From 9e79fb6a6d5d551c798e2e35dece96c1f1da7661 Mon Sep 17 00:00:00 2001 From: sunyugang Date: Fri, 14 Mar 2025 15:44:41 +0800 Subject: [PATCH] =?UTF-8?q?=E6=94=B9=E8=BF=9B=E9=A1=B9=E7=9B=AE=E7=BB=93?= =?UTF-8?q?=E6=9E=84?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- app/api/business/project_detect_api.py | 6 +- app/model/schemas/project_detect_schemas.py | 2 + app/service/project_detect_service.py | 20 +- app/util/yolov5/models/common.py | 1149 ++++++++++++++ app/util/yolov5/models/experimental.py | 130 ++ app/util/yolov5/models/hub/anchors.yaml | 57 + app/util/yolov5/models/hub/yolov3-spp.yaml | 52 + app/util/yolov5/models/hub/yolov3-tiny.yaml | 42 + app/util/yolov5/models/hub/yolov3.yaml | 52 + app/util/yolov5/models/hub/yolov5-bifpn.yaml | 49 + app/util/yolov5/models/hub/yolov5-fpn.yaml | 43 + app/util/yolov5/models/hub/yolov5-p2.yaml | 55 + app/util/yolov5/models/hub/yolov5-p34.yaml | 42 + app/util/yolov5/models/hub/yolov5-p6.yaml | 57 + app/util/yolov5/models/hub/yolov5-p7.yaml | 68 + app/util/yolov5/models/hub/yolov5-panet.yaml | 49 + app/util/yolov5/models/hub/yolov5l6.yaml | 61 + app/util/yolov5/models/hub/yolov5m6.yaml | 61 + app/util/yolov5/models/hub/yolov5n6.yaml | 61 + .../yolov5/models/hub/yolov5s-LeakyReLU.yaml | 50 + app/util/yolov5/models/hub/yolov5s-ghost.yaml | 49 + .../models/hub/yolov5s-transformer.yaml | 49 + app/util/yolov5/models/hub/yolov5s6.yaml | 61 + app/util/yolov5/models/hub/yolov5x6.yaml | 61 + .../yolov5/models/segment/yolov5l-seg.yaml | 49 + .../yolov5/models/segment/yolov5m-seg.yaml | 49 + .../yolov5/models/segment/yolov5n-seg.yaml | 49 + .../yolov5/models/segment/yolov5s-seg.yaml | 49 + .../yolov5/models/segment/yolov5x-seg.yaml | 49 + app/util/yolov5/models/tf.py | 797 ++++++++++ app/util/yolov5/models/yolo.py | 495 ++++++ app/util/yolov5/models/yolov5l.yaml | 49 + app/util/yolov5/models/yolov5m.yaml | 49 + app/util/yolov5/models/yolov5n.yaml | 49 + app/util/yolov5/models/yolov5s.yaml | 49 + app/util/yolov5/models/yolov5x.yaml | 49 + app/util/yolov5/utils/__init__.py | 97 ++ app/util/yolov5/utils/activations.py | 134 ++ app/util/yolov5/utils/augmentations.py | 440 ++++++ app/util/yolov5/utils/autoanchor.py | 175 +++ app/util/yolov5/utils/autobatch.py | 70 + app/util/yolov5/utils/aws/__init__.py | 1 + app/util/yolov5/utils/aws/mime.sh | 26 + app/util/yolov5/utils/aws/resume.py | 42 + app/util/yolov5/utils/aws/userdata.sh | 27 + app/util/yolov5/utils/callbacks.py | 72 + app/util/yolov5/utils/dataloaders.py | 1378 +++++++++++++++++ app/util/yolov5/utils/docker/Dockerfile | 73 + app/util/yolov5/utils/docker/Dockerfile-arm64 | 40 + app/util/yolov5/utils/docker/Dockerfile-cpu | 42 + app/util/yolov5/utils/downloads.py | 136 ++ .../yolov5/utils/flask_rest_api/README.md | 70 + .../yolov5/utils/flask_rest_api/__init__.py | 0 .../utils/flask_rest_api/example_request.py | 17 + .../yolov5/utils/flask_rest_api/restapi.py | 49 + app/util/yolov5/utils/general.py | 1314 ++++++++++++++++ .../yolov5/utils/google_app_engine/Dockerfile | 25 + .../additional_requirements.txt | 6 + .../yolov5/utils/google_app_engine/app.yaml | 16 + app/util/yolov5/utils/loggers/__init__.py | 476 ++++++ .../yolov5/utils/loggers/clearml/README.md | 222 +++ .../yolov5/utils/loggers/clearml/__init__.py | 1 + .../utils/loggers/clearml/clearml_utils.py | 228 +++ app/util/yolov5/utils/loggers/clearml/hpo.py | 90 ++ app/util/yolov5/utils/loggers/comet/README.md | 250 +++ .../yolov5/utils/loggers/comet/__init__.py | 549 +++++++ .../yolov5/utils/loggers/comet/comet_utils.py | 151 ++ app/util/yolov5/utils/loggers/comet/hpo.py | 126 ++ .../utils/loggers/comet/optimizer_config.json | 135 ++ .../yolov5/utils/loggers/wandb/__init__.py | 1 + .../yolov5/utils/loggers/wandb/wandb_utils.py | 210 +++ app/util/yolov5/utils/loss.py | 254 +++ app/util/yolov5/utils/metrics.py | 381 +++++ app/util/yolov5/utils/plots.py | 517 +++++++ app/util/yolov5/utils/segment/__init__.py | 1 + .../yolov5/utils/segment/augmentations.py | 92 ++ app/util/yolov5/utils/segment/dataloaders.py | 366 +++++ app/util/yolov5/utils/segment/general.py | 160 ++ app/util/yolov5/utils/segment/loss.py | 197 +++ app/util/yolov5/utils/segment/metrics.py | 225 +++ app/util/yolov5/utils/segment/plots.py | 152 ++ app/util/yolov5/utils/torch_utils.py | 482 ++++++ app/util/yolov5/utils/triton.py | 90 ++ app/websocket/web_socket_server.py | 14 +- yolov5/.gitignore | 2 +- yolov5/models/common.py | 16 +- yolov5/models/experimental.py | 4 +- yolov5/segment/val.py | 2 +- yolov5/train.py | 4 +- yolov5/utils/augmentations.py | 4 +- yolov5/utils/dataloaders.py | 6 +- yolov5/utils/general.py | 29 +- yolov5/utils/metrics.py | 2 +- yolov5/utils/plots.py | 8 +- yolov5/utils/torch_utils.py | 4 +- 95 files changed, 13745 insertions(+), 64 deletions(-) create mode 100644 app/util/yolov5/models/common.py create mode 100644 app/util/yolov5/models/experimental.py create mode 100644 app/util/yolov5/models/hub/anchors.yaml create mode 100644 app/util/yolov5/models/hub/yolov3-spp.yaml create mode 100644 app/util/yolov5/models/hub/yolov3-tiny.yaml create mode 100644 app/util/yolov5/models/hub/yolov3.yaml create mode 100644 app/util/yolov5/models/hub/yolov5-bifpn.yaml create mode 100644 app/util/yolov5/models/hub/yolov5-fpn.yaml create mode 100644 app/util/yolov5/models/hub/yolov5-p2.yaml create mode 100644 app/util/yolov5/models/hub/yolov5-p34.yaml create mode 100644 app/util/yolov5/models/hub/yolov5-p6.yaml create mode 100644 app/util/yolov5/models/hub/yolov5-p7.yaml create mode 100644 app/util/yolov5/models/hub/yolov5-panet.yaml create mode 100644 app/util/yolov5/models/hub/yolov5l6.yaml create mode 100644 app/util/yolov5/models/hub/yolov5m6.yaml create mode 100644 app/util/yolov5/models/hub/yolov5n6.yaml create mode 100644 app/util/yolov5/models/hub/yolov5s-LeakyReLU.yaml create mode 100644 app/util/yolov5/models/hub/yolov5s-ghost.yaml create mode 100644 app/util/yolov5/models/hub/yolov5s-transformer.yaml create mode 100644 app/util/yolov5/models/hub/yolov5s6.yaml create mode 100644 app/util/yolov5/models/hub/yolov5x6.yaml create mode 100644 app/util/yolov5/models/segment/yolov5l-seg.yaml create mode 100644 app/util/yolov5/models/segment/yolov5m-seg.yaml create mode 100644 app/util/yolov5/models/segment/yolov5n-seg.yaml create mode 100644 app/util/yolov5/models/segment/yolov5s-seg.yaml create mode 100644 app/util/yolov5/models/segment/yolov5x-seg.yaml create mode 100644 app/util/yolov5/models/tf.py create mode 100644 app/util/yolov5/models/yolo.py create mode 100644 app/util/yolov5/models/yolov5l.yaml create mode 100644 app/util/yolov5/models/yolov5m.yaml create mode 100644 app/util/yolov5/models/yolov5n.yaml create mode 100644 app/util/yolov5/models/yolov5s.yaml create mode 100644 app/util/yolov5/models/yolov5x.yaml create mode 100644 app/util/yolov5/utils/__init__.py create mode 100644 app/util/yolov5/utils/activations.py create mode 100644 app/util/yolov5/utils/augmentations.py create mode 100644 app/util/yolov5/utils/autoanchor.py create mode 100644 app/util/yolov5/utils/autobatch.py create mode 100644 app/util/yolov5/utils/aws/__init__.py create mode 100644 app/util/yolov5/utils/aws/mime.sh create mode 100644 app/util/yolov5/utils/aws/resume.py create mode 100644 app/util/yolov5/utils/aws/userdata.sh create mode 100644 app/util/yolov5/utils/callbacks.py create mode 100644 app/util/yolov5/utils/dataloaders.py create mode 100644 app/util/yolov5/utils/docker/Dockerfile create mode 100644 app/util/yolov5/utils/docker/Dockerfile-arm64 create mode 100644 app/util/yolov5/utils/docker/Dockerfile-cpu create mode 100644 app/util/yolov5/utils/downloads.py create mode 100644 app/util/yolov5/utils/flask_rest_api/README.md create mode 100644 app/util/yolov5/utils/flask_rest_api/__init__.py create mode 100644 app/util/yolov5/utils/flask_rest_api/example_request.py create mode 100644 app/util/yolov5/utils/flask_rest_api/restapi.py create mode 100644 app/util/yolov5/utils/general.py create mode 100644 app/util/yolov5/utils/google_app_engine/Dockerfile create mode 100644 app/util/yolov5/utils/google_app_engine/additional_requirements.txt create mode 100644 app/util/yolov5/utils/google_app_engine/app.yaml create mode 100644 app/util/yolov5/utils/loggers/__init__.py create mode 100644 app/util/yolov5/utils/loggers/clearml/README.md create mode 100644 app/util/yolov5/utils/loggers/clearml/__init__.py create mode 100644 app/util/yolov5/utils/loggers/clearml/clearml_utils.py create mode 100644 app/util/yolov5/utils/loggers/clearml/hpo.py create mode 100644 app/util/yolov5/utils/loggers/comet/README.md create mode 100644 app/util/yolov5/utils/loggers/comet/__init__.py create mode 100644 app/util/yolov5/utils/loggers/comet/comet_utils.py create mode 100644 app/util/yolov5/utils/loggers/comet/hpo.py create mode 100644 app/util/yolov5/utils/loggers/comet/optimizer_config.json create mode 100644 app/util/yolov5/utils/loggers/wandb/__init__.py create mode 100644 app/util/yolov5/utils/loggers/wandb/wandb_utils.py create mode 100644 app/util/yolov5/utils/loss.py create mode 100644 app/util/yolov5/utils/metrics.py create mode 100644 app/util/yolov5/utils/plots.py create mode 100644 app/util/yolov5/utils/segment/__init__.py create mode 100644 app/util/yolov5/utils/segment/augmentations.py create mode 100644 app/util/yolov5/utils/segment/dataloaders.py create mode 100644 app/util/yolov5/utils/segment/general.py create mode 100644 app/util/yolov5/utils/segment/loss.py create mode 100644 app/util/yolov5/utils/segment/metrics.py create mode 100644 app/util/yolov5/utils/segment/plots.py create mode 100644 app/util/yolov5/utils/torch_utils.py create mode 100644 app/util/yolov5/utils/triton.py diff --git a/app/api/business/project_detect_api.py b/app/api/business/project_detect_api.py index b3bd38b..3d7f304 100644 --- a/app/api/business/project_detect_api.py +++ b/app/api/business/project_detect_api.py @@ -118,7 +118,7 @@ def run_detect_yolo(detect_log_in: ProjectDetectLogIn, session: Session = Depend if train is None: return rc.response_error("训练权重不存在") detect_img_count = pdc.check_detect_img(detect_log_in.detect_id, session) - if detect_img_count == 0: + if detect_img_count == 0 and detect.rtsp_url is None: return rc.response_error("推理集合中没有内容，请先到推理集合中上传图片") if detect.file_type == 'img' or detect.file_type == 'video': detect_log = pds.run_detect_yolo(detect_log_in, detect, train, session) @@ -131,8 +131,8 @@ def run_detect_yolo(detect_log_in: ProjectDetectLogIn, session: Session = Depend weights_pt = train.best_pt else: weights_pt = train.last_pt - thread_train = threading.Thread(target=pds.run_detect_rtsp, - args=(weights_pt, detect.rtsp_url, train.train_data,)) + thread_train = threading.Thread(target=run_rtsp_loop, + args=(weights_pt, detect.rtsp_url, train.train_data, detect.id)) thread_train.start() return rc.response_success(msg="执行成功") diff --git a/app/model/schemas/project_detect_schemas.py b/app/model/schemas/project_detect_schemas.py index 8dc6d90..5922809 100644 --- a/app/model/schemas/project_detect_schemas.py +++ b/app/model/schemas/project_detect_schemas.py @@ -7,6 +7,7 @@ class ProjectDetectIn(BaseModel): project_id: Optional[int] = Field(..., description="项目id") file_type: Optional[str] = Field('img', description="推理集合文件类别") detect_name: Optional[str] = Field(..., description="推理集合名称") + rtsp_url: Optional[str] = Field(None, description="视频流地址") class ProjectDetectPager(BaseModel): @@ -36,6 +37,7 @@ class ProjectDetectOut(BaseModel): class ProjectDetectList(BaseModel): id: Optional[int] + file_type: Optional[str] detect_name: Optional[str] class Config: diff --git a/app/service/project_detect_service.py b/app/service/project_detect_service.py index e3c2733..073e2ba 100644 --- a/app/service/project_detect_service.py +++ b/app/service/project_detect_service.py @@ -2,14 +2,12 @@ from sqlalchemy.orm import Session from typing import List from fastapi import UploadFile import subprocess -from yolov5.models.common import DetectMultiBackend -from yolov5.utils.torch_utils import select_device -from yolov5.utils.dataloaders import LoadStreams -from yolov5.utils.general import check_img_size, Profile, non_max_suppression, cv2, scale_boxes import torch -from pathlib import Path -from ultralytics.utils.plotting import Annotator, colors, save_one_box -import platform +from app.util.yolov5.models.common import DetectMultiBackend +from app.util.yolov5.utils.torch_utils import select_device +from app.util.yolov5.utils.dataloaders import LoadStreams +from app.util.yolov5.utils.general import check_img_size, Profile, non_max_suppression, cv2, scale_boxes +from ultralytics.utils.plotting import Annotator, colors from app.model.crud import project_detect_crud as pdc from app.model.schemas.project_detect_schemas import ProjectDetectIn, ProjectDetectOut, ProjectDetectLogIn @@ -188,6 +186,8 @@ async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id: :param data: yaml文件 :return: """ + room = 'detect_rtsp_' + str(detect_id) + await room_manager.send_to_room(room, '开始推理rtsp视频流') # 选择设备（CPU 或 GPU） device = select_device('cpu') @@ -247,7 +247,11 @@ async def run_detect_rtsp(weights_pt: str, rtsp_url: str, data: str, detect_id: # Stream results im0 = annotator.result() - + # 将帧编码为 JPEG + ret, jpeg = cv2.imencode('.jpg', im0) + if ret: + frame_data = jpeg.tobytes() + await room_manager.send_stream_to_room(room, frame_data) diff --git a/app/util/yolov5/models/common.py b/app/util/yolov5/models/common.py new file mode 100644 index 0000000..935430b --- /dev/null +++ b/app/util/yolov5/models/common.py @@ -0,0 +1,1149 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Common modules.""" + +import ast +import contextlib +import json +import math +import platform +import warnings +import zipfile +from collections import OrderedDict, namedtuple +from copy import copy +from pathlib import Path +from urllib.parse import urlparse + +import cv2 +import numpy as np +import pandas as pd +import requests +import torch +import torch.nn as nn +from PIL import Image +from torch.cuda import amp + +# Import 'ultralytics' package or install if missing +try: + import ultralytics + + assert hasattr(ultralytics, "__version__") # verify package is not directory +except (ImportError, AssertionError): + import os + + os.system("pip install -U ultralytics") + import ultralytics + +from ultralytics.utils.plotting import Annotator, colors, save_one_box + +from app.util.yolov5.utils import TryExcept +from app.util.yolov5.utils.dataloaders import exif_transpose, letterbox +from app.util.yolov5.utils.general import ( + LOGGER, + ROOT, + Profile, + check_requirements, + check_suffix, + check_version, + colorstr, + increment_path, + is_jupyter, + make_divisible, + non_max_suppression, + scale_boxes, + xywh2xyxy, + xyxy2xywh, + yaml_load, +) +from app.util.yolov5.utils.torch_utils import copy_attr, smart_inference_mode + + +def autopad(k, p=None, d=1): + """ + Pads kernel to 'same' output shape, adjusting for optional dilation; returns padding size. + + `k`: kernel, `p`: padding, `d`: dilation. + """ + if d > 1: + k = d * (k - 1) + 1 if isinstance(k, int) else [d * (x - 1) + 1 for x in k] # actual kernel-size + if p is None: + p = k // 2 if isinstance(k, int) else [x // 2 for x in k] # auto-pad + return p + + +class Conv(nn.Module): + """Applies a convolution, batch normalization, and activation function to an input tensor in a neural network.""" + + default_act = nn.SiLU() # default activation + + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, d=1, act=True): + """Initializes a standard convolution layer with optional batch normalization and activation.""" + super().__init__() + self.conv = nn.Conv2d(c1, c2, k, s, autopad(k, p, d), groups=g, dilation=d, bias=False) + self.bn = nn.BatchNorm2d(c2) + self.act = self.default_act if act is True else act if isinstance(act, nn.Module) else nn.Identity() + + def forward(self, x): + """Applies a convolution followed by batch normalization and an activation function to the input tensor `x`.""" + return self.act(self.bn(self.conv(x))) + + def forward_fuse(self, x): + """Applies a fused convolution and activation function to the input tensor `x`.""" + return self.act(self.conv(x)) + + +class DWConv(Conv): + """Implements a depth-wise convolution layer with optional activation for efficient spatial filtering.""" + + def __init__(self, c1, c2, k=1, s=1, d=1, act=True): + """Initializes a depth-wise convolution layer with optional activation; args: input channels (c1), output + channels (c2), kernel size (k), stride (s), dilation (d), and activation flag (act). + """ + super().__init__(c1, c2, k, s, g=math.gcd(c1, c2), d=d, act=act) + + +class DWConvTranspose2d(nn.ConvTranspose2d): + """A depth-wise transpose convolutional layer for upsampling in neural networks, particularly in YOLOv5 models.""" + + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0): + """Initializes a depth-wise transpose convolutional layer for YOLOv5; args: input channels (c1), output channels + (c2), kernel size (k), stride (s), input padding (p1), output padding (p2). + """ + super().__init__(c1, c2, k, s, p1, p2, groups=math.gcd(c1, c2)) + + +class TransformerLayer(nn.Module): + """Transformer layer with multihead attention and linear layers, optimized by removing LayerNorm.""" + + def __init__(self, c, num_heads): + """ + Initializes a transformer layer, sans LayerNorm for performance, with multihead attention and linear layers. + + See as described in https://arxiv.org/abs/2010.11929. + """ + super().__init__() + self.q = nn.Linear(c, c, bias=False) + self.k = nn.Linear(c, c, bias=False) + self.v = nn.Linear(c, c, bias=False) + self.ma = nn.MultiheadAttention(embed_dim=c, num_heads=num_heads) + self.fc1 = nn.Linear(c, c, bias=False) + self.fc2 = nn.Linear(c, c, bias=False) + + def forward(self, x): + """Performs forward pass using MultiheadAttention and two linear transformations with residual connections.""" + x = self.ma(self.q(x), self.k(x), self.v(x))[0] + x + x = self.fc2(self.fc1(x)) + x + return x + + +class TransformerBlock(nn.Module): + """A Transformer block for vision tasks with convolution, position embeddings, and Transformer layers.""" + + def __init__(self, c1, c2, num_heads, num_layers): + """Initializes a Transformer block for vision tasks, adapting dimensions if necessary and stacking specified + layers. + """ + super().__init__() + self.conv = None + if c1 != c2: + self.conv = Conv(c1, c2) + self.linear = nn.Linear(c2, c2) # learnable position embedding + self.tr = nn.Sequential(*(TransformerLayer(c2, num_heads) for _ in range(num_layers))) + self.c2 = c2 + + def forward(self, x): + """Processes input through an optional convolution, followed by Transformer layers and position embeddings for + object detection. + """ + if self.conv is not None: + x = self.conv(x) + b, _, w, h = x.shape + p = x.flatten(2).permute(2, 0, 1) + return self.tr(p + self.linear(p)).permute(1, 2, 0).reshape(b, self.c2, w, h) + + +class Bottleneck(nn.Module): + """A bottleneck layer with optional shortcut and group convolution for efficient feature extraction.""" + + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5): + """Initializes a standard bottleneck layer with optional shortcut and group convolution, supporting channel + expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_, c2, 3, 1, g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + """Processes input through two convolutions, optionally adds shortcut if channel dimensions match; input is a + tensor. + """ + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class BottleneckCSP(nn.Module): + """CSP bottleneck layer for feature extraction with cross-stage partial connections and optional shortcuts.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes CSP bottleneck with optional shortcuts; args: ch_in, ch_out, number of repeats, shortcut bool, + groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = nn.Conv2d(c1, c_, 1, 1, bias=False) + self.cv3 = nn.Conv2d(c_, c_, 1, 1, bias=False) + self.cv4 = Conv(2 * c_, c2, 1, 1) + self.bn = nn.BatchNorm2d(2 * c_) # applied to cat(cv2, cv3) + self.act = nn.SiLU() + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + """Performs forward pass by applying layers, activation, and concatenation on input x, returning feature- + enhanced output. + """ + y1 = self.cv3(self.m(self.cv1(x))) + y2 = self.cv2(x) + return self.cv4(self.act(self.bn(torch.cat((y1, y2), 1)))) + + +class CrossConv(nn.Module): + """Implements a cross convolution layer with downsampling, expansion, and optional shortcut.""" + + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False): + """ + Initializes CrossConv with downsampling, expanding, and optionally shortcutting; `c1` input, `c2` output + channels. + + Inputs are ch_in, ch_out, kernel, stride, groups, expansion, shortcut. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, (1, k), (1, s)) + self.cv2 = Conv(c_, c2, (k, 1), (s, 1), g=g) + self.add = shortcut and c1 == c2 + + def forward(self, x): + """Performs feature sampling, expanding, and applies shortcut if channels match; expects `x` input tensor.""" + return x + self.cv2(self.cv1(x)) if self.add else self.cv2(self.cv1(x)) + + +class C3(nn.Module): + """Implements a CSP Bottleneck module with three convolutions for enhanced feature extraction in neural networks.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes C3 module with options for channel count, bottleneck repetition, shortcut usage, group + convolutions, and expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c1, c_, 1, 1) + self.cv3 = Conv(2 * c_, c2, 1) # optional act=FReLU(c2) + self.m = nn.Sequential(*(Bottleneck(c_, c_, shortcut, g, e=1.0) for _ in range(n))) + + def forward(self, x): + """Performs forward propagation using concatenated outputs from two convolutions and a Bottleneck sequence.""" + return self.cv3(torch.cat((self.m(self.cv1(x)), self.cv2(x)), 1)) + + +class C3x(C3): + """Extends the C3 module with cross-convolutions for enhanced feature extraction in neural networks.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes C3x module with cross-convolutions, extending C3 with customizable channel dimensions, groups, + and expansion. + """ + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = nn.Sequential(*(CrossConv(c_, c_, 3, 1, g, 1.0, shortcut) for _ in range(n))) + + +class C3TR(C3): + """C3 module with TransformerBlock for enhanced feature extraction in object detection models.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes C3 module with TransformerBlock for enhanced feature extraction, accepts channel sizes, shortcut + config, group, and expansion. + """ + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = TransformerBlock(c_, c_, 4, n) + + +class C3SPP(C3): + """Extends the C3 module with an SPP layer for enhanced spatial feature extraction and customizable channels.""" + + def __init__(self, c1, c2, k=(5, 9, 13), n=1, shortcut=True, g=1, e=0.5): + """Initializes a C3 module with SPP layer for advanced spatial feature extraction, given channel sizes, kernel + sizes, shortcut, group, and expansion ratio. + """ + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) + self.m = SPP(c_, c_, k) + + +class C3Ghost(C3): + """Implements a C3 module with Ghost Bottlenecks for efficient feature extraction in YOLOv5.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5): + """Initializes YOLOv5's C3 module with Ghost Bottlenecks for efficient feature extraction.""" + super().__init__(c1, c2, n, shortcut, g, e) + c_ = int(c2 * e) # hidden channels + self.m = nn.Sequential(*(GhostBottleneck(c_, c_) for _ in range(n))) + + +class SPP(nn.Module): + """Implements Spatial Pyramid Pooling (SPP) for feature extraction, ref: https://arxiv.org/abs/1406.4729.""" + + def __init__(self, c1, c2, k=(5, 9, 13)): + """Initializes SPP layer with Spatial Pyramid Pooling, ref: https://arxiv.org/abs/1406.4729, args: c1 (input channels), c2 (output channels), k (kernel sizes).""" + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * (len(k) + 1), c2, 1, 1) + self.m = nn.ModuleList([nn.MaxPool2d(kernel_size=x, stride=1, padding=x // 2) for x in k]) + + def forward(self, x): + """Applies convolution and max pooling layers to the input tensor `x`, concatenates results, and returns output + tensor. + """ + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning + return self.cv2(torch.cat([x] + [m(x) for m in self.m], 1)) + + +class SPPF(nn.Module): + """Implements a fast Spatial Pyramid Pooling (SPPF) layer for efficient feature extraction in YOLOv5 models.""" + + def __init__(self, c1, c2, k=5): + """ + Initializes YOLOv5 SPPF layer with given channels and kernel size for YOLOv5 model, combining convolution and + max pooling. + + Equivalent to SPP(k=(5, 9, 13)). + """ + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = Conv(c1, c_, 1, 1) + self.cv2 = Conv(c_ * 4, c2, 1, 1) + self.m = nn.MaxPool2d(kernel_size=k, stride=1, padding=k // 2) + + def forward(self, x): + """Processes input through a series of convolutions and max pooling operations for feature extraction.""" + x = self.cv1(x) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress torch 1.9.0 max_pool2d() warning + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(torch.cat((x, y1, y2, self.m(y2)), 1)) + + +class Focus(nn.Module): + """Focuses spatial information into channel space using slicing and convolution for efficient feature extraction.""" + + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True): + """Initializes Focus module to concentrate width-height info into channel space with configurable convolution + parameters. + """ + super().__init__() + self.conv = Conv(c1 * 4, c2, k, s, p, g, act=act) + # self.contract = Contract(gain=2) + + def forward(self, x): + """Processes input through Focus mechanism, reshaping (b,c,w,h) to (b,4c,w/2,h/2) then applies convolution.""" + return self.conv(torch.cat((x[..., ::2, ::2], x[..., 1::2, ::2], x[..., ::2, 1::2], x[..., 1::2, 1::2]), 1)) + # return self.conv(self.contract(x)) + + +class GhostConv(nn.Module): + """Implements Ghost Convolution for efficient feature extraction, see https://github.com/huawei-noah/ghostnet.""" + + def __init__(self, c1, c2, k=1, s=1, g=1, act=True): + """Initializes GhostConv with in/out channels, kernel size, stride, groups, and activation; halves out channels + for efficiency. + """ + super().__init__() + c_ = c2 // 2 # hidden channels + self.cv1 = Conv(c1, c_, k, s, None, g, act=act) + self.cv2 = Conv(c_, c_, 5, 1, None, c_, act=act) + + def forward(self, x): + """Performs forward pass, concatenating outputs of two convolutions on input `x`: shape (B,C,H,W).""" + y = self.cv1(x) + return torch.cat((y, self.cv2(y)), 1) + + +class GhostBottleneck(nn.Module): + """Efficient bottleneck layer using Ghost Convolutions, see https://github.com/huawei-noah/ghostnet.""" + + def __init__(self, c1, c2, k=3, s=1): + """Initializes GhostBottleneck with ch_in `c1`, ch_out `c2`, kernel size `k`, stride `s`; see https://github.com/huawei-noah/ghostnet.""" + super().__init__() + c_ = c2 // 2 + self.conv = nn.Sequential( + GhostConv(c1, c_, 1, 1), # pw + DWConv(c_, c_, k, s, act=False) if s == 2 else nn.Identity(), # dw + GhostConv(c_, c2, 1, 1, act=False), + ) # pw-linear + self.shortcut = ( + nn.Sequential(DWConv(c1, c1, k, s, act=False), Conv(c1, c2, 1, 1, act=False)) if s == 2 else nn.Identity() + ) + + def forward(self, x): + """Processes input through conv and shortcut layers, returning their summed output.""" + return self.conv(x) + self.shortcut(x) + + +class Contract(nn.Module): + """Contracts spatial dimensions into channel dimensions for efficient processing in neural networks.""" + + def __init__(self, gain=2): + """Initializes a layer to contract spatial dimensions (width-height) into channels, e.g., input shape + (1,64,80,80) to (1,256,40,40). + """ + super().__init__() + self.gain = gain + + def forward(self, x): + """Processes input tensor to expand channel dimensions by contracting spatial dimensions, yielding output shape + `(b, c*s*s, h//s, w//s)`. + """ + b, c, h, w = x.size() # assert (h / s == 0) and (W / s == 0), 'Indivisible gain' + s = self.gain + x = x.view(b, c, h // s, s, w // s, s) # x(1,64,40,2,40,2) + x = x.permute(0, 3, 5, 1, 2, 4).contiguous() # x(1,2,2,64,40,40) + return x.view(b, c * s * s, h // s, w // s) # x(1,256,40,40) + + +class Expand(nn.Module): + """Expands spatial dimensions by redistributing channels, e.g., from (1,64,80,80) to (1,16,160,160).""" + + def __init__(self, gain=2): + """ + Initializes the Expand module to increase spatial dimensions by redistributing channels, with an optional gain + factor. + + Example: x(1,64,80,80) to x(1,16,160,160). + """ + super().__init__() + self.gain = gain + + def forward(self, x): + """Processes input tensor x to expand spatial dimensions by redistributing channels, requiring C / gain^2 == + 0. + """ + b, c, h, w = x.size() # assert C / s ** 2 == 0, 'Indivisible gain' + s = self.gain + x = x.view(b, s, s, c // s**2, h, w) # x(1,2,2,16,80,80) + x = x.permute(0, 3, 4, 1, 5, 2).contiguous() # x(1,16,80,2,80,2) + return x.view(b, c // s**2, h * s, w * s) # x(1,16,160,160) + + +class Concat(nn.Module): + """Concatenates tensors along a specified dimension for efficient tensor manipulation in neural networks.""" + + def __init__(self, dimension=1): + """Initializes a Concat module to concatenate tensors along a specified dimension.""" + super().__init__() + self.d = dimension + + def forward(self, x): + """Concatenates a list of tensors along a specified dimension; `x` is a list of tensors, `dimension` is an + int. + """ + return torch.cat(x, self.d) + + +class DetectMultiBackend(nn.Module): + """YOLOv5 MultiBackend class for inference on various backends including PyTorch, ONNX, TensorRT, and more.""" + + def __init__(self, weights="yolov5s.pt", device=torch.device("cpu"), dnn=False, data=None, fp16=False, fuse=True): + """Initializes DetectMultiBackend with support for various inference backends, including PyTorch and ONNX.""" + # PyTorch: weights = *.pt + # TorchScript: *.torchscript + # ONNX Runtime: *.onnx + # ONNX OpenCV DNN: *.onnx --dnn + # OpenVINO: *_openvino_model + # CoreML: *.mlpackage + # TensorRT: *.engine + # TensorFlow SavedModel: *_saved_model + # TensorFlow GraphDef: *.pb + # TensorFlow Lite: *.tflite + # TensorFlow Edge TPU: *_edgetpu.tflite + # PaddlePaddle: *_paddle_model + from app.util.yolov5.models.experimental import attempt_download, attempt_load # scoped to avoid circular import + + super().__init__() + w = str(weights[0] if isinstance(weights, list) else weights) + pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle, triton = self._model_type(w) + fp16 &= pt or jit or onnx or engine or triton # FP16 + nhwc = coreml or saved_model or pb or tflite or edgetpu # BHWC formats (vs torch BCWH) + stride = 32 # default stride + cuda = torch.cuda.is_available() and device.type != "cpu" # use CUDA + if not (pt or triton): + w = attempt_download(w) # download if not local + + if pt: # PyTorch + model = attempt_load(weights if isinstance(weights, list) else w, device=device, inplace=True, fuse=fuse) + stride = max(int(model.stride.max()), 32) # model stride + names = model.module.names if hasattr(model, "module") else model.names # get class names + model.half() if fp16 else model.float() + self.model = model # explicitly assign for to(), cpu(), cuda(), half() + elif jit: # TorchScript + LOGGER.info(f"Loading {w} for TorchScript inference...") + extra_files = {"config.txt": ""} # model metadata + model = torch.jit.load(w, _extra_files=extra_files, map_location=device) + model.half() if fp16 else model.float() + if extra_files["config.txt"]: # load metadata dict + d = json.loads( + extra_files["config.txt"], + object_hook=lambda d: {int(k) if k.isdigit() else k: v for k, v in d.items()}, + ) + stride, names = int(d["stride"]), d["names"] + elif dnn: # ONNX OpenCV DNN + LOGGER.info(f"Loading {w} for ONNX OpenCV DNN inference...") + check_requirements("opencv-python>=4.5.4") + net = cv2.dnn.readNetFromONNX(w) + elif onnx: # ONNX Runtime + LOGGER.info(f"Loading {w} for ONNX Runtime inference...") + check_requirements(("onnx", "onnxruntime-gpu" if cuda else "onnxruntime")) + import onnxruntime + + providers = ["CUDAExecutionProvider", "CPUExecutionProvider"] if cuda else ["CPUExecutionProvider"] + session = onnxruntime.InferenceSession(w, providers=providers) + output_names = [x.name for x in session.get_outputs()] + meta = session.get_modelmeta().custom_metadata_map # metadata + if "stride" in meta: + stride, names = int(meta["stride"]), eval(meta["names"]) + elif xml: # OpenVINO + LOGGER.info(f"Loading {w} for OpenVINO inference...") + check_requirements("openvino>=2023.0") # requires openvino-dev: https://pypi.org/project/openvino-dev/ + from openvino.runtime import Core, Layout, get_batch + + core = Core() + if not Path(w).is_file(): # if not *.xml + w = next(Path(w).glob("*.xml")) # get *.xml file from *_openvino_model dir + ov_model = core.read_model(model=w, weights=Path(w).with_suffix(".bin")) + if ov_model.get_parameters()[0].get_layout().empty: + ov_model.get_parameters()[0].set_layout(Layout("NCHW")) + batch_dim = get_batch(ov_model) + if batch_dim.is_static: + batch_size = batch_dim.get_length() + ov_compiled_model = core.compile_model(ov_model, device_name="AUTO") # AUTO selects best available device + stride, names = self._load_metadata(Path(w).with_suffix(".yaml")) # load metadata + elif engine: # TensorRT + LOGGER.info(f"Loading {w} for TensorRT inference...") + import tensorrt as trt # https://developer.nvidia.com/nvidia-tensorrt-download + + check_version(trt.__version__, "7.0.0", hard=True) # require tensorrt>=7.0.0 + if device.type == "cpu": + device = torch.device("cuda:0") + Binding = namedtuple("Binding", ("name", "dtype", "shape", "data", "ptr")) + logger = trt.Logger(trt.Logger.INFO) + with open(w, "rb") as f, trt.Runtime(logger) as runtime: + model = runtime.deserialize_cuda_engine(f.read()) + context = model.create_execution_context() + bindings = OrderedDict() + output_names = [] + fp16 = False # default updated below + dynamic = False + is_trt10 = not hasattr(model, "num_bindings") + num = range(model.num_io_tensors) if is_trt10 else range(model.num_bindings) + for i in num: + if is_trt10: + name = model.get_tensor_name(i) + dtype = trt.nptype(model.get_tensor_dtype(name)) + is_input = model.get_tensor_mode(name) == trt.TensorIOMode.INPUT + if is_input: + if -1 in tuple(model.get_tensor_shape(name)): # dynamic + dynamic = True + context.set_input_shape(name, tuple(model.get_profile_shape(name, 0)[2])) + if dtype == np.float16: + fp16 = True + else: # output + output_names.append(name) + shape = tuple(context.get_tensor_shape(name)) + else: + name = model.get_binding_name(i) + dtype = trt.nptype(model.get_binding_dtype(i)) + if model.binding_is_input(i): + if -1 in tuple(model.get_binding_shape(i)): # dynamic + dynamic = True + context.set_binding_shape(i, tuple(model.get_profile_shape(0, i)[2])) + if dtype == np.float16: + fp16 = True + else: # output + output_names.append(name) + shape = tuple(context.get_binding_shape(i)) + im = torch.from_numpy(np.empty(shape, dtype=dtype)).to(device) + bindings[name] = Binding(name, dtype, shape, im, int(im.data_ptr())) + binding_addrs = OrderedDict((n, d.ptr) for n, d in bindings.items()) + batch_size = bindings["images"].shape[0] # if dynamic, this is instead max batch size + elif coreml: # CoreML + LOGGER.info(f"Loading {w} for CoreML inference...") + import coremltools as ct + + model = ct.models.MLModel(w) + elif saved_model: # TF SavedModel + LOGGER.info(f"Loading {w} for TensorFlow SavedModel inference...") + import tensorflow as tf + + keras = False # assume TF1 saved_model + model = tf.keras.models.load_model(w) if keras else tf.saved_model.load(w) + elif pb: # GraphDef https://www.tensorflow.org/guide/migrate#a_graphpb_or_graphpbtxt + LOGGER.info(f"Loading {w} for TensorFlow GraphDef inference...") + import tensorflow as tf + + def wrap_frozen_graph(gd, inputs, outputs): + """Wraps a TensorFlow GraphDef for inference, returning a pruned function.""" + x = tf.compat.v1.wrap_function(lambda: tf.compat.v1.import_graph_def(gd, name=""), []) # wrapped + ge = x.graph.as_graph_element + return x.prune(tf.nest.map_structure(ge, inputs), tf.nest.map_structure(ge, outputs)) + + def gd_outputs(gd): + """Generates a sorted list of graph outputs excluding NoOp nodes and inputs, formatted as ':0'.""" + name_list, input_list = [], [] + for node in gd.node: # tensorflow.core.framework.node_def_pb2.NodeDef + name_list.append(node.name) + input_list.extend(node.input) + return sorted(f"{x}:0" for x in list(set(name_list) - set(input_list)) if not x.startswith("NoOp")) + + gd = tf.Graph().as_graph_def() # TF GraphDef + with open(w, "rb") as f: + gd.ParseFromString(f.read()) + frozen_func = wrap_frozen_graph(gd, inputs="x:0", outputs=gd_outputs(gd)) + elif tflite or edgetpu: # https://www.tensorflow.org/lite/guide/python#install_tensorflow_lite_for_python + try: # https://coral.ai/docs/edgetpu/tflite-python/#update-existing-tf-lite-code-for-the-edge-tpu + from tflite_runtime.interpreter import Interpreter, load_delegate + except ImportError: + import tensorflow as tf + + Interpreter, load_delegate = ( + tf.lite.Interpreter, + tf.lite.experimental.load_delegate, + ) + if edgetpu: # TF Edge TPU https://coral.ai/software/#edgetpu-runtime + LOGGER.info(f"Loading {w} for TensorFlow Lite Edge TPU inference...") + delegate = {"Linux": "libedgetpu.so.1", "Darwin": "libedgetpu.1.dylib", "Windows": "edgetpu.dll"}[ + platform.system() + ] + interpreter = Interpreter(model_path=w, experimental_delegates=[load_delegate(delegate)]) + else: # TFLite + LOGGER.info(f"Loading {w} for TensorFlow Lite inference...") + interpreter = Interpreter(model_path=w) # load TFLite model + interpreter.allocate_tensors() # allocate + input_details = interpreter.get_input_details() # inputs + output_details = interpreter.get_output_details() # outputs + # load metadata + with contextlib.suppress(zipfile.BadZipFile): + with zipfile.ZipFile(w, "r") as model: + meta_file = model.namelist()[0] + meta = ast.literal_eval(model.read(meta_file).decode("utf-8")) + stride, names = int(meta["stride"]), meta["names"] + elif tfjs: # TF.js + raise NotImplementedError("ERROR: YOLOv5 TF.js inference is not supported") + elif paddle: # PaddlePaddle + LOGGER.info(f"Loading {w} for PaddlePaddle inference...") + check_requirements("paddlepaddle-gpu" if cuda else "paddlepaddle") + import paddle.inference as pdi + + if not Path(w).is_file(): # if not *.pdmodel + w = next(Path(w).rglob("*.pdmodel")) # get *.pdmodel file from *_paddle_model dir + weights = Path(w).with_suffix(".pdiparams") + config = pdi.Config(str(w), str(weights)) + if cuda: + config.enable_use_gpu(memory_pool_init_size_mb=2048, device_id=0) + predictor = pdi.create_predictor(config) + input_handle = predictor.get_input_handle(predictor.get_input_names()[0]) + output_names = predictor.get_output_names() + elif triton: # NVIDIA Triton Inference Server + LOGGER.info(f"Using {w} as Triton Inference Server...") + check_requirements("tritonclient[all]") + from app.util.yolov5.utils.triton import TritonRemoteModel + + model = TritonRemoteModel(url=w) + nhwc = model.runtime.startswith("tensorflow") + else: + raise NotImplementedError(f"ERROR: {w} is not a supported format") + + # class names + if "names" not in locals(): + names = yaml_load(data)["names"] if data else {i: f"class{i}" for i in range(999)} + if names[0] == "n01440764" and len(names) == 1000: # ImageNet + names = yaml_load(ROOT / "data/ImageNet.yaml")["names"] # human-readable names + + self.__dict__.update(locals()) # assign all variables to self + + def forward(self, im, augment=False, visualize=False): + """Performs YOLOv5 inference on input images with options for augmentation and visualization.""" + b, ch, h, w = im.shape # batch, channel, height, width + if self.fp16 and im.dtype != torch.float16: + im = im.half() # to FP16 + if self.nhwc: + im = im.permute(0, 2, 3, 1) # torch BCHW to numpy BHWC shape(1,320,192,3) + + if self.pt: # PyTorch + y = self.model(im, augment=augment, visualize=visualize) if augment or visualize else self.model(im) + elif self.jit: # TorchScript + y = self.model(im) + elif self.dnn: # ONNX OpenCV DNN + im = im.cpu().numpy() # torch to numpy + self.net.setInput(im) + y = self.net.forward() + elif self.onnx: # ONNX Runtime + im = im.cpu().numpy() # torch to numpy + y = self.session.run(self.output_names, {self.session.get_inputs()[0].name: im}) + elif self.xml: # OpenVINO + im = im.cpu().numpy() # FP32 + y = list(self.ov_compiled_model(im).values()) + elif self.engine: # TensorRT + if self.dynamic and im.shape != self.bindings["images"].shape: + i = self.model.get_binding_index("images") + self.context.set_binding_shape(i, im.shape) # reshape if dynamic + self.bindings["images"] = self.bindings["images"]._replace(shape=im.shape) + for name in self.output_names: + i = self.model.get_binding_index(name) + self.bindings[name].data.resize_(tuple(self.context.get_binding_shape(i))) + s = self.bindings["images"].shape + assert im.shape == s, f"input size {im.shape} {'>' if self.dynamic else 'not equal to'} max model size {s}" + self.binding_addrs["images"] = int(im.data_ptr()) + self.context.execute_v2(list(self.binding_addrs.values())) + y = [self.bindings[x].data for x in sorted(self.output_names)] + elif self.coreml: # CoreML + im = im.cpu().numpy() + im = Image.fromarray((im[0] * 255).astype("uint8")) + # im = im.resize((192, 320), Image.BILINEAR) + y = self.model.predict({"image": im}) # coordinates are xywh normalized + if "confidence" in y: + box = xywh2xyxy(y["coordinates"] * [[w, h, w, h]]) # xyxy pixels + conf, cls = y["confidence"].max(1), y["confidence"].argmax(1).astype(np.float) + y = np.concatenate((box, conf.reshape(-1, 1), cls.reshape(-1, 1)), 1) + else: + y = list(reversed(y.values())) # reversed for segmentation models (pred, proto) + elif self.paddle: # PaddlePaddle + im = im.cpu().numpy().astype(np.float32) + self.input_handle.copy_from_cpu(im) + self.predictor.run() + y = [self.predictor.get_output_handle(x).copy_to_cpu() for x in self.output_names] + elif self.triton: # NVIDIA Triton Inference Server + y = self.model(im) + else: # TensorFlow (SavedModel, GraphDef, Lite, Edge TPU) + im = im.cpu().numpy() + if self.saved_model: # SavedModel + y = self.model(im, training=False) if self.keras else self.model(im) + elif self.pb: # GraphDef + y = self.frozen_func(x=self.tf.constant(im)) + else: # Lite or Edge TPU + input = self.input_details[0] + int8 = input["dtype"] == np.uint8 # is TFLite quantized uint8 model + if int8: + scale, zero_point = input["quantization"] + im = (im / scale + zero_point).astype(np.uint8) # de-scale + self.interpreter.set_tensor(input["index"], im) + self.interpreter.invoke() + y = [] + for output in self.output_details: + x = self.interpreter.get_tensor(output["index"]) + if int8: + scale, zero_point = output["quantization"] + x = (x.astype(np.float32) - zero_point) * scale # re-scale + y.append(x) + if len(y) == 2 and len(y[1].shape) != 4: + y = list(reversed(y)) + y = [x if isinstance(x, np.ndarray) else x.numpy() for x in y] + y[0][..., :4] *= [w, h, w, h] # xywh normalized to pixels + + if isinstance(y, (list, tuple)): + return self.from_numpy(y[0]) if len(y) == 1 else [self.from_numpy(x) for x in y] + else: + return self.from_numpy(y) + + def from_numpy(self, x): + """Converts a NumPy array to a torch tensor, maintaining device compatibility.""" + return torch.from_numpy(x).to(self.device) if isinstance(x, np.ndarray) else x + + def warmup(self, imgsz=(1, 3, 640, 640)): + """Performs a single inference warmup to initialize model weights, accepting an `imgsz` tuple for image size.""" + warmup_types = self.pt, self.jit, self.onnx, self.engine, self.saved_model, self.pb, self.triton + if any(warmup_types) and (self.device.type != "cpu" or self.triton): + im = torch.empty(*imgsz, dtype=torch.half if self.fp16 else torch.float, device=self.device) # input + for _ in range(2 if self.jit else 1): # + self.forward(im) # warmup + + @staticmethod + def _model_type(p="path/to/model.pt"): + """ + Determines model type from file path or URL, supporting various export formats. + + Example: path='path/to/model.onnx' -> type=onnx + """ + # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] + from app.util.yolov5.utils.downloads import is_url + + sf = list(export_formats().Suffix) # export suffixes + if not is_url(p, check=False): + check_suffix(p, sf) # checks + url = urlparse(p) # if url may be Triton inference server + types = [s in Path(p).name for s in sf] + types[8] &= not types[9] # tflite &= not edgetpu + triton = not any(types) and all([any(s in url.scheme for s in ["http", "grpc"]), url.netloc]) + return types + [triton] + + @staticmethod + def _load_metadata(f=Path("path/to/meta.yaml")): + """Loads metadata from a YAML file, returning strides and names if the file exists, otherwise `None`.""" + if f.exists(): + d = yaml_load(f) + return d["stride"], d["names"] # assign stride, names + return None, None + + +class AutoShape(nn.Module): + """AutoShape class for robust YOLOv5 inference with preprocessing, NMS, and support for various input formats.""" + + conf = 0.25 # NMS confidence threshold + iou = 0.45 # NMS IoU threshold + agnostic = False # NMS class-agnostic + multi_label = False # NMS multiple labels per box + classes = None # (optional list) filter by class, i.e. = [0, 15, 16] for COCO persons, cats and dogs + max_det = 1000 # maximum number of detections per image + amp = False # Automatic Mixed Precision (AMP) inference + + def __init__(self, model, verbose=True): + """Initializes YOLOv5 model for inference, setting up attributes and preparing model for evaluation.""" + super().__init__() + if verbose: + LOGGER.info("Adding AutoShape... ") + copy_attr(self, model, include=("yaml", "nc", "hyp", "names", "stride", "abc"), exclude=()) # copy attributes + self.dmb = isinstance(model, DetectMultiBackend) # DetectMultiBackend() instance + self.pt = not self.dmb or model.pt # PyTorch model + self.model = model.eval() + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.inplace = False # Detect.inplace=False for safe multithread inference + m.export = True # do not output loss values + + def _apply(self, fn): + """ + Applies to(), cpu(), cuda(), half() etc. + + to model tensors excluding parameters or registered buffers. + """ + self = super()._apply(fn) + if self.pt: + m = self.model.model.model[-1] if self.dmb else self.model.model[-1] # Detect() + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + @smart_inference_mode() + def forward(self, ims, size=640, augment=False, profile=False): + """ + Performs inference on inputs with optional augment & profiling. + + Supports various formats including file, URI, OpenCV, PIL, numpy, torch. + """ + # For size(height=640, width=1280), RGB images example inputs are: + # file: ims = 'data/images/zidane.jpg' # str or PosixPath + # URI: = 'https://ultralytics.com/images/zidane.jpg' + # OpenCV: = cv2.imread('image.jpg')[:,:,::-1] # HWC BGR to RGB x(640,1280,3) + # PIL: = Image.open('image.jpg') or ImageGrab.grab() # HWC x(640,1280,3) + # numpy: = np.zeros((640,1280,3)) # HWC + # torch: = torch.zeros(16,3,320,640) # BCHW (scaled to size=640, 0-1 values) + # multiple: = [Image.open('image1.jpg'), Image.open('image2.jpg'), ...] # list of images + + dt = (Profile(), Profile(), Profile()) + with dt[0]: + if isinstance(size, int): # expand + size = (size, size) + p = next(self.model.parameters()) if self.pt else torch.empty(1, device=self.model.device) # param + autocast = self.amp and (p.device.type != "cpu") # Automatic Mixed Precision (AMP) inference + if isinstance(ims, torch.Tensor): # torch + with amp.autocast(autocast): + return self.model(ims.to(p.device).type_as(p), augment=augment) # inference + + # Pre-process + n, ims = (len(ims), list(ims)) if isinstance(ims, (list, tuple)) else (1, [ims]) # number, list of images + shape0, shape1, files = [], [], [] # image and inference shapes, filenames + for i, im in enumerate(ims): + f = f"image{i}" # filename + if isinstance(im, (str, Path)): # filename or uri + im, f = Image.open(requests.get(im, stream=True).raw if str(im).startswith("http") else im), im + im = np.asarray(exif_transpose(im)) + elif isinstance(im, Image.Image): # PIL Image + im, f = np.asarray(exif_transpose(im)), getattr(im, "filename", f) or f + files.append(Path(f).with_suffix(".jpg").name) + if im.shape[0] < 5: # image in CHW + im = im.transpose((1, 2, 0)) # reverse dataloader .transpose(2, 0, 1) + im = im[..., :3] if im.ndim == 3 else cv2.cvtColor(im, cv2.COLOR_GRAY2BGR) # enforce 3ch input + s = im.shape[:2] # HWC + shape0.append(s) # image shape + g = max(size) / max(s) # gain + shape1.append([int(y * g) for y in s]) + ims[i] = im if im.data.contiguous else np.ascontiguousarray(im) # update + shape1 = [make_divisible(x, self.stride) for x in np.array(shape1).max(0)] # inf shape + x = [letterbox(im, shape1, auto=False)[0] for im in ims] # pad + x = np.ascontiguousarray(np.array(x).transpose((0, 3, 1, 2))) # stack and BHWC to BCHW + x = torch.from_numpy(x).to(p.device).type_as(p) / 255 # uint8 to fp16/32 + + with amp.autocast(autocast): + # Inference + with dt[1]: + y = self.model(x, augment=augment) # forward + + # Post-process + with dt[2]: + y = non_max_suppression( + y if self.dmb else y[0], + self.conf, + self.iou, + self.classes, + self.agnostic, + self.multi_label, + max_det=self.max_det, + ) # NMS + for i in range(n): + scale_boxes(shape1, y[i][:, :4], shape0[i]) + + return Detections(ims, y, files, dt, self.names, x.shape) + + +class Detections: + """Manages YOLOv5 detection results with methods for visualization, saving, cropping, and exporting detections.""" + + def __init__(self, ims, pred, files, times=(0, 0, 0), names=None, shape=None): + """Initializes the YOLOv5 Detections class with image info, predictions, filenames, timing and normalization.""" + super().__init__() + d = pred[0].device # device + gn = [torch.tensor([*(im.shape[i] for i in [1, 0, 1, 0]), 1, 1], device=d) for im in ims] # normalizations + self.ims = ims # list of images as numpy arrays + self.pred = pred # list of tensors pred[0] = (xyxy, conf, cls) + self.names = names # class names + self.files = files # image filenames + self.times = times # profiling times + self.xyxy = pred # xyxy pixels + self.xywh = [xyxy2xywh(x) for x in pred] # xywh pixels + self.xyxyn = [x / g for x, g in zip(self.xyxy, gn)] # xyxy normalized + self.xywhn = [x / g for x, g in zip(self.xywh, gn)] # xywh normalized + self.n = len(self.pred) # number of images (batch size) + self.t = tuple(x.t / self.n * 1e3 for x in times) # timestamps (ms) + self.s = tuple(shape) # inference BCHW shape + + def _run(self, pprint=False, show=False, save=False, crop=False, render=False, labels=True, save_dir=Path("")): + """Executes model predictions, displaying and/or saving outputs with optional crops and labels.""" + s, crops = "", [] + for i, (im, pred) in enumerate(zip(self.ims, self.pred)): + s += f"\nimage {i + 1}/{len(self.pred)}: {im.shape[0]}x{im.shape[1]} " # string + if pred.shape[0]: + for c in pred[:, -1].unique(): + n = (pred[:, -1] == c).sum() # detections per class + s += f"{n} {self.names[int(c)]}{'s' * (n > 1)}, " # add to string + s = s.rstrip(", ") + if show or save or render or crop: + annotator = Annotator(im, example=str(self.names)) + for *box, conf, cls in reversed(pred): # xyxy, confidence, class + label = f"{self.names[int(cls)]} {conf:.2f}" + if crop: + file = save_dir / "crops" / self.names[int(cls)] / self.files[i] if save else None + crops.append( + { + "box": box, + "conf": conf, + "cls": cls, + "label": label, + "im": save_one_box(box, im, file=file, save=save), + } + ) + else: # all others + annotator.box_label(box, label if labels else "", color=colors(cls)) + im = annotator.im + else: + s += "(no detections)" + + im = Image.fromarray(im.astype(np.uint8)) if isinstance(im, np.ndarray) else im # from np + if show: + if is_jupyter(): + from IPython.display import display + + display(im) + else: + im.show(self.files[i]) + if save: + f = self.files[i] + im.save(save_dir / f) # save + if i == self.n - 1: + LOGGER.info(f"Saved {self.n} image{'s' * (self.n > 1)} to {colorstr('bold', save_dir)}") + if render: + self.ims[i] = np.asarray(im) + if pprint: + s = s.lstrip("\n") + return f"{s}\nSpeed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {self.s}" % self.t + if crop: + if save: + LOGGER.info(f"Saved results to {save_dir}\n") + return crops + + @TryExcept("Showing images is not supported in this environment") + def show(self, labels=True): + """ + Displays detection results with optional labels. + + Usage: show(labels=True) + """ + self._run(show=True, labels=labels) # show results + + def save(self, labels=True, save_dir="runs/detect/exp", exist_ok=False): + """ + Saves detection results with optional labels to a specified directory. + + Usage: save(labels=True, save_dir='runs/detect/exp', exist_ok=False) + """ + save_dir = increment_path(save_dir, exist_ok, mkdir=True) # increment save_dir + self._run(save=True, labels=labels, save_dir=save_dir) # save results + + def crop(self, save=True, save_dir="runs/detect/exp", exist_ok=False): + """ + Crops detection results, optionally saves them to a directory. + + Args: save (bool), save_dir (str), exist_ok (bool). + """ + save_dir = increment_path(save_dir, exist_ok, mkdir=True) if save else None + return self._run(crop=True, save=save, save_dir=save_dir) # crop results + + def render(self, labels=True): + """Renders detection results with optional labels on images; args: labels (bool) indicating label inclusion.""" + self._run(render=True, labels=labels) # render results + return self.ims + + def pandas(self): + """ + Returns detections as pandas DataFrames for various box formats (xyxy, xyxyn, xywh, xywhn). + + Example: print(results.pandas().xyxy[0]). + """ + new = copy(self) # return copy + ca = "xmin", "ymin", "xmax", "ymax", "confidence", "class", "name" # xyxy columns + cb = "xcenter", "ycenter", "width", "height", "confidence", "class", "name" # xywh columns + for k, c in zip(["xyxy", "xyxyn", "xywh", "xywhn"], [ca, ca, cb, cb]): + a = [[x[:5] + [int(x[5]), self.names[int(x[5])]] for x in x.tolist()] for x in getattr(self, k)] # update + setattr(new, k, [pd.DataFrame(x, columns=c) for x in a]) + return new + + def tolist(self): + """ + Converts a Detections object into a list of individual detection results for iteration. + + Example: for result in results.tolist(): + """ + r = range(self.n) # iterable + return [ + Detections( + [self.ims[i]], + [self.pred[i]], + [self.files[i]], + self.times, + self.names, + self.s, + ) + for i in r + ] + + def print(self): + """Logs the string representation of the current object's state via the LOGGER.""" + LOGGER.info(self.__str__()) + + def __len__(self): + """Returns the number of results stored, overrides the default len(results).""" + return self.n + + def __str__(self): + """Returns a string representation of the model's results, suitable for printing, overrides default + print(results). + """ + return self._run(pprint=True) # print results + + def __repr__(self): + """Returns a string representation of the YOLOv5 object, including its class and formatted results.""" + return f"YOLOv5 {self.__class__} instance\n" + self.__str__() + + +class Proto(nn.Module): + """YOLOv5 mask Proto module for segmentation models, performing convolutions and upsampling on input tensors.""" + + def __init__(self, c1, c_=256, c2=32): + """Initializes YOLOv5 Proto module for segmentation with input, proto, and mask channels configuration.""" + super().__init__() + self.cv1 = Conv(c1, c_, k=3) + self.upsample = nn.Upsample(scale_factor=2, mode="nearest") + self.cv2 = Conv(c_, c_, k=3) + self.cv3 = Conv(c_, c2) + + def forward(self, x): + """Performs a forward pass using convolutional layers and upsampling on input tensor `x`.""" + return self.cv3(self.cv2(self.upsample(self.cv1(x)))) + + +class Classify(nn.Module): + """YOLOv5 classification head with convolution, pooling, and dropout layers for channel transformation.""" + + def __init__( + self, c1, c2, k=1, s=1, p=None, g=1, dropout_p=0.0 + ): # ch_in, ch_out, kernel, stride, padding, groups, dropout probability + """Initializes YOLOv5 classification head with convolution, pooling, and dropout layers for input to output + channel transformation. + """ + super().__init__() + c_ = 1280 # efficientnet_b0 size + self.conv = Conv(c1, c_, k, s, autopad(k, p), g) + self.pool = nn.AdaptiveAvgPool2d(1) # to x(b,c_,1,1) + self.drop = nn.Dropout(p=dropout_p, inplace=True) + self.linear = nn.Linear(c_, c2) # to x(b,c2) + + def forward(self, x): + """Processes input through conv, pool, drop, and linear layers; supports list concatenation input.""" + if isinstance(x, list): + x = torch.cat(x, 1) + return self.linear(self.drop(self.pool(self.conv(x)).flatten(1))) + +def export_formats(): + r""" + Returns a DataFrame of supported YOLOv5 model export formats and their properties. + + Returns: + pandas.DataFrame: A DataFrame containing supported export formats and their properties. The DataFrame + includes columns for format name, CLI argument suffix, file extension or directory name, and boolean flags + indicating if the export format supports training and detection. + + Examples: + ```python + formats = export_formats() + print(f"Supported export formats:\n{formats}") + ``` + + Notes: + The DataFrame contains the following columns: + - Format: The name of the model format (e.g., PyTorch, TorchScript, ONNX, etc.). + - Include Argument: The argument to use with the export script to include this format. + - File Suffix: File extension or directory name associated with the format. + - Supports Training: Whether the format supports training. + - Supports Detection: Whether the format supports detection. + """ + x = [ + ["PyTorch", "-", ".pt", True, True], + ["TorchScript", "torchscript", ".torchscript", True, True], + ["ONNX", "onnx", ".onnx", True, True], + ["OpenVINO", "openvino", "_openvino_model", True, False], + ["TensorRT", "engine", ".engine", False, True], + ["CoreML", "coreml", ".mlpackage", True, False], + ["TensorFlow SavedModel", "saved_model", "_saved_model", True, True], + ["TensorFlow GraphDef", "pb", ".pb", True, True], + ["TensorFlow Lite", "tflite", ".tflite", True, False], + ["TensorFlow Edge TPU", "edgetpu", "_edgetpu.tflite", False, False], + ["TensorFlow.js", "tfjs", "_web_model", False, False], + ["PaddlePaddle", "paddle", "_paddle_model", True, True], + ] + return pd.DataFrame(x, columns=["Format", "Argument", "Suffix", "CPU", "GPU"]) \ No newline at end of file diff --git a/app/util/yolov5/models/experimental.py b/app/util/yolov5/models/experimental.py new file mode 100644 index 0000000..8ebd539 --- /dev/null +++ b/app/util/yolov5/models/experimental.py @@ -0,0 +1,130 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Experimental modules.""" + +import math + +import numpy as np +import torch +import torch.nn as nn + +from app.util.yolov5.utils.downloads import attempt_download + + +class Sum(nn.Module): + """Weighted sum of 2 or more layers https://arxiv.org/abs/1911.09070.""" + + def __init__(self, n, weight=False): + """Initializes a module to sum outputs of layers with number of inputs `n` and optional weighting, supporting 2+ + inputs. + """ + super().