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dearleiii / PIRM-2018-SISR-Challenge

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https://www.pirm2018.org/PIRM-SR.html
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RuntimeError: size mismatch, m1: [7 x 1036320], m2: [1048576 x 256] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:249 #19

Open dearleiii opened 6 years ago

dearleiii commented 6 years ago 
  (2): Linear(in_features=256, out_features=1, bias=True)
)

) ) intpus: torch.Size([50, 3, 1020, 2040]) scores: torch.Size([50, 1]) Traceback (most recent call last): File "load_model_test.py", line 69, in outputs = model1(inputs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(*input, kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/data_parallel.py", line 114, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/data_parallel.py", line 124, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/parallel_apply.py", line 65, in parallel_apply raise output File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/parallel_apply.py", line 41, in _worker output = module(*input, *kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(input, kwargs) File "/usr/project/xtmp/superresoluter/approximator/model1/apxm.py", line 60, in forward output = self.regressor(x) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(*input, *kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/container.py", line 91, in forward input = module(input) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(input, **kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/linear.py", line 55, in forward return F.linear(input, self.weight, self.bias) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/functional.py", line 992, in linear return torch.addmm(bias, input, weight.t()) RuntimeError: size mismatch, m1: [7 x 1036320], m2: [1048576 x 256] at /pytorch/aten/src/THC/generic/THCTensorMathBlas.cu:249

dearleiii commented 6 years ago

File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(*input, kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/data_parallel.py", line 114, in forward outputs = self.parallel_apply(replicas, inputs, kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/data_parallel.py", line 124, in parallel_apply return parallel_apply(replicas, inputs, kwargs, self.device_ids[:len(replicas)]) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/parallel_apply.py", line 65, in parallel_apply raise output File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/parallel/parallel_apply.py", line 41, in _worker output = module(*input, *kwargs) File "/home/home2/leichen/.local/lib/python3.5/site-packages/torch/nn/modules/module.py", line 491, in call result = self.forward(input, kwargs)

dearleiii commented 6 years ago

Model is using Data_parallel

dearleiii commented 6 years ago

DataParallel( (module): APXM_conv3( (main): Sequential( (0): Conv2d(3, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.2, inplace) (2): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.2, inplace) (4): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (5): LeakyReLU(negative_slope=0.2, inplace) ) (regressor): Sequential( (0): Linear(in_features=1048576, out_features=256, bias=True) (1): LeakyReLU(negative_slope=0.01) (2): Linear(in_features=256, out_features=1, bias=True) ) )

dearleiii commented 6 years ago

ataParallel( (module): APXM_conv3( (main): Sequential( (0): Conv2d(3, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.2, inplace) (2): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.2, inplace) (4): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (5): LeakyReLU(negative_slope=0.2, inplace) ) (regressor): Sequential( (0): Linear(in_features=1048576, out_features=256, bias=True) (1): LeakyReLU(negative_slope=0.01) (2): Linear(in_features=256, out_features=1, bias=True) ) ) ) DataParallel( (module): APXM_conv3( (main): Sequential( (0): Conv2d(3, 8, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.2, inplace) (2): Conv2d(8, 16, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.2, inplace) (4): Conv2d(16, 32, kernel_size=(4, 4), stride=(2, 2), padding=(1, 1), bias=False) (5): LeakyReLU(negative_slope=0.2, inplace) ) (regressor): Sequential( (0): Linear(in_features=1048576, out_features=256, bias=True) (1): LeakyReLU(negative_slope=0.01) (2): Linear(in_features=256, out_features=1, bias=True) ) )