Error while using Vgg16 (transfter learning) - RuntimeError: Failed to run torchinfo
Error while using Vgg16 (transfter learning) - RuntimeError: Failed to run torchinfo
我正在尝试将 VGG16 与迁移学习结合使用，但出现错误：
model = torchvision.models.vgg16(pretrained=True)
print(model)
for param in model.parameters():
param.requires_grad = False
input_size = model.classifier[0].in_features
model.classifier[0] = nn.Sequential(
nn.Linear(input_size, 128), nn.ReLU(),
nn.Linear(128, 2))
torchinfo.summary(model, (64, 3, 224, 224))
VGG16:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace=True)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace=True)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace=True)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace=True)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(inplace=True)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace=True)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace=True)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(inplace=True)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(inplace=True)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace=True)
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace=True)
(2): Dropout(p=0.5, inplace=False)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
260 if isinstance(x, (list, tuple)):
--> 261 _ = model.to(device)(*x, **kwargs)
262 elif isinstance(x, dict):
~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
~/.local/lib/python3.8/site-packages/torchvision/models/vgg.py in forward(self, x)
51 x = torch.flatten(x, 1)
---> 52 x = self.classifier(x)
53 return x
~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1119
-> 1120 result = forward_call(*input, **kwargs)
1121 if _global_forward_hooks or self._forward_hooks:
~/.local/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
140 for module in self:
--> 141 input = module(input)
142 return input
~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1119
-> 1120 result = forward_call(*input, **kwargs)
1121 if _global_forward_hooks or self._forward_hooks:
~/.local/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
102 def forward(self, input: Tensor) -> Tensor:
--> 103 return F.linear(input, self.weight, self.bias)
104
~/.local/lib/python3.8/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1847 return handle_torch_function(linear, (input, weight, bias), input, weight, bias=bias)
-> 1848 return torch._C._nn.linear(input, weight, bias)
1849
RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x2 and 4096x4096)
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_8204/406510959.py in <module>
11 nn.Linear(128, 2))
12
---> 13 torchinfo.summary(model, (64, 3, 224, 224))
~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in summary(model, input_size, input_data, batch_dim, cache_forward_pass, col_names, col_width, depth, device, dtypes, row_settings, verbose, **kwargs)
192 input_data, input_size, batch_dim, device, dtypes
193 )
--> 194 summary_list = forward_pass(
195 model, x, batch_dim, cache_forward_pass, device, **kwargs
196 )
~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
268 except Exception as e:
269 executed_layers = [layer for layer in summary_list if layer.executed]
--> 270 raise RuntimeError(
271 "Failed to run torchinfo. See above stack traces for more details. "
272 f"Executed layers up to: {executed_layers}"
RuntimeError: Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [Sequential: 1, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, AdaptiveAvgPool2d: 1, Sequential: 2, Linear: 3, ReLU: 3, Linear: 3, ReLU: 2, Dropout: 2]
我正在使用以下 pytorch 包版本：
torch==1.10.0
torchinfo==1.5.3
torchvision==0.11.1
- 怎么了？
- 我需要更改什么才能使用 VGG16（带迁移学习）？
如果您尝试更改最终分类器，您应该更改整个分类器，而不仅仅是一层：
model.classifier = nn.Sequential(
nn.Linear(input_size, 128), nn.ReLU(),
nn.Linear(128, 2))
我正在尝试将 VGG16 与迁移学习结合使用，但出现错误：
model = torchvision.models.vgg16(pretrained=True)
print(model)
for param in model.parameters():
param.requires_grad = False
input_size = model.classifier[0].in_features
model.classifier[0] = nn.Sequential(
nn.Linear(input_size, 128), nn.ReLU(),
nn.Linear(128, 2))
torchinfo.summary(model, (64, 3, 224, 224))
VGG16:
VGG(
(features): Sequential(
(0): Conv2d(3, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(1): ReLU(inplace=True)
(2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(3): ReLU(inplace=True)
(4): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(5): Conv2d(64, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(6): ReLU(inplace=True)
(7): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(8): ReLU(inplace=True)
