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Eric-mingjie / network-slimming

Network Slimming (Pytorch) (ICCV 2017)
MIT License
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A tough question about network-slimming used on SSD #54

Open windyjunfeng opened 3 years ago

windyjunfeng commented 3 years ago

Hello, would you please give me a hand? I met a tough question when I tried to use the method to prune the channels of VGG16_reducedfc, the basebone of SSD. I was stuck in the problem for several weeks. I would appreciate it if you could show me the direction for solving the problem. Thank you very much!

Initializing weights...
DataParallel(
  (module): SSD(
    (vgg): ModuleList(
      (0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (2): ReLU(inplace=True)
      (3): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (4): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (5): ReLU(inplace=True)
      (6): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (7): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (8): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (9): ReLU(inplace=True)
      (10): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (11): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (12): ReLU(inplace=True)
      (13): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (14): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (15): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (16): ReLU(inplace=True)
      (17): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (18): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (19): ReLU(inplace=True)
      (20): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (21): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (22): ReLU(inplace=True)
      (23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=True)
      (24): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (25): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (26): ReLU(inplace=True)
      (27): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (28): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (29): ReLU(inplace=True)
      (30): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (31): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (32): ReLU(inplace=True)
      (33): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
      (34): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (35): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (36): ReLU(inplace=True)
      (37): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (38): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (39): ReLU(inplace=True)
      (40): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (41): BatchNorm2d(512, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True)
      (42): ReLU(inplace=True)
      (43): MaxPool2d(kernel_size=3, stride=1, padding=1, dilation=1, ceil_mode=False)
      (44): Conv2d(512, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(6, 6), dilation=(6, 6))
      (45): ReLU(inplace=True)
      (46): Conv2d(1024, 1024, kernel_size=(1, 1), stride=(1, 1))
      (47): ReLU(inplace=True)
    )
    (L2Norm): L2Norm()
    (extras): ModuleList(
      (0): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))
      (1): Conv2d(256, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (2): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
      (3): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1))
      (4): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (5): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))
      (6): Conv2d(256, 128, kernel_size=(1, 1), stride=(1, 1))
      (7): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1))
    )
    (loc): ModuleList(
      (0): Conv2d(512, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): Conv2d(1024, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (2): Conv2d(512, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): Conv2d(256, 24, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (4): Conv2d(256, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (5): Conv2d(256, 16, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
    (conf): ModuleList(
      (0): Conv2d(512, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (1): Conv2d(1024, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (2): Conv2d(512, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (3): Conv2d(256, 126, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (4): Conv2d(256, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
      (5): Conv2d(256, 84, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
    )
  )
)
Loading the dataset...
Training SSD on: VOC0712
Using the specified args:
Namespace(basenet='vgg16_reducedfc.pth', batch_size=32, cuda=True, dataset='VOC', dataset_root='/root/data/VOCdevkit/', gamma=0.1, lr=0.004, momentum=0.9, num_workers=4, refine='', resume=None, save_folder='weights/', sp=0.0001, sr=True, start_iter=0, visdom=False, weight_decay=0.0005)
[W python_anomaly_mode.cpp:60] Warning: Error detected in DivBackward0. Traceback of forward call that caused the error:
  File "train.py", line 312, in <module>
    train()
  File "train.py", line 192, in train
    out = net(images)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/parallel/data_parallel.py", line 153, in forward
    return self.module(*inputs[0], **kwargs[0])
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/content/gdrive/My Drive/pytorch/ssd/ssd.py", line 82, in forward
    s = self.L2Norm(x)
  File "/usr/local/lib/python3.6/dist-packages/torch/nn/modules/module.py", line 722, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/content/gdrive/My Drive/pytorch/ssd/layers/modules/l2norm.py", line 22, in forward
    x = torch.div(x,norm)
 (function print_stack)
Traceback (most recent call last):
  File "train.py", line 312, in <module>
    train()
  File "train.py", line 205, in train
    loss.backward()
  File "/usr/local/lib/python3.6/dist-packages/torch/tensor.py", line 185, in backward
    torch.autograd.backward(self, gradient, retain_graph, create_graph)
  File "/usr/local/lib/python3.6/dist-packages/torch/autograd/__init__.py", line 127, in backward
    allow_unreachable=True)  # allow_unreachable flag
RuntimeError: one of the variables needed for gradient computation has been modified by an inplace operation: [torch.cuda.FloatTensor [32, 512, 38, 38]], which is output 0 of CudnnBatchNormBackward, is at version 1; expected version 0 instead. Hint: the backtrace further above shows the operation that failed to compute its gradient. The variable in question was changed in there or anywhere later. Good luck!
Eric-mingjie commented 3 years ago

Sorry, i am not familar with SSD. Maybe you can check those repos that use network slimming for object detection.