__init__() + self.weight = weight # apply weights boolean + self.iter = range(n - 1) # iter object + if weight: + self.w = nn.Parameter(-torch.arange(1.0, n) / 2, requires_grad=True) # layer weights + + def forward(self, x): + """Processes input through a customizable weighted sum of `n` inputs, optionally applying learned weights.""" + y = x[0] # no weight + if self.weight: + w = torch.sigmoid(self.w) * 2 + for i in self.iter: + y = y + x[i + 1] * w[i] + else: + for i in self.iter: + y = y + x[i + 1] + return y + + +class MixConv2d(nn.Module): + """Mixed Depth-wise Conv https://arxiv.org/abs/1907.09595.""" + + def __init__(self, c1, c2, k=(1, 3), s=1, equal_ch=True): + """Initializes MixConv2d with mixed depth-wise convolutional layers, taking input and output channels (c1, c2), + kernel sizes (k), stride (s), and channel distribution strategy (equal_ch). + """ + super().__init__() + n = len(k) # number of convolutions + if equal_ch: # equal c_ per group + i = torch.linspace(0, n - 1e-6, c2).floor() # c2 indices + c_ = [(i == g).sum() for g in range(n)] # intermediate channels + else: # equal weight.numel() per group + b = [c2] + [0] * n + a = np.eye(n + 1, n, k=-1) + a -= np.roll(a, 1, axis=1) + a *= np.array(k) ** 2 + a[0] = 1 + c_ = np.linalg.lstsq(a, b, rcond=None)[0].round() # solve for equal weight indices, ax = b + + self.m = nn.ModuleList( + [nn.Conv2d(c1, int(c_), k, s, k // 2, groups=math.gcd(c1, int(c_)), bias=False) for k, c_ in zip(k, c_)] + ) + self.bn = nn.BatchNorm2d(c2) + self.act = nn.SiLU() + + def forward(self, x): + """Performs forward pass by applying SiLU activation on batch-normalized concatenated convolutional layer + outputs. + """ + return self.act(self.bn(torch.cat([m(x) for m in self.m], 1))) + + +class Ensemble(nn.ModuleList): + """Ensemble of models.""" + + def __init__(self): + """Initializes an ensemble of models to be used for aggregated predictions.""" + super().__init__() + + def forward(self, x, augment=False, profile=False, visualize=False): + """Performs forward pass aggregating outputs from an ensemble of models..""" + y = [module(x, augment, profile, visualize)[0] for module in self] + # y = torch.stack(y).max(0)[0] # max ensemble + # y = torch.stack(y).mean(0) # mean ensemble + y = torch.cat(y, 1) # nms ensemble + return y, None # inference, train output + + +def attempt_load(weights, device=None, inplace=True, fuse=True): + """ + Loads and fuses an ensemble or single YOLOv5 model from weights, handling device placement and model adjustments. + + Example inputs: weights=[a,b,c] or a single model weights=[a] or weights=a. + """ + from app.util.yolov5.models.yolo import Detect, Model + + model = Ensemble() + for w in weights if isinstance(weights, list) else [weights]: + ckpt = torch.load(attempt_download(w), map_location="cpu") # load + ckpt = (ckpt.get("ema") or ckpt["model"]).to(device).float() # FP32 model + + # Model compatibility updates + if not hasattr(ckpt, "stride"): + ckpt.stride = torch.tensor([32.0]) + if hasattr(ckpt, "names") and isinstance(ckpt.names, (list, tuple)): + ckpt.names = dict(enumerate(ckpt.names)) # convert to dict + + model.append(ckpt.fuse().eval() if fuse and hasattr(ckpt, "fuse") else ckpt.eval()) # model in eval mode + + # Module updates + for m in model.modules(): + t = type(m) + if t in (nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU, Detect, Model): + m.inplace = inplace + if t is Detect and not isinstance(m.anchor_grid, list): + delattr(m, "anchor_grid") + setattr(m, "anchor_grid", [torch.zeros(1)] * m.nl) + elif t is nn.Upsample and not hasattr(m, "recompute_scale_factor"): + m.recompute_scale_factor = None # torch 1.11.0 compatibility + + # Return model + if len(model) == 1: + return model[-1] + + # Return detection ensemble + print(f"Ensemble created with {weights}\n") + for k in "names", "nc", "yaml": + setattr(model, k, getattr(model[0], k)) + model.stride = model[torch.argmax(torch.tensor([m.stride.max() for m in model])).int()].stride # max stride + assert all(model[0].nc == m.nc for m in model), f"Models have different class counts: {[m.nc for m in model]}" + return model diff --git a/app/util/yolov5/models/hub/anchors.yaml b/app/util/yolov5/models/hub/anchors.yaml new file mode 100644 index 0000000..0f3e288 --- /dev/null +++ b/app/util/yolov5/models/hub/anchors.yaml @@ -0,0 +1,57 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Default anchors for COCO data + +# P5 ------------------------------------------------------------------------------------------------------------------- +# P5-640: +anchors_p5_640: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# P6 ------------------------------------------------------------------------------------------------------------------- +# P6-640: thr=0.25: 0.9964 BPR, 5.54 anchors past thr, n=12, img_size=640, metric_all=0.281/0.716-mean/best, past_thr=0.469-mean: 9,11, 21,19, 17,41, 43,32, 39,70, 86,64, 65,131, 134,130, 120,265, 282,180, 247,354, 512,387 +anchors_p6_640: + - [9, 11, 21, 19, 17, 41] # P3/8 + - [43, 32, 39, 70, 86, 64] # P4/16 + - [65, 131, 134, 130, 120, 265] # P5/32 + - [282, 180, 247, 354, 512, 387] # P6/64 + +# P6-1280: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1280, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 19,27, 44,40, 38,94, 96,68, 86,152, 180,137, 140,301, 303,264, 238,542, 436,615, 739,380, 925,792 +anchors_p6_1280: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# P6-1920: thr=0.25: 0.9950 BPR, 5.55 anchors past thr, n=12, img_size=1920, metric_all=0.281/0.714-mean/best, past_thr=0.468-mean: 28,41, 67,59, 57,141, 144,103, 129,227, 270,205, 209,452, 455,396, 358,812, 653,922, 1109,570, 1387,1187 +anchors_p6_1920: + - [28, 41, 67, 59, 57, 141] # P3/8 + - [144, 103, 129, 227, 270, 205] # P4/16 + - [209, 452, 455, 396, 358, 812] # P5/32 + - [653, 922, 1109, 570, 1387, 1187] # P6/64 + +# P7 ------------------------------------------------------------------------------------------------------------------- +# P7-640: thr=0.25: 0.9962 BPR, 6.76 anchors past thr, n=15, img_size=640, metric_all=0.275/0.733-mean/best, past_thr=0.466-mean: 11,11, 13,30, 29,20, 30,46, 61,38, 39,92, 78,80, 146,66, 79,163, 149,150, 321,143, 157,303, 257,402, 359,290, 524,372 +anchors_p7_640: + - [11, 11, 13, 30, 29, 20] # P3/8 + - [30, 46, 61, 38, 39, 92] # P4/16 + - [78, 80, 146, 66, 79, 163] # P5/32 + - [149, 150, 321, 143, 157, 303] # P6/64 + - [257, 402, 359, 290, 524, 372] # P7/128 + +# P7-1280: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1280, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 19,22, 54,36, 32,77, 70,83, 138,71, 75,173, 165,159, 148,334, 375,151, 334,317, 251,626, 499,474, 750,326, 534,814, 1079,818 +anchors_p7_1280: + - [19, 22, 54, 36, 32, 77] # P3/8 + - [70, 83, 138, 71, 75, 173] # P4/16 + - [165, 159, 148, 334, 375, 151] # P5/32 + - [334, 317, 251, 626, 499, 474] # P6/64 + - [750, 326, 534, 814, 1079, 818] # P7/128 + +# P7-1920: thr=0.25: 0.9968 BPR, 6.71 anchors past thr, n=15, img_size=1920, metric_all=0.273/0.732-mean/best, past_thr=0.463-mean: 29,34, 81,55, 47,115, 105,124, 207,107, 113,259, 247,238, 222,500, 563,227, 501,476, 376,939, 749,711, 1126,489, 801,1222, 1618,1227 +anchors_p7_1920: + - [29, 34, 81, 55, 47, 115] # P3/8 + - [105, 124, 207, 107, 113, 259] # P4/16 + - [247, 238, 222, 500, 563, 227] # P5/32 + - [501, 476, 376, 939, 749, 711] # P6/64 + - [1126, 489, 801, 1222, 1618, 1227] # P7/128 diff --git a/app/util/yolov5/models/hub/yolov3-spp.yaml b/app/util/yolov5/models/hub/yolov3-spp.yaml new file mode 100644 index 0000000..34c2d51 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov3-spp.yaml @@ -0,0 +1,52 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3-SPP head +head: [ + [-1, 1, Bottleneck, [1024, False]], + [-1, 1, SPP, [512, [5, 9, 13]]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov3-tiny.yaml b/app/util/yolov5/models/hub/yolov3-tiny.yaml new file mode 100644 index 0000000..f186101 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov3-tiny.yaml @@ -0,0 +1,42 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 14, 23, 27, 37, 58] # P4/16 + - [81, 82, 135, 169, 344, 319] # P5/32 + +# YOLOv3-tiny backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [16, 3, 1]], # 0 + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 1-P1/2 + [-1, 1, Conv, [32, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 3-P2/4 + [-1, 1, Conv, [64, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 5-P3/8 + [-1, 1, Conv, [128, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 7-P4/16 + [-1, 1, Conv, [256, 3, 1]], + [-1, 1, nn.MaxPool2d, [2, 2, 0]], # 9-P5/32 + [-1, 1, Conv, [512, 3, 1]], + [-1, 1, nn.ZeroPad2d, [[0, 1, 0, 1]]], # 11 + [-1, 1, nn.MaxPool2d, [2, 1, 0]], # 12 + ] + +# YOLOv3-tiny head +head: [ + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [256, 3, 1]], # 19 (P4/16-medium) + + [[19, 15], 1, Detect, [nc, anchors]], # Detect(P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov3.yaml b/app/util/yolov5/models/hub/yolov3.yaml new file mode 100644 index 0000000..15cb68a --- /dev/null +++ b/app/util/yolov5/models/hub/yolov3.yaml @@ -0,0 +1,52 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# darknet53 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [32, 3, 1]], # 0 + [-1, 1, Conv, [64, 3, 2]], # 1-P1/2 + [-1, 1, Bottleneck, [64]], + [-1, 1, Conv, [128, 3, 2]], # 3-P2/4 + [-1, 2, Bottleneck, [128]], + [-1, 1, Conv, [256, 3, 2]], # 5-P3/8 + [-1, 8, Bottleneck, [256]], + [-1, 1, Conv, [512, 3, 2]], # 7-P4/16 + [-1, 8, Bottleneck, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P5/32 + [-1, 4, Bottleneck, [1024]], # 10 + ] + +# YOLOv3 head +head: [ + [-1, 1, Bottleneck, [1024, False]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, Conv, [1024, 3, 1]], # 15 (P5/32-large) + + [-2, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Bottleneck, [512, False]], + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, Conv, [512, 3, 1]], # 22 (P4/16-medium) + + [-2, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Bottleneck, [256, False]], + [-1, 2, Bottleneck, [256, False]], # 27 (P3/8-small) + + [[27, 22, 15], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5-bifpn.yaml b/app/util/yolov5/models/hub/yolov5-bifpn.yaml new file mode 100644 index 0000000..fba3fe5 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5-bifpn.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 BiFPN head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14, 6], 1, Concat, [1]], # cat P4 <--- BiFPN change + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5-fpn.yaml b/app/util/yolov5/models/hub/yolov5-fpn.yaml new file mode 100644 index 0000000..4411d1c --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5-fpn.yaml @@ -0,0 +1,43 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 FPN head +head: [ + [-1, 3, C3, [1024, False]], # 10 (P5/32-large) + + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 1, Conv, [512, 1, 1]], + [-1, 3, C3, [512, False]], # 14 (P4/16-medium) + + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 1, Conv, [256, 1, 1]], + [-1, 3, C3, [256, False]], # 18 (P3/8-small) + + [[18, 14, 10], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5-p2.yaml b/app/util/yolov5/models/hub/yolov5-p2.yaml new file mode 100644 index 0000000..e47d39e --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5-p2.yaml @@ -0,0 +1,55 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head with (P2, P3, P4, P5) outputs +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [128, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 2], 1, Concat, [1]], # cat backbone P2 + [-1, 1, C3, [128, False]], # 21 (P2/4-xsmall) + + [-1, 1, Conv, [128, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P3 + [-1, 3, C3, [256, False]], # 24 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 27 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 30 (P5/32-large) + + [[21, 24, 27, 30], 1, Detect, [nc, anchors]], # Detect(P2, P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5-p34.yaml b/app/util/yolov5/models/hub/yolov5-p34.yaml new file mode 100644 index 0000000..17e46f7 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5-p34.yaml @@ -0,0 +1,42 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head with (P3, P4) outputs +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [[17, 20], 1, Detect, [nc, anchors]], # Detect(P3, P4) + ] diff --git a/app/util/yolov5/models/hub/yolov5-p6.yaml b/app/util/yolov5/models/hub/yolov5-p6.yaml new file mode 100644 index 0000000..dbc1ae4 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5-p6.yaml @@ -0,0 +1,57 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6) outputs +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/app/util/yolov5/models/hub/yolov5-p7.yaml b/app/util/yolov5/models/hub/yolov5-p7.yaml new file mode 100644 index 0000000..2c17069 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5-p7.yaml @@ -0,0 +1,68 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: 3 # AutoAnchor evolves 3 anchors per P output layer + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, Conv, [1280, 3, 2]], # 11-P7/128 + [-1, 3, C3, [1280]], + [-1, 1, SPPF, [1280, 5]], # 13 + ] + +# YOLOv5 v6.0 head with (P3, P4, P5, P6, P7) outputs +head: [ + [-1, 1, Conv, [1024, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 10], 1, Concat, [1]], # cat backbone P6 + [-1, 3, C3, [1024, False]], # 17 + + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 21 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 25 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 29 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 26], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 32 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 22], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 35 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 18], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 38 (P6/64-xlarge) + + [-1, 1, Conv, [1024, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P7 + [-1, 3, C3, [1280, False]], # 41 (P7/128-xxlarge) + + [[29, 32, 35, 38, 41], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6, P7) + ] diff --git a/app/util/yolov5/models/hub/yolov5-panet.yaml b/app/util/yolov5/models/hub/yolov5-panet.yaml new file mode 100644 index 0000000..68a7175 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5-panet.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 PANet head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5l6.yaml b/app/util/yolov5/models/hub/yolov5l6.yaml new file mode 100644 index 0000000..223f681 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5l6.yaml @@ -0,0 +1,61 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/app/util/yolov5/models/hub/yolov5m6.yaml b/app/util/yolov5/models/hub/yolov5m6.yaml new file mode 100644 index 0000000..6878d89 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5m6.yaml @@ -0,0 +1,61 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/app/util/yolov5/models/hub/yolov5n6.yaml b/app/util/yolov5/models/hub/yolov5n6.yaml new file mode 100644 index 0000000..0d454c9 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5n6.yaml @@ -0,0 +1,61 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/app/util/yolov5/models/hub/yolov5s-LeakyReLU.yaml b/app/util/yolov5/models/hub/yolov5s-LeakyReLU.yaml new file mode 100644 index 0000000..61d6d33 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5s-LeakyReLU.yaml @@ -0,0 +1,50 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +activation: nn.LeakyReLU(0.1) # <----- Conv() activation used throughout entire YOLOv5 model +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5s-ghost.yaml b/app/util/yolov5/models/hub/yolov5s-ghost.yaml new file mode 100644 index 0000000..53695ae --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5s-ghost.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, GhostConv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3Ghost, [128]], + [-1, 1, GhostConv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3Ghost, [256]], + [-1, 1, GhostConv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3Ghost, [512]], + [-1, 1, GhostConv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3Ghost, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, GhostConv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3Ghost, [512, False]], # 13 + + [-1, 1, GhostConv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3Ghost, [256, False]], # 17 (P3/8-small) + + [-1, 1, GhostConv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3Ghost, [512, False]], # 20 (P4/16-medium) + + [-1, 1, GhostConv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3Ghost, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5s-transformer.yaml b/app/util/yolov5/models/hub/yolov5s-transformer.yaml new file mode 100644 index 0000000..213e4da --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5s-transformer.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3TR, [1024]], # 9 <--- C3TR() Transformer module + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/hub/yolov5s6.yaml b/app/util/yolov5/models/hub/yolov5s6.yaml new file mode 100644 index 0000000..6e69964 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5s6.yaml @@ -0,0 +1,61 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/app/util/yolov5/models/hub/yolov5x6.yaml b/app/util/yolov5/models/hub/yolov5x6.yaml new file mode 100644 index 0000000..33a8525 --- /dev/null +++ b/app/util/yolov5/models/hub/yolov5x6.yaml @@ -0,0 +1,61 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [19, 27, 44, 40, 38, 94] # P3/8 + - [96, 68, 86, 152, 180, 137] # P4/16 + - [140, 301, 303, 264, 238, 542] # P5/32 + - [436, 615, 739, 380, 925, 792] # P6/64 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [768, 3, 2]], # 7-P5/32 + [-1, 3, C3, [768]], + [-1, 1, Conv, [1024, 3, 2]], # 9-P6/64 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 11 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [768, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 8], 1, Concat, [1]], # cat backbone P5 + [-1, 3, C3, [768, False]], # 15 + + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 19 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 23 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 20], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 26 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 16], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [768, False]], # 29 (P5/32-large) + + [-1, 1, Conv, [768, 3, 2]], + [[-1, 12], 1, Concat, [1]], # cat head P6 + [-1, 3, C3, [1024, False]], # 32 (P6/64-xlarge) + + [[23, 26, 29, 32], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5, P6) + ] diff --git a/app/util/yolov5/models/segment/yolov5l-seg.yaml b/app/util/yolov5/models/segment/yolov5l-seg.yaml new file mode 100644 index 0000000..824e8ae --- /dev/null +++ b/app/util/yolov5/models/segment/yolov5l-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/segment/yolov5m-seg.yaml b/app/util/yolov5/models/segment/yolov5m-seg.yaml new file mode 100644 index 0000000..c3c1e66 --- /dev/null +++ b/app/util/yolov5/models/segment/yolov5m-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/segment/yolov5n-seg.yaml b/app/util/yolov5/models/segment/yolov5n-seg.yaml new file mode 100644 index 0000000..2461e41 --- /dev/null +++ b/app/util/yolov5/models/segment/yolov5n-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/segment/yolov5s-seg.yaml b/app/util/yolov5/models/segment/yolov5s-seg.yaml new file mode 100644 index 0000000..fac7664 --- /dev/null +++ b/app/util/yolov5/models/segment/yolov5s-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.5 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/segment/yolov5x-seg.yaml b/app/util/yolov5/models/segment/yolov5x-seg.yaml new file mode 100644 index 0000000..d3c457a --- /dev/null +++ b/app/util/yolov5/models/segment/yolov5x-seg.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Segment, [nc, anchors, 32, 256]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/tf.py b/app/util/yolov5/models/tf.py new file mode 100644 index 0000000..c2cad39 --- /dev/null +++ b/app/util/yolov5/models/tf.py @@ -0,0 +1,797 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +""" +TensorFlow, Keras and TFLite versions of YOLOv5 +Authored by https://github.com/zldrobit in PR https://github.com/ultralytics/yolov5/pull/1127. + +Usage: + $ python models/tf.py --weights yolov5s.pt + +Export: + $ python export.py --weights yolov5s.pt --include saved_model pb tflite tfjs +""" + +import argparse +import sys +from copy import deepcopy +from pathlib import Path + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +# ROOT = ROOT.relative_to(Path.cwd()) # relative + +import numpy as np +import tensorflow as tf +import torch +import torch.nn as nn +from tensorflow import keras + +from models.common import ( + C3, + SPP, + SPPF, + Bottleneck, + BottleneckCSP, + C3x, + Concat, + Conv, + CrossConv, + DWConv, + DWConvTranspose2d, + Focus, + autopad, +) +from models.experimental import MixConv2d, attempt_load +from models.yolo import Detect, Segment +from utils.activations import SiLU +from utils.general import LOGGER, make_divisible, print_args + + +class TFBN(keras.layers.Layer): + """TensorFlow BatchNormalization wrapper for initializing with optional pretrained weights.""" + + def __init__(self, w=None): + """Initializes a TensorFlow BatchNormalization layer with optional pretrained weights.""" + super().__init__() + self.bn = keras.layers.BatchNormalization( + beta_initializer=keras.initializers.Constant(w.bias.numpy()), + gamma_initializer=keras.initializers.Constant(w.weight.numpy()), + moving_mean_initializer=keras.initializers.Constant(w.running_mean.numpy()), + moving_variance_initializer=keras.initializers.Constant(w.running_var.numpy()), + epsilon=w.eps, + ) + + def call(self, inputs): + """Applies batch normalization to the inputs.""" + return self.bn(inputs) + + +class TFPad(keras.layers.Layer): + """Pads input tensors in spatial dimensions 1 and 2 with specified integer or tuple padding values.""" + + def __init__(self, pad): + """ + Initializes a padding layer for spatial dimensions 1 and 2 with specified padding, supporting both int and tuple + inputs. + + Inputs are + """ + super().__init__() + if isinstance(pad, int): + self.pad = tf.constant([[0, 0], [pad, pad], [pad, pad], [0, 0]]) + else: # tuple/list + self.pad = tf.constant([[0, 0], [pad[0], pad[0]], [pad[1], pad[1]], [0, 0]]) + + def call(self, inputs): + """Pads input tensor with zeros using specified padding, suitable for int and tuple pad dimensions.""" + return tf.pad(inputs, self.pad, mode="constant", constant_values=0) + + +class TFConv(keras.layers.Layer): + """Implements a standard convolutional layer with optional batch normalization and activation for TensorFlow.""" + + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + """ + Initializes a standard convolution layer with optional batch normalization and activation; supports only + group=1. + + Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups. + """ + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + # TensorFlow convolution padding is inconsistent with PyTorch (e.g. k=3 s=2 'SAME' padding) + # see https://stackoverflow.com/questions/52975843/comparing-conv2d-with-padding-between-tensorflow-and-pytorch + conv = keras.layers.Conv2D( + filters=c2, + kernel_size=k, + strides=s, + padding="SAME" if s == 1 else "VALID", + use_bias=not hasattr(w, "bn"), + kernel_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), + ) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + """Applies convolution, batch normalization, and activation function to input tensors.""" + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConv(keras.layers.Layer): + """Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow.""" + + def __init__(self, c1, c2, k=1, s=1, p=None, act=True, w=None): + """ + Initializes a depthwise convolution layer with optional batch normalization and activation for TensorFlow + models. + + Input are ch_in, ch_out, weights, kernel, stride, padding, groups. + """ + super().__init__() + assert c2 % c1 == 0, f"TFDWConv() output={c2} must be a multiple of input={c1} channels" + conv = keras.layers.DepthwiseConv2D( + kernel_size=k, + depth_multiplier=c2 // c1, + strides=s, + padding="SAME" if s == 1 else "VALID", + use_bias=not hasattr(w, "bn"), + depthwise_initializer=keras.initializers.Constant(w.conv.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer="zeros" if hasattr(w, "bn") else keras.initializers.Constant(w.conv.bias.numpy()), + ) + self.conv = conv if s == 1 else keras.Sequential([TFPad(autopad(k, p)), conv]) + self.bn = TFBN(w.bn) if hasattr(w, "bn") else tf.identity + self.act = activations(w.act) if act else tf.identity + + def call(self, inputs): + """Applies convolution, batch normalization, and activation function to input tensors.""" + return self.act(self.bn(self.conv(inputs))) + + +class TFDWConvTranspose2d(keras.layers.Layer): + """Implements a depthwise ConvTranspose2D layer for TensorFlow with specific settings.""" + + def __init__(self, c1, c2, k=1, s=1, p1=0, p2=0, w=None): + """ + Initializes depthwise ConvTranspose2D layer with specific channel, kernel, stride, and padding settings. + + Inputs are ch_in, ch_out, weights, kernel, stride, padding, groups. + """ + super().__init__() + assert c1 == c2, f"TFDWConv() output={c2} must be equal to input={c1} channels" + assert k == 4 and p1 == 1, "TFDWConv() only valid for k=4 and p1=1" + weight, bias = w.weight.permute(2, 3, 1, 0).numpy(), w.bias.numpy() + self.c1 = c1 + self.conv = [ + keras.layers.Conv2DTranspose( + filters=1, + kernel_size=k, + strides=s, + padding="VALID", + output_padding=p2, + use_bias=True, + kernel_initializer=keras.initializers.Constant(weight[..., i : i + 1]), + bias_initializer=keras.initializers.Constant(bias[i]), + ) + for i in range(c1) + ] + + def call(self, inputs): + """Processes input through parallel convolutions and concatenates results, trimming border pixels.""" + return tf.concat([m(x) for m, x in zip(self.conv, tf.split(inputs, self.c1, 3))], 3)[:, 1:-1, 1:-1] + + +class TFFocus(keras.layers.Layer): + """Focuses spatial information into channel space using pixel shuffling and convolution for TensorFlow models.""" + + def __init__(self, c1, c2, k=1, s=1, p=None, g=1, act=True, w=None): + """ + Initializes TFFocus layer to focus width and height information into channel space with custom convolution + parameters. + + Inputs are ch_in, ch_out, kernel, stride, padding, groups. + """ + super().__init__() + self.conv = TFConv(c1 * 4, c2, k, s, p, g, act, w.conv) + + def call(self, inputs): + """ + Performs pixel shuffling and convolution on input tensor, downsampling by 2 and expanding channels by 4. + + Example x(b,w,h,c) -> y(b,w/2,h/2,4c). + """ + inputs = [inputs[:, ::2, ::2, :], inputs[:, 1::2, ::2, :], inputs[:, ::2, 1::2, :], inputs[:, 1::2, 1::2, :]] + return self.conv(tf.concat(inputs, 3)) + + +class TFBottleneck(keras.layers.Layer): + """Implements a TensorFlow bottleneck layer with optional shortcut connections for efficient feature extraction.""" + + def __init__(self, c1, c2, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes a standard bottleneck layer for TensorFlow models, expanding and contracting channels with optional + shortcut. + + Arguments are ch_in, ch_out, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_, c2, 3, 1, g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + """Performs forward pass; if shortcut is True & input/output channels match, adds input to the convolution + result. + """ + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFCrossConv(keras.layers.Layer): + """Implements a cross convolutional layer with optional expansion, grouping, and shortcut for TensorFlow.""" + + def __init__(self, c1, c2, k=3, s=1, g=1, e=1.0, shortcut=False, w=None): + """Initializes cross convolution layer with optional expansion, grouping, and shortcut addition capabilities.""" + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, (1, k), (1, s), w=w.cv1) + self.cv2 = TFConv(c_, c2, (k, 1), (s, 1), g=g, w=w.cv2) + self.add = shortcut and c1 == c2 + + def call(self, inputs): + """Passes input through two convolutions optionally adding the input if channel dimensions match.""" + return inputs + self.cv2(self.cv1(inputs)) if self.add else self.cv2(self.cv1(inputs)) + + +class TFConv2d(keras.layers.Layer): + """Implements a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D for specified filters and stride.""" + + def __init__(self, c1, c2, k, s=1, g=1, bias=True, w=None): + """Initializes a TensorFlow 2D convolution layer, mimicking PyTorch's nn.Conv2D functionality for given filter + sizes and stride. + """ + super().__init__() + assert g == 1, "TF v2.2 Conv2D does not support 'groups' argument" + self.conv = keras.layers.Conv2D( + filters=c2, + kernel_size=k, + strides=s, + padding="VALID", + use_bias=bias, + kernel_initializer=keras.initializers.Constant(w.weight.permute(2, 3, 1, 0).numpy()), + bias_initializer=keras.initializers.Constant(w.bias.numpy()) if bias else None, + ) + + def call(self, inputs): + """Applies a convolution operation to the inputs and returns the result.""" + return self.conv(inputs) + + +class TFBottleneckCSP(keras.layers.Layer): + """Implements a CSP bottleneck layer for TensorFlow models to enhance gradient flow and efficiency.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes CSP bottleneck layer with specified channel sizes, count, shortcut option, groups, and expansion + ratio. + + Inputs are ch_in, ch_out, number, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv2d(c1, c_, 1, 1, bias=False, w=w.cv2) + self.cv3 = TFConv2d(c_, c_, 1, 1, bias=False, w=w.cv3) + self.cv4 = TFConv(2 * c_, c2, 1, 1, w=w.cv4) + self.bn = TFBN(w.bn) + self.act = lambda x: keras.activations.swish(x) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + """Processes input through the model layers, concatenates, normalizes, activates, and reduces the output + dimensions. + """ + y1 = self.cv3(self.m(self.cv1(inputs))) + y2 = self.cv2(inputs) + return self.cv4(self.act(self.bn(tf.concat((y1, y2), axis=3)))) + + +class TFC3(keras.layers.Layer): + """CSP bottleneck layer with 3 convolutions for TensorFlow, supporting optional shortcuts and group convolutions.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes CSP Bottleneck with 3 convolutions, supporting optional shortcuts and group convolutions. + + Inputs are ch_in, ch_out, number, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential([TFBottleneck(c_, c_, shortcut, g, e=1.0, w=w.m[j]) for j in range(n)]) + + def call(self, inputs): + """ + Processes input through a sequence of transformations for object detection (YOLOv5). + + See https://github.com/ultralytics/yolov5. + """ + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFC3x(keras.layers.Layer): + """A TensorFlow layer for enhanced feature extraction using cross-convolutions in object detection models.""" + + def __init__(self, c1, c2, n=1, shortcut=True, g=1, e=0.5, w=None): + """ + Initializes layer with cross-convolutions for enhanced feature extraction in object detection models. + + Inputs are ch_in, ch_out, number, shortcut, groups, expansion. + """ + super().__init__() + c_ = int(c2 * e) # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c1, c_, 1, 1, w=w.cv2) + self.cv3 = TFConv(2 * c_, c2, 1, 1, w=w.cv3) + self.m = keras.Sequential( + [TFCrossConv(c_, c_, k=3, s=1, g=g, e=1.0, shortcut=shortcut, w=w.m[j]) for j in range(n)] + ) + + def call(self, inputs): + """Processes input through cascaded convolutions and merges features, returning the final tensor output.""" + return self.cv3(tf.concat((self.m(self.cv1(inputs)), self.cv2(inputs)), axis=3)) + + +class TFSPP(keras.layers.Layer): + """Implements spatial pyramid pooling for YOLOv3-SPP with specific channels and kernel sizes.""" + + def __init__(self, c1, c2, k=(5, 9, 13), w=None): + """Initializes a YOLOv3-SPP layer with specific input/output channels and kernel sizes for pooling.""" + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * (len(k) + 1), c2, 1, 1, w=w.cv2) + self.m = [keras.layers.MaxPool2D(pool_size=x, strides=1, padding="SAME") for x in k] + + def call(self, inputs): + """Processes input through two TFConv layers and concatenates with max-pooled outputs at intermediate stage.""" + x = self.cv1(inputs) + return self.cv2(tf.concat([x] + [m(x) for m in self.m], 3)) + + +class TFSPPF(keras.layers.Layer): + """Implements a fast spatial pyramid pooling layer for TensorFlow with optimized feature extraction.""" + + def __init__(self, c1, c2, k=5, w=None): + """Initializes a fast spatial pyramid pooling layer with customizable in/out channels, kernel size, and + weights. + """ + super().__init__() + c_ = c1 // 2 # hidden channels + self.cv1 = TFConv(c1, c_, 1, 1, w=w.cv1) + self.cv2 = TFConv(c_ * 4, c2, 1, 1, w=w.cv2) + self.m = keras.layers.MaxPool2D(pool_size=k, strides=1, padding="SAME") + + def call(self, inputs): + """Executes the model's forward pass, concatenating input features with three max-pooled versions before final + convolution. + """ + x = self.cv1(inputs) + y1 = self.m(x) + y2 = self.m(y1) + return self.cv2(tf.concat([x, y1, y2, self.m(y2)], 3)) + + +class TFDetect(keras.layers.Layer): + """Implements YOLOv5 object detection layer in TensorFlow for predicting bounding boxes and class probabilities.""" + + def __init__(self, nc=80, anchors=(), ch=(), imgsz=(640, 640), w=None): + """Initializes YOLOv5 detection layer for TensorFlow with configurable classes, anchors, channels, and image + size. + """ + super().__init__() + self.stride = tf.convert_to_tensor(w.stride.numpy(), dtype=tf.float32) + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [tf.zeros(1)] * self.nl # init grid + self.anchors = tf.convert_to_tensor(w.anchors.numpy(), dtype=tf.float32) + self.anchor_grid = tf.reshape(self.anchors * tf.reshape(self.stride, [self.nl, 1, 1]), [self.nl, 1, -1, 1, 2]) + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] + self.training = False # set to False after building model + self.imgsz = imgsz + for i in range(self.nl): + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + self.grid[i] = self._make_grid(nx, ny) + + def call(self, inputs): + """Performs forward pass through the model layers to predict object bounding boxes and classifications.""" + z = [] # inference output + x = [] + for i in range(self.nl): + x.append(self.m[i](inputs[i])) + # x(bs,20,20,255) to x(bs,3,20,20,85) + ny, nx = self.imgsz[0] // self.stride[i], self.imgsz[1] // self.stride[i] + x[i] = tf.reshape(x[i], [-1, ny * nx, self.na, self.no]) + + if not self.training: # inference + y = x[i] + grid = tf.transpose(self.grid[i], [0, 2, 1, 3]) - 0.5 + anchor_grid = tf.transpose(self.anchor_grid[i], [0, 2, 1, 3]) * 4 + xy = (tf.sigmoid(y[..., 0:2]) * 2 + grid) * self.stride[i] # xy + wh = tf.sigmoid(y[..., 2:4]) ** 2 * anchor_grid + # Normalize xywh to 0-1 to reduce calibration error + xy /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + wh /= tf.constant([[self.imgsz[1], self.imgsz[0]]], dtype=tf.float32) + y = tf.concat([xy, wh, tf.sigmoid(y[..., 4 : 5 + self.nc]), y[..., 5 + self.nc :]], -1) + z.append(tf.reshape(y, [-1, self.na * ny * nx, self.no])) + + return tf.transpose(x, [0, 2, 1, 3]) if self.training else (tf.concat(z, 1),) + + @staticmethod + def _make_grid(nx=20, ny=20): + """Generates a 2D grid of coordinates in (x, y) format with shape [1, 1, ny*nx, 2].""" + # return torch.stack((xv, yv), 2).view((1, 1, ny, nx, 2)).float() + xv, yv = tf.meshgrid(tf.range(nx), tf.range(ny)) + return tf.cast(tf.reshape(tf.stack([xv, yv], 2), [1, 1, ny * nx, 2]), dtype=tf.float32) + + +class TFSegment(TFDetect): + """YOLOv5 segmentation head for TensorFlow, combining detection and segmentation.""" + + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), imgsz=(640, 640), w=None): + """Initializes YOLOv5 Segment head with specified channel depths, anchors, and input size for segmentation + models. + """ + super().__init__(nc, anchors, ch, imgsz, w) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = [TFConv2d(x, self.no * self.na, 1, w=w.m[i]) for i, x in enumerate(ch)] # output conv + self.proto = TFProto(ch[0], self.npr, self.nm, w=w.proto) # protos + self.detect = TFDetect.call + + def call(self, x): + """Applies detection and proto layers on input, returning detections and optionally protos if training.""" + p = self.proto(x[0]) + # p = TFUpsample(None, scale_factor=4, mode='nearest')(self.proto(x[0])) # (optional) full-size protos + p = tf.transpose(p, [0, 3, 1, 2]) # from shape(1,160,160,32) to shape(1,32,160,160) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) + + +class TFProto(keras.layers.Layer): + """Implements convolutional and upsampling layers for feature extraction in YOLOv5 segmentation.""" + + def __init__(self, c1, c_=256, c2=32, w=None): + """Initializes TFProto layer with convolutional and upsampling layers for feature extraction and + transformation. + """ + super().__init__() + self.cv1 = TFConv(c1, c_, k=3, w=w.cv1) + self.upsample = TFUpsample(None, scale_factor=2, mode="nearest") + self.cv2 = TFConv(c_, c_, k=3, w=w.cv2) + self.cv3 = TFConv(c_, c2, w=w.cv3) + + def call(self, inputs): + """Performs forward pass through the model, applying convolutions and upscaling on input tensor.""" + return self.cv3(self.cv2(self.upsample(self.cv1(inputs)))) + + +class TFUpsample(keras.layers.Layer): + """Implements a TensorFlow upsampling layer with specified size, scale factor, and interpolation mode.""" + + def __init__(self, size, scale_factor, mode, w=None): + """ + Initializes a TensorFlow upsampling layer with specified size, scale_factor, and mode, ensuring scale_factor is + even. + + Warning: all arguments needed including 'w' + """ + super().__init__() + assert scale_factor % 2 == 0, "scale_factor must be multiple of 2" + self.upsample = lambda x: tf.image.resize(x, (x.shape[1] * scale_factor, x.shape[2] * scale_factor), mode) + # self.upsample = keras.layers.UpSampling2D(size=scale_factor, interpolation=mode) + # with default arguments: align_corners=False, half_pixel_centers=False + # self.upsample = lambda x: tf.raw_ops.ResizeNearestNeighbor(images=x, + # size=(x.shape[1] * 2, x.shape[2] * 2)) + + def call(self, inputs): + """Applies upsample operation to inputs using nearest neighbor interpolation.""" + return self.upsample(inputs) + + +class TFConcat(keras.layers.Layer): + """Implements TensorFlow's version of torch.concat() for concatenating tensors along the last dimension.""" + + def __init__(self, dimension=1, w=None): + """Initializes a TensorFlow layer for NCHW to NHWC concatenation, requiring dimension=1.""" + super().__init__() + assert dimension == 1, "convert only NCHW to NHWC concat" + self.d = 3 + + def call(self, inputs): + """Concatenates a list of tensors along the last dimension, used for NCHW to NHWC conversion.""" + return tf.concat(inputs, self.d) + + +def parse_model(d, ch, model, imgsz): + """Parses a model definition dict `d` to create YOLOv5 model layers, including dynamic channel adjustments.""" + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw, ch_mul = ( + d["anchors"], + d["nc"], + d["depth_multiple"], + d["width_multiple"], + d.get("channel_multiple"), + ) + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + if not ch_mul: + ch_mul = 8 + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args + m_str = m + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + try: + args[j] = eval(a) if isinstance(a, str) else a # eval strings + except NameError: + pass + + n = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in [ + nn.Conv2d, + Conv, + DWConv, + DWConvTranspose2d, + Bottleneck, + SPP, + SPPF, + MixConv2d, + Focus, + CrossConv, + BottleneckCSP, + C3, + C3x, + ]: + c1, c2 = ch[f], args[0] + c2 = make_divisible(c2 * gw, ch_mul) if c2 != no else c2 + + args = [c1, c2, *args[1:]] + if m in [BottleneckCSP, C3, C3x]: + args.insert(2, n) + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[-1 if x == -1 else x + 1] for x in f) + elif m in [Detect, Segment]: + args.append([ch[x + 1] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, ch_mul) + args.append(imgsz) + else: + c2 = ch[f] + + tf_m = eval("TF" + m_str.replace("nn.", "")) + m_ = ( + keras.Sequential([tf_m(*args, w=model.model[i][j]) for j in range(n)]) + if n > 1 + else tf_m(*args, w=model.model[i]) + ) # module + + torch_m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace("__main__.", "") # module type + np = sum(x.numel() for x in torch_m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f"{i:>3}{str(f):>18}{str(n):>3}{np:>10} {t:<40}{str(args):<30}") # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + ch.append(c2) + return keras.Sequential(layers), sorted(save) + + +class TFModel: + """Implements YOLOv5 model in TensorFlow, supporting TensorFlow, Keras, and TFLite formats for object detection.""" + + def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, model=None, imgsz=(640, 640)): + """Initializes TF YOLOv5 model with specified configuration, channels, classes, model instance, and input + size. + """ + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + + self.yaml_file = Path(cfg).name + with open(cfg) as f: + self.yaml = yaml.load(f, Loader=yaml.FullLoader) # model dict + + # Define model + if nc and nc != self.yaml["nc"]: + LOGGER.info(f"Overriding {cfg} nc={self.yaml['nc']} with nc={nc}") + self.yaml["nc"] = nc # override yaml value + self.model, self.savelist = parse_model(deepcopy(self.yaml), ch=[ch], model=model, imgsz=imgsz) + + def predict( + self, + inputs, + tf_nms=False, + agnostic_nms=False, + topk_per_class=100, + topk_all=100, + iou_thres=0.45, + conf_thres=0.25, + ): + """Runs inference on input data, with an option for TensorFlow NMS.""" + y = [] # outputs + x = inputs + for m in self.model.layers: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + + x = m(x) # run + y.append(x if m.i in self.savelist else None) # save output + + # Add TensorFlow NMS + if tf_nms: + boxes = self._xywh2xyxy(x[0][..., :4]) + probs = x[0][:, :, 4:5] + classes = x[0][:, :, 5:] + scores = probs * classes + if agnostic_nms: + nms = AgnosticNMS()((boxes, classes, scores), topk_all, iou_thres, conf_thres) + else: + boxes = tf.expand_dims(boxes, 2) + nms = tf.image.combined_non_max_suppression( + boxes, scores, topk_per_class, topk_all, iou_thres, conf_thres, clip_boxes=False + ) + return (nms,) + return x # output [1,6300,85] = [xywh, conf, class0, class1, ...] + # x = x[0] # [x(1,6300,85), ...] to x(6300,85) + # xywh = x[..., :4] # x(6300,4) boxes + # conf = x[..., 4:5] # x(6300,1) confidences + # cls = tf.reshape(tf.cast(tf.argmax(x[..., 5:], axis=1), tf.float32), (-1, 1)) # x(6300,1) classes + # return tf.concat([conf, cls, xywh], 1) + + @staticmethod + def _xywh2xyxy(xywh): + """Converts bounding box format from [x, y, w, h] to [x1, y1, x2, y2], where xy1=top-left and xy2=bottom- + right. + """ + x, y, w, h = tf.split(xywh, num_or_size_splits=4, axis=-1) + return tf.concat([x - w / 2, y - h / 2, x + w / 2, y + h / 2], axis=-1) + + +class AgnosticNMS(keras.layers.Layer): + """Performs agnostic non-maximum suppression (NMS) on detected objects using IoU and confidence thresholds.""" + + def call(self, input, topk_all, iou_thres, conf_thres): + """Performs agnostic NMS on input tensors using given thresholds and top-K selection.""" + return tf.map_fn( + lambda x: self._nms(x, topk_all, iou_thres, conf_thres), + input, + fn_output_signature=(tf.float32, tf.float32, tf.float32, tf.int32), + name="agnostic_nms", + ) + + @staticmethod + def _nms(x, topk_all=100, iou_thres=0.45, conf_thres=0.25): + """Performs agnostic non-maximum suppression (NMS) on detected objects, filtering based on IoU and confidence + thresholds. + """ + boxes, classes, scores = x + class_inds = tf.cast(tf.argmax(classes, axis=-1), tf.float32) + scores_inp = tf.reduce_max(scores, -1) + selected_inds = tf.image.non_max_suppression( + boxes, scores_inp, max_output_size=topk_all, iou_threshold=iou_thres, score_threshold=conf_thres + ) + selected_boxes = tf.gather(boxes, selected_inds) + padded_boxes = tf.pad( + selected_boxes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]], [0, 0]], + mode="CONSTANT", + constant_values=0.0, + ) + selected_scores = tf.gather(scores_inp, selected_inds) + padded_scores = tf.pad( + selected_scores, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode="CONSTANT", + constant_values=-1.0, + ) + selected_classes = tf.gather(class_inds, selected_inds) + padded_classes = tf.pad( + selected_classes, + paddings=[[0, topk_all - tf.shape(selected_boxes)[0]]], + mode="CONSTANT", + constant_values=-1.0, + ) + valid_detections = tf.shape(selected_inds)[0] + return padded_boxes, padded_scores, padded_classes, valid_detections + + +def activations(act=nn.SiLU): + """Converts PyTorch activations to TensorFlow equivalents, supporting LeakyReLU, Hardswish, and SiLU/Swish.""" + if isinstance(act, nn.LeakyReLU): + return lambda x: keras.activations.relu(x, alpha=0.1) + elif isinstance(act, nn.Hardswish): + return lambda x: x * tf.nn.relu6(x + 3) * 0.166666667 + elif isinstance(act, (nn.SiLU, SiLU)): + return lambda x: keras.activations.swish(x) + else: + raise Exception(f"no matching TensorFlow activation found for PyTorch activation {act}") + + +def representative_dataset_gen(dataset, ncalib=100): + """Generates a representative dataset for calibration by yielding transformed numpy arrays from the input + dataset. + """ + for n, (path, img, im0s, vid_cap, string) in enumerate(dataset): + im = np.transpose(img, [1, 2, 0]) + im = np.expand_dims(im, axis=0).astype(np.float32) + im /= 255 + yield [im] + if n >= ncalib: + break + + +def run( + weights=ROOT / "yolov5s.pt", # weights path + imgsz=(640, 640), # inference size h,w + batch_size=1, # batch size + dynamic=False, # dynamic batch size +): + # PyTorch model + """Exports YOLOv5 model from PyTorch to TensorFlow and Keras formats, performing inference for validation.""" + im = torch.zeros((batch_size, 3, *imgsz)) # BCHW image + model = attempt_load(weights, device=torch.device("cpu"), inplace=True, fuse=False) + _ = model(im) # inference + model.info() + + # TensorFlow model + im = tf.zeros((batch_size, *imgsz, 3)) # BHWC image + tf_model = TFModel(cfg=model.yaml, model=model, nc=model.nc, imgsz=imgsz) + _ = tf_model.predict(im) # inference + + # Keras model + im = keras.Input(shape=(*imgsz, 3), batch_size=None if dynamic else batch_size) + keras_model = keras.Model(inputs=im, outputs=tf_model.predict(im)) + keras_model.summary() + + LOGGER.info("PyTorch, TensorFlow and Keras models successfully verified.\nUse export.py for TF model export.") + + +def parse_opt(): + """Parses and returns command-line options for model inference, including weights path, image size, batch size, and + dynamic batching. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="weights path") + parser.add_argument("--imgsz", "--img", "--img-size", nargs="+", type=int, default=[640], help="inference size h,w") + parser.add_argument("--batch-size", type=int, default=1, help="batch size") + parser.add_argument("--dynamic", action="store_true", help="dynamic batch size") + opt = parser.parse_args() + opt.imgsz *= 2 if len(opt.imgsz) == 1 else 1 # expand + print_args(vars(opt)) + return opt + + +def main(opt): + """Executes the YOLOv5 model run function with parsed command line options.""" + run(**vars(opt)) + + +if __name__ == "__main__": + opt = parse_opt() + main(opt) diff --git a/app/util/yolov5/models/yolo.py b/app/util/yolov5/models/yolo.py new file mode 100644 index 0000000..13498ac --- /dev/null +++ b/app/util/yolov5/models/yolo.py @@ -0,0 +1,495 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +""" +YOLO-specific modules. + +Usage: + $ python models/yolo.py --cfg yolov5s.yaml +""" + +import argparse +import contextlib +import math +import os +import platform +import sys +from copy import deepcopy +from pathlib import Path + +import torch +import torch.nn as nn + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +if platform.system() != "Windows": + ROOT = Path(os.path.relpath(ROOT, Path.cwd())) # relative + +from models.common import ( + C3, + C3SPP, + C3TR, + SPP, + SPPF, + Bottleneck, + BottleneckCSP, + C3Ghost, + C3x, + Classify, + Concat, + Contract, + Conv, + CrossConv, + DetectMultiBackend, + DWConv, + DWConvTranspose2d, + Expand, + Focus, + GhostBottleneck, + GhostConv, + Proto, +) +from models.experimental import MixConv2d +from utils.autoanchor import check_anchor_order +from utils.general import LOGGER, check_version, check_yaml, colorstr, make_divisible, print_args +from utils.plots import feature_visualization +from utils.torch_utils import ( + fuse_conv_and_bn, + initialize_weights, + model_info, + profile, + scale_img, + select_device, + time_sync, +) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + + +class Detect(nn.Module): + """YOLOv5 Detect head for processing input tensors and generating detection outputs in object detection models.""" + + stride = None # strides computed during build + dynamic = False # force grid reconstruction + export = False # export mode + + def __init__(self, nc=80, anchors=(), ch=(), inplace=True): + """Initializes YOLOv5 detection layer with specified classes, anchors, channels, and inplace operations.""" + super().__init__() + self.nc = nc # number of classes + self.no = nc + 5 # number of outputs per anchor + self.nl = len(anchors) # number of detection layers + self.na = len(anchors[0]) // 2 # number of anchors + self.grid = [torch.empty(0) for _ in range(self.nl)] # init grid + self.anchor_grid = [torch.empty(0) for _ in range(self.nl)] # init anchor grid + self.register_buffer("anchors", torch.tensor(anchors).float().view(self.nl, -1, 2)) # shape(nl,na,2) + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.inplace = inplace # use inplace ops (e.g. slice assignment) + + def forward(self, x): + """Processes input through YOLOv5 layers, altering shape for detection: `x(bs, 3, ny, nx, 85)`.""" + z = [] # inference output + for i in range(self.nl): + x[i] = self.m[i](x[i]) # conv + bs, _, ny, nx = x[i].shape # x(bs,255,20,20) to x(bs,3,20,20,85) + x[i] = x[i].view(bs, self.na, self.no, ny, nx).permute(0, 1, 3, 4, 2).contiguous() + + if not self.training: # inference + if self.dynamic or self.grid[i].shape[2:4] != x[i].shape[2:4]: + self.grid[i], self.anchor_grid[i] = self._make_grid(nx, ny, i) + + if isinstance(self, Segment): # (boxes + masks) + xy, wh, conf, mask = x[i].split((2, 2, self.nc + 1, self.no - self.nc - 5), 4) + xy = (xy.sigmoid() * 2 + self.grid[i]) * self.stride[i] # xy + wh = (wh.sigmoid() * 2) ** 2 * self.anchor_grid[i] # wh + y = torch.cat((xy, wh, conf.sigmoid(), mask), 4) + else: # Detect (boxes only) + xy, wh, conf = x[i].sigmoid().split((2, 2, self.nc + 1), 4) + xy = (xy * 2 + self.grid[i]) * self.stride[i] # xy + wh = (wh * 2) ** 2 * self.anchor_grid[i] # wh + y = torch.cat((xy, wh, conf), 4) + z.append(y.view(bs, self.na * nx * ny, self.no)) + + return x if self.training else (torch.cat(z, 1),) if self.export else (torch.cat(z, 1), x) + + def _make_grid(self, nx=20, ny=20, i=0, torch_1_10=check_version(torch.__version__, "1.10.0")): + """Generates a mesh grid for anchor boxes with optional compatibility for torch versions < 1.10.""" + d = self.anchors[i].device + t = self.anchors[i].dtype + shape = 1, self.na, ny, nx, 2 # grid shape + y, x = torch.arange(ny, device=d, dtype=t), torch.arange(nx, device=d, dtype=t) + yv, xv = torch.meshgrid(y, x, indexing="ij") if torch_1_10 else torch.meshgrid(y, x) # torch>=0.7 compatibility + grid = torch.stack((xv, yv), 2).expand(shape) - 0.5 # add grid offset, i.e. y = 2.0 * x - 0.5 + anchor_grid = (self.anchors[i] * self.stride[i]).view((1, self.na, 1, 1, 2)).expand(shape) + return grid, anchor_grid + + +class Segment(Detect): + """YOLOv5 Segment head for segmentation models, extending Detect with mask and prototype layers.""" + + def __init__(self, nc=80, anchors=(), nm=32, npr=256, ch=(), inplace=True): + """Initializes YOLOv5 Segment head with options for mask count, protos, and channel adjustments.""" + super().__init__(nc, anchors, ch, inplace) + self.nm = nm # number of masks + self.npr = npr # number of protos + self.no = 5 + nc + self.nm # number of outputs per anchor + self.m = nn.ModuleList(nn.Conv2d(x, self.no * self.na, 1) for x in ch) # output conv + self.proto = Proto(ch[0], self.npr, self.nm) # protos + self.detect = Detect.forward + + def forward(self, x): + """Processes input through the network, returning detections and prototypes; adjusts output based on + training/export mode. + """ + p = self.proto(x[0]) + x = self.detect(self, x) + return (x, p) if self.training else (x[0], p) if self.export else (x[0], p, x[1]) + + +class BaseModel(nn.Module): + """YOLOv5 base model.""" + + def forward(self, x, profile=False, visualize=False): + """Executes a single-scale inference or training pass on the YOLOv5 base model, with options for profiling and + visualization. + """ + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_once(self, x, profile=False, visualize=False): + """Performs a forward pass on the YOLOv5 model, enabling profiling and feature visualization options.""" + y, dt = [], [] # outputs + for m in self.model: + if m.f != -1: # if not from previous layer + x = y[m.f] if isinstance(m.f, int) else [x if j == -1 else y[j] for j in m.f] # from earlier layers + if profile: + self._profile_one_layer(m, x, dt) + x = m(x) # run + y.append(x if m.i in self.save else None) # save output + if visualize: + feature_visualization(x, m.type, m.i, save_dir=visualize) + return x + + def _profile_one_layer(self, m, x, dt): + """Profiles a single layer's performance by computing GFLOPs, execution time, and parameters.""" + c = m == self.model[-1] # is final layer, copy input as inplace fix + o = thop.profile(m, inputs=(x.copy() if c else x,), verbose=False)[0] / 1e9 * 2 if thop else 0 # FLOPs + t = time_sync() + for _ in range(10): + m(x.copy() if c else x) + dt.append((time_sync() - t) * 100) + if m == self.model[0]: + LOGGER.info(f"{'time (ms)':>10s} {'GFLOPs':>10s} {'params':>10s} module") + LOGGER.info(f"{dt[-1]:10.2f} {o:10.2f} {m.np:10.0f} {m.type}") + if c: + LOGGER.info(f"{sum(dt):10.2f} {'-':>10s} {'-':>10s} Total") + + def fuse(self): + """Fuses Conv2d() and BatchNorm2d() layers in the model to improve inference speed.""" + LOGGER.info("Fusing layers... ") + for m in self.model.modules(): + if isinstance(m, (Conv, DWConv)) and hasattr(m, "bn"): + m.conv = fuse_conv_and_bn(m.conv, m.bn) # update conv + delattr(m, "bn") # remove batchnorm + m.forward = m.forward_fuse # update forward + self.info() + return self + + def info(self, verbose=False, img_size=640): + """Prints model information given verbosity and image size, e.g., `info(verbose=True, img_size=640)`.""" + model_info(self, verbose, img_size) + + def _apply(self, fn): + """Applies transformations like to(), cpu(), cuda(), half() to model tensors excluding parameters or registered + buffers. + """ + self = super()._apply(fn) + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + m.stride = fn(m.stride) + m.grid = list(map(fn, m.grid)) + if isinstance(m.anchor_grid, list): + m.anchor_grid = list(map(fn, m.anchor_grid)) + return self + + +class DetectionModel(BaseModel): + """YOLOv5 detection model class for object detection tasks, supporting custom configurations and anchors.""" + + def __init__(self, cfg="yolov5s.yaml", ch=3, nc=None, anchors=None): + """Initializes YOLOv5 model with configuration file, input channels, number of classes, and custom anchors.""" + super().__init__() + if isinstance(cfg, dict): + self.yaml = cfg # model dict + else: # is *.yaml + import yaml # for torch hub + + self.yaml_file = Path(cfg).name + with open(cfg, encoding="ascii", errors="ignore") as f: + self.yaml = yaml.safe_load(f) # model dict + + # Define model + ch = self.yaml["ch"] = self.yaml.get("ch", ch) # input channels + if nc and nc != self.yaml["nc"]: + LOGGER.info(f"Overriding model.yaml nc={self.yaml['nc']} with nc={nc}") + self.yaml["nc"] = nc # override yaml value + if anchors: + LOGGER.info(f"Overriding model.yaml anchors with anchors={anchors}") + self.yaml["anchors"] = round(anchors) # override yaml value + self.model, self.save = parse_model(deepcopy(self.yaml), ch=[ch]) # model, savelist + self.names = [str(i) for i in range(self.yaml["nc"])] # default names + self.inplace = self.yaml.get("inplace", True) + + # Build strides, anchors + m = self.model[-1] # Detect() + if isinstance(m, (Detect, Segment)): + + def _forward(x): + """Passes the input 'x' through the model and returns the processed output.""" + return self.forward(x)[0] if isinstance(m, Segment) else self.forward(x) + + s = 256 # 2x min stride + m.inplace = self.inplace + m.stride = torch.tensor([s / x.shape[-2] for x in _forward(torch.zeros(1, ch, s, s))]) # forward + check_anchor_order(m) + m.anchors /= m.stride.view(-1, 1, 1) + self.stride = m.stride + self._initialize_biases() # only run once + + # Init weights, biases + initialize_weights(self) + self.info() + LOGGER.info("") + + def forward(self, x, augment=False, profile=False, visualize=False): + """Performs single-scale or augmented inference and may include profiling or visualization.""" + if augment: + return self._forward_augment(x) # augmented inference, None + return self._forward_once(x, profile, visualize) # single-scale inference, train + + def _forward_augment(self, x): + """Performs augmented inference across different scales and flips, returning combined detections.""" + img_size = x.shape[-2:] # height, width + s = [1, 0.83, 0.67] # scales + f = [None, 3, None] # flips (2-ud, 3-lr) + y = [] # outputs + for si, fi in zip(s, f): + xi = scale_img(x.flip(fi) if fi else x, si, gs=int(self.stride.max())) + yi = self._forward_once(xi)[0] # forward + # cv2.imwrite(f'img_{si}.jpg', 255 * xi[0].cpu().numpy().transpose((1, 2, 0))[:, :, ::-1]) # save + yi = self._descale_pred(yi, fi, si, img_size) + y.append(yi) + y = self._clip_augmented(y) # clip augmented tails + return torch.cat(y, 1), None # augmented inference, train + + def _descale_pred(self, p, flips, scale, img_size): + """De-scales predictions from augmented inference, adjusting for flips and image size.""" + if self.inplace: + p[..., :4] /= scale # de-scale + if flips == 2: + p[..., 1] = img_size[0] - p[..., 1] # de-flip ud + elif flips == 3: + p[..., 0] = img_size[1] - p[..., 0] # de-flip lr + else: + x, y, wh = p[..., 0:1] / scale, p[..., 1:2] / scale, p[..., 2:4] / scale # de-scale + if flips == 2: + y = img_size[0] - y # de-flip ud + elif flips == 3: + x = img_size[1] - x # de-flip lr + p = torch.cat((x, y, wh, p[..., 4:]), -1) + return p + + def _clip_augmented(self, y): + """Clips augmented inference tails for YOLOv5 models, affecting first and last tensors based on grid points and + layer counts. + """ + nl = self.model[-1].nl # number of detection layers (P3-P5) + g = sum(4**x for x in range(nl)) # grid points + e = 1 # exclude layer count + i = (y[0].shape[1] // g) * sum(4**x for x in range(e)) # indices + y[0] = y[0][:, :-i] # large + i = (y[-1].shape[1] // g) * sum(4 ** (nl - 1 - x) for x in range(e)) # indices + y[-1] = y[-1][:, i:] # small + return y + + def _initialize_biases(self, cf=None): + """ + Initializes biases for YOLOv5's Detect() module, optionally using class frequencies (cf). + + For details see https://arxiv.org/abs/1708.02002 section 3.3. + """ + # cf = torch.bincount(torch.tensor(np.concatenate(dataset.labels, 0)[:, 0]).long(), minlength=nc) + 1. + m = self.model[-1] # Detect() module + for mi, s in zip(m.m, m.stride): # from + b = mi.bias.view(m.na, -1) # conv.bias(255) to (3,85) + b.data[:, 4] += math.log(8 / (640 / s) ** 2) # obj (8 objects per 640 image) + b.data[:, 5 : 5 + m.nc] += ( + math.log(0.6 / (m.nc - 0.99999)) if cf is None else torch.log(cf / cf.sum()) + ) # cls + mi.bias = torch.nn.Parameter(b.view(-1), requires_grad=True) + + +Model = DetectionModel # retain YOLOv5 'Model' class for backwards compatibility + + +class SegmentationModel(DetectionModel): + """YOLOv5 segmentation model for object detection and segmentation tasks with configurable parameters.""" + + def __init__(self, cfg="yolov5s-seg.yaml", ch=3, nc=None, anchors=None): + """Initializes a YOLOv5 segmentation model with configurable params: cfg (str) for configuration, ch (int) for channels, nc (int) for num classes, anchors (list).""" + super().