# Copyright 2021 Huawei Technologies Co., Ltd
#
# 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 numpy as np
import pytest

import mindspore.context as context
import mindspore.nn as nn
from mindspore import Tensor
from mindspore.common.parameter import ParameterTuple
from mindspore.ops import operations as P
from mindspore.ops import composite as C


class NetConv3d(nn.Cell):
    def __init__(self):
        super(NetConv3d, self).__init__()
        out_channel = 4
        kernel_size = 2
        self.conv = P.Conv3D(out_channel,
                             kernel_size,
                             mode=1,
                             pad_mode="valid",
                             pad=0,
                             stride=1,
                             dilation=1,
                             group=1)

    def construct(self, x, w):
        return self.conv(x, w)


@pytest.mark.level0
@pytest.mark.platform_x86_gpu_training
@pytest.mark.env_onecard
def test_conv3d():
    x = Tensor(np.arange(1 * 3 * 3 * 3 * 3).reshape(1, 3, 3, 3, 3).astype(np.float32))
    w = Tensor(np.arange(4 * 3 * 2 * 2 * 2).reshape(4, 3, 2, 2, 2).astype(np.float32))
    expect = np.array([[[[[12960., 13236.],
                          [13788., 14064.]],
                         [[15444., 15720.],
                          [16272., 16548.]]],
                        [[[32256., 33108.],
                          [34812., 35664.]],
                         [[39924., 40776.],
                          [42480., 43332.]]],
                        [[[51552., 52980.],
                          [55836., 57264.]],
                         [[64404., 65832.],
                          [68688., 70116.]]],
                        [[[70848., 72852.],
                          [76860., 78864.]],
                         [[88884., 90888.],
                          [94896., 96900.]]]]]).astype(np.float32)

    context.set_context(mode=context.PYNATIVE_MODE, device_target="GPU")
    net = NetConv3d()
    output = net(x, w)
    assert (output.asnumpy() == expect).all()
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    net = NetConv3d()
    output = net(x, w)
    assert (output.asnumpy() == expect).all()


class MSConv3dNet(nn.Cell):
    def __init__(self, in_channels, out_channels, kernel_size, pad_mode='pad', padding=0, stride=1, dilation=1,
                 has_bias=False, weight_init='normal'):
        super(MSConv3dNet, self).__init__()
        self.cv1 = nn.Conv3d(in_channels=in_channels,
                             out_channels=out_channels,
                             kernel_size=kernel_size,
                             pad_mode=pad_mode,
                             padding=padding,
                             stride=stride,
                             dilation=dilation,
                             group=1,
                             has_bias=has_bias,
                             weight_init=weight_init,
                             data_format='NCDHW')

    def construct(self, x):
        x = self.cv1(x)
        return x


class MSGradNet(nn.Cell):
    def __init__(self, network):
        super(MSGradNet, self).__init__()
        self.grad = C.GradOperation(get_all=True, sens_param=True, get_by_list=True)
        self.network = network
        self.params = ParameterTuple(network.trainable_params())

    def construct(self, x, dy):
        grad_op = self.grad(self.network, self.params)
        output = grad_op(x, dy)
        return output


def test_conv3d_grad():
    context.set_context(mode=context.GRAPH_MODE, device_target="GPU")
    dtype = np.float32
    out_c = 2
    kernel_size = (2, 2, 2)
    x = Tensor(np.array([[[[[1.6924546, 0.05080776, -0.6369957],
                            [0.19091548, 2.1002553, 0.12015896],
                            [0.6172031, 0.30017033, -0.35224986]],
                           [[-1.1425182, -0.34934273, -0.20889424],
                            [0.5866232, 0.8389834, 0.9311021],
                            [0.2855873, 0.8851412, -0.7543979]],
                           [[1.2528682, 0.5129298, -0.29809284],
                            [0.48851815, -0.07557172, 1.1316293],
                            [1.5198169, 2.1855755, -1.3964963]]]]]).astype(dtype))
    dy = Tensor(np.array([[[[[-1.4441139, -0.5044659],
                             [0.16003707, 0.8761689]],
                            [[0.31563494, -2.0222013],
                             [-0.30620402, 0.8279746]]],
                           [[[0.23009473, 0.7620112],
                             [-0.22232814, -0.20075807]],
                            [[0.18656139, 0.41005164],
                             [0.19829972, 0.11900865]]]]]).astype(dtype))
    w = Tensor(np.array([[[[[-0.9358, -0.2679],
                            [0.5304, -0.6917]],
                           [[-0.3968, -0.6872],
                            [-0.8452, -0.6712]]]],
                         [[[[-0.0127, -1.1173],
                            [0.2344, 1.6598]],
                           [[0.7420, -0.1918],
                            [-0.8876, -0.7472]]]]]).astype(dtype))
    w_exp = np.array([[[[[-0.9384, -0.2830],
                         [0.5487, -0.6330]],
                        [[-0.4148, -0.7200],
                         [-0.8572, -0.6079]]]],
                      [[[[-0.0109, -1.1089],
                         [0.2138, 1.6478]],
                        [[0.7450, -0.1866],
                         [-0.8992, -0.7629]]]]]).astype(dtype)
    net = MSConv3dNet(x.shape[1], out_c, kernel_size, weight_init=w)
    grad_net = MSGradNet(net)
    optimizer = nn.SGD(net.trainable_params(), learning_rate=0.01, momentum=0.9)
    grad_net.set_train(True)
    output = grad_net(x, dy)
    optimizer(output[1])
    assert np.allclose(net.cv1.weight.asnumpy(), w_exp, atol=1.0e-4)