Refactoring - #5
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sonechiman
commented
Dec 18, 2017
| # for this project | ||
| Image_check.ipynb | ||
| /snapshot | ||
| /cfp-snapshot |
sonechiman
commented
Dec 18, 2017
| end = start + args.batch_size | ||
| batch_image = torch.FloatTensor(images[start:end]) | ||
| batch_pose_code = torch.FloatTensor(pose_code[start:end]) # Condition 付に使用 | ||
| batch_pose_code = torch.FloatTensor(pose_code[start:end]) # Condition 付に使用 |
sonechiman
commented
Dec 18, 2017
| import pdb | ||
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| # TODO: Use pytorch's dataloader |
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やらないといけないことは TODO:で書いておくと検索しやすい
sonechiman
commented
Dec 18, 2017
| else: | ||
| if args.images_perID==0: | ||
| if args.multi_DRGAN and args.images_perID == 0: | ||
| print("Please specify -images-perID of your data to input to multi_DRGAN") |
sonechiman
commented
Dec 18, 2017
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| def create_convs(channel_num, n_last=320): | ||
| return [ |
sonechiman
commented
Dec 18, 2017
| return x | ||
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| def weight_sum_feature(x, n): |
sonechiman
commented
Dec 18, 2017
| def __init__(self, Np, Nz, channel_num, multi=False, images_perID=None): | ||
| super(Generator, self).__init__() | ||
| self.features = [] | ||
| self.multi_flag = multi |
sonechiman
commented
Dec 18, 2017
| self.multi_flag = multi | ||
| self.Nf = 320 | ||
| if self.multi_flag: | ||
| self.Nf += 1 |
sonechiman
commented
Dec 18, 2017
| if isinstance(m, nn.Conv2d): | ||
| m.weight.data.normal_(0, 0.02) | ||
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| elif isinstance(m, nn.Linear): |
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