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test file can't find gpu #60

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ovipaul opened this issue Feb 6, 2022 · 5 comments
Open

test file can't find gpu #60

ovipaul opened this issue Feb 6, 2022 · 5 comments

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@ovipaul
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ovipaul commented Feb 6, 2022

------------ 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))
)
)
)
)
@ovipaul ovipaul changed the title How to run the test file? executing both with test.sh and test.py shows this issue. test file can't find gpu Feb 6, 2022
@ansarker
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ansarker commented Feb 7, 2022

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
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kieutrongthien commented Jun 9, 2022 •
edited
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What is GPU you both using and cuda version ?

@ansarker
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ansarker commented Jun 9, 2022

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

@kieutrongthien
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kieutrongthien commented Jun 9, 2022 •
edited
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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
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Hello, I have encountered the same problem as you. Have you solved it? Can you share your solution with me? Thank you！

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@kieutrongthien @ansarker @ovipaul @zbq777sc