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geyuying / PF-AFN

Official code for "Parser-Free Virtual Try-on via Distilling Appearance Flows", CVPR 2021.
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test file can't find gpu #60

Open ovipaul opened 2 years ago

ovipaul commented 2 years ago

------------ Options ------------- batchSize: 1 data_type: 32 dataroot: dataset/ display_winsize: 512 fineSize: 512 gen_checkpoint: checkpoints/PFAFN/gen_model_final.pth gpu_ids: [0] input_nc: 3 isTrain: False loadSize: 512 max_dataset_size: inf nThreads: 1 name: demo no_flip: False norm: instance output_nc: 3 phase: test resize_or_crop: None serial_batches: False tf_log: False use_dropout: False verbose: False warp_checkpoint: checkpoints/PFAFN/warp_model_final.pth -------------- End ---------------- CustomDatasetDataLoader dataset [AlignedDataset] was created 6 AFWM( (image_features): FeatureEncoder( (encoders): ModuleList( (0): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (1): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (2): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (3): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (4): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) ) ) (cond_features): FeatureEncoder( (encoders): ModuleList( (0): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(3, 64, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(64, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (1): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(64, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(64, 128, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(128, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (2): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(128, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(128, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (3): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) (4): Sequential( (0): DownSample( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), bias=False) ) ) (1): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) (2): ResBlock( (block): Sequential( (0): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (1): ReLU(inplace) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) (3): BatchNorm2d(256, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) (4): ReLU(inplace) (5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), bias=False) ) ) ) ) ) (image_FPN): RefinePyramid( (adaptive): ModuleList( (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) ) (smooth): ModuleList( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (cond_FPN): RefinePyramid( (adaptive): ModuleList( (0): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (1): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1)) (3): Conv2d(128, 256, kernel_size=(1, 1), stride=(1, 1)) (4): Conv2d(64, 256, kernel_size=(1, 1), stride=(1, 1)) ) (smooth): ModuleList( (0): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (aflow_net): AFlowNet( (netMain): ModuleList( (0): Sequential( (0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (1): Sequential( (0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (2): Sequential( (0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (3): Sequential( (0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (4): Sequential( (0): Conv2d(49, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) (netRefine): ModuleList( (0): Sequential( (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (1): Sequential( (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (2): Sequential( (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (3): Sequential( (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) (4): Sequential( (0): Conv2d(512, 128, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (1): LeakyReLU(negative_slope=0.1) (2): Conv2d(128, 64, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (3): LeakyReLU(negative_slope=0.1) (4): Conv2d(64, 32, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) (5): LeakyReLU(negative_slope=0.1) (6): Conv2d(32, 2, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)) ) ) ) )

ansarker commented 2 years ago

I got the same error and a Traceback error when I try demo test. It freezes while showing the model summary and then it shows the error below.

Traceback (most recent call last): File "test.py", line 58, in

File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, kwargs) File "/home/anis/Virtual_Trial_Room/codes/PF-AFN/PF-AFN_test/models/afwm.py", line 192, in forward cond_pyramids = self.cond_FPN(self.cond_features(cond_input)) # maybe use nn.Sequential File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, *kwargs) File "/home/anis/Virtual_Trial_Room/codes/PF-AFN/PF-AFN_test/models/afwm.py", line 73, in forward x = encoder(x) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(input, kwargs) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward input = module(input) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, kwargs) File "/home/anis/Virtual_Trial_Room/codes/PF-AFN/PF-AFN_test/models/afwm.py", line 47, in forward return self.block(x) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(*input, *kwargs) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/container.py", line 92, in forward input = module(input) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/module.py", line 493, in call result = self.forward(input, kwargs) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/modules/activation.py", line 99, in forward return F.relu(input, inplace=self.inplace) File "/home/anis/anaconda3/envs/tryon/lib/python3.6/site-packages/torch/nn/functional.py", line 941, in relu result = torch.relu_(input) RuntimeError: CUDA error: no kernel image is available for execution on the device

kieutrongthien commented 2 years ago

What is GPU you both using and cuda version ?

ansarker commented 2 years ago

GPU: Nvidia RTX 3080 Ti CUDA: 11.3 CUDNN: 8.2.1

kieutrongthien commented 2 years ago

GPU: Nvidia RTX 3080 Ti CUDA: 11.3 CUDNN: 8.2.1

Make sure you installed Pytorch version is >= 1.8.1 and cudatoolkit 11.3.

Can you print result of the command conda list in your environment ?

Note: from NVIDIA RTX series you need to install CUDA >= 11 to make it work, because CUDA 11.x is support compute capability 8.6. If you run on older version you'll get this error:

RuntimeError: CUDA error: no kernel image is available for execution on the device

zbq777sc commented 2 years ago

Hello, I have encountered the same problem as you. Have you solved it? Can you share your solution with me? Thank you！