85 lines
2.7 KiB
Python
85 lines
2.7 KiB
Python
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
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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 paddle.nn as nn
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import paddle.nn.functional as F
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from ppcls.utils.initializer import kaiming_normal_
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class MGDLoss(nn.Layer):
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"""Paddle version of `Masked Generative Distillation`
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MGDLoss
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Reference: https://arxiv.org/abs/2205.01529
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Code was heavily based on https://github.com/yzd-v/MGD
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"""
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def __init__(
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self,
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student_channels,
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teacher_channels,
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alpha_mgd=1.756,
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lambda_mgd=0.15, ):
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super().__init__()
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self.alpha_mgd = alpha_mgd
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self.lambda_mgd = lambda_mgd
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if student_channels != teacher_channels:
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self.align = nn.Conv2D(
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student_channels,
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teacher_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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else:
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self.align = None
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self.generation = nn.Sequential(
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nn.Conv2D(
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teacher_channels, teacher_channels, kernel_size=3, padding=1),
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nn.ReLU(),
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nn.Conv2D(
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teacher_channels, teacher_channels, kernel_size=3, padding=1))
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self.apply(self._init_weights)
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def _init_weights(self, m):
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if isinstance(m, nn.Conv2D):
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kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
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def forward(self, pred_s, pred_t):
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"""Forward function.
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Args:
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pred_s(Tensor): Bs*C*H*W, student's feature map
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pred_t(Tensor): Bs*C*H*W, teacher's feature map
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"""
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assert pred_s.shape[-2:] == pred_t.shape[-2:]
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if self.align is not None:
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pred_s = self.align(pred_s)
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loss = self.get_dis_loss(pred_s, pred_t) * self.alpha_mgd
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return loss
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def get_dis_loss(self, pred_s, pred_t):
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loss_mse = nn.MSELoss(reduction='mean')
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N, C, _, _ = pred_t.shape
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mat = paddle.rand([N, C, 1, 1])
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mat = paddle.where(mat < self.lambda_mgd, 0, 1).astype("float32")
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masked_fea = paddle.multiply(pred_s, mat)
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new_fea = self.generation(masked_fea)
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dis_loss = loss_mse(new_fea, pred_t)
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return dis_loss
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