153 lines
5.6 KiB
Python
153 lines
5.6 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 Dict
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import paddle
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import paddle.nn as nn
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from ppcls.loss.xbm import CrossBatchMemory
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class ContrastiveLoss(nn.Layer):
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"""ContrastiveLoss
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Args:
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margin (float): margin
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embedding_size (int): number of embedding's dimension
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normalize_feature (bool, optional): whether to normalize embedding. Defaults to True.
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epsilon (float, optional): epsilon. Defaults to 1e-5.
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feature_from (str, optional): which key embedding from input dict. Defaults to "features".
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"""
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def __init__(self,
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margin: float,
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embedding_size: int,
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normalize_feature=True,
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epsilon: float=1e-5,
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feature_from: str="features"):
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super(ContrastiveLoss, self).__init__()
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self.margin = margin
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self.embedding_size = embedding_size
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self.normalize_feature = normalize_feature
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self.epsilon = epsilon
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self.feature_from = feature_from
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def forward(self, input: Dict[str, paddle.Tensor],
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target: paddle.Tensor) -> Dict[str, paddle.Tensor]:
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feats = input[self.feature_from]
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labels = target
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# normalize along feature dim
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if self.normalize_feature:
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feats = nn.functional.normalize(feats, p=2, axis=1)
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# squeeze labels to shape (batch_size, )
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if labels.ndim >= 2 and labels.shape[-1] == 1:
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labels = paddle.squeeze(labels, axis=[-1])
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loss = self._compute_loss(feats, target, feats, target)
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return {'ContrastiveLoss': loss}
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def _compute_loss(self,
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inputs_q: paddle.Tensor,
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targets_q: paddle.Tensor,
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inputs_k: paddle.Tensor,
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targets_k: paddle.Tensor) -> paddle.Tensor:
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batch_size = inputs_q.shape[0]
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# Compute similarity matrix
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sim_mat = paddle.matmul(inputs_q, inputs_k.t())
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loss = []
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for i in range(batch_size):
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pos_pair_ = paddle.masked_select(sim_mat[i],
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targets_q[i] == targets_k)
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pos_pair_ = paddle.masked_select(pos_pair_,
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pos_pair_ < 1 - self.epsilon)
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neg_pair_ = paddle.masked_select(sim_mat[i],
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targets_q[i] != targets_k)
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neg_pair = paddle.masked_select(neg_pair_, neg_pair_ > self.margin)
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pos_loss = paddle.sum(-pos_pair_ + 1)
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if len(neg_pair) > 0:
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neg_loss = paddle.sum(neg_pair)
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else:
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neg_loss = 0
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loss.append(pos_loss + neg_loss)
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loss = sum(loss) / batch_size
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return loss
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class ContrastiveLoss_XBM(ContrastiveLoss):
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"""ContrastiveLoss with CrossBatchMemory
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Args:
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xbm_size (int): size of memory bank
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xbm_weight (int): weight of CrossBatchMemory's loss
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start_iter (int): store embeddings after start_iter
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margin (float): margin
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embedding_size (int): number of embedding's dimension
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epsilon (float, optional): epsilon. Defaults to 1e-5.
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normalize_feature (bool, optional): whether to normalize embedding. Defaults to True.
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feature_from (str, optional): which key embedding from input dict. Defaults to "features".
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"""
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def __init__(self,
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xbm_size: int,
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xbm_weight: int,
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start_iter: int,
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margin: float,
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embedding_size: int,
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epsilon: float=1e-5,
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normalize_feature=True,
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feature_from: str="features"):
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super(ContrastiveLoss_XBM, self).__init__(
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margin, embedding_size, normalize_feature, epsilon, feature_from)
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self.xbm = CrossBatchMemory(xbm_size, embedding_size)
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self.xbm_weight = xbm_weight
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self.start_iter = start_iter
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self.iter = 0
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def __call__(self, input: Dict[str, paddle.Tensor],
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target: paddle.Tensor) -> Dict[str, paddle.Tensor]:
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feats = input[self.feature_from]
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labels = target
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# normalize along feature dim
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if self.normalize_feature:
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feats = nn.functional.normalize(feats, p=2, axis=1)
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# squeeze labels to shape (batch_size, )
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if labels.ndim >= 2 and labels.shape[-1] == 1:
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labels = paddle.squeeze(labels, axis=[-1])
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loss = self._compute_loss(feats, labels, feats, labels)
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# compute contrastive loss from memory bank
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self.iter += 1
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if self.iter > self.start_iter:
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self.xbm.enqueue_dequeue(feats.detach(), labels.detach())
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xbm_feats, xbm_labels = self.xbm.get()
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xbm_loss = self._compute_loss(feats, labels, xbm_feats, xbm_labels)
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loss = loss + self.xbm_weight * xbm_loss
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return {'ContrastiveLoss_XBM': loss}
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