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			59 lines
		
	
	
		
			1.9 KiB
		
	
	
	
		
			Python
		
	
	
	
	
	
			
		
		
	
	
			59 lines
		
	
	
		
			1.9 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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