add instance_norm.py

x2paddle/op_mapper/dygraph/pytorch2paddle/pytorch_custom_layer/instance_norm.py

+# Copyright (c) 2020  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
+from paddle.nn.functional import instance_norm
+from paddle.fluid.initializer import Constant
+
+class InstanceNorm(paddle.nn.Layer):
+    """
+    This class is based class for InstanceNorm1D, 2d, 3d. 
+    See InstaceNorm1D, InstanceNorm2D or InstanceNorm3D for more details.
+    """
+
+    def __init__(self,
+                 num_features,
+                 epsilon=1e-5,
+                 momentum=0.9,
+                 weight_attr=None,
+                 bias_attr=None,
+                 data_format="NCHW",
+                 name=None):
+        super(InstanceNorm, self).__init__()
+
+        if weight_attr == False or bias_attr == False:
+            assert weight_attr == bias_attr, "weight_attr and bias_attr must be set to Fasle at the same time in InstanceNorm"
+        self._epsilon = epsilon
+        self._weight_attr = weight_attr
+        self._bias_attr = bias_attr
+
+        if weight_attr != False and bias_attr != False:
+            self.scale = self.create_parameter(
+                attr=self._weight_attr,
+                shape=[num_features],
+                default_initializer=Constant(1.0),
+                is_bias=False)
+            self.bias = self.create_parameter(
+                attr=self._bias_attr,
+                shape=[num_features],
+                default_initializer=Constant(0.0),
+                is_bias=True)
+        else:
+            self.scale = None
+            self.bias = None
+
+    def forward(self, input):
+        return instance_norm(
+            input, weight=self.scale, bias=self.bias, eps=self._epsilon)
+
+    def extra_repr(self):
+        return 'num_features={}, epsilon={}'.format(self.scale.shape[0],
+                                                    self._epsilon)