__init__(cfg, ch, nc, anchors) + + +class ClassificationModel(BaseModel): + """YOLOv5 classification model for image classification tasks, initialized with a config file or detection model.""" + + def __init__(self, cfg=None, model=None, nc=1000, cutoff=10): + """Initializes YOLOv5 model with config file `cfg`, input channels `ch`, number of classes `nc`, and `cuttoff` + index. + """ + super().__init__() + self._from_detection_model(model, nc, cutoff) if model is not None else self._from_yaml(cfg) + + def _from_detection_model(self, model, nc=1000, cutoff=10): + """Creates a classification model from a YOLOv5 detection model, slicing at `cutoff` and adding a classification + layer. + """ + if isinstance(model, DetectMultiBackend): + model = model.model # unwrap DetectMultiBackend + model.model = model.model[:cutoff] # backbone + m = model.model[-1] # last layer + ch = m.conv.in_channels if hasattr(m, "conv") else m.cv1.conv.in_channels # ch into module + c = Classify(ch, nc) # Classify() + c.i, c.f, c.type = m.i, m.f, "models.common.Classify" # index, from, type + model.model[-1] = c # replace + self.model = model.model + self.stride = model.stride + self.save = [] + self.nc = nc + + def _from_yaml(self, cfg): + """Creates a YOLOv5 classification model from a specified *.yaml configuration file.""" + self.model = None + + +def parse_model(d, ch): + """Parses a YOLOv5 model from a dict `d`, configuring layers based on input channels `ch` and model architecture.""" + LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}") + anchors, nc, gd, gw, act, ch_mul = ( + d["anchors"], + d["nc"], + d["depth_multiple"], + d["width_multiple"], + d.get("activation"), + d.get("channel_multiple"), + ) + if act: + Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU() + LOGGER.info(f"{colorstr('activation:')} {act}") # print + if not ch_mul: + ch_mul = 8 + na = (len(anchors[0]) // 2) if isinstance(anchors, list) else anchors # number of anchors + no = na * (nc + 5) # number of outputs = anchors * (classes + 5) + + layers, save, c2 = [], [], ch[-1] # layers, savelist, ch out + for i, (f, n, m, args) in enumerate(d["backbone"] + d["head"]): # from, number, module, args + m = eval(m) if isinstance(m, str) else m # eval strings + for j, a in enumerate(args): + with contextlib.suppress(NameError): + args[j] = eval(a) if isinstance(a, str) else a # eval strings + + n = n_ = max(round(n * gd), 1) if n > 1 else n # depth gain + if m in { + Conv, + GhostConv, + Bottleneck, + GhostBottleneck, + SPP, + SPPF, + DWConv, + MixConv2d, + Focus, + CrossConv, + BottleneckCSP, + C3, + C3TR, + C3SPP, + C3Ghost, + nn.ConvTranspose2d, + DWConvTranspose2d, + C3x, + }: + c1, c2 = ch[f], args[0] + if c2 != no: # if not output + c2 = make_divisible(c2 * gw, ch_mul) + + args = [c1, c2, *args[1:]] + if m in {BottleneckCSP, C3, C3TR, C3Ghost, C3x}: + args.insert(2, n) # number of repeats + n = 1 + elif m is nn.BatchNorm2d: + args = [ch[f]] + elif m is Concat: + c2 = sum(ch[x] for x in f) + # TODO: channel, gw, gd + elif m in {Detect, Segment}: + args.append([ch[x] for x in f]) + if isinstance(args[1], int): # number of anchors + args[1] = [list(range(args[1] * 2))] * len(f) + if m is Segment: + args[3] = make_divisible(args[3] * gw, ch_mul) + elif m is Contract: + c2 = ch[f] * args[0] ** 2 + elif m is Expand: + c2 = ch[f] // args[0] ** 2 + else: + c2 = ch[f] + + m_ = nn.Sequential(*(m(*args) for _ in range(n))) if n > 1 else m(*args) # module + t = str(m)[8:-2].replace("__main__.", "") # module type + np = sum(x.numel() for x in m_.parameters()) # number params + m_.i, m_.f, m_.type, m_.np = i, f, t, np # attach index, 'from' index, type, number params + LOGGER.info(f"{i:>3}{str(f):>18}{n_:>3}{np:10.0f} {t:<40}{str(args):<30}") # print + save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist + layers.append(m_) + if i == 0: + ch = [] + ch.append(c2) + return nn.Sequential(*layers), sorted(save) + + +if __name__ == "__main__": + parser = argparse.ArgumentParser() + parser.add_argument("--cfg", type=str, default="yolov5s.yaml", help="model.yaml") + parser.add_argument("--batch-size", type=int, default=1, help="total batch size for all GPUs") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--profile", action="store_true", help="profile model speed") + parser.add_argument("--line-profile", action="store_true", help="profile model speed layer by layer") + parser.add_argument("--test", action="store_true", help="test all yolo*.yaml") + opt = parser.parse_args() + opt.cfg = check_yaml(opt.cfg) # check YAML + print_args(vars(opt)) + device = select_device(opt.device) + + # Create model + im = torch.rand(opt.batch_size, 3, 640, 640).to(device) + model = Model(opt.cfg).to(device) + + # Options + if opt.line_profile: # profile layer by layer + model(im, profile=True) + + elif opt.profile: # profile forward-backward + results = profile(input=im, ops=[model], n=3) + + elif opt.test: # test all models + for cfg in Path(ROOT / "models").rglob("yolo*.yaml"): + try: + _ = Model(cfg) + except Exception as e: + print(f"Error in {cfg}: {e}") + + else: # report fused model summary + model.fuse() diff --git a/app/util/yolov5/models/yolov5l.yaml b/app/util/yolov5/models/yolov5l.yaml new file mode 100644 index 0000000..c6c878a --- /dev/null +++ b/app/util/yolov5/models/yolov5l.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.0 # model depth multiple +width_multiple: 1.0 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/yolov5m.yaml b/app/util/yolov5/models/yolov5m.yaml new file mode 100644 index 0000000..41d9c22 --- /dev/null +++ b/app/util/yolov5/models/yolov5m.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.67 # model depth multiple +width_multiple: 0.75 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/yolov5n.yaml b/app/util/yolov5/models/yolov5n.yaml new file mode 100644 index 0000000..5886749 --- /dev/null +++ b/app/util/yolov5/models/yolov5n.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/yolov5s.yaml b/app/util/yolov5/models/yolov5s.yaml new file mode 100644 index 0000000..11ff790 --- /dev/null +++ b/app/util/yolov5/models/yolov5s.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 0.33 # model depth multiple +width_multiple: 0.50 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/models/yolov5x.yaml b/app/util/yolov5/models/yolov5x.yaml new file mode 100644 index 0000000..817b4f9 --- /dev/null +++ b/app/util/yolov5/models/yolov5x.yaml @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Parameters +nc: 80 # number of classes +depth_multiple: 1.33 # model depth multiple +width_multiple: 1.25 # layer channel multiple +anchors: + - [10, 13, 16, 30, 33, 23] # P3/8 + - [30, 61, 62, 45, 59, 119] # P4/16 + - [116, 90, 156, 198, 373, 326] # P5/32 + +# YOLOv5 v6.0 backbone +backbone: + # [from, number, module, args] + [ + [-1, 1, Conv, [64, 6, 2, 2]], # 0-P1/2 + [-1, 1, Conv, [128, 3, 2]], # 1-P2/4 + [-1, 3, C3, [128]], + [-1, 1, Conv, [256, 3, 2]], # 3-P3/8 + [-1, 6, C3, [256]], + [-1, 1, Conv, [512, 3, 2]], # 5-P4/16 + [-1, 9, C3, [512]], + [-1, 1, Conv, [1024, 3, 2]], # 7-P5/32 + [-1, 3, C3, [1024]], + [-1, 1, SPPF, [1024, 5]], # 9 + ] + +# YOLOv5 v6.0 head +head: [ + [-1, 1, Conv, [512, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 6], 1, Concat, [1]], # cat backbone P4 + [-1, 3, C3, [512, False]], # 13 + + [-1, 1, Conv, [256, 1, 1]], + [-1, 1, nn.Upsample, [None, 2, "nearest"]], + [[-1, 4], 1, Concat, [1]], # cat backbone P3 + [-1, 3, C3, [256, False]], # 17 (P3/8-small) + + [-1, 1, Conv, [256, 3, 2]], + [[-1, 14], 1, Concat, [1]], # cat head P4 + [-1, 3, C3, [512, False]], # 20 (P4/16-medium) + + [-1, 1, Conv, [512, 3, 2]], + [[-1, 10], 1, Concat, [1]], # cat head P5 + [-1, 3, C3, [1024, False]], # 23 (P5/32-large) + + [[17, 20, 23], 1, Detect, [nc, anchors]], # Detect(P3, P4, P5) + ] diff --git a/app/util/yolov5/utils/__init__.py b/app/util/yolov5/utils/__init__.py new file mode 100644 index 0000000..3c43c9b --- /dev/null +++ b/app/util/yolov5/utils/__init__.py @@ -0,0 +1,97 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""utils/initialization.""" + +import contextlib +import platform +import threading + + +def emojis(str=""): + """Returns an emoji-safe version of a string, stripped of emojis on Windows platforms.""" + return str.encode().decode("ascii", "ignore") if platform.system() == "Windows" else str + + +class TryExcept(contextlib.ContextDecorator): + """A context manager and decorator for error handling that prints an optional message with emojis on exception.""" + + def __init__(self, msg=""): + """Initializes TryExcept with an optional message, used as a decorator or context manager for error handling.""" + self.msg = msg + + def __enter__(self): + """Enter the runtime context related to this object for error handling with an optional message.""" + pass + + def __exit__(self, exc_type, value, traceback): + """Context manager exit method that prints an error message with emojis if an exception occurred, always returns + True. + """ + if value: + print(emojis(f"{self.msg}{': ' if self.msg else ''}{value}")) + return True + + +def threaded(func): + """Decorator @threaded to run a function in a separate thread, returning the thread instance.""" + + def wrapper(*args, **kwargs): + """Runs the decorated function in a separate daemon thread and returns the thread instance.""" + thread = threading.Thread(target=func, args=args, kwargs=kwargs, daemon=True) + thread.start() + return thread + + return wrapper + + +def join_threads(verbose=False): + """ + Joins all daemon threads, optionally printing their names if verbose is True. + + Example: atexit.register(lambda: join_threads()) + """ + main_thread = threading.current_thread() + for t in threading.enumerate(): + if t is not main_thread: + if verbose: + print(f"Joining thread {t.name}") + t.join() + + +def notebook_init(verbose=True): + """Initializes notebook environment by checking requirements, cleaning up, and displaying system info.""" + print("Checking setup...") + + import os + import shutil + + from ultralytics.utils.checks import check_requirements + + from utils.general import check_font, is_colab + from utils.torch_utils import select_device # imports + + check_font() + + import psutil + + if check_requirements("wandb", install=False): + os.system("pip uninstall -y wandb") # eliminate unexpected account creation prompt with infinite hang + if is_colab(): + shutil.rmtree("/content/sample_data", ignore_errors=True) # remove colab /sample_data directory + + # System info + display = None + if verbose: + gb = 1 << 30 # bytes to GiB (1024 ** 3) + ram = psutil.virtual_memory().total + total, used, free = shutil.disk_usage("/") + with contextlib.suppress(Exception): # clear display if ipython is installed + from IPython import display + + display.clear_output() + s = f"({os.cpu_count()} CPUs, {ram / gb:.1f} GB RAM, {(total - free) / gb:.1f}/{total / gb:.1f} GB disk)" + else: + s = "" + + select_device(newline=False) + print(emojis(f"Setup complete ✅ {s}")) + return display diff --git a/app/util/yolov5/utils/activations.py b/app/util/yolov5/utils/activations.py new file mode 100644 index 0000000..4652540 --- /dev/null +++ b/app/util/yolov5/utils/activations.py @@ -0,0 +1,134 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Activation functions.""" + +import torch +import torch.nn as nn +import torch.nn.functional as F + + +class SiLU(nn.Module): + """Applies the Sigmoid-weighted Linear Unit (SiLU) activation function, also known as Swish.""" + + @staticmethod + def forward(x): + """ + Applies the Sigmoid-weighted Linear Unit (SiLU) activation function. + + https://arxiv.org/pdf/1606.08415.pdf. + """ + return x * torch.sigmoid(x) + + +class Hardswish(nn.Module): + """Applies the Hardswish activation function, which is efficient for mobile and embedded devices.""" + + @staticmethod + def forward(x): + """ + Applies the Hardswish activation function, compatible with TorchScript, CoreML, and ONNX. + + Equivalent to x * F.hardsigmoid(x) + """ + return x * F.hardtanh(x + 3, 0.0, 6.0) / 6.0 # for TorchScript, CoreML and ONNX + + +class Mish(nn.Module): + """Mish activation https://github.com/digantamisra98/Mish.""" + + @staticmethod + def forward(x): + """Applies the Mish activation function, a smooth alternative to ReLU.""" + return x * F.softplus(x).tanh() + + +class MemoryEfficientMish(nn.Module): + """Efficiently applies the Mish activation function using custom autograd for reduced memory usage.""" + + class F(torch.autograd.Function): + """Implements a custom autograd function for memory-efficient Mish activation.""" + + @staticmethod + def forward(ctx, x): + """Applies the Mish activation function, a smooth ReLU alternative, to the input tensor `x`.""" + ctx.save_for_backward(x) + return x.mul(torch.tanh(F.softplus(x))) # x * tanh(ln(1 + exp(x))) + + @staticmethod + def backward(ctx, grad_output): + """Computes the gradient of the Mish activation function with respect to input `x`.""" + x = ctx.saved_tensors[0] + sx = torch.sigmoid(x) + fx = F.softplus(x).tanh() + return grad_output * (fx + x * sx * (1 - fx * fx)) + + def forward(self, x): + """Applies the Mish activation function to the input tensor `x`.""" + return self.F.apply(x) + + +class FReLU(nn.Module): + """FReLU activation https://arxiv.org/abs/2007.11824.""" + + def __init__(self, c1, k=3): # ch_in, kernel + """Initializes FReLU activation with channel `c1` and kernel size `k`.""" + super().__init__() + self.conv = nn.Conv2d(c1, c1, k, 1, 1, groups=c1, bias=False) + self.bn = nn.BatchNorm2d(c1) + + def forward(self, x): + """ + Applies FReLU activation with max operation between input and BN-convolved input. + + https://arxiv.org/abs/2007.11824 + """ + return torch.max(x, self.bn(self.conv(x))) + + +class AconC(nn.Module): + """ + ACON activation (activate or not) function. + + AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter + See "Activate or Not: Learning Customized Activation" https://arxiv.org/pdf/2009.04759.pdf. + """ + + def __init__(self, c1): + """Initializes AconC with learnable parameters p1, p2, and beta for channel-wise activation control.""" + super().__init__() + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.beta = nn.Parameter(torch.ones(1, c1, 1, 1)) + + def forward(self, x): + """Applies AconC activation function with learnable parameters for channel-wise control on input tensor x.""" + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(self.beta * dpx) + self.p2 * x + + +class MetaAconC(nn.Module): + """ + ACON activation (activate or not) function. + + AconC: (p1*x-p2*x) * sigmoid(beta*(p1*x-p2*x)) + p2*x, beta is a learnable parameter + See "Activate or Not: Learning Customized Activation" https://arxiv.org/pdf/2009.04759.pdf. + """ + + def __init__(self, c1, k=1, s=1, r=16): + """Initializes MetaAconC with params: channel_in (c1), kernel size (k=1), stride (s=1), reduction (r=16).""" + super().__init__() + c2 = max(r, c1 // r) + self.p1 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.p2 = nn.Parameter(torch.randn(1, c1, 1, 1)) + self.fc1 = nn.Conv2d(c1, c2, k, s, bias=True) + self.fc2 = nn.Conv2d(c2, c1, k, s, bias=True) + # self.bn1 = nn.BatchNorm2d(c2) + # self.bn2 = nn.BatchNorm2d(c1) + + def forward(self, x): + """Applies a forward pass transforming input `x` using learnable parameters and sigmoid activation.""" + y = x.mean(dim=2, keepdims=True).mean(dim=3, keepdims=True) + # batch-size 1 bug/instabilities https://github.com/ultralytics/yolov5/issues/2891 + # beta = torch.sigmoid(self.bn2(self.fc2(self.bn1(self.fc1(y))))) # bug/unstable + beta = torch.sigmoid(self.fc2(self.fc1(y))) # bug patch BN layers removed + dpx = (self.p1 - self.p2) * x + return dpx * torch.sigmoid(beta * dpx) + self.p2 * x diff --git a/app/util/yolov5/utils/augmentations.py b/app/util/yolov5/utils/augmentations.py new file mode 100644 index 0000000..eed772c --- /dev/null +++ b/app/util/yolov5/utils/augmentations.py @@ -0,0 +1,440 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Image augmentation functions.""" + +import math +import random + +import cv2 +import numpy as np +import torch +import torchvision.transforms as T +import torchvision.transforms.functional as TF + +from app.util.yolov5.utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy +from app.util.yolov5.utils.metrics import bbox_ioa + +IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean +IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation + + +class Albumentations: + """Provides optional data augmentation for YOLOv5 using Albumentations library if installed.""" + + def __init__(self, size=640): + """Initializes Albumentations class for optional data augmentation in YOLOv5 with specified input size.""" + self.transform = None + prefix = colorstr("albumentations: ") + try: + import albumentations as A + + check_version(A.__version__, "1.0.3", hard=True) # version requirement + + T = [ + A.RandomResizedCrop(height=size, width=size, scale=(0.8, 1.0), ratio=(0.9, 1.11), p=0.0), + A.Blur(p=0.01), + A.MedianBlur(p=0.01), + A.ToGray(p=0.01), + A.CLAHE(p=0.01), + A.RandomBrightnessContrast(p=0.0), + A.RandomGamma(p=0.0), + A.ImageCompression(quality_lower=75, p=0.0), + ] # transforms + self.transform = A.Compose(T, bbox_params=A.BboxParams(format="yolo", label_fields=["class_labels"])) + + LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) + except ImportError: # package not installed, skip + pass + except Exception as e: + LOGGER.info(f"{prefix}{e}") + + def __call__(self, im, labels, p=1.0): + """Applies transformations to an image and labels with probability `p`, returning updated image and labels.""" + if self.transform and random.random() < p: + new = self.transform(image=im, bboxes=labels[:, 1:], class_labels=labels[:, 0]) # transformed + im, labels = new["image"], np.array([[c, *b] for c, b in zip(new["class_labels"], new["bboxes"])]) + return im, labels + + +def normalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD, inplace=False): + """ + Applies ImageNet normalization to RGB images in BCHW format, modifying them in-place if specified. + + Example: y = (x - mean) / std + """ + return TF.normalize(x, mean, std, inplace=inplace) + + +def denormalize(x, mean=IMAGENET_MEAN, std=IMAGENET_STD): + """Reverses ImageNet normalization for BCHW format RGB images by applying `x = x * std + mean`.""" + for i in range(3): + x[:, i] = x[:, i] * std[i] + mean[i] + return x + + +def augment_hsv(im, hgain=0.5, sgain=0.5, vgain=0.5): + """Applies HSV color-space augmentation to an image with random gains for hue, saturation, and value.""" + if hgain or sgain or vgain: + r = np.random.uniform(-1, 1, 3) * [hgain, sgain, vgain] + 1 # random gains + hue, sat, val = cv2.split(cv2.cvtColor(im, cv2.COLOR_BGR2HSV)) + dtype = im.dtype # uint8 + + x = np.arange(0, 256, dtype=r.dtype) + lut_hue = ((x * r[0]) % 180).astype(dtype) + lut_sat = np.clip(x * r[1], 0, 255).astype(dtype) + lut_val = np.clip(x * r[2], 0, 255).astype(dtype) + + im_hsv = cv2.merge((cv2.LUT(hue, lut_hue), cv2.LUT(sat, lut_sat), cv2.LUT(val, lut_val))) + cv2.cvtColor(im_hsv, cv2.COLOR_HSV2BGR, dst=im) # no return needed + + +def hist_equalize(im, clahe=True, bgr=False): + """Equalizes image histogram, with optional CLAHE, for BGR or RGB image with shape (n,m,3) and range 0-255.""" + yuv = cv2.cvtColor(im, cv2.COLOR_BGR2YUV if bgr else cv2.COLOR_RGB2YUV) + if clahe: + c = cv2.createCLAHE(clipLimit=2.0, tileGridSize=(8, 8)) + yuv[:, :, 0] = c.apply(yuv[:, :, 0]) + else: + yuv[:, :, 0] = cv2.equalizeHist(yuv[:, :, 0]) # equalize Y channel histogram + return cv2.cvtColor(yuv, cv2.COLOR_YUV2BGR if bgr else cv2.COLOR_YUV2RGB) # convert YUV image to RGB + + +def replicate(im, labels): + """ + Replicates half of the smallest object labels in an image for data augmentation. + + Returns augmented image and labels. + """ + h, w = im.shape[:2] + boxes = labels[:, 1:].astype(int) + x1, y1, x2, y2 = boxes.T + s = ((x2 - x1) + (y2 - y1)) / 2 # side length (pixels) + for i in s.argsort()[: round(s.size * 0.5)]: # smallest indices + x1b, y1b, x2b, y2b = boxes[i] + bh, bw = y2b - y1b, x2b - x1b + yc, xc = int(random.uniform(0, h - bh)), int(random.uniform(0, w - bw)) # offset x, y + x1a, y1a, x2a, y2a = [xc, yc, xc + bw, yc + bh] + im[y1a:y2a, x1a:x2a] = im[y1b:y2b, x1b:x2b] # im4[ymin:ymax, xmin:xmax] + labels = np.append(labels, [[labels[i, 0], x1a, y1a, x2a, y2a]], axis=0) + + return im, labels + + +def letterbox(im, new_shape=(640, 640), color=(114, 114, 114), auto=True, scaleFill=False, scaleup=True, stride=32): + """Resizes and pads image to new_shape with stride-multiple constraints, returns resized image, ratio, padding.""" + shape = im.shape[:2] # current shape [height, width] + if isinstance(new_shape, int): + new_shape = (new_shape, new_shape) + + # Scale ratio (new / old) + r = min(new_shape[0] / shape[0], new_shape[1] / shape[1]) + if not scaleup: # only scale down, do not scale up (for better val mAP) + r = min(r, 1.0) + + # Compute padding + ratio = r, r # width, height ratios + new_unpad = int(round(shape[1] * r)), int(round(shape[0] * r)) + dw, dh = new_shape[1] - new_unpad[0], new_shape[0] - new_unpad[1] # wh padding + if auto: # minimum rectangle + dw, dh = np.mod(dw, stride), np.mod(dh, stride) # wh padding + elif scaleFill: # stretch + dw, dh = 0.0, 0.0 + new_unpad = (new_shape[1], new_shape[0]) + ratio = new_shape[1] / shape[1], new_shape[0] / shape[0] # width, height ratios + + dw /= 2 # divide padding into 2 sides + dh /= 2 + + if shape[::-1] != new_unpad: # resize + im = cv2.resize(im, new_unpad, interpolation=cv2.INTER_LINEAR) + top, bottom = int(round(dh - 0.1)), int(round(dh + 0.1)) + left, right = int(round(dw - 0.1)), int(round(dw + 0.1)) + im = cv2.copyMakeBorder(im, top, bottom, left, right, cv2.BORDER_CONSTANT, value=color) # add border + return im, ratio, (dw, dh) + + +def random_perspective( + im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) +): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(0.1, 0.1), scale=(0.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + """Applies random perspective transformation to an image, modifying the image and corresponding labels.""" + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + if n := len(targets): + use_segments = any(x.any() for x in segments) and len(segments) == n + new = np.zeros((n, 4)) + if use_segments: # warp segments + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + + else: # warp boxes + xy = np.ones((n * 4, 3)) + xy[:, :2] = targets[:, [1, 2, 3, 4, 1, 4, 3, 2]].reshape(n * 4, 2) # x1y1, x2y2, x1y2, x2y1 + xy = xy @ M.T # transform + xy = (xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2]).reshape(n, 8) # perspective rescale or affine + + # create new boxes + x = xy[:, [0, 2, 4, 6]] + y = xy[:, [1, 3, 5, 7]] + new = np.concatenate((x.min(1), y.min(1), x.max(1), y.max(1))).reshape(4, n).T + + # clip + new[:, [0, 2]] = new[:, [0, 2]].clip(0, width) + new[:, [1, 3]] = new[:, [1, 3]].clip(0, height) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01 if use_segments else 0.10) + targets = targets[i] + targets[:, 1:5] = new[i] + + return im, targets + + +def copy_paste(im, labels, segments, p=0.5): + """ + Applies Copy-Paste augmentation by flipping and merging segments and labels on an image. + + Details at https://arxiv.org/abs/2012.07177. + """ + n = len(segments) + if p and n: + h, w, c = im.shape # height, width, channels + im_new = np.zeros(im.shape, np.uint8) + for j in random.sample(range(n), k=round(p * n)): + l, s = labels[j], segments[j] + box = w - l[3], l[2], w - l[1], l[4] + ioa = bbox_ioa(box, labels[:, 1:5]) # intersection over area + if (ioa < 0.30).all(): # allow 30% obscuration of existing labels + labels = np.concatenate((labels, [[l[0], *box]]), 0) + segments.append(np.concatenate((w - s[:, 0:1], s[:, 1:2]), 1)) + cv2.drawContours(im_new, [segments[j].astype(np.int32)], -1, (1, 1, 1), cv2.FILLED) + + result = cv2.flip(im, 1) # augment segments (flip left-right) + i = cv2.flip(im_new, 1).astype(bool) + im[i] = result[i] # cv2.imwrite('debug.jpg', im) # debug + + return im, labels, segments + + +def cutout(im, labels, p=0.5): + """ + Applies cutout augmentation to an image with optional label adjustment, using random masks of varying sizes. + + Details at https://arxiv.org/abs/1708.04552. + """ + if random.random() < p: + h, w = im.shape[:2] + scales = [0.5] * 1 + [0.25] * 2 + [0.125] * 4 + [0.0625] * 8 + [0.03125] * 16 # image size fraction + for s in scales: + mask_h = random.randint(1, int(h * s)) # create random masks + mask_w = random.randint(1, int(w * s)) + + # box + xmin = max(0, random.randint(0, w) - mask_w // 2) + ymin = max(0, random.randint(0, h) - mask_h // 2) + xmax = min(w, xmin + mask_w) + ymax = min(h, ymin + mask_h) + + # apply random color mask + im[ymin:ymax, xmin:xmax] = [random.randint(64, 191) for _ in range(3)] + + # return unobscured labels + if len(labels) and s > 0.03: + box = np.array([xmin, ymin, xmax, ymax], dtype=np.float32) + ioa = bbox_ioa(box, xywhn2xyxy(labels[:, 1:5], w, h)) # intersection over area + labels = labels[ioa < 0.60] # remove >60% obscured labels + + return labels + + +def mixup(im, labels, im2, labels2): + """ + Applies MixUp augmentation by blending images and labels. + + See https://arxiv.org/pdf/1710.09412.pdf for details. + """ + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + return im, labels + + +def box_candidates(box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): + """ + Filters bounding box candidates by minimum width-height threshold `wh_thr` (pixels), aspect ratio threshold + `ar_thr`, and area ratio threshold `area_thr`. + + box1(4,n) is before augmentation, box2(4,n) is after augmentation. + """ + w1, h1 = box1[2] - box1[0], box1[3] - box1[1] + w2, h2 = box2[2] - box2[0], box2[3] - box2[1] + ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio + return (w2 > wh_thr) & (h2 > wh_thr) & (w2 * h2 / (w1 * h1 + eps) > area_thr) & (ar < ar_thr) # candidates + + +def classify_albumentations( + augment=True, + size=224, + scale=(0.08, 1.0), + ratio=(0.75, 1.0 / 0.75), # 0.75, 1.33 + hflip=0.5, + vflip=0.0, + jitter=0.4, + mean=IMAGENET_MEAN, + std=IMAGENET_STD, + auto_aug=False, +): + # YOLOv5 classification Albumentations (optional, only used if package is installed) + """Sets up and returns Albumentations transforms for YOLOv5 classification tasks depending on augmentation + settings. + """ + prefix = colorstr("albumentations: ") + try: + import albumentations as A + from albumentations.pytorch import ToTensorV2 + + check_version(A.__version__, "1.0.3", hard=True) # version requirement + if augment: # Resize and crop + T = [A.RandomResizedCrop(height=size, width=size, scale=scale, ratio=ratio)] + if auto_aug: + # TODO: implement AugMix, AutoAug & RandAug in albumentation + LOGGER.info(f"{prefix}auto augmentations are currently not supported") + else: + if hflip > 0: + T += [A.HorizontalFlip(p=hflip)] + if vflip > 0: + T += [A.VerticalFlip(p=vflip)] + if jitter > 0: + color_jitter = (float(jitter),) * 3 # repeat value for brightness, contrast, saturation, 0 hue + T += [A.ColorJitter(*color_jitter, 0)] + else: # Use fixed crop for eval set (reproducibility) + T = [A.SmallestMaxSize(max_size=size), A.CenterCrop(height=size, width=size)] + T += [A.Normalize(mean=mean, std=std), ToTensorV2()] # Normalize and convert to Tensor + LOGGER.info(prefix + ", ".join(f"{x}".replace("always_apply=False, ", "") for x in T if x.p)) + return A.Compose(T) + + except ImportError: # package not installed, skip + LOGGER.warning(f"{prefix}⚠️ not found, install with `pip install albumentations` (recommended)") + except Exception as e: + LOGGER.info(f"{prefix}{e}") + + +def classify_transforms(size=224): + """Applies a series of transformations including center crop, ToTensor, and normalization for classification.""" + assert isinstance(size, int), f"ERROR: classify_transforms size {size} must be integer, not (list, tuple)" + # T.Compose([T.ToTensor(), T.Resize(size), T.CenterCrop(size), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + return T.Compose([CenterCrop(size), ToTensor(), T.Normalize(IMAGENET_MEAN, IMAGENET_STD)]) + + +class LetterBox: + """Resizes and pads images to specified dimensions while maintaining aspect ratio for YOLOv5 preprocessing.""" + + def __init__(self, size=(640, 640), auto=False, stride=32): + """Initializes a LetterBox object for YOLOv5 image preprocessing with optional auto sizing and stride + adjustment. + """ + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + self.auto = auto # pass max size integer, automatically solve for short side using stride + self.stride = stride # used with auto + + def __call__(self, im): + """ + Resizes and pads input image `im` (HWC format) to specified dimensions, maintaining aspect ratio. + + im = np.array HWC + """ + imh, imw = im.shape[:2] + r = min(self.h / imh, self.w / imw) # ratio of new/old + h, w = round(imh * r), round(imw * r) # resized image + hs, ws = (math.ceil(x / self.stride) * self.stride for x in (h, w)) if self.auto else self.h, self.w + top, left = round((hs - h) / 2 - 0.1), round((ws - w) / 2 - 0.1) + im_out = np.full((self.h, self.w, 3), 114, dtype=im.dtype) + im_out[top : top + h, left : left + w] = cv2.resize(im, (w, h), interpolation=cv2.INTER_LINEAR) + return im_out + + +class CenterCrop: + """Applies center crop to an image, resizing it to the specified size while maintaining aspect ratio.""" + + def __init__(self, size=640): + """Initializes CenterCrop for image preprocessing, accepting single int or tuple for size, defaults to 640.""" + super().__init__() + self.h, self.w = (size, size) if isinstance(size, int) else size + + def __call__(self, im): + """ + Applies center crop to the input image and resizes it to a specified size, maintaining aspect ratio. + + im = np.array HWC + """ + imh, imw = im.shape[:2] + m = min(imh, imw) # min dimension + top, left = (imh - m) // 2, (imw - m) // 2 + return cv2.resize(im[top : top + m, left : left + m], (self.w, self.h), interpolation=cv2.INTER_LINEAR) + + +class ToTensor: + """Converts BGR np.array image from HWC to RGB CHW format, normalizes to [0, 1], and supports FP16 if half=True.""" + + def __init__(self, half=False): + """Initializes ToTensor for YOLOv5 image preprocessing, with optional half precision (half=True for FP16).""" + super().__init__() + self.half = half + + def __call__(self, im): + """ + Converts BGR np.array image from HWC to RGB CHW format, and normalizes to [0, 1], with support for FP16 if + `half=True`. + + im = np.array HWC in BGR order + """ + im = np.ascontiguousarray(im.transpose((2, 0, 1))[::-1]) # HWC to CHW -> BGR to RGB -> contiguous + im = torch.from_numpy(im) # to torch + im = im.half() if self.half else im.float() # uint8 to fp16/32 + im /= 255.0 # 0-255 to 0.0-1.0 + return im diff --git a/app/util/yolov5/utils/autoanchor.py b/app/util/yolov5/utils/autoanchor.py new file mode 100644 index 0000000..c86fa0b --- /dev/null +++ b/app/util/yolov5/utils/autoanchor.py @@ -0,0 +1,175 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""AutoAnchor utils.""" + +import random + +import numpy as np +import torch +import yaml +from tqdm import tqdm + +from app.util.yolov5.utils import TryExcept +from app.util.yolov5.utils.general import LOGGER, TQDM_BAR_FORMAT, colorstr + +PREFIX = colorstr("AutoAnchor: ") + + +def check_anchor_order(m): + """Checks and corrects anchor order against stride in YOLOv5 Detect() module if necessary.""" + a = m.anchors.prod(-1).mean(-1).view(-1) # mean anchor area per output layer + da = a[-1] - a[0] # delta a + ds = m.stride[-1] - m.stride[0] # delta s + if da and (da.sign() != ds.sign()): # same order + LOGGER.info(f"{PREFIX}Reversing anchor order") + m.anchors[:] = m.anchors.flip(0) + + +@TryExcept(f"{PREFIX}ERROR") +def check_anchors(dataset, model, thr=4.0, imgsz=640): + """Evaluates anchor fit to dataset and adjusts if necessary, supporting customizable threshold and image size.""" + m = model.module.model[-1] if hasattr(model, "module") else model.model[-1] # Detect() + shapes = imgsz * dataset.shapes / dataset.shapes.max(1, keepdims=True) + scale = np.random.uniform(0.9, 1.1, size=(shapes.shape[0], 1)) # augment scale + wh = torch.tensor(np.concatenate([l[:, 3:5] * s for s, l in zip(shapes * scale, dataset.labels)])).float() # wh + + def metric(k): # compute metric + """Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation.""" + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + best = x.max(1)[0] # best_x + aat = (x > 1 / thr).float().sum(1).mean() # anchors above threshold + bpr = (best > 1 / thr).float().mean() # best possible recall + return bpr, aat + + stride = m.stride.to(m.anchors.device).view(-1, 1, 1) # model strides + anchors = m.anchors.clone() * stride # current anchors + bpr, aat = metric(anchors.cpu().view(-1, 2)) + s = f"\n{PREFIX}{aat:.2f} anchors/target, {bpr:.3f} Best Possible Recall (BPR). " + if bpr > 0.98: # threshold to recompute + LOGGER.info(f"{s}Current anchors are a good fit to dataset ✅") + else: + LOGGER.info(f"{s}Anchors are a poor fit to dataset ⚠️, attempting to improve...") + na = m.anchors.numel() // 2 # number of anchors + anchors = kmean_anchors(dataset, n=na, img_size=imgsz, thr=thr, gen=1000, verbose=False) + new_bpr = metric(anchors)[0] + if new_bpr > bpr: # replace anchors + anchors = torch.tensor(anchors, device=m.anchors.device).type_as(m.anchors) + m.anchors[:] = anchors.clone().view_as(m.anchors) + check_anchor_order(m) # must be in pixel-space (not grid-space) + m.anchors /= stride + s = f"{PREFIX}Done ✅ (optional: update model *.yaml to use these anchors in the future)" + else: + s = f"{PREFIX}Done ⚠️ (original anchors better than new anchors, proceeding with original anchors)" + LOGGER.info(s) + + +def kmean_anchors(dataset="./data/coco128.yaml", n=9, img_size=640, thr=4.0, gen=1000, verbose=True): + """ + Creates kmeans-evolved anchors from training dataset. + + Arguments: + dataset: path to data.yaml, or a loaded dataset + n: number of anchors + img_size: image size used for training + thr: anchor-label wh ratio threshold hyperparameter hyp['anchor_t'] used for training, default=4.0 + gen: generations to evolve anchors using genetic algorithm + verbose: print all results + + Return: + k: kmeans evolved anchors + + Usage: + from utils.autoanchor import *; _ = kmean_anchors() + """ + from scipy.cluster.vq import kmeans + + npr = np.random + thr = 1 / thr + + def metric(k, wh): # compute metrics + """Computes ratio metric, anchors above threshold, and best possible recall for YOLOv5 anchor evaluation.""" + r = wh[:, None] / k[None] + x = torch.min(r, 1 / r).min(2)[0] # ratio metric + # x = wh_iou(wh, torch.tensor(k)) # iou metric + return x, x.max(1)[0] # x, best_x + + def anchor_fitness(k): # mutation fitness + """Evaluates fitness of YOLOv5 anchors by computing recall and ratio metrics for an anchor evolution process.""" + _, best = metric(torch.tensor(k, dtype=torch.float32), wh) + return (best * (best > thr).float()).mean() # fitness + + def print_results(k, verbose=True): + """Sorts and logs kmeans-evolved anchor metrics and best possible recall values for YOLOv5 anchor evaluation.""" + k = k[np.argsort(k.prod(1))] # sort small to large + x, best = metric(k, wh0) + bpr, aat = (best > thr).float().mean(), (x > thr).float().mean() * n # best possible recall, anch > thr + s = ( + f"{PREFIX}thr={thr:.2f}: {bpr:.4f} best possible recall, {aat:.2f} anchors past thr\n" + f"{PREFIX}n={n}, img_size={img_size}, metric_all={x.mean():.3f}/{best.mean():.3f}-mean/best, " + f"past_thr={x[x > thr].mean():.3f}-mean: " + ) + for x in k: + s += "%i,%i, " % (round(x[0]), round(x[1])) + if verbose: + LOGGER.info(s[:-2]) + return k + + if isinstance(dataset, str): # *.yaml file + with open(dataset, errors="ignore") as f: + data_dict = yaml.safe_load(f) # model dict + from utils.dataloaders import LoadImagesAndLabels + + dataset = LoadImagesAndLabels(data_dict["train"], augment=True, rect=True) + + # Get label wh + shapes = img_size * dataset.shapes / dataset.shapes.max(1, keepdims=True) + wh0 = np.concatenate([l[:, 3:5] * s for s, l in zip(shapes, dataset.labels)]) # wh + + # Filter + i = (wh0 < 3.0).any(1).sum() + if i: + LOGGER.info(f"{PREFIX}WARNING ⚠️ Extremely small objects found: {i} of {len(wh0)} labels are <3 pixels in size") + wh = wh0[(wh0 >= 2.0).any(1)].astype(np.float32) # filter > 2 pixels + # wh = wh * (npr.rand(wh.shape[0], 1) * 0.9 + 0.1) # multiply by random scale 0-1 + + # Kmeans init + try: + LOGGER.info(f"{PREFIX}Running kmeans for {n} anchors on {len(wh)} points...") + assert n <= len(wh) # apply overdetermined constraint + s = wh.std(0) # sigmas for whitening + k = kmeans(wh / s, n, iter=30)[0] * s # points + assert n == len(k) # kmeans may return fewer points than requested if wh is insufficient or too similar + except Exception: + LOGGER.warning(f"{PREFIX}WARNING ⚠️ switching strategies from kmeans to random init") + k = np.sort(npr.rand(n * 2)).reshape(n, 2) * img_size # random init + wh, wh0 = (torch.tensor(x, dtype=torch.float32) for x in (wh, wh0)) + k = print_results(k, verbose=False) + + # Plot + # k, d = [None] * 20, [None] * 20 + # for i in tqdm(range(1, 21)): + # k[i-1], d[i-1] = kmeans(wh / s, i) # points, mean distance + # fig, ax = plt.subplots(1, 2, figsize=(14, 7), tight_layout=True) + # ax = ax.ravel() + # ax[0].plot(np.arange(1, 21), np.array(d) ** 2, marker='.') + # fig, ax = plt.subplots(1, 2, figsize=(14, 7)) # plot wh + # ax[0].hist(wh[wh[:, 0]<100, 0],400) + # ax[1].hist(wh[wh[:, 1]<100, 1],400) + # fig.savefig('wh.png', dpi=200) + + # Evolve + f, sh, mp, s = anchor_fitness(k), k.shape, 0.9, 0.1 # fitness, generations, mutation prob, sigma + pbar = tqdm(range(gen), bar_format=TQDM_BAR_FORMAT) # progress bar + for _ in pbar: + v = np.ones(sh) + while (v == 1).all(): # mutate until a change occurs (prevent duplicates) + v = ((npr.random(sh) < mp) * random.random() * npr.randn(*sh) * s + 1).clip(0.3, 3.0) + kg = (k.copy() * v).clip(min=2.0) + fg = anchor_fitness(kg) + if fg > f: + f, k = fg, kg.copy() + pbar.desc = f"{PREFIX}Evolving anchors with Genetic Algorithm: fitness = {f:.4f}" + if verbose: + print_results(k, verbose) + + return print_results(k).astype(np.float32) diff --git a/app/util/yolov5/utils/autobatch.py b/app/util/yolov5/utils/autobatch.py new file mode 100644 index 0000000..5a485f9 --- /dev/null +++ b/app/util/yolov5/utils/autobatch.py @@ -0,0 +1,70 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Auto-batch utils.""" + +from copy import deepcopy + +import numpy as np +import torch + +from app.util.yolov5.utils.general import LOGGER, colorstr +from app.util.yolov5.utils.torch_utils import profile + + +def check_train_batch_size(model, imgsz=640, amp=True): + """Checks and computes optimal training batch size for YOLOv5 model, given image size and AMP setting.""" + with torch.cuda.amp.autocast(amp): + return autobatch(deepcopy(model).train(), imgsz) # compute optimal batch size + + +def autobatch(model, imgsz=640, fraction=0.8, batch_size=16): + """Estimates optimal YOLOv5 batch size using `fraction` of CUDA memory.""" + # Usage: + # import torch + # from utils.autobatch import autobatch + # model = torch.hub.load('ultralytics/yolov5', 'yolov5s', autoshape=False) + # print(autobatch(model)) + + # Check device + prefix = colorstr("AutoBatch: ") + LOGGER.info(f"{prefix}Computing optimal batch size for --imgsz {imgsz}") + device = next(model.parameters()).device # get model device + if device.type == "cpu": + LOGGER.info(f"{prefix}CUDA not detected, using default CPU batch-size {batch_size}") + return batch_size + if torch.backends.cudnn.benchmark: + LOGGER.info(f"{prefix} ⚠️ Requires torch.backends.cudnn.benchmark=False, using default batch-size {batch_size}") + return batch_size + + # Inspect CUDA memory + gb = 1 << 30 # bytes to GiB (1024 ** 3) + d = str(device).upper() # 'CUDA:0' + properties = torch.cuda.get_device_properties(device) # device properties + t = properties.total_memory / gb # GiB total + r = torch.cuda.memory_reserved(device) / gb # GiB reserved + a = torch.cuda.memory_allocated(device) / gb # GiB allocated + f = t - (r + a) # GiB free + LOGGER.info(f"{prefix}{d} ({properties.name}) {t:.2f}G total, {r:.2f}G reserved, {a:.2f}G allocated, {f:.2f}G free") + + # Profile batch sizes + batch_sizes = [1, 2, 4, 8, 16] + try: + img = [torch.empty(b, 3, imgsz, imgsz) for b in batch_sizes] + results = profile(img, model, n=3, device=device) + except Exception as e: + LOGGER.warning(f"{prefix}{e}") + + # Fit a solution + y = [x[2] for x in results if x] # memory [2] + p = np.polyfit(batch_sizes[: len(y)], y, deg=1) # first degree polynomial fit + b = int((f * fraction - p[1]) / p[0]) # y intercept (optimal batch size) + if None in results: # some sizes failed + i = results.index(None) # first fail index + if b >= batch_sizes[i]: # y intercept above failure point + b = batch_sizes[max(i - 1, 0)] # select prior safe point + if b < 1 or b > 1024: # b outside of safe range + b = batch_size + LOGGER.warning(f"{prefix}WARNING ⚠️ CUDA anomaly detected, recommend restart environment and retry command.") + + fraction = (np.polyval(p, b) + r + a) / t # actual fraction predicted + LOGGER.info(f"{prefix}Using batch-size {b} for {d} {t * fraction:.2f}G/{t:.2f}G ({fraction * 100:.0f}%) ✅") + return b diff --git a/app/util/yolov5/utils/aws/__init__.py b/app/util/yolov5/utils/aws/__init__.py new file mode 100644 index 0000000..77a19dc --- /dev/null +++ b/app/util/yolov5/utils/aws/__init__.py @@ -0,0 +1 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license diff --git a/app/util/yolov5/utils/aws/mime.sh b/app/util/yolov5/utils/aws/mime.sh new file mode 100644 index 0000000..c319a83 --- /dev/null +++ b/app/util/yolov5/utils/aws/mime.sh @@ -0,0 +1,26 @@ +# AWS EC2 instance startup 'MIME' script https://aws.amazon.com/premiumsupport/knowledge-center/execute-user-data-ec2/ +# This script will run on every instance restart, not only on first start +# --- DO NOT COPY ABOVE COMMENTS WHEN PASTING INTO USERDATA --- + +Content-Type: multipart/mixed; boundary="//" +MIME-Version: 1.0 + +--// +Content-Type: text/cloud-config; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="cloud-config.txt" + +#cloud-config +cloud_final_modules: +- [scripts-user, always] + +--// +Content-Type: text/x-shellscript; charset="us-ascii" +MIME-Version: 1.0 +Content-Transfer-Encoding: 7bit +Content-Disposition: attachment; filename="userdata.txt" + +#!/bin/bash +# --- paste contents of userdata.sh here --- +--// diff --git a/app/util/yolov5/utils/aws/resume.py b/app/util/yolov5/utils/aws/resume.py new file mode 100644 index 0000000..5b80fd4 --- /dev/null +++ b/app/util/yolov5/utils/aws/resume.py @@ -0,0 +1,42 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# Resume all interrupted trainings in yolov5/ dir including DDP trainings +# Usage: $ python utils/aws/resume.py + +import os +import sys +from pathlib import Path + +import torch +import yaml + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[2] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +port = 0 # --master_port +path = Path("").resolve() +for last in path.rglob("*/**/last.pt"): + ckpt = torch.load(last) + if ckpt["optimizer"] is None: + continue + + # Load opt.yaml + with open(last.parent.parent / "opt.yaml", errors="ignore") as f: + opt = yaml.safe_load(f) + + # Get device count + d = opt["device"].split(",") # devices + nd = len(d) # number of devices + ddp = nd > 1 or (nd == 0 and torch.cuda.device_count() > 1) # distributed data parallel + + if ddp: # multi-GPU + port += 1 + cmd = f"python -m torch.distributed.run --nproc_per_node {nd} --master_port {port} train.py --resume {last}" + else: # single-GPU + cmd = f"python train.py --resume {last}" + + cmd += " > /dev/null 2>&1 &" # redirect output to dev/null and run in daemon thread + print(cmd) + os.system(cmd) diff --git a/app/util/yolov5/utils/aws/userdata.sh b/app/util/yolov5/utils/aws/userdata.sh new file mode 100644 index 0000000..5fc1332 --- /dev/null +++ b/app/util/yolov5/utils/aws/userdata.sh @@ -0,0 +1,27 @@ +#!/bin/bash +# AWS EC2 instance startup script https://docs.aws.amazon.com/AWSEC2/latest/UserGuide/user-data.html +# This script will run only once on first instance start (for a re-start script see mime.sh) +# /home/ubuntu (ubuntu) or /home/ec2-user (amazon-linux) is working dir +# Use >300 GB SSD + +cd home/ubuntu +if [ ! -d yolov5 ]; then + echo "Running first-time script." # install dependencies, download COCO, pull Docker + git clone https://github.com/ultralytics/yolov5 -b master && sudo chmod -R 777 yolov5 + cd yolov5 + bash data/scripts/get_coco.sh && echo "COCO done." & + sudo docker pull ultralytics/yolov5:latest && echo "Docker done." & + python -m pip install --upgrade pip && pip install -r requirements.txt && python detect.py && echo "Requirements done." & + wait && echo "All tasks done." # finish background tasks +else + echo "Running re-start script." # resume interrupted runs + i=0 + list=$(sudo docker ps -qa) # container list i.e. $'one\ntwo\nthree\nfour' + while IFS= read -r id; do + ((i++)) + echo "restarting container $i: $id" + sudo docker start $id + # sudo docker exec -it $id python train.py --resume # single-GPU + sudo docker exec -d $id python utils/aws/resume.py # multi-scenario + done <<<"$list" +fi diff --git a/app/util/yolov5/utils/callbacks.py b/app/util/yolov5/utils/callbacks.py new file mode 100644 index 0000000..1a60928 --- /dev/null +++ b/app/util/yolov5/utils/callbacks.py @@ -0,0 +1,72 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Callback utils.""" + +import threading + + +class Callbacks: + """Handles all registered callbacks for YOLOv5 Hooks.""" + + def __init__(self): + """Initializes a Callbacks object to manage registered YOLOv5 training event hooks.""" + self._callbacks = { + "on_pretrain_routine_start": [], + "on_pretrain_routine_end": [], + "on_train_start": [], + "on_train_epoch_start": [], + "on_train_batch_start": [], + "optimizer_step": [], + "on_before_zero_grad": [], + "on_train_batch_end": [], + "on_train_epoch_end": [], + "on_val_start": [], + "on_val_batch_start": [], + "on_val_image_end": [], + "on_val_batch_end": [], + "on_val_end": [], + "on_fit_epoch_end": [], # fit = train + val + "on_model_save": [], + "on_train_end": [], + "on_params_update": [], + "teardown": [], + } + self.stop_training = False # set True to interrupt training + + def register_action(self, hook, name="", callback=None): + """ + Register a new action to a callback hook. + + Args: + hook: The callback hook name to register the action to + name: The name of the action for later reference + callback: The callback to fire + """ + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + assert callable(callback), f"callback '{callback}' is not callable" + self._callbacks[hook].append({"name": name, "callback": callback}) + + def get_registered_actions(self, hook=None): + """ + Returns all the registered actions by callback hook. + + Args: + hook: The name of the hook to check, defaults to all + """ + return self._callbacks[hook] if hook else self._callbacks + + def run(self, hook, *args, thread=False, **kwargs): + """ + Loop through the registered actions and fire all callbacks on main thread. + + Args: + hook: The name of the hook to check, defaults to all + args: Arguments to receive from YOLOv5 + thread: (boolean) Run callbacks in daemon thread + kwargs: Keyword Arguments to receive from YOLOv5 + """ + assert hook in self._callbacks, f"hook '{hook}' not found in callbacks {self._callbacks}" + for logger in self._callbacks[hook]: + if thread: + threading.Thread(target=logger["callback"], args=args, kwargs=kwargs, daemon=True).start() + else: + logger["callback"](*args, **kwargs) diff --git a/app/util/yolov5/utils/dataloaders.py b/app/util/yolov5/utils/dataloaders.py new file mode 100644 index 0000000..6d26cfd --- /dev/null +++ b/app/util/yolov5/utils/dataloaders.py @@ -0,0 +1,1378 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Dataloaders and dataset utils.""" + +import contextlib +import glob +import hashlib +import json +import math +import os +import random +import shutil +import time +from itertools import repeat +from multiprocessing.pool import Pool, ThreadPool +from pathlib import Path +from threading import Thread +from urllib.parse import urlparse + +import numpy as np +import psutil +import torch +import torch.nn.functional as F +import torchvision +import yaml +from PIL import ExifTags, Image, ImageOps +from torch.utils.data import DataLoader, Dataset, dataloader, distributed +from tqdm import tqdm + +from app.util.yolov5.utils.augmentations import ( + Albumentations, + augment_hsv, + classify_albumentations, + classify_transforms, + copy_paste, + letterbox, + mixup, + random_perspective, +) +from app.util.yolov5.utils.general import ( + DATASETS_DIR, + LOGGER, + NUM_THREADS, + TQDM_BAR_FORMAT, + check_dataset, + check_requirements, + check_yaml, + clean_str, + cv2, + is_colab, + is_kaggle, + segments2boxes, + unzip_file, + xyn2xy, + xywh2xyxy, + xywhn2xyxy, + xyxy2xywhn, +) +from app.util.yolov5.utils.torch_utils import torch_distributed_zero_first + +# Parameters +HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data" +IMG_FORMATS = "bmp", "dng", "jpeg", "jpg", "mpo", "png", "tif", "tiff", "webp", "pfm" # include image suffixes +VID_FORMATS = "asf", "avi", "gif", "m4v", "mkv", "mov", "mp4", "mpeg", "mpg", "ts", "wmv" # include video suffixes +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) +PIN_MEMORY = str(os.getenv("PIN_MEMORY", True)).lower() == "true" # global pin_memory for dataloaders + +# Get orientation exif tag +for orientation in ExifTags.TAGS.keys(): + if ExifTags.TAGS[orientation] == "Orientation": + break + + +def get_hash(paths): + """Generates a single SHA256 hash for a list of file or directory paths by combining their sizes and paths.""" + size = sum(os.path.getsize(p) for p in paths if os.path.exists(p)) # sizes + h = hashlib.sha256(str(size).encode()) # hash sizes + h.update("".join(paths).encode()) # hash paths + return h.hexdigest() # return hash + + +def exif_size(img): + """Returns corrected PIL image size (width, height) considering EXIF orientation.""" + s = img.size # (width, height) + with contextlib.suppress(Exception): + rotation = dict(img._getexif().items())[orientation] + if rotation in [6, 8]: # rotation 270 or 90 + s = (s[1], s[0]) + return s + + +def exif_transpose(image): + """ + Transpose a PIL image accordingly if it has an EXIF Orientation tag. + Inplace version of https://github.com/python-pillow/Pillow/blob/master/src/PIL/ImageOps.py exif_transpose(). + + :param image: The image to transpose. + :return: An image. + """ + exif = image.getexif() + orientation = exif.get(0x0112, 1) # default 1 + if orientation > 1: + method = { + 2: Image.FLIP_LEFT_RIGHT, + 3: Image.ROTATE_180, + 4: Image.FLIP_TOP_BOTTOM, + 5: Image.TRANSPOSE, + 6: Image.ROTATE_270, + 7: Image.TRANSVERSE, + 8: Image.ROTATE_90, + }.get(orientation) + if method is not None: + image = image.transpose(method) + del exif[0x0112] + image.info["exif"] = exif.tobytes() + return image + + +def seed_worker(worker_id): + """ + Sets the seed for a dataloader worker to ensure reproducibility, based on PyTorch's randomness notes. + + See https://pytorch.org/docs/stable/notes/randomness.html#dataloader. + """ + worker_seed = torch.initial_seed() % 2**32 + np.random.seed(worker_seed) + random.seed(worker_seed) + + +# Inherit from DistributedSampler and override iterator +# https://github.com/pytorch/pytorch/blob/master/torch/utils/data/distributed.py +class SmartDistributedSampler(distributed.DistributedSampler): + """A distributed sampler ensuring deterministic shuffling and balanced data distribution across GPUs.""" + + def __iter__(self): + """Yields indices for distributed data sampling, shuffled deterministically based on epoch and seed.""" + g = torch.Generator() + g.manual_seed(self.seed + self.epoch) + + # determine the eventual size (n) of self.indices (DDP indices) + n = int((len(self.dataset) - self.rank - 1) / self.num_replicas) + 1 # num_replicas == WORLD_SIZE + idx = torch.randperm(n, generator=g) + if not self.shuffle: + idx = idx.sort()[0] + + idx = idx.tolist() + if self.drop_last: + idx = idx[: self.num_samples] + else: + padding_size = self.num_samples - len(idx) + if padding_size <= len(idx): + idx += idx[:padding_size] + else: + idx += (idx * math.ceil(padding_size / len(idx)))[:padding_size] + + return iter(idx) + + +def create_dataloader( + path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix="", + shuffle=False, + seed=0, +): + """Creates and returns a configured DataLoader instance for loading and processing image datasets.""" + if rect and shuffle: + LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabels( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix, + rank=rank, + ) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + drop_last=quad, + pin_memory=PIN_MEMORY, + collate_fn=LoadImagesAndLabels.collate_fn4 if quad else LoadImagesAndLabels.collate_fn, + worker_init_fn=seed_worker, + generator=generator, + ), dataset + + +class InfiniteDataLoader(dataloader.DataLoader): + """ + Dataloader that reuses workers. + + Uses same syntax as vanilla DataLoader + """ + + def __init__(self, *args, **kwargs): + """Initializes an InfiniteDataLoader that reuses workers with standard DataLoader syntax, augmenting with a + repeating sampler. + """ + super().__init__(*args, **kwargs) + object.__setattr__(self, "batch_sampler", _RepeatSampler(self.batch_sampler)) + self.iterator = super().__iter__() + + def __len__(self): + """Returns the length of the batch sampler's sampler in the InfiniteDataLoader.""" + return len(self.batch_sampler.sampler) + + def __iter__(self): + """Yields batches of data indefinitely in a loop by resetting the sampler when exhausted.""" + for _ in range(len(self)): + yield next(self.iterator) + + +class _RepeatSampler: + """ + Sampler that repeats forever. + + Args: + sampler (Sampler) + """ + + def __init__(self, sampler): + """Initializes a perpetual sampler wrapping a provided `Sampler` instance for endless data iteration.""" + self.sampler = sampler + + def __iter__(self): + """Returns an infinite iterator over the dataset by repeatedly yielding from the given sampler.""" + while True: + yield from iter(self.sampler) + + +class LoadScreenshots: + """Loads and processes screenshots for YOLOv5 detection from specified screen regions using mss.""" + + def __init__(self, source, img_size=640, stride=32, auto=True, transforms=None): + """ + Initializes a screenshot dataloader for YOLOv5 with specified source region, image size, stride, auto, and + transforms. + + Source = [screen_number left top width height] (pixels) + """ + check_requirements("mss") + import mss + + source, *params = source.split() + self.screen, left, top, width, height = 0, None, None, None, None # default to full screen 0 + if len(params) == 1: + self.screen = int(params[0]) + elif len(params) == 4: + left, top, width, height = (int(x) for x in params) + elif len(params) == 5: + self.screen, left, top, width, height = (int(x) for x in params) + self.img_size = img_size + self.stride = stride + self.transforms = transforms + self.auto = auto + self.mode = "stream" + self.frame = 0 + self.sct = mss.mss() + + # Parse monitor shape + monitor = self.sct.monitors[self.screen] + self.top = monitor["top"] if top is None else (monitor["top"] + top) + self.left = monitor["left"] if left is None else (monitor["left"] + left) + self.width = width or monitor["width"] + self.height = height or monitor["height"] + self.monitor = {"left": self.left, "top": self.top, "width": self.width, "height": self.height} + + def __iter__(self): + """Iterates over itself, enabling use in loops and iterable contexts.""" + return self + + def __next__(self): + """Captures and returns the next screen frame as a BGR numpy array, cropping to only the first three channels + from BGRA. + """ + im0 = np.array(self.sct.grab(self.monitor))[:, :, :3] # [:, :, :3] BGRA to BGR + s = f"screen {self.screen} (LTWH): {self.left},{self.top},{self.width},{self.height}: " + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + self.frame += 1 + return str(self.screen), im, im0, None, s # screen, img, original img, im0s, s + + +class LoadImages: + """YOLOv5 image/video dataloader, i.e. `python detect.py --source image.jpg/vid.mp4`.""" + + def __init__(self, path, img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + """Initializes YOLOv5 loader for images/videos, supporting glob patterns, directories, and lists of paths.""" + if isinstance(path, str) and Path(path).suffix == ".txt": # *.txt file with img/vid/dir on each line + path = Path(path).read_text().rsplit() + files = [] + for p in sorted(path) if isinstance(path, (list, tuple)) else [path]: + p = str(Path(p).resolve()) + if "*" in p: + files.extend(sorted(glob.glob(p, recursive=True))) # glob + elif os.path.isdir(p): + files.extend(sorted(glob.glob(os.path.join(p, "*.*")))) # dir + elif os.path.isfile(p): + files.append(p) # files + else: + raise FileNotFoundError(f"{p} does not exist") + + images = [x for x in files if x.split(".")[-1].lower() in IMG_FORMATS] + videos = [x for x in files if x.split(".")[-1].lower() in VID_FORMATS] + ni, nv = len(images), len(videos) + + self.img_size = img_size + self.stride = stride + self.files = images + videos + self.nf = ni + nv # number of files + self.video_flag = [False] * ni + [True] * nv + self.mode = "image" + self.auto = auto + self.transforms = transforms # optional + self.vid_stride = vid_stride # video frame-rate stride + if any(videos): + self._new_video(videos[0]) # new video + else: + self.cap = None + assert self.nf > 0, ( + f"No images or videos found in {p}. Supported formats are:\nimages: {IMG_FORMATS}\nvideos: {VID_FORMATS}" + ) + + def __iter__(self): + """Initializes iterator by resetting count and returns the iterator object itself.""" + self.count = 0 + return self + + def __next__(self): + """Advances to the next file in the dataset, raising StopIteration if at the end.""" + if self.count == self.nf: + raise StopIteration + path = self.files[self.count] + + if self.video_flag[self.count]: + # Read video + self.mode = "video" + for _ in range(self.vid_stride): + self.cap.grab() + ret_val, im0 = self.cap.retrieve() + while not ret_val: + self.count += 1 + self.cap.release() + if self.count == self.nf: # last video + raise StopIteration + path = self.files[self.count] + self._new_video(path) + ret_val, im0 = self.cap.read() + + self.frame += 1 + # im0 = self._cv2_rotate(im0) # for use if cv2 autorotation is False + s = f"video {self.count + 1}/{self.nf} ({self.frame}/{self.frames}) {path}: " + + else: + # Read image + self.count += 1 + im0 = cv2.imread(path) # BGR + assert im0 is not None, f"Image Not Found {path}" + s = f"image {self.count}/{self.nf} {path}: " + + if self.transforms: + im = self.transforms(im0) # transforms + else: + im = letterbox(im0, self.img_size, stride=self.stride, auto=self.auto)[0] # padded resize + im = im.