Refactor image preprocessing iterators to subclass Sequence. #7853
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Dref360
reviewed
Sep 8, 2017
| batch_x = np.zeros(tuple([current_batch_size] + list(self.x.shape)[1:]), dtype=K.floatx()) | ||
| def _get_batches_of_transformed_samples(self, index_array): | ||
| batch_x = np.zeros(tuple([len(index_array)] + list(self.x.shape)[1:]), | ||
| dtype=K.floatx()) | ||
| for i, j in enumerate(index_array): |
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Do not need the enumerate here. i is not used.
| for i, j in enumerate(index_array): | ||
| x = self.x[j] | ||
| x = self.image_data_generator.random_transform(x.astype(K.floatx())) | ||
| x = self.image_data_generator.standardize(x) | ||
| batch_x[i] = x | ||
| if self.save_to_dir: | ||
| for i in range(current_batch_size): | ||
| for i, j in enumerate(index_array): |
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enumerate is not needed here. i is not used
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i is used below to retrieve sample of index i in the batch, batch_x[i]
j is the index of the same sample in the original array, which we use in the filename
| @@ -1037,10 +1057,10 @@ def next(self): | |||
| batch_x[i] = x | |||
| # optionally save augmented images to disk for debugging purposes | |||
| if self.save_to_dir: | |||
| for i in range(current_batch_size): | |||
| for i, j in enumerate(index_array): | |||
| break | ||
|
|
||
| # Test `flow` behavior as Sequence | ||
| seq = generator.flow(images, np.arange(images.shape[0]), | ||
| shuffle=True, save_to_dir=str(tmpdir), |
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Need a test to validate that shuffle works and on_epoch_end works
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Added tests for shuffling and reshuffling upon on_epoch_end
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CC @Dref360