# Copyright 2020 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.
# ============================================================================
"""ResNet."""
import numpy as np
import mindspore.nn as nn
import mindspore.common.dtype as mstype
from mindspore.ops import operations as P
from mindspore.ops import functional as F
from mindspore.common.tensor import Tensor
from scipy.stats import truncnorm

def _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size):
    fan_in = in_channel * kernel_size * kernel_size
    scale = 1.0
    scale /= max(1., fan_in)
    stddev = (scale ** 0.5) / .87962566103423978
    mu, sigma = 0, stddev
    weight = truncnorm(-2, 2, loc=mu, scale=sigma).rvs(out_channel * in_channel * kernel_size * kernel_size)
    weight = np.reshape(weight, (out_channel, in_channel, kernel_size, kernel_size))
    return Tensor(weight, dtype=mstype.float32)

def _weight_variable(shape, factor=0.01):
    init_value = np.random.randn(*shape).astype(np.float32) * factor
    return Tensor(init_value)


def _conv3x3(in_channel, out_channel, stride=1, use_se=False):
    if use_se:
        weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=3)
    else:
        weight_shape = (out_channel, in_channel, 3, 3)
        weight = _weight_variable(weight_shape)
    return nn.Conv2d(in_channel, out_channel,
                     kernel_size=3, stride=stride, padding=0, pad_mode='same', weight_init=weight)


def _conv1x1(in_channel, out_channel, stride=1, use_se=False):
    if use_se:
        weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=1)
    else:
        weight_shape = (out_channel, in_channel, 1, 1)
        weight = _weight_variable(weight_shape)
    return nn.Conv2d(in_channel, out_channel,
                     kernel_size=1, stride=stride, padding=0, pad_mode='same', weight_init=weight)


def _conv7x7(in_channel, out_channel, stride=1, use_se=False):
    if use_se:
        weight = _conv_variance_scaling_initializer(in_channel, out_channel, kernel_size=7)
    else:
        weight_shape = (out_channel, in_channel, 7, 7)
        weight = _weight_variable(weight_shape)
    return nn.Conv2d(in_channel, out_channel,
                     kernel_size=7, stride=stride, padding=0, pad_mode='same', weight_init=weight)


def _bn(channel):
    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
                          gamma_init=1, beta_init=0, moving_mean_init=0, moving_var_init=1)


def _bn_last(channel):
    return nn.BatchNorm2d(channel, eps=1e-4, momentum=0.9,
                          gamma_init=0, beta_init=0, moving_mean_init=0, moving_var_init=1)


def _fc(in_channel, out_channel, use_se=False):
    if use_se:
        weight = np.random.normal(loc=0, scale=0.01, size=out_channel*in_channel)
        weight = Tensor(np.reshape(weight, (out_channel, in_channel)), dtype=mstype.float32)
    else:
        weight_shape = (out_channel, in_channel)
        weight = _weight_variable(weight_shape)
    return nn.Dense(in_channel, out_channel, has_bias=True, weight_init=weight, bias_init=0)


class ResidualBlock(nn.Cell):
    """
    ResNet V1 residual block definition.

    Args:
        in_channel (int): Input channel.
        out_channel (int): Output channel.
        stride (int): Stride size for the first convolutional layer. Default: 1.
        use_se (bool): enable SE-ResNet50 net. Default: False.
        se_block(bool): use se block in SE-ResNet50 net. Default: False.

    Returns:
        Tensor, output tensor.

    Examples:
        >>> ResidualBlock(3, 256, stride=2)
    """
    expansion = 4

    def __init__(self,
                 in_channel,
                 out_channel,
                 stride=1,
                 use_se=False, se_block=False):
        super(ResidualBlock, self).__init__()
        self.stride = stride
        self.use_se = use_se
        self.se_block = se_block
        channel = out_channel // self.expansion
        self.conv1 = _conv1x1(in_channel, channel, stride=1, use_se=self.use_se)
        self.bn1 = _bn(channel)
        if self.use_se and self.stride != 1:
            self.e2 = nn.SequentialCell([_conv3x3(channel, channel, stride=1, use_se=True), _bn(channel),
                                         nn.ReLU(), nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same')])
        else:
            self.conv2 = _conv3x3(channel, channel, stride=stride, use_se=self.use_se)
            self.bn2 = _bn(channel)

