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Kylin9511 / ACRNet

This is an implementation of ACRNet for results reproduction on COST2100
MIT License
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'Module not Callable' #2

Open EyuT777 opened 3 years ago

EyuT777 commented 3 years ago

I was working on the algorithm, running all the parameters as required. This is the output shown. Can you please help with it?

C:\Users\User\Desktop\Home>python C:\Users\User\Desktop\Home>python C:\Users\User\Desktop\Home\ACRNet\main.py --data-dir C:\Users\User\Desktop\Home\COST2100 --reduction 4 --expansion 1 --batch-size 200 --scenario in --workers 0 I 09.13/12:19 C:\Users\User\Desktop\Home\ACRNet\main.py:35 ] => PyTorch Version: 1.9.0+cpu I 09.13/12:19 C:\Users\User\Desktop\Home\ACRNet\main.py:14 ] Running on CPU I 09.13/12:20 C:\Users\User\Desktop\Home\ACRNet\model\acrnet.py:117 ] => Model ACRNet with reduction=4, expansion=1 I 09.13/12:20 C:\Users\User\Desktop\Home\ACRNet\main.py:24 ] => Model Name: ACRNet I 09.13/12:20 C:\Users\User\Desktop\Home\ACRNet\main.py:24 ] => Model Config: compression ratio=1/4; expansion=1 I 09.13/12:20 C:\Users\User\Desktop\Home\ACRNet\main.py:24 ]

ACRNet( (encoder_feature): Sequential( (conv5x5_bn): ConvBN( (conv): Conv2d(2, 2, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu): PReLU(num_parameters=2) (ACREncoderBlock1): ACREncoderBlock( (conv_bn1): ConvBN( (conv): Conv2d(2, 2, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu1): PReLU(num_parameters=2) (conv_bn2): ConvBN( (conv): Conv2d(2, 2, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu2): PReLU(num_parameters=2) (identity): Identity() ) (ACREncoderBlock2): ACREncoderBlock( (conv_bn1): ConvBN( (conv): Conv2d(2, 2, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu1): PReLU(num_parameters=2) (conv_bn2): ConvBN( (conv): Conv2d(2, 2, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu2): PReLU(num_parameters=2) (identity): Identity() ) ) (encoder_fc): Linear(in_features=2048, out_features=512, bias=True) (decoder_fc): Linear(in_features=512, out_features=2048, bias=True) (decoder_feature): Sequential( (conv5x5_bn): ConvBN( (conv): Conv2d(2, 2, kernel_size=(5, 5), stride=(1, 1), padding=(2, 2), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu): PReLU(num_parameters=2) (ACRDecoderBlock1): ACRDecoderBlock( (conv1_bn): ConvBN( (conv): Conv2d(2, 8, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (bn): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu1): PReLU(num_parameters=8) (conv2_bn): ConvBN( (conv): Conv2d(8, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=4, bias=False) (bn): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu2): PReLU(num_parameters=8) (conv3_bn): ConvBN( (conv): Conv2d(8, 2, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu3): PReLU(num_parameters=2) (identity): Identity() ) (ACRDecoderBlock2): ACRDecoderBlock( (conv1_bn): ConvBN( (conv): Conv2d(2, 8, kernel_size=(1, 9), stride=(1, 1), padding=(0, 4), bias=False) (bn): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu1): PReLU(num_parameters=8) (conv2_bn): ConvBN( (conv): Conv2d(8, 8, kernel_size=(7, 7), stride=(1, 1), padding=(3, 3), groups=4, bias=False) (bn): BatchNorm2d(8, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu2): PReLU(num_parameters=8) (conv3_bn): ConvBN( (conv): Conv2d(8, 2, kernel_size=(9, 1), stride=(1, 1), padding=(4, 0), bias=False) (bn): BatchNorm2d(2, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) ) (prelu3): PReLU(num_parameters=2) (identity): Identity() ) (sigmoid): Sigmoid() ) )

Traceback (most recent call last): File "C:\Users\User\Desktop\Home\ACRNet\main.py", line 35, in main() File "C:\Users\User\Desktop\Home\ACRNet\main.py", line 31, in main Tester(model, device, criterion, print_freq=20)(test_loader) File "C:\Users\User\Desktop\Home\ACRNet\utils\solver.py", line 35, in call loss, rho, nmse = self._iteration(test_data) File "C:\Users\User\Desktop\Home\ACRNet\utils\solver.py", line 56, in _iteration rho, nmse = evaluator(sparse_pred, sparse_gt, raw_gt) File "C:\Users\User\Desktop\Home\ACRNet\utils\statics.py", line 60, in evaluator raw_pred = torch.fft(sparse_pred, signal_ndim=1)[:, :, :125, :] TypeError: 'module' object is not callable

shubhamsrivast4u commented 1 year ago

The following adjustment solved this error at my end. use

raw_pred = torch.fft.fft(sparse_pred, dim=1)[:, :, :125, :]