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + im = np.ascontiguousarray(im) # contiguous + + return path, im, im0, self.cap, s + + def _new_video(self, path): + """Initializes a new video capture object with path, frame count adjusted by stride, and orientation + metadata. + """ + self.frame = 0 + self.cap = cv2.VideoCapture(path) + self.frames = int(self.cap.get(cv2.CAP_PROP_FRAME_COUNT) / self.vid_stride) + self.orientation = int(self.cap.get(cv2.CAP_PROP_ORIENTATION_META)) # rotation degrees + # self.cap.set(cv2.CAP_PROP_ORIENTATION_AUTO, 0) # disable https://github.com/ultralytics/yolov5/issues/8493 + + def _cv2_rotate(self, im): + """Rotates a cv2 image based on its orientation; supports 0, 90, and 180 degrees rotations.""" + if self.orientation == 0: + return cv2.rotate(im, cv2.ROTATE_90_CLOCKWISE) + elif self.orientation == 180: + return cv2.rotate(im, cv2.ROTATE_90_COUNTERCLOCKWISE) + elif self.orientation == 90: + return cv2.rotate(im, cv2.ROTATE_180) + return im + + def __len__(self): + """Returns the number of files in the dataset.""" + return self.nf # number of files + + +class LoadStreams: + """Loads and processes video streams for YOLOv5, supporting various sources including YouTube and IP cameras.""" + + def __init__(self, sources="file.streams", img_size=640, stride=32, auto=True, transforms=None, vid_stride=1): + """Initializes a stream loader for processing video streams with YOLOv5, supporting various sources including + YouTube. + """ + torch.backends.cudnn.benchmark = True # faster for fixed-size inference + self.mode = "stream" + self.img_size = img_size + self.stride = stride + self.vid_stride = vid_stride # video frame-rate stride + sources = Path(sources).read_text().rsplit() if os.path.isfile(sources) else [sources] + n = len(sources) + self.sources = [clean_str(x) for x in sources] # clean source names for later + self.imgs, self.fps, self.frames, self.threads = [None] * n, [0] * n, [0] * n, [None] * n + for i, s in enumerate(sources): # index, source + # Start thread to read frames from video stream + st = f"{i + 1}/{n}: {s}... " + if urlparse(s).hostname in ("www.youtube.com", "youtube.com", "youtu.be"): # if source is YouTube video + # YouTube format i.e. 'https://www.youtube.com/watch?v=Zgi9g1ksQHc' or 'https://youtu.be/LNwODJXcvt4' + check_requirements(("pafy", "youtube_dl==2020.12.2")) + import pafy + + s = pafy.new(s).getbest(preftype="mp4").url # YouTube URL + s = eval(s) if s.isnumeric() else s # i.e. s = '0' local webcam + if s == 0: + assert not is_colab(), "--source 0 webcam unsupported on Colab. Rerun command in a local environment." + assert not is_kaggle(), "--source 0 webcam unsupported on Kaggle. Rerun command in a local environment." + cap = cv2.VideoCapture(s) + assert cap.isOpened(), f"{st}Failed to open {s}" + w = int(cap.get(cv2.CAP_PROP_FRAME_WIDTH)) + h = int(cap.get(cv2.CAP_PROP_FRAME_HEIGHT)) + fps = cap.get(cv2.CAP_PROP_FPS) # warning: may return 0 or nan + self.frames[i] = max(int(cap.get(cv2.CAP_PROP_FRAME_COUNT)), 0) or float("inf") # infinite stream fallback + self.fps[i] = max((fps if math.isfinite(fps) else 0) % 100, 0) or 30 # 30 FPS fallback + + _, self.imgs[i] = cap.read() # guarantee first frame + self.threads[i] = Thread(target=self.update, args=([i, cap, s]), daemon=True) + LOGGER.info(f"{st} Success ({self.frames[i]} frames {w}x{h} at {self.fps[i]:.2f} FPS)") + self.threads[i].start() + LOGGER.info("") # newline + + # check for common shapes + s = np.stack([letterbox(x, img_size, stride=stride, auto=auto)[0].shape for x in self.imgs]) + self.rect = np.unique(s, axis=0).shape[0] == 1 # rect inference if all shapes equal + self.auto = auto and self.rect + self.transforms = transforms # optional + if not self.rect: + LOGGER.warning("WARNING ⚠️ Stream shapes differ. For optimal performance supply similarly-shaped streams.") + + def update(self, i, cap, stream): + """Reads frames from stream `i`, updating imgs array; handles stream reopening on signal loss.""" + n, f = 0, self.frames[i] # frame number, frame array + while cap.isOpened() and n < f: + n += 1 + cap.grab() # .read() = .grab() followed by .retrieve() + if n % self.vid_stride == 0: + success, im = cap.retrieve() + if success: + self.imgs[i] = im + else: + LOGGER.warning("WARNING ⚠️ Video stream unresponsive, please check your IP camera connection.") + self.imgs[i] = np.zeros_like(self.imgs[i]) + cap.open(stream) # re-open stream if signal was lost + time.sleep(0.0) # wait time + + def __iter__(self): + """Resets and returns the iterator for iterating over video frames or images in a dataset.""" + self.count = -1 + return self + + def __next__(self): + """Iterates over video frames or images, halting on thread stop or 'q' key press, raising `StopIteration` when + done. + """ + self.count += 1 + if not all(x.is_alive() for x in self.threads) or cv2.waitKey(1) == ord("q"): # q to quit + cv2.destroyAllWindows() + raise StopIteration + + im0 = self.imgs.copy() + if self.transforms: + im = np.stack([self.transforms(x) for x in im0]) # transforms + else: + im = np.stack([letterbox(x, self.img_size, stride=self.stride, auto=self.auto)[0] for x in im0]) # resize + im = im[..., ::-1].transpose((0, 3, 1, 2)) # BGR to RGB, BHWC to BCHW + im = np.ascontiguousarray(im) # contiguous + + return self.sources, im, im0, None, "" + + def __len__(self): + """Returns the number of sources in the dataset, supporting up to 32 streams at 30 FPS over 30 years.""" + return len(self.sources) # 1E12 frames = 32 streams at 30 FPS for 30 years + + +def img2label_paths(img_paths): + """Generates label file paths from corresponding image file paths by replacing `/images/` with `/labels/` and + extension with `.txt`. + """ + sa, sb = f"{os.sep}images{os.sep}", f"{os.sep}labels{os.sep}" # /images/, /labels/ substrings + return [sb.join(x.rsplit(sa, 1)).rsplit(".", 1)[0] + ".txt" for x in img_paths] + + +class LoadImagesAndLabels(Dataset): + """Loads images and their corresponding labels for training and validation in YOLOv5.""" + + cache_version = 0.6 # dataset labels *.cache version + rand_interp_methods = [cv2.INTER_NEAREST, cv2.INTER_LINEAR, cv2.INTER_CUBIC, cv2.INTER_AREA, cv2.INTER_LANCZOS4] + + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0.0, + min_items=0, + prefix="", + rank=-1, + seed=0, + ): + """Initializes the YOLOv5 dataset loader, handling images and their labels, caching, and preprocessing.""" + self.img_size = img_size + self.augment = augment + self.hyp = hyp + self.image_weights = image_weights + self.rect = False if image_weights else rect + self.mosaic = self.augment and not self.rect # load 4 images at a time into a mosaic (only during training) + self.mosaic_border = [-img_size // 2, -img_size // 2] + self.stride = stride + self.path = path + self.albumentations = Albumentations(size=img_size) if augment else None + + try: + f = [] # image files + for p in path if isinstance(path, list) else [path]: + p = Path(p) # os-agnostic + if p.is_dir(): # dir + f += glob.glob(str(p / "**" / "*.*"), recursive=True) + # f = list(p.rglob('*.*')) # pathlib + elif p.is_file(): # file + with open(p) as t: + t = t.read().strip().splitlines() + parent = str(p.parent) + os.sep + f += [x.replace("./", parent, 1) if x.startswith("./") else x for x in t] # to global path + # f += [p.parent / x.lstrip(os.sep) for x in t] # to global path (pathlib) + else: + raise FileNotFoundError(f"{prefix}{p} does not exist") + self.im_files = sorted(x.replace("/", os.sep) for x in f if x.split(".")[-1].lower() in IMG_FORMATS) + # self.img_files = sorted([x for x in f if x.suffix[1:].lower() in IMG_FORMATS]) # pathlib + assert self.im_files, f"{prefix}No images found" + except Exception as e: + raise Exception(f"{prefix}Error loading data from {path}: {e}\n{HELP_URL}") from e + + # Check cache + self.label_files = img2label_paths(self.im_files) # labels + cache_path = (p if p.is_file() else Path(self.label_files[0]).parent).with_suffix(".cache") + try: + cache, exists = np.load(cache_path, allow_pickle=True).item(), True # load dict + assert cache["version"] == self.cache_version # matches current version + assert cache["hash"] == get_hash(self.label_files + self.im_files) # identical hash + except Exception: + cache, exists = self.cache_labels(cache_path, prefix), False # run cache ops + + # Display cache + nf, nm, ne, nc, n = cache.pop("results") # found, missing, empty, corrupt, total + if exists and LOCAL_RANK in {-1, 0}: + d = f"Scanning {cache_path}... {nf} images, {nm + ne} backgrounds, {nc} corrupt" + tqdm(None, desc=prefix + d, total=n, initial=n, bar_format=TQDM_BAR_FORMAT) # display cache results + if cache["msgs"]: + LOGGER.info("\n".join(cache["msgs"])) # display warnings + assert nf > 0 or not augment, f"{prefix}No labels found in {cache_path}, can not start training. {HELP_URL}" + + # Read cache + [cache.pop(k) for k in ("hash", "version", "msgs")] # remove items + labels, shapes, self.segments = zip(*cache.values()) + nl = len(np.concatenate(labels, 0)) # number of labels + assert nl > 0 or not augment, f"{prefix}All labels empty in {cache_path}, can not start training. {HELP_URL}" + self.labels = list(labels) + self.shapes = np.array(shapes) + self.im_files = list(cache.keys()) # update + self.label_files = img2label_paths(cache.keys()) # update + + # Filter images + if min_items: + include = np.array([len(x) >= min_items for x in self.labels]).nonzero()[0].astype(int) + LOGGER.info(f"{prefix}{n - len(include)}/{n} images filtered from dataset") + self.im_files = [self.im_files[i] for i in include] + self.label_files = [self.label_files[i] for i in include] + self.labels = [self.labels[i] for i in include] + self.segments = [self.segments[i] for i in include] + self.shapes = self.shapes[include] # wh + + # Create indices + n = len(self.shapes) # number of images + bi = np.floor(np.arange(n) / batch_size).astype(int) # batch index + nb = bi[-1] + 1 # number of batches + self.batch = bi # batch index of image + self.n = n + self.indices = np.arange(n) + if rank > -1: # DDP indices (see: SmartDistributedSampler) + # force each rank (i.e. GPU process) to sample the same subset of data on every epoch + self.indices = self.indices[np.random.RandomState(seed=seed).permutation(n) % WORLD_SIZE == RANK] + + # Update labels + include_class = [] # filter labels to include only these classes (optional) + self.segments = list(self.segments) + include_class_array = np.array(include_class).reshape(1, -1) + for i, (label, segment) in enumerate(zip(self.labels, self.segments)): + if include_class: + j = (label[:, 0:1] == include_class_array).any(1) + self.labels[i] = label[j] + if segment: + self.segments[i] = [segment[idx] for idx, elem in enumerate(j) if elem] + if single_cls: # single-class training, merge all classes into 0 + self.labels[i][:, 0] = 0 + + # Rectangular Training + if self.rect: + # Sort by aspect ratio + s = self.shapes # wh + ar = s[:, 1] / s[:, 0] # aspect ratio + irect = ar.argsort() + self.im_files = [self.im_files[i] for i in irect] + self.label_files = [self.label_files[i] for i in irect] + self.labels = [self.labels[i] for i in irect] + self.segments = [self.segments[i] for i in irect] + self.shapes = s[irect] # wh + ar = ar[irect] + + # Set training image shapes + shapes = [[1, 1]] * nb + for i in range(nb): + ari = ar[bi == i] + mini, maxi = ari.min(), ari.max() + if maxi < 1: + shapes[i] = [maxi, 1] + elif mini > 1: + shapes[i] = [1, 1 / mini] + + self.batch_shapes = np.ceil(np.array(shapes) * img_size / stride + pad).astype(int) * stride + + # Cache images into RAM/disk for faster training + if cache_images == "ram" and not self.check_cache_ram(prefix=prefix): + cache_images = False + self.ims = [None] * n + self.npy_files = [Path(f).with_suffix(".npy") for f in self.im_files] + if cache_images: + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + self.im_hw0, self.im_hw = [None] * n, [None] * n + fcn = self.cache_images_to_disk if cache_images == "disk" else self.load_image + results = ThreadPool(NUM_THREADS).imap(lambda i: (i, fcn(i)), self.indices) + pbar = tqdm(results, total=len(self.indices), bar_format=TQDM_BAR_FORMAT, disable=LOCAL_RANK > 0) + for i, x in pbar: + if cache_images == "disk": + b += self.npy_files[i].stat().st_size + else: # 'ram' + self.ims[i], self.im_hw0[i], self.im_hw[i] = x # im, hw_orig, hw_resized = load_image(self, i) + b += self.ims[i].nbytes * WORLD_SIZE + pbar.desc = f"{prefix}Caching images ({b / gb:.1f}GB {cache_images})" + pbar.close() + + def check_cache_ram(self, safety_margin=0.1, prefix=""): + """Checks if available RAM is sufficient for caching images, adjusting for a safety margin.""" + b, gb = 0, 1 << 30 # bytes of cached images, bytes per gigabytes + n = min(self.n, 30) # extrapolate from 30 random images + for _ in range(n): + im = cv2.imread(random.choice(self.im_files)) # sample image + ratio = self.img_size / max(im.shape[0], im.shape[1]) # max(h, w) # ratio + b += im.nbytes * ratio**2 + mem_required = b * self.n / n # GB required to cache dataset into RAM + mem = psutil.virtual_memory() + cache = mem_required * (1 + safety_margin) < mem.available # to cache or not to cache, that is the question + if not cache: + LOGGER.info( + f"{prefix}{mem_required / gb:.1f}GB RAM required, " + f"{mem.available / gb:.1f}/{mem.total / gb:.1f}GB available, " + f"{'caching images ✅' if cache else 'not caching images ⚠️'}" + ) + return cache + + def cache_labels(self, path=Path("./labels.cache"), prefix=""): + """Caches dataset labels, verifies images, reads shapes, and tracks dataset integrity.""" + x = {} # dict + nm, nf, ne, nc, msgs = 0, 0, 0, 0, [] # number missing, found, empty, corrupt, messages + desc = f"{prefix}Scanning {path.parent / path.stem}..." + with Pool(NUM_THREADS) as pool: + pbar = tqdm( + pool.imap(verify_image_label, zip(self.im_files, self.label_files, repeat(prefix))), + desc=desc, + total=len(self.im_files), + bar_format=TQDM_BAR_FORMAT, + ) + for im_file, lb, shape, segments, nm_f, nf_f, ne_f, nc_f, msg in pbar: + nm += nm_f + nf += nf_f + ne += ne_f + nc += nc_f + if im_file: + x[im_file] = [lb, shape, segments] + if msg: + msgs.append(msg) + pbar.desc = f"{desc} {nf} images, {nm + ne} backgrounds, {nc} corrupt" + + pbar.close() + if msgs: + LOGGER.info("\n".join(msgs)) + if nf == 0: + LOGGER.warning(f"{prefix}WARNING ⚠️ No labels found in {path}. {HELP_URL}") + x["hash"] = get_hash(self.label_files + self.im_files) + x["results"] = nf, nm, ne, nc, len(self.im_files) + x["msgs"] = msgs # warnings + x["version"] = self.cache_version # cache version + try: + np.save(path, x) # save cache for next time + path.with_suffix(".cache.npy").rename(path) # remove .npy suffix + LOGGER.info(f"{prefix}New cache created: {path}") + except Exception as e: + LOGGER.warning(f"{prefix}WARNING ⚠️ Cache directory {path.parent} is not writeable: {e}") # not writeable + return x + + def __len__(self): + """Returns the number of images in the dataset.""" + return len(self.im_files) + + # def __iter__(self): + # self.count = -1 + # print('ran dataset iter') + # #self.shuffled_vector = np.random.permutation(self.nF) if self.augment else np.arange(self.nF) + # return self + + def __getitem__(self, index): + """Fetches the dataset item at the given index, considering linear, shuffled, or weighted sampling.""" + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + if mosaic := self.mosaic and random.random() < hyp["mosaic"]: + # Load mosaic + img, labels = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp["mixup"]: + img, labels = mixup(img, labels, *self.load_mosaic(random.choice(self.indices))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels = random_perspective( + img, + labels, + degrees=hyp["degrees"], + translate=hyp["translate"], + scale=hyp["scale"], + shear=hyp["shear"], + perspective=hyp["perspective"], + ) + + nl = len(labels) # number of labels + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) + + if self.augment: + # Albumentations + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) + + # Flip up-down + if random.random() < hyp["flipud"]: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + + # Flip left-right + if random.random() < hyp["fliplr"]: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + + # Cutouts + # labels = cutout(img, labels, p=0.5) + # nl = len(labels) # update after cutout + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return torch.from_numpy(img), labels_out, self.im_files[index], shapes + + def load_image(self, i): + """ + Loads an image by index, returning the image, its original dimensions, and resized dimensions. + + Returns (im, original hw, resized hw) + """ + im, f, fn = ( + self.ims[i], + self.im_files[i], + self.npy_files[i], + ) + if im is None: # not cached in RAM + if fn.exists(): # load npy + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + assert im is not None, f"Image Not Found {f}" + h0, w0 = im.shape[:2] # orig hw + r = self.img_size / max(h0, w0) # ratio + if r != 1: # if sizes are not equal + interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA + im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp) + return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized + return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized + + def cache_images_to_disk(self, i): + """Saves an image to disk as an *.npy file for quicker loading, identified by index `i`.""" + f = self.npy_files[i] + if not f.exists(): + np.save(f.as_posix(), cv2.imread(self.im_files[i])) + + def load_mosaic(self, index): + """Loads a 4-image mosaic for YOLOv5, combining 1 selected and 3 random images, with labels and segments.""" + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + random.shuffle(indices) + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) + img4, labels4 = random_perspective( + img4, + labels4, + segments4, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + + return img4, labels4 + + def load_mosaic9(self, index): + """Loads 1 image + 8 random images into a 9-image mosaic for augmented YOLOv5 training, returning labels and + segments. + """ + labels9, segments9 = [], [] + s = self.img_size + indices = [index] + random.choices(self.indices, k=8) # 8 additional image indices + random.shuffle(indices) + hp, wp = -1, -1 # height, width previous + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img9 + if i == 0: # center + img9 = np.full((s * 3, s * 3, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + h0, w0 = h, w + c = s, s, s + w, s + h # xmin, ymin, xmax, ymax (base) coordinates + elif i == 1: # top + c = s, s - h, s + w, s + elif i == 2: # top right + c = s + wp, s - h, s + wp + w, s + elif i == 3: # right + c = s + w0, s, s + w0 + w, s + h + elif i == 4: # bottom right + c = s + w0, s + hp, s + w0 + w, s + hp + h + elif i == 5: # bottom + c = s + w0 - w, s + h0, s + w0, s + h0 + h + elif i == 6: # bottom left + c = s + w0 - wp - w, s + h0, s + w0 - wp, s + h0 + h + elif i == 7: # left + c = s - w, s + h0 - h, s, s + h0 + elif i == 8: # top left + c = s - w, s + h0 - hp - h, s, s + h0 - hp + + padx, pady = c[:2] + x1, y1, x2, y2 = (max(x, 0) for x in c) # allocate coords + + # Labels + labels, segments = self.labels[index].copy(), self.segments[index].copy() + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padx, pady) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padx, pady) for x in segments] + labels9.append(labels) + segments9.extend(segments) + + # Image + img9[y1:y2, x1:x2] = img[y1 - pady :, x1 - padx :] # img9[ymin:ymax, xmin:xmax] + hp, wp = h, w # height, width previous + + # Offset + yc, xc = (int(random.uniform(0, s)) for _ in self.mosaic_border) # mosaic center x, y + img9 = img9[yc : yc + 2 * s, xc : xc + 2 * s] + + # Concat/clip labels + labels9 = np.concatenate(labels9, 0) + labels9[:, [1, 3]] -= xc + labels9[:, [2, 4]] -= yc + c = np.array([xc, yc]) # centers + segments9 = [x - c for x in segments9] + + for x in (labels9[:, 1:], *segments9): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img9, labels9 = replicate(img9, labels9) # replicate + + # Augment + img9, labels9, segments9 = copy_paste(img9, labels9, segments9, p=self.hyp["copy_paste"]) + img9, labels9 = random_perspective( + img9, + labels9, + segments9, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + + return img9, labels9 + + @staticmethod + def collate_fn(batch): + """Batches images, labels, paths, and shapes, assigning unique indices to targets in merged label tensor.""" + im, label, path, shapes = zip(*batch) # transposed + for i, lb in enumerate(label): + lb[:, 0] = i # add target image index for build_targets() + return torch.stack(im, 0), torch.cat(label, 0), path, shapes + + @staticmethod + def collate_fn4(batch): + """Bundles a batch's data by quartering the number of shapes and paths, preparing it for model input.""" + im, label, path, shapes = zip(*batch) # transposed + n = len(shapes) // 4 + im4, label4, path4, shapes4 = [], [], path[:n], shapes[:n] + + ho = torch.tensor([[0.0, 0, 0, 1, 0, 0]]) + wo = torch.tensor([[0.0, 0, 1, 0, 0, 0]]) + s = torch.tensor([[1, 1, 0.5, 0.5, 0.5, 0.5]]) # scale + for i in range(n): # zidane torch.zeros(16,3,720,1280) # BCHW + i *= 4 + if random.random() < 0.5: + im1 = F.interpolate(im[i].unsqueeze(0).float(), scale_factor=2.0, mode="bilinear", align_corners=False)[ + 0 + ].type(im[i].type()) + lb = label[i] + else: + im1 = torch.cat((torch.cat((im[i], im[i + 1]), 1), torch.cat((im[i + 2], im[i + 3]), 1)), 2) + lb = torch.cat((label[i], label[i + 1] + ho, label[i + 2] + wo, label[i + 3] + ho + wo), 0) * s + im4.append(im1) + label4.append(lb) + + for i, lb in enumerate(label4): + lb[:, 0] = i # add target image index for build_targets() + + return torch.stack(im4, 0), torch.cat(label4, 0), path4, shapes4 + + +# Ancillary functions -------------------------------------------------------------------------------------------------- +def flatten_recursive(path=DATASETS_DIR / "coco128"): + """Flattens a directory by copying all files from subdirectories to a new top-level directory, preserving + filenames. + """ + new_path = Path(f"{str(path)}_flat") + if os.path.exists(new_path): + shutil.rmtree(new_path) # delete output folder + os.makedirs(new_path) # make new output folder + for file in tqdm(glob.glob(f"{str(Path(path))}/**/*.*", recursive=True)): + shutil.copyfile(file, new_path / Path(file).name) + + +def extract_boxes(path=DATASETS_DIR / "coco128"): + """ + Converts a detection dataset to a classification dataset, creating a directory for each class and extracting + bounding boxes. + + Example: from utils.dataloaders import *; extract_boxes() + """ + path = Path(path) # images dir + shutil.rmtree(path / "classification") if (path / "classification").is_dir() else None # remove existing + files = list(path.rglob("*.*")) + n = len(files) # number of files + for im_file in tqdm(files, total=n): + if im_file.suffix[1:] in IMG_FORMATS: + # image + im = cv2.imread(str(im_file))[..., ::-1] # BGR to RGB + h, w = im.shape[:2] + + # labels + lb_file = Path(img2label_paths([str(im_file)])[0]) + if Path(lb_file).exists(): + with open(lb_file) as f: + lb = np.array([x.split() for x in f.read().strip().splitlines()], dtype=np.float32) # labels + + for j, x in enumerate(lb): + c = int(x[0]) # class + f = (path / "classification") / f"{c}" / f"{path.stem}_{im_file.stem}_{j}.jpg" # new filename + if not f.parent.is_dir(): + f.parent.mkdir(parents=True) + + b = x[1:] * [w, h, w, h] # box + # b[2:] = b[2:].max() # rectangle to square + b[2:] = b[2:] * 1.2 + 3 # pad + b = xywh2xyxy(b.reshape(-1, 4)).ravel().astype(int) + + b[[0, 2]] = np.clip(b[[0, 2]], 0, w) # clip boxes outside of image + b[[1, 3]] = np.clip(b[[1, 3]], 0, h) + assert cv2.imwrite(str(f), im[b[1] : b[3], b[0] : b[2]]), f"box failure in {f}" + + +def autosplit(path=DATASETS_DIR / "coco128/images", weights=(0.9, 0.1, 0.0), annotated_only=False): + """Autosplit a dataset into train/val/test splits and save path/autosplit_*.txt files + Usage: from utils.dataloaders import *; autosplit(). + + Arguments: + path: Path to images directory + weights: Train, val, test weights (list, tuple) + annotated_only: Only use images with an annotated txt file + """ + path = Path(path) # images dir + files = sorted(x for x in path.rglob("*.*") if x.suffix[1:].lower() in IMG_FORMATS) # image files only + n = len(files) # number of files + random.seed(0) # for reproducibility + indices = random.choices([0, 1, 2], weights=weights, k=n) # assign each image to a split + + txt = ["autosplit_train.txt", "autosplit_val.txt", "autosplit_test.txt"] # 3 txt files + for x in txt: + if (path.parent / x).exists(): + (path.parent / x).unlink() # remove existing + + print(f"Autosplitting images from {path}" + ", using *.txt labeled images only" * annotated_only) + for i, img in tqdm(zip(indices, files), total=n): + if not annotated_only or Path(img2label_paths([str(img)])[0]).exists(): # check label + with open(path.parent / txt[i], "a") as f: + f.write(f"./{img.relative_to(path.parent).as_posix()}" + "\n") # add image to txt file + + +def verify_image_label(args): + """Verifies a single image-label pair, ensuring image format, size, and legal label values.""" + im_file, lb_file, prefix = args + nm, nf, ne, nc, msg, segments = 0, 0, 0, 0, "", [] # number (missing, found, empty, corrupt), message, segments + try: + # verify images + im = Image.open(im_file) + im.verify() # PIL verify + shape = exif_size(im) # image size + assert (shape[0] > 9) & (shape[1] > 9), f"image size {shape} <10 pixels" + assert im.format.lower() in IMG_FORMATS, f"invalid image format {im.format}" + if im.format.lower() in ("jpg", "jpeg"): + with open(im_file, "rb") as f: + f.seek(-2, 2) + if f.read() != b"\xff\xd9": # corrupt JPEG + ImageOps.exif_transpose(Image.open(im_file)).save(im_file, "JPEG", subsampling=0, quality=100) + msg = f"{prefix}WARNING ⚠️ {im_file}: corrupt JPEG restored and saved" + + # verify labels + if os.path.isfile(lb_file): + nf = 1 # label found + with open(lb_file) as f: + lb = [x.split() for x in f.read().strip().splitlines() if len(x)] + if any(len(x) > 6 for x in lb): # is segment + classes = np.array([x[0] for x in lb], dtype=np.float32) + segments = [np.array(x[1:], dtype=np.float32).reshape(-1, 2) for x in lb] # (cls, xy1...) + lb = np.concatenate((classes.reshape(-1, 1), segments2boxes(segments)), 1) # (cls, xywh) + lb = np.array(lb, dtype=np.float32) + if nl := len(lb): + assert lb.shape[1] == 5, f"labels require 5 columns, {lb.shape[1]} columns detected" + assert (lb >= 0).all(), f"negative label values {lb[lb < 0]}" + assert (lb[:, 1:] <= 1).all(), f"non-normalized or out of bounds coordinates {lb[:, 1:][lb[:, 1:] > 1]}" + _, i = np.unique(lb, axis=0, return_index=True) + if len(i) < nl: # duplicate row check + lb = lb[i] # remove duplicates + if segments: + segments = [segments[x] for x in i] + msg = f"{prefix}WARNING ⚠️ {im_file}: {nl - len(i)} duplicate labels removed" + else: + ne = 1 # label empty + lb = np.zeros((0, 5), dtype=np.float32) + else: + nm = 1 # label missing + lb = np.zeros((0, 5), dtype=np.float32) + return im_file, lb, shape, segments, nm, nf, ne, nc, msg + except Exception as e: + nc = 1 + msg = f"{prefix}WARNING ⚠️ {im_file}: ignoring corrupt image/label: {e}" + return [None, None, None, None, nm, nf, ne, nc, msg] + + +class HUBDatasetStats: + """ + Class for generating HUB dataset JSON and `-hub` dataset directory. + + Arguments: + path: Path to data.yaml or data.zip (with data.yaml inside data.zip) + autodownload: Attempt to download dataset if not found locally + + Usage + from utils.dataloaders import HUBDatasetStats + stats = HUBDatasetStats('coco128.yaml', autodownload=True) # usage 1 + stats = HUBDatasetStats('path/to/coco128.zip') # usage 2 + stats.get_json(save=False) + stats.process_images() + """ + + def __init__(self, path="coco128.yaml", autodownload=False): + """Initializes HUBDatasetStats with optional auto-download for datasets, given a path to dataset YAML or ZIP + file. + """ + zipped, data_dir, yaml_path = self._unzip(Path(path)) + try: + with open(check_yaml(yaml_path), errors="ignore") as f: + data = yaml.safe_load(f) # data dict + if zipped: + data["path"] = data_dir + except Exception as e: + raise Exception("error/HUB/dataset_stats/yaml_load") from e + + check_dataset(data, autodownload) # download dataset if missing + self.hub_dir = Path(data["path"] + "-hub") + self.im_dir = self.hub_dir / "images" + self.im_dir.mkdir(parents=True, exist_ok=True) # makes /images + self.stats = {"nc": data["nc"], "names": list(data["names"].values())} # statistics dictionary + self.data = data + + @staticmethod + def _find_yaml(dir): + """Finds and returns the path to a single '.yaml' file in the specified directory, preferring files that match + the directory name. + """ + files = list(dir.glob("*.yaml")) or list(dir.rglob("*.yaml")) # try root level first and then recursive + assert files, f"No *.yaml file found in {dir}" + if len(files) > 1: + files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name + assert files, f"Multiple *.yaml files found in {dir}, only 1 *.yaml file allowed" + assert len(files) == 1, f"Multiple *.yaml files found: {files}, only 1 *.yaml file allowed in {dir}" + return files[0] + + def _unzip(self, path): + """Unzips a .zip file at 'path', returning success status, unzipped directory, and path to YAML file within.""" + if not str(path).endswith(".zip"): # path is data.yaml + return False, None, path + assert Path(path).is_file(), f"Error unzipping {path}, file not found" + unzip_file(path, path=path.parent) + dir = path.with_suffix("") # dataset directory == zip name + assert dir.is_dir(), f"Error unzipping {path}, {dir} not found. path/to/abc.zip MUST unzip to path/to/abc/" + return True, str(dir), self._find_yaml(dir) # zipped, data_dir, yaml_path + + def _hub_ops(self, f, max_dim=1920): + """Resizes and saves an image at reduced quality for web/app viewing, supporting both PIL and OpenCV.""" + f_new = self.im_dir / Path(f).name # dataset-hub image filename + try: # use PIL + im = Image.open(f) + r = max_dim / max(im.height, im.width) # ratio + if r < 1.0: # image too large + im = im.resize((int(im.width * r), int(im.height * r))) + im.save(f_new, "JPEG", quality=50, optimize=True) # save + except Exception as e: # use OpenCV + LOGGER.info(f"WARNING ⚠️ HUB ops PIL failure {f}: {e}") + im = cv2.imread(f) + im_height, im_width = im.shape[:2] + r = max_dim / max(im_height, im_width) # ratio + if r < 1.0: # image too large + im = cv2.resize(im, (int(im_width * r), int(im_height * r)), interpolation=cv2.INTER_AREA) + cv2.imwrite(str(f_new), im) + + def get_json(self, save=False, verbose=False): + """Generates dataset JSON for Ultralytics HUB, optionally saves or prints it; save=bool, verbose=bool.""" + + def _round(labels): + """Rounds class labels to integers and coordinates to 4 decimal places for improved label accuracy.""" + return [[int(c), *(round(x, 4) for x in points)] for c, *points in labels] + + for split in "train", "val", "test": + if self.data.get(split) is None: + self.stats[split] = None # i.e. no test set + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + x = np.array( + [ + np.bincount(label[:, 0].astype(int), minlength=self.data["nc"]) + for label in tqdm(dataset.labels, total=dataset.n, desc="Statistics") + ] + ) # shape(128x80) + self.stats[split] = { + "instance_stats": {"total": int(x.sum()), "per_class": x.sum(0).tolist()}, + "image_stats": { + "total": dataset.n, + "unlabelled": int(np.all(x == 0, 1).sum()), + "per_class": (x > 0).sum(0).tolist(), + }, + "labels": [{str(Path(k).name): _round(v.tolist())} for k, v in zip(dataset.im_files, dataset.labels)], + } + + # Save, print and return + if save: + stats_path = self.hub_dir / "stats.json" + print(f"Saving {stats_path.resolve()}...") + with open(stats_path, "w") as f: + json.dump(self.stats, f) # save stats.json + if verbose: + print(json.dumps(self.stats, indent=2, sort_keys=False)) + return self.stats + + def process_images(self): + """Compresses images for Ultralytics HUB across 'train', 'val', 'test' splits and saves to specified + directory. + """ + for split in "train", "val", "test": + if self.data.get(split) is None: + continue + dataset = LoadImagesAndLabels(self.data[split]) # load dataset + desc = f"{split} images" + for _ in tqdm(ThreadPool(NUM_THREADS).imap(self._hub_ops, dataset.im_files), total=dataset.n, desc=desc): + pass + print(f"Done. All images saved to {self.im_dir}") + return self.im_dir + + +# Classification dataloaders ------------------------------------------------------------------------------------------- +class ClassificationDataset(torchvision.datasets.ImageFolder): + """ + YOLOv5 Classification Dataset. + + Arguments: + root: Dataset path + transform: torchvision transforms, used by default + album_transform: Albumentations transforms, used if installed + """ + + def __init__(self, root, augment, imgsz, cache=False): + """Initializes YOLOv5 Classification Dataset with optional caching, augmentations, and transforms for image + classification. + """ + super().__init__(root=root) + self.torch_transforms = classify_transforms(imgsz) + self.album_transforms = classify_albumentations(augment, imgsz) if augment else None + self.cache_ram = cache is True or cache == "ram" + self.cache_disk = cache == "disk" + self.samples = [list(x) + [Path(x[0]).with_suffix(".npy"), None] for x in self.samples] # file, index, npy, im + + def __getitem__(self, i): + """Fetches and transforms an image sample by index, supporting RAM/disk caching and Augmentations.""" + f, j, fn, im = self.samples[i] # filename, index, filename.with_suffix('.npy'), image + if self.cache_ram and im is None: + im = self.samples[i][3] = cv2.imread(f) + elif self.cache_disk: + if not fn.exists(): # load npy + np.save(fn.as_posix(), cv2.imread(f)) + im = np.load(fn) + else: # read image + im = cv2.imread(f) # BGR + if self.album_transforms: + sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"] + else: + sample = self.torch_transforms(im) + return sample, j + + +def create_classification_dataloader( + path, imgsz=224, batch_size=16, augment=True, cache=False, rank=-1, workers=8, shuffle=True +): + # Returns Dataloader object to be used with YOLOv5 Classifier + """Creates a DataLoader for image classification, supporting caching, augmentation, and distributed training.""" + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = ClassificationDataset(root=path, imgsz=imgsz, augment=augment, cache=cache) + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) + sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle) + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + RANK) + return InfiniteDataLoader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + pin_memory=PIN_MEMORY, + worker_init_fn=seed_worker, + generator=generator, + ) # or DataLoader(persistent_workers=True) diff --git a/app/util/yolov5/utils/docker/Dockerfile b/app/util/yolov5/utils/docker/Dockerfile new file mode 100644 index 0000000..f472716 --- /dev/null +++ b/app/util/yolov5/utils/docker/Dockerfile @@ -0,0 +1,73 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Builds ultralytics/yolov5:latest image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CUDA-optimized for YOLOv5 single/multi-GPU training and inference + +# Start FROM PyTorch image https://hub.docker.com/r/pytorch/pytorch +FROM pytorch/pytorch:2.0.0-cuda11.7-cudnn8-runtime + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y gcc git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg +# RUN alias python=python3 + +# Security updates +# https://security.snyk.io/vuln/SNYK-UBUNTU1804-OPENSSL-3314796 +RUN apt upgrade --no-install-recommends -y openssl + +# Create working directory +RUN rm -rf /usr/src/app && mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations comet gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' + # tensorflow tensorflowjs \ + +# Set environment variables +ENV OMP_NUM_THREADS=1 + +# Cleanup +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest && sudo docker build -f utils/docker/Dockerfile -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all $t + +# Pull and Run with local directory access +# t=ultralytics/yolov5:latest && sudo docker pull $t && sudo docker run -it --ipc=host --gpus all -v "$(pwd)"/datasets:/usr/src/datasets $t + +# Kill all +# sudo docker kill $(sudo docker ps -q) + +# Kill all image-based +# sudo docker kill $(sudo docker ps -qa --filter ancestor=ultralytics/yolov5:latest) + +# DockerHub tag update +# t=ultralytics/yolov5:latest tnew=ultralytics/yolov5:v6.2 && sudo docker pull $t && sudo docker tag $t $tnew && sudo docker push $tnew + +# Clean up +# sudo docker system prune -a --volumes + +# Update Ubuntu drivers +# https://www.maketecheasier.com/install-nvidia-drivers-ubuntu/ + +# DDP test +# python -m torch.distributed.run --nproc_per_node 2 --master_port 1 train.py --epochs 3 + +# GCP VM from Image +# docker.io/ultralytics/yolov5:latest diff --git a/app/util/yolov5/utils/docker/Dockerfile-arm64 b/app/util/yolov5/utils/docker/Dockerfile-arm64 new file mode 100644 index 0000000..0de85bf --- /dev/null +++ b/app/util/yolov5/utils/docker/Dockerfile-arm64 @@ -0,0 +1,40 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Builds ultralytics/yolov5:latest-arm64 image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is aarch64-compatible for Apple M1 and other ARM architectures i.e. Jetson Nano and Raspberry Pi + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM arm64v8/ubuntu:22.10 + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +ENV DEBIAN_FRONTEND noninteractive +RUN apt update +RUN TZ=Etc/UTC apt install -y tzdata +RUN apt install --no-install-recommends -y python3-pip git zip curl htop gcc libgl1 libglib2.0-0 libpython3-dev +# RUN alias python=python3 + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnxruntime + # tensorflow-aarch64 tensorflowjs \ + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app +ENV DEBIAN_FRONTEND teletype + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-arm64 && sudo docker build --platform linux/arm64 -f utils/docker/Dockerfile-arm64 -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-arm64 && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/app/util/yolov5/utils/docker/Dockerfile-cpu b/app/util/yolov5/utils/docker/Dockerfile-cpu new file mode 100644 index 0000000..573ad32 --- /dev/null +++ b/app/util/yolov5/utils/docker/Dockerfile-cpu @@ -0,0 +1,42 @@ +# YOLOv5 🚀 by Ultralytics, AGPL-3.0 license +# Builds ultralytics/yolov5:latest-cpu image on DockerHub https://hub.docker.com/r/ultralytics/yolov5 +# Image is CPU-optimized for ONNX, OpenVINO and PyTorch YOLOv5 deployments + +# Start FROM Ubuntu image https://hub.docker.com/_/ubuntu +FROM ubuntu:23.10 + +# Downloads to user config dir +ADD https://ultralytics.com/assets/Arial.ttf https://ultralytics.com/assets/Arial.Unicode.ttf /root/.config/Ultralytics/ + +# Install linux packages +# g++ required to build 'tflite_support' and 'lap' packages, libusb-1.0-0 required for 'tflite_support' package +RUN apt update \ + && apt install --no-install-recommends -y python3-pip git zip curl htop libgl1 libglib2.0-0 libpython3-dev gnupg g++ libusb-1.0-0 +# RUN alias python=python3 + +# Remove python3.11/EXTERNALLY-MANAGED or use 'pip install --break-system-packages' avoid 'externally-managed-environment' Ubuntu nightly error +RUN rm -rf /usr/lib/python3.11/EXTERNALLY-MANAGED + +# Install pip packages +COPY requirements.txt . +RUN python3 -m pip install --upgrade pip wheel +RUN pip install --no-cache -r requirements.txt albumentations gsutil notebook \ + coremltools onnx onnx-simplifier onnxruntime 'openvino-dev>=2023.0' \ + # tensorflow tensorflowjs \ + --extra-index-url https://download.pytorch.org/whl/cpu + +# Create working directory +RUN mkdir -p /usr/src/app +WORKDIR /usr/src/app + +# Copy contents +COPY . /usr/src/app + + +# Usage Examples ------------------------------------------------------------------------------------------------------- + +# Build and Push +# t=ultralytics/yolov5:latest-cpu && sudo docker build -f utils/docker/Dockerfile-cpu -t $t . && sudo docker push $t + +# Pull and Run +# t=ultralytics/yolov5:latest-cpu && sudo docker pull $t && sudo docker run -it --ipc=host -v "$(pwd)"/datasets:/usr/src/datasets $t diff --git a/app/util/yolov5/utils/downloads.py b/app/util/yolov5/utils/downloads.py new file mode 100644 index 0000000..f51d67a --- /dev/null +++ b/app/util/yolov5/utils/downloads.py @@ -0,0 +1,136 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Download utils.""" + +import logging +import subprocess +import urllib +from pathlib import Path + +import requests +import torch + + +def is_url(url, check=True): + """Determines if a string is a URL and optionally checks its existence online, returning a boolean.""" + try: + url = str(url) + result = urllib.parse.urlparse(url) + assert all([result.scheme, result.netloc]) # check if is url + return (urllib.request.urlopen(url).getcode() == 200) if check else True # check if exists online + except (AssertionError, urllib.request.HTTPError): + return False + + +def gsutil_getsize(url=""): + """ + Returns the size in bytes of a file at a Google Cloud Storage URL using `gsutil du`. + + Returns 0 if the command fails or output is empty. + """ + output = subprocess.check_output(["gsutil", "du", url], shell=True, encoding="utf-8") + return int(output.split()[0]) if output else 0 + + +def url_getsize(url="https://ultralytics.com/images/bus.jpg"): + """Returns the size in bytes of a downloadable file at a given URL; defaults to -1 if not found.""" + response = requests.head(url, allow_redirects=True) + return int(response.headers.get("content-length", -1)) + + +def curl_download(url, filename, *, silent: bool = False) -> bool: + """Download a file from a url to a filename using curl.""" + silent_option = "sS" if silent else "" # silent + proc = subprocess.run( + [ + "curl", + "-#", + f"-{silent_option}L", + url, + "--output", + filename, + "--retry", + "9", + "-C", + "-", + ] + ) + return proc.returncode == 0 + + +def safe_download(file, url, url2=None, min_bytes=1e0, error_msg=""): + """ + Downloads a file from a URL (or alternate URL) to a specified path if file is above a minimum size. + + Removes incomplete downloads. + """ + from utils.general import LOGGER + + file = Path(file) + assert_msg = f"Downloaded file '{file}' does not exist or size is < min_bytes={min_bytes}" + try: # url1 + LOGGER.info(f"Downloading {url} to {file}...") + torch.hub.download_url_to_file(url, str(file), progress=LOGGER.level <= logging.INFO) + assert file.exists() and file.stat().st_size > min_bytes, assert_msg # check + except Exception as e: # url2 + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f"ERROR: {e}\nRe-attempting {url2 or url} to {file}...") + # curl download, retry and resume on fail + curl_download(url2 or url, file) + finally: + if not file.exists() or file.stat().st_size < min_bytes: # check + if file.exists(): + file.unlink() # remove partial downloads + LOGGER.info(f"ERROR: {assert_msg}\n{error_msg}") + LOGGER.info("") + + +def attempt_download(file, repo="ultralytics/yolov5", release="v7.0"): + """Downloads a file from GitHub release assets or via direct URL if not found locally, supporting backup + versions. + """ + from utils.general import LOGGER + + def github_assets(repository, version="latest"): + """Fetches GitHub repository release tag and asset names using the GitHub API.""" + if version != "latest": + version = f"tags/{version}" # i.e. tags/v7.0 + response = requests.get(f"https://api.github.com/repos/{repository}/releases/{version}").json() # github api + return response["tag_name"], [x["name"] for x in response["assets"]] # tag, assets + + file = Path(str(file).strip().replace("'", "")) + if not file.exists(): + # URL specified + name = Path(urllib.parse.unquote(str(file))).name # decode '%2F' to '/' etc. + if str(file).startswith(("http:/", "https:/")): # download + url = str(file).replace(":/", "://") # Pathlib turns :// -> :/ + file = name.split("?")[0] # parse authentication https://url.com/file.txt?auth... + if Path(file).is_file(): + LOGGER.info(f"Found {url} locally at {file}") # file already exists + else: + safe_download(file=file, url=url, min_bytes=1e5) + return file + + # GitHub assets + assets = [f"yolov5{size}{suffix}.pt" for size in "nsmlx" for suffix in ("", "6", "-cls", "-seg")] # default + try: + tag, assets = github_assets(repo, release) + except Exception: + try: + tag, assets = github_assets(repo) # latest release + except Exception: + try: + tag = subprocess.check_output("git tag", shell=True, stderr=subprocess.STDOUT).decode().split()[-1] + except Exception: + tag = release + + if name in assets: + file.parent.mkdir(parents=True, exist_ok=True) # make parent dir (if required) + safe_download( + file, + url=f"https://github.com/{repo}/releases/download/{tag}/{name}", + min_bytes=1e5, + error_msg=f"{file} missing, try downloading from https://github.com/{repo}/releases/{tag}", + ) + + return str(file) diff --git a/app/util/yolov5/utils/flask_rest_api/README.md b/app/util/yolov5/utils/flask_rest_api/README.md new file mode 100644 index 0000000..d3ffaa2 --- /dev/null +++ b/app/util/yolov5/utils/flask_rest_api/README.md @@ -0,0 +1,70 @@ +# Flask REST API + +[REST](https://en.wikipedia.org/wiki/Representational_state_transfer) [API](https://en.wikipedia.org/wiki/API)s are commonly used to expose Machine Learning (ML) models to other services. This folder contains an example REST API created using Flask to expose the YOLOv5s model from [PyTorch Hub](https://pytorch.org/hub/ultralytics_yolov5/). + +## Requirements + +[Flask](https://palletsprojects.com/projects/flask/) is required. Install with: + +```shell +$ pip install Flask +``` + +## Run + +After Flask installation run: + +```shell +$ python3 restapi.py --port 5000 +``` + +Then use [curl](https://curl.se/) to perform a request: + +```shell +$ curl -X POST -F image=@zidane.jpg 'http://localhost:5000/v1/object-detection/yolov5s' +``` + +The model inference results are returned as a JSON response: + +```json +[ + { + "class": 0, + "confidence": 0.8900438547, + "height": 0.9318675399, + "name": "person", + "width": 0.3264600933, + "xcenter": 0.7438579798, + "ycenter": 0.5207948685 + }, + { + "class": 0, + "confidence": 0.8440024257, + "height": 0.7155083418, + "name": "person", + "width": 0.6546785235, + "xcenter": 0.427829951, + "ycenter": 0.6334488392 + }, + { + "class": 27, + "confidence": 0.3771208823, + "height": 0.3902671337, + "name": "tie", + "width": 0.0696444362, + "xcenter": 0.3675483763, + "ycenter": 0.7991207838 + }, + { + "class": 27, + "confidence": 0.3527112305, + "height": 0.1540903747, + "name": "tie", + "width": 0.0336618312, + "xcenter": 0.7814827561, + "ycenter": 0.5065554976 + } +] +``` + +An example python script to perform inference using [requests](https://docs.python-requests.org/en/master/) is given in `example_request.py` diff --git a/app/util/yolov5/utils/flask_rest_api/__init__.py b/app/util/yolov5/utils/flask_rest_api/__init__.py new file mode 100644 index 0000000..e69de29 diff --git a/app/util/yolov5/utils/flask_rest_api/example_request.py b/app/util/yolov5/utils/flask_rest_api/example_request.py new file mode 100644 index 0000000..db88e80 --- /dev/null +++ b/app/util/yolov5/utils/flask_rest_api/example_request.py @@ -0,0 +1,17 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Perform test request.""" + +import pprint + +import requests + +DETECTION_URL = "http://localhost:5000/v1/object-detection/yolov5s" +IMAGE = "zidane.jpg" + +# Read image +with open(IMAGE, "rb") as f: + image_data = f.read() + +response = requests.post(DETECTION_URL, files={"image": image_data}).json() + +pprint.pprint(response) diff --git a/app/util/yolov5/utils/flask_rest_api/restapi.py b/app/util/yolov5/utils/flask_rest_api/restapi.py new file mode 100644 index 0000000..410ae26 --- /dev/null +++ b/app/util/yolov5/utils/flask_rest_api/restapi.py @@ -0,0 +1,49 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Run a Flask REST API exposing one or more YOLOv5s models.""" + +import argparse +import io + +import torch +from flask import Flask, request +from PIL import Image + +app = Flask(__name__) +models = {} + +DETECTION_URL = "/v1/object-detection/" + + +@app.route(DETECTION_URL, methods=["POST"]) +def predict(model): + """Predict and return object detections in JSON format given an image and model name via a Flask REST API POST + request. + """ + if request.method != "POST": + return + + if request.files.get("image"): + # Method 1 + # with request.files["image"] as f: + # im = Image.open(io.BytesIO(f.read())) + + # Method 2 + im_file = request.files["image"] + im_bytes = im_file.read() + im = Image.open(io.BytesIO(im_bytes)) + + if model in models: + results = models[model](im, size=640) # reduce size=320 for faster inference + return results.pandas().xyxy[0].to_json(orient="records") + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(description="Flask API exposing YOLOv5 model") + parser.add_argument("--port", default=5000, type=int, help="port number") + parser.add_argument("--model", nargs="+", default=["yolov5s"], help="model(s) to run, i.e. --model yolov5n yolov5s") + opt = parser.parse_args() + + for m in opt.model: + models[m] = torch.hub.load("ultralytics/yolov5", m, force_reload=True, skip_validation=True) + + app.run(host="0.0.0.0", port=opt.port) # debug=True causes Restarting with stat diff --git a/app/util/yolov5/utils/general.py b/app/util/yolov5/utils/general.py new file mode 100644 index 0000000..d016ca3 --- /dev/null +++ b/app/util/yolov5/utils/general.py @@ -0,0 +1,1314 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""General utils.""" + +import contextlib +import glob +import inspect +import logging +import logging.config +import math +import os +import platform +import random +import re +import signal +import subprocess +import sys +import time +import urllib +from copy import deepcopy +from datetime import datetime +from itertools import repeat +from multiprocessing.pool import ThreadPool +from pathlib import Path +from subprocess import check_output +from tarfile import is_tarfile +from typing import Optional +from zipfile import ZipFile, is_zipfile + +import cv2 +import numpy as np +import pandas as pd +import pkg_resources as pkg +import torch +import torchvision +import yaml + +# Import 'ultralytics' package or install if missing +try: + import ultralytics + + assert hasattr(ultralytics, "__version__") # verify package is not directory +except (ImportError, AssertionError): + os.system("pip install -U ultralytics") + import ultralytics + +from ultralytics.utils.checks import check_requirements + +from app.util.yolov5.utils import TryExcept, emojis +from app.util.yolov5.utils.downloads import curl_download, gsutil_getsize +from app.util.yolov5.utils.metrics import box_iou, fitness + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[1] # YOLOv5 root directory +RANK = int(os.getenv("RANK", -1)) + +# Settings +NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiprocessing threads +DATASETS_DIR = Path(os.getenv("YOLOv5_DATASETS_DIR", ROOT.parent / "datasets")) # global datasets directory +AUTOINSTALL = str(os.getenv("YOLOv5_AUTOINSTALL", True)).lower() == "true" # global auto-install mode +VERBOSE = str(os.getenv("YOLOv5_VERBOSE", True)).lower() == "true" # global verbose mode +TQDM_BAR_FORMAT = "{l_bar}{bar:10}{r_bar}" # tqdm bar format +FONT = "Arial.ttf" # https://github.com/ultralytics/assets/releases/download/v0.0.0/Arial.ttf + +torch.set_printoptions(linewidth=320, precision=5, profile="long") +np.set_printoptions(linewidth=320, formatter={"float_kind": "{:11.5g}".format}) # format short g, %precision=5 +pd.options.display.max_columns = 10 +cv2.setNumThreads(0) # prevent OpenCV from multithreading (incompatible with PyTorch DataLoader) +os.environ["NUMEXPR_MAX_THREADS"] = str(NUM_THREADS) # NumExpr max threads +os.environ["OMP_NUM_THREADS"] = "1" if platform.system() == "darwin" else str(NUM_THREADS) # OpenMP (PyTorch and SciPy) +os.environ["TF_CPP_MIN_LOG_LEVEL"] = "2" # suppress verbose TF compiler warnings in Colab +os.environ["TORCH_CPP_LOG_LEVEL"] = "ERROR" # suppress "NNPACK.cpp could not initialize NNPACK" warnings +os.environ["KINETO_LOG_LEVEL"] = "5" # suppress verbose PyTorch profiler output when computing FLOPs + + +def is_ascii(s=""): + """Checks if input string `s` contains only ASCII characters; returns `True` if so, otherwise `False`.""" + s = str(s) # convert list, tuple, None, etc. to str + return len(s.encode().decode("ascii", "ignore")) == len(s) + + +def is_chinese(s="人工智能"): + """Determines if a string `s` contains any Chinese characters; returns `True` if so, otherwise `False`.""" + return bool(re.search("[\u4e00-\u9fff]", str(s))) + + +def is_colab(): + """Checks if the current environment is a Google Colab instance; returns `True` for Colab, otherwise `False`.""" + return "google.colab" in sys.modules + + +def is_jupyter(): + """ + Check if the current script is running inside a Jupyter Notebook. Verified on Colab, Jupyterlab, Kaggle, Paperspace. + + Returns: + bool: True if running inside a Jupyter Notebook, False otherwise. + """ + with contextlib.suppress(Exception): + from IPython import get_ipython + + return get_ipython() is not None + return False + + +def is_kaggle(): + """Checks if the current environment is a Kaggle Notebook by validating environment variables.""" + return os.environ.get("PWD") == "/kaggle/working" and os.environ.get("KAGGLE_URL_BASE") == "https://www.kaggle.com" + + +def is_docker() -> bool: + """Check if the process runs inside a docker container.""" + if Path("/.dockerenv").exists(): + return True + try: # check if docker is in control groups + with open("/proc/self/cgroup") as file: + return any("docker" in line for line in file) + except OSError: + return False + + +def is_writeable(dir, test=False): + """Checks if a directory is writable, optionally testing by creating a temporary file if `test=True`.""" + if not test: + return os.access(dir, os.W_OK) # possible issues on Windows + file = Path(dir) / "tmp.txt" + try: + with open(file, "w"): # open file with write permissions + pass + file.unlink() # remove file + return True + except OSError: + return False + + +LOGGING_NAME = "yolov5" + + +def set_logging(name=LOGGING_NAME, verbose=True): + """Configures logging with specified verbosity; `name` sets the logger's name, `verbose` controls logging level.""" + rank = int(os.getenv("RANK", -1)) # rank in world for Multi-GPU trainings + level = logging.INFO if verbose and rank in {-1, 0} else logging.ERROR + logging.config.dictConfig( + { + "version": 1, + "disable_existing_loggers": False, + "formatters": {name: {"format": "%(message)s"}}, + "handlers": { + name: { + "class": "logging.StreamHandler", + "formatter": name, + "level": level, + } + }, + "loggers": { + name: { + "level": level, + "handlers": [name], + "propagate": False, + } + }, + } + ) + + +set_logging(LOGGING_NAME) # run before defining LOGGER +LOGGER = logging.getLogger(LOGGING_NAME) # define globally (used in train.py, val.py, detect.py, etc.) +if platform.system() == "Windows": + for fn in LOGGER.info, LOGGER.warning: + setattr(LOGGER, fn.__name__, lambda x: fn(emojis(x))) # emoji safe logging + + +def user_config_dir(dir="Ultralytics", env_var="YOLOV5_CONFIG_DIR"): + """Returns user configuration directory path, preferring environment variable `YOLOV5_CONFIG_DIR` if set, else OS- + specific. + """ + if env := os.getenv(env_var): + path = Path(env) # use environment variable + else: + cfg = {"Windows": "AppData/Roaming", "Linux": ".config", "Darwin": "Library/Application Support"} # 3 OS dirs + path = Path.home() / cfg.get(platform.system(), "") # OS-specific config dir + path = (path if is_writeable(path) else Path("/tmp")) / dir # GCP and AWS lambda fix, only /tmp is writeable + path.mkdir(exist_ok=True) # make if required + return path + + +CONFIG_DIR = user_config_dir() # Ultralytics settings dir + + +class Profile(contextlib.ContextDecorator): + """Context manager and decorator for profiling code execution time, with optional CUDA synchronization.""" + + def __init__(self, t=0.0, device: torch.device = None): + """Initializes a profiling context for YOLOv5 with optional timing threshold and device specification.""" + self.t = t + self.device = device + self.cuda = bool(device and str(device).startswith("cuda")) + + def __enter__(self): + """Initializes timing at the start of a profiling context block for performance measurement.""" + self.start = self.time() + return self + + def __exit__(self, type, value, traceback): + """Concludes timing, updating duration for profiling upon exiting a context block.""" + self.dt = self.time() - self.start # delta-time + self.t += self.dt # accumulate dt + + def time(self): + """Measures and returns the current time, synchronizing CUDA operations if `cuda` is True.""" + if self.cuda: + torch.cuda.synchronize(self.device) + return time.time() + + +class Timeout(contextlib.ContextDecorator): + """Enforces a timeout on code execution, raising TimeoutError if the specified duration is exceeded.""" + + def __init__(self, seconds, *, timeout_msg="", suppress_timeout_errors=True): + """Initializes a timeout context/decorator with defined seconds, optional message, and error suppression.""" + self.seconds = int(seconds) + self.timeout_message = timeout_msg + self.suppress = bool(suppress_timeout_errors) + + def _timeout_handler(self, signum, frame): + """Raises a TimeoutError with a custom message when a timeout event occurs.""" + raise TimeoutError(self.timeout_message) + + def __enter__(self): + """Initializes timeout mechanism on non-Windows platforms, starting a countdown to raise TimeoutError.""" + if platform.system() != "Windows": # not supported on Windows + signal.signal(signal.SIGALRM, self._timeout_handler) # Set handler for SIGALRM + signal.alarm(self.seconds) # start countdown for SIGALRM to be raised + + def __exit__(self, exc_type, exc_val, exc_tb): + """Disables active alarm on non-Windows systems and optionally suppresses TimeoutError if set.""" + if platform.system() != "Windows": + signal.alarm(0) # Cancel SIGALRM if it's scheduled + if self.suppress and exc_type is TimeoutError: # Suppress TimeoutError + return True + + +class WorkingDirectory(contextlib.ContextDecorator): + """Context manager/decorator to temporarily change the working directory within a 'with' statement or decorator.""" + + def __init__(self, new_dir): + """Initializes a context manager/decorator to temporarily change the working directory.""" + self.dir = new_dir # new dir + self.cwd = Path.cwd().resolve() # current dir + + def __enter__(self): + """Temporarily changes the working directory within a 'with' statement context.""" + os.chdir(self.dir) + + def __exit__(self, exc_type, exc_val, exc_tb): + """Restores the original working directory upon exiting a 'with' statement context.""" + os.chdir(self.cwd) + + +def methods(instance): + """Returns list of method names for a class/instance excluding dunder methods.""" + return [f for f in dir(instance) if callable(getattr(instance, f)) and not f.startswith("__")] + + +def print_args(args: Optional[dict] = None, show_file=True, show_func=False): + """Logs the arguments of the calling function, with options to include the filename and function name.""" + x = inspect.currentframe().f_back # previous frame + file, _, func, _, _ = inspect.getframeinfo(x) + if args is None: # get args automatically + args, _, _, frm = inspect.getargvalues(x) + args = {k: v for k, v in frm.items() if k in args} + try: + file = Path(file).resolve().relative_to(ROOT).with_suffix("") + except ValueError: + file = Path(file).stem + s = (f"{file}: " if show_file else "") + (f"{func}: " if show_func else "") + LOGGER.info(colorstr(s) + ", ".join(f"{k}={v}" for k, v in args.items())) + + +def init_seeds(seed=0, deterministic=False): + """ + Initializes RNG seeds and sets deterministic options if specified. + + See https://pytorch.org/docs/stable/notes/randomness.html + """ + random.seed(seed) + np.random.seed(seed) + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + torch.cuda.manual_seed_all(seed) # for Multi-GPU, exception safe + # torch.backends.cudnn.benchmark = True # AutoBatch problem https://github.com/ultralytics/yolov5/issues/9287 + if deterministic and check_version(torch.