(9): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(10): Conv2d(128, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(11): ReLU(inplace=True)
(12): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(13): ReLU(inplace=True)
(14): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(15): ReLU(inplace=True)
(16): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(17): Conv2d(256, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(18): ReLU(inplace=True)
(19): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(20): ReLU(inplace=True)
(21): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(22): ReLU(inplace=True)
(23): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
(24): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(25): ReLU(inplace=True)
(26): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(27): ReLU(inplace=True)
(28): Conv2d(512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))
(29): ReLU(inplace=True)
(30): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)
)
(avgpool): AdaptiveAvgPool2d(output_size=(7, 7))
(classifier): Sequential(
(0): Linear(in_features=25088, out_features=4096, bias=True)
(1): ReLU(inplace=True)
(2): Dropout(p=0.5, inplace=False)
(3): Linear(in_features=4096, out_features=4096, bias=True)
(4): ReLU(inplace=True)
(5): Dropout(p=0.5, inplace=False)
(6): Linear(in_features=4096, out_features=1000, bias=True)
)
)
错误：
---------------------------------------------------------------------------
RuntimeError Traceback (most recent call last)
~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
260 if isinstance(x, (list, tuple)):
--> 261 _ = model.to(device)(*x, **kwargs)
262 elif isinstance(x, dict):
~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1101 or _global_forward_hooks or _global_forward_pre_hooks):
-> 1102 return forward_call(*input, **kwargs)
1103 # Do not call functions when jit is used
~/.local/lib/python3.8/site-packages/torchvision/models/vgg.py in forward(self, x)
51 x = torch.flatten(x, 1)
---> 52 x = self.classifier(x)
53 return x
~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1119
-> 1120 result = forward_call(*input, **kwargs)
1121 if _global_forward_hooks or self._forward_hooks:
~/.local/lib/python3.8/site-packages/torch/nn/modules/container.py in forward(self, input)
140 for module in self:
--> 141 input = module(input)
142 return input
~/.local/lib/python3.8/site-packages/torch/nn/modules/module.py in _call_impl(self, *input, **kwargs)
1119
-> 1120 result = forward_call(*input, **kwargs)
1121 if _global_forward_hooks or self._forward_hooks:
~/.local/lib/python3.8/site-packages/torch/nn/modules/linear.py in forward(self, input)
102 def forward(self, input: Tensor) -> Tensor:
--> 103 return F.linear(input, self.weight, self.bias)
104
~/.local/lib/python3.8/site-packages/torch/nn/functional.py in linear(input, weight, bias)
1847 return handle_torch_function(linear, (input, weight, bias), input, weight, bias=bias)
-> 1848 return torch._C._nn.linear(input, weight, bias)
1849
RuntimeError: mat1 and mat2 shapes cannot be multiplied (64x2 and 4096x4096)
The above exception was the direct cause of the following exception:
RuntimeError Traceback (most recent call last)
/tmp/ipykernel_8204/406510959.py in <module>
11 nn.Linear(128, 2))
12
---> 13 torchinfo.summary(model, (64, 3, 224, 224))
~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in summary(model, input_size, input_data, batch_dim, cache_forward_pass, col_names, col_width, depth, device, dtypes, row_settings, verbose, **kwargs)
192 input_data, input_size, batch_dim, device, dtypes
193 )
--> 194 summary_list = forward_pass(
195 model, x, batch_dim, cache_forward_pass, device, **kwargs
196 )
~/.local/lib/python3.8/site-packages/torchinfo/torchinfo.py in forward_pass(model, x, batch_dim, cache_forward_pass, device, **kwargs)
268 except Exception as e:
269 executed_layers = [layer for layer in summary_list if layer.executed]
--> 270 raise RuntimeError(
271 "Failed to run torchinfo. See above stack traces for more details. "
272 f"Executed layers up to: {executed_layers}"
RuntimeError: Failed to run torchinfo. See above stack traces for more details. Executed layers up to: [Sequential: 1, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, Conv2d: 2, ReLU: 2, MaxPool2d: 2, AdaptiveAvgPool2d: 1, Sequential: 2, Linear: 3, ReLU: 3, Linear: 3, ReLU: 2, Dropout: 2]
我正在使用以下 pytorch 包版本：
torch==1.10.0
torchinfo==1.5.3
torchvision==0.11.1
- 怎么了？
- 我需要更改什么才能使用 VGG16（带迁移学习）？
如果您尝试更改最终分类器，您应该更改整个分类器，而不仅仅是一层：
model.classifier = nn.Sequential(
nn.Linear(input_size, 128), nn.ReLU(),
nn.Linear(128, 2))