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jaskiratsingh2000 / Research-Scalable-Vehicle-Detection-on-Edge-Devices

Research project tracker: Scalable Vehicle Detection on Edge Devices
MIT License
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[Testing] Finding PyTorch Model Information inside YOLOv5 #25

Open jaskiratsingh2000 opened 3 years ago

jaskiratsingh2000 commented 3 years ago

Was able to load the PyTorch Model by using the following command:

>>> import torch
>>> model = torch.hub.load('ultralytics/yolov5', 'yolov5s')
>>> print(model)
jaskiratsingh2000 commented 3 years ago

Output:

Downloading: "https://github.com/ultralytics/yolov5/archive/master.zip" to /home/jaskirat/.cache/torch/hub/master.zip
Matplotlib is building the font cache; this may take a moment.
YOLOv5 🚀 2021-11-3 torch 1.10.0+cu102 CPU

Downloading https://github.com/ultralytics/yolov5/releases/download/v6.0/yolov5s.pt to /home/jaskirat/.cache/torch/hub/ultralytics_yolov5_master/yolov5s.pt...
100%|██████████████████████████████████████████████████████████████████████████████████████████████████████| 14.0M/14.0M [00:01<00:00, 10.0MB/s]

Fusing layers... 
Model Summary: 213 layers, 7225885 parameters, 0 gradients
Adding AutoShape... 
>>> print(model)
AutoShape(
  (model): Model(
    (model): Sequential(
      (0): Conv(
        (conv): Conv2d(3, 32, kernel_size=(6, 6), stride=(2, 2), padding=(2, 2))
        (act): SiLU(inplace=True)
      )
      (1): Conv(
        (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (act): SiLU(inplace=True)
      )
      (2): C3(
        (cv1): Conv(
          (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (3): Conv(
        (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (act): SiLU(inplace=True)
      )
      (4): C3(
        (cv1): Conv(
          (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
          (1): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (5): Conv(
        (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (act): SiLU(inplace=True)
      )
      (6): C3(
        (cv1): Conv(
          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
          (1): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
          (2): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (7): Conv(
        (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (act): SiLU(inplace=True)
      )
      (8): C3(
        (cv1): Conv(
          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (9): SPPF(
        (cv1): Conv(
          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
      )
      (10): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): SiLU(inplace=True)
      )
      (11): Upsample(scale_factor=2.0, mode=nearest)
      (12): Concat()
      (13): C3(
        (cv1): Conv(
          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (14): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): SiLU(inplace=True)
      )
      (15): Upsample(scale_factor=2.0, mode=nearest)
      (16): Concat()
      (17): C3(
        (cv1): Conv(
          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (18): Conv(
        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (act): SiLU(inplace=True)
      )
      (19): Concat()
      (20): C3(
        (cv1): Conv(
          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (21): Conv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
        (act): SiLU(inplace=True)
      )
      (22): Concat()
      (23): C3(
        (cv1): Conv(
          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv2): Conv(
          (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (cv3): Conv(
          (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
          (act): SiLU(inplace=True)
        )
        (m): Sequential(
          (0): Bottleneck(
            (cv1): Conv(
              (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
              (act): SiLU(inplace=True)
            )
            (cv2): Conv(
              (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
              (act): SiLU(inplace=True)
            )
          )
        )
      )
      (24): Detect(
        (m): ModuleList(
          (0): Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1))
          (1): Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1))
          (2): Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1))
        )
      )
    )
  )
)