__version__, "1.12.0"): # https://github.com/ultralytics/yolov5/pull/8213 + torch.use_deterministic_algorithms(True) + torch.backends.cudnn.deterministic = True + os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8" + os.environ["PYTHONHASHSEED"] = str(seed) + + +def intersect_dicts(da, db, exclude=()): + """Returns intersection of `da` and `db` dicts with matching keys and shapes, excluding `exclude` keys; uses `da` + values. + """ + return {k: v for k, v in da.items() if k in db and all(x not in k for x in exclude) and v.shape == db[k].shape} + + +def get_default_args(func): + """Returns a dict of `func` default arguments by inspecting its signature.""" + signature = inspect.signature(func) + return {k: v.default for k, v in signature.parameters.items() if v.default is not inspect.Parameter.empty} + + +def get_latest_run(search_dir="."): + """Returns the path to the most recent 'last.pt' file in /runs to resume from, searches in `search_dir`.""" + last_list = glob.glob(f"{search_dir}/**/last*.pt", recursive=True) + return max(last_list, key=os.path.getctime) if last_list else "" + + +def file_age(path=__file__): + """Calculates and returns the age of a file in days based on its last modification time.""" + dt = datetime.now() - datetime.fromtimestamp(Path(path).stat().st_mtime) # delta + return dt.days # + dt.seconds / 86400 # fractional days + + +def file_date(path=__file__): + """Returns a human-readable file modification date in 'YYYY-M-D' format, given a file path.""" + t = datetime.fromtimestamp(Path(path).stat().st_mtime) + return f"{t.year}-{t.month}-{t.day}" + + +def file_size(path): + """Returns file or directory size in megabytes (MB) for a given path, where directories are recursively summed.""" + mb = 1 << 20 # bytes to MiB (1024 ** 2) + path = Path(path) + if path.is_file(): + return path.stat().st_size / mb + elif path.is_dir(): + return sum(f.stat().st_size for f in path.glob("**/*") if f.is_file()) / mb + else: + return 0.0 + + +def check_online(): + """Checks internet connectivity by attempting to create a connection to "1.1.1.1" on port 443, retries once if the + first attempt fails. + """ + import socket + + def run_once(): + """Checks internet connectivity by attempting to create a connection to "1.1.1.1" on port 443.""" + try: + socket.create_connection(("1.1.1.1", 443), 5) # check host accessibility + return True + except OSError: + return False + + return run_once() or run_once() # check twice to increase robustness to intermittent connectivity issues + + +def git_describe(path=ROOT): + """ + Returns a human-readable git description of the repository at `path`, or an empty string on failure. + + Example output is 'fv5.0-5-g3e25f1e'. See https://git-scm.com/docs/git-describe. + """ + try: + assert (Path(path) / ".git").is_dir() + return check_output(f"git -C {path} describe --tags --long --always", shell=True).decode()[:-1] + except Exception: + return "" + + +@TryExcept() +@WorkingDirectory(ROOT) +def check_git_status(repo="ultralytics/yolov5", branch="master"): + """Checks if YOLOv5 code is up-to-date with the repository, advising 'git pull' if behind; errors return informative + messages. + """ + url = f"https://github.com/{repo}" + msg = f", for updates see {url}" + s = colorstr("github: ") # string + assert Path(".git").exists(), s + "skipping check (not a git repository)" + msg + assert check_online(), s + "skipping check (offline)" + msg + + splits = re.split(pattern=r"\s", string=check_output("git remote -v", shell=True).decode()) + matches = [repo in s for s in splits] + if any(matches): + remote = splits[matches.index(True) - 1] + else: + remote = "ultralytics" + check_output(f"git remote add {remote} {url}", shell=True) + check_output(f"git fetch {remote}", shell=True, timeout=5) # git fetch + local_branch = check_output("git rev-parse --abbrev-ref HEAD", shell=True).decode().strip() # checked out + n = int(check_output(f"git rev-list {local_branch}..{remote}/{branch} --count", shell=True)) # commits behind + if n > 0: + pull = "git pull" if remote == "origin" else f"git pull {remote} {branch}" + s += f"⚠️ YOLOv5 is out of date by {n} commit{'s' * (n > 1)}. Use '{pull}' or 'git clone {url}' to update." + else: + s += f"up to date with {url} ✅" + LOGGER.info(s) + + +@WorkingDirectory(ROOT) +def check_git_info(path="."): + """Checks YOLOv5 git info, returning a dict with remote URL, branch name, and commit hash.""" + check_requirements("gitpython") + import git + + try: + repo = git.Repo(path) + remote = repo.remotes.origin.url.replace(".git", "") # i.e. 'https://github.com/ultralytics/yolov5' + commit = repo.head.commit.hexsha # i.e. '3134699c73af83aac2a481435550b968d5792c0d' + try: + branch = repo.active_branch.name # i.e. 'main' + except TypeError: # not on any branch + branch = None # i.e. 'detached HEAD' state + return {"remote": remote, "branch": branch, "commit": commit} + except git.exc.InvalidGitRepositoryError: # path is not a git dir + return {"remote": None, "branch": None, "commit": None} + + +def check_python(minimum="3.8.0"): + """Checks if current Python version meets the minimum required version, exits if not.""" + check_version(platform.python_version(), minimum, name="Python ", hard=True) + + +def check_version(current="0.0.0", minimum="0.0.0", name="version ", pinned=False, hard=False, verbose=False): + """Checks if the current version meets the minimum required version, exits or warns based on parameters.""" + current, minimum = (pkg.parse_version(x) for x in (current, minimum)) + result = (current == minimum) if pinned else (current >= minimum) # bool + s = f"WARNING ⚠️ {name}{minimum} is required by YOLOv5, but {name}{current} is currently installed" # string + if hard: + assert result, emojis(s) # assert min requirements met + if verbose and not result: + LOGGER.warning(s) + return result + + +def check_img_size(imgsz, s=32, floor=0): + """Adjusts image size to be divisible by stride `s`, supports int or list/tuple input, returns adjusted size.""" + if isinstance(imgsz, int): # integer i.e. img_size=640 + new_size = max(make_divisible(imgsz, int(s)), floor) + else: # list i.e. img_size=[640, 480] + imgsz = list(imgsz) # convert to list if tuple + new_size = [max(make_divisible(x, int(s)), floor) for x in imgsz] + if new_size != imgsz: + LOGGER.warning(f"WARNING ⚠️ --img-size {imgsz} must be multiple of max stride {s}, updating to {new_size}") + return new_size + + +def check_imshow(warn=False): + """Checks environment support for image display; warns on failure if `warn=True`.""" + try: + assert not is_jupyter() + assert not is_docker() + cv2.imshow("test", np.zeros((1, 1, 3))) + cv2.waitKey(1) + cv2.destroyAllWindows() + cv2.waitKey(1) + return True + except Exception as e: + if warn: + LOGGER.warning(f"WARNING ⚠️ Environment does not support cv2.imshow() or PIL Image.show()\n{e}") + return False + + +def check_suffix(file="yolov5s.pt", suffix=(".pt",), msg=""): + """Validates if a file or files have an acceptable suffix, raising an error if not.""" + if file and suffix: + if isinstance(suffix, str): + suffix = [suffix] + for f in file if isinstance(file, (list, tuple)) else [file]: + s = Path(f).suffix.lower() # file suffix + if len(s): + assert s in suffix, f"{msg}{f} acceptable suffix is {suffix}" + + +def check_yaml(file, suffix=(".yaml", ".yml")): + """Searches/downloads a YAML file, verifies its suffix (.yaml or .yml), and returns the file path.""" + return check_file(file, suffix) + + +def check_file(file, suffix=""): + """Searches/downloads a file, checks its suffix (if provided), and returns the file path.""" + check_suffix(file, suffix) # optional + file = str(file) # convert to str() + if os.path.isfile(file) or not file: # exists + return file + elif file.startswith(("http:/", "https:/")): # download + url = file # warning: Pathlib turns :// -> :/ + file = Path(urllib.parse.unquote(file).split("?")[0]).name # '%2F' to '/', split https://url.com/file.txt?auth + if os.path.isfile(file): + LOGGER.info(f"Found {url} locally at {file}") # file already exists + else: + LOGGER.info(f"Downloading {url} to {file}...") + torch.hub.download_url_to_file(url, file) + assert Path(file).exists() and Path(file).stat().st_size > 0, f"File download failed: {url}" # check + return file + elif file.startswith("clearml://"): # ClearML Dataset ID + assert "clearml" in sys.modules, ( + "ClearML is not installed, so cannot use ClearML dataset. Try running 'pip install clearml'." + ) + return file + else: # search + files = [] + for d in "data", "models", "utils": # search directories + files.extend(glob.glob(str(ROOT / d / "**" / file), recursive=True)) # find file + assert len(files), f"File not found: {file}" # assert file was found + assert len(files) == 1, f"Multiple files match '{file}', specify exact path: {files}" # assert unique + return files[0] # return file + + +def check_font(font=FONT, progress=False): + """Ensures specified font exists or downloads it from Ultralytics assets, optionally displaying progress.""" + font = Path(font) + file = CONFIG_DIR / font.name + if not font.exists() and not file.exists(): + url = f"https://github.com/ultralytics/assets/releases/download/v0.0.0/{font.name}" + LOGGER.info(f"Downloading {url} to {file}...") + torch.hub.download_url_to_file(url, str(file), progress=progress) + + +def check_dataset(data, autodownload=True): + """Validates and/or auto-downloads a dataset, returning its configuration as a dictionary.""" + # Download (optional) + extract_dir = "" + if isinstance(data, (str, Path)) and (is_zipfile(data) or is_tarfile(data)): + download(data, dir=f"{DATASETS_DIR}/{Path(data).stem}", unzip=True, delete=False, curl=False, threads=1) + data = next((DATASETS_DIR / Path(data).stem).rglob("*.yaml")) + extract_dir, autodownload = data.parent, False + + # Read yaml (optional) + if isinstance(data, (str, Path)): + data = yaml_load(data) # dictionary + + # Checks + for k in "train", "val", "names": + assert k in data, emojis(f"data.yaml '{k}:' field missing ❌") + if isinstance(data["names"], (list, tuple)): # old array format + data["names"] = dict(enumerate(data["names"])) # convert to dict + assert all(isinstance(k, int) for k in data["names"].keys()), "data.yaml names keys must be integers, i.e. 2: car" + data["nc"] = len(data["names"]) + + # Resolve paths + path = Path(extract_dir or data.get("path") or "") # optional 'path' default to '.' + if not path.is_absolute(): + path = (ROOT / path).resolve() + data["path"] = path # download scripts + for k in "train", "val", "test": + if data.get(k): # prepend path + if isinstance(data[k], str): + x = (path / data[k]).resolve() + if not x.exists() and data[k].startswith("../"): + x = (path / data[k][3:]).resolve() + data[k] = str(x) + else: + data[k] = [str((path / x).resolve()) for x in data[k]] + + # Parse yaml + train, val, test, s = (data.get(x) for x in ("train", "val", "test", "download")) + if val: + val = [Path(x).resolve() for x in (val if isinstance(val, list) else [val])] # val path + if not all(x.exists() for x in val): + LOGGER.info("\nDataset not found ⚠️, missing paths %s" % [str(x) for x in val if not x.exists()]) + if not s or not autodownload: + raise Exception("Dataset not found ❌") + t = time.time() + if s.startswith("http") and s.endswith(".zip"): # URL + f = Path(s).name # filename + LOGGER.info(f"Downloading {s} to {f}...") + torch.hub.download_url_to_file(s, f) + Path(DATASETS_DIR).mkdir(parents=True, exist_ok=True) # create root + unzip_file(f, path=DATASETS_DIR) # unzip + Path(f).unlink() # remove zip + r = None # success + elif s.startswith("bash "): # bash script + LOGGER.info(f"Running {s} ...") + r = subprocess.run(s, shell=True) + else: # python script + r = exec(s, {"yaml": data}) # return None + dt = f"({round(time.time() - t, 1)}s)" + s = f"success ✅ {dt}, saved to {colorstr('bold', DATASETS_DIR)}" if r in (0, None) else f"failure {dt} ❌" + LOGGER.info(f"Dataset download {s}") + check_font("Arial.ttf" if is_ascii(data["names"]) else "Arial.Unicode.ttf", progress=True) # download fonts + return data # dictionary + + +def check_amp(model): + """Checks PyTorch AMP functionality for a model, returns True if AMP operates correctly, otherwise False.""" + from app.util.yolov5.models.common import AutoShape, DetectMultiBackend + + def amp_allclose(model, im): + """Compares FP32 and AMP model inference outputs, ensuring they are close within a 10% absolute tolerance.""" + m = AutoShape(model, verbose=False) # model + a = m(im).xywhn[0] # FP32 inference + m.amp = True + b = m(im).xywhn[0] # AMP inference + return a.shape == b.shape and torch.allclose(a, b, atol=0.1) # close to 10% absolute tolerance + + prefix = colorstr("AMP: ") + device = next(model.parameters()).device # get model device + if device.type in ("cpu", "mps"): + return False # AMP only used on CUDA devices + f = ROOT / "data" / "images" / "bus.jpg" # image to check + im = f if f.exists() else "https://ultralytics.com/images/bus.jpg" if check_online() else np.ones((640, 640, 3)) + try: + assert amp_allclose(deepcopy(model), im) or amp_allclose(DetectMultiBackend("yolov5n.pt", device), im) + LOGGER.info(f"{prefix}checks passed ✅") + return True + except Exception: + help_url = "https://github.com/ultralytics/yolov5/issues/7908" + LOGGER.warning(f"{prefix}checks failed ❌, disabling Automatic Mixed Precision. See {help_url}") + return False + + +def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): + # Rescale coords (xyxy) from img1_shape to img0_shape + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + coords[:, [0, 2]] -= pad[0] # x padding + coords[:, [1, 3]] -= pad[1] # y padding + coords[:, :4] /= gain + + # Clip bounding xyxy bounding boxes to image shape (height, width) + coords[:, 0].clamp_(0, img0_shape[1]) # x1 + coords[:, 1].clamp_(0, img0_shape[0]) # y1 + coords[:, 2].clamp_(0, img0_shape[1]) # x2 + coords[:, 3].clamp_(0, img0_shape[0]) # y2 + return coords + + +def yaml_load(file="data.yaml"): + """Safely loads and returns the contents of a YAML file specified by `file` argument.""" + with open(file, errors="ignore") as f: + return yaml.safe_load(f) + + +def yaml_save(file="data.yaml", data=None): + """Safely saves `data` to a YAML file specified by `file`, converting `Path` objects to strings; `data` is a + dictionary. + """ + if data is None: + data = {} + with open(file, "w") as f: + yaml.safe_dump({k: str(v) if isinstance(v, Path) else v for k, v in data.items()}, f, sort_keys=False) + + +def unzip_file(file, path=None, exclude=(".DS_Store", "__MACOSX")): + """Unzips `file` to `path` (default: file's parent), excluding filenames containing any in `exclude` (`.DS_Store`, + `__MACOSX`). + """ + if path is None: + path = Path(file).parent # default path + with ZipFile(file) as zipObj: + for f in zipObj.namelist(): # list all archived filenames in the zip + if all(x not in f for x in exclude): + zipObj.extract(f, path=path) + + +def url2file(url): + """ + Converts a URL string to a valid filename by stripping protocol, domain, and any query parameters. + + Example https://url.com/file.txt?auth -> file.txt + """ + url = str(Path(url)).replace(":/", "://") # Pathlib turns :// -> :/ + return Path(urllib.parse.unquote(url)).name.split("?")[0] # '%2F' to '/', split https://url.com/file.txt?auth + + +def download(url, dir=".", unzip=True, delete=True, curl=False, threads=1, retry=3): + """Downloads and optionally unzips files concurrently, supporting retries and curl fallback.""" + + def download_one(url, dir): + """Downloads a single file from `url` to `dir`, with retry support and optional curl fallback.""" + success = True + if os.path.isfile(url): + f = Path(url) # filename + else: # does not exist + f = dir / Path(url).name + LOGGER.info(f"Downloading {url} to {f}...") + for i in range(retry + 1): + if curl: + success = curl_download(url, f, silent=(threads > 1)) + else: + torch.hub.download_url_to_file(url, f, progress=threads == 1) # torch download + success = f.is_file() + if success: + break + elif i < retry: + LOGGER.warning(f"⚠️ Download failure, retrying {i + 1}/{retry} {url}...") + else: + LOGGER.warning(f"❌ Failed to download {url}...") + + if unzip and success and (f.suffix == ".gz" or is_zipfile(f) or is_tarfile(f)): + LOGGER.info(f"Unzipping {f}...") + if is_zipfile(f): + unzip_file(f, dir) # unzip + elif is_tarfile(f): + subprocess.run(["tar", "xf", f, "--directory", f.parent], check=True) # unzip + elif f.suffix == ".gz": + subprocess.run(["tar", "xfz", f, "--directory", f.parent], check=True) # unzip + if delete: + f.unlink() # remove zip + + dir = Path(dir) + dir.mkdir(parents=True, exist_ok=True) # make directory + if threads > 1: + pool = ThreadPool(threads) + pool.imap(lambda x: download_one(*x), zip(url, repeat(dir))) # multithreaded + pool.close() + pool.join() + else: + for u in [url] if isinstance(url, (str, Path)) else url: + download_one(u, dir) + + +def make_divisible(x, divisor): + """Adjusts `x` to be divisible by `divisor`, returning the nearest greater or equal value.""" + if isinstance(divisor, torch.Tensor): + divisor = int(divisor.max()) # to int + return math.ceil(x / divisor) * divisor + + +def clean_str(s): + """Cleans a string by replacing special characters with underscore, e.g., `clean_str('#example!')` returns + '_example_'. + """ + return re.sub(pattern="[|@#!¡·$€%&()=?¿^*;:,¨´><+]", repl="_", string=s) + + +def one_cycle(y1=0.0, y2=1.0, steps=100): + """ + Generates a lambda for a sinusoidal ramp from y1 to y2 over 'steps'. + + See https://arxiv.org/pdf/1812.01187.pdf for details. + """ + return lambda x: ((1 - math.cos(x * math.pi / steps)) / 2) * (y2 - y1) + y1 + + +def colorstr(*input): + """ + Colors a string using ANSI escape codes, e.g., colorstr('blue', 'hello world'). + + See https://en.wikipedia.org/wiki/ANSI_escape_code. + """ + *args, string = input if len(input) > 1 else ("blue", "bold", input[0]) # color arguments, string + colors = { + "black": "\033[30m", # basic colors + "red": "\033[31m", + "green": "\033[32m", + "yellow": "\033[33m", + "blue": "\033[34m", + "magenta": "\033[35m", + "cyan": "\033[36m", + "white": "\033[37m", + "bright_black": "\033[90m", # bright colors + "bright_red": "\033[91m", + "bright_green": "\033[92m", + "bright_yellow": "\033[93m", + "bright_blue": "\033[94m", + "bright_magenta": "\033[95m", + "bright_cyan": "\033[96m", + "bright_white": "\033[97m", + "end": "\033[0m", # misc + "bold": "\033[1m", + "underline": "\033[4m", + } + return "".join(colors[x] for x in args) + f"{string}" + colors["end"] + + +def labels_to_class_weights(labels, nc=80): + """Calculates class weights from labels to handle class imbalance in training; input shape: (n, 5).""" + if labels[0] is None: # no labels loaded + return torch.Tensor() + + labels = np.concatenate(labels, 0) # labels.shape = (866643, 5) for COCO + classes = labels[:, 0].astype(int) # labels = [class xywh] + weights = np.bincount(classes, minlength=nc) # occurrences per class + + # Prepend gridpoint count (for uCE training) + # gpi = ((320 / 32 * np.array([1, 2, 4])) ** 2 * 3).sum() # gridpoints per image + # weights = np.hstack([gpi * len(labels) - weights.sum() * 9, weights * 9]) ** 0.5 # prepend gridpoints to start + + weights[weights == 0] = 1 # replace empty bins with 1 + weights = 1 / weights # number of targets per class + weights /= weights.sum() # normalize + return torch.from_numpy(weights).float() + + +def labels_to_image_weights(labels, nc=80, class_weights=np.ones(80)): + """Calculates image weights from labels using class weights for weighted sampling.""" + # Usage: index = random.choices(range(n), weights=image_weights, k=1) # weighted image sample + class_counts = np.array([np.bincount(x[:, 0].astype(int), minlength=nc) for x in labels]) + return (class_weights.reshape(1, nc) * class_counts).sum(1) + + +def coco80_to_coco91_class(): + """ + Converts COCO 80-class index to COCO 91-class index used in the paper. + + Reference: https://tech.amikelive.com/node-718/what-object-categories-labels-are-in-coco-dataset/ + """ + # a = np.loadtxt('data/coco.names', dtype='str', delimiter='\n') + # b = np.loadtxt('data/coco_paper.names', dtype='str', delimiter='\n') + # x1 = [list(a[i] == b).index(True) + 1 for i in range(80)] # darknet to coco + # x2 = [list(b[i] == a).index(True) if any(b[i] == a) else None for i in range(91)] # coco to darknet + return [ + 1, + 2, + 3, + 4, + 5, + 6, + 7, + 8, + 9, + 10, + 11, + 13, + 14, + 15, + 16, + 17, + 18, + 19, + 20, + 21, + 22, + 23, + 24, + 25, + 27, + 28, + 31, + 32, + 33, + 34, + 35, + 36, + 37, + 38, + 39, + 40, + 41, + 42, + 43, + 44, + 46, + 47, + 48, + 49, + 50, + 51, + 52, + 53, + 54, + 55, + 56, + 57, + 58, + 59, + 60, + 61, + 62, + 63, + 64, + 65, + 67, + 70, + 72, + 73, + 74, + 75, + 76, + 77, + 78, + 79, + 80, + 81, + 82, + 84, + 85, + 86, + 87, + 88, + 89, + 90, + ] + + +def xyxy2xywh(x): + """Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right.""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center + y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center + y[..., 2] = x[..., 2] - x[..., 0] # width + y[..., 3] = x[..., 3] - x[..., 1] # height + return y + + +def xywh2xyxy(x): + """Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right.""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x + y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y + y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x + y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y + return y + + +def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0): + """Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right.""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x + y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y + y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x + y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y + return y + + +def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0): + """Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right.""" + if clip: + clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center + y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center + y[..., 2] = (x[..., 2] - x[..., 0]) / w # width + y[..., 3] = (x[..., 3] - x[..., 1]) / h # height + return y + + +def xyn2xy(x, w=640, h=640, padw=0, padh=0): + """Convert normalized segments into pixel segments, shape (n,2).""" + y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x) + y[..., 0] = w * x[..., 0] + padw # top left x + y[..., 1] = h * x[..., 1] + padh # top left y + return y + + +def segment2box(segment, width=640, height=640): + """Convert 1 segment label to 1 box label, applying inside-image constraint, i.e. (xy1, xy2, ...) to (xyxy).""" + x, y = segment.T # segment xy + inside = (x >= 0) & (y >= 0) & (x <= width) & (y <= height) + ( + x, + y, + ) = x[inside], y[inside] + return np.array([x.min(), y.min(), x.max(), y.max()]) if any(x) else np.zeros((1, 4)) # xyxy + + +def segments2boxes(segments): + """Convert segment labels to box labels, i.e. (cls, xy1, xy2, ...) to (cls, xywh).""" + boxes = [] + for s in segments: + x, y = s.T # segment xy + boxes.append([x.min(), y.min(), x.max(), y.max()]) # cls, xyxy + return xyxy2xywh(np.array(boxes)) # cls, xywh + + +def resample_segments(segments, n=1000): + """Resamples an (n,2) segment to a fixed number of points for consistent representation.""" + for i, s in enumerate(segments): + s = np.concatenate((s, s[0:1, :]), axis=0) + x = np.linspace(0, len(s) - 1, n) + xp = np.arange(len(s)) + segments[i] = np.concatenate([np.interp(x, xp, s[:, i]) for i in range(2)]).reshape(2, -1).T # segment xy + return segments + + +def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None): + """Rescales (xyxy) bounding boxes from img1_shape to img0_shape, optionally using provided `ratio_pad`.""" + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + boxes[..., [0, 2]] -= pad[0] # x padding + boxes[..., [1, 3]] -= pad[1] # y padding + boxes[..., :4] /= gain + clip_boxes(boxes, img0_shape) + return boxes + + +def scale_segments(img1_shape, segments, img0_shape, ratio_pad=None, normalize=False): + """Rescales segment coordinates from img1_shape to img0_shape, optionally normalizing them with custom padding.""" + if ratio_pad is None: # calculate from img0_shape + gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new + pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding + else: + gain = ratio_pad[0][0] + pad = ratio_pad[1] + + segments[:, 0] -= pad[0] # x padding + segments[:, 1] -= pad[1] # y padding + segments /= gain + clip_segments(segments, img0_shape) + if normalize: + segments[:, 0] /= img0_shape[1] # width + segments[:, 1] /= img0_shape[0] # height + return segments + + +def clip_boxes(boxes, shape): + """Clips bounding box coordinates (xyxy) to fit within the specified image shape (height, width).""" + if isinstance(boxes, torch.Tensor): # faster individually + boxes[..., 0].clamp_(0, shape[1]) # x1 + boxes[..., 1].clamp_(0, shape[0]) # y1 + boxes[..., 2].clamp_(0, shape[1]) # x2 + boxes[..., 3].clamp_(0, shape[0]) # y2 + else: # np.array (faster grouped) + boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2 + boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2 + + +def clip_segments(segments, shape): + """Clips segment coordinates (xy1, xy2, ...) to an image's boundaries given its shape (height, width).""" + if isinstance(segments, torch.Tensor): # faster individually + segments[:, 0].clamp_(0, shape[1]) # x + segments[:, 1].clamp_(0, shape[0]) # y + else: # np.array (faster grouped) + segments[:, 0] = segments[:, 0].clip(0, shape[1]) # x + segments[:, 1] = segments[:, 1].clip(0, shape[0]) # y + + +def non_max_suppression( + prediction, + conf_thres=0.25, + iou_thres=0.45, + classes=None, + agnostic=False, + multi_label=False, + labels=(), + max_det=300, + nm=0, # number of masks +): + """ + Non-Maximum Suppression (NMS) on inference results to reject overlapping detections. + + Returns: + list of detections, on (n,6) tensor per image [xyxy, conf, cls] + """ + # Checks + assert 0 <= conf_thres <= 1, f"Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0" + assert 0 <= iou_thres <= 1, f"Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0" + if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out) + prediction = prediction[0] # select only inference output + + device = prediction.device + mps = "mps" in device.type # Apple MPS + if mps: # MPS not fully supported yet, convert tensors to CPU before NMS + prediction = prediction.cpu() + bs = prediction.shape[0] # batch size + nc = prediction.shape[2] - nm - 5 # number of classes + xc = prediction[..., 4] > conf_thres # candidates + + # Settings + # min_wh = 2 # (pixels) minimum box width and height + max_wh = 7680 # (pixels) maximum box width and height + max_nms = 30000 # maximum number of boxes into torchvision.ops.nms() + time_limit = 0.5 + 0.05 * bs # seconds to quit after + redundant = True # require redundant detections + multi_label &= nc > 1 # multiple labels per box (adds 0.5ms/img) + merge = False # use merge-NMS + + t = time.time() + mi = 5 + nc # mask start index + output = [torch.zeros((0, 6 + nm), device=prediction.device)] * bs + for xi, x in enumerate(prediction): # image index, image inference + # Apply constraints + # x[((x[..., 2:4] < min_wh) | (x[..., 2:4] > max_wh)).any(1), 4] = 0 # width-height + x = x[xc[xi]] # confidence + + # Cat apriori labels if autolabelling + if labels and len(labels[xi]): + lb = labels[xi] + v = torch.zeros((len(lb), nc + nm + 5), device=x.device) + v[:, :4] = lb[:, 1:5] # box + v[:, 4] = 1.0 # conf + v[range(len(lb)), lb[:, 0].long() + 5] = 1.0 # cls + x = torch.cat((x, v), 0) + + # If none remain process next image + if not x.shape[0]: + continue + + # Compute conf + x[:, 5:] *= x[:, 4:5] # conf = obj_conf * cls_conf + + # Box/Mask + box = xywh2xyxy(x[:, :4]) # center_x, center_y, width, height) to (x1, y1, x2, y2) + mask = x[:, mi:] # zero columns if no masks + + # Detections matrix nx6 (xyxy, conf, cls) + if multi_label: + i, j = (x[:, 5:mi] > conf_thres).nonzero(as_tuple=False).T + x = torch.cat((box[i], x[i, 5 + j, None], j[:, None].float(), mask[i]), 1) + else: # best class only + conf, j = x[:, 5:mi].max(1, keepdim=True) + x = torch.cat((box, conf, j.float(), mask), 1)[conf.view(-1) > conf_thres] + + # Filter by class + if classes is not None: + x = x[(x[:, 5:6] == torch.tensor(classes, device=x.device)).any(1)] + + # Apply finite constraint + # if not torch.isfinite(x).all(): + # x = x[torch.isfinite(x).all(1)] + + # Check shape + n = x.shape[0] # number of boxes + if not n: # no boxes + continue + x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes + + # Batched NMS + c = x[:, 5:6] * (0 if agnostic else max_wh) # classes + boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores + i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS + i = i[:max_det] # limit detections + if merge and (1 < n < 3e3): # Merge NMS (boxes merged using weighted mean) + # update boxes as boxes(i,4) = weights(i,n) * boxes(n,4) + iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix + weights = iou * scores[None] # box weights + x[i, :4] = torch.mm(weights, x[:, :4]).float() / weights.sum(1, keepdim=True) # merged boxes + if redundant: + i = i[iou.sum(1) > 1] # require redundancy + + output[xi] = x[i] + if mps: + output[xi] = output[xi].to(device) + if (time.time() - t) > time_limit: + LOGGER.warning(f"WARNING ⚠️ NMS time limit {time_limit:.3f}s exceeded") + break # time limit exceeded + + return output + + +def strip_optimizer(f="best.pt", s=""): + """ + Strips optimizer and optionally saves checkpoint to finalize training; arguments are file path 'f' and save path + 's'. + + Example: from utils.general import *; strip_optimizer() + """ + x = torch.load(f, map_location=torch.device("cpu")) + if x.get("ema"): + x["model"] = x["ema"] # replace model with ema + for k in "optimizer", "best_fitness", "ema", "updates": # keys + x[k] = None + x["epoch"] = -1 + x["model"].half() # to FP16 + for p in x["model"].parameters(): + p.requires_grad = False + torch.save(x, s or f) + mb = os.path.getsize(s or f) / 1e6 # filesize + LOGGER.info(f"Optimizer stripped from {f},{f' saved as {s},' if s else ''} {mb:.1f}MB") + + +def print_mutation(keys, results, hyp, save_dir, bucket, prefix=colorstr("evolve: ")): + """Logs evolution results and saves to CSV and YAML in `save_dir`, optionally syncs with `bucket`.""" + evolve_csv = save_dir / "evolve.csv" + evolve_yaml = save_dir / "hyp_evolve.yaml" + keys = tuple(keys) + tuple(hyp.keys()) # [results + hyps] + keys = tuple(x.strip() for x in keys) + vals = results + tuple(hyp.values()) + n = len(keys) + + # Download (optional) + if bucket: + url = f"gs://{bucket}/evolve.csv" + if gsutil_getsize(url) > (evolve_csv.stat().st_size if evolve_csv.exists() else 0): + subprocess.run(["gsutil", "cp", f"{url}", f"{save_dir}"]) # download evolve.csv if larger than local + + # Log to evolve.csv + s = "" if evolve_csv.exists() else (("%20s," * n % keys).rstrip(",") + "\n") # add header + with open(evolve_csv, "a") as f: + f.write(s + ("%20.5g," * n % vals).rstrip(",") + "\n") + + # Save yaml + with open(evolve_yaml, "w") as f: + data = pd.read_csv(evolve_csv, skipinitialspace=True) + data = data.rename(columns=lambda x: x.strip()) # strip keys + i = np.argmax(fitness(data.values[:, :4])) # + generations = len(data) + f.write( + "# YOLOv5 Hyperparameter Evolution Results\n" + + f"# Best generation: {i}\n" + + f"# Last generation: {generations - 1}\n" + + "# " + + ", ".join(f"{x.strip():>20s}" for x in keys[:7]) + + "\n" + + "# " + + ", ".join(f"{x:>20.5g}" for x in data.values[i, :7]) + + "\n\n" + ) + yaml.safe_dump(data.loc[i][7:].to_dict(), f, sort_keys=False) + + # Print to screen + LOGGER.info( + prefix + + f"{generations} generations finished, current result:\n" + + prefix + + ", ".join(f"{x.strip():>20s}" for x in keys) + + "\n" + + prefix + + ", ".join(f"{x:20.5g}" for x in vals) + + "\n\n" + ) + + if bucket: + subprocess.run(["gsutil", "cp", f"{evolve_csv}", f"{evolve_yaml}", f"gs://{bucket}"]) # upload + + +def apply_classifier(x, model, img, im0): + """Applies second-stage classifier to YOLO outputs, filtering detections by class match.""" + # Example model = torchvision.models.__dict__['efficientnet_b0'](pretrained=True).to(device).eval() + im0 = [im0] if isinstance(im0, np.ndarray) else im0 + for i, d in enumerate(x): # per image + if d is not None and len(d): + d = d.clone() + + # Reshape and pad cutouts + b = xyxy2xywh(d[:, :4]) # boxes + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # rectangle to square + b[:, 2:] = b[:, 2:] * 1.3 + 30 # pad + d[:, :4] = xywh2xyxy(b).long() + + # Rescale boxes from img_size to im0 size + scale_boxes(img.shape[2:], d[:, :4], im0[i].shape) + + # Classes + pred_cls1 = d[:, 5].long() + ims = [] + for a in d: + cutout = im0[i][int(a[1]) : int(a[3]), int(a[0]) : int(a[2])] + im = cv2.resize(cutout, (224, 224)) # BGR + + im = im[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 + im = np.ascontiguousarray(im, dtype=np.float32) # uint8 to float32 + im /= 255 # 0 - 255 to 0.0 - 1.0 + ims.append(im) + + pred_cls2 = model(torch.Tensor(ims).to(d.device)).argmax(1) # classifier prediction + x[i] = x[i][pred_cls1 == pred_cls2] # retain matching class detections + + return x + + +def increment_path(path, exist_ok=False, sep="", mkdir=False): + """ + Generates an incremented file or directory path if it exists, with optional mkdir; args: path, exist_ok=False, + sep="", mkdir=False. + + Example: runs/exp --> runs/exp{sep}2, runs/exp{sep}3, ... etc + """ + path = Path(path) # os-agnostic + if path.exists() and not exist_ok: + path, suffix = (path.with_suffix(""), path.suffix) if path.is_file() else (path, "") + + # Method 1 + for n in range(2, 9999): + p = f"{path}{sep}{n}{suffix}" # increment path + if not os.path.exists(p): # + break + path = Path(p) + + # Method 2 (deprecated) + # dirs = glob.glob(f"{path}{sep}*") # similar paths + # matches = [re.search(rf"{path.stem}{sep}(\d+)", d) for d in dirs] + # i = [int(m.groups()[0]) for m in matches if m] # indices + # n = max(i) + 1 if i else 2 # increment number + # path = Path(f"{path}{sep}{n}{suffix}") # increment path + + if mkdir: + path.mkdir(parents=True, exist_ok=True) # make directory + + return path + + +# OpenCV Multilanguage-friendly functions ------------------------------------------------------------------------------------ +imshow_ = cv2.imshow # copy to avoid recursion errors + + +def imread(filename, flags=cv2.IMREAD_COLOR): + """Reads an image from a file and returns it as a numpy array, using OpenCV's imdecode to support multilanguage + paths. + """ + return cv2.imdecode(np.fromfile(filename, np.uint8), flags) + + +def imwrite(filename, img): + """Writes an image to a file, returns True on success and False on failure, supports multilanguage paths.""" + try: + cv2.imencode(Path(filename).suffix, img)[1].tofile(filename) + return True + except Exception: + return False + + +def imshow(path, im): + """Displays an image using Unicode path, requires encoded path and image matrix as input.""" + imshow_(path.encode("unicode_escape").decode(), im) + + +if Path(inspect.stack()[0].filename).parent.parent.as_posix() in inspect.stack()[-1].filename: + cv2.imread, cv2.imwrite, cv2.imshow = imread, imwrite, imshow # redefine + +# Variables ------------------------------------------------------------------------------------------------------------ diff --git a/app/util/yolov5/utils/google_app_engine/Dockerfile b/app/util/yolov5/utils/google_app_engine/Dockerfile new file mode 100644 index 0000000..0155618 --- /dev/null +++ b/app/util/yolov5/utils/google_app_engine/Dockerfile @@ -0,0 +1,25 @@ +FROM gcr.io/google-appengine/python + +# Create a virtualenv for dependencies. This isolates these packages from +# system-level packages. +# Use -p python3 or -p python3.7 to select python version. Default is version 2. +RUN virtualenv /env -p python3 + +# Setting these environment variables are the same as running +# source /env/bin/activate. +ENV VIRTUAL_ENV /env +ENV PATH /env/bin:$PATH + +RUN apt-get update && apt-get install -y python-opencv + +# Copy the application's requirements.txt and run pip to install all +# dependencies into the virtualenv. +ADD requirements.txt /app/requirements.txt +RUN pip install -r /app/requirements.txt + +# Add the application source code. +ADD . /app + +# Run a WSGI server to serve the application. gunicorn must be declared as +# a dependency in requirements.txt. +CMD gunicorn -b :$PORT main:app diff --git a/app/util/yolov5/utils/google_app_engine/additional_requirements.txt b/app/util/yolov5/utils/google_app_engine/additional_requirements.txt new file mode 100644 index 0000000..08c276f --- /dev/null +++ b/app/util/yolov5/utils/google_app_engine/additional_requirements.txt @@ -0,0 +1,6 @@ +# add these requirements in your app on top of the existing ones +pip==23.3 +Flask==2.3.2 +gunicorn==22.0.0 +werkzeug>=3.0.1 # not directly required, pinned by Snyk to avoid a vulnerability +zipp>=3.19.1 # not directly required, pinned by Snyk to avoid a vulnerability diff --git a/app/util/yolov5/utils/google_app_engine/app.yaml b/app/util/yolov5/utils/google_app_engine/app.yaml new file mode 100644 index 0000000..6fb9d5f --- /dev/null +++ b/app/util/yolov5/utils/google_app_engine/app.yaml @@ -0,0 +1,16 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +runtime: custom +env: flex + +service: yolov5app + +liveness_check: + initial_delay_sec: 600 + +manual_scaling: + instances: 1 +resources: + cpu: 1 + memory_gb: 4 + disk_size_gb: 20 diff --git a/app/util/yolov5/utils/loggers/__init__.py b/app/util/yolov5/utils/loggers/__init__.py new file mode 100644 index 0000000..9ff5f56 --- /dev/null +++ b/app/util/yolov5/utils/loggers/__init__.py @@ -0,0 +1,476 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Logging utils.""" + +import json +import os +import warnings +from pathlib import Path + +import pkg_resources as pkg +import torch + +from utils.general import LOGGER, colorstr, cv2 +from utils.loggers.clearml.clearml_utils import ClearmlLogger +from utils.loggers.wandb.wandb_utils import WandbLogger +from utils.plots import plot_images, plot_labels, plot_results +from utils.torch_utils import de_parallel + +LOGGERS = ("csv", "tb", "wandb", "clearml", "comet") # *.csv, TensorBoard, Weights & Biases, ClearML +RANK = int(os.getenv("RANK", -1)) + +try: + from torch.utils.tensorboard import SummaryWriter +except ImportError: + + def SummaryWriter(*args): + """Fall back to SummaryWriter returning None if TensorBoard is not installed.""" + return None # None = SummaryWriter(str) + + +try: + import wandb + + assert hasattr(wandb, "__version__") # verify package import not local dir + if pkg.parse_version(wandb.__version__) >= pkg.parse_version("0.12.2") and RANK in {0, -1}: + try: + wandb_login_success = wandb.login(timeout=30) + except wandb.errors.UsageError: # known non-TTY terminal issue + wandb_login_success = False + if not wandb_login_success: + wandb = None +except (ImportError, AssertionError): + wandb = None + +try: + import clearml + + assert hasattr(clearml, "__version__") # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + +try: + if RANK in {0, -1}: + import comet_ml + + assert hasattr(comet_ml, "__version__") # verify package import not local dir + from utils.loggers.comet import CometLogger + + else: + comet_ml = None +except (ImportError, AssertionError): + comet_ml = None + + +def _json_default(value): + """ + Format `value` for JSON serialization (e.g. unwrap tensors). + + Fall back to strings. + """ + if isinstance(value, torch.Tensor): + try: + value = value.item() + except ValueError: # "only one element tensors can be converted to Python scalars" + pass + return value if isinstance(value, float) else str(value) + + +class Loggers: + """Initializes and manages various logging utilities for tracking YOLOv5 training and validation metrics.""" + + def __init__(self, save_dir=None, weights=None, opt=None, hyp=None, logger=None, include=LOGGERS): + """Initializes loggers for YOLOv5 training and validation metrics, paths, and options.""" + self.save_dir = save_dir + self.weights = weights + self.opt = opt + self.hyp = hyp + self.plots = not opt.noplots # plot results + self.logger = logger # for printing results to console + self.include = include + self.keys = [ + "train/box_loss", + "train/obj_loss", + "train/cls_loss", # train loss + "metrics/precision", + "metrics/recall", + "metrics/mAP_0.5", + "metrics/mAP_0.5:0.95", # metrics + "val/box_loss", + "val/obj_loss", + "val/cls_loss", # val loss + "x/lr0", + "x/lr1", + "x/lr2", + ] # params + self.best_keys = ["best/epoch", "best/precision", "best/recall", "best/mAP_0.5", "best/mAP_0.5:0.95"] + for k in LOGGERS: + setattr(self, k, None) # init empty logger dictionary + self.csv = True # always log to csv + self.ndjson_console = "ndjson_console" in self.include # log ndjson to console + self.ndjson_file = "ndjson_file" in self.include # log ndjson to file + + # Messages + if not comet_ml: + prefix = colorstr("Comet: ") + s = f"{prefix}run 'pip install comet_ml' to automatically track and visualize YOLOv5 🚀 runs in Comet" + self.logger.info(s) + # TensorBoard + s = self.save_dir + if "tb" in self.include and not self.opt.evolve: + prefix = colorstr("TensorBoard: ") + self.logger.info(f"{prefix}Start with 'tensorboard --logdir {s.parent}', view at http://localhost:6006/") + self.tb = SummaryWriter(str(s)) + + # W&B + if wandb and "wandb" in self.include: + self.opt.hyp = self.hyp # add hyperparameters + self.wandb = WandbLogger(self.opt) + else: + self.wandb = None + + # ClearML + if clearml and "clearml" in self.include: + try: + self.clearml = ClearmlLogger(self.opt, self.hyp) + except Exception: + self.clearml = None + prefix = colorstr("ClearML: ") + LOGGER.warning( + f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging." + f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration#readme" + ) + + else: + self.clearml = None + + # Comet + if comet_ml and "comet" in self.include: + if isinstance(self.opt.resume, str) and self.opt.resume.startswith("comet://"): + run_id = self.opt.resume.split("/")[-1] + self.comet_logger = CometLogger(self.opt, self.hyp, run_id=run_id) + + else: + self.comet_logger = CometLogger(self.opt, self.hyp) + + else: + self.comet_logger = None + + @property + def remote_dataset(self): + """Fetches dataset dictionary from remote logging services like ClearML, Weights & Biases, or Comet ML.""" + data_dict = None + if self.clearml: + data_dict = self.clearml.data_dict + if self.wandb: + data_dict = self.wandb.data_dict + if self.comet_logger: + data_dict = self.comet_logger.data_dict + + return data_dict + + def on_train_start(self): + """Initializes the training process for Comet ML logger if it's configured.""" + if self.comet_logger: + self.comet_logger.on_train_start() + + def on_pretrain_routine_start(self): + """Invokes pre-training routine start hook for Comet ML logger if available.""" + if self.comet_logger: + self.comet_logger.on_pretrain_routine_start() + + def on_pretrain_routine_end(self, labels, names): + """Callback that runs at the end of pre-training routine, logging label plots if enabled.""" + if self.plots: + plot_labels(labels, names, self.save_dir) + paths = self.save_dir.glob("*labels*.jpg") # training labels + if self.wandb: + self.wandb.log({"Labels": [wandb.Image(str(x), caption=x.name) for x in paths]}) + if self.comet_logger: + self.comet_logger.on_pretrain_routine_end(paths) + if self.clearml: + for path in paths: + self.clearml.log_plot(title=path.stem, plot_path=path) + + def on_train_batch_end(self, model, ni, imgs, targets, paths, vals): + """Logs training batch end events, plots images, and updates external loggers with batch-end data.""" + log_dict = dict(zip(self.keys[:3], vals)) + # Callback runs on train batch end + # ni: number integrated batches (since train start) + if self.plots: + if ni < 3: + f = self.save_dir / f"train_batch{ni}.jpg" # filename + plot_images(imgs, targets, paths, f) + if ni == 0 and self.tb and not self.opt.sync_bn: + log_tensorboard_graph(self.tb, model, imgsz=(self.opt.imgsz, self.opt.imgsz)) + if ni == 10 and (self.wandb or self.clearml): + files = sorted(self.save_dir.glob("train*.jpg")) + if self.wandb: + self.wandb.log({"Mosaics": [wandb.Image(str(f), caption=f.name) for f in files if f.exists()]}) + if self.clearml: + self.clearml.log_debug_samples(files, title="Mosaics") + + if self.comet_logger: + self.comet_logger.on_train_batch_end(log_dict, step=ni) + + def on_train_epoch_end(self, epoch): + """Callback that updates the current epoch in Weights & Biases at the end of a training epoch.""" + if self.wandb: + self.wandb.current_epoch = epoch + 1 + + if self.comet_logger: + self.comet_logger.on_train_epoch_end(epoch) + + def on_val_start(self): + """Callback that signals the start of a validation phase to the Comet logger.""" + if self.comet_logger: + self.comet_logger.on_val_start() + + def on_val_image_end(self, pred, predn, path, names, im): + """Callback that logs a validation image and its predictions to WandB or ClearML.""" + if self.wandb: + self.wandb.val_one_image(pred, predn, path, names, im) + if self.clearml: + self.clearml.log_image_with_boxes(path, pred, names, im) + + def on_val_batch_end(self, batch_i, im, targets, paths, shapes, out): + """Logs validation batch results to Comet ML during training at the end of each validation batch.""" + if self.comet_logger: + self.comet_logger.on_val_batch_end(batch_i, im, targets, paths, shapes, out) + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + """Logs validation results to WandB or ClearML at the end of the validation process.""" + if self.wandb or self.clearml: + files = sorted(self.save_dir.glob("val*.jpg")) + if self.wandb: + self.wandb.log({"Validation": [wandb.Image(str(f), caption=f.name) for f in files]}) + if self.clearml: + self.clearml.log_debug_samples(files, title="Validation") + + if self.comet_logger: + self.comet_logger.on_val_end(nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix) + + def on_fit_epoch_end(self, vals, epoch, best_fitness, fi): + """Callback that logs metrics and saves them to CSV or NDJSON at the end of each fit (train+val) epoch.""" + x = dict(zip(self.keys, vals)) + if self.csv: + file = self.save_dir / "results.csv" + n = len(x) + 1 # number of cols + s = "" if file.exists() else (("%20s," * n % tuple(["epoch"] + self.keys)).rstrip(",") + "\n") # add header + with open(file, "a") as f: + f.write(s + ("%20.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n") + if self.ndjson_console or self.ndjson_file: + json_data = json.dumps(dict(epoch=epoch, **x), default=_json_default) + if self.ndjson_console: + print(json_data) + if self.ndjson_file: + file = self.save_dir / "results.ndjson" + with open(file, "a") as f: + print(json_data, file=f) + + if self.tb: + for k, v in x.items(): + self.tb.add_scalar(k, v, epoch) + elif self.clearml: # log to ClearML if TensorBoard not used + self.clearml.log_scalars(x, epoch) + + if self.wandb: + if best_fitness == fi: + best_results = [epoch] + vals[3:7] + for i, name in enumerate(self.best_keys): + self.wandb.wandb_run.summary[name] = best_results[i] # log best results in the summary + self.wandb.log(x) + self.wandb.end_epoch() + + if self.clearml: + self.clearml.current_epoch_logged_images = set() # reset epoch image limit + self.clearml.current_epoch += 1 + + if self.comet_logger: + self.comet_logger.on_fit_epoch_end(x, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + """Callback that handles model saving events, logging to Weights & Biases or ClearML if enabled.""" + if (epoch + 1) % self.opt.save_period == 0 and not final_epoch and self.opt.save_period != -1: + if self.wandb: + self.wandb.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + if self.clearml: + self.clearml.task.update_output_model( + model_path=str(last), model_name="Latest Model", auto_delete_file=False + ) + + if self.comet_logger: + self.comet_logger.on_model_save(last, epoch, final_epoch, best_fitness, fi) + + def on_train_end(self, last, best, epoch, results): + """Callback that runs at the end of training to save plots and log results.""" + if self.plots: + plot_results(file=self.save_dir / "results.csv") # save results.png + files = ["results.png", "confusion_matrix.png", *(f"{x}_curve.png" for x in ("F1", "PR", "P", "R"))] + files = [(self.save_dir / f) for f in files if (self.save_dir / f).exists()] # filter + self.logger.info(f"Results saved to {colorstr('bold', self.save_dir)}") + + if self.tb and not self.clearml: # These images are already captured by ClearML by now, we don't want doubles + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") + + if self.wandb: + self.wandb.log(dict(zip(self.keys[3:10], results))) + self.wandb.log({"Results": [wandb.Image(str(f), caption=f.name) for f in files]}) + # Calling wandb.log. TODO: Refactor this into WandbLogger.log_model + if not self.opt.evolve: + wandb.log_artifact( + str(best if best.exists() else last), + type="model", + name=f"run_{self.wandb.wandb_run.id}_model", + aliases=["latest", "best", "stripped"], + ) + self.wandb.finish_run() + + if self.clearml and not self.opt.evolve: + self.clearml.log_summary(dict(zip(self.keys[3:10], results))) + [self.clearml.log_plot(title=f.stem, plot_path=f) for f in files] + self.clearml.log_model( + str(best if best.exists() else last), "Best Model" if best.exists() else "Last Model", epoch + ) + + if self.comet_logger: + final_results = dict(zip(self.keys[3:10], results)) + self.comet_logger.on_train_end(files, self.save_dir, last, best, epoch, final_results) + + def on_params_update(self, params: dict): + """Updates experiment hyperparameters or configurations in WandB, Comet, or ClearML.""" + if self.wandb: + self.wandb.wandb_run.config.update(params, allow_val_change=True) + if self.comet_logger: + self.comet_logger.on_params_update(params) + if self.clearml: + self.clearml.task.connect(params) + + +class GenericLogger: + """ + YOLOv5 General purpose logger for non-task specific logging + Usage: from utils.loggers import GenericLogger; logger = GenericLogger(...). + + Arguments: + opt: Run arguments + console_logger: Console logger + include: loggers to include + """ + + def __init__(self, opt, console_logger, include=("tb", "wandb", "clearml")): + """Initializes a generic logger with optional TensorBoard, W&B, and ClearML support.""" + self.save_dir = Path(opt.save_dir) + self.include = include + self.console_logger = console_logger + self.csv = self.save_dir / "results.csv" # CSV logger + if "tb" in self.include: + prefix = colorstr("TensorBoard: ") + self.console_logger.info( + f"{prefix}Start with 'tensorboard --logdir {self.save_dir.parent}', view at http://localhost:6006/" + ) + self.tb = SummaryWriter(str(self.save_dir)) + + if wandb and "wandb" in self.include: + self.wandb = wandb.init( + project=web_project_name(str(opt.project)), name=None if opt.name == "exp" else opt.name, config=opt + ) + else: + self.wandb = None + + if clearml and "clearml" in self.include: + try: + # Hyp is not available in classification mode + hyp = {} if "hyp" not in opt else opt.hyp + self.clearml = ClearmlLogger(opt, hyp) + except Exception: + self.clearml = None + prefix = colorstr("ClearML: ") + LOGGER.warning( + f"{prefix}WARNING ⚠️ ClearML is installed but not configured, skipping ClearML logging." + f" See https://docs.ultralytics.com/yolov5/tutorials/clearml_logging_integration" + ) + else: + self.clearml = None + + def log_metrics(self, metrics, epoch): + """Logs metrics to CSV, TensorBoard, W&B, and ClearML; `metrics` is a dict, `epoch` is an int.""" + if self.csv: + keys, vals = list(metrics.keys()), list(metrics.values()) + n = len(metrics) + 1 # number of cols + s = "" if self.csv.exists() else (("%23s," * n % tuple(["epoch"] + keys)).rstrip(",") + "\n") # header + with open(self.csv, "a") as f: + f.write(s + ("%23.5g," * n % tuple([epoch] + vals)).rstrip(",") + "\n") + + if self.tb: + for k, v in metrics.items(): + self.tb.add_scalar(k, v, epoch) + + if self.wandb: + self.wandb.log(metrics, step=epoch) + + if self.clearml: + self.clearml.log_scalars(metrics, epoch) + + def log_images(self, files, name="Images", epoch=0): + """Logs images to all loggers with optional naming and epoch specification.""" + files = [Path(f) for f in (files if isinstance(files, (tuple, list)) else [files])] # to Path + files = [f for f in files if f.exists()] # filter by exists + + if self.tb: + for f in files: + self.tb.add_image(f.stem, cv2.imread(str(f))[..., ::-1], epoch, dataformats="HWC") + + if self.wandb: + self.wandb.log({name: [wandb.Image(str(f), caption=f.name) for f in files]}, step=epoch) + + if self.clearml: + if name == "Results": + [self.clearml.log_plot(f.stem, f) for f in files] + else: + self.clearml.log_debug_samples(files, title=name) + + def log_graph(self, model, imgsz=(640, 640)): + """Logs model graph to all configured loggers with specified input image size.""" + if self.tb: + log_tensorboard_graph(self.tb, model, imgsz) + + def log_model(self, model_path, epoch=0, metadata=None): + """Logs the model to all configured loggers with optional epoch and metadata.""" + if metadata is None: + metadata = {} + # Log model to all loggers + if self.wandb: + art = wandb.Artifact(name=f"run_{wandb.run.id}_model", type="model", metadata=metadata) + art.add_file(str(model_path)) + wandb.log_artifact(art) + if self.clearml: + self.clearml.log_model(model_path=model_path, model_name=model_path.stem) + + def update_params(self, params): + """Updates logged parameters in WandB and/or ClearML if enabled.""" + if self.wandb: + wandb.run.config.update(params, allow_val_change=True) + if self.clearml: + self.clearml.task.connect(params) + + +def log_tensorboard_graph(tb, model, imgsz=(640, 640)): + """Logs the model graph to TensorBoard with specified image size and model.""" + try: + p = next(model.parameters()) # for device, type + imgsz = (imgsz, imgsz) if isinstance(imgsz, int) else imgsz # expand + im = torch.zeros((1, 3, *imgsz)).to(p.device).type_as(p) # input image (WARNING: must be zeros, not empty) + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress jit trace warning + tb.add_graph(torch.jit.trace(de_parallel(model), im, strict=False), []) + except Exception as e: + LOGGER.warning(f"WARNING ⚠️ TensorBoard graph visualization failure {e}") + + +def web_project_name(project): + """Converts a local project name to a standardized web project name with optional suffixes.""" + if not project.startswith("runs/train"): + return project + suffix = "-Classify" if project.endswith("-cls") else "-Segment" if project.endswith("-seg") else "" + return f"YOLOv5{suffix}" diff --git a/app/util/yolov5/utils/loggers/clearml/README.md b/app/util/yolov5/utils/loggers/clearml/README.md new file mode 100644 index 0000000..374765d --- /dev/null +++ b/app/util/yolov5/utils/loggers/clearml/README.md @@ -0,0 +1,222 @@ +# ClearML Integration + +Clear|MLClear|ML + +## About ClearML + +[ClearML](https://clear.ml/) is an [open-source](https://github.com/clearml/clearml) toolbox designed to save you time ⏱️. + +🔨 Track every YOLOv5 training run in the experiment manager + +🔧 Version and easily access your custom training data with the integrated ClearML Data Versioning Tool + +🔦 Remotely train and monitor your YOLOv5 training runs using ClearML Agent + +🔬 Get the very best mAP using ClearML Hyperparameter Optimization + +🔭 Turn your newly trained YOLOv5 model into an API with just a few commands using ClearML Serving + +And so much more. It's up to you how many of these tools you want to use, you can stick to the experiment manager, or chain them all together into an impressive pipeline! + +![ClearML scalars dashboard](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/experiment_manager_with_compare.gif) + +## 🦾 Setting Things Up + +To keep track of your experiments and/or data, ClearML needs to communicate to a server. You have 2 options to get one: + +Either sign up for free to the [ClearML Hosted Service](https://clear.ml/) or you can set up your own server, see [here](https://clear.ml/docs/latest/docs/deploying_clearml/clearml_server). Even the server is open-source, so even if you're dealing with sensitive data, you should be good to go! + +1. Install the `clearml` python package: + + ```bash + pip install clearml + ``` + +2. Connect the ClearML SDK to the server by [creating credentials](https://app.clear.ml/settings/workspace-configuration) (go right top to Settings -> Workspace -> Create new credentials), then execute the command below and follow the instructions: + + ```bash + clearml-init + ``` + +That's it! You're done 😎 + +## 🚀 Training YOLOv5 With ClearML + +To enable ClearML experiment tracking, simply install the ClearML pip package. + +```bash +pip install clearml>=1.2.0 +``` + +This will enable integration with the YOLOv5 training script. Every training run from now on, will be captured and stored by the ClearML experiment manager. + +If you want to change the `project_name` or `task_name`, use the `--project` and `--name` arguments of the `train.py` script, by default the project will be called `YOLOv5` and the task `Training`. PLEASE NOTE: ClearML uses `/` as a delimiter for subprojects, so be careful when using `/` in your project name! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +or with custom project and task name: + +```bash +python train.py --project my_project --name my_training --img 640 --batch 16 --epochs 3 --data coco128.yaml --weights yolov5s.pt --cache +``` + +This will capture: + +- Source code + uncommitted changes +- Installed packages +- (Hyper)parameters +- Model files (use `--save-period n` to save a checkpoint every n epochs) +- Console output +- Scalars (mAP_0.5, mAP_0.5:0.95, precision, recall, losses, learning rates, ...) +- General info such as machine details, runtime, creation date etc. +- All produced plots such as label correlogram and confusion matrix +- Images with bounding boxes per epoch +- Mosaic per epoch +- Validation images per epoch +- ... + +That's a lot right? 