danusya@werewolf:~/PyTorch-Multi-Style-Transfer/experiments$ python main.py train --dataset ~/dataset/ --vgg-model-dir caleido_vgg --save-model-dir caleido_model --epochs 2 --cuda 0
/usr/local/lib/python2.7/dist-packages/torchvision/transforms/transforms.py:156: UserWarning: The use of the transforms.Scale transform is deprecated, please use transforms.Resize instead.
"please use transforms.Resize instead.")
Net(
(gram): GramMatrix(
)
(model1): Sequential(
(0): ConvLayer(
(reflection_pad): ReflectionPad2d((3, 3, 3, 3))
(conv2d): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
)
(1): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
(2): ReLU(inplace)
(3): Bottleneck(
(residual_layer): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
(conv_block): Sequential(
(0): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(4): Bottleneck(
(residual_layer): Conv2d(128, 512, kernel_size=(1, 1), stride=(2, 2))
(conv_block): Sequential(
(0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
)
(ins): Inspiration(N x 512)
(model): Sequential(
(0): Sequential(
(0): ConvLayer(
(reflection_pad): ReflectionPad2d((3, 3, 3, 3))
(conv2d): Conv2d(3, 64, kernel_size=(7, 7), stride=(1, 1))
)
(1): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
(2): ReLU(inplace)
(3): Bottleneck(
(residual_layer): Conv2d(64, 128, kernel_size=(1, 1), stride=(2, 2))
(conv_block): Sequential(
(0): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(64, 32, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(32, 32, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(4): Bottleneck(
(residual_layer): Conv2d(128, 512, kernel_size=(1, 1), stride=(2, 2))
(conv_block): Sequential(
(0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(128, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(2, 2))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
)
(1): Inspiration(N x 512)
(2): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(3): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(4): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(5): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(6): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(7): Bottleneck(
(conv_block): Sequential(
(0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): ConvLayer(
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(128, 512, kernel_size=(1, 1), stride=(1, 1))
)
)
(8): UpBottleneck(
(residual_layer): UpsampleConvLayer(
(upsample_layer): Upsample(scale_factor=2, mode=nearest)
(conv2d): Conv2d(512, 128, kernel_size=(1, 1), stride=(1, 1))
)
(conv_block): Sequential(
(0): InstanceNorm2d(512, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(512, 32, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): UpsampleConvLayer(
(upsample_layer): Upsample(scale_factor=2, mode=nearest)
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(32, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(32, 128, kernel_size=(1, 1), stride=(1, 1))
)
)
(9): UpBottleneck(
(upsample_layer): Upsample(scale_factor=2, mode=nearest)
(conv2d): Conv2d(128, 64, kernel_size=(1, 1), stride=(1, 1))
)
(conv_block): Sequential(
(0): InstanceNorm2d(128, eps=1e-05, momentum=0.1, affine=False)
(1): ReLU(inplace)
(2): Conv2d(128, 16, kernel_size=(1, 1), stride=(1, 1))
(3): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=False)
(4): ReLU(inplace)
(5): UpsampleConvLayer(
(upsample_layer): Upsample(scale_factor=2, mode=nearest)
(reflection_pad): ReflectionPad2d((1, 1, 1, 1))
(conv2d): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1))
)
(6): InstanceNorm2d(16, eps=1e-05, momentum=0.1, affine=False)
(7): ReLU(inplace)
(8): Conv2d(16, 64, kernel_size=(1, 1), stride=(1, 1))
)
)
(10): InstanceNorm2d(64, eps=1e-05, momentum=0.1, affine=False)
(11): ReLU(inplace)
(12): ConvLayer(
(reflection_pad): ReflectionPad2d((3, 3, 3, 3))
(conv2d): Conv2d(64, 3, kernel_size=(7, 7), stride=(1, 1))
)
)
)
Traceback (most recent call last):
File "main.py", line 287, in <module>
main()
File "main.py", line 40, in main
train(args)
File "main.py", line 159, in train
style_model.setTarget(style_v)
File "/home/danusya/PyTorch-Multi-Style-Transfer/experiments/net.py", line 293, in setTarget
F = self.model1(Xs)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 357, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/container.py", line 67, in forward
input = module(input)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 357, in __call__
result = self.forward(*input, **kwargs)
File "/home/danusya/PyTorch-Multi-Style-Transfer/experiments/net.py", line 153, in forward
out = self.conv2d(out)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/module.py", line 357, in __call__
result = self.forward(*input, **kwargs)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/modules/conv.py", line 282, in forward
self.padding, self.dilation, self.groups)
File "/usr/local/lib/python2.7/dist-packages/torch/nn/functional.py", line 90, in conv2d
return f(input, weight, bias)
RuntimeError: Input type (CUDAFloatTensor) and weight type (CPUFloatTensor) should be the same