People
 :bowtie: |
😄 :smile: |
😆 :laughing: |
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😊 :blush: |
😃 :smiley: |
:relaxed: |
😏 :smirk: |
😍 :heart_eyes: |
😘 :kissing_heart: |
😚 :kissing_closed_eyes: |
😳 :flushed: |
😌 :relieved: |
😆 :satisfied: |
😁 :grin: |
😉 :wink: |
😜 :stuck_out_tongue_winking_eye: |
😝 :stuck_out_tongue_closed_eyes: |
😀 :grinning: |
😗 :kissing: |
😙 :kissing_smiling_eyes: |
😛 :stuck_out_tongue: |
| import numpy | |
| from scipy.ndimage.interpolation import map_coordinates | |
| from scipy.ndimage.filters import gaussian_filter | |
| def elastic_transform(image, alpha, sigma, random_state=None): | |
| """Elastic deformation of images as described in [Simard2003]_. | |
| .. [Simard2003] Simard, Steinkraus and Platt, "Best Practices for | |
| Convolutional Neural Networks applied to Visual Document Analysis", in |
| def _sequence_mask(sequence_length, max_len=None): | |
| if max_len is None: | |
| max_len = sequence_length.data.max() | |
| batch_size = sequence_length.size(0) | |
| seq_range = torch.range(0, max_len - 1).long() | |
| seq_range_expand = seq_range.unsqueeze(0).expand(batch_size, max_len) | |
| seq_range_expand = Variable(seq_range_expand) | |
| if sequence_length.is_cuda: | |
| seq_range_expand = seq_range_expand.cuda() | |
| seq_length_expand = (sequence_length.unsqueeze(1) |
