Remove paddle.incubate.hapi.loss and reuse paddle.nn.layer.loss in high level API (#25590)
* Remove paddle.incubate.hapi.loss and reuse the paddle.nn.layer.loss in high level APIfix_copy_if_different
qingqing01
5 years ago
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GitHub
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2a191d8fa5
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# Copyright (c) 2020 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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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import os
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from paddle import fluid
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from paddle.fluid.framework import in_dygraph_mode, Variable
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from paddle.fluid.dygraph.base import to_variable
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from .utils import to_list
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__all__ = ['Loss', 'CrossEntropy', 'SoftmaxWithCrossEntropy']
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class Loss(object):
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"""
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Base class for loss, encapsulates loss logic and APIs
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Usage:
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custom_loss = CustomLoss()
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loss = custom_loss(inputs, labels)
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Examples:
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.. code-block:: python
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from paddle.incubate.hapi.loss import Loss
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from paddle import fluid
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class SoftmaxWithCrossEntropy(Loss):
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def __init__(self, average=True):
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super(SoftmaxWithCrossEntropy, self).__init__(average)
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def forward(self, outputs, labels):
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return [
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fluid.layers.softmax_with_cross_entropy(
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o, l, return_softmax=False) for o, l in zip(outputs, labels)
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]
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"""
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def __init__(self, average=True):
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super(Loss, self).__init__()
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self.average = average
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def forward(self, outputs, labels):
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raise NotImplementedError()
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def __call__(self, outputs, labels=None):
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labels = to_list(labels)
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if in_dygraph_mode() and labels:
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labels = [to_variable(l) for l in labels]
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losses = to_list(self.forward(to_list(outputs), labels))
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if self.average:
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losses = [fluid.layers.reduce_mean(l) for l in losses]
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else:
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losses = [fluid.layers.reduce_sum(l) for l in losses]
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return losses
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class CrossEntropy(Loss):
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"""
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Args:
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input (list[Variable]): Input tensor, the data type is float32,
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float64, int32, int64.
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label (list[Variable]): Label tensor, the data type is float32,
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float64, int32, int64.
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average (bool, optional): Indicate whether to average the loss, Default: True.
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Returns:
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list[Variable]: The tensor variable storing the cross_entropy_loss of inputs and labels.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import paddle.incubate.hapi as hapi
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fluid.enable_dygraph()
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model = hapi.Model(hapi.vision.LeNet())
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model.prepare(loss_function=hapi.loss.CrossEntropy())
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"""
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def __init__(self, average=True):
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super(CrossEntropy, self).__init__(average)
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def forward(self, outputs, labels):
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return [
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fluid.layers.cross_entropy(o, l) for o, l in zip(outputs, labels)
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]
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class SoftmaxWithCrossEntropy(Loss):
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"""
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this op combined softmax and cross entropy.
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Args:
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input (list[Variable]): Input tensor, the data type is float32,
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float64, int32, int64.
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label (list[Variable]): Label tensor, the data type is float32,
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float64, int32, int64.
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average (bool, optional): Indicate whether to average the loss, Default: True.
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Returns:
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list[Variable]: The tensor variable storing the cross_entropy_loss of inputs and labels.
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Examples:
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.. code-block:: python
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import paddle.fluid as fluid
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import paddle.incubate.hapi as hapi
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fluid.enable_dygraph()
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model = hapi.Model(hapi.vision.LeNet(classifier_activation=None))
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loss = hapi.loss.SoftmaxWithCrossEntropy()
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model.prepare(loss_function=loss)
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"""
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def __init__(self, average=True):
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super(SoftmaxWithCrossEntropy, self).__init__(average)
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def forward(self, outputs, labels):
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return [
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fluid.layers.softmax_with_cross_entropy(
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o, l, return_softmax=False) for o, l in zip(outputs, labels)
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]
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File diff suppressed because it is too large
Load Diff
@ -1,111 +0,0 @@
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# Copyright (c) 2020 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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from __future__ import division
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from __future__ import print_function
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import unittest
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import os
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import six
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import numpy as np
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import shutil
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import copy
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import paddle
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from paddle import fluid
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from paddle.incubate.hapi.model import Model, Input
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from paddle.incubate.hapi.loss import CrossEntropy, SoftmaxWithCrossEntropy
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def stable_softmax(x):
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"""Compute the softmax of vector x in a numerically stable way."""
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# clip to shiftx, otherwise, when calc loss with
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# log(exp(shiftx)), may get log(0)=INF
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shiftx = (x - np.max(x)).clip(-64.)
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exps = np.exp(shiftx)
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return exps / np.sum(exps)
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def randomize_probability(batch_size, class_num, dtype='float32'):
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prob = np.random.uniform(
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0.1, 1.0, size=(batch_size, class_num)).astype(dtype)
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prob_sum = prob.sum(axis=1)
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for i in six.moves.xrange(len(prob)):
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prob[i] /= prob_sum[i]
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return prob
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def numpy_ce(x, label):
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return np.asmatrix(
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[[-np.log(x[i][label[i][0]])] for i in range(x.shape[0])],
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dtype="float32").mean()
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class TestLoss(unittest.TestCase):
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def test_cross_entropy(self):
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class_num = 100
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batch_size = 128
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inputs = [randomize_probability(128, class_num) for _ in range(2)]
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labels = [
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np.random.randint(
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0, class_num, (batch_size, 1), dtype="int64") for _ in range(2)
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]
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gt_out = [numpy_ce(inputs[i], labels[i]) for i in range(2)]
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fluid.enable_dygraph()
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cross_entropy = CrossEntropy()
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out = cross_entropy(
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[fluid.dygraph.to_variable(x) for x in inputs],
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[fluid.dygraph.to_variable(label) for label in labels])
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out = [o.numpy() for o in out]
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for o, g in zip(out, gt_out):
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np.testing.assert_allclose(o, g, atol=1e-5)
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def test_soft_cross_entronpy(self):
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class_num = 100
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batch_size = 128
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inputs = [randomize_probability(128, class_num) for _ in range(2)]
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labels = [
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np.random.randint(
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0, class_num, (batch_size, 1), dtype="int64") for _ in range(2)
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]
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fluid.enable_dygraph()
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softmax_cross_entropy = SoftmaxWithCrossEntropy()
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softmax_cross_entropy(
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[fluid.dygraph.to_variable(x) for x in inputs],
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[fluid.dygraph.to_variable(label) for label in labels])
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softmax_cross_entropy = SoftmaxWithCrossEntropy(average=False)
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inputs = [randomize_probability(128, class_num)]
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labels = [
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np.random.randint(
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0, class_num, (batch_size, 1), dtype="int64")
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]
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softmax_cross_entropy([fluid.dygraph.to_variable(x) for x in inputs],
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fluid.dygraph.to_variable(labels[0]))
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if __name__ == '__main__':
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unittest.main()
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