🤯 Now, we can visualize all of this information in the ClearML UI to get an overview of our training progress. Add custom columns to the table view (such as e.g. mAP_0.5) so you can easily sort on the best performing model. Or select multiple experiments and directly compare them! + +There even more we can do with all of this information, like hyperparameter optimization and remote execution, so keep reading if you want to see how that works! + +## 🔗 Dataset Version Management + +Versioning your data separately from your code is generally a good idea and makes it easy to acquire the latest version too. This repository supports supplying a dataset version ID, and it will make sure to get the data if it's not there yet. Next to that, this workflow also saves the used dataset ID as part of the task parameters, so you will always know for sure which data was used in which experiment! + +![ClearML Dataset Interface](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/clearml_data.gif) + +### Prepare Your Dataset + +The YOLOv5 repository supports a number of different datasets by using yaml files containing their information. By default datasets are downloaded to the `../datasets` folder in relation to the repository root folder. So if you downloaded the `coco128` dataset using the link in the yaml or with the scripts provided by yolov5, you get this folder structure: + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ LICENSE + |_ README.txt +``` + +But this can be any dataset you wish. Feel free to use your own, as long as you keep to this folder structure. + +Next, ⚠️**copy the corresponding yaml file to the root of the dataset folder**⚠️. This yaml files contains the information ClearML will need to properly use the dataset. You can make this yourself too, of course, just follow the structure of the example yamls. + +Basically we need the following keys: `path`, `train`, `test`, `val`, `nc`, `names`. + +``` +.. +|_ yolov5 +|_ datasets + |_ coco128 + |_ images + |_ labels + |_ coco128.yaml # <---- HERE! + |_ LICENSE + |_ README.txt +``` + +### Upload Your Dataset + +To get this dataset into ClearML as a versioned dataset, go to the dataset root folder and run the following command: + +```bash +cd coco128 +clearml-data sync --project YOLOv5 --name coco128 --folder . +``` + +The command `clearml-data sync` is actually a shorthand command. You could also run these commands one after the other: + +```bash +# Optionally add --parent if you want to base +# this version on another dataset version, so no duplicate files are uploaded! +clearml-data create --name coco128 --project YOLOv5 +clearml-data add --files . +clearml-data close +``` + +### Run Training Using A ClearML Dataset + +Now that you have a ClearML dataset, you can very simply use it to train custom YOLOv5 🚀 models! + +```bash +python train.py --img 640 --batch 16 --epochs 3 --data clearml:// --weights yolov5s.pt --cache +``` + +## 👀 Hyperparameter Optimization + +Now that we have our experiments and data versioned, it's time to take a look at what we can build on top! + +Using the code information, installed packages and environment details, the experiment itself is now **completely reproducible**. In fact, ClearML allows you to clone an experiment and even change its parameters. We can then just rerun it with these new parameters automatically, this is basically what HPO does! + +To **run hyperparameter optimization locally**, we've included a pre-made script for you. Just make sure a training task has been run at least once, so it is in the ClearML experiment manager, we will essentially clone it and change its hyperparameters. + +You'll need to fill in the ID of this `template task` in the script found at `utils/loggers/clearml/hpo.py` and then just run it :) You can change `task.execute_locally()` to `task.execute()` to put it in a ClearML queue and have a remote agent work on it instead. + +```bash +# To use optuna, install it first, otherwise you can change the optimizer to just be RandomSearch +pip install optuna +python utils/loggers/clearml/hpo.py +``` + +![HPO](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/hpo.png) + +## 🤯 Remote Execution (advanced) + +Running HPO locally is really handy, but what if we want to run our experiments on a remote machine instead? Maybe you have access to a very powerful GPU machine on-site, or you have some budget to use cloud GPUs. This is where the ClearML Agent comes into play. Check out what the agent can do here: + +- [YouTube video](https://www.youtube.com/watch?v=MX3BrXnaULs&feature=youtu.be) +- [Documentation](https://clear.ml/docs/latest/docs/clearml_agent) + +In short: every experiment tracked by the experiment manager contains enough information to reproduce it on a different machine (installed packages, uncommitted changes etc.). So a ClearML agent does just that: it listens to a queue for incoming tasks and when it finds one, it recreates the environment and runs it while still reporting scalars, plots etc. to the experiment manager. + +You can turn any machine (a cloud VM, a local GPU machine, your own laptop ... ) into a ClearML agent by simply running: + +```bash +clearml-agent daemon --queue [--docker] +``` + +### Cloning, Editing And Enqueuing + +With our agent running, we can give it some work. Remember from the HPO section that we can clone a task and edit the hyperparameters? We can do that from the interface too! + +🪄 Clone the experiment by right-clicking it + +🎯 Edit the hyperparameters to what you wish them to be + +⏳ Enqueue the task to any of the queues by right-clicking it + +![Enqueue a task from the UI](https://raw.githubusercontent.com/thepycoder/clearml_screenshots/main/enqueue.gif) + +### Executing A Task Remotely + +Now you can clone a task like we explained above, or simply mark your current script by adding `task.execute_remotely()` and on execution it will be put into a queue, for the agent to start working on! + +To run the YOLOv5 training script remotely, all you have to do is add this line to the training.py script after the clearml logger has been instantiated: + +```python +# ... +# Loggers +data_dict = None +if RANK in {-1, 0}: + loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance + if loggers.clearml: + loggers.clearml.task.execute_remotely(queue="my_queue") # <------ ADD THIS LINE + # Data_dict is either None is user did not choose for ClearML dataset or is filled in by ClearML + data_dict = loggers.clearml.data_dict +# ... +``` + +When running the training script after this change, python will run the script up until that line, after which it will package the code and send it to the queue instead! + +### Autoscaling workers + +ClearML comes with autoscalers too! This tool will automatically spin up new remote machines in the cloud of your choice (AWS, GCP, Azure) and turn them into ClearML agents for you whenever there are experiments detected in the queue. Once the tasks are processed, the autoscaler will automatically shut down the remote machines, and you stop paying! + +Check out the autoscalers getting started video below. + +[![Watch the video](https://img.youtube.com/vi/j4XVMAaUt3E/0.jpg)](https://youtu.be/j4XVMAaUt3E) diff --git a/app/util/yolov5/utils/loggers/clearml/__init__.py b/app/util/yolov5/utils/loggers/clearml/__init__.py new file mode 100644 index 0000000..77a19dc --- /dev/null +++ b/app/util/yolov5/utils/loggers/clearml/__init__.py @@ -0,0 +1 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license diff --git a/app/util/yolov5/utils/loggers/clearml/clearml_utils.py b/app/util/yolov5/utils/loggers/clearml/clearml_utils.py new file mode 100644 index 0000000..67553bd --- /dev/null +++ b/app/util/yolov5/utils/loggers/clearml/clearml_utils.py @@ -0,0 +1,228 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Main Logger class for ClearML experiment tracking.""" + +import glob +import re +from pathlib import Path + +import matplotlib.image as mpimg +import matplotlib.pyplot as plt +import numpy as np +import yaml +from ultralytics.utils.plotting import Annotator, colors + +try: + import clearml + from clearml import Dataset, Task + + assert hasattr(clearml, "__version__") # verify package import not local dir +except (ImportError, AssertionError): + clearml = None + + +def construct_dataset(clearml_info_string): + """Load in a clearml dataset and fill the internal data_dict with its contents.""" + dataset_id = clearml_info_string.replace("clearml://", "") + dataset = Dataset.get(dataset_id=dataset_id) + dataset_root_path = Path(dataset.get_local_copy()) + + # We'll search for the yaml file definition in the dataset + yaml_filenames = list(glob.glob(str(dataset_root_path / "*.yaml")) + glob.glob(str(dataset_root_path / "*.yml"))) + if len(yaml_filenames) > 1: + raise ValueError( + "More than one yaml file was found in the dataset root, cannot determine which one contains " + "the dataset definition this way." + ) + elif not yaml_filenames: + raise ValueError( + "No yaml definition found in dataset root path, check that there is a correct yaml file " + "inside the dataset root path." + ) + with open(yaml_filenames[0]) as f: + dataset_definition = yaml.safe_load(f) + + assert set(dataset_definition.keys()).issuperset({"train", "test", "val", "nc", "names"}), ( + "The right keys were not found in the yaml file, make sure it at least has the following keys: ('train', 'test', 'val', 'nc', 'names')" + ) + + data_dict = { + "train": ( + str((dataset_root_path / dataset_definition["train"]).resolve()) if dataset_definition["train"] else None + ) + } + data_dict["test"] = ( + str((dataset_root_path / dataset_definition["test"]).resolve()) if dataset_definition["test"] else None + ) + data_dict["val"] = ( + str((dataset_root_path / dataset_definition["val"]).resolve()) if dataset_definition["val"] else None + ) + data_dict["nc"] = dataset_definition["nc"] + data_dict["names"] = dataset_definition["names"] + + return data_dict + + +class ClearmlLogger: + """ + Log training runs, datasets, models, and predictions to ClearML. + + This logger sends information to ClearML at app.clear.ml or to your own hosted server. By default, this information + includes hyperparameters, system configuration and metrics, model metrics, code information and basic data metrics + and analyses. + + By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. + """ + + def __init__(self, opt, hyp): + """ + - Initialize ClearML Task, this object will capture the experiment + - Upload dataset version to ClearML Data if opt.upload_dataset is True. + + Arguments: + opt (namespace) -- Commandline arguments for this run + hyp (dict) -- Hyperparameters for this run + + """ + self.current_epoch = 0 + # Keep tracked of amount of logged images to enforce a limit + self.current_epoch_logged_images = set() + # Maximum number of images to log to clearML per epoch + self.max_imgs_to_log_per_epoch = 16 + # Get the interval of epochs when bounding box images should be logged + # Only for detection task though! + if "bbox_interval" in opt: + self.bbox_interval = opt.bbox_interval + self.clearml = clearml + self.task = None + self.data_dict = None + if self.clearml: + self.task = Task.init( + project_name="YOLOv5" if str(opt.project).startswith("runs/") else opt.project, + task_name=opt.name if opt.name != "exp" else "Training", + tags=["YOLOv5"], + output_uri=True, + reuse_last_task_id=opt.exist_ok, + auto_connect_frameworks={"pytorch": False, "matplotlib": False}, + # We disconnect pytorch auto-detection, because we added manual model save points in the code + ) + # ClearML's hooks will already grab all general parameters + # Only the hyperparameters coming from the yaml config file + # will have to be added manually! + self.task.connect(hyp, name="Hyperparameters") + self.task.connect(opt, name="Args") + + # Make sure the code is easily remotely runnable by setting the docker image to use by the remote agent + self.task.set_base_docker( + "ultralytics/yolov5:latest", + docker_arguments='--ipc=host -e="CLEARML_AGENT_SKIP_PYTHON_ENV_INSTALL=1"', + docker_setup_bash_script="pip install clearml", + ) + + # Get ClearML Dataset Version if requested + if opt.data.startswith("clearml://"): + # data_dict should have the following keys: + # names, nc (number of classes), test, train, val (all three relative paths to ../datasets) + self.data_dict = construct_dataset(opt.data) + # Set data to data_dict because wandb will crash without this information and opt is the best way + # to give it to them + opt.data = self.data_dict + + def log_scalars(self, metrics, epoch): + """ + Log scalars/metrics to ClearML. + + Arguments: + metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} + epoch (int) iteration number for the current set of metrics + """ + for k, v in metrics.items(): + title, series = k.split("/") + self.task.get_logger().report_scalar(title, series, v, epoch) + + def log_model(self, model_path, model_name, epoch=0): + """ + Log model weights to ClearML. + + Arguments: + model_path (PosixPath or str) Path to the model weights + model_name (str) Name of the model visible in ClearML + epoch (int) Iteration / epoch of the model weights + """ + self.task.update_output_model( + model_path=str(model_path), name=model_name, iteration=epoch, auto_delete_file=False + ) + + def log_summary(self, metrics): + """ + Log final metrics to a summary table. + + Arguments: + metrics (dict) Metrics in dict format: {"metrics/mAP": 0.8, ...} + """ + for k, v in metrics.items(): + self.task.get_logger().report_single_value(k, v) + + def log_plot(self, title, plot_path): + """ + Log image as plot in the plot section of ClearML. + + Arguments: + title (str) Title of the plot + plot_path (PosixPath or str) Path to the saved image file + """ + img = mpimg.imread(plot_path) + fig = plt.figure() + ax = fig.add_axes([0, 0, 1, 1], frameon=False, aspect="auto", xticks=[], yticks=[]) # no ticks + ax.imshow(img) + + self.task.get_logger().report_matplotlib_figure(title, "", figure=fig, report_interactive=False) + + def log_debug_samples(self, files, title="Debug Samples"): + """ + Log files (images) as debug samples in the ClearML task. + + Arguments: + files (List(PosixPath)) a list of file paths in PosixPath format + title (str) A title that groups together images with the same values + """ + for f in files: + if f.exists(): + it = re.search(r"_batch(\d+)", f.name) + iteration = int(it.groups()[0]) if it else 0 + self.task.get_logger().report_image( + title=title, series=f.name.replace(f"_batch{iteration}", ""), local_path=str(f), iteration=iteration + ) + + def log_image_with_boxes(self, image_path, boxes, class_names, image, conf_threshold=0.25): + """ + Draw the bounding boxes on a single image and report the result as a ClearML debug sample. + + Arguments: + image_path (PosixPath) the path the original image file + boxes (list): list of scaled predictions in the format - [xmin, ymin, xmax, ymax, confidence, class] + class_names (dict): dict containing mapping of class int to class name + image (Tensor): A torch tensor containing the actual image data + """ + if ( + len(self.current_epoch_logged_images) < self.max_imgs_to_log_per_epoch + and self.current_epoch >= 0 + and (self.current_epoch % self.bbox_interval == 0 and image_path not in self.current_epoch_logged_images) + ): + im = np.ascontiguousarray(np.moveaxis(image.mul(255).clamp(0, 255).byte().cpu().numpy(), 0, 2)) + annotator = Annotator(im=im, pil=True) + for i, (conf, class_nr, box) in enumerate(zip(boxes[:, 4], boxes[:, 5], boxes[:, :4])): + color = colors(i) + + class_name = class_names[int(class_nr)] + confidence_percentage = round(float(conf) * 100, 2) + label = f"{class_name}: {confidence_percentage}%" + + if conf > conf_threshold: + annotator.rectangle(box.cpu().numpy(), outline=color) + annotator.box_label(box.cpu().numpy(), label=label, color=color) + + annotated_image = annotator.result() + self.task.get_logger().report_image( + title="Bounding Boxes", series=image_path.name, iteration=self.current_epoch, image=annotated_image + ) + self.current_epoch_logged_images.add(image_path) diff --git a/app/util/yolov5/utils/loggers/clearml/hpo.py b/app/util/yolov5/utils/loggers/clearml/hpo.py new file mode 100644 index 0000000..099a87f --- /dev/null +++ b/app/util/yolov5/utils/loggers/clearml/hpo.py @@ -0,0 +1,90 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +from clearml import Task + +# Connecting ClearML with the current process, +# from here on everything is logged automatically +from clearml.automation import HyperParameterOptimizer, UniformParameterRange +from clearml.automation.optuna import OptimizerOptuna + +task = Task.init( + project_name="Hyper-Parameter Optimization", + task_name="YOLOv5", + task_type=Task.TaskTypes.optimizer, + reuse_last_task_id=False, +) + +# Example use case: +optimizer = HyperParameterOptimizer( + # This is the experiment we want to optimize + base_task_id="", + # here we define the hyper-parameters to optimize + # Notice: The parameter name should exactly match what you see in the UI: / + # For Example, here we see in the base experiment a section Named: "General" + # under it a parameter named "batch_size", this becomes "General/batch_size" + # If you have `argparse` for example, then arguments will appear under the "Args" section, + # and you should instead pass "Args/batch_size" + hyper_parameters=[ + UniformParameterRange("Hyperparameters/lr0", min_value=1e-5, max_value=1e-1), + UniformParameterRange("Hyperparameters/lrf", min_value=0.01, max_value=1.0), + UniformParameterRange("Hyperparameters/momentum", min_value=0.6, max_value=0.98), + UniformParameterRange("Hyperparameters/weight_decay", min_value=0.0, max_value=0.001), + UniformParameterRange("Hyperparameters/warmup_epochs", min_value=0.0, max_value=5.0), + UniformParameterRange("Hyperparameters/warmup_momentum", min_value=0.0, max_value=0.95), + UniformParameterRange("Hyperparameters/warmup_bias_lr", min_value=0.0, max_value=0.2), + UniformParameterRange("Hyperparameters/box", min_value=0.02, max_value=0.2), + UniformParameterRange("Hyperparameters/cls", min_value=0.2, max_value=4.0), + UniformParameterRange("Hyperparameters/cls_pw", min_value=0.5, max_value=2.0), + UniformParameterRange("Hyperparameters/obj", min_value=0.2, max_value=4.0), + UniformParameterRange("Hyperparameters/obj_pw", min_value=0.5, max_value=2.0), + UniformParameterRange("Hyperparameters/iou_t", min_value=0.1, max_value=0.7), + UniformParameterRange("Hyperparameters/anchor_t", min_value=2.0, max_value=8.0), + UniformParameterRange("Hyperparameters/fl_gamma", min_value=0.0, max_value=4.0), + UniformParameterRange("Hyperparameters/hsv_h", min_value=0.0, max_value=0.1), + UniformParameterRange("Hyperparameters/hsv_s", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/hsv_v", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/degrees", min_value=0.0, max_value=45.0), + UniformParameterRange("Hyperparameters/translate", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/scale", min_value=0.0, max_value=0.9), + UniformParameterRange("Hyperparameters/shear", min_value=0.0, max_value=10.0), + UniformParameterRange("Hyperparameters/perspective", min_value=0.0, max_value=0.001), + UniformParameterRange("Hyperparameters/flipud", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/fliplr", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/mosaic", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/mixup", min_value=0.0, max_value=1.0), + UniformParameterRange("Hyperparameters/copy_paste", min_value=0.0, max_value=1.0), + ], + # this is the objective metric we want to maximize/minimize + objective_metric_title="metrics", + objective_metric_series="mAP_0.5", + # now we decide if we want to maximize it or minimize it (accuracy we maximize) + objective_metric_sign="max", + # let us limit the number of concurrent experiments, + # this in turn will make sure we don't bombard the scheduler with experiments. + # if we have an auto-scaler connected, this, by proxy, will limit the number of machine + max_number_of_concurrent_tasks=1, + # this is the optimizer class (actually doing the optimization) + # Currently, we can choose from GridSearch, RandomSearch or OptimizerBOHB (Bayesian optimization Hyper-Band) + optimizer_class=OptimizerOptuna, + # If specified only the top K performing Tasks will be kept, the others will be automatically archived + save_top_k_tasks_only=5, # 5, + compute_time_limit=None, + total_max_jobs=20, + min_iteration_per_job=None, + max_iteration_per_job=None, +) + +# report every 10 seconds, this is way too often, but we are testing here +optimizer.set_report_period(10 / 60) +# You can also use the line below instead to run all the optimizer tasks locally, without using queues or agent +# an_optimizer.start_locally(job_complete_callback=job_complete_callback) +# set the time limit for the optimization process (2 hours) +optimizer.set_time_limit(in_minutes=120.0) +# Start the optimization process in the local environment +optimizer.start_locally() +# wait until process is done (notice we are controlling the optimization process in the background) +optimizer.wait() +# make sure background optimization stopped +optimizer.stop() + +print("We are done, good bye") diff --git a/app/util/yolov5/utils/loggers/comet/README.md b/app/util/yolov5/utils/loggers/comet/README.md new file mode 100644 index 0000000..52f344d --- /dev/null +++ b/app/util/yolov5/utils/loggers/comet/README.md @@ -0,0 +1,250 @@ + + +# YOLOv5 with Comet + +This guide will cover how to use YOLOv5 with [Comet](https://bit.ly/yolov5-readme-comet2) + +# About Comet + +Comet builds tools that help data scientists, engineers, and team leaders accelerate and optimize machine learning and deep learning models. + +Track and visualize model metrics in real time, save your hyperparameters, datasets, and model checkpoints, and visualize your model predictions with [Comet Custom Panels](https://www.comet.com/docs/v2/guides/comet-dashboard/code-panels/about-panels/?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github)! Comet makes sure you never lose track of your work and makes it easy to share results and collaborate across teams of all sizes! + +# Getting Started + +## Install Comet + +```shell +pip install comet_ml +``` + +## Configure Comet Credentials + +There are two ways to configure Comet with YOLOv5. + +You can either set your credentials through environment variables + +**Environment Variables** + +```shell +export COMET_API_KEY= +export COMET_PROJECT_NAME= # This will default to 'yolov5' +``` + +Or create a `.comet.config` file in your working directory and set your credentials there. + +**Comet Configuration File** + +``` +[comet] +api_key= +project_name= # This will default to 'yolov5' +``` + +## Run the Training Script + +```shell +# Train YOLOv5s on COCO128 for 5 epochs +python train.py --img 640 --batch 16 --epochs 5 --data coco128.yaml --weights yolov5s.pt +``` + +That's it! Comet will automatically log your hyperparameters, command line arguments, training and validation metrics. You can visualize and analyze your runs in the Comet UI + +yolo-ui + +# Try out an Example! + +Check out an example of a [completed run here](https://www.comet.com/examples/comet-example-yolov5/a0e29e0e9b984e4a822db2a62d0cb357?experiment-tab=chart&showOutliers=true&smoothing=0&transformY=smoothing&xAxis=step&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +Or better yet, try it out yourself in this Colab Notebook + +[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/comet-ml/comet-examples/blob/master/integrations/model-training/yolov5/notebooks/Comet_and_YOLOv5.ipynb) + +# Log automatically + +By default, Comet will log the following items + +## Metrics + +- Box Loss, Object Loss, Classification Loss for the training and validation data +- mAP_0.5, mAP_0.5:0.95 metrics for the validation data. +- Precision and Recall for the validation data + +## Parameters + +- Model Hyperparameters +- All parameters passed through the command line options + +## Visualizations + +- Confusion Matrix of the model predictions on the validation data +- Plots for the PR and F1 curves across all classes +- Correlogram of the Class Labels + +# Configure Comet Logging + +Comet can be configured to log additional data either through command line flags passed to the training script or through environment variables. + +```shell +export COMET_MODE=online # Set whether to run Comet in 'online' or 'offline' mode. Defaults to online +export COMET_MODEL_NAME= #Set the name for the saved model. Defaults to yolov5 +export COMET_LOG_CONFUSION_MATRIX=false # Set to disable logging a Comet Confusion Matrix. Defaults to true +export COMET_MAX_IMAGE_UPLOADS= # Controls how many total image predictions to log to Comet. Defaults to 100. +export COMET_LOG_PER_CLASS_METRICS=true # Set to log evaluation metrics for each detected class at the end of training. Defaults to false +export COMET_DEFAULT_CHECKPOINT_FILENAME= # Set this if you would like to resume training from a different checkpoint. Defaults to 'last.pt' +export COMET_LOG_BATCH_LEVEL_METRICS=true # Set this if you would like to log training metrics at the batch level. Defaults to false. +export COMET_LOG_PREDICTIONS=true # Set this to false to disable logging model predictions +``` + +## Logging Checkpoints with Comet + +Logging Models to Comet is disabled by default. To enable it, pass the `save-period` argument to the training script. This will save the logged checkpoints to Comet based on the interval value provided by `save-period` + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--save-period 1 +``` + +## Logging Model Predictions + +By default, model predictions (images, ground truth labels and bounding boxes) will be logged to Comet. + +You can control the frequency of logged predictions and the associated images by passing the `bbox_interval` command line argument. Predictions can be visualized using Comet's Object Detection Custom Panel. This frequency corresponds to every Nth batch of data per epoch. In the example below, we are logging every 2nd batch of data for each epoch. + +**Note:** The YOLOv5 validation dataloader will default to a batch size of 32, so you will have to set the logging frequency accordingly. + +Here is an [example project using the Panel](https://www.comet.com/examples/comet-example-yolov5?shareable=YcwMiJaZSXfcEXpGOHDD12vA1&utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 2 +``` + +### Controlling the number of Prediction Images logged to Comet + +When logging predictions from YOLOv5, Comet will log the images associated with each set of predictions. By default a maximum of 100 validation images are logged. You can increase or decrease this number using the `COMET_MAX_IMAGE_UPLOADS` environment variable. + +```shell +env COMET_MAX_IMAGE_UPLOADS=200 python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--bbox_interval 1 +``` + +### Logging Class Level Metrics + +Use the `COMET_LOG_PER_CLASS_METRICS` environment variable to log mAP, precision, recall, f1 for each class. + +```shell +env COMET_LOG_PER_CLASS_METRICS=true python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt +``` + +## Uploading a Dataset to Comet Artifacts + +If you would like to store your data using [Comet Artifacts](https://www.comet.com/docs/v2/guides/data-management/using-artifacts/#learn-more?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github), you can do so using the `upload_dataset` flag. + +The dataset be organized in the way described in the [YOLOv5 documentation](https://docs.ultralytics.com/yolov5/tutorials/train_custom_data/). The dataset config `yaml` file must follow the same format as that of the `coco128.yaml` file. + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data coco128.yaml \ +--weights yolov5s.pt \ +--upload_dataset +``` + +You can find the uploaded dataset in the Artifacts tab in your Comet Workspace artifact-1 + +You can preview the data directly in the Comet UI. artifact-2 + +Artifacts are versioned and also support adding metadata about the dataset. Comet will automatically log the metadata from your dataset `yaml` file artifact-3 + +### Using a saved Artifact + +If you would like to use a dataset from Comet Artifacts, set the `path` variable in your dataset `yaml` file to point to the following Artifact resource URL. + +``` +# contents of artifact.yaml file +path: "comet:///:" +``` + +Then pass this file to your training script in the following way + +```shell +python train.py \ +--img 640 \ +--batch 16 \ +--epochs 5 \ +--data artifact.yaml \ +--weights yolov5s.pt +``` + +Artifacts also allow you to track the lineage of data as it flows through your Experimentation workflow. Here you can see a graph that shows you all the experiments that have used your uploaded dataset. artifact-4 + +## Resuming a Training Run + +If your training run is interrupted for any reason, e.g. disrupted internet connection, you can resume the run using the `resume` flag and the Comet Run Path. + +The Run Path has the following format `comet:////`. + +This will restore the run to its state before the interruption, which includes restoring the model from a checkpoint, restoring all hyperparameters and training arguments and downloading Comet dataset Artifacts if they were used in the original run. The resumed run will continue logging to the existing Experiment in the Comet UI + +```shell +python train.py \ +--resume "comet://" +``` + +## Hyperparameter Search with the Comet Optimizer + +YOLOv5 is also integrated with Comet's Optimizer, making is simple to visualize hyperparameter sweeps in the Comet UI. + +### Configuring an Optimizer Sweep + +To configure the Comet Optimizer, you will have to create a JSON file with the information about the sweep. An example file has been provided in `utils/loggers/comet/optimizer_config.json` + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" +``` + +The `hpo.py` script accepts the same arguments as `train.py`. If you wish to pass additional arguments to your sweep simply add them after the script. + +```shell +python utils/loggers/comet/hpo.py \ + --comet_optimizer_config "utils/loggers/comet/optimizer_config.json" \ + --save-period 1 \ + --bbox_interval 1 +``` + +### Running a Sweep in Parallel + +```shell +comet optimizer -j utils/loggers/comet/hpo.py \ + utils/loggers/comet/optimizer_config.json" +``` + +### Visualizing Results + +Comet provides a number of ways to visualize the results of your sweep. Take a look at a [project with a completed sweep here](https://www.comet.com/examples/comet-example-yolov5/view/PrlArHGuuhDTKC1UuBmTtOSXD/panels?utm_source=yolov5&utm_medium=partner&utm_campaign=partner_yolov5_2022&utm_content=github) + +hyperparameter-yolo diff --git a/app/util/yolov5/utils/loggers/comet/__init__.py b/app/util/yolov5/utils/loggers/comet/__init__.py new file mode 100644 index 0000000..1ad44b9 --- /dev/null +++ b/app/util/yolov5/utils/loggers/comet/__init__.py @@ -0,0 +1,549 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +import glob +import json +import logging +import os +import sys +from pathlib import Path + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +try: + import comet_ml + + # Project Configuration + config = comet_ml.config.get_config() + COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") +except ImportError: + comet_ml = None + COMET_PROJECT_NAME = None + +import PIL +import torch +import torchvision.transforms as T +import yaml + +from utils.dataloaders import img2label_paths +from utils.general import check_dataset, scale_boxes, xywh2xyxy +from utils.metrics import box_iou + +COMET_PREFIX = "comet://" + +COMET_MODE = os.getenv("COMET_MODE", "online") + +# Model Saving Settings +COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") + +# Dataset Artifact Settings +COMET_UPLOAD_DATASET = os.getenv("COMET_UPLOAD_DATASET", "false").lower() == "true" + +# Evaluation Settings +COMET_LOG_CONFUSION_MATRIX = os.getenv("COMET_LOG_CONFUSION_MATRIX", "true").lower() == "true" +COMET_LOG_PREDICTIONS = os.getenv("COMET_LOG_PREDICTIONS", "true").lower() == "true" +COMET_MAX_IMAGE_UPLOADS = int(os.getenv("COMET_MAX_IMAGE_UPLOADS", 100)) + +# Confusion Matrix Settings +CONF_THRES = float(os.getenv("CONF_THRES", 0.001)) +IOU_THRES = float(os.getenv("IOU_THRES", 0.6)) + +# Batch Logging Settings +COMET_LOG_BATCH_METRICS = os.getenv("COMET_LOG_BATCH_METRICS", "false").lower() == "true" +COMET_BATCH_LOGGING_INTERVAL = os.getenv("COMET_BATCH_LOGGING_INTERVAL", 1) +COMET_PREDICTION_LOGGING_INTERVAL = os.getenv("COMET_PREDICTION_LOGGING_INTERVAL", 1) +COMET_LOG_PER_CLASS_METRICS = os.getenv("COMET_LOG_PER_CLASS_METRICS", "false").lower() == "true" + +RANK = int(os.getenv("RANK", -1)) + +to_pil = T.ToPILImage() + + +class CometLogger: + """Log metrics, parameters, source code, models and much more with Comet.""" + + def __init__(self, opt, hyp, run_id=None, job_type="Training", **experiment_kwargs) -> None: + """Initializes CometLogger with given options, hyperparameters, run ID, job type, and additional experiment + arguments. + """ + self.job_type = job_type + self.opt = opt + self.hyp = hyp + + # Comet Flags + self.comet_mode = COMET_MODE + + self.save_model = opt.save_period > -1 + self.model_name = COMET_MODEL_NAME + + # Batch Logging Settings + self.log_batch_metrics = COMET_LOG_BATCH_METRICS + self.comet_log_batch_interval = COMET_BATCH_LOGGING_INTERVAL + + # Dataset Artifact Settings + self.upload_dataset = self.opt.upload_dataset or COMET_UPLOAD_DATASET + self.resume = self.opt.resume + + self.default_experiment_kwargs = { + "log_code": False, + "log_env_gpu": True, + "log_env_cpu": True, + "project_name": COMET_PROJECT_NAME, + } | experiment_kwargs + self.experiment = self._get_experiment(self.comet_mode, run_id) + self.experiment.set_name(self.opt.name) + + self.data_dict = self.check_dataset(self.opt.data) + self.class_names = self.data_dict["names"] + self.num_classes = self.data_dict["nc"] + + self.logged_images_count = 0 + self.max_images = COMET_MAX_IMAGE_UPLOADS + + if run_id is None: + self.experiment.log_other("Created from", "YOLOv5") + if not isinstance(self.experiment, comet_ml.OfflineExperiment): + workspace, project_name, experiment_id = self.experiment.url.split("/")[-3:] + self.experiment.log_other( + "Run Path", + f"{workspace}/{project_name}/{experiment_id}", + ) + self.log_parameters(vars(opt)) + self.log_parameters(self.opt.hyp) + self.log_asset_data( + self.opt.hyp, + name="hyperparameters.json", + metadata={"type": "hyp-config-file"}, + ) + self.log_asset( + f"{self.opt.save_dir}/opt.yaml", + metadata={"type": "opt-config-file"}, + ) + + self.comet_log_confusion_matrix = COMET_LOG_CONFUSION_MATRIX + + if hasattr(self.opt, "conf_thres"): + self.conf_thres = self.opt.conf_thres + else: + self.conf_thres = CONF_THRES + if hasattr(self.opt, "iou_thres"): + self.iou_thres = self.opt.iou_thres + else: + self.iou_thres = IOU_THRES + + self.log_parameters({"val_iou_threshold": self.iou_thres, "val_conf_threshold": self.conf_thres}) + + self.comet_log_predictions = COMET_LOG_PREDICTIONS + if self.opt.bbox_interval == -1: + self.comet_log_prediction_interval = 1 if self.opt.epochs < 10 else self.opt.epochs // 10 + else: + self.comet_log_prediction_interval = self.opt.bbox_interval + + if self.comet_log_predictions: + self.metadata_dict = {} + self.logged_image_names = [] + + self.comet_log_per_class_metrics = COMET_LOG_PER_CLASS_METRICS + + self.experiment.log_others( + { + "comet_mode": COMET_MODE, + "comet_max_image_uploads": COMET_MAX_IMAGE_UPLOADS, + "comet_log_per_class_metrics": COMET_LOG_PER_CLASS_METRICS, + "comet_log_batch_metrics": COMET_LOG_BATCH_METRICS, + "comet_log_confusion_matrix": COMET_LOG_CONFUSION_MATRIX, + "comet_model_name": COMET_MODEL_NAME, + } + ) + + # Check if running the Experiment with the Comet Optimizer + if hasattr(self.opt, "comet_optimizer_id"): + self.experiment.log_other("optimizer_id", self.opt.comet_optimizer_id) + self.experiment.log_other("optimizer_objective", self.opt.comet_optimizer_objective) + self.experiment.log_other("optimizer_metric", self.opt.comet_optimizer_metric) + self.experiment.log_other("optimizer_parameters", json.dumps(self.hyp)) + + def _get_experiment(self, mode, experiment_id=None): + """Returns a new or existing Comet.ml experiment based on mode and optional experiment_id.""" + if mode == "offline": + return ( + comet_ml.ExistingOfflineExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + if experiment_id is not None + else comet_ml.OfflineExperiment( + **self.default_experiment_kwargs, + ) + ) + try: + if experiment_id is not None: + return comet_ml.ExistingExperiment( + previous_experiment=experiment_id, + **self.default_experiment_kwargs, + ) + + return comet_ml.Experiment(**self.default_experiment_kwargs) + + except ValueError: + logger.warning( + "COMET WARNING: " + "Comet credentials have not been set. " + "Comet will default to offline logging. " + "Please set your credentials to enable online logging." + ) + return self._get_experiment("offline", experiment_id) + + return + + def log_metrics(self, log_dict, **kwargs): + """Logs metrics to the current experiment, accepting a dictionary of metric names and values.""" + self.experiment.log_metrics(log_dict, **kwargs) + + def log_parameters(self, log_dict, **kwargs): + """Logs parameters to the current experiment, accepting a dictionary of parameter names and values.""" + self.experiment.log_parameters(log_dict, **kwargs) + + def log_asset(self, asset_path, **kwargs): + """Logs a file or directory as an asset to the current experiment.""" + self.experiment.log_asset(asset_path, **kwargs) + + def log_asset_data(self, asset, **kwargs): + """Logs in-memory data as an asset to the current experiment, with optional kwargs.""" + self.experiment.log_asset_data(asset, **kwargs) + + def log_image(self, img, **kwargs): + """Logs an image to the current experiment with optional kwargs.""" + self.experiment.log_image(img, **kwargs) + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + """Logs model checkpoint to experiment with path, options, epoch, fitness, and best model flag.""" + if not self.save_model: + return + + model_metadata = { + "fitness_score": fitness_score[-1], + "epochs_trained": epoch + 1, + "save_period": opt.save_period, + "total_epochs": opt.epochs, + } + + model_files = glob.glob(f"{path}/*.pt") + for model_path in model_files: + name = Path(model_path).name + + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + metadata=model_metadata, + overwrite=True, + ) + + def check_dataset(self, data_file): + """Validates the dataset configuration by loading the YAML file specified in `data_file`.""" + with open(data_file) as f: + data_config = yaml.safe_load(f) + + path = data_config.get("path") + if path and path.startswith(COMET_PREFIX): + path = data_config["path"].replace(COMET_PREFIX, "") + return self.download_dataset_artifact(path) + self.log_asset(self.opt.data, metadata={"type": "data-config-file"}) + + return check_dataset(data_file) + + def log_predictions(self, image, labelsn, path, shape, predn): + """Logs predictions with IOU filtering, given image, labels, path, shape, and predictions.""" + if self.logged_images_count >= self.max_images: + return + detections = predn[predn[:, 4] > self.conf_thres] + iou = box_iou(labelsn[:, 1:], detections[:, :4]) + mask, _ = torch.where(iou > self.iou_thres) + if len(mask) == 0: + return + + filtered_detections = detections[mask] + filtered_labels = labelsn[mask] + + image_id = path.split("/")[-1].split(".")[0] + image_name = f"{image_id}_curr_epoch_{self.experiment.curr_epoch}" + if image_name not in self.logged_image_names: + native_scale_image = PIL.Image.open(path) + self.log_image(native_scale_image, name=image_name) + self.logged_image_names.append(image_name) + + metadata = [ + { + "label": f"{self.class_names[int(cls)]}-gt", + "score": 100, + "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, + } + for cls, *xyxy in filtered_labels.tolist() + ] + metadata.extend( + { + "label": f"{self.class_names[int(cls)]}", + "score": conf * 100, + "box": {"x": xyxy[0], "y": xyxy[1], "x2": xyxy[2], "y2": xyxy[3]}, + } + for *xyxy, conf, cls in filtered_detections.tolist() + ) + self.metadata_dict[image_name] = metadata + self.logged_images_count += 1 + + return + + def preprocess_prediction(self, image, labels, shape, pred): + """Processes prediction data, resizing labels and adding dataset metadata.""" + nl, _ = labels.shape[0], pred.shape[0] + + # Predictions + if self.opt.single_cls: + pred[:, 5] = 0 + + predn = pred.clone() + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) + + labelsn = None + if nl: + tbox = xywh2xyxy(labels[:, 1:5]) # target boxes + scale_boxes(image.shape[1:], tbox, shape[0], shape[1]) # native-space labels + labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels + scale_boxes(image.shape[1:], predn[:, :4], shape[0], shape[1]) # native-space pred + + return predn, labelsn + + def add_assets_to_artifact(self, artifact, path, asset_path, split): + """Adds image and label assets to a wandb artifact given dataset split and paths.""" + img_paths = sorted(glob.glob(f"{asset_path}/*")) + label_paths = img2label_paths(img_paths) + + for image_file, label_file in zip(img_paths, label_paths): + image_logical_path, label_logical_path = map(lambda x: os.path.relpath(x, path), [image_file, label_file]) + + try: + artifact.add( + image_file, + logical_path=image_logical_path, + metadata={"split": split}, + ) + artifact.add( + label_file, + logical_path=label_logical_path, + metadata={"split": split}, + ) + except ValueError as e: + logger.error("COMET ERROR: Error adding file to Artifact. Skipping file.") + logger.error(f"COMET ERROR: {e}") + continue + + return artifact + + def upload_dataset_artifact(self): + """Uploads a YOLOv5 dataset as an artifact to the Comet.ml platform.""" + dataset_name = self.data_dict.get("dataset_name", "yolov5-dataset") + path = str((ROOT / Path(self.data_dict["path"])).resolve()) + + metadata = self.data_dict.copy() + for key in ["train", "val", "test"]: + split_path = metadata.get(key) + if split_path is not None: + metadata[key] = split_path.replace(path, "") + + artifact = comet_ml.Artifact(name=dataset_name, artifact_type="dataset", metadata=metadata) + for key in metadata.keys(): + if key in ["train", "val", "test"]: + if isinstance(self.upload_dataset, str) and (key != self.upload_dataset): + continue + + asset_path = self.data_dict.get(key) + if asset_path is not None: + artifact = self.add_assets_to_artifact(artifact, path, asset_path, key) + + self.experiment.log_artifact(artifact) + + return + + def download_dataset_artifact(self, artifact_path): + """Downloads a dataset artifact to a specified directory using the experiment's logged artifact.""" + logged_artifact = self.experiment.get_artifact(artifact_path) + artifact_save_dir = str(Path(self.opt.save_dir) / logged_artifact.name) + logged_artifact.download(artifact_save_dir) + + metadata = logged_artifact.metadata + data_dict = metadata.copy() + data_dict["path"] = artifact_save_dir + + metadata_names = metadata.get("names") + if isinstance(metadata_names, dict): + data_dict["names"] = {int(k): v for k, v in metadata.get("names").items()} + elif isinstance(metadata_names, list): + data_dict["names"] = {int(k): v for k, v in zip(range(len(metadata_names)), metadata_names)} + else: + raise "Invalid 'names' field in dataset yaml file. Please use a list or dictionary" + + return self.update_data_paths(data_dict) + + def update_data_paths(self, data_dict): + """Updates data paths in the dataset dictionary, defaulting 'path' to an empty string if not present.""" + path = data_dict.get("path", "") + + for split in ["train", "val", "test"]: + if data_dict.get(split): + split_path = data_dict.get(split) + data_dict[split] = ( + f"{path}/{split_path}" if isinstance(split, str) else [f"{path}/{x}" for x in split_path] + ) + + return data_dict + + def on_pretrain_routine_end(self, paths): + """Called at the end of pretraining routine to handle paths if training is not being resumed.""" + if self.opt.resume: + return + + for path in paths: + self.log_asset(str(path)) + + if self.upload_dataset and not self.resume: + self.upload_dataset_artifact() + + return + + def on_train_start(self): + """Logs hyperparameters at the start of training.""" + self.log_parameters(self.hyp) + + def on_train_epoch_start(self): + """Called at the start of each training epoch.""" + return + + def on_train_epoch_end(self, epoch): + """Updates the current epoch in the experiment tracking at the end of each epoch.""" + self.experiment.curr_epoch = epoch + + return + + def on_train_batch_start(self): + """Called at the start of each training batch.""" + return + + def on_train_batch_end(self, log_dict, step): + """Callback function that updates and logs metrics at the end of each training batch if conditions are met.""" + self.experiment.curr_step = step + if self.log_batch_metrics and (step % self.comet_log_batch_interval == 0): + self.log_metrics(log_dict, step=step) + + return + + def on_train_end(self, files, save_dir, last, best, epoch, results): + """Logs metadata and optionally saves model files at the end of training.""" + if self.comet_log_predictions: + curr_epoch = self.experiment.curr_epoch + self.experiment.log_asset_data(self.metadata_dict, "image-metadata.json", epoch=curr_epoch) + + for f in files: + self.log_asset(f, metadata={"epoch": epoch}) + self.log_asset(f"{save_dir}/results.csv", metadata={"epoch": epoch}) + + if not self.opt.evolve: + model_path = str(best if best.exists() else last) + name = Path(model_path).name + if self.save_model: + self.experiment.log_model( + self.model_name, + file_or_folder=model_path, + file_name=name, + overwrite=True, + ) + + # Check if running Experiment with Comet Optimizer + if hasattr(self.opt, "comet_optimizer_id"): + metric = results.get(self.opt.comet_optimizer_metric) + self.experiment.log_other("optimizer_metric_value", metric) + + self.finish_run() + + def on_val_start(self): + """Called at the start of validation, currently a placeholder with no functionality.""" + return + + def on_val_batch_start(self): + """Placeholder called at the start of a validation batch with no current functionality.""" + return + + def on_val_batch_end(self, batch_i, images, targets, paths, shapes, outputs): + """Callback executed at the end of a validation batch, conditionally logs predictions to Comet ML.""" + if not (self.comet_log_predictions and ((batch_i + 1) % self.comet_log_prediction_interval == 0)): + return + + for si, pred in enumerate(outputs): + if len(pred) == 0: + continue + + image = images[si] + labels = targets[targets[:, 0] == si, 1:] + shape = shapes[si] + path = paths[si] + predn, labelsn = self.preprocess_prediction(image, labels, shape, pred) + if labelsn is not None: + self.log_predictions(image, labelsn, path, shape, predn) + + return + + def on_val_end(self, nt, tp, fp, p, r, f1, ap, ap50, ap_class, confusion_matrix): + """Logs per-class metrics to Comet.ml after validation if enabled and more than one class exists.""" + if self.comet_log_per_class_metrics and self.num_classes > 1: + for i, c in enumerate(ap_class): + class_name = self.class_names[c] + self.experiment.log_metrics( + { + "mAP@.5": ap50[i], + "mAP@.5:.95": ap[i], + "precision": p[i], + "recall": r[i], + "f1": f1[i], + "true_positives": tp[i], + "false_positives": fp[i], + "support": nt[c], + }, + prefix=class_name, + ) + + if self.comet_log_confusion_matrix: + epoch = self.experiment.curr_epoch + class_names = list(self.class_names.values()) + class_names.append("background") + num_classes = len(class_names) + + self.experiment.log_confusion_matrix( + matrix=confusion_matrix.matrix, + max_categories=num_classes, + labels=class_names, + epoch=epoch, + column_label="Actual Category", + row_label="Predicted Category", + file_name=f"confusion-matrix-epoch-{epoch}.json", + ) + + def on_fit_epoch_end(self, result, epoch): + """Logs metrics at the end of each training epoch.""" + self.log_metrics(result, epoch=epoch) + + def on_model_save(self, last, epoch, final_epoch, best_fitness, fi): + """Callback to save model checkpoints periodically if conditions are met.""" + if ((epoch + 1) % self.opt.save_period == 0 and not final_epoch) and self.opt.save_period != -1: + self.log_model(last.parent, self.opt, epoch, fi, best_model=best_fitness == fi) + + def on_params_update(self, params): + """Logs updated parameters during training.""" + self.log_parameters(params) + + def finish_run(self): + """Ends the current experiment and logs its completion.""" + self.experiment.end() diff --git a/app/util/yolov5/utils/loggers/comet/comet_utils.py b/app/util/yolov5/utils/loggers/comet/comet_utils.py new file mode 100644 index 0000000..1dc572c --- /dev/null +++ b/app/util/yolov5/utils/loggers/comet/comet_utils.py @@ -0,0 +1,151 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +import logging +import os +from urllib.parse import urlparse + +try: + import comet_ml +except ImportError: + comet_ml = None + +import yaml + +logger = logging.getLogger(__name__) + +COMET_PREFIX = "comet://" +COMET_MODEL_NAME = os.getenv("COMET_MODEL_NAME", "yolov5") +COMET_DEFAULT_CHECKPOINT_FILENAME = os.getenv("COMET_DEFAULT_CHECKPOINT_FILENAME", "last.pt") + + +def download_model_checkpoint(opt, experiment): + """Downloads YOLOv5 model checkpoint from Comet ML experiment, updating `opt.weights` with download path.""" + model_dir = f"{opt.project}/{experiment.name}" + os.makedirs(model_dir, exist_ok=True) + + model_name = COMET_MODEL_NAME + model_asset_list = experiment.get_model_asset_list(model_name) + + if len(model_asset_list) == 0: + logger.error(f"COMET ERROR: No checkpoints found for model name : {model_name}") + return + + model_asset_list = sorted( + model_asset_list, + key=lambda x: x["step"], + reverse=True, + ) + logged_checkpoint_map = {asset["fileName"]: asset["assetId"] for asset in model_asset_list} + + resource_url = urlparse(opt.weights) + checkpoint_filename = resource_url.query + + if checkpoint_filename: + asset_id = logged_checkpoint_map.get(checkpoint_filename) + else: + asset_id = logged_checkpoint_map.get(COMET_DEFAULT_CHECKPOINT_FILENAME) + checkpoint_filename = COMET_DEFAULT_CHECKPOINT_FILENAME + + if asset_id is None: + logger.error(f"COMET ERROR: Checkpoint {checkpoint_filename} not found in the given Experiment") + return + + try: + logger.info(f"COMET INFO: Downloading checkpoint {checkpoint_filename}") + asset_filename = checkpoint_filename + + model_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) + model_download_path = f"{model_dir}/{asset_filename}" + with open(model_download_path, "wb") as f: + f.write(model_binary) + + opt.weights = model_download_path + + except Exception as e: + logger.warning("COMET WARNING: Unable to download checkpoint from Comet") + logger.exception(e) + + +def set_opt_parameters(opt, experiment): + """ + Update the opts Namespace with parameters from Comet's ExistingExperiment when resuming a run. + + Args: + opt (argparse.Namespace): Namespace of command line options + experiment (comet_ml.APIExperiment): Comet API Experiment object + """ + asset_list = experiment.get_asset_list() + resume_string = opt.resume + + for asset in asset_list: + if asset["fileName"] == "opt.yaml": + asset_id = asset["assetId"] + asset_binary = experiment.get_asset(asset_id, return_type="binary", stream=False) + opt_dict = yaml.safe_load(asset_binary) + for key, value in opt_dict.items(): + setattr(opt, key, value) + opt.resume = resume_string + + # Save hyperparameters to YAML file + # Necessary to pass checks in training script + save_dir = f"{opt.project}/{experiment.name}" + os.makedirs(save_dir, exist_ok=True) + + hyp_yaml_path = f"{save_dir}/hyp.yaml" + with open(hyp_yaml_path, "w") as f: + yaml.dump(opt.hyp, f) + opt.hyp = hyp_yaml_path + + +def check_comet_weights(opt): + """ + Downloads model weights from Comet and updates the weights path to point to saved weights location. + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if weights are successfully downloaded + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.weights, str) and opt.weights.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.weights) + experiment_path = f"{resource.netloc}{resource.path}" + experiment = api.get(experiment_path) + download_model_checkpoint(opt, experiment) + return True + + return None + + +def check_comet_resume(opt): + """ + Restores run parameters to its original state based on the model checkpoint and logged Experiment parameters. + + Args: + opt (argparse.Namespace): Command Line arguments passed + to YOLOv5 training script + + Returns: + None/bool: Return True if the run is restored successfully + else return None + """ + if comet_ml is None: + return + + if isinstance(opt.resume, str) and opt.resume.startswith(COMET_PREFIX): + api = comet_ml.API() + resource = urlparse(opt.resume) + experiment_path = f"{resource.netloc}{resource.path}" + experiment = api.get(experiment_path) + set_opt_parameters(opt, experiment) + download_model_checkpoint(opt, experiment) + + return True + + return None diff --git a/app/util/yolov5/utils/loggers/comet/hpo.py b/app/util/yolov5/utils/loggers/comet/hpo.py new file mode 100644 index 0000000..dc171e2 --- /dev/null +++ b/app/util/yolov5/utils/loggers/comet/hpo.py @@ -0,0 +1,126 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +import argparse +import json +import logging +import os +import sys +from pathlib import Path + +import comet_ml + +logger = logging.getLogger(__name__) + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH + +from train import train +from utils.callbacks import Callbacks +from utils.general import increment_path +from utils.torch_utils import select_device + +# Project Configuration +config = comet_ml.config.get_config() +COMET_PROJECT_NAME = config.get_string(os.getenv("COMET_PROJECT_NAME"), "comet.project_name", default="yolov5") + + +def get_args(known=False): + """Parses command-line arguments for YOLOv5 training, supporting configuration of weights, data paths, + hyperparameters, and more. + """ + parser = argparse.ArgumentParser() + parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") + parser.add_argument("--cfg", type=str, default="", help="model.