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