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| # Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. | |
| # | |
| # Licensed under the Apache License, Version 2.0 (the "License"); | |
| # you may not use this file except in compliance with the License. | |
| # You may obtain a copy of the License at | |
| # | |
| # http://www.apache.org/licenses/LICENSE-2.0 | |
| # | |
| # Unless required by applicable law or agreed to in writing, software | |
| # distributed under the License is distributed on an "AS IS" BASIS, | |
| # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
| # See the License for the specific language governing permissions and | |
| # limitations under the License. | |
| import paddle | |
| import numbers | |
| import numpy as np | |
| from collections import defaultdict | |
| class DictCollator(object): | |
| """ | |
| data batch | |
| """ | |
| def __call__(self, batch): | |
| # todo：support batch operators | |
| data_dict = defaultdict(list) | |
| to_tensor_keys = [] | |
| for sample in batch: | |
| for k, v in sample.items(): | |
| if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)): | |
| if k not in to_tensor_keys: | |
| to_tensor_keys.append(k) | |
| data_dict[k].append(v) | |
| for k in to_tensor_keys: | |
| data_dict[k] = paddle.to_tensor(data_dict[k]) | |
| return data_dict | |
| class ListCollator(object): | |
| """ | |
| data batch | |
| """ | |
| def __call__(self, batch): | |
| # todo：support batch operators | |
| data_dict = defaultdict(list) | |
| to_tensor_idxs = [] | |
| for sample in batch: | |
| for idx, v in enumerate(sample): | |
| if isinstance(v, (np.ndarray, paddle.Tensor, numbers.Number)): | |
| if idx not in to_tensor_idxs: | |
| to_tensor_idxs.append(idx) | |
| data_dict[idx].append(v) | |
| for idx in to_tensor_idxs: | |
| data_dict[idx] = paddle.to_tensor(data_dict[idx]) | |
| return list(data_dict.values()) | |
| class SSLRotateCollate(object): | |
| """ | |
| bach: [ | |
| [(4*3xH*W), (4,)] | |
| [(4*3xH*W), (4,)] | |
| ... | |
| ] | |
| """ | |
| def __call__(self, batch): | |
| output = [np.concatenate(d, axis=0) for d in zip(*batch)] | |
| return output | |
| class DyMaskCollator(object): | |
| """ | |
| batch: [ | |
| image [batch_size, channel, maxHinbatch, maxWinbatch] | |
| image_mask [batch_size, channel, maxHinbatch, maxWinbatch] | |
| label [batch_size, maxLabelLen] | |
| label_mask [batch_size, maxLabelLen] | |
| ... | |
| ] | |
| """ | |
| def __call__(self, batch): | |
| max_width, max_height, max_length = 0, 0, 0 | |
| bs, channel = len(batch), batch[0][0].shape[0] | |
| proper_items = [] | |
| for item in batch: | |
| if item[0].shape[1] * max_width > 1600 * 320 or item[0].shape[ | |
| 2] * max_height > 1600 * 320: | |
| continue | |
| max_height = item[0].shape[1] if item[0].shape[ | |
| 1] > max_height else max_height | |
| max_width = item[0].shape[2] if item[0].shape[ | |
| 2] > max_width else max_width | |
| max_length = len(item[1]) if len(item[ | |
| 1]) > max_length else max_length | |
| proper_items.append(item) | |
| images, image_masks = np.zeros( | |
| (len(proper_items), channel, max_height, max_width), | |
| dtype='float32'), np.zeros( | |
| (len(proper_items), 1, max_height, max_width), dtype='float32') | |
| labels, label_masks = np.zeros( | |
| (len(proper_items), max_length), dtype='int64'), np.zeros( | |
| (len(proper_items), max_length), dtype='int64') | |
| for i in range(len(proper_items)): | |
| _, h, w = proper_items[i][0].shape | |
| images[i][:, :h, :w] = proper_items[i][0] | |
| image_masks[i][:, :h, :w] = 1 | |
| l = len(proper_items[i][1]) | |
| labels[i][:l] = proper_items[i][1] | |
| label_masks[i][:l] = 1 | |
| return images, image_masks, labels, label_masks | |