import numpy as np
import paddle
import paddle.fluid as fluid
from paddle.fluid.dygraph.nn import Conv2D, Pool2D, BatchNorm, Linear, Conv2DTranspose


class ConvBNLayer(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 groups=1,
                 act=None):
        super(ConvBNLayer, self).__init__()

        self._conv = Conv2D(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            groups=groups,
            act=None,
            bias_attr=False)

        self._batch_norm = BatchNorm(num_filters, act=act)

    def forward(self, inputs):
        y = self._conv(inputs)
        y = self._batch_norm(y)
        return y


class BottleneckBlock(fluid.dygraph.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True):
        super(BottleneckBlock, self).__init__()

        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            act='relu')
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act='relu')
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            act=None)

        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                stride=stride)

        self.shortcut = shortcut

        self._num_channels_out = num_filters * 4

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        conv2 = self.conv2(conv1)

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)

        y = fluid.layers.elementwise_add(x=short, y=conv2)

        return y


class ResNetv1(fluid.dygraph.Layer):
    """
        输出Hotmap
    """
    def __init__(self, layers=50, class_dim=1, act='relu'):
        super(ResNetv1, self).__init__()

        self.layers = layers
        supported_layers = [50, 101, 152]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(supported_layers, layers)

        if layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        num_filters = [64, 128, 256, 512]

        self.conv = ConvBNLayer(
            num_channels=3,
            num_filters=64,
            filter_size=7,
            stride=2,
            act='relu')
        self.pool2d_max = Pool2D(
            pool_size=3,
            pool_stride=2,
            pool_padding=1,
            pool_type='max')

        self.bottleneck_block_list = []
        num_channels = 64
        for block in range(len(depth)):
            shortcut = False
            for i in range(depth[block]):
                bottleneck_block = BottleneckBlock(
                    num_channels=num_channels,
                    num_filters=num_filters[block],
                    stride=2 if i == 0 and block != 0 else 1,
                    shortcut=shortcut)
                num_channels = bottleneck_block._num_channels_out
                self.bottleneck_block_list.append(bottleneck_block)
                shortcut = True
        self.bottleneck_block = paddle.nn.Sequential(*self.bottleneck_block_list)

        self.conv2d_transpose1 = Conv2DTranspose(num_channels, 256, filter_size=4, padding=1, stride=2)
        self.batch_norm1 = BatchNorm(256, act=act)
        self.conv2d_transpose2 = Conv2DTranspose(256, 256, filter_size=4, padding=1, stride=2)
        self.batch_norm2 = BatchNorm(256, act=act)
        self.conv2d_transpose3 = Conv2DTranspose(256, 256, filter_size=4, padding=1, stride=2)
        self.batch_norm3 = BatchNorm(256, act=act)
        self.conv2d_transpose4 = Conv2DTranspose(256, 256, filter_size=4, padding=1, stride=2)
        self.batch_norm4 = BatchNorm(256, act=act)
        self.conv2d_transpose5 = Conv2DTranspose(256, 256, filter_size=4, padding=1, stride=2)
        self.batch_norm5 = BatchNorm(256, act=act)
        self.out1 = Conv2D(256, class_dim, filter_size=1, stride=1, padding=0)
        self.out2 = BatchNorm(class_dim, act=act)

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)

        y = self.bottleneck_block(y)

        y = self.conv2d_transpose1(y)
        y = self.batch_norm1(y)
        y = self.conv2d_transpose2(y)
        y = self.batch_norm2(y)
        y = self.conv2d_transpose3(y)
        y = self.batch_norm3(y)
        y = self.conv2d_transpose4(y)
        y = self.batch_norm3(y)
        y = self.conv2d_transpose4(y)
        y = self.batch_norm3(y)
        
        y = self.out1(y)
        y = self.out2(y)

        y = paddle.nn.functional.softmax(y, axis=1)

        y = paddle.transpose(y, [0, 2, 3, 1])

        return y


if __name__ == '__main__':
    with fluid.dygraph.guard():
        network = ResNetv1(50)
        paddle.summary(network, (1, 3, 512, 512))