(self, meta_file, *batch_files)
| 822 | return np.concatenate(images) |
| 823 | |
| 824 | def load_labels(self, meta_file, *batch_files): |
| 825 | classes = [] |
| 826 | with open(meta_file, "r") as fin: |
| 827 | for line in fin: |
| 828 | line = line.strip() |
| 829 | if line: |
| 830 | classes.append(line) |
| 831 | classes = np.asarray(classes) |
| 832 | labels = [] |
| 833 | for batch_file in batch_files: |
| 834 | batch = np.fromfile(batch_file, dtype=np.uint8) |
| 835 | batch = batch.reshape(-1, 32*32*3 + 1) |
| 836 | labels.append(batch[:, 0]) |
| 837 | return classes[np.concatenate(labels)] |
| 838 | |
| 839 | def train_image_data_preprocess(self, raw_path, save_file): |
| 840 | batch_files = glob.glob(os.path.join(raw_path, "cifar-10-batches-bin/data_batch_*.bin")) |
no outgoing calls