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+      <option name="project" value="LOCAL" />
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   <component name="TypeScriptGeneratedFilesManager">
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+  <component name="Vcs.Log.Tabs.Properties">
+    <option name="TAB_STATES">
+      <map>
+        <entry key="7fa0cafa-346f-4108-8391-ef5b1a6ab08f">
+          <value>
+            <State>
+              <option name="SHOW_ONLY_AFFECTED_CHANGES" value="true" />
+              <option name="FILTERS">
+                <map>
+                  <entry key="branch">
+                    <value>
+                      <list>
+                        <option value="HEAD" />
+                      </list>
+                    </value>
+                  </entry>
+                  <entry key="roots">
+                    <value>
+                      <list>
+                        <option value="$PROJECT_DIR$" />
+                      </list>
+                    </value>
+                  </entry>
+                </map>
+              </option>
+            </State>
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+    <option name="OPEN_GENERIC_TABS">
+      <map>
+        <entry key="7fa0cafa-346f-4108-8391-ef5b1a6ab08f" value="TOOL_WINDOW" />
+      </map>
+    </option>
+  </component>
   <component name="VcsManagerConfiguration">
     <MESSAGE value="first commit" />
     <option name="LAST_COMMIT_MESSAGE" value="first commit" />
@@ -373,12 +291,13 @@
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+    <SUITE FILE_PATH="coverage/server_develop$multi_thread_test.coverage" NAME="multi_thread_test Coverage Results" MODIFIED="1646357967827" SOURCE_PROVIDER="com.intellij.coverage.DefaultCoverageFileProvider" RUNNER="coverage.py" COVERAGE_BY_TEST_ENABLED="true" COVERAGE_TRACING_ENABLED="false" WORKING_DIRECTORY="$PROJECT_DIR$/src_code/test/test_src" />
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src_code/model_zoo/yolov5_model_zoo/coco.yaml

@@ -0,0 +1,44 @@
+# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
+# COCO 2017 dataset http://cocodataset.org by Microsoft
+# Example usage: python train.py --data coco.yaml
+# parent
+# ├── yolov5
+# └── datasets
+#     └── coco  ← downloads here
+
+
+# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
+path: ../datasets/coco  # dataset root dir
+train: train2017.txt  # train images (relative to 'path') 118287 images
+val: val2017.txt  # val images (relative to 'path') 5000 images
+test: test-dev2017.txt  # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
+
+# Classes
+nc: 80  # number of classes
+names: ['person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train', 'truck', 'boat', 'traffic light',
+        'fire hydrant', 'stop sign', 'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
+        'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag', 'tie', 'suitcase', 'frisbee',
+        'skis', 'snowboard', 'sports ball', 'kite', 'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
+        'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon', 'bowl', 'banana', 'apple',
+        'sandwich', 'orange', 'broccoli', 'carrot', 'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch',
+        'potted plant', 'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote', 'keyboard', 'cell phone',
+        'microwave', 'oven', 'toaster', 'sink', 'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
+        'hair drier', 'toothbrush']  # class names
+
+
+# Download script/URL (optional)
+download: |
+  from utils.general import download, Path
+
+  # Download labels
+  segments = False  # segment or box labels
+  dir = Path(yaml['path'])  # dataset root dir
+  url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
+  urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')]  # labels
+  download(urls, dir=dir.parent)
+
+  # Download data
+  urls = ['http://images.cocodataset.org/zips/train2017.zip',  # 19G, 118k images
+          'http://images.cocodataset.org/zips/val2017.zip',  # 1G, 5k images
+          'http://images.cocodataset.org/zips/test2017.zip']  # 7G, 41k images (optional)
+  download(urls, dir=dir / 'images', threads=3)

src_code/src/model_structure/yolov5_structure/yolov5-master/utils/torch_utils.py

@@ -20,6 +20,7 @@ import torch.nn as nn
 import torch.nn.functional as F
 
 from utils.general import LOGGER
+import logging
 
 try:
     import thop  # for FLOPs computation
@@ -156,12 +157,15 @@ def profile(input, ops, n=10, device=None):
 
 
 def is_parallel(model):
+
     # Returns True if model is of type DP or DDP
+    logging.info("!!!!!!!!!!!!!!!!!!!!!!"*100)
     return type(model) in (nn.parallel.DataParallel, nn.parallel.DistributedDataParallel)
 
 
 def de_parallel(model):
     # De-parallelize a model: returns single-GPU model if model is of type DP or DDP
+    logging.info("@@@@@@@@@@@@@@@"*100)
     return model.module if is_parallel(model) else model
 
 

src_code/src/model_structure/yolov5_structure/yolov5.py

@@ -12,15 +12,27 @@ from cf import yolov5_yaml_config
 from models.yolo import Model
 from utils.general import *
 from models.common import *
+from utils.torch_utils import select_device
 
 
 class YOLOV5(object):
     def __init__(self):
         LOGGER.setLevel(logging.WARNING)
         self.model = None
+
         self.config = yolov5_yaml_config
-        self.device = self.config.get("basic_params").get("device")
-        self.device = self.device if self.device else -1
+        # self.device = self.config.get("basic_params").get("device")
+        self.gpu_id = self.config.get("basic_params").get("gpu_id")
+        # self.use_multi_gpu = False
+        # if len(gpu_id.split(",")) > 1 and torch.cuda.device_count() > 1:
+        #     self.use_multi_gpu = True
+        # if torch.cuda.is_available() and gpu_id != "-1":
+        #     device_id = torch.device("cuda:0")
+        # else:
+        #     device_id = torch.device("cpu")
+        # self.device = torch.device(device_id)
+
+
         self.root_dir = self.config.get("basic_params").get("root_dir", None)
         self.structure_yaml = self.config.get("basic_params").get("struct_path", None)
         self.model_weight = self.config.get("basic_params").get("weight_path", None)
@@ -32,8 +44,6 @@ class YOLOV5(object):
         self.multi_label = self.config.get("advanced_params").get("multi_label", False)
         self.max_det = self.config.get("advanced_params").get("max_det", 1000)
         self.amp = self.config.get("advanced_params").get("amp", False)
-        self.structure_yaml_path = ""
-        self.model_weight_path = ""
         if self.root_dir is not None:
             if self.structure_yaml is not None:
                 self.structure_yaml_path = os.path.join(self.root_dir, self.structure_yaml)
@@ -43,6 +53,10 @@ class YOLOV5(object):
 
     def create(self):
         model = Model(self.structure_yaml_path, nc=self.class_num)
+        device = "cpu"
+        if torch.cuda.is_available():
+            device = select_device(self.gpu_id, batch_size=2)
+        model = model.to(device)
         ckpt = torch.load(self.model_weight_path)
         csd = ckpt['model'].float().state_dict()
         model.load_state_dict(csd)  # load
@@ -56,8 +70,6 @@ class YOLOV5(object):
         model.multi_label = self.multi_label
         model.max_det = self.max_det
         model.amp = self.amp
-        if self.device != -1:
-            model = model.cuda()
         model.eval()
         return model