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77 lines
2.3 KiB
77 lines
2.3 KiB
# Copyright (c) 2022 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 paddle.nn as nn
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from paddle.autograd import PyLayer
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class GradientReversalFunction(PyLayer):
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"""Gradient Reversal Layer from:
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Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
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Forward pass is the identity function. In the backward pass,
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the upstream gradients are multiplied by -lambda (i.e. gradient is reversed)
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"""
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@staticmethod
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def forward(ctx, x, lambda_=1):
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"""Forward in networks
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"""
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ctx.save_for_backward(lambda_)
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return x.clone()
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@staticmethod
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def backward(ctx, grads):
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"""Backward in networks
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"""
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lambda_, = ctx.saved_tensor()
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dx = -lambda_ * grads
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return dx
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class GradientReversalLayer(nn.Layer):
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"""Gradient Reversal Layer from:
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Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
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Forward pass is the identity function. In the backward pass,
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the upstream gradients are multiplied by -lambda (i.e. gradient is reversed)
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"""
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def __init__(self, lambda_=1):
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super(GradientReversalLayer, self).__init__()
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self.lambda_ = lambda_
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def forward(self, x):
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"""Forward in networks
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"""
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return GradientReversalFunction.apply(x, self.lambda_)
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if __name__ == "__main__":
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paddle.set_device("cpu")
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data = paddle.randn([2, 3], dtype="float64")
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data.stop_gradient = False
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grl = GradientReversalLayer(1)
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out = grl(data)
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out.mean().backward()
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print(data.grad)
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data = paddle.randn([2, 3], dtype="float64")
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data.stop_gradient = False
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grl = GradientReversalLayer(-1)
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out = grl(data)
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out.mean().backward()
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print(data.grad)
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