#   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.core as core


def affine_channel(x, scale, bias, layout):
    C = x.shape[1] if layout == 'NCHW' else x.shape[-1]
    if len(x.shape) == 4:
        new_shape = (1, C, 1, 1) if layout == 'NCHW' else (1, 1, 1, C)
    else:
        new_shape = (1, C)
    scale = scale.reshape(new_shape)
    bias = bias.reshape(new_shape)
    return x * scale + bias


class TestAffineChannelOp(OpTest):
    def setUp(self):
        self.op_type = "affine_channel"
        self.init_test_case()

        x = np.random.random(self.shape).astype("float32")
        scale = np.random.random(self.C).astype("float32")
        bias = np.random.random(self.C).astype("float32")

        y = affine_channel(x, scale, bias, self.layout)

        self.inputs = {'X': x, 'Scale': scale, 'Bias': bias}
        self.attrs = {'data_layout': self.layout}
        self.outputs = {'Out': y}

    def test_check_output(self):
        self.check_output()

    def test_check_grad(self):
        self.check_grad(['X', 'Scale', 'Bias'], 'Out')

    def test_check_grad_stopgrad_dx(self):
        self.check_grad(['Scale', 'Bias'], 'Out', no_grad_set=set('X'))

    def test_check_grad_stopgrad_dscale_dbias(self):
        self.check_grad(['X'], 'Out', no_grad_set=set(['Scale', 'Bias']))

    def init_test_case(self):
        self.shape = [2, 32, 14, 14]
        self.C = 32
        self.layout = 'NCHW'


class TestAffineChannelNHWC(TestAffineChannelOp):
    def init_test_case(self):
        self.shape = [2, 14, 14, 32]
        self.C = 32
        self.layout = 'NHWC'

    def test_check_grad_stopgrad_dx(self):
        return

    def test_check_grad_stopgrad_dscale_dbias(self):
        return


class TestAffineChannel2D(TestAffineChannelOp):
    def init_test_case(self):
        self.shape = [16, 64]
        self.C = 64
        self.layout = 'NCHW'

    def test_check_grad_stopgrad_dx(self):
        return

    def test_check_grad_stopgrad_dscale_dbias(self):
        return


class TestAffineChannelNCHWLargeShape(TestAffineChannelOp):
    def init_test_case(self):
        self.shape = [4, 128, 112, 112]
        self.C = 128
        self.layout = 'NCHW'

    # since the gradient check is very slow in large shape, so skip check_grad
    def test_check_grad(self):
        pass

    def test_check_grad_stopgrad_dx(self):
        pass

    def test_check_grad_stopgrad_dscale_dbias(self):
        pass


class TestAffineChannelNHWCLargeShape(TestAffineChannelNCHWLargeShape):
    def init_test_case(self):
        self.shape = [64, 32, 32, 512]
        self.C = 512
        self.layout = 'NHWC'


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