shamazharikh / pirl Goto Github PK

Model_Dict ={
    'resnet': ['layer4'],
    'densenet': ['features'],
    'shufflenet': ['conv5'],
    'alexnet': ['avgpool']

----------------------------------------------------------------
        Layer (type)               Output Shape         Param #
================================================================
            Conv2d-1           [-1, 64, 19, 19]          23,296
              ReLU-2           [-1, 64, 19, 19]               0
         MaxPool2d-3             [-1, 64, 9, 9]               0
            Conv2d-4            [-1, 192, 9, 9]         307,392
              ReLU-5            [-1, 192, 9, 9]               0
         MaxPool2d-6            [-1, 192, 4, 4]               0
            Conv2d-7            [-1, 384, 4, 4]         663,936
              ReLU-8            [-1, 384, 4, 4]               0
            Conv2d-9            [-1, 256, 4, 4]         884,992
             ReLU-10            [-1, 256, 4, 4]               0
           Conv2d-11            [-1, 256, 4, 4]         590,080
             ReLU-12            [-1, 256, 4, 4]               0
        MaxPool2d-13            [-1, 256, 1, 1]               0
AdaptiveAvgPool2d-14            [-1, 256, 6, 6]               0
          Dropout-15                 [-1, 9216]               0
           Linear-16                 [-1, 4096]      37,752,832
             ReLU-17                 [-1, 4096]               0
          Dropout-18                 [-1, 4096]               0
           Linear-19                 [-1, 4096]      16,781,312
             ReLU-20                 [-1, 4096]               0
           Linear-21                 [-1, 1000]       4,097,000
          AlexNet-22                 [-1, 1000]               0
AdaptiveAvgPool2d-23            [-1, 256, 1, 1]               0
           Linear-24                   [-1, 64]          16,384
      BatchNorm1d-25                   [-1, 64]             128
        LeakyReLU-26                   [-1, 64]               0
      GenericTask-27                   [-1, 64]               0
================================================================
Total params: 61,117,352
Trainable params: 61,117,352
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.07
Forward/backward pass size (MB): 1.19
Params size (MB): 233.14
Estimated Total Size (MB): 234.41
----------------------------------------------------------------

---------------------------------------------------------------------------
RuntimeError                              Traceback (most recent call last)
<ipython-input-9-00e192360482> in <module>
     18 
     19         # compute output
---> 20         image_features, transformed_image_features = model(image.cuda(), transformed_image.cuda())
     21         transformed_output, output, _ = memorybank(image_features, transformed_image_features, index)
     22 

~\Anaconda3\envs\nlp\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

D:\Ramdhan\SSL\PIRL-master\Models\PIRL.py in forward(self, image, transformed_image)
     37             transformed_image = torch.cat([*transformed_image], dim=0)
     38             #Collapsing batch and patch dimensions
---> 39             _ = self.net(transformed_image)
     40             image_activations = self.ILG.activations[self.layer_names[0]][transformed_image.get_device()]
     41             transformed_image_features = self.Jigsaw(image_activations)

~\Anaconda3\envs\nlp\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

~\Anaconda3\envs\nlp\lib\site-packages\torchvision\models\alexnet.py in forward(self, x)
     43 
     44     def forward(self, x):
---> 45         x = self.features(x)
     46         x = self.avgpool(x)
     47         x = torch.flatten(x, 1)

~\Anaconda3\envs\nlp\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

~\Anaconda3\envs\nlp\lib\site-packages\torch\nn\modules\container.py in forward(self, input)
     98     def forward(self, input):
     99         for module in self:
--> 100             input = module(input)
    101         return input
    102 

~\Anaconda3\envs\nlp\lib\site-packages\torch\nn\modules\module.py in __call__(self, *input, **kwargs)
    548             result = self._slow_forward(*input, **kwargs)
    549         else:
--> 550             result = self.forward(*input, **kwargs)
    551         for hook in self._forward_hooks.values():
    552             hook_result = hook(self, input, result)

~\Anaconda3\envs\nlp\lib\site-packages\torch\nn\modules\pooling.py in forward(self, input)
    139         return F.max_pool2d(input, self.kernel_size, self.stride,
    140                             self.padding, self.dilation, self.ceil_mode,
--> 141                             self.return_indices)
    142 
    143 

~\Anaconda3\envs\nlp\lib\site-packages\torch\_jit_internal.py in fn(*args, **kwargs)
    207             return if_true(*args, **kwargs)
    208         else:
--> 209             return if_false(*args, **kwargs)
    210 
    211     if if_true.__doc__ is None and if_false.__doc__ is not None:

~\Anaconda3\envs\nlp\lib\site-packages\torch\nn\functional.py in _max_pool2d(input, kernel_size, stride, padding, dilation, ceil_mode, return_indices)
    537         stride = torch.jit.annotate(List[int], [])
    538     return torch.max_pool2d(
--> 539         input, kernel_size, stride, padding, dilation, ceil_mode)
    540 
    541 max_pool2d = boolean_dispatch(

RuntimeError: Given input size: (256x1x1). Calculated output size: (256x0x0). Output size is too small