Sourcery refactored master branch#1
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| def deconv(c_in, c_out, k_size, stride=2, pad=1, bn=True): | ||
| layers = [] | ||
| layers.append(nn.ConvTranspose2d(c_in, c_out, k_size, stride, pad)) | ||
| layers = [nn.ConvTranspose2d(c_in, c_out, k_size, stride, pad)] |
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Function deconv refactored with the following changes:
- Merge append into list declaration (
merge-list-append)
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| layers = [] | ||
| layers.append(nn.Conv2d(c_in, c_out, k_size, stride, pad)) | ||
| layers = [nn.Conv2d(c_in, c_out, k_size, stride, pad)] |
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Function conv refactored with the following changes:
- Merge append into list declaration (
merge-list-append)
| p = p / np.sum(p) | ||
| c = np.random.choice(vocab_size, 1, p=p)[0] | ||
| return c | ||
| return np.random.choice(vocab_size, 1, p=p)[0] |
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Function pick_top_n refactored with the following changes:
- Inline variable that is immediately returned (
inline-immediately-returned-variable)
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| samples = [c for c in prime] | ||
| samples = list(prime) |
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Function sample refactored with the following changes:
- Replace identity comprehension with call to collection constructor (
identity-comprehension) - Replace unused for index with underscore (
for-index-underscore)
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| print('Epoch: {} '.format(e + 1), 'Loss: {:.4f} '.format(batch_loss), | ||
| '{:.4f} sec/batch'.format((end - start))) | ||
| print( | ||
| f'Epoch: {e + 1} ', | ||
| 'Loss: {:.4f} '.format(batch_loss), | ||
| '{:.4f} sec/batch'.format((end - start)), | ||
| ) | ||
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| if (counter % save_every_n == 0): | ||
| saver.save(sess, "saves/{}.ckpt".format(counter)) | ||
| saver.save(sess, f"saves/{counter}.ckpt") | ||
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| saver.save(sess, "saves/{}.ckpt".format(counter)) | ||
| saver.save(sess, f"saves/{counter}.ckpt") |
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Function train refactored with the following changes:
- Replace call to format with f-string [×3] (
use-fstring-for-formatting)
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| layers = [] | ||
| layers.append( | ||
| block(self.in_channels, out_channels, stride, downsample)) | ||
| layers = [block(self.in_channels, out_channels, stride, downsample)] | ||
| self.in_channels = out_channels | ||
| for i in range(1, blocks): | ||
| layers.append(block(out_channels, out_channels)) | ||
| layers.extend(block(out_channels, out_channels) for _ in range(1, blocks)) |
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Function ResNet.make_layer refactored with the following changes:
- Merge append into list declaration (
merge-list-append) - Replace unused for index with underscore (
for-index-underscore) - Replace a for append loop with list extend (
for-append-to-extend)
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