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") + parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") + parser.add_argument("--epochs", type=int, default=300, help="total training epochs") + parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") + parser.add_argument("--imgsz", "--img", "--img-size", type=int, default=640, help="train, val image size (pixels)") + parser.add_argument("--rect", action="store_true", help="rectangular training") + parser.add_argument("--resume", nargs="?", const=True, default=False, help="resume most recent training") + parser.add_argument("--nosave", action="store_true", help="only save final checkpoint") + parser.add_argument("--noval", action="store_true", help="only validate final epoch") + parser.add_argument("--noautoanchor", action="store_true", help="disable AutoAnchor") + parser.add_argument("--noplots", action="store_true", help="save no plot files") + parser.add_argument("--evolve", type=int, nargs="?", const=300, help="evolve hyperparameters for x generations") + parser.add_argument("--bucket", type=str, default="", help="gsutil bucket") + parser.add_argument("--cache", type=str, nargs="?", const="ram", help='--cache images in "ram" (default) or "disk"') + parser.add_argument("--image-weights", action="store_true", help="use weighted image selection for training") + parser.add_argument("--device", default="", help="cuda device, i.e. 0 or 0,1,2,3 or cpu") + parser.add_argument("--multi-scale", action="store_true", help="vary img-size +/- 50%%") + parser.add_argument("--single-cls", action="store_true", help="train multi-class data as single-class") + parser.add_argument("--optimizer", type=str, choices=["SGD", "Adam", "AdamW"], default="SGD", help="optimizer") + parser.add_argument("--sync-bn", action="store_true", help="use SyncBatchNorm, only available in DDP mode") + parser.add_argument("--workers", type=int, default=8, help="max dataloader workers (per RANK in DDP mode)") + parser.add_argument("--project", default=ROOT / "runs/train", help="save to project/name") + parser.add_argument("--name", default="exp", help="save to project/name") + parser.add_argument("--exist-ok", action="store_true", help="existing project/name ok, do not increment") + parser.add_argument("--quad", action="store_true", help="quad dataloader") + parser.add_argument("--cos-lr", action="store_true", help="cosine LR scheduler") + parser.add_argument("--label-smoothing", type=float, default=0.0, help="Label smoothing epsilon") + parser.add_argument("--patience", type=int, default=100, help="EarlyStopping patience (epochs without improvement)") + parser.add_argument("--freeze", nargs="+", type=int, default=[0], help="Freeze layers: backbone=10, first3=0 1 2") + parser.add_argument("--save-period", type=int, default=-1, help="Save checkpoint every x epochs (disabled if < 1)") + parser.add_argument("--seed", type=int, default=0, help="Global training seed") + parser.add_argument("--local_rank", type=int, default=-1, help="Automatic DDP Multi-GPU argument, do not modify") + + # Weights & Biases arguments + parser.add_argument("--entity", default=None, help="W&B: Entity") + parser.add_argument("--upload_dataset", nargs="?", const=True, default=False, help='W&B: Upload data, "val" option') + parser.add_argument("--bbox_interval", type=int, default=-1, help="W&B: Set bounding-box image logging interval") + parser.add_argument("--artifact_alias", type=str, default="latest", help="W&B: Version of dataset artifact to use") + + # Comet Arguments + parser.add_argument("--comet_optimizer_config", type=str, help="Comet: Path to a Comet Optimizer Config File.") + parser.add_argument("--comet_optimizer_id", type=str, help="Comet: ID of the Comet Optimizer sweep.") + parser.add_argument("--comet_optimizer_objective", type=str, help="Comet: Set to 'minimize' or 'maximize'.") + parser.add_argument("--comet_optimizer_metric", type=str, help="Comet: Metric to Optimize.") + parser.add_argument( + "--comet_optimizer_workers", + type=int, + default=1, + help="Comet: Number of Parallel Workers to use with the Comet Optimizer.", + ) + + return parser.parse_known_args()[0] if known else parser.parse_args() + + +def run(parameters, opt): + """Executes YOLOv5 training with given hyperparameters and options, setting up device and training directories.""" + hyp_dict = {k: v for k, v in parameters.items() if k not in ["epochs", "batch_size"]} + + opt.save_dir = str(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok or opt.evolve)) + opt.batch_size = parameters.get("batch_size") + opt.epochs = parameters.get("epochs") + + device = select_device(opt.device, batch_size=opt.batch_size) + train(hyp_dict, opt, device, callbacks=Callbacks()) + + +if __name__ == "__main__": + opt = get_args(known=True) + + opt.weights = str(opt.weights) + opt.cfg = str(opt.cfg) + opt.data = str(opt.data) + opt.project = str(opt.project) + + optimizer_id = os.getenv("COMET_OPTIMIZER_ID") + if optimizer_id is None: + with open(opt.comet_optimizer_config) as f: + optimizer_config = json.load(f) + optimizer = comet_ml.Optimizer(optimizer_config) + else: + optimizer = comet_ml.Optimizer(optimizer_id) + + opt.comet_optimizer_id = optimizer.id + status = optimizer.status() + + opt.comet_optimizer_objective = status["spec"]["objective"] + opt.comet_optimizer_metric = status["spec"]["metric"] + + logger.info("COMET INFO: Starting Hyperparameter Sweep") + for parameter in optimizer.get_parameters(): + run(parameter["parameters"], opt) diff --git a/app/util/yolov5/utils/loggers/comet/optimizer_config.json b/app/util/yolov5/utils/loggers/comet/optimizer_config.json new file mode 100644 index 0000000..0218f16 --- /dev/null +++ b/app/util/yolov5/utils/loggers/comet/optimizer_config.json @@ -0,0 +1,135 @@ +{ + "algorithm": "random", + "parameters": { + "anchor_t": { + "type": "discrete", + "values": [2, 8] + }, + "batch_size": { + "type": "discrete", + "values": [16, 32, 64] + }, + "box": { + "type": "discrete", + "values": [0.02, 0.2] + }, + "cls": { + "type": "discrete", + "values": [0.2] + }, + "cls_pw": { + "type": "discrete", + "values": [0.5] + }, + "copy_paste": { + "type": "discrete", + "values": [1] + }, + "degrees": { + "type": "discrete", + "values": [0, 45] + }, + "epochs": { + "type": "discrete", + "values": [5] + }, + "fl_gamma": { + "type": "discrete", + "values": [0] + }, + "fliplr": { + "type": "discrete", + "values": [0] + }, + "flipud": { + "type": "discrete", + "values": [0] + }, + "hsv_h": { + "type": "discrete", + "values": [0] + }, + "hsv_s": { + "type": "discrete", + "values": [0] + }, + "hsv_v": { + "type": "discrete", + "values": [0] + }, + "iou_t": { + "type": "discrete", + "values": [0.7] + }, + "lr0": { + "type": "discrete", + "values": [1e-5, 0.1] + }, + "lrf": { + "type": "discrete", + "values": [0.01, 1] + }, + "mixup": { + "type": "discrete", + "values": [1] + }, + "momentum": { + "type": "discrete", + "values": [0.6] + }, + "mosaic": { + "type": "discrete", + "values": [0] + }, + "obj": { + "type": "discrete", + "values": [0.2] + }, + "obj_pw": { + "type": "discrete", + "values": [0.5] + }, + "optimizer": { + "type": "categorical", + "values": ["SGD", "Adam", "AdamW"] + }, + "perspective": { + "type": "discrete", + "values": [0] + }, + "scale": { + "type": "discrete", + "values": [0] + }, + "shear": { + "type": "discrete", + "values": [0] + }, + "translate": { + "type": "discrete", + "values": [0] + }, + "warmup_bias_lr": { + "type": "discrete", + "values": [0, 0.2] + }, + "warmup_epochs": { + "type": "discrete", + "values": [5] + }, + "warmup_momentum": { + "type": "discrete", + "values": [0, 0.95] + }, + "weight_decay": { + "type": "discrete", + "values": [0, 0.001] + } + }, + "spec": { + "maxCombo": 0, + "metric": "metrics/mAP_0.5", + "objective": "maximize" + }, + "trials": 1 +} diff --git a/app/util/yolov5/utils/loggers/wandb/__init__.py b/app/util/yolov5/utils/loggers/wandb/__init__.py new file mode 100644 index 0000000..77a19dc --- /dev/null +++ b/app/util/yolov5/utils/loggers/wandb/__init__.py @@ -0,0 +1 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license diff --git a/app/util/yolov5/utils/loggers/wandb/wandb_utils.py b/app/util/yolov5/utils/loggers/wandb/wandb_utils.py new file mode 100644 index 0000000..83c5ee1 --- /dev/null +++ b/app/util/yolov5/utils/loggers/wandb/wandb_utils.py @@ -0,0 +1,210 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +# WARNING ⚠️ wandb is deprecated and will be removed in future release. +# See supported integrations at https://github.com/ultralytics/yolov5#integrations + +import logging +import os +import sys +from contextlib import contextmanager +from pathlib import Path + +from utils.general import LOGGER, colorstr + +FILE = Path(__file__).resolve() +ROOT = FILE.parents[3] # YOLOv5 root directory +if str(ROOT) not in sys.path: + sys.path.append(str(ROOT)) # add ROOT to PATH +RANK = int(os.getenv("RANK", -1)) +DEPRECATION_WARNING = ( + f"{colorstr('wandb')}: WARNING ⚠️ wandb is deprecated and will be removed in a future release. " + f"See supported integrations at https://github.com/ultralytics/yolov5#integrations." +) + +try: + import wandb + + assert hasattr(wandb, "__version__") # verify package import not local dir + LOGGER.warning(DEPRECATION_WARNING) +except (ImportError, AssertionError): + wandb = None + + +class WandbLogger: + """ + Log training runs, datasets, models, and predictions to Weights & Biases. + + This logger sends information to W&B at wandb.ai. By default, this information includes hyperparameters, system + configuration and metrics, model metrics, and basic data metrics and analyses. + + By providing additional command line arguments to train.py, datasets, models and predictions can also be logged. + + For more on how this logger is used, see the Weights & Biases documentation: + https://docs.wandb.com/guides/integrations/yolov5 + """ + + def __init__(self, opt, run_id=None, job_type="Training"): + """ + - Initialize WandbLogger instance + - Upload dataset if opt.upload_dataset is True + - Setup training processes if job_type is 'Training'. + + Arguments: + opt (namespace) -- Commandline arguments for this run + run_id (str) -- Run ID of W&B run to be resumed + job_type (str) -- To set the job_type for this run + + """ + # Pre-training routine -- + self.job_type = job_type + self.wandb, self.wandb_run = wandb, wandb.run if wandb else None + self.val_artifact, self.train_artifact = None, None + self.train_artifact_path, self.val_artifact_path = None, None + self.result_artifact = None + self.val_table, self.result_table = None, None + self.max_imgs_to_log = 16 + self.data_dict = None + if self.wandb: + self.wandb_run = wandb.run or wandb.init( + config=opt, + resume="allow", + project="YOLOv5" if opt.project == "runs/train" else Path(opt.project).stem, + entity=opt.entity, + name=opt.name if opt.name != "exp" else None, + job_type=job_type, + id=run_id, + allow_val_change=True, + ) + + if self.wandb_run and self.job_type == "Training": + if isinstance(opt.data, dict): + # This means another dataset manager has already processed the dataset info (e.g. ClearML) + # and they will have stored the already processed dict in opt.data + self.data_dict = opt.data + self.setup_training(opt) + + def setup_training(self, opt): + """ + Setup the necessary processes for training YOLO models: + - Attempt to download model checkpoint and dataset artifacts if opt.resume stats with WANDB_ARTIFACT_PREFIX + - Update data_dict, to contain info of previous run if resumed and the paths of dataset artifact if downloaded + - Setup log_dict, initialize bbox_interval. + + Arguments: + opt (namespace) -- commandline arguments for this run + + """ + self.log_dict, self.current_epoch = {}, 0 + self.bbox_interval = opt.bbox_interval + if isinstance(opt.resume, str): + model_dir, _ = self.download_model_artifact(opt) + if model_dir: + self.weights = Path(model_dir) / "last.pt" + config = self.wandb_run.config + opt.weights, opt.save_period, opt.batch_size, opt.bbox_interval, opt.epochs, opt.hyp, opt.imgsz = ( + str(self.weights), + config.save_period, + config.batch_size, + config.bbox_interval, + config.epochs, + config.hyp, + config.imgsz, + ) + + if opt.bbox_interval == -1: + self.bbox_interval = opt.bbox_interval = (opt.epochs // 10) if opt.epochs > 10 else 1 + if opt.evolve or opt.noplots: + self.bbox_interval = opt.bbox_interval = opt.epochs + 1 # disable bbox_interval + + def log_model(self, path, opt, epoch, fitness_score, best_model=False): + """ + Log the model checkpoint as W&B artifact. + + Arguments: + path (Path) -- Path of directory containing the checkpoints + opt (namespace) -- Command line arguments for this run + epoch (int) -- Current epoch number + fitness_score (float) -- fitness score for current epoch + best_model (boolean) -- Boolean representing if the current checkpoint is the best yet. + """ + model_artifact = wandb.Artifact( + f"run_{wandb.run.id}_model", + type="model", + metadata={ + "original_url": str(path), + "epochs_trained": epoch + 1, + "save period": opt.save_period, + "project": opt.project, + "total_epochs": opt.epochs, + "fitness_score": fitness_score, + }, + ) + model_artifact.add_file(str(path / "last.pt"), name="last.pt") + wandb.log_artifact( + model_artifact, + aliases=[ + "latest", + "last", + f"epoch {str(self.current_epoch)}", + "best" if best_model else "", + ], + ) + LOGGER.info(f"Saving model artifact on epoch {epoch + 1}") + + def val_one_image(self, pred, predn, path, names, im): + """Evaluates model prediction for a single image, returning metrics and visualizations.""" + pass + + def log(self, log_dict): + """ + Save the metrics to the logging dictionary. + + Arguments: + log_dict (Dict) -- metrics/media to be logged in current step + """ + if self.wandb_run: + for key, value in log_dict.items(): + self.log_dict[key] = value + + def end_epoch(self): + """ + Commit the log_dict, model artifacts and Tables to W&B and flush the log_dict. + + Arguments: + best_result (boolean): Boolean representing if the result of this evaluation is best or not + """ + if self.wandb_run: + with all_logging_disabled(): + try: + wandb.log(self.log_dict) + except BaseException as e: + LOGGER.info( + f"An error occurred in wandb logger. The training will proceed without interruption. More info\n{e}" + ) + self.wandb_run.finish() + self.wandb_run = None + self.log_dict = {} + + def finish_run(self): + """Log metrics if any and finish the current W&B run.""" + if self.wandb_run: + if self.log_dict: + with all_logging_disabled(): + wandb.log(self.log_dict) + wandb.run.finish() + LOGGER.warning(DEPRECATION_WARNING) + + +@contextmanager +def all_logging_disabled(highest_level=logging.CRITICAL): + """Source - https://gist.github.com/simon-weber/7853144 + A context manager that will prevent any logging messages triggered during the body from being processed. + :param highest_level: the maximum logging level in use. + This would only need to be changed if a custom level greater than CRITICAL is defined. + """ + previous_level = logging.root.manager.disable + logging.disable(highest_level) + try: + yield + finally: + logging.disable(previous_level) diff --git a/app/util/yolov5/utils/loss.py b/app/util/yolov5/utils/loss.py new file mode 100644 index 0000000..6525acd --- /dev/null +++ b/app/util/yolov5/utils/loss.py @@ -0,0 +1,254 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Loss functions.""" + +import torch +import torch.nn as nn + +from app.util.yolov5.utils.metrics import bbox_iou +from app.util.yolov5.utils.torch_utils import de_parallel + + +def smooth_BCE(eps=0.1): + """Returns label smoothing BCE targets for reducing overfitting; pos: `1.0 - 0.5*eps`, neg: `0.5*eps`. For details see https://github.com/ultralytics/yolov3/issues/238#issuecomment-598028441.""" + return 1.0 - 0.5 * eps, 0.5 * eps + + +class BCEBlurWithLogitsLoss(nn.Module): + """Modified BCEWithLogitsLoss to reduce missing label effects in YOLOv5 training with optional alpha smoothing.""" + + def __init__(self, alpha=0.05): + """Initializes a modified BCEWithLogitsLoss with reduced missing label effects, taking optional alpha smoothing + parameter. + """ + super().__init__() + self.loss_fcn = nn.BCEWithLogitsLoss(reduction="none") # must be nn.BCEWithLogitsLoss() + self.alpha = alpha + + def forward(self, pred, true): + """Computes modified BCE loss for YOLOv5 with reduced missing label effects, taking pred and true tensors, + returns mean loss. + """ + loss = self.loss_fcn(pred, true) + pred = torch.sigmoid(pred) # prob from logits + dx = pred - true # reduce only missing label effects + # dx = (pred - true).abs() # reduce missing label and false label effects + alpha_factor = 1 - torch.exp((dx - 1) / (self.alpha + 1e-4)) + loss *= alpha_factor + return loss.mean() + + +class FocalLoss(nn.Module): + """Applies focal loss to address class imbalance by modifying BCEWithLogitsLoss with gamma and alpha parameters.""" + + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + """Initializes FocalLoss with specified loss function, gamma, and alpha values; modifies loss reduction to + 'none'. + """ + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = "none" # required to apply FL to each element + + def forward(self, pred, true): + """Calculates the focal loss between predicted and true labels using a modified BCEWithLogitsLoss.""" + loss = self.loss_fcn(pred, true) + # p_t = torch.exp(-loss) + # loss *= self.alpha * (1.000001 - p_t) ** self.gamma # non-zero power for gradient stability + + # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py + pred_prob = torch.sigmoid(pred) # prob from logits + p_t = true * pred_prob + (1 - true) * (1 - pred_prob) + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = (1.0 - p_t) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == "mean": + return loss.mean() + elif self.reduction == "sum": + return loss.sum() + else: # 'none' + return loss + + +class QFocalLoss(nn.Module): + """Implements Quality Focal Loss to address class imbalance by modulating loss based on prediction confidence.""" + + def __init__(self, loss_fcn, gamma=1.5, alpha=0.25): + """Initializes Quality Focal Loss with given loss function, gamma, alpha; modifies reduction to 'none'.""" + super().__init__() + self.loss_fcn = loss_fcn # must be nn.BCEWithLogitsLoss() + self.gamma = gamma + self.alpha = alpha + self.reduction = loss_fcn.reduction + self.loss_fcn.reduction = "none" # required to apply FL to each element + + def forward(self, pred, true): + """Computes the focal loss between `pred` and `true` using BCEWithLogitsLoss, adjusting for imbalance with + `gamma` and `alpha`. + """ + loss = self.loss_fcn(pred, true) + + pred_prob = torch.sigmoid(pred) # prob from logits + alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha) + modulating_factor = torch.abs(true - pred_prob) ** self.gamma + loss *= alpha_factor * modulating_factor + + if self.reduction == "mean": + return loss.mean() + elif self.reduction == "sum": + return loss.sum() + else: # 'none' + return loss + + +class ComputeLoss: + """Computes the total loss for YOLOv5 model predictions, including classification, box, and objectness losses.""" + + sort_obj_iou = False + + # Compute losses + def __init__(self, model, autobalance=False): + """Initializes ComputeLoss with model and autobalance option, autobalances losses if True.""" + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.anchors = m.anchors + self.device = device + + def __call__(self, p, targets): # predictions, targets + """Performs forward pass, calculating class, box, and object loss for given predictions and targets.""" + lcls = torch.zeros(1, device=self.device) # class loss + lbox = torch.zeros(1, device=self.device) # box loss + lobj = torch.zeros(1, device=self.device) # object loss + tcls, tbox, indices, anchors = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + if n := b.shape[0]: + # pxy, pwh, _, pcls = pi[b, a, gj, gi].tensor_split((2, 4, 5), dim=1) # faster, requires torch 1.8.0 + pxy, pwh, _, pcls = pi[b, a, gj, gi].split((2, 2, 1, self.nc), 1) # target-subset of predictions + + # Regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + bs = tobj.shape[0] # batch size + + return (lbox + lobj + lcls) * bs, torch.cat((lbox, lobj, lcls)).detach() + + def build_targets(self, p, targets): + """Prepares model targets from input targets (image,class,x,y,w,h) for loss computation, returning class, box, + indices, and anchors. + """ + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch = [], [], [], [] + gain = torch.ones(7, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, a = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + a, (b, c) = a.long().view(-1), bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + + return tcls, tbox, indices, anch diff --git a/app/util/yolov5/utils/metrics.py b/app/util/yolov5/utils/metrics.py new file mode 100644 index 0000000..5dc462b --- /dev/null +++ b/app/util/yolov5/utils/metrics.py @@ -0,0 +1,381 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Model validation metrics.""" + +import math +import warnings +from pathlib import Path + +import matplotlib.pyplot as plt +import numpy as np +import torch + +from app.util.yolov5.utils import TryExcept, threaded + + +def fitness(x): + """Calculates fitness of a model using weighted sum of metrics P, R, mAP@0.5, mAP@0.5:0.95.""" + w = [0.0, 0.0, 0.1, 0.9] # weights for [P, R, mAP@0.5, mAP@0.5:0.95] + return (x[:, :4] * w).sum(1) + + +def smooth(y, f=0.05): + """Applies box filter smoothing to array `y` with fraction `f`, yielding a smoothed array.""" + nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd) + p = np.ones(nf // 2) # ones padding + yp = np.concatenate((p * y[0], y, p * y[-1]), 0) # y padded + return np.convolve(yp, np.ones(nf) / nf, mode="valid") # y-smoothed + + +def ap_per_class(tp, conf, pred_cls, target_cls, plot=False, save_dir=".", names=(), eps=1e-16, prefix=""): + """ + Compute the average precision, given the recall and precision curves. + + Source: https://github.com/rafaelpadilla/Object-Detection-Metrics. + # Arguments + tp: True positives (nparray, nx1 or nx10). + conf: Objectness value from 0-1 (nparray). + pred_cls: Predicted object classes (nparray). + target_cls: True object classes (nparray). + plot: Plot precision-recall curve at mAP@0.5 + save_dir: Plot save directory + # Returns + The average precision as computed in py-faster-rcnn. + """ + # Sort by objectness + i = np.argsort(-conf) + tp, conf, pred_cls = tp[i], conf[i], pred_cls[i] + + # Find unique classes + unique_classes, nt = np.unique(target_cls, return_counts=True) + nc = unique_classes.shape[0] # number of classes, number of detections + + # Create Precision-Recall curve and compute AP for each class + px, py = np.linspace(0, 1, 1000), [] # for plotting + ap, p, r = np.zeros((nc, tp.shape[1])), np.zeros((nc, 1000)), np.zeros((nc, 1000)) + for ci, c in enumerate(unique_classes): + i = pred_cls == c + n_l = nt[ci] # number of labels + n_p = i.sum() # number of predictions + if n_p == 0 or n_l == 0: + continue + + # Accumulate FPs and TPs + fpc = (1 - tp[i]).cumsum(0) + tpc = tp[i].cumsum(0) + + # Recall + recall = tpc / (n_l + eps) # recall curve + r[ci] = np.interp(-px, -conf[i], recall[:, 0], left=0) # negative x, xp because xp decreases + + # Precision + precision = tpc / (tpc + fpc) # precision curve + p[ci] = np.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score + + # AP from recall-precision curve + for j in range(tp.shape[1]): + ap[ci, j], mpre, mrec = compute_ap(recall[:, j], precision[:, j]) + if plot and j == 0: + py.append(np.interp(px, mrec, mpre)) # precision at mAP@0.5 + + # Compute F1 (harmonic mean of precision and recall) + f1 = 2 * p * r / (p + r + eps) + names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data + names = dict(enumerate(names)) # to dict + if plot: + plot_pr_curve(px, py, ap, Path(save_dir) / f"{prefix}PR_curve.png", names) + plot_mc_curve(px, f1, Path(save_dir) / f"{prefix}F1_curve.png", names, ylabel="F1") + plot_mc_curve(px, p, Path(save_dir) / f"{prefix}P_curve.png", names, ylabel="Precision") + plot_mc_curve(px, r, Path(save_dir) / f"{prefix}R_curve.png", names, ylabel="Recall") + + i = smooth(f1.mean(0), 0.1).argmax() # max F1 index + p, r, f1 = p[:, i], r[:, i], f1[:, i] + tp = (r * nt).round() # true positives + fp = (tp / (p + eps) - tp).round() # false positives + return tp, fp, p, r, f1, ap, unique_classes.astype(int) + + +def compute_ap(recall, precision): + """Compute the average precision, given the recall and precision curves + # Arguments + recall: The recall curve (list) + precision: The precision curve (list) + # Returns + Average precision, precision curve, recall curve. + """ + # Append sentinel values to beginning and end + mrec = np.concatenate(([0.0], recall, [1.0])) + mpre = np.concatenate(([1.0], precision, [0.0])) + + # Compute the precision envelope + mpre = np.flip(np.maximum.accumulate(np.flip(mpre))) + + # Integrate area under curve + method = "interp" # methods: 'continuous', 'interp' + if method == "interp": + x = np.linspace(0, 1, 101) # 101-point interp (COCO) + ap = np.trapz(np.interp(x, mrec, mpre), x) # integrate + else: # 'continuous' + i = np.where(mrec[1:] != mrec[:-1])[0] # points where x axis (recall) changes + ap = np.sum((mrec[i + 1] - mrec[i]) * mpre[i + 1]) # area under curve + + return ap, mpre, mrec + + +class ConfusionMatrix: + """Generates and visualizes a confusion matrix for evaluating object detection classification performance.""" + + def __init__(self, nc, conf=0.25, iou_thres=0.45): + """Initializes ConfusionMatrix with given number of classes, confidence, and IoU threshold.""" + self.matrix = np.zeros((nc + 1, nc + 1)) + self.nc = nc # number of classes + self.conf = conf + self.iou_thres = iou_thres + + def process_batch(self, detections, labels): + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + + Arguments: + detections (Array[N, 6]), x1, y1, x2, y2, conf, class + labels (Array[M, 5]), class, x1, y1, x2, y2 + Returns: + None, updates confusion matrix accordingly + """ + if detections is None: + gt_classes = labels.int() + for gc in gt_classes: + self.matrix[self.nc, gc] += 1 # background FN + return + + detections = detections[detections[:, 4] > self.conf] + gt_classes = labels[:, 0].int() + detection_classes = detections[:, 5].int() + iou = box_iou(labels[:, 1:], detections[:, :4]) + + x = torch.where(iou > self.iou_thres) + if x[0].shape[0]: + matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() + if x[0].shape[0] > 1: + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 1], return_index=True)[1]] + matches = matches[matches[:, 2].argsort()[::-1]] + matches = matches[np.unique(matches[:, 0], return_index=True)[1]] + else: + matches = np.zeros((0, 3)) + + n = matches.shape[0] > 0 + m0, m1, _ = matches.transpose().astype(int) + for i, gc in enumerate(gt_classes): + j = m0 == i + if n and sum(j) == 1: + self.matrix[detection_classes[m1[j]], gc] += 1 # correct + else: + self.matrix[self.nc, gc] += 1 # true background + + if n: + for i, dc in enumerate(detection_classes): + if not any(m1 == i): + self.matrix[dc, self.nc] += 1 # predicted background + + def tp_fp(self): + """Calculates true positives (tp) and false positives (fp) excluding the background class from the confusion + matrix. + """ + tp = self.matrix.diagonal() # true positives + fp = self.matrix.sum(1) - tp # false positives + # fn = self.matrix.sum(0) - tp # false negatives (missed detections) + return tp[:-1], fp[:-1] # remove background class + + @TryExcept("WARNING ⚠️ ConfusionMatrix plot failure") + def plot(self, normalize=True, save_dir="", names=()): + """Plots confusion matrix using seaborn, optional normalization; can save plot to specified directory.""" + import seaborn as sn + + array = self.matrix / ((self.matrix.sum(0).reshape(1, -1) + 1e-9) if normalize else 1) # normalize columns + array[array < 0.005] = np.nan # don't annotate (would appear as 0.00) + + fig, ax = plt.subplots(1, 1, figsize=(12, 9), tight_layout=True) + nc, nn = self.nc, len(names) # number of classes, names + sn.set(font_scale=1.0 if nc < 50 else 0.8) # for label size + labels = (0 < nn < 99) and (nn == nc) # apply names to ticklabels + ticklabels = (names + ["background"]) if labels else "auto" + with warnings.catch_warnings(): + warnings.simplefilter("ignore") # suppress empty matrix RuntimeWarning: All-NaN slice encountered + sn.heatmap( + array, + ax=ax, + annot=nc < 30, + annot_kws={"size": 8}, + cmap="Blues", + fmt=".2f", + square=True, + vmin=0.0, + xticklabels=ticklabels, + yticklabels=ticklabels, + ).set_facecolor((1, 1, 1)) + ax.set_xlabel("True") + ax.set_ylabel("Predicted") + ax.set_title("Confusion Matrix") + fig.savefig(Path(save_dir) / "confusion_matrix.png", dpi=250) + plt.close(fig) + + def print(self): + """Prints the confusion matrix row-wise, with each class and its predictions separated by spaces.""" + for i in range(self.nc + 1): + print(" ".join(map(str, self.matrix[i]))) + + +def bbox_iou(box1, box2, xywh=True, GIoU=False, DIoU=False, CIoU=False, eps=1e-7): + """ + Calculates IoU, GIoU, DIoU, or CIoU between two boxes, supporting xywh/xyxy formats. + + Input shapes are box1(1,4) to box2(n,4). + """ + # Get the coordinates of bounding boxes + if xywh: # transform from xywh to xyxy + (x1, y1, w1, h1), (x2, y2, w2, h2) = box1.chunk(4, -1), box2.chunk(4, -1) + w1_, h1_, w2_, h2_ = w1 / 2, h1 / 2, w2 / 2, h2 / 2 + b1_x1, b1_x2, b1_y1, b1_y2 = x1 - w1_, x1 + w1_, y1 - h1_, y1 + h1_ + b2_x1, b2_x2, b2_y1, b2_y2 = x2 - w2_, x2 + w2_, y2 - h2_, y2 + h2_ + else: # x1, y1, x2, y2 = box1 + b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1) + b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1) + w1, h1 = b1_x2 - b1_x1, (b1_y2 - b1_y1).clamp(eps) + w2, h2 = b2_x2 - b2_x1, (b2_y2 - b2_y1).clamp(eps) + + # Intersection area + inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * ( + b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1) + ).clamp(0) + + # Union Area + union = w1 * h1 + w2 * h2 - inter + eps + + # IoU + iou = inter / union + if CIoU or DIoU or GIoU: + cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width + ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height + if CIoU or DIoU: # Distance or Complete IoU https://arxiv.org/abs/1911.08287v1 + c2 = cw**2 + ch**2 + eps # convex diagonal squared + rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2 + if CIoU: # https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47 + v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2) + with torch.no_grad(): + alpha = v / (v - iou + (1 + eps)) + return iou - (rho2 / c2 + v * alpha) # CIoU + return iou - rho2 / c2 # DIoU + c_area = cw * ch + eps # convex area + return iou - (c_area - union) / c_area # GIoU https://arxiv.org/pdf/1902.09630.pdf + return iou # IoU + + +def box_iou(box1, box2, eps=1e-7): + # https://github.com/pytorch/vision/blob/master/torchvision/ops/boxes.py + """ + Return intersection-over-union (Jaccard index) of boxes. + + Both sets of boxes are expected to be in (x1, y1, x2, y2) format. + + Arguments: + box1 (Tensor[N, 4]) + box2 (Tensor[M, 4]) + + Returns: + iou (Tensor[N, M]): the NxM matrix containing the pairwise + IoU values for every element in boxes1 and boxes2 + """ + # inter(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2) + (a1, a2), (b1, b2) = box1.unsqueeze(1).chunk(2, 2), box2.unsqueeze(0).chunk(2, 2) + inter = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2) + + # IoU = inter / (area1 + area2 - inter) + return inter / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - inter + eps) + + +def bbox_ioa(box1, box2, eps=1e-7): + """ + Returns the intersection over box2 area given box1, box2. + + Boxes are x1y1x2y2 + box1: np.array of shape(4) + box2: np.array of shape(nx4) + returns: np.array of shape(n) + """ + # Get the coordinates of bounding boxes + b1_x1, b1_y1, b1_x2, b1_y2 = box1 + b2_x1, b2_y1, b2_x2, b2_y2 = box2.T + + # Intersection area + inter_area = (np.minimum(b1_x2, b2_x2) - np.maximum(b1_x1, b2_x1)).clip(0) * ( + np.minimum(b1_y2, b2_y2) - np.maximum(b1_y1, b2_y1) + ).clip(0) + + # box2 area + box2_area = (b2_x2 - b2_x1) * (b2_y2 - b2_y1) + eps + + # Intersection over box2 area + return inter_area / box2_area + + +def wh_iou(wh1, wh2, eps=1e-7): + """Calculates the Intersection over Union (IoU) for two sets of widths and heights; `wh1` and `wh2` should be nx2 + and mx2 tensors. + """ + wh1 = wh1[:, None] # [N,1,2] + wh2 = wh2[None] # [1,M,2] + inter = torch.min(wh1, wh2).prod(2) # [N,M] + return inter / (wh1.prod(2) + wh2.prod(2) - inter + eps) # iou = inter / (area1 + area2 - inter) + + +# Plots ---------------------------------------------------------------------------------------------------------------- + + +@threaded +def plot_pr_curve(px, py, ap, save_dir=Path("pr_curve.png"), names=()): + """Plots precision-recall curve, optionally per class, saving to `save_dir`; `px`, `py` are lists, `ap` is Nx2 + array, `names` optional. + """ + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + py = np.stack(py, axis=1) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py.T): + ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision) + else: + ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision) + + ax.plot(px, py.mean(1), linewidth=3, color="blue", label=f"all classes {ap[:, 0].mean():.3f} mAP@0.5") + ax.set_xlabel("Recall") + ax.set_ylabel("Precision") + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title("Precision-Recall Curve") + fig.savefig(save_dir, dpi=250) + plt.close(fig) + + +@threaded +def plot_mc_curve(px, py, save_dir=Path("mc_curve.png"), names=(), xlabel="Confidence", ylabel="Metric"): + """Plots a metric-confidence curve for model predictions, supporting per-class visualization and smoothing.""" + fig, ax = plt.subplots(1, 1, figsize=(9, 6), tight_layout=True) + + if 0 < len(names) < 21: # display per-class legend if < 21 classes + for i, y in enumerate(py): + ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric) + else: + ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric) + + y = smooth(py.mean(0), 0.05) + ax.plot(px, y, linewidth=3, color="blue", label=f"all classes {y.max():.2f} at {px[y.argmax()]:.3f}") + ax.set_xlabel(xlabel) + ax.set_ylabel(ylabel) + ax.set_xlim(0, 1) + ax.set_ylim(0, 1) + ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left") + ax.set_title(f"{ylabel}-Confidence Curve") + fig.savefig(save_dir, dpi=250) + plt.close(fig) diff --git a/app/util/yolov5/utils/plots.py b/app/util/yolov5/utils/plots.py new file mode 100644 index 0000000..e166f14 --- /dev/null +++ b/app/util/yolov5/utils/plots.py @@ -0,0 +1,517 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Plotting utils.""" + +import contextlib +import math +import os +from copy import copy +from pathlib import Path + +import cv2 +import matplotlib +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import seaborn as sn +import torch +from PIL import Image, ImageDraw +from scipy.ndimage.filters import gaussian_filter1d +from ultralytics.utils.plotting import Annotator + +from app.util.yolov5.utils import TryExcept, threaded +from app.util.yolov5.utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh +from app.util.yolov5.utils.metrics import fitness + +# Settings +RANK = int(os.getenv("RANK", -1)) +matplotlib.rc("font", **{"size": 11}) +matplotlib.use("Agg") # for writing to files only + + +class Colors: + """Provides an RGB color palette derived from Ultralytics color scheme for visualization tasks.""" + + def __init__(self): + """ + Initializes the Colors class with a palette derived from Ultralytics color scheme, converting hex codes to RGB. + + Colors derived from `hex = matplotlib.colors.TABLEAU_COLORS.values()`. + """ + hexs = ( + "FF3838", + "FF9D97", + "FF701F", + "FFB21D", + "CFD231", + "48F90A", + "92CC17", + "3DDB86", + "1A9334", + "00D4BB", + "2C99A8", + "00C2FF", + "344593", + "6473FF", + "0018EC", + "8438FF", + "520085", + "CB38FF", + "FF95C8", + "FF37C7", + ) + self.palette = [self.hex2rgb(f"#{c}") for c in hexs] + self.n = len(self.palette) + + def __call__(self, i, bgr=False): + """Returns color from palette by index `i`, in BGR format if `bgr=True`, else RGB; `i` is an integer index.""" + c = self.palette[int(i) % self.n] + return (c[2], c[1], c[0]) if bgr else c + + @staticmethod + def hex2rgb(h): + """Converts hexadecimal color `h` to an RGB tuple (PIL-compatible) with order (R, G, B).""" + return tuple(int(h[1 + i : 1 + i + 2], 16) for i in (0, 2, 4)) + + +colors = Colors() # create instance for 'from utils.plots import colors' + + +def feature_visualization(x, module_type, stage, n=32, save_dir=Path("runs/detect/exp")): + """ + x: Features to be visualized + module_type: Module type + stage: Module stage within model + n: Maximum number of feature maps to plot + save_dir: Directory to save results. + """ + if ("Detect" not in module_type) and ( + "Segment" not in module_type + ): # 'Detect' for Object Detect task,'Segment' for Segment task + batch, channels, height, width = x.shape # batch, channels, height, width + if height > 1 and width > 1: + f = save_dir / f"stage{stage}_{module_type.split('.')[-1]}_features.png" # filename + + blocks = torch.chunk(x[0].cpu(), channels, dim=0) # select batch index 0, block by channels + n = min(n, channels) # number of plots + fig, ax = plt.subplots(math.ceil(n / 8), 8, tight_layout=True) # 8 rows x n/8 cols + ax = ax.ravel() + plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze()) # cmap='gray' + ax[i].axis("off") + + LOGGER.info(f"Saving {f}... ({n}/{channels})") + plt.savefig(f, dpi=300, bbox_inches="tight") + plt.close() + np.save(str(f.with_suffix(".npy")), x[0].cpu().numpy()) # npy save + + +def hist2d(x, y, n=100): + """ + Generates a logarithmic 2D histogram, useful for visualizing label or evolution distributions. + + Used in used in labels.png and evolve.png. + """ + xedges, yedges = np.linspace(x.min(), x.max(), n), np.linspace(y.min(), y.max(), n) + hist, xedges, yedges = np.histogram2d(x, y, (xedges, yedges)) + xidx = np.clip(np.digitize(x, xedges) - 1, 0, hist.shape[0] - 1) + yidx = np.clip(np.digitize(y, yedges) - 1, 0, hist.shape[1] - 1) + return np.log(hist[xidx, yidx]) + + +def butter_lowpass_filtfilt(data, cutoff=1500, fs=50000, order=5): + """Applies a low-pass Butterworth filter to `data` with specified `cutoff`, `fs`, and `order`.""" + from scipy.signal import butter, filtfilt + + # https://stackoverflow.com/questions/28536191/how-to-filter-smooth-with-scipy-numpy + def butter_lowpass(cutoff, fs, order): + """Applies a low-pass Butterworth filter to a signal with specified cutoff frequency, sample rate, and filter + order. + """ + nyq = 0.5 * fs + normal_cutoff = cutoff / nyq + return butter(order, normal_cutoff, btype="low", analog=False) + + b, a = butter_lowpass(cutoff, fs, order=order) + return filtfilt(b, a, data) # forward-backward filter + + +def output_to_target(output, max_det=300): + """Converts YOLOv5 model output to [batch_id, class_id, x, y, w, h, conf] format for plotting, limiting detections + to `max_det`. + """ + targets = [] + for i, o in enumerate(output): + box, conf, cls = o[:max_det, :6].cpu().split((4, 1, 1), 1) + j = torch.full((conf.shape[0], 1), i) + targets.append(torch.cat((j, cls, xyxy2xywh(box), conf), 1)) + return torch.cat(targets, 0).numpy() + + +@threaded +def plot_images(images, targets, paths=None, fname="images.jpg", names=None): + """Plots an image grid with labels from YOLOv5 predictions or targets, saving to `fname`.""" + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs**0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y : y + h, x : x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(bs): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + ti = targets[targets[:, 0] == i] # image targets + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype("int") + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" + annotator.box_label(box, label, color=color) + annotator.im.save(fname) # save + + +def plot_lr_scheduler(optimizer, scheduler, epochs=300, save_dir=""): + """Plots learning rate schedule for given optimizer and scheduler, saving plot to `save_dir`.""" + optimizer, scheduler = copy(optimizer), copy(scheduler) # do not modify originals + y = [] + for _ in range(epochs): + scheduler.step() + y.append(optimizer.param_groups[0]["lr"]) + plt.plot(y, ".-", label="LR") + plt.xlabel("epoch") + plt.ylabel("LR") + plt.grid() + plt.xlim(0, epochs) + plt.ylim(0) + plt.savefig(Path(save_dir) / "LR.png", dpi=200) + plt.close() + + +def plot_val_txt(): + """ + Plots 2D and 1D histograms of bounding box centers from 'val.txt' using matplotlib, saving as 'hist2d.png' and + 'hist1d.png'. + + Example: from utils.plots import *; plot_val() + """ + x = np.loadtxt("val.txt", dtype=np.float32) + box = xyxy2xywh(x[:, :4]) + cx, cy = box[:, 0], box[:, 1] + + fig, ax = plt.subplots(1, 1, figsize=(6, 6), tight_layout=True) + ax.hist2d(cx, cy, bins=600, cmax=10, cmin=0) + ax.set_aspect("equal") + plt.savefig("hist2d.png", dpi=300) + + fig, ax = plt.subplots(1, 2, figsize=(12, 6), tight_layout=True) + ax[0].hist(cx, bins=600) + ax[1].hist(cy, bins=600) + plt.savefig("hist1d.png", dpi=200) + + +def plot_targets_txt(): + """ + Plots histograms of object detection targets from 'targets.txt', saving the figure as 'targets.jpg'. + + Example: from utils.plots import *; plot_targets_txt() + """ + x = np.loadtxt("targets.txt", dtype=np.float32).T + s = ["x targets", "y targets", "width targets", "height targets"] + fig, ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True) + ax = ax.ravel() + for i in range(4): + ax[i].hist(x[i], bins=100, label=f"{x[i].mean():.3g} +/- {x[i].std():.3g}") + ax[i].legend() + ax[i].set_title(s[i]) + plt.savefig("targets.jpg", dpi=200) + + +def plot_val_study(file="", dir="", x=None): + """ + Plots validation study results from 'study*.txt' files in a directory or a specific file, comparing model + performance and speed. + + Example: from utils.plots import *; plot_val_study() + """ + save_dir = Path(file).parent if file else Path(dir) + plot2 = False # plot additional results + if plot2: + ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() + + fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) + # for f in [save_dir / f'study_coco_{x}.txt' for x in ['yolov5n6', 'yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: + for f in sorted(save_dir.glob("study*.txt")): + y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T + x = np.arange(y.shape[1]) if x is None else np.array(x) + if plot2: + s = ["P", "R", "mAP@.5", "mAP@.5:.95", "t_preprocess (ms/img)", "t_inference (ms/img)", "t_NMS (ms/img)"] + for i in range(7): + ax[i].plot(x, y[i], ".-", linewidth=2, markersize=8) + ax[i].set_title(s[i]) + + j = y[3].argmax() + 1 + ax2.plot( + y[5, 1:j], + y[3, 1:j] * 1e2, + ".-", + linewidth=2, + markersize=8, + label=f.stem.replace("study_coco_", "").replace("yolo", "YOLO"), + ) + + ax2.plot( + 1e3 / np.array([209, 140, 97, 58, 35, 18]), + [34.6, 40.5, 43.0, 47.5, 49.7, 51.5], + "k.-", + linewidth=2, + markersize=8, + alpha=0.25, + label="EfficientDet", + ) + + ax2.grid(alpha=0.2) + ax2.set_yticks(np.arange(20, 60, 5)) + ax2.set_xlim(0, 57) + ax2.set_ylim(25, 55) + ax2.set_xlabel("GPU Speed (ms/img)") + ax2.set_ylabel("COCO AP val") + ax2.legend(loc="lower right") + f = save_dir / "study.png" + print(f"Saving {f}...") + plt.savefig(f, dpi=300) + + +@TryExcept() # known issue https://github.com/ultralytics/yolov5/issues/5395 +def plot_labels(labels, names=(), save_dir=Path("")): + """Plots dataset labels, saving correlogram and label images, handles classes, and visualizes bounding boxes.""" + LOGGER.info(f"Plotting labels to {save_dir / 'labels.jpg'}... ") + c, b = labels[:, 0], labels[:, 1:].transpose() # classes, boxes + nc = int(c.max() + 1) # number of classes + x = pd.DataFrame(b.transpose(), columns=["x", "y", "width", "height"]) + + # seaborn correlogram + sn.pairplot(x, corner=True, diag_kind="auto", kind="hist", diag_kws=dict(bins=50), plot_kws=dict(pmax=0.9)) + plt.savefig(save_dir / "labels_correlogram.jpg", dpi=200) + plt.close() + + # matplotlib labels + matplotlib.use("svg") # faster + ax = plt.subplots(2, 2, figsize=(8, 8), tight_layout=True)[1].ravel() + y = ax[0].hist(c, bins=np.linspace(0, nc, nc + 1) - 0.5, rwidth=0.8) + with contextlib.suppress(Exception): # color histogram bars by class + [y[2].patches[i].set_color([x / 255 for x in colors(i)]) for i in range(nc)] # known issue #3195 + ax[0].set_ylabel("instances") + if 0 < len(names) < 30: + ax[0].set_xticks(range(len(names))) + ax[0].set_xticklabels(list(names.values()), rotation=90, fontsize=10) + else: + ax[0].set_xlabel("classes") + sn.histplot(x, x="x", y="y", ax=ax[2], bins=50, pmax=0.9) + sn.histplot(x, x="width", y="height", ax=ax[3], bins=50, pmax=0.9) + + # rectangles + labels[:, 1:3] = 0.5 # center + labels[:, 1:] = xywh2xyxy(labels[:, 1:]) * 2000 + img = Image.fromarray(np.ones((2000, 2000, 3), dtype=np.uint8) * 255) + for cls, *box in labels[:1000]: + ImageDraw.Draw(img).rectangle(box, width=1, outline=colors(cls)) # plot + ax[1].imshow(img) + ax[1].axis("off") + + for a in [0, 1, 2, 3]: + for s in ["top", "right", "left", "bottom"]: + ax[a].spines[s].set_visible(False) + + plt.savefig(save_dir / "labels.jpg", dpi=200) + matplotlib.use("Agg") + plt.close() + + +def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")): + """Displays a grid of images with optional labels and predictions, saving to a file.""" + from app.util.yolov5.utils.augmentations import denormalize + + names = names or [f"class{i}" for i in range(1000)] + blocks = torch.chunk( + denormalize(im.clone()).cpu().float(), len(im), dim=0 + ) # select batch index 0, block by channels + n = min(len(blocks), nmax) # number of plots + m = min(8, round(n**0.5)) # 8 x 8 default + fig, ax = plt.subplots(math.ceil(n / m), m) # 8 rows x n/8 cols + ax = ax.ravel() if m > 1 else [ax] + # plt.subplots_adjust(wspace=0.05, hspace=0.05) + for i in range(n): + ax[i].imshow(blocks[i].squeeze().permute((1, 2, 0)).numpy().clip(0.0, 1.0)) + ax[i].axis("off") + if labels is not None: + s = names[labels[i]] + (f"—{names[pred[i]]}" if pred is not None else "") + ax[i].set_title(s, fontsize=8, verticalalignment="top") + plt.savefig(f, dpi=300, bbox_inches="tight") + plt.close() + if verbose: + LOGGER.info(f"Saving {f}") + if labels is not None: + LOGGER.info("True: " + " ".join(f"{names[i]:3s}" for i in labels[:nmax])) + if pred is not None: + LOGGER.info("Predicted:" + " ".join(f"{names[i]:3s}" for i in pred[:nmax])) + return f + + +def plot_evolve(evolve_csv="path/to/evolve.csv"): + """ + Plots hyperparameter evolution results from a given CSV, saving the plot and displaying best results. + + Example: from utils.plots import *; plot_evolve() + """ + evolve_csv = Path(evolve_csv) + data = pd.read_csv(evolve_csv) + keys = [x.strip() for x in data.columns] + x = data.values + f = fitness(x) + j = np.argmax(f) # max fitness index + plt.figure(figsize=(10, 12), tight_layout=True) + matplotlib.rc("font", **{"size": 8}) + print(f"Best results from row {j} of {evolve_csv}:") + for i, k in enumerate(keys[7:]): + v = x[:, 7 + i] + mu = v[j] # best single result + plt.subplot(6, 5, i + 1) + plt.scatter(v, f, c=hist2d(v, f, 20), cmap="viridis", alpha=0.8, edgecolors="none") + plt.plot(mu, f.max(), "k+", markersize=15) + plt.title(f"{k} = {mu:.3g}", fontdict={"size": 9}) # limit to 40 characters + if i % 5 != 0: + plt.yticks([]) + print(f"{k:>15}: {mu:.3g}") + f = evolve_csv.with_suffix(".png") # filename + plt.savefig(f, dpi=200) + plt.close() + print(f"Saved {f}") + + +def plot_results(file="path/to/results.csv", dir=""): + """ + Plots training results from a 'results.csv' file; accepts file path and directory as arguments. + + Example: from utils.plots import *; plot_results('path/to/results.csv') + """ + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 5, figsize=(12, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob("results*.csv")) + assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." + for f in files: + try: + data = pd.read_csv(f) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 8, 9, 10, 6, 7]): + y = data.values[:, j].astype("float") + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=8) # actual results + ax[i].plot(x, gaussian_filter1d(y, sigma=3), ":", label="smooth", linewidth=2) # smoothing line + ax[i].set_title(s[j], fontsize=12) + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + LOGGER.info(f"Warning: Plotting error for {f}: {e}") + ax[1].legend() + fig.savefig(save_dir / "results.png", dpi=200) + plt.close() + + +def profile_idetection(start=0, stop=0, labels=(), save_dir=""): + """ + Plots per-image iDetection logs, comparing metrics like storage and performance over time. + + Example: from utils.plots import *; profile_idetection() + """ + ax = plt.subplots(2, 4, figsize=(12, 6), tight_layout=True)[1].ravel() + s = ["Images", "Free Storage (GB)", "RAM Usage (GB)", "Battery", "dt_raw (ms)", "dt_smooth (ms)", "real-world FPS"] + files = list(Path(save_dir).glob("frames*.txt")) + for fi, f in enumerate(files): + try: + results = np.loadtxt(f, ndmin=2).T[:, 90:-30] # clip first and last rows + n = results.shape[1] # number of rows + x = np.arange(start, min(stop, n) if stop else n) + results = results[:, x] + t = results[0] - results[0].min() # set t0=0s + results[0] = x + for i, a in enumerate(ax): + if i < len(results): + label = labels[fi] if len(labels) else f.stem.replace("frames_", "") + a.plot(t, results[i], marker=".", label=label, linewidth=1, markersize=5) + a.set_title(s[i]) + a.set_xlabel("time (s)") + # if fi == len(files) - 1: + # a.set_ylim(bottom=0) + for side in ["top", "right"]: + a.spines[side].set_visible(False) + else: + a.remove() + except Exception as e: + print(f"Warning: Plotting error for {f}; {e}") + ax[1].legend() + plt.savefig(Path(save_dir) / "idetection_profile.png", dpi=200) + + +def save_one_box(xyxy, im, file=Path("im.jpg"), gain=1.02, pad=10, square=False, BGR=False, save=True): + """Crops and saves an image from bounding box `xyxy`, applied with `gain` and `pad`, optionally squares and adjusts + for BGR. + """ + xyxy = torch.tensor(xyxy).view(-1, 4) + b = xyxy2xywh(xyxy) # boxes + if square: + b[:, 2:] = b[:, 2:].max(1)[0].unsqueeze(1) # attempt rectangle to square + b[:, 2:] = b[:, 2:] * gain + pad # box wh * gain + pad + xyxy = xywh2xyxy(b).long() + clip_boxes(xyxy, im.shape) + crop = im[int(xyxy[0, 1]) : int(xyxy[0, 3]), int(xyxy[0, 0]) : int(xyxy[0, 2]), :: (1 if BGR else -1)] + if save: + file.parent.mkdir(parents=True, exist_ok=True) # make directory + f = str(increment_path(file).with_suffix(".jpg")) + # cv2.imwrite(f, crop) # save BGR, https://github.com/ultralytics/yolov5/issues/7007 chroma subsampling issue + Image.fromarray(crop[..., ::-1]).save(f, quality=95, subsampling=0) # save RGB + return crop diff --git a/app/util/yolov5/utils/segment/__init__.py b/app/util/yolov5/utils/segment/__init__.py new file mode 100644 index 0000000..77a19dc --- /dev/null +++ b/app/util/yolov5/utils/segment/__init__.py @@ -0,0 +1 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license diff --git a/app/util/yolov5/utils/segment/augmentations.py b/app/util/yolov5/utils/segment/augmentations.py new file mode 100644 index 0000000..14a81cf --- /dev/null +++ b/app/util/yolov5/utils/segment/augmentations.py @@ -0,0 +1,92 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Image augmentation functions.""" + +import math +import random + +import cv2 +import numpy as np + +from ..augmentations import box_candidates +from ..general import resample_segments, segment2box + + +def mixup(im, labels, segments, im2, labels2, segments2): + """ + Applies MixUp augmentation blending two images, labels, and segments with a random ratio. + + See https://arxiv.org/pdf/1710.09412.pdf + """ + r = np.random.beta(32.0, 32.0) # mixup ratio, alpha=beta=32.0 + im = (im * r + im2 * (1 - r)).astype(np.uint8) + labels = np.concatenate((labels, labels2), 0) + segments = np.concatenate((segments, segments2), 0) + return im, labels, segments + + +def random_perspective( + im, targets=(), segments=(), degrees=10, translate=0.1, scale=0.1, shear=10, perspective=0.0, border=(0, 0) +): + # torchvision.transforms.RandomAffine(degrees=(-10, 10), translate=(.1, .1), scale=(.9, 1.1), shear=(-10, 10)) + # targets = [cls, xyxy] + """Applies random perspective, rotation, scale, shear, and translation augmentations to an image and targets.""" + height = im.shape[0] + border[0] * 2 # shape(h,w,c) + width = im.shape[1] + border[1] * 2 + + # Center + C = np.eye(3) + C[0, 2] = -im.shape[1] / 2 # x translation (pixels) + C[1, 2] = -im.shape[0] / 2 # y translation (pixels) + + # Perspective + P = np.eye(3) + P[2, 0] = random.uniform(-perspective, perspective) # x perspective (about y) + P[2, 1] = random.uniform(-perspective, perspective) # y perspective (about x) + + # Rotation and Scale + R = np.eye(3) + a = random.uniform(-degrees, degrees) + # a += random.choice([-180, -90, 0, 90]) # add 90deg rotations to small rotations + s = random.uniform(1 - scale, 1 + scale) + # s = 2 ** random.uniform(-scale, scale) + R[:2] = cv2.getRotationMatrix2D(angle=a, center=(0, 0), scale=s) + + # Shear + S = np.eye(3) + S[0, 1] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # x shear (deg) + S[1, 0] = math.tan(random.uniform(-shear, shear) * math.pi / 180) # y shear (deg) + + # Translation + T = np.eye(3) + T[0, 2] = random.uniform(0.5 - translate, 0.5 + translate) * width # x translation (pixels) + T[1, 2] = random.uniform(0.5 - translate, 0.5 + translate) * height # y translation (pixels) + + # Combined rotation matrix + M = T @ S @ R @ P @ C # order of operations (right to left) is IMPORTANT + if (border[0] != 0) or (border[1] != 0) or (M != np.eye(3)).any(): # image changed + if perspective: + im = cv2.warpPerspective(im, M, dsize=(width, height), borderValue=(114, 114, 114)) + else: # affine + im = cv2.warpAffine(im, M[:2], dsize=(width, height), borderValue=(114, 114, 114)) + + new_segments = [] + if n := len(targets): + new = np.zeros((n, 4)) + segments = resample_segments(segments) # upsample + for i, segment in enumerate(segments): + xy = np.ones((len(segment), 3)) + xy[:, :2] = segment + xy = xy @ M.T # transform + xy = xy[:, :2] / xy[:, 2:3] if perspective else xy[:, :2] # perspective rescale or affine + + # clip + new[i] = segment2box(xy, width, height) + new_segments.append(xy) + + # filter candidates + i = box_candidates(box1=targets[:, 1:5].T * s, box2=new.T, area_thr=0.01) + targets = targets[i] + targets[:, 1:5] = new[i] + new_segments = np.array(new_segments)[i] + + return im, targets, new_segments diff --git a/app/util/yolov5/utils/segment/dataloaders.py b/app/util/yolov5/utils/segment/dataloaders.py new file mode 100644 index 0000000..2363d72 --- /dev/null +++ b/app/util/yolov5/utils/segment/dataloaders.py @@ -0,0 +1,366 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Dataloaders.""" + +import os +import random + +import cv2 +import numpy as np +import torch +from torch.utils.data import DataLoader + +from ..augmentations import augment_hsv, copy_paste, letterbox +from ..dataloaders import InfiniteDataLoader, LoadImagesAndLabels, SmartDistributedSampler, seed_worker +from ..general import LOGGER, xyn2xy, xywhn2xyxy, xyxy2xywhn +from ..torch_utils import torch_distributed_zero_first +from .augmentations import mixup, random_perspective + +RANK = int(os.getenv("RANK", -1)) + + +def create_dataloader( + path, + imgsz, + batch_size, + stride, + single_cls=False, + hyp=None, + augment=False, + cache=False, + pad=0.0, + rect=False, + rank=-1, + workers=8, + image_weights=False, + quad=False, + prefix="", + shuffle=False, + mask_downsample_ratio=1, + overlap_mask=False, + seed=0, +): + """Creates a dataloader for training, validating, or testing YOLO models with various dataset options.""" + if rect and shuffle: + LOGGER.warning("WARNING ⚠️ --rect is incompatible with DataLoader shuffle, setting shuffle=False") + shuffle = False + with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP + dataset = LoadImagesAndLabelsAndMasks( + path, + imgsz, + batch_size, + augment=augment, # augmentation + hyp=hyp, # hyperparameters + rect=rect, # rectangular batches + cache_images=cache, + single_cls=single_cls, + stride=int(stride), + pad=pad, + image_weights=image_weights, + prefix=prefix, + downsample_ratio=mask_downsample_ratio, + overlap=overlap_mask, + rank=rank, + ) + + batch_size = min(batch_size, len(dataset)) + nd = torch.cuda.device_count() # number of CUDA devices + nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers + sampler = None if rank == -1 else SmartDistributedSampler(dataset, shuffle=shuffle) + loader = DataLoader if image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates + generator = torch.Generator() + generator.manual_seed(6148914691236517205 + seed + RANK) + return loader( + dataset, + batch_size=batch_size, + shuffle=shuffle and sampler is None, + num_workers=nw, + sampler=sampler, + drop_last=quad, + pin_memory=True, + collate_fn=LoadImagesAndLabelsAndMasks.collate_fn4 if quad else LoadImagesAndLabelsAndMasks.collate_fn, + worker_init_fn=seed_worker, + generator=generator, + ), dataset + + +class LoadImagesAndLabelsAndMasks(LoadImagesAndLabels): # for training/testing + """Loads images, labels, and segmentation masks for training and testing YOLO models with augmentation support.""" + + def __init__( + self, + path, + img_size=640, + batch_size=16, + augment=False, + hyp=None, + rect=False, + image_weights=False, + cache_images=False, + single_cls=False, + stride=32, + pad=0, + min_items=0, + prefix="", + downsample_ratio=1, + overlap=False, + rank=-1, + seed=0, + ): + """Initializes the dataset with image, label, and mask loading capabilities for training/testing.""" + super().