test_huber_loss_op.py

#   Copyright (c) 2018 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 print_function

import unittest
import numpy as np
from op_test import OpTest
import paddle.fluid as fluid
import paddle
from paddle.fluid import compiler, Program, program_guard


def huber_loss_forward(val, delta):
    abs_val = abs(val)
    if abs_val <= delta:
        return 0.5 * val * val
    else:
        return delta * (abs_val - 0.5 * delta)


class TestHuberLossOp(OpTest):
    def setUp(self):
        self.op_type = 'huber_loss'
        self.python_api = paddle.fluid.layers.huber_loss
        self.python_out_sig = ["Out"]
        self.delta = 1.0
        self.init_input()
        shape = self.set_shape()
        residual = self.inputs['Y'] - self.inputs['X']
        loss = np.vectorize(huber_loss_forward)(residual,
                                                self.delta).astype('float32')
        self.attrs = {'delta': self.delta}
        self.outputs = {'Residual': residual, 'Out': loss.reshape(shape)}

    def init_input(self):
        shape = self.set_shape()
        self.inputs = {
            'X': np.random.uniform(0, 1., shape).astype('float32'),
            'Y': np.random.uniform(0, 1., shape).astype('float32'),
        }

    def set_shape(self):
        return (100, 1)

    def test_check_output(self):
        self.check_output(check_eager=True)

    def test_check_grad_normal(self):
        self.check_grad(['X', 'Y'], 'Out', check_eager=True)

    def test_check_grad_ingore_x(self):
        self.check_grad(
            ['Y'], 'Out', max_relative_error=0.008, no_grad_set=set("residual"))

    def test_check_grad_ingore_y(self):
        self.check_grad(
            ['X'], 'Out', max_relative_error=0.008, no_grad_set=set('residual'))


def TestHuberLossOp1(TestHuberLossOp):
    def set_shape(self):
        return (64)


def TestHuberLossOp2(TestHuberLossOp):
    def set_shape(self):
        return (6, 6)


def TestHuberLossOp3(TestHuberLossOp):
    def set_shape(self):
        return (6, 6, 1)


class TestHuberLossOpError(unittest.TestCase):
    def test_errors(self):
        with program_guard(Program(), Program()):
            # the input and label must be Variable
            xw = np.random.random((6, 6)).astype("float32")
            xr = fluid.data(name='xr', shape=[None, 6], dtype="float32")
            lw = np.random.random((6, 6)).astype("float32")
            lr = fluid.data(name='lr', shape=[None, 6], dtype="float32")
            delta = 1.0
            self.assertRaises(TypeError, fluid.layers.huber_loss, xr, lw, delta)
            self.assertRaises(TypeError, fluid.layers.huber_loss, xw, lr, delta)

            # the dtype of input and label must be float32 or float64
            xw2 = fluid.data(name='xw2', shape=[None, 6], dtype="int32")
            lw2 = fluid.data(name='lw2', shape=[None, 6], dtype="int32")
            self.assertRaises(TypeError, fluid.layers.huber_loss, xw2, lr,
                              delta)
            self.assertRaises(TypeError, fluid.layers.huber_loss, xr, lw2,
                              delta)


if __name__ == '__main__':
    paddle.enable_static()
    unittest.main()