第一次调试

app/yolov5/classify/predict.py

@@ -39,13 +39,13 @@ if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from models.common import DetectMultiBackend
-from utils.augmentations import classify_transforms
-from utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
-from utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
+from app.yolov5.models.common import DetectMultiBackend
+from app.yolov5.utils.augmentations import classify_transforms
+from app.yolov5.utils.dataloaders import IMG_FORMATS, VID_FORMATS, LoadImages, LoadStreams
+from app.yolov5.utils.general import (LOGGER, Profile, check_file, check_img_size, check_imshow, check_requirements, colorstr, cv2,
                            increment_path, print_args, strip_optimizer)
-from utils.plots import Annotator
-from utils.torch_utils import select_device, smart_inference_mode
+from app.yolov5.utils.plots import Annotator
+from app.yolov5.utils.torch_utils import select_device, smart_inference_mode
 
 
 @smart_inference_mode()

app/yolov5/classify/train.py

@@ -36,15 +36,15 @@ if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from classify import val as validate
-from models.experimental import attempt_load
-from models.yolo import ClassificationModel, DetectionModel
-from utils.dataloaders import create_classification_dataloader
-from utils.general import (DATASETS_DIR, LOGGER, WorkingDirectory, check_git_status, check_requirements, colorstr,
+from app.yolov5.classify import val as validate
+from app.yolov5.models.experimental import attempt_load
+from app.yolov5.models.yolo import ClassificationModel, DetectionModel
+from app.yolov5.utils.dataloaders import create_classification_dataloader
+from app.yolov5.utils.general import (DATASETS_DIR, LOGGER, WorkingDirectory, check_git_status, check_requirements, colorstr,
                            download, increment_path, init_seeds, print_args, yaml_save)
-from utils.loggers import GenericLogger
-from utils.plots import imshow_cls
-from utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
+from app.yolov5.utils.loggers import GenericLogger
+from app.yolov5.utils.plots import imshow_cls
+from app.yolov5.utils.torch_utils import (ModelEMA, model_info, reshape_classifier_output, select_device, smart_DDP,
                                smart_optimizer, smartCrossEntropyLoss, torch_distributed_zero_first)
 
 LOCAL_RANK = int(os.getenv('LOCAL_RANK', -1))  # https://pytorch.org/docs/stable/elastic/run.html

app/yolov5/classify/val.py

@@ -33,10 +33,10 @@ if str(ROOT) not in sys.path:
     sys.path.append(str(ROOT))  # add ROOT to PATH
 ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from models.common import DetectMultiBackend
-from utils.dataloaders import create_classification_dataloader
-from utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
-from utils.torch_utils import select_device, smart_inference_mode
+from app.yolov5.models.common import DetectMultiBackend
+from app.yolov5.utils.dataloaders import create_classification_dataloader
+from app.yolov5.utils.general import LOGGER, Profile, check_img_size, check_requirements, colorstr, increment_path, print_args
+from app.yolov5.utils.torch_utils import select_device, smart_inference_mode
 
 
 @smart_inference_mode()

app/yolov5/export.py

@@ -64,12 +64,12 @@ if str(ROOT) not in sys.path:
 if platform.system() != 'Windows':
     ROOT = Path(os.path.relpath(ROOT, Path.cwd()))  # relative
 
-from models.experimental import attempt_load
-from models.yolo import ClassificationModel, Detect
-from utils.dataloaders import LoadImages
-from utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
+from app.yolov5.models.experimental import attempt_load
+from app.yolov5.models.yolo import ClassificationModel, Detect
+from app.yolov5.utils.dataloaders import LoadImages
+from app.yolov5.utils.general import (LOGGER, Profile, check_dataset, check_img_size, check_requirements, check_version,
                            check_yaml, colorstr, file_size, get_default_args, print_args, url2file)
-from utils.torch_utils import select_device, smart_inference_mode
+from app.yolov5.utils.torch_utils import select_device, smart_inference_mode
 
 
 def export_formats():
@@ -309,7 +309,7 @@ def export_saved_model(model,
     import tensorflow as tf
     from tensorflow.python.framework.convert_to_constants import convert_variables_to_constants_v2
 
