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#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import paddle
import paddle.nn as nn
from paddle.autograd import PyLayer
[docs]class GradientReversalFunction(PyLayer):
"""Gradient Reversal Layer from:
Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
Forward pass is the identity function. In the backward pass,
the upstream gradients are multiplied by -lambda (i.e. gradient is reversed)
"""
[docs] @staticmethod
def forward(ctx, x, lambda_=1):
"""Forward in networks
"""
ctx.save_for_backward(lambda_)
return x.clone()
[docs] @staticmethod
def backward(ctx, grads):
"""Backward in networks
"""
lambda_, = ctx.saved_tensor()
dx = -lambda_ * grads
return dx
[docs]class GradientReversalLayer(nn.Layer):
"""Gradient Reversal Layer from:
Unsupervised Domain Adaptation by Backpropagation (Ganin & Lempitsky, 2015)
Forward pass is the identity function. In the backward pass,
the upstream gradients are multiplied by -lambda (i.e. gradient is reversed)
"""
def __init__(self, lambda_=1):
super(GradientReversalLayer, self).__init__()
self.lambda_ = lambda_
[docs] def forward(self, x):
"""Forward in networks
"""
return GradientReversalFunction.apply(x, self.lambda_)
if __name__ == "__main__":
paddle.set_device("cpu")
data = paddle.randn([2, 3], dtype="float64")
data.stop_gradient = False
grl = GradientReversalLayer(1)
out = grl(data)
out.mean().backward()
print(data.grad)
data = paddle.randn([2, 3], dtype="float64")
data.stop_gradient = False
grl = GradientReversalLayer(-1)
out = grl(data)
out.mean().backward()
print(data.grad)