90 lines
3.1 KiB
Python
90 lines
3.1 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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from typing import Tuple
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import paddle
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class CrossBatchMemory(paddle.nn.Layer):
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"""
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CrossBatchMemory Implementation. refer to "Cross-Batch Memory for Embedding Learning".
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code heavily based on https://github.com/msight-tech/research-xbm/blob/master/ret_benchmark/modeling/xbm.py
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Args:
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size (int): Size of memory bank
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embedding_size (int): number of embedding dimension for memory bank
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"""
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def __init__(self, size: int, embedding_size: int):
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super().__init__()
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self.size = size
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self.embedding_size = embedding_size
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# initialize and register feature queue for resume training
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feats = paddle.zeros([self.size, self.embedding_size])
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self.register_buffer("feats", feats)
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# initialize and register label queue for resume training
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targets = paddle.zeros([self.size, ], dtype="int64")
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self.register_buffer("targets", targets)
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self.ptr = 0
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# self.accumulated_size = 0
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@property
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def _is_full(self) -> bool:
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# return self.accumulated_size >= self.size
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return self.targets[-1].item() != 0 # author's usage
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def get(self) -> Tuple[paddle.Tensor, paddle.Tensor]:
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"""return features and targets in memory bank
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Returns:
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Tuple[paddle.Tensor, paddle.Tensor]: [features, targets]
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"""
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if self._is_full:
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return self.feats, self.targets
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else:
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return self.feats[:self.ptr], self.targets[:self.ptr]
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def enqueue_dequeue(self, feats: paddle.Tensor,
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targets: paddle.Tensor) -> None:
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"""put newest feats and targets into memory bank and pop oldest feats and targets from momory bank
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Args:
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feats (paddle.Tensor): features to enque
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targets (paddle.Tensor): targets to enque
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"""
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input_size = len(targets)
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if self.ptr + input_size > self.size:
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self.feats[-input_size:] = feats
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self.targets[-input_size:] = targets
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self.ptr = 0
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else:
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self.feats[self.ptr:self.ptr + input_size] = feats
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self.targets[self.ptr:self.ptr + input_size] = targets
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self.ptr += input_size
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# self.accumulated_size += input_size
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def forward(self, *kargs, **kwargs):
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raise NotImplementedError(
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"CrossBatchMemory module is for memory-bank, forward method is not needed"
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)
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