        self.conv3 = _conv1x1(channel, out_channel, stride=1, use_se=self.use_se)
        self.bn3 = _bn_last(out_channel)
        if self.se_block:
            self.se_global_pool = P.ReduceMean(keep_dims=False)
            self.se_dense_0 = _fc(out_channel, int(out_channel/4), use_se=self.use_se)
            self.se_dense_1 = _fc(int(out_channel/4), out_channel, use_se=self.use_se)
            self.se_sigmoid = nn.Sigmoid()
            self.se_mul = P.Mul()
        self.relu = nn.ReLU()

        self.down_sample = False

        if stride != 1 or in_channel != out_channel:
            self.down_sample = True
        self.down_sample_layer = None

        if self.down_sample:
            if self.use_se:
                if stride == 1:
                    self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel,
                                                                         stride, use_se=self.use_se), _bn(out_channel)])
                else:
                    self.down_sample_layer = nn.SequentialCell([nn.MaxPool2d(kernel_size=2, stride=2, pad_mode='same'),
                                                                _conv1x1(in_channel, out_channel, 1,
                                                                         use_se=self.use_se), _bn(out_channel)])
            else:
                self.down_sample_layer = nn.SequentialCell([_conv1x1(in_channel, out_channel, stride,
                                                                     use_se=self.use_se), _bn(out_channel)])
        self.add = P.Add()

    def construct(self, x):
        identity = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)
        if self.use_se and self.stride != 1:
            out = self.e2(out)
        else:
            out = self.conv2(out)
            out = self.bn2(out)
            out = self.relu(out)
        out = self.conv3(out)
        out = self.bn3(out)
        if self.se_block:
            out_se = out
            out = self.se_global_pool(out, (2, 3))
            out = self.se_dense_0(out)
            out = self.relu(out)
            out = self.se_dense_1(out)
            out = self.se_sigmoid(out)
            out = F.reshape(out, F.shape(out) + (1, 1))
            out = self.se_mul(out, out_se)

        if self.down_sample:
            identity = self.down_sample_layer(identity)

        out = self.add(out, identity)
        out = self.relu(out)

        return out


class ResNet(nn.Cell):
    """
    ResNet architecture.

    Args:
        block (Cell): Block for network.
        layer_nums (list): Numbers of block in different layers.
        in_channels (list): Input channel in each layer.
        out_channels (list): Output channel in each layer.
        strides (list):  Stride size in each layer.
        num_classes (int): The number of classes that the training images are belonging to.
        use_se (bool): enable SE-ResNet50 net. Default: False.
        se_block(bool): use se block in SE-ResNet50 net in layer 3 and layer 4. Default: False.
    Returns:
        Tensor, output tensor.

    Examples:
        >>> ResNet(ResidualBlock,
        >>>        [3, 4, 6, 3],
        >>>        [64, 256, 512, 1024],
        >>>        [256, 512, 1024, 2048],
        >>>        [1, 2, 2, 2],
        >>>        10)
    """

    def __init__(self,
                 block,
                 layer_nums,
                 in_channels,
                 out_channels,
                 strides,
                 num_classes,
                 use_se=False):
        super(ResNet, self).__init__()

        if not len(layer_nums) == len(in_channels) == len(out_channels) == 4:
            raise ValueError("the length of layer_num, in_channels, out_channels list must be 4!")
        self.use_se = use_se
        self.se_block = False
        if self.use_se:
            self.se_block = True