-    from models.tf import TFModel
+    from app.yolov5.models.tf import TFModel
 
     LOGGER.info(f'\n{prefix} starting export with tensorflow {tf.__version__}...')
     f = str(file).replace('.pt', '_saved_model')
@@ -371,7 +371,7 @@ def export_tflite(keras_model, im, file, int8, data, nms, agnostic_nms, prefix=c
     converter.target_spec.supported_types = [tf.float16]
     converter.optimizations = [tf.lite.Optimize.DEFAULT]
     if int8:
-        from models.tf import representative_dataset_gen
+        from app.yolov5.models.tf import representative_dataset_gen
         dataset = LoadImages(check_dataset(check_yaml(data))['train'], img_size=imgsz, auto=False)
         converter.representative_dataset = lambda: representative_dataset_gen(dataset, ncalib=100)
         converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]

app/yolov5/hubconf.py

@@ -28,12 +28,12 @@ def _create(name, pretrained=True, channels=3, classes=80, autoshape=True, verbo
     """
     from pathlib import Path
 
-    from models.common import AutoShape, DetectMultiBackend
-    from models.experimental import attempt_load
-    from models.yolo import ClassificationModel, DetectionModel
-    from utils.downloads import attempt_download
-    from utils.general import LOGGER, check_requirements, intersect_dicts, logging
-    from utils.torch_utils import select_device
+    from app.yolov5.models.common import AutoShape, DetectMultiBackend
+    from app.yolov5.models.experimental import attempt_load
+    from app.yolov5.models.yolo import ClassificationModel, DetectionModel
+    from app.yolov5.utils.downloads import attempt_download
+    from app.yolov5.utils.general import LOGGER, check_requirements, intersect_dicts, logging
+    from app.yolov5.utils.torch_utils import select_device
 
     if not verbose:
         LOGGER.setLevel(logging.WARNING)

app/yolov5/models/experimental.py

@@ -72,7 +72,7 @@ class Ensemble(nn.ModuleList):
 
 def attempt_load(weights, device=None, inplace=True, fuse=True):
     # Loads an ensemble of models weights=[a,b,c] or a single model weights=[a] or weights=a
-    from models.yolo import Detect, Model
+    from app.yolov5.models.yolo import Detect, Model
 
     model = Ensemble()
     for w in weights if isinstance(weights, list) else [weights]:

app/yolov5/models/tf.py

@@ -27,12 +27,12 @@ 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,
+from app.yolov5.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
-from utils.activations import SiLU
-from utils.general import LOGGER, make_divisible, print_args
+from app.yolov5.models.experimental import MixConv2d, attempt_load
+from app.yolov5.models.yolo import Detect
+from app.yolov5.utils.activations import SiLU
+from app.yolov5.utils.general import LOGGER, make_divisible, print_args
 
 
 class TFBN(keras.layers.Layer):

app/yolov5/train_server.py

@@ -462,7 +462,7 @@ def train(hyp, opt, device, data_list,id,callbacks):  # hyp is path/to/hyp.yaml
         #     break  # must break all DDP ranks
         ######实时传输训练精度参数#############
         #gain_train_report(int(epoch + 1), float(results[0]), pro, version, epochs)
-        report_cellback(epoch, epochs, float(results[0]),train_num)
+        report_cellback(epoch, epochs, float(results[0]))
         # end epoch ----------------------------------------------------------------------------------------------------
     ########训练数量和保存模型###########
     #save_train_report_result(pro, train_num, best)

app/yolov5/utils/downloads.py

@@ -64,7 +64,7 @@ def safe_download(file, url, url2=None, min_bytes=1E0, error_msg=''):
 
 def attempt_download(file, repo='ultralytics/yolov5', release='v6.2'):
     # Attempt file download from GitHub release assets if not found locally. release = 'latest', 'v6.2', etc.
-    from utils.general import LOGGER
+    from app.yolov5.utils.general import LOGGER
 
     def github_assets(repository, version='latest'):
         # Return GitHub repo tag (i.e. 'v6.2') and assets (i.e. ['yolov5s.pt', 'yolov5m.pt', ...])

app/yolov5/utils/torch_utils.py

@@ -64,7 +64,7 @@ def smart_DDP(model):
 
 def reshape_classifier_output(model, n=1000):
     # Update a TorchVision classification model to class count 'n' if required
-    from models.common import Classify
+    from app.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: