[vec][layer] add GRL to domain adaptation, test=doc fix #1724

paddlespeech/vector/modules/layer.py

@@ -0,0 +1,76 @@
+# Copyright (c) 2022 PaddlePaddle Authors. All Rights Reserved.
+#
+# 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
+
+
+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)
+    """
+
+    @staticmethod
+    def forward(ctx, x, lambda_=1):
+        """Forward in networks
+        """
+        ctx.save_for_backward(lambda_)
+        return x.clone()
+
+    @staticmethod
+    def backward(ctx, grads):
+        """Backward in networks
+        """
+        lambda_, = ctx.saved_tensor()
+        dx = -lambda_ * grads
+        return dx
+
+
+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_
+
+    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)