Comments (6)
Hi @PeterouZh, I think the reason might be that f3 parse "-1" as string instead of int. Can you try to replace that line with f3 = self.frame.replace(-1, 0) or f3 = self.frame.replace("-1", 0)? Please let me know whether it works or not.
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Hi @yingcong , I'm facing the same problem, and I tried your method to solve it. But after change that line, it keeps run time error at the start of training(showing "stack overflow")
Then follows lots of lines of "***********/data/attributeDataset.py", line 244 in __getitem__
"
Here's some first lines of the error message.
loading default VGG
loading default VGG
loading default VGG
* Total Images: 162770
* Total Images: 19962
save_dir: checkpoints/CelebA_my
n_discrim: 5
command: train
gpu: 0
batch_size: 128
data_dir: ../celebra-dataset
attr: Mouth_Slightly_Open@Smiling,Male@No_Beard@Mustache@Goatee@Sideburns,Black_Hair@Blond_Hair@Brown_Hair@Gray_Hair,Bald@Receding_Hairline@Bangs,Young
dec_type: v1
continue_train: False
epoch: 10000
0%| | 0/1271 [00:00<?, ?it/s]Fatal Python error: Cannot recover from stack overflow.
Current thread 0x00007f8675a0b740 (most recent call first):
File "/....../HomoInterpGAN/util/util.py", line 144 in readRGB
File "/....../HomoInterpGAN/data/attributeDataset.py", line 244 in __getitem__
File "/....../HomoInterpGAN/data/attributeDataset.py", line 266 in __getitem__
File "/....../HomoInterpGAN/data/attributeDataset.py", line 266 in __getitem__
File "/....../HomoInterpGAN/data/attributeDataset.py", line 266 in __getitem__
File "/....../HomoInterpGAN/data/attributeDataset.py", line 266 in __getitem__
......
I'm using pytorch 1.0.1 with cuda 9.1, other modules are installed as requirement.txt
I'm looking forward to your help, thanks!
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Hi @oscardhc It seems that images are not successfully loaded with getitem. Have you checked if the celeba images (jpg format) are in the ../celebra-dataset file? If yes, could you remove the "try -- except" block in the getitem method? This could give more information about why images cannot be loaded.
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@yingcong Thank you for the advice! It turns out that I've put all images in a subdirectory in the dataset path(as the default result of unzipping). After moving the images, your solutions above work and it starts to train.
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I fix this issue by replacing "f3 = self.frame.iloc[:, 1:] > 0" url with "f3 = self.frame.iloc[:, 3:-1] > 0".
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Hi @PeterouZh I've replaced f3 = self.frame.iloc[:, 1:] > 0 with f3 = self.frame.replace(-1, 0) in the later commit, which seems more general for different pandas versions.
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Related Issues (12)
- supplementary material HOT 1
- TypeError: 'NoneType' object has no attribute 'getitem' HOT 1
- weird results HOT 10
- Questions about the results in supplementary material HOT 5
- How to train this code on RafD dataset? Could you also share the pretrained model on RaFD? Thanks HOT 4
- ModuleNotFoundError: No module named 'colored' HOT 1
- Why we need "Rigorous Training" for attribute classifier ? HOT 2
- Interpolation between dogs and cats HOT 7
- AttributeError: 'Namespace' object has no attribute 'random_crop_bias'
- About the file of Rafd_image.csv HOT 2
- Is it possible to generate an image without reference image ?
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