补全yolov5的代码

yolov5/utils/augmentations.py

@@ -10,8 +10,8 @@ import torch
 import torchvision.transforms as T
 import torchvision.transforms.functional as TF
 
-from utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
-from utils.metrics import bbox_ioa
+from yolov5.utils.general import LOGGER, check_version, colorstr, resample_segments, segment2box, xywhn2xyxy
+from 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

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 utils.augmentations import (
+from yolov5.utils.augmentations import (
     Albumentations,
     augment_hsv,
     classify_albumentations,
@@ -36,7 +36,7 @@ from utils.augmentations import (
     mixup,
     random_perspective,
 )
-from utils.general import (
+from yolov5.utils.general import (
     DATASETS_DIR,
     LOGGER,
     NUM_THREADS,
@@ -55,7 +55,7 @@ from utils.general import (
     xywhn2xyxy,
     xyxy2xywhn,
 )
-from utils.torch_utils import torch_distributed_zero_first
+from yolov5.utils.torch_utils import torch_distributed_zero_first
 
 # Parameters
 HELP_URL = "See https://docs.ultralytics.com/yolov5/tutorials/train_custom_data"

yolov5/utils/general.py

@@ -45,9 +45,9 @@ except (ImportError, AssertionError):
 
 from ultralytics.utils.checks import check_requirements
 
-from utils import TryExcept, emojis
-from utils.downloads import curl_download, gsutil_getsize
-from utils.metrics import box_iou, fitness
+from yolov5.utils import TryExcept, emojis
+from yolov5.utils.downloads import curl_download, gsutil_getsize
+from yolov5.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 models.common import AutoShape, DetectMultiBackend
+    from 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."""
@@ -611,6 +611,27 @@ 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:

yolov5/utils/loggers/comet/optimizer_config.json

@@ -1,135 +0,0 @@
-{
-  "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
-}

yolov5/utils/metrics.py

@@ -9,7 +9,7 @@ import matplotlib.pyplot as plt
 import numpy as np
 import torch
 
-from utils import TryExcept, threaded
+from yolov5.utils import TryExcept, threaded
 
 
 def fitness(x):

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 utils import TryExcept, threaded
-from utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh
-from utils.metrics import fitness
+from yolov5.utils import TryExcept, threaded
+from yolov5.utils.general import LOGGER, clip_boxes, increment_path, xywh2xyxy, xyxy2xywh
+from yolov5.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 utils.augmentations import denormalize
+    from yolov5.utils.augmentations import denormalize
 
     names = names or [f"class{i}" for i in range(1000)]
     blocks = torch.chunk(

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 utils.general import LOGGER, check_version, colorstr, file_date, git_describe
+from 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))
@@ -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 models.common import Classify
+    from 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