Model Summary: 224 layers, 7266973 parameters, 0 gradients, 17.0 GFLOPS
Namespace(batch_size=1, img_size=[640, 640], weights='./yolov5s.pt')
Fusing layers... 
------------ BEGIN : DG._module_to_name ----------------------
{Model(
  (model): Sequential(
    (0): Focus(
      (conv): Conv(
        (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (act): ReLU()
      )
    )
    (1): Conv(
      (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): ReLU()
    )
    (2): C3(
      (cv1): Conv(
        (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (3): Conv(
      (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): ReLU()
    )
    (4): C3(
      (cv1): Conv(
        (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
        (1): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
        (2): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (5): Conv(
      (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): ReLU()
    )
    (6): C3(
      (cv1): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
        (1): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
        (2): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (7): Conv(
      (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): ReLU()
    )
    (8): SPP(
      (cv1): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): ModuleList(
        (0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
        (1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
        (2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)
      )
    )
    (9): C3(
      (cv1): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (10): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (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): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (14): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (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): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (18): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): ReLU()
    )
    (19): Concat()
    (20): C3(
      (cv1): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (21): Conv(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (act): ReLU()
    )
    (22): Concat()
    (23): C3(
      (cv1): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv3): Conv(
        (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (m): Sequential(
        (0): Bottleneck(
          (cv1): Conv(
            (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
            (act): ReLU()
          )
          (cv2): Conv(
            (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
            (act): ReLU()
          )
        )
      )
    )
    (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))
      )
    )
  )
): '', Sequential(
  (0): Focus(
    (conv): Conv(
      (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): ReLU()
    )
  )
  (1): Conv(
    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (2): C3(
    (cv1): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (3): Conv(
    (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (4): C3(
    (cv1): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (1): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (2): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (5): Conv(
    (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (6): C3(
    (cv1): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (1): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (2): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (7): Conv(
    (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (8): SPP(
    (cv1): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): ModuleList(
      (0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
      (1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
      (2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)
    )
  )
  (9): C3(
    (cv1): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (10): Conv(
    (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (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): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (14): Conv(
    (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (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): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (18): Conv(
    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (19): Concat()
  (20): C3(
    (cv1): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (21): Conv(
    (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (22): Concat()
  (23): C3(
    (cv1): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (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))
    )
  )
): 'model', Focus(
  (conv): Conv(
    (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (act): ReLU()
  )
): 'model.0', Conv(
  (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (act): ReLU()
): 'model.0.conv', Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'model.0.conv.conv', ReLU(): 'model.0.conv.act', Conv(
  (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  (act): ReLU()
): 'model.1', Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)): 'model.1.conv', ReLU(): 'model.1.act', C3(
  (cv1): Conv(
    (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv2): Conv(
    (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv3): Conv(
    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (m): Sequential(
    (0): Bottleneck(
      (cv1): Conv(
        (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (act): ReLU()
      )
    )
  )
