image-inpaintingcolab-notebookhigh-resolutioncolabgenerative-adversarial-networkscnngenerative-adversarial-networkganfourier-transformfourier-convolutionspytorchfourierinpainting-methodsdeep-neural-networksinpainting-algorithmdeep-learninginpaintingcomputer-vision
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42 lines
1.5 KiB
42 lines
1.5 KiB
import os |
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import random |
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test_files_path = os.path.abspath('.') + '/places_standard_dataset/original/test/' |
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list_of_random_test_files = os.path.abspath('.') + '/places_standard_dataset/original/test_random_files.txt' |
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test_files = [ |
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test_files_path + image for image in os.listdir(test_files_path) |
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] |
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print(f'Sampling 2000 images out of {len(test_files)} images in {test_files_path}' + \ |
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f'and put their paths to {list_of_random_test_files}') |
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print('Our training procedure will pick best checkpoints according to metrics, computed on these images.') |
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random.shuffle(test_files) |
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test_files_random = test_files[0:2000] |
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with open(list_of_random_test_files, 'w') as fw: |
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for filename in test_files_random: |
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fw.write(filename+'\n') |
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print('...done') |
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# -------------------------------- |
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val_files_path = os.path.abspath('.') + '/places_standard_dataset/original/val/' |
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list_of_random_val_files = os.path.abspath('.') + '/places_standard_dataset/original/val_random_files.txt' |
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val_files = [ |
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val_files_path + image for image in os.listdir(val_files_path) |
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] |
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print(f'Sampling 100 images out of {len(val_files)} in {val_files_path} ' + \ |
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f'and put their paths to {list_of_random_val_files}') |
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print('We use these images for visual check up of evolution of inpainting algorithm epoch to epoch' ) |
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random.shuffle(val_files) |
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val_files_random = val_files[0:100] |
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with open(list_of_random_val_files, 'w') as fw: |
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for filename in val_files_random: |
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fw.write(filename+'\n') |
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print('...done') |
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