move loss to loss.py

hapi/loss.py

+# Copyright (c) 2019 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.
+
+from __future__ import absolute_import
+
+import os
+
+from paddle import fluid
+from paddle.fluid.framework import in_dygraph_mode, Variable
+from paddle.fluid.dygraph.base import to_variable
+
+__all__ = ['Loss', 'CrossEntropy', 'SoftmaxWithCrossEntropy']
+
+
+def to_list(value):
+    if value is None:
+        return value
+    if isinstance(value, (list, tuple)):
+        return list(value)
+    return [value]
+
+
+class Loss(object):
+    """
+    Base class for loss, encapsulates loss logic and APIs
+
+    Usage:
+    custom_loss = CustomLoss()
+    loss = custom_loss(inputs, labels)
+    """
+
+    def __init__(self, average=True):
+        super(Loss, self).__init__()
+        self.average = average
+
+    def forward(self, outputs, labels):
+        raise NotImplementedError()
+
+    def __call__(self, outputs, labels=None):
+        labels = to_list(labels)
+        if in_dygraph_mode() and labels:
+            labels = [to_variable(l) for l in labels]
+        losses = to_list(self.forward(to_list(outputs), labels))
+        if self.average:
+            losses = [fluid.layers.reduce_mean(l) for l in losses]
+        else:
+            losses = [fluid.layers.reduce_sum(l) for l in losses]
+        return losses
+
+
+class CrossEntropy(Loss):
+    """
+    Args:
+        input (list[Variable]): Input tensor, the data type is float32,
+            float64, int32, int64.
+        label (list[Variable]): Label tensor, the data type is float32,
+            float64, int32, int64.
+        average (bool, optional): Indicate whether to average the loss, Default: True.
+    Returns:
+        list[Variable]: The tensor variable storing the cross_entropy_loss of inputs and labels.
+    """
+
+    def __init__(self, average=True):
+        super(CrossEntropy, self).__init__()
+
+    def forward(self, outputs, labels):
+        return [
+            fluid.layers.cross_entropy(o, l) for o, l in zip(outputs, labels)
+        ]
+
+
+class SoftmaxWithCrossEntropy(Loss):
+    """
+    this op combined softmax and cross entropy.
+    Args:
+        input (list[Variable]): Input tensor, the data type is float32,
+            float64, int32, int64.
+        label (list[Variable]): Label tensor, the data type is float32,
+            float64, int32, int64.
+        average (bool, optional): Indicate whether to average the loss, Default: True.
+    Returns:
+        list[Variable]: The tensor variable storing the cross_entropy_loss of inputs and labels.
+    """
+
+    def __init__(self, average=True):
+        super(SoftmaxWithCrossEntropy, self).__init__()
+
+    def forward(self, outputs, labels):
+        return [
+            fluid.layers.softmax_with_cross_entropy(
+                o, l, return_softmax=False) for o, l in zip(outputs, labels)
+        ]