kylin9511 / acrnet Goto Github PK

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

Does this set of code not include the code of quantization part and the code of full connection layer binarization?