        if self.use_se:
            self.conv1_0 = _conv3x3(3, 32, stride=2, use_se=self.use_se)
            self.bn1_0 = _bn(32)
            self.conv1_1 = _conv3x3(32, 32, stride=1, use_se=self.use_se)
            self.bn1_1 = _bn(32)
            self.conv1_2 = _conv3x3(32, 64, stride=1, use_se=self.use_se)
        else:
            self.conv1 = _conv7x7(3, 64, stride=2)
        self.bn1 = _bn(64)
        self.relu = P.ReLU()
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, pad_mode="same")
        self.layer1 = self._make_layer(block,
                                       layer_nums[0],
                                       in_channel=in_channels[0],
                                       out_channel=out_channels[0],
                                       stride=strides[0],
                                       use_se=self.use_se)
        self.layer2 = self._make_layer(block,
                                       layer_nums[1],
                                       in_channel=in_channels[1],
                                       out_channel=out_channels[1],
                                       stride=strides[1],
                                       use_se=self.use_se)
        self.layer3 = self._make_layer(block,
                                       layer_nums[2],
                                       in_channel=in_channels[2],
                                       out_channel=out_channels[2],
                                       stride=strides[2],
                                       use_se=self.use_se,
                                       se_block=self.se_block)
        self.layer4 = self._make_layer(block,
                                       layer_nums[3],
                                       in_channel=in_channels[3],
                                       out_channel=out_channels[3],
                                       stride=strides[3],
                                       use_se=self.use_se,
                                       se_block=self.se_block)

        self.mean = P.ReduceMean(keep_dims=True)
        self.flatten = nn.Flatten()
        self.end_point = _fc(out_channels[3], num_classes, use_se=self.use_se)

    def _make_layer(self, block, layer_num, in_channel, out_channel, stride, use_se=False, se_block=False):
        """
        Make stage network of ResNet.

        Args:
            block (Cell): Resnet block.
            layer_num (int): Layer number.
            in_channel (int): Input channel.
            out_channel (int): Output channel.
            stride (int): Stride size for the first convolutional layer.
            se_block(bool): use se block in SE-ResNet50 net. Default: False.
        Returns:
            SequentialCell, the output layer.

        Examples:
            >>> _make_layer(ResidualBlock, 3, 128, 256, 2)
        """
        layers = []

        resnet_block = block(in_channel, out_channel, stride=stride, use_se=use_se)
        layers.append(resnet_block)
        if se_block:
            for _ in range(1, layer_num - 1):
                resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se)
                layers.append(resnet_block)
            resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se, se_block=se_block)
            layers.append(resnet_block)
        else:
            for _ in range(1, layer_num):
                resnet_block = block(out_channel, out_channel, stride=1, use_se=use_se)
                layers.append(resnet_block)
        return nn.SequentialCell(layers)

    def construct(self, x):
        if self.use_se:
            x = self.conv1_0(x)
            x = self.bn1_0(x)
            x = self.relu(x)
            x = self.conv1_1(x)
            x = self.bn1_1(x)
            x = self.relu(x)
            x = self.conv1_2(x)
        else:
            x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        c1 = self.maxpool(x)

        c2 = self.layer1(c1)
        c3 = self.layer2(c2)
        c4 = self.layer3(c3)
        c5 = self.layer4(c4)

        out = self.mean(c5, (2, 3))
        out = self.flatten(out)
        out = self.end_point(out)

        return out


def resnet50(class_num=10):
    """
    Get ResNet50 neural network.

    Args:
        class_num (int): Class number.

    Returns:
        Cell, cell instance of ResNet50 neural network.

    Examples:
        >>> net = resnet50(10)
    """
    return ResNet(ResidualBlock,
                  [3, 4, 6, 3],
                  [64, 256, 512, 1024],
                  [256, 512, 1024, 2048],
                  [1, 2, 2, 2],
                  class_num)

def se_resnet50(class_num=1001):
    """
    Get SE-ResNet50 neural network.

    Args:
        class_num (int): Class number.

    Returns:
        Cell, cell instance of SE-ResNet50 neural network.

    Examples:
        >>> net = se-resnet50(1001)
    """
    return ResNet(ResidualBlock,
                  [3, 4, 6, 3],
                  [64, 256, 512, 1024],
                  [256, 512, 1024, 2048],
                  [1, 2, 2, 2],
                  class_num,
                  use_se=True)

def resnet101(class_num=1001):
    """
    Get ResNet101 neural network.

    Args:
        class_num (int): Class number.

    Returns:
        Cell, cell instance of ResNet101 neural network.

    Examples:
        >>> net = resnet101(1001)
    """
    return ResNet(ResidualBlock,
                  [3, 4, 23, 3],
                  [64, 256, 512, 1024],
                  [256, 512, 1024, 2048],
                  [1, 2, 2, 2],
                  class_num)