): 'model.2', Conv(
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  )
): 'model.13.m.0', Conv(
  (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.13.m.0.cv1', Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)): 'model.13.m.0.cv1.conv', ReLU(): 'model.13.m.0.cv1.act', Conv(
  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (act): ReLU()
): 'model.13.m.0.cv2', Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'model.13.m.0.cv2.conv', ReLU(): 'model.13.m.0.cv2.act', Conv(
  (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.14', Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)): 'model.14.conv', ReLU(): 'model.14.act', Upsample(scale_factor=2.0, mode=nearest): 'model.15', Concat(): 'model.16', C3(
  (cv1): Conv(
    (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv2): Conv(
    (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv3): Conv(
    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (m): Sequential(
    (0): Bottleneck(
      (cv1): Conv(
        (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (act): ReLU()
      )
    )
  )
): 'model.17', Conv(
  (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.17.cv1', Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)): 'model.17.cv1.conv', ReLU(): 'model.17.cv1.act', Conv(
  (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.17.cv2', Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1)): 'model.17.cv2.conv', ReLU(): 'model.17.cv2.act', Conv(
  (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.17.cv3', Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)): 'model.17.cv3.conv', ReLU(): 'model.17.cv3.act', Sequential(
  (0): Bottleneck(
    (cv1): Conv(
      (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): ReLU()
    )
  )
): 'model.17.m', Bottleneck(
  (cv1): Conv(
    (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv2): Conv(
    (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (act): ReLU()
  )
): 'model.17.m.0', Conv(
  (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.17.m.0.cv1', Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1)): 'model.17.m.0.cv1.conv', ReLU(): 'model.17.m.0.cv1.act', Conv(
  (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (act): ReLU()
): 'model.17.m.0.cv2', Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'model.17.m.0.cv2.conv', ReLU(): 'model.17.m.0.cv2.act', Conv(
  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  (act): ReLU()
): 'model.18', Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)): 'model.18.conv', ReLU(): 'model.18.act', Concat(): 'model.19', C3(
  (cv1): Conv(
    (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv2): Conv(
    (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv3): Conv(
    (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (m): Sequential(
    (0): Bottleneck(
      (cv1): Conv(
        (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (act): ReLU()
      )
    )
  )
): 'model.20', Conv(
  (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.20.cv1', Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)): 'model.20.cv1.conv', ReLU(): 'model.20.cv1.act', Conv(
  (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.20.cv2', Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1)): 'model.20.cv2.conv', ReLU(): 'model.20.cv2.act', Conv(
  (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.20.cv3', Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)): 'model.20.cv3.conv', ReLU(): 'model.20.cv3.act', Sequential(
  (0): Bottleneck(
    (cv1): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): ReLU()
    )
  )
): 'model.20.m', Bottleneck(
  (cv1): Conv(
    (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv2): Conv(
    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (act): ReLU()
  )
): 'model.20.m.0', Conv(
  (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.20.m.0.cv1', Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1)): 'model.20.m.0.cv1.conv', ReLU(): 'model.20.m.0.cv1.act', Conv(
  (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (act): ReLU()
): 'model.20.m.0.cv2', Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'model.20.m.0.cv2.conv', ReLU(): 'model.20.m.0.cv2.act', Conv(
  (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  (act): ReLU()
): 'model.21', Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1)): 'model.21.conv', ReLU(): 'model.21.act', Concat(): 'model.22', C3(
  (cv1): Conv(
    (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv2): Conv(
    (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv3): Conv(
    (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (m): Sequential(
    (0): Bottleneck(
      (cv1): Conv(
        (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
        (act): ReLU()
      )
      (cv2): Conv(
        (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
        (act): ReLU()
      )
    )
  )
): 'model.23', Conv(
  (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.23.cv1', Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)): 'model.23.cv1.conv', ReLU(): 'model.23.cv1.act', Conv(
  (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.23.cv2', Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1)): 'model.23.cv2.conv', ReLU(): 'model.23.cv2.act', Conv(
  (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.23.cv3', Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1)): 'model.23.cv3.conv', ReLU(): 'model.23.cv3.act', Sequential(
  (0): Bottleneck(
    (cv1): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): ReLU()
    )
  )
): 'model.23.m', Bottleneck(
  (cv1): Conv(