__init__( + path, + img_size, + batch_size, + augment, + hyp, + rect, + image_weights, + cache_images, + single_cls, + stride, + pad, + min_items, + prefix, + rank, + seed, + ) + self.downsample_ratio = downsample_ratio + self.overlap = overlap + + def __getitem__(self, index): + """Returns a transformed item from the dataset at the specified index, handling indexing and image weighting.""" + index = self.indices[index] # linear, shuffled, or image_weights + + hyp = self.hyp + if mosaic := self.mosaic and random.random() < hyp["mosaic"]: + # Load mosaic + img, labels, segments = self.load_mosaic(index) + shapes = None + + # MixUp augmentation + if random.random() < hyp["mixup"]: + img, labels, segments = mixup(img, labels, segments, *self.load_mosaic(random.randint(0, self.n - 1))) + + else: + # Load image + img, (h0, w0), (h, w) = self.load_image(index) + + # Letterbox + shape = self.batch_shapes[self.batch[index]] if self.rect else self.img_size # final letterboxed shape + img, ratio, pad = letterbox(img, shape, auto=False, scaleup=self.augment) + shapes = (h0, w0), ((h / h0, w / w0), pad) # for COCO mAP rescaling + + labels = self.labels[index].copy() + # [array, array, ....], array.shape=(num_points, 2), xyxyxyxy + segments = self.segments[index].copy() + if len(segments): + for i_s in range(len(segments)): + segments[i_s] = xyn2xy( + segments[i_s], + ratio[0] * w, + ratio[1] * h, + padw=pad[0], + padh=pad[1], + ) + if labels.size: # normalized xywh to pixel xyxy format + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], ratio[0] * w, ratio[1] * h, padw=pad[0], padh=pad[1]) + + if self.augment: + img, labels, segments = random_perspective( + img, + labels, + segments=segments, + degrees=hyp["degrees"], + translate=hyp["translate"], + scale=hyp["scale"], + shear=hyp["shear"], + perspective=hyp["perspective"], + ) + + nl = len(labels) # number of labels + masks = [] + if nl: + labels[:, 1:5] = xyxy2xywhn(labels[:, 1:5], w=img.shape[1], h=img.shape[0], clip=True, eps=1e-3) + if self.overlap: + masks, sorted_idx = polygons2masks_overlap( + img.shape[:2], segments, downsample_ratio=self.downsample_ratio + ) + masks = masks[None] # (640, 640) -> (1, 640, 640) + labels = labels[sorted_idx] + else: + masks = polygons2masks(img.shape[:2], segments, color=1, downsample_ratio=self.downsample_ratio) + + masks = ( + torch.from_numpy(masks) + if len(masks) + else torch.zeros( + 1 if self.overlap else nl, img.shape[0] // self.downsample_ratio, img.shape[1] // self.downsample_ratio + ) + ) + # TODO: albumentations support + if self.augment: + # Albumentations + # there are some augmentation that won't change boxes and masks, + # so just be it for now. + img, labels = self.albumentations(img, labels) + nl = len(labels) # update after albumentations + + # HSV color-space + augment_hsv(img, hgain=hyp["hsv_h"], sgain=hyp["hsv_s"], vgain=hyp["hsv_v"]) + + # Flip up-down + if random.random() < hyp["flipud"]: + img = np.flipud(img) + if nl: + labels[:, 2] = 1 - labels[:, 2] + masks = torch.flip(masks, dims=[1]) + + # Flip left-right + if random.random() < hyp["fliplr"]: + img = np.fliplr(img) + if nl: + labels[:, 1] = 1 - labels[:, 1] + masks = torch.flip(masks, dims=[2]) + + # Cutouts # labels = cutout(img, labels, p=0.5) + + labels_out = torch.zeros((nl, 6)) + if nl: + labels_out[:, 1:] = torch.from_numpy(labels) + + # Convert + img = img.transpose((2, 0, 1))[::-1] # HWC to CHW, BGR to RGB + img = np.ascontiguousarray(img) + + return (torch.from_numpy(img), labels_out, self.im_files[index], shapes, masks) + + def load_mosaic(self, index): + """Loads 1 image + 3 random images into a 4-image YOLOv5 mosaic, adjusting labels and segments accordingly.""" + labels4, segments4 = [], [] + s = self.img_size + yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.mosaic_border) # mosaic center x, y + + # 3 additional image indices + indices = [index] + random.choices(self.indices, k=3) # 3 additional image indices + for i, index in enumerate(indices): + # Load image + img, _, (h, w) = self.load_image(index) + + # place img in img4 + if i == 0: # top left + img4 = np.full((s * 2, s * 2, img.shape[2]), 114, dtype=np.uint8) # base image with 4 tiles + x1a, y1a, x2a, y2a = max(xc - w, 0), max(yc - h, 0), xc, yc # xmin, ymin, xmax, ymax (large image) + x1b, y1b, x2b, y2b = w - (x2a - x1a), h - (y2a - y1a), w, h # xmin, ymin, xmax, ymax (small image) + elif i == 1: # top right + x1a, y1a, x2a, y2a = xc, max(yc - h, 0), min(xc + w, s * 2), yc + x1b, y1b, x2b, y2b = 0, h - (y2a - y1a), min(w, x2a - x1a), h + elif i == 2: # bottom left + x1a, y1a, x2a, y2a = max(xc - w, 0), yc, xc, min(s * 2, yc + h) + x1b, y1b, x2b, y2b = w - (x2a - x1a), 0, w, min(y2a - y1a, h) + elif i == 3: # bottom right + x1a, y1a, x2a, y2a = xc, yc, min(xc + w, s * 2), min(s * 2, yc + h) + x1b, y1b, x2b, y2b = 0, 0, min(w, x2a - x1a), min(y2a - y1a, h) + + img4[y1a:y2a, x1a:x2a] = img[y1b:y2b, x1b:x2b] # img4[ymin:ymax, xmin:xmax] + padw = x1a - x1b + padh = y1a - y1b + + labels, segments = self.labels[index].copy(), self.segments[index].copy() + + if labels.size: + labels[:, 1:] = xywhn2xyxy(labels[:, 1:], w, h, padw, padh) # normalized xywh to pixel xyxy format + segments = [xyn2xy(x, w, h, padw, padh) for x in segments] + labels4.append(labels) + segments4.extend(segments) + + # Concat/clip labels + labels4 = np.concatenate(labels4, 0) + for x in (labels4[:, 1:], *segments4): + np.clip(x, 0, 2 * s, out=x) # clip when using random_perspective() + # img4, labels4 = replicate(img4, labels4) # replicate + + # Augment + img4, labels4, segments4 = copy_paste(img4, labels4, segments4, p=self.hyp["copy_paste"]) + img4, labels4, segments4 = random_perspective( + img4, + labels4, + segments4, + degrees=self.hyp["degrees"], + translate=self.hyp["translate"], + scale=self.hyp["scale"], + shear=self.hyp["shear"], + perspective=self.hyp["perspective"], + border=self.mosaic_border, + ) # border to remove + return img4, labels4, segments4 + + @staticmethod + def collate_fn(batch): + """Custom collation function for DataLoader, batches images, labels, paths, shapes, and segmentation masks.""" + img, label, path, shapes, masks = zip(*batch) # transposed + batched_masks = torch.cat(masks, 0) + for i, l in enumerate(label): + l[:, 0] = i # add target image index for build_targets() + return torch.stack(img, 0), torch.cat(label, 0), path, shapes, batched_masks + + +def polygon2mask(img_size, polygons, color=1, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (np.ndarray): [N, M], N is the number of polygons, + M is the number of points(Be divided by 2). + """ + mask = np.zeros(img_size, dtype=np.uint8) + polygons = np.asarray(polygons) + polygons = polygons.astype(np.int32) + shape = polygons.shape + polygons = polygons.reshape(shape[0], -1, 2) + cv2.fillPoly(mask, polygons, color=color) + nh, nw = (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio) + # NOTE: fillPoly firstly then resize is trying the keep the same way + # of loss calculation when mask-ratio=1. + mask = cv2.resize(mask, (nw, nh)) + return mask + + +def polygons2masks(img_size, polygons, color, downsample_ratio=1): + """ + Args: + img_size (tuple): The image size. + polygons (list[np.ndarray]): each polygon is [N, M], + N is the number of polygons, + M is the number of points(Be divided by 2). + """ + masks = [] + for si in range(len(polygons)): + mask = polygon2mask(img_size, [polygons[si].reshape(-1)], color, downsample_ratio) + masks.append(mask) + return np.array(masks) + + +def polygons2masks_overlap(img_size, segments, downsample_ratio=1): + """Return a (640, 640) overlap mask.""" + masks = np.zeros( + (img_size[0] // downsample_ratio, img_size[1] // downsample_ratio), + dtype=np.int32 if len(segments) > 255 else np.uint8, + ) + areas = [] + ms = [] + for si in range(len(segments)): + mask = polygon2mask( + img_size, + [segments[si].reshape(-1)], + downsample_ratio=downsample_ratio, + color=1, + ) + ms.append(mask) + areas.append(mask.sum()) + areas = np.asarray(areas) + index = np.argsort(-areas) + ms = np.array(ms)[index] + for i in range(len(segments)): + mask = ms[i] * (i + 1) + masks = masks + mask + masks = np.clip(masks, a_min=0, a_max=i + 1) + return masks, index diff --git a/app/util/yolov5/utils/segment/general.py b/app/util/yolov5/utils/segment/general.py new file mode 100644 index 0000000..6a71c25 --- /dev/null +++ b/app/util/yolov5/utils/segment/general.py @@ -0,0 +1,160 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +import cv2 +import numpy as np +import torch +import torch.nn.functional as F + + +def crop_mask(masks, boxes): + """ + "Crop" predicted masks by zeroing out everything not in the predicted bbox. Vectorized by Chong (thanks Chong). + + Args: + - masks should be a size [n, h, w] tensor of masks + - boxes should be a size [n, 4] tensor of bbox coords in relative point form + """ + n, h, w = masks.shape + x1, y1, x2, y2 = torch.chunk(boxes[:, :, None], 4, 1) # x1 shape(1,1,n) + r = torch.arange(w, device=masks.device, dtype=x1.dtype)[None, None, :] # rows shape(1,w,1) + c = torch.arange(h, device=masks.device, dtype=x1.dtype)[None, :, None] # cols shape(h,1,1) + + return masks * ((r >= x1) * (r < x2) * (c >= y1) * (c < y2)) + + +def process_mask_upsample(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w). + + return: h, w, n + """ + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def process_mask(protos, masks_in, bboxes, shape, upsample=False): + """ + Crop before upsample. + proto_out: [mask_dim, mask_h, mask_w] + out_masks: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape:input_image_size, (h, w). + + return: h, w, n + """ + c, mh, mw = protos.shape # CHW + ih, iw = shape + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) # CHW + + downsampled_bboxes = bboxes.clone() + downsampled_bboxes[:, 0] *= mw / iw + downsampled_bboxes[:, 2] *= mw / iw + downsampled_bboxes[:, 3] *= mh / ih + downsampled_bboxes[:, 1] *= mh / ih + + masks = crop_mask(masks, downsampled_bboxes) # CHW + if upsample: + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW + return masks.gt_(0.5) + + +def process_mask_native(protos, masks_in, bboxes, shape): + """ + Crop after upsample. + protos: [mask_dim, mask_h, mask_w] + masks_in: [n, mask_dim], n is number of masks after nms + bboxes: [n, 4], n is number of masks after nms + shape: input_image_size, (h, w). + + return: h, w, n + """ + c, mh, mw = protos.shape # CHW + masks = (masks_in @ protos.float().view(c, -1)).sigmoid().view(-1, mh, mw) + gain = min(mh / shape[0], mw / shape[1]) # gain = old / new + pad = (mw - shape[1] * gain) / 2, (mh - shape[0] * gain) / 2 # wh padding + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(mh - pad[1]), int(mw - pad[0]) + masks = masks[:, top:bottom, left:right] + + masks = F.interpolate(masks[None], shape, mode="bilinear", align_corners=False)[0] # CHW + masks = crop_mask(masks, bboxes) # CHW + return masks.gt_(0.5) + + +def scale_image(im1_shape, masks, im0_shape, ratio_pad=None): + """ + img1_shape: model input shape, [h, w] + img0_shape: origin pic shape, [h, w, 3] + masks: [h, w, num]. + """ + # Rescale coordinates (xyxy) from im1_shape to im0_shape + if ratio_pad is None: # calculate from im0_shape + gain = min(im1_shape[0] / im0_shape[0], im1_shape[1] / im0_shape[1]) # gain = old / new + pad = (im1_shape[1] - im0_shape[1] * gain) / 2, (im1_shape[0] - im0_shape[0] * gain) / 2 # wh padding + else: + pad = ratio_pad[1] + top, left = int(pad[1]), int(pad[0]) # y, x + bottom, right = int(im1_shape[0] - pad[1]), int(im1_shape[1] - pad[0]) + + if len(masks.shape) < 2: + raise ValueError(f'"len of masks shape" should be 2 or 3, but got {len(masks.shape)}') + masks = masks[top:bottom, left:right] + # masks = masks.permute(2, 0, 1).contiguous() + # masks = F.interpolate(masks[None], im0_shape[:2], mode='bilinear', align_corners=False)[0] + # masks = masks.permute(1, 2, 0).contiguous() + masks = cv2.resize(masks, (im0_shape[1], im0_shape[0])) + + if len(masks.shape) == 2: + masks = masks[:, :, None] + return masks + + +def mask_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [M, n] m2 means number of gt objects + Note: n means image_w x image_h. + + return: masks iou, [N, M] + """ + intersection = torch.matmul(mask1, mask2.t()).clamp(0) + union = (mask1.sum(1)[:, None] + mask2.sum(1)[None]) - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks_iou(mask1, mask2, eps=1e-7): + """ + mask1: [N, n] m1 means number of predicted objects + mask2: [N, n] m2 means number of gt objects + Note: n means image_w x image_h. + + return: masks iou, (N, ) + """ + intersection = (mask1 * mask2).sum(1).clamp(0) # (N, ) + union = (mask1.sum(1) + mask2.sum(1))[None] - intersection # (area1 + area2) - intersection + return intersection / (union + eps) + + +def masks2segments(masks, strategy="largest"): + """Converts binary (n,160,160) masks to polygon segments with options for concatenation or selecting the largest + segment. + """ + segments = [] + for x in masks.int().cpu().numpy().astype("uint8"): + c = cv2.findContours(x, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)[0] + if c: + if strategy == "concat": # concatenate all segments + c = np.concatenate([x.reshape(-1, 2) for x in c]) + elif strategy == "largest": # select largest segment + c = np.array(c[np.array([len(x) for x in c]).argmax()]).reshape(-1, 2) + else: + c = np.zeros((0, 2)) # no segments found + segments.append(c.astype("float32")) + return segments diff --git a/app/util/yolov5/utils/segment/loss.py b/app/util/yolov5/utils/segment/loss.py new file mode 100644 index 0000000..6ef5906 --- /dev/null +++ b/app/util/yolov5/utils/segment/loss.py @@ -0,0 +1,197 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +import torch +import torch.nn as nn +import torch.nn.functional as F + +from ..general import xywh2xyxy +from ..loss import FocalLoss, smooth_BCE +from ..metrics import bbox_iou +from ..torch_utils import de_parallel +from .general import crop_mask + + +class ComputeLoss: + """Computes the YOLOv5 model's loss components including classification, objectness, box, and mask losses.""" + + def __init__(self, model, autobalance=False, overlap=False): + """Initializes the compute loss function for YOLOv5 models with options for autobalancing and overlap + handling. + """ + self.sort_obj_iou = False + self.overlap = overlap + device = next(model.parameters()).device # get model device + h = model.hyp # hyperparameters + + # Define criteria + BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["cls_pw"]], device=device)) + BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h["obj_pw"]], device=device)) + + # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 + self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets + + # Focal loss + g = h["fl_gamma"] # focal loss gamma + if g > 0: + BCEcls, BCEobj = FocalLoss(BCEcls, g), FocalLoss(BCEobj, g) + + m = de_parallel(model).model[-1] # Detect() module + self.balance = {3: [4.0, 1.0, 0.4]}.get(m.nl, [4.0, 1.0, 0.25, 0.06, 0.02]) # P3-P7 + self.ssi = list(m.stride).index(16) if autobalance else 0 # stride 16 index + self.BCEcls, self.BCEobj, self.gr, self.hyp, self.autobalance = BCEcls, BCEobj, 1.0, h, autobalance + self.na = m.na # number of anchors + self.nc = m.nc # number of classes + self.nl = m.nl # number of layers + self.nm = m.nm # number of masks + self.anchors = m.anchors + self.device = device + + def __call__(self, preds, targets, masks): # predictions, targets, model + """Evaluates YOLOv5 model's loss for given predictions, targets, and masks; returns total loss components.""" + p, proto = preds + bs, nm, mask_h, mask_w = proto.shape # batch size, number of masks, mask height, mask width + lcls = torch.zeros(1, device=self.device) + lbox = torch.zeros(1, device=self.device) + lobj = torch.zeros(1, device=self.device) + lseg = torch.zeros(1, device=self.device) + tcls, tbox, indices, anchors, tidxs, xywhn = self.build_targets(p, targets) # targets + + # Losses + for i, pi in enumerate(p): # layer index, layer predictions + b, a, gj, gi = indices[i] # image, anchor, gridy, gridx + tobj = torch.zeros(pi.shape[:4], dtype=pi.dtype, device=self.device) # target obj + + if n := b.shape[0]: + pxy, pwh, _, pcls, pmask = pi[b, a, gj, gi].split((2, 2, 1, self.nc, nm), 1) # subset of predictions + + # Box regression + pxy = pxy.sigmoid() * 2 - 0.5 + pwh = (pwh.sigmoid() * 2) ** 2 * anchors[i] + pbox = torch.cat((pxy, pwh), 1) # predicted box + iou = bbox_iou(pbox, tbox[i], CIoU=True).squeeze() # iou(prediction, target) + lbox += (1.0 - iou).mean() # iou loss + + # Objectness + iou = iou.detach().clamp(0).type(tobj.dtype) + if self.sort_obj_iou: + j = iou.argsort() + b, a, gj, gi, iou = b[j], a[j], gj[j], gi[j], iou[j] + if self.gr < 1: + iou = (1.0 - self.gr) + self.gr * iou + tobj[b, a, gj, gi] = iou # iou ratio + + # Classification + if self.nc > 1: # cls loss (only if multiple classes) + t = torch.full_like(pcls, self.cn, device=self.device) # targets + t[range(n), tcls[i]] = self.cp + lcls += self.BCEcls(pcls, t) # BCE + + # Mask regression + if tuple(masks.shape[-2:]) != (mask_h, mask_w): # downsample + masks = F.interpolate(masks[None], (mask_h, mask_w), mode="nearest")[0] + marea = xywhn[i][:, 2:].prod(1) # mask width, height normalized + mxyxy = xywh2xyxy(xywhn[i] * torch.tensor([mask_w, mask_h, mask_w, mask_h], device=self.device)) + for bi in b.unique(): + j = b == bi # matching index + if self.overlap: + mask_gti = torch.where(masks[bi][None] == tidxs[i][j].view(-1, 1, 1), 1.0, 0.0) + else: + mask_gti = masks[tidxs[i]][j] + lseg += self.single_mask_loss(mask_gti, pmask[j], proto[bi], mxyxy[j], marea[j]) + + obji = self.BCEobj(pi[..., 4], tobj) + lobj += obji * self.balance[i] # obj loss + if self.autobalance: + self.balance[i] = self.balance[i] * 0.9999 + 0.0001 / obji.detach().item() + + if self.autobalance: + self.balance = [x / self.balance[self.ssi] for x in self.balance] + lbox *= self.hyp["box"] + lobj *= self.hyp["obj"] + lcls *= self.hyp["cls"] + lseg *= self.hyp["box"] / bs + + loss = lbox + lobj + lcls + lseg + return loss * bs, torch.cat((lbox, lseg, lobj, lcls)).detach() + + def single_mask_loss(self, gt_mask, pred, proto, xyxy, area): + """Calculates and normalizes single mask loss for YOLOv5 between predicted and ground truth masks.""" + pred_mask = (pred @ proto.view(self.nm, -1)).view(-1, *proto.shape[1:]) # (n,32) @ (32,80,80) -> (n,80,80) + loss = F.binary_cross_entropy_with_logits(pred_mask, gt_mask, reduction="none") + return (crop_mask(loss, xyxy).mean(dim=(1, 2)) / area).mean() + + def build_targets(self, p, targets): + """Prepares YOLOv5 targets for loss computation; inputs targets (image, class, x, y, w, h), output target + classes/boxes. + """ + na, nt = self.na, targets.shape[0] # number of anchors, targets + tcls, tbox, indices, anch, tidxs, xywhn = [], [], [], [], [], [] + gain = torch.ones(8, device=self.device) # normalized to gridspace gain + ai = torch.arange(na, device=self.device).float().view(na, 1).repeat(1, nt) # same as .repeat_interleave(nt) + if self.overlap: + batch = p[0].shape[0] + ti = [] + for i in range(batch): + num = (targets[:, 0] == i).sum() # find number of targets of each image + ti.append(torch.arange(num, device=self.device).float().view(1, num).repeat(na, 1) + 1) # (na, num) + ti = torch.cat(ti, 1) # (na, nt) + else: + ti = torch.arange(nt, device=self.device).float().view(1, nt).repeat(na, 1) + targets = torch.cat((targets.repeat(na, 1, 1), ai[..., None], ti[..., None]), 2) # append anchor indices + + g = 0.5 # bias + off = ( + torch.tensor( + [ + [0, 0], + [1, 0], + [0, 1], + [-1, 0], + [0, -1], # j,k,l,m + # [1, 1], [1, -1], [-1, 1], [-1, -1], # jk,jm,lk,lm + ], + device=self.device, + ).float() + * g + ) # offsets + + for i in range(self.nl): + anchors, shape = self.anchors[i], p[i].shape + gain[2:6] = torch.tensor(shape)[[3, 2, 3, 2]] # xyxy gain + + # Match targets to anchors + t = targets * gain # shape(3,n,7) + if nt: + # Matches + r = t[..., 4:6] / anchors[:, None] # wh ratio + j = torch.max(r, 1 / r).max(2)[0] < self.hyp["anchor_t"] # compare + # j = wh_iou(anchors, t[:, 4:6]) > model.hyp['iou_t'] # iou(3,n)=wh_iou(anchors(3,2), gwh(n,2)) + t = t[j] # filter + + # Offsets + gxy = t[:, 2:4] # grid xy + gxi = gain[[2, 3]] - gxy # inverse + j, k = ((gxy % 1 < g) & (gxy > 1)).T + l, m = ((gxi % 1 < g) & (gxi > 1)).T + j = torch.stack((torch.ones_like(j), j, k, l, m)) + t = t.repeat((5, 1, 1))[j] + offsets = (torch.zeros_like(gxy)[None] + off[:, None])[j] + else: + t = targets[0] + offsets = 0 + + # Define + bc, gxy, gwh, at = t.chunk(4, 1) # (image, class), grid xy, grid wh, anchors + (a, tidx), (b, c) = at.long().T, bc.long().T # anchors, image, class + gij = (gxy - offsets).long() + gi, gj = gij.T # grid indices + + # Append + indices.append((b, a, gj.clamp_(0, shape[2] - 1), gi.clamp_(0, shape[3] - 1))) # image, anchor, grid + tbox.append(torch.cat((gxy - gij, gwh), 1)) # box + anch.append(anchors[a]) # anchors + tcls.append(c) # class + tidxs.append(tidx) + xywhn.append(torch.cat((gxy, gwh), 1) / gain[2:6]) # xywh normalized + + return tcls, tbox, indices, anch, tidxs, xywhn diff --git a/app/util/yolov5/utils/segment/metrics.py b/app/util/yolov5/utils/segment/metrics.py new file mode 100644 index 0000000..3bb7aee --- /dev/null +++ b/app/util/yolov5/utils/segment/metrics.py @@ -0,0 +1,225 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Model validation metrics.""" + +import numpy as np + +from ..metrics import ap_per_class + + +def fitness(x): + """Evaluates model fitness by a weighted sum of 8 metrics, `x`: [N,8] array, weights: [0.1, 0.9] for mAP and F1.""" + w = [0.0, 0.0, 0.1, 0.9, 0.0, 0.0, 0.1, 0.9] + return (x[:, :8] * w).sum(1) + + +def ap_per_class_box_and_mask( + tp_m, + tp_b, + conf, + pred_cls, + target_cls, + plot=False, + save_dir=".", + names=(), +): + """ + Args: + tp_b: tp of boxes. + tp_m: tp of masks. + other arguments see `func: ap_per_class`. + """ + results_boxes = ap_per_class( + tp_b, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Box" + )[2:] + results_masks = ap_per_class( + tp_m, conf, pred_cls, target_cls, plot=plot, save_dir=save_dir, names=names, prefix="Mask" + )[2:] + + return { + "boxes": { + "p": results_boxes[0], + "r": results_boxes[1], + "ap": results_boxes[3], + "f1": results_boxes[2], + "ap_class": results_boxes[4], + }, + "masks": { + "p": results_masks[0], + "r": results_masks[1], + "ap": results_masks[3], + "f1": results_masks[2], + "ap_class": results_masks[4], + }, + } + + +class Metric: + """Computes performance metrics like precision, recall, F1 score, and average precision for model evaluation.""" + + def __init__(self) -> None: + """Initializes performance metric attributes for precision, recall, F1 score, average precision, and class + indices. + """ + self.p = [] # (nc, ) + self.r = [] # (nc, ) + self.f1 = [] # (nc, ) + self.all_ap = [] # (nc, 10) + self.ap_class_index = [] # (nc, ) + + @property + def ap50(self): + """ + AP@0.5 of all classes. + + Return: + (nc, ) or []. + """ + return self.all_ap[:, 0] if len(self.all_ap) else [] + + @property + def ap(self): + """AP@0.5:0.95 + Return: + (nc, ) or []. + """ + return self.all_ap.mean(1) if len(self.all_ap) else [] + + @property + def mp(self): + """ + Mean precision of all classes. + + Return: + float. + """ + return self.p.mean() if len(self.p) else 0.0 + + @property + def mr(self): + """ + Mean recall of all classes. + + Return: + float. + """ + return self.r.mean() if len(self.r) else 0.0 + + @property + def map50(self): + """ + Mean AP@0.5 of all classes. + + Return: + float. + """ + return self.all_ap[:, 0].mean() if len(self.all_ap) else 0.0 + + @property + def map(self): + """ + Mean AP@0.5:0.95 of all classes. + + Return: + float. + """ + return self.all_ap.mean() if len(self.all_ap) else 0.0 + + def mean_results(self): + """Mean of results, return mp, mr, map50, map.""" + return (self.mp, self.mr, self.map50, self.map) + + def class_result(self, i): + """Class-aware result, return p[i], r[i], ap50[i], ap[i].""" + return (self.p[i], self.r[i], self.ap50[i], self.ap[i]) + + def get_maps(self, nc): + """Calculates and returns mean Average Precision (mAP) for each class given number of classes `nc`.""" + maps = np.zeros(nc) + self.map + for i, c in enumerate(self.ap_class_index): + maps[c] = self.ap[i] + return maps + + def update(self, results): + """ + Args: + results: tuple(p, r, ap, f1, ap_class). + """ + p, r, all_ap, f1, ap_class_index = results + self.p = p + self.r = r + self.all_ap = all_ap + self.f1 = f1 + self.ap_class_index = ap_class_index + + +class Metrics: + """Metric for boxes and masks.""" + + def __init__(self) -> None: + """Initializes Metric objects for bounding boxes and masks to compute performance metrics in the Metrics + class. + """ + self.metric_box = Metric() + self.metric_mask = Metric() + + def update(self, results): + """ + Args: + results: Dict{'boxes': Dict{}, 'masks': Dict{}}. + """ + self.metric_box.update(list(results["boxes"].values())) + self.metric_mask.update(list(results["masks"].values())) + + def mean_results(self): + """Computes and returns the mean results for both box and mask metrics by summing their individual means.""" + return self.metric_box.mean_results() + self.metric_mask.mean_results() + + def class_result(self, i): + """Returns the sum of box and mask metric results for a specified class index `i`.""" + return self.metric_box.class_result(i) + self.metric_mask.class_result(i) + + def get_maps(self, nc): + """Calculates and returns the sum of mean average precisions (mAPs) for both box and mask metrics for `nc` + classes. + """ + return self.metric_box.get_maps(nc) + self.metric_mask.get_maps(nc) + + @property + def ap_class_index(self): + """Returns the class index for average precision, shared by both box and mask metrics.""" + return self.metric_box.ap_class_index + + +KEYS = [ + "train/box_loss", + "train/seg_loss", # train loss + "train/obj_loss", + "train/cls_loss", + "metrics/precision(B)", + "metrics/recall(B)", + "metrics/mAP_0.5(B)", + "metrics/mAP_0.5:0.95(B)", # metrics + "metrics/precision(M)", + "metrics/recall(M)", + "metrics/mAP_0.5(M)", + "metrics/mAP_0.5:0.95(M)", # metrics + "val/box_loss", + "val/seg_loss", # val loss + "val/obj_loss", + "val/cls_loss", + "x/lr0", + "x/lr1", + "x/lr2", +] + +BEST_KEYS = [ + "best/epoch", + "best/precision(B)", + "best/recall(B)", + "best/mAP_0.5(B)", + "best/mAP_0.5:0.95(B)", + "best/precision(M)", + "best/recall(M)", + "best/mAP_0.5(M)", + "best/mAP_0.5:0.95(M)", +] diff --git a/app/util/yolov5/utils/segment/plots.py b/app/util/yolov5/utils/segment/plots.py new file mode 100644 index 0000000..5619e9d --- /dev/null +++ b/app/util/yolov5/utils/segment/plots.py @@ -0,0 +1,152 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license + +import contextlib +import math +from pathlib import Path + +import cv2 +import matplotlib.pyplot as plt +import numpy as np +import pandas as pd +import torch + +from .. import threaded +from ..general import xywh2xyxy +from ..plots import Annotator, colors + + +@threaded +def plot_images_and_masks(images, targets, masks, paths=None, fname="images.jpg", names=None): + """Plots a grid of images, their labels, and masks with optional resizing and annotations, saving to fname.""" + if isinstance(images, torch.Tensor): + images = images.cpu().float().numpy() + if isinstance(targets, torch.Tensor): + targets = targets.cpu().numpy() + if isinstance(masks, torch.Tensor): + masks = masks.cpu().numpy().astype(int) + + max_size = 1920 # max image size + max_subplots = 16 # max image subplots, i.e. 4x4 + bs, _, h, w = images.shape # batch size, _, height, width + bs = min(bs, max_subplots) # limit plot images + ns = np.ceil(bs**0.5) # number of subplots (square) + if np.max(images[0]) <= 1: + images *= 255 # de-normalise (optional) + + # Build Image + mosaic = np.full((int(ns * h), int(ns * w), 3), 255, dtype=np.uint8) # init + for i, im in enumerate(images): + if i == max_subplots: # if last batch has fewer images than we expect + break + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + im = im.transpose(1, 2, 0) + mosaic[y : y + h, x : x + w, :] = im + + # Resize (optional) + scale = max_size / ns / max(h, w) + if scale < 1: + h = math.ceil(scale * h) + w = math.ceil(scale * w) + mosaic = cv2.resize(mosaic, tuple(int(x * ns) for x in (w, h))) + + # Annotate + fs = int((h + w) * ns * 0.01) # font size + annotator = Annotator(mosaic, line_width=round(fs / 10), font_size=fs, pil=True, example=names) + for i in range(i + 1): + x, y = int(w * (i // ns)), int(h * (i % ns)) # block origin + annotator.rectangle([x, y, x + w, y + h], None, (255, 255, 255), width=2) # borders + if paths: + annotator.text([x + 5, y + 5], text=Path(paths[i]).name[:40], txt_color=(220, 220, 220)) # filenames + if len(targets) > 0: + idx = targets[:, 0] == i + ti = targets[idx] # image targets + + boxes = xywh2xyxy(ti[:, 2:6]).T + classes = ti[:, 1].astype("int") + labels = ti.shape[1] == 6 # labels if no conf column + conf = None if labels else ti[:, 6] # check for confidence presence (label vs pred) + + if boxes.shape[1]: + if boxes.max() <= 1.01: # if normalized with tolerance 0.01 + boxes[[0, 2]] *= w # scale to pixels + boxes[[1, 3]] *= h + elif scale < 1: # absolute coords need scale if image scales + boxes *= scale + boxes[[0, 2]] += x + boxes[[1, 3]] += y + for j, box in enumerate(boxes.T.tolist()): + cls = classes[j] + color = colors(cls) + cls = names[cls] if names else cls + if labels or conf[j] > 0.25: # 0.25 conf thresh + label = f"{cls}" if labels else f"{cls} {conf[j]:.1f}" + annotator.box_label(box, label, color=color) + + # Plot masks + if len(masks): + if masks.max() > 1.0: # mean that masks are overlap + image_masks = masks[[i]] # (1, 640, 640) + nl = len(ti) + index = np.arange(nl).reshape(nl, 1, 1) + 1 + image_masks = np.repeat(image_masks, nl, axis=0) + image_masks = np.where(image_masks == index, 1.0, 0.0) + else: + image_masks = masks[idx] + + im = np.asarray(annotator.im).copy() + for j, box in enumerate(boxes.T.tolist()): + if labels or conf[j] > 0.25: # 0.25 conf thresh + color = colors(classes[j]) + mh, mw = image_masks[j].shape + if mh != h or mw != w: + mask = image_masks[j].astype(np.uint8) + mask = cv2.resize(mask, (w, h)) + mask = mask.astype(bool) + else: + mask = image_masks[j].astype(bool) + with contextlib.suppress(Exception): + im[y : y + h, x : x + w, :][mask] = ( + im[y : y + h, x : x + w, :][mask] * 0.4 + np.array(color) * 0.6 + ) + annotator.fromarray(im) + annotator.im.save(fname) # save + + +def plot_results_with_masks(file="path/to/results.csv", dir="", best=True): + """ + Plots training results from CSV files, plotting best or last result highlights based on `best` parameter. + + Example: from utils.plots import *; plot_results('path/to/results.csv') + """ + save_dir = Path(file).parent if file else Path(dir) + fig, ax = plt.subplots(2, 8, figsize=(18, 6), tight_layout=True) + ax = ax.ravel() + files = list(save_dir.glob("results*.csv")) + assert len(files), f"No results.csv files found in {save_dir.resolve()}, nothing to plot." + for f in files: + try: + data = pd.read_csv(f) + index = np.argmax( + 0.9 * data.values[:, 8] + 0.1 * data.values[:, 7] + 0.9 * data.values[:, 12] + 0.1 * data.values[:, 11] + ) + s = [x.strip() for x in data.columns] + x = data.values[:, 0] + for i, j in enumerate([1, 2, 3, 4, 5, 6, 9, 10, 13, 14, 15, 16, 7, 8, 11, 12]): + y = data.values[:, j] + # y[y == 0] = np.nan # don't show zero values + ax[i].plot(x, y, marker=".", label=f.stem, linewidth=2, markersize=2) + if best: + # best + ax[i].scatter(index, y[index], color="r", label=f"best:{index}", marker="*", linewidth=3) + ax[i].set_title(s[j] + f"\n{round(y[index], 5)}") + else: + # last + ax[i].scatter(x[-1], y[-1], color="r", label="last", marker="*", linewidth=3) + ax[i].set_title(s[j] + f"\n{round(y[-1], 5)}") + # if j in [8, 9, 10]: # share train and val loss y axes + # ax[i].get_shared_y_axes().join(ax[i], ax[i - 5]) + except Exception as e: + print(f"Warning: Plotting error for {f}: {e}") + ax[1].legend() + fig.savefig(save_dir / "results.png", dpi=200) + plt.close() diff --git a/app/util/yolov5/utils/torch_utils.py b/app/util/yolov5/utils/torch_utils.py new file mode 100644 index 0000000..75bbfe1 --- /dev/null +++ b/app/util/yolov5/utils/torch_utils.py @@ -0,0 +1,482 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""PyTorch utils.""" + +import math +import os +import platform +import subprocess +import time +import warnings +from contextlib import contextmanager +from copy import deepcopy +from pathlib import Path + +import torch +import torch.distributed as dist +import torch.nn as nn +import torch.nn.functional as F +from torch.nn.parallel import DistributedDataParallel as DDP + +from app.util.yolov5.utils.general import LOGGER, check_version, colorstr, file_date, git_describe + +LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html +RANK = int(os.getenv("RANK", -1)) +WORLD_SIZE = int(os.getenv("WORLD_SIZE", 1)) + +try: + import thop # for FLOPs computation +except ImportError: + thop = None + +# Suppress PyTorch warnings +warnings.filterwarnings("ignore", message="User provided device_type of 'cuda', but CUDA is not available. Disabling") +warnings.filterwarnings("ignore", category=UserWarning) + + +def smart_inference_mode(torch_1_9=check_version(torch.__version__, "1.9.0")): + """Applies torch.inference_mode() if torch>=1.9.0, else torch.no_grad() as a decorator for functions.""" + + def decorate(fn): + """Applies torch.inference_mode() if torch>=1.9.0, else torch.no_grad() to the decorated function.""" + return (torch.inference_mode if torch_1_9 else torch.no_grad)()(fn) + + return decorate + + +def smartCrossEntropyLoss(label_smoothing=0.0): + """Returns a CrossEntropyLoss with optional label smoothing for torch>=1.10.0; warns if smoothing on lower + versions. + """ + if check_version(torch.__version__, "1.10.0"): + return nn.CrossEntropyLoss(label_smoothing=label_smoothing) + if label_smoothing > 0: + LOGGER.warning(f"WARNING ⚠️ label smoothing {label_smoothing} requires torch>=1.10.0") + return nn.CrossEntropyLoss() + + +def smart_DDP(model): + """Initializes DistributedDataParallel (DDP) for model training, respecting torch version constraints.""" + assert not check_version(torch.__version__, "1.12.0", pinned=True), ( + "torch==1.12.0 torchvision==0.13.0 DDP training is not supported due to a known issue. " + "Please upgrade or downgrade torch to use DDP. See https://github.com/ultralytics/yolov5/issues/8395" + ) + if check_version(torch.__version__, "1.11.0"): + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK, static_graph=True) + else: + return DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK) + + +def reshape_classifier_output(model, n=1000): + """Reshapes last layer of model to match class count 'n', supporting Classify, Linear, Sequential types.""" + from app.util.yolov5.models.common import Classify + + name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module + if isinstance(m, Classify): # YOLOv5 Classify() head + if m.linear.out_features != n: + m.linear = nn.Linear(m.linear.in_features, n) + elif isinstance(m, nn.Linear): # ResNet, EfficientNet + if m.out_features != n: + setattr(model, name, nn.Linear(m.in_features, n)) + elif isinstance(m, nn.Sequential): + types = [type(x) for x in m] + if nn.Linear in types: + i = len(types) - 1 - types[::-1].index(nn.Linear) # last nn.Linear index + if m[i].out_features != n: + m[i] = nn.Linear(m[i].in_features, n) + elif nn.Conv2d in types: + i = len(types) - 1 - types[::-1].index(nn.Conv2d) # last nn.Conv2d index + if m[i].out_channels != n: + m[i] = nn.Conv2d(m[i].in_channels, n, m[i].kernel_size, m[i].stride, bias=m[i].bias is not None) + + +@contextmanager +def torch_distributed_zero_first(local_rank: int): + """Context manager ensuring ordered operations in distributed training by making all processes wait for the leading + process. + """ + if local_rank not in [-1, 0]: + dist.barrier(device_ids=[local_rank]) + yield + if local_rank == 0: + dist.barrier(device_ids=[0]) + + +def device_count(): + """Returns the number of available CUDA devices; works on Linux and Windows by invoking `nvidia-smi`.""" + assert platform.system() in ("Linux", "Windows"), "device_count() only supported on Linux or Windows" + try: + cmd = "nvidia-smi -L | wc -l" if platform.system() == "Linux" else 'nvidia-smi -L | find /c /v ""' # Windows + return int(subprocess.run(cmd, shell=True, capture_output=True, check=True).stdout.decode().split()[-1]) + except Exception: + return 0 + + +def select_device(device="", batch_size=0, newline=True): + """Selects computing device (CPU, CUDA GPU, MPS) for YOLOv5 model deployment, logging device info.""" + s = f"YOLOv5 🚀 {git_describe() or file_date()} Python-{platform.python_version()} torch-{torch.__version__} " + device = str(device).strip().lower().replace("cuda:", "").replace("none", "") # to string, 'cuda:0' to '0' + cpu = device == "cpu" + mps = device == "mps" # Apple Metal Performance Shaders (MPS) + if cpu or mps: + os.environ["CUDA_VISIBLE_DEVICES"] = "-1" # force torch.cuda.is_available() = False + elif device: # non-cpu device requested + os.environ["CUDA_VISIBLE_DEVICES"] = device # set environment variable - must be before assert is_available() + assert torch.cuda.is_available() and torch.cuda.device_count() >= len(device.replace(",", "")), ( + f"Invalid CUDA '--device {device}' requested, use '--device cpu' or pass valid CUDA device(s)" + ) + + if not cpu and not mps and torch.cuda.is_available(): # prefer GPU if available + devices = device.split(",") if device else "0" # range(torch.cuda.device_count()) # i.e. 0,1,6,7 + n = len(devices) # device count + if n > 1 and batch_size > 0: # check batch_size is divisible by device_count + assert batch_size % n == 0, f"batch-size {batch_size} not multiple of GPU count {n}" + space = " " * (len(s) + 1) + for i, d in enumerate(devices): + p = torch.cuda.get_device_properties(i) + s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB + arg = "cuda:0" + elif mps and getattr(torch, "has_mps", False) and torch.backends.mps.is_available(): # prefer MPS if available + s += "MPS\n" + arg = "mps" + else: # revert to CPU + s += "CPU\n" + arg = "cpu" + + if not newline: + s = s.rstrip() + LOGGER.info(s) + return torch.device(arg) + + +def time_sync(): + """Synchronizes PyTorch for accurate timing, leveraging CUDA if available, and returns the current time.""" + if torch.cuda.is_available(): + torch.cuda.synchronize() + return time.time() + + +def profile(input, ops, n=10, device=None): + """YOLOv5 speed/memory/FLOPs profiler + Usage: + input = torch.randn(16, 3, 640, 640) + m1 = lambda x: x * torch.sigmoid(x) + m2 = nn.SiLU() + profile(input, [m1, m2], n=100) # profile over 100 iterations. + """ + results = [] + if not isinstance(device, torch.device): + device = select_device(device) + print( + f"{'Params':>12s}{'GFLOPs':>12s}{'GPU_mem (GB)':>14s}{'forward (ms)':>14s}{'backward (ms)':>14s}" + f"{'input':>24s}{'output':>24s}" + ) + + for x in input if isinstance(input, list) else [input]: + x = x.to(device) + x.requires_grad = True + for m in ops if isinstance(ops, list) else [ops]: + m = m.to(device) if hasattr(m, "to") else m # device + m = m.half() if hasattr(m, "half") and isinstance(x, torch.Tensor) and x.dtype is torch.float16 else m + tf, tb, t = 0, 0, [0, 0, 0] # dt forward, backward + try: + flops = thop.profile(m, inputs=(x,), verbose=False)[0] / 1e9 * 2 # GFLOPs + except Exception: + flops = 0 + + try: + for _ in range(n): + t[0] = time_sync() + y = m(x) + t[1] = time_sync() + try: + _ = (sum(yi.sum() for yi in y) if isinstance(y, list) else y).sum().backward() + t[2] = time_sync() + except Exception: # no backward method + # print(e) # for debug + t[2] = float("nan") + tf += (t[1] - t[0]) * 1000 / n # ms per op forward + tb += (t[2] - t[1]) * 1000 / n # ms per op backward + mem = torch.cuda.memory_reserved() / 1e9 if torch.cuda.is_available() else 0 # (GB) + s_in, s_out = (tuple(x.shape) if isinstance(x, torch.Tensor) else "list" for x in (x, y)) # shapes + p = sum(x.numel() for x in m.parameters()) if isinstance(m, nn.Module) else 0 # parameters + print(f"{p:12}{flops:12.4g}{mem:>14.3f}{tf:14.4g}{tb:14.4g}{str(s_in):>24s}{str(s_out):>24s}") + results.append([p, flops, mem, tf, tb, s_in, s_out]) + except Exception as e: + print(e) + results.append(None) + torch.cuda.empty_cache() + return results + + +def is_parallel(model): + """Checks if the model is using Data Parallelism (DP) or Distributed Data Parallelism (DDP).""" + return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel) + + +def de_parallel(model): + """Returns a single-GPU model by removing Data Parallelism (DP) or Distributed Data Parallelism (DDP) if applied.""" + return model.module if is_parallel(model) else model + + +def initialize_weights(model): + """Initializes weights of Conv2d, BatchNorm2d, and activations (Hardswish, LeakyReLU, ReLU, ReLU6, SiLU) in the + model. + """ + for m in model.modules(): + t = type(m) + if t is nn.Conv2d: + pass # nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu') + elif t is nn.BatchNorm2d: + m.eps = 1e-3 + m.momentum = 0.03 + elif t in [nn.Hardswish, nn.LeakyReLU, nn.ReLU, nn.ReLU6, nn.SiLU]: + m.inplace = True + + +def find_modules(model, mclass=nn.Conv2d): + """Finds and returns list of layer indices in `model.module_list` matching the specified `mclass`.""" + return [i for i, m in enumerate(model.module_list) if isinstance(m, mclass)] + + +def sparsity(model): + """Calculates and returns the global sparsity of a model as the ratio of zero-valued parameters to total + parameters. + """ + a, b = 0, 0 + for p in model.parameters(): + a += p.numel() + b += (p == 0).sum() + return b / a + + +def prune(model, amount=0.3): + """Prunes Conv2d layers in a model to a specified sparsity using L1 unstructured pruning.""" + import torch.nn.utils.prune as prune + + for name, m in model.named_modules(): + if isinstance(m, nn.Conv2d): + prune.l1_unstructured(m, name="weight", amount=amount) # prune + prune.remove(m, "weight") # make permanent + LOGGER.info(f"Model pruned to {sparsity(model):.3g} global sparsity") + + +def fuse_conv_and_bn(conv, bn): + """ + Fuses Conv2d and BatchNorm2d layers into a single Conv2d layer. + + See https://tehnokv.com/posts/fusing-batchnorm-and-conv/. + """ + fusedconv = ( + nn.Conv2d( + conv.in_channels, + conv.out_channels, + kernel_size=conv.kernel_size, + stride=conv.stride, + padding=conv.padding, + dilation=conv.dilation, + groups=conv.groups, + bias=True, + ) + .requires_grad_(False) + .to(conv.weight.device) + ) + + # Prepare filters + w_conv = conv.weight.clone().view(conv.out_channels, -1) + w_bn = torch.diag(bn.weight.div(torch.sqrt(bn.eps + bn.running_var))) + fusedconv.weight.copy_(torch.mm(w_bn, w_conv).view(fusedconv.weight.shape)) + + # Prepare spatial bias + b_conv = torch.zeros(conv.weight.size(0), device=conv.weight.device) if conv.bias is None else conv.bias + b_bn = bn.bias - bn.weight.mul(bn.running_mean).div(torch.sqrt(bn.running_var + bn.eps)) + fusedconv.bias.copy_(torch.mm(w_bn, b_conv.reshape(-1, 1)).reshape(-1) + b_bn) + + return fusedconv + + +def model_info(model, verbose=False, imgsz=640): + """ + Prints model summary including layers, parameters, gradients, and FLOPs; imgsz may be int or list. + + Example: img_size=640 or img_size=[640, 320] + """ + n_p = sum(x.numel() for x in model.parameters()) # number parameters + n_g = sum(x.numel() for x in model.parameters() if x.requires_grad) # number gradients + if verbose: + print(f"{'layer':>5} {'name':>40} {'gradient':>9} {'parameters':>12} {'shape':>20} {'mu':>10} {'sigma':>10}") + for i, (name, p) in enumerate(model.named_parameters()): + name = name.replace("module_list.", "") + print( + "%5g %40s %9s %12g %20s %10.3g %10.3g" + % (i, name, p.requires_grad, p.numel(), list(p.shape), p.mean(), p.std()) + ) + + try: # FLOPs + p = next(model.parameters()) + stride = max(int(model.stride.max()), 32) if hasattr(model, "stride") else 32 # max stride + im = torch.empty((1, p.shape[1], stride, stride), device=p.device) # input image in BCHW format + flops = thop.profile(deepcopy(model), inputs=(im,), verbose=False)[0] / 1e9 * 2 # stride GFLOPs + imgsz = imgsz if isinstance(imgsz, list) else [imgsz, imgsz] # expand if int/float + fs = f", {flops * imgsz[0] / stride * imgsz[1] / stride:.1f} GFLOPs" # 640x640 GFLOPs + except Exception: + fs = "" + + name = Path(model.yaml_file).stem.replace("yolov5", "YOLOv5") if hasattr(model, "yaml_file") else "Model" + LOGGER.info(f"{name} summary: {len(list(model.modules()))} layers, {n_p} parameters, {n_g} gradients{fs}") + + +def scale_img(img, ratio=1.0, same_shape=False, gs=32): # img(16,3,256,416) + """Scales an image tensor `img` of shape (bs,3,y,x) by `ratio`, optionally maintaining the original shape, padded to + multiples of `gs`. + """ + if ratio == 1.0: + return img + h, w = img.shape[2:] + s = (int(h * ratio), int(w * ratio)) # new size + img = F.interpolate(img, size=s, mode="bilinear", align_corners=False) # resize + if not same_shape: # pad/crop img + h, w = (math.ceil(x * ratio / gs) * gs for x in (h, w)) + return F.pad(img, [0, w - s[1], 0, h - s[0]], value=0.447) # value = imagenet mean + + +def copy_attr(a, b, include=(), exclude=()): + """Copies attributes from object b to a, optionally filtering with include and exclude lists.""" + for k, v in b.__dict__.items(): + if (len(include) and k not in include) or k.startswith("_") or k in exclude: + continue + else: + setattr(a, k, v) + + +def smart_optimizer(model, name="Adam", lr=0.001, momentum=0.9, decay=1e-5): + """ + Initializes YOLOv5 smart optimizer with 3 parameter groups for different decay configurations. + + Groups are 0) weights with decay, 1) weights no decay, 2) biases no decay. + """ + g = [], [], [] # optimizer parameter groups + bn = tuple(v for k, v in nn.__dict__.items() if "Norm" in k) # normalization layers, i.e. BatchNorm2d() + for v in model.modules(): + for p_name, p in v.named_parameters(recurse=0): + if p_name == "bias": # bias (no decay) + g[2].append(p) + elif p_name == "weight" and isinstance(v, bn): # weight (no decay) + g[1].append(p) + else: + g[0].append(p) # weight (with decay) + + if name == "Adam": + optimizer = torch.optim.Adam(g[2], lr=lr, betas=(momentum, 0.999)) # adjust beta1 to momentum + elif name == "AdamW": + optimizer = torch.optim.AdamW(g[2], lr=lr, betas=(momentum, 0.999), weight_decay=0.0) + elif name == "RMSProp": + optimizer = torch.optim.RMSprop(g[2], lr=lr, momentum=momentum) + elif name == "SGD": + optimizer = torch.optim.SGD(g[2], lr=lr, momentum=momentum, nesterov=True) + else: + raise NotImplementedError(f"Optimizer {name} not implemented.") + + optimizer.add_param_group({"params": g[0], "weight_decay": decay}) # add g0 with weight_decay + optimizer.add_param_group({"params": g[1], "weight_decay": 0.0}) # add g1 (BatchNorm2d weights) + LOGGER.info( + f"{colorstr('optimizer:')} {type(optimizer).__name__}(lr={lr}) with parameter groups " + f"{len(g[1])} weight(decay=0.0), {len(g[0])} weight(decay={decay}), {len(g[2])} bias" + ) + return optimizer + + +def smart_hub_load(repo="ultralytics/yolov5", model="yolov5s", **kwargs): + """YOLOv5 torch.hub.load() wrapper with smart error handling, adjusting torch arguments for compatibility.""" + if check_version(torch.__version__, "1.9.1"): + kwargs["skip_validation"] = True # validation causes GitHub API rate limit errors + if check_version(torch.__version__, "1.12.0"): + kwargs["trust_repo"] = True # argument required starting in torch 0.12 + try: + return torch.hub.load(repo, model, **kwargs) + except Exception: + return torch.hub.load(repo, model, force_reload=True, **kwargs) + + +def smart_resume(ckpt, optimizer, ema=None, weights="yolov5s.pt", epochs=300, resume=True): + """Resumes training from a checkpoint, updating optimizer, ema, and epochs, with optional resume verification.""" + best_fitness = 0.0 + start_epoch = ckpt["epoch"] + 1 + if ckpt["optimizer"] is not None: + optimizer.load_state_dict(ckpt["optimizer"]) # optimizer + best_fitness = ckpt["best_fitness"] + if ema and ckpt.get("ema"): + ema.ema.load_state_dict(ckpt["ema"].float().state_dict()) # EMA + ema.updates = ckpt["updates"] + if resume: + assert start_epoch > 0, ( + f"{weights} training to {epochs} epochs is finished, nothing to resume.\n" + f"Start a new training without --resume, i.e. 'python train.py --weights {weights}'" + ) + LOGGER.info(f"Resuming training from {weights} from epoch {start_epoch} to {epochs} total epochs") + if epochs < start_epoch: + LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.") + epochs += ckpt["epoch"] # finetune additional epochs + return best_fitness, start_epoch, epochs + + +class EarlyStopping: + """Implements early stopping to halt training when no improvement is observed for a specified number of epochs.""" + + def __init__(self, patience=30): + """Initializes simple early stopping mechanism for YOLOv5, with adjustable patience for non-improving epochs.""" + self.best_fitness = 0.0 # i.e. mAP + self.best_epoch = 0 + self.patience = patience or float("inf") # epochs to wait after fitness stops improving to stop + self.possible_stop = False # possible stop may occur next epoch + + def __call__(self, epoch, fitness): + """Evaluates if training should stop based on fitness improvement and patience, returning a boolean.""" + if fitness >= self.best_fitness: # >= 0 to allow for early zero-fitness stage of training + self.best_epoch = epoch + self.best_fitness = fitness + delta = epoch - self.best_epoch # epochs without improvement + self.possible_stop = delta >= (self.patience - 1) # possible stop may occur next epoch + stop = delta >= self.patience # stop training if patience exceeded + if stop: + LOGGER.info( + f"Stopping training early as no improvement observed in last {self.patience} epochs. " + f"Best results observed at epoch {self.best_epoch}, best model saved as best.pt.\n" + f"To update EarlyStopping(patience={self.patience}) pass a new patience value, " + f"i.e. `python train.py --patience 300` or use `--patience 0` to disable EarlyStopping." + ) + return stop + + +class ModelEMA: + """Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models + Keeps a moving average of everything in the model state_dict (parameters and buffers) + For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage. + """ + + def __init__(self, model, decay=0.9999, tau=2000, updates=0): + """Initializes EMA with model parameters, decay rate, tau for decay adjustment, and update count; sets model to + evaluation mode. + """ + self.ema = deepcopy(de_parallel(model)).eval() # FP32 EMA + self.updates = updates # number of EMA updates + self.decay = lambda x: decay * (1 - math.exp(-x / tau)) # decay exponential ramp (to help early epochs) + for p in self.ema.parameters(): + p.requires_grad_(False) + + def update(self, model): + """Updates the Exponential Moving Average (EMA) parameters based on the current model's parameters.""" + self.updates += 1 + d = self.decay(self.updates) + + msd = de_parallel(model).state_dict() # model state_dict + for k, v in self.ema.state_dict().items(): + if v.dtype.is_floating_point: # true for FP16 and FP32 + v *= d + v += (1 - d) * msd[k].detach() + # assert v.dtype == msd[k].dtype == torch.float32, f'{k}: EMA {v.dtype} and model {msd[k].dtype} must be FP32' + + def update_attr(self, model, include=(), exclude=("process_group", "reducer")): + """Updates EMA attributes by copying specified attributes from model to EMA, excluding certain attributes by + default. + """ + copy_attr(self.ema, model, include, exclude) diff --git a/app/util/yolov5/utils/triton.py b/app/util/yolov5/utils/triton.py new file mode 100644 index 0000000..88c0bd7 --- /dev/null +++ b/app/util/yolov5/utils/triton.py @@ -0,0 +1,90 @@ +# Ultralytics 🚀 AGPL-3.0 License - https://ultralytics.com/license +"""Utils to interact with the Triton Inference Server.""" + +import typing +from urllib.parse import urlparse + +import torch + + +class TritonRemoteModel: + """ + A wrapper over a model served by the Triton Inference Server. + + It can be configured to communicate over GRPC or HTTP. It accepts Torch Tensors as input and returns them as + outputs. + """ + + def __init__(self, url: str): + """ + Keyword Arguments: + url: Fully qualified address of the Triton server - for e.g. grpc://localhost:8000. + """ + parsed_url = urlparse(url) + if parsed_url.scheme == "grpc": + from tritonclient.grpc import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton GRPC client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository.models[0].name + self.metadata = self.client.get_model_metadata(self.model_name, as_json=True) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] + ] + + else: + from tritonclient.http import InferenceServerClient, InferInput + + self.client = InferenceServerClient(parsed_url.netloc) # Triton HTTP client + model_repository = self.client.get_model_repository_index() + self.model_name = model_repository[0]["name"] + self.metadata = self.client.get_model_metadata(self.model_name) + + def create_input_placeholders() -> typing.List[InferInput]: + return [ + InferInput(i["name"], [int(s) for s in i["shape"]], i["datatype"]) for i in self.metadata["inputs"] + ] + + self._create_input_placeholders_fn = create_input_placeholders + + @property + def runtime(self): + """Returns the model runtime.""" + return self.metadata.get("backend", self.metadata.get("platform")) + + def __call__(self, *args, **kwargs) -> typing.Union[torch.Tensor, typing.Tuple[torch.Tensor, ...]]: + """ + Invokes the model. + + Parameters can be provided via args or kwargs. args, if provided, are assumed to match the order of inputs of + the model. kwargs are matched with the model input names. + """ + inputs = self._create_inputs(*args, **kwargs) + response = self.client.infer(model_name=self.model_name, inputs=inputs) + result = [] + for output in self.metadata["outputs"]: + tensor = torch.as_tensor(response.as_numpy(output["name"])) + result.append(tensor) + return result[0] if len(result) == 1 else result + + def _create_inputs(self, *args, **kwargs): + """Creates input tensors from args or kwargs, not both; raises error if none or both are provided.""" + args_len, kwargs_len = len(args), len(kwargs) + if not args_len and not kwargs_len: + raise RuntimeError("No inputs provided.") + if args_len and kwargs_len: + raise RuntimeError("Cannot specify args and kwargs at the same time") + + placeholders = self._create_input_placeholders_fn() + if args_len: + if args_len != len(placeholders): + raise RuntimeError(f"Expected {len(placeholders)} inputs, got {args_len}.") + for input, value in zip(placeholders, args): + input.set_data_from_numpy(value.cpu().numpy()) + else: + for input in placeholders: + value = kwargs[input.name] + input.set_data_from_numpy(value.cpu().numpy()) + return placeholders diff --git a/app/websocket/web_socket_server.py b/app/websocket/web_socket_server.py index 24b29fe..2a19c9c 100644 --- a/app/websocket/web_socket_server.py +++ b/app/websocket/web_socket_server.py @@ -15,6 +15,8 @@ class SocketManager: self.rooms[room].remove(websocket) if len(self.rooms[room]) == 0: del self.rooms[room] + if room.startswith('detect_rtsp_'): + print() async def broadcast_to_room(self, room: str, message: str, exclude_websocket: WebSocket = None): if room in self.rooms: @@ -30,8 +32,16 @@ class SocketManager: for ws in self.rooms[room]: try: await ws.send_text(message) - except: - await self.remove_from_room(room, ws) + except Exception as e: + print(e) + + async def send_stream_to_room(self, room: str, message): + if room in self.rooms: + for ws in self.rooms[room]: + try: + await ws.send_bytes(message) + except Exception as e: + print(e) room_manager = SocketManager() diff --git a/yolov5/.gitignore b/yolov5/.gitignore index 7f683c9..d8b9c06 100644 --- a/yolov5/.gitignore +++ b/yolov5/.gitignore @@ -24,7 +24,7 @@ !cfg/yolov3*.cfg storage.googleapis.com -../runs/* +runs/* data/* data/images/* !data/*.yaml diff --git a/yolov5/models/common.py b/yolov5/models/common.py index 6aa6217..ea893db 100644 --- a/yolov5/models/common.py +++ b/yolov5/models/common.py @@ -35,9 +35,9 @@ except (ImportError, AssertionError): from ultralytics.utils.plotting import Annotator, colors, save_one_box -from yolov5.utils import TryExcept -from yolov5.utils.dataloaders import exif_transpose, letterbox -from yolov5.utils.general import ( +from utils import TryExcept +from utils.dataloaders import exif_transpose, letterbox +from utils.general import ( LOGGER, ROOT, Profile, @@ -54,7 +54,7 @@ from yolov5.utils.general import ( xyxy2xywh, yaml_load, ) -from yolov5.utils.torch_utils import copy_attr, smart_inference_mode +from utils.torch_utils import copy_attr, smart_inference_mode def autopad(k, p=None, d=1): @@ -473,7 +473,7 @@ class DetectMultiBackend(nn.Module): # TensorFlow Lite: *.tflite # TensorFlow Edge TPU: *_edgetpu.tflite # PaddlePaddle: *_paddle_model - from yolov5.models.experimental import attempt_download, attempt_load # scoped to avoid circular import + from models.experimental import attempt_download, attempt_load # scoped to avoid circular import super().__init__() w = str(weights[0] if isinstance(weights, list) else weights) @@ -661,7 +661,7 @@ class DetectMultiBackend(nn.Module): elif triton: # NVIDIA Triton Inference Server LOGGER.info(f"Using {w} as Triton Inference Server...") check_requirements("tritonclient[all]") - from yolov5.utils.triton import TritonRemoteModel + from utils.triton import TritonRemoteModel model = TritonRemoteModel(url=w) nhwc = model.runtime.startswith("tensorflow") @@ -780,8 +780,8 @@ class DetectMultiBackend(nn.Module): Example: path='path/to/model.onnx' -> type=onnx """ # types = [pt, jit, onnx, xml, engine, coreml, saved_model, pb, tflite, edgetpu, tfjs, paddle] - from yolov5.export import export_formats - from yolov5.utils.downloads import is_url + from export import export_formats + from utils.downloads import is_url sf = list(export_formats().Suffix) # export suffixes if not is_url(p, check=False): diff --git a/yolov5/models/experimental.py b/yolov5/models/experimental.py index c3fff90..63d9c46 100644 --- a/yolov5/models/experimental.py +++ b/yolov5/models/experimental.py @@ -7,7 +7,7 @@ import numpy as np import torch import torch.nn as nn -from yolov5.utils.downloads import attempt_download +from utils.downloads import attempt_download class Sum(nn.Module): @@ -91,7 +91,7 @@ def attempt_load(weights, device=None, inplace=True, fuse=True): Example inputs: weights=[a,b,c] or a single model weights=[a] or weights=a. """ - from yolov5.models.yolo import Detect, Model + from models.yolo import Detect, Model model = Ensemble() for w in weights if isinstance(weights, list) else [weights]: diff --git a/yolov5/segment/val.py b/yolov5/segment/val.py index 29ca803..edd6a08 100644 --- a/yolov5/segment/val.py +++ b/yolov5/segment/val.py @@ -413,7 +413,7 @@ def run( # Save JSON if save_json and len(jdict): w = Path(weights[0] if isinstance(weights, list) else weights).stem if weights is not None else "" # weights - anno_json = str(Path("../../datasets/coco/annotations/instances_val2017.json")) # annotations + anno_json = str(Path("../datasets/coco/annotations/instances_val2017.json")) # annotations pred_json = str(save_dir / f"{w}_predictions.json") # predictions LOGGER.info(f"\nEvaluating pycocotools mAP... saving {pred_json}...") with open(pred_json, "w") as f: diff --git a/yolov5/train.py b/yolov5/train.py index 04a87a4..1401ccb 100644 --- a/yolov5/train.py +++ b/yolov5/train.py @@ -409,7 +409,7 @@ def train(hyp, opt, device, callbacks): imgs = nn.functional.interpolate(imgs, size=ns, mode="bilinear", align_corners=False) # Forward - with torch.amp.autocast(device_type='cuda',enabled=amp): + with torch.cuda.amp.autocast(amp): pred = model(imgs) # forward loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size if RANK != -1: @@ -566,7 +566,7 @@ def parse_opt(known=False): parser = argparse.ArgumentParser() parser.add_argument("--weights", type=str, default=ROOT / "yolov5s.pt", help="initial weights path") parser.add_argument("--cfg", type=str, default="", help="model.yaml path") - parser.add_argument("--data", type=str, default=ROOT / "data/5t5.yaml", help="dataset.yaml path") + parser.add_argument("--data", type=str, default=ROOT / "data/coco128.yaml", help="dataset.yaml path") parser.add_argument("--hyp", type=str, default=ROOT / "data/hyps/hyp.scratch-low.yaml", help="hyperparameters path") parser.add_argument("--epochs", type=int, default=100, help="total training epochs") parser.add_argument("--batch-size", type=int, default=16, help="total batch size for all GPUs, -1 for autobatch") diff --git a/yolov5/utils/augmentations.py b/yolov5/utils/augmentations.py index 07b5f9f..79e7afc 100644 --- a/yolov5/utils/augmentations.py +++ b/yolov5/utils/augmentations.py @@ -10,8 +10,8 @@ import torch import torchvision.transforms as T import torchvision.transforms.functional as TF -from yolov5.utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy -from yolov5.utils.metrics import bbox_ioa +from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy +from utils.metrics import bbox_ioa IMAGENET_MEAN = 0.485, 0.456, 0.406 # RGB mean IMAGENET_STD = 0.229, 0.224, 0.225 # RGB standard deviation diff --git a/yolov5/utils/dataloaders.py b/yolov5/utils/dataloaders.py index f4251c1..7ca5a85 100644 --- a/yolov5/utils/dataloaders.py +++ b/yolov5/utils/dataloaders.py @@ -26,7 +26,7 @@ from PIL import ExifTags, Image, ImageOps from torch.utils.data import DataLoader, Dataset, dataloader, distributed from tqdm import tqdm -from yolov5.utils.augmentations import ( +from utils.augmentations import ( Albumentations, augment_hsv, classify_albumentations, @@ -36,7 +36,7 @@ from yolov5.utils.augmentations import ( mixup, random_perspective, ) -from yolov5.utils.general import ( +from utils.general import ( DATASETS_DIR, LOGGER, NUM_THREADS, @@ -55,7 +55,7 @@ from yolov5.utils.general import ( xywhn2xyxy, xyxy2xywhn, ) -from yolov5.utils.torch_utils import torch_distributed_zero_first +from utils.torch_utils import torch_distributed_zero_first # Parameters HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data" diff --git a/yolov5/utils/general.py b/yolov5/utils/general.py index 574ef48..89bbc61 100644 --- a/yolov5/utils/general.py +++ b/yolov5/utils/general.py @@ -45,9 +45,9 @@ except (ImportError, AssertionError): from ultralytics.utils.checks import check_requirements -from yolov5.utils import TryExcept, emojis -from yolov5.utils.downloads import curl_download, gsutil_getsize -from yolov5.utils.metrics import box_iou, fitness +from utils import TryExcept, emojis +from utils.downloads import curl_download, gsutil_getsize +from utils.metrics import box_iou, fitness FILE = Path(__file__).resolve() ROOT = FILE.parents[1] # YOLOv5 root directory @@ -585,7 +585,7 @@ def check_dataset(data, autodownload=True): def check_amp(model): """Checks PyTorch AMP functionality for a model, returns True if AMP operates correctly, otherwise False.""" - from yolov5.models.common import AutoShape, DetectMultiBackend + from models.common import AutoShape, DetectMultiBackend def amp_allclose(model, im): """Compares FP32 and AMP model inference outputs, ensuring they are close within a 10% absolute tolerance.""" @@ -611,27 +611,6 @@ def check_amp(model): return False -def scale_coords(img1_shape, coords, img0_shape, ratio_pad=None): - # Rescale coords (xyxy) from img1_shape to img0_shape - if ratio_pad is None: # calculate from img0_shape - gain = min(img1_shape[0] / img0_shape[0], img1_shape[1] / img0_shape[1]) # gain = old / new - pad = (img1_shape[1] - img0_shape[1] * gain) / 2, (img1_shape[0] - img0_shape[0] * gain) / 2 # wh padding - else: - gain = ratio_pad[0][0] - pad = ratio_pad[1] - - coords[:, [0, 2]] -= pad[0] # x padding - coords[:, [1, 3]] -= pad[1] # y padding - coords[:, :4] /= gain - - # Clip bounding xyxy bounding boxes to image shape (height, width) - coords[:, 0].clamp_(0, img0_shape[1]) # x1 - coords[:, 1].clamp_(0, img0_shape[0]) # y1 - coords[:, 2].clamp_(0, img0_shape[1]) # x2 - coords[:, 3].clamp_(0, img0_shape[0]) # y2 - return coords - - def yaml_load(file="data.yaml"): """Safely loads and returns the contents of a YAML file specified by `file` argument.""" with open(file, errors="ignore") as f: diff --git a/yolov5/utils/metrics.py b/yolov5/utils/metrics.py index 362e098..03013f4 100644 --- a/yolov5/utils/metrics.py +++ b/yolov5/utils/metrics.py @@ -9,7 +9,7 @@ import matplotlib.pyplot as plt import numpy as np import torch -from yolov5.utils import TryExcept, threaded +from utils import TryExcept, threaded def fitness(x): diff --git a/yolov5/utils/plots.py b/yolov5/utils/plots.py index 3e08097..f70775f 100644 --- a/yolov5/utils/plots.py +++ b/yolov5/utils/plots.py @@ -18,9 +18,9 @@ from PIL import Image, ImageDraw from scipy.ndimage.filters import gaussian_filter1d from ultralytics.utils.plotting import Annotator -from yolov5.utils import TryExcept, threaded -from yolov5.utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh -from yolov5.utils.metrics import fitness +from utils import TryExcept, threaded +from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh +from utils.metrics import fitness # Settings RANK = int(os.getenv("RANK", -1)) @@ -372,7 +372,7 @@ def plot_labels(labels, names=(), save_dir=Path("")): def imshow_cls(im, labels=None, pred=None, names=None, nmax=25, verbose=False, f=Path("images.jpg")): """Displays a grid of images with optional labels and predictions, saving to a file.""" - from yolov5.utils.augmentations import denormalize + from utils.augmentations import denormalize names = names or [f"class{i}" for i in range(1000)] blocks = torch.chunk( diff --git a/yolov5/utils/torch_utils.py b/yolov5/utils/torch_utils.py index 3210731..8b3c43b 100644 --- a/yolov5/utils/torch_utils.py +++ b/yolov5/utils/torch_utils.py @@ -17,7 +17,7 @@ import torch.nn as nn import torch.nn.functional as F from torch.nn.parallel import DistributedDataParallel as DDP -from yolov5.utils.general import LOGGER, check_version, colorstr, file_date, git_describe +from utils.general import LOGGER, check_version, colorstr, file_date, git_describe LOCAL_RANK = int(os.getenv("LOCAL_RANK", -1)) # https://pytorch.org/docs/stable/elastic/run.html RANK = int(os.getenv("RANK", -1)) @@ -68,7 +68,7 @@ def smart_DDP(model): def reshape_classifier_output(model, n=1000): """Reshapes last layer of model to match class count 'n', supporting Classify, Linear, Sequential types.""" - from yolov5.models.common import Classify + from models.common import Classify name, m = list((model.model if hasattr(model, "model") else model).named_children())[-1] # last module if isinstance(m, Classify): # YOLOv5 Classify() head