    (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (cv2): Conv(
    (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (act): ReLU()
  )
): 'model.23.m.0', Conv(
  (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
  (act): ReLU()
): 'model.23.m.0.cv1', Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)): 'model.23.m.0.cv1.conv', ReLU(): 'model.23.m.0.cv1.act', Conv(
  (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (act): ReLU()
): 'model.23.m.0.cv2', Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)): 'model.23.m.0.cv2.conv', ReLU(): 'model.23.m.0.cv2.act', 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))
  )
): 'model.24', 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))
): 'model.24.m', Conv2d(128, 255, kernel_size=(1, 1), stride=(1, 1)): 'model.24.m.0', Conv2d(256, 255, kernel_size=(1, 1), stride=(1, 1)): 'model.24.m.1', Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1)): 'model.24.m.2'}
------------ END   : DG._module_to_name ----------------------
------------ BEGIN : DG.module_to_node  ---------------------
{Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1)): <Node: (model.24.m.2 (Conv2d(512, 255, kernel_size=(1, 1), stride=(1, 1))), None)>}
------------ END   : DG.module_to_node  ---------------------
***** BEGIN PRUNING ***********************************************************
LEVEL 1 :  model Sequential(
  (0): Focus(
    (conv): Conv(
      (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (act): ReLU()
    )
  )
  (1): Conv(
    (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (2): C3(
    (cv1): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(32, 32, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (3): Conv(
    (conv): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (4): C3(
    (cv1): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (1): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (2): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (5): Conv(
    (conv): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (6): C3(
    (cv1): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (1): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
      (2): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (7): Conv(
    (conv): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (8): SPP(
    (cv1): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(1024, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): ModuleList(
      (0): MaxPool2d(kernel_size=5, stride=1, padding=2, dilation=1, ceil_mode=False)
      (1): MaxPool2d(kernel_size=9, stride=1, padding=4, dilation=1, ceil_mode=False)
      (2): MaxPool2d(kernel_size=13, stride=1, padding=6, dilation=1, ceil_mode=False)
    )
  )
  (9): C3(
    (cv1): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (10): Conv(
    (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (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): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (14): Conv(
    (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
    (act): ReLU()
  )
  (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): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 64, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(64, 64, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (18): Conv(
    (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (19): Concat()
  (20): C3(
    (cv1): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (21): Conv(
    (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
    (act): ReLU()
  )
  (22): Concat()
  (23): C3(
    (cv1): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv2): Conv(
      (conv): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (cv3): Conv(
      (conv): Conv2d(512, 512, kernel_size=(1, 1), stride=(1, 1))
      (act): ReLU()
    )
    (m): Sequential(
      (0): Bottleneck(
        (cv1): Conv(
          (conv): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))
          (act): ReLU()
        )
        (cv2): Conv(
          (conv): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
          (act): ReLU()
        )
      )
    )
  )
  (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))
    )
  )
)
____ LEVEL 2 :  0 Focus(
  (conv): Conv(
    (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    (act): ReLU()
  )
)
________ LEVEL 3 :  conv Conv(
  (conv): Conv2d(12, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
  (act): ReLU()
)
________ END CHILD LEVEL 3 ______________________
____ END CHILD LEVEL 2 ______________________
____ LEVEL 2 :  1 Conv(
  (conv): Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
  (act): ReLU()
)
________ LEVEL 3 :  conv Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
Conv2d detected. Attempting to prune......
======== prune_conv() ========
prune_index =  [11, 0, 17, 4, 19, 29, 8, 33, 13, 31, 23, 38, 53, 46, 63, 20, 45, 40, 58, 15, 36, 28, 57, 27, 16, 55, 37, 5, 41, 34, 48, 12]
num_pruned =  32
Traceback (most recent call last):
  File "models/prune_TP_git_issue.py", line 117, in <module>
    new_model = prune_model(model, img)
  File "models/prune_TP_git_issue.py", line 64, in prune_model
    prune_conv(mm3, SPARSITY)
  File "models/prune_TP_git_issue.py", line 48, in prune_conv
    raise e
  File "models/prune_TP_git_issue.py", line 46, in prune_conv
    plan = DG.get_pruning_plan(conv, tp.prune_conv, prune_index)
  File "/home/tfs/venv_yolov5_Torch_Pruning/lib/python3.6/site-packages/torch_pruning-0.2.2-py3.6.egg/torch_pruning/dependency.py", line 330, in get_pruning_plan
KeyError: Conv2d(32, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))