# copyright (c) 2020 PaddlePaddle Authors. All Rights Reserve.
#
# 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 paddle
import paddle.nn as nn
import paddle.nn.functional as F

from paddleseg.cvlibs import manager
from paddleseg.models import layers
from paddleseg.utils import utils

__all__ = [
    "ResNet18_vd", "ResNet34_vd", "ResNet50_vd", "ResNet101_vd", "ResNet152_vd"
]


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 kernel_size,
                 stride=1,
                 dilation=1,
                 groups=1,
                 is_vd_mode=False,
                 act=None,
                 data_format='NCHW'):
        super(ConvBNLayer, self).__init__()
        if dilation != 1 and kernel_size != 3:
            raise RuntimeError("When the dilation isn't 1," \
                "the kernel_size should be 3.")

        self.is_vd_mode = is_vd_mode
        self._pool2d_avg = nn.AvgPool2D(
            kernel_size=2,
            stride=2,
            padding=0,
            ceil_mode=True,
            data_format=data_format)
        self._conv = nn.Conv2D(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=kernel_size,
            stride=stride,
            padding=(kernel_size - 1) // 2 \
                if dilation == 1 else dilation,
            dilation=dilation,
            groups=groups,
            bias_attr=False,
            data_format=data_format)

        self._batch_norm = layers.SyncBatchNorm(
            out_channels, data_format=data_format)
        self._act_op = layers.Activation(act=act)

    def forward(self, inputs):
        if self.is_vd_mode:
            inputs = self._pool2d_avg(inputs)
        y = self._conv(inputs)
        y = self._batch_norm(y)
        y = self._act_op(y)

        return y


class BottleneckBlock(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 shortcut=True,
                 if_first=False,
                 dilation=1,
                 data_format='NCHW'):
        super(BottleneckBlock, self).__init__()

        self.data_format = data_format
        self.conv0 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=1,
            act='relu',
            data_format=data_format)

        self.dilation = dilation

        self.conv1 = ConvBNLayer(
            in_channels=out_channels,
            out_channels=out_channels,
            kernel_size=3,
            stride=stride,
            act='relu',
            dilation=dilation,
            data_format=data_format)
        self.conv2 = ConvBNLayer(
            in_channels=out_channels,
            out_channels=out_channels * 4,
            kernel_size=1,
            act=None,
            data_format=data_format)

        if not shortcut:
            self.short = ConvBNLayer(
                in_channels=in_channels,
                out_channels=out_channels * 4,
                kernel_size=1,
                stride=1,
                is_vd_mode=False if if_first or stride == 1 else True,
                data_format=data_format)

        self.shortcut = shortcut
        # NOTE: Use the wrap layer for quantization training
        self.add = layers.Add()
        self.relu = layers.Activation(act="relu")

    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 = self.add(short, conv2)
        y = self.relu(y)
        return y


class BasicBlock(nn.Layer):
    def __init__(self,
                 in_channels,
                 out_channels,
                 stride,
                 dilation=1,
                 shortcut=True,
                 if_first=False,
                 data_format='NCHW'):
        super(BasicBlock, self).__init__()
        self.conv0 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=out_channels,
            kernel_size=3,
            stride=stride,
            dilation=dilation,
            act='relu',
            data_format=data_format)
        self.conv1 = ConvBNLayer(
            in_channels=out_channels,
            out_channels=out_channels,
            kernel_size=3,
            dilation=dilation,
            act=None,
            data_format=data_format)

        if not shortcut:
            self.short = ConvBNLayer(
                in_channels=in_channels,
                out_channels=out_channels,
                kernel_size=1,
                stride=1,
                is_vd_mode=False if if_first or stride == 1 else True,
                data_format=data_format)

        self.shortcut = shortcut
        self.dilation = dilation
        self.data_format = data_format
        self.add = layers.Add()
        self.relu = layers.Activation(act="relu")

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

        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)
        y = self.add(short, conv1)
        y = self.relu(y)

        return y


class ResNet_vd(nn.Layer):
    """
    The ResNet_vd implementation based on PaddlePaddle.

    The original article refers to Jingdong
    Tong He, et, al. "Bag of Tricks for Image Classification with Convolutional Neural Networks"
    (https://arxiv.org/pdf/1812.01187.pdf).

    Args:
        layers (int, optional): The layers of ResNet_vd. The supported layers are (18, 34, 50, 101, 152, 200). Default: 50.
        output_stride (int, optional): The stride of output features compared to input images. It is 8 or 16. Default: 8.
        multi_grid (tuple|list, optional): The grid of stage4. Defult: (1, 1, 1).
        in_channels (int, optional): The channels of input image. Default: 3.
        pretrained (str, optional): The path of pretrained model.

    """

    def __init__(self,
                 layers=50,
                 output_stride=8,
                 multi_grid=(1, 1, 1),
                 in_channels=3,
                 pretrained=None,
                 data_format='NCHW'):
        super(ResNet_vd, self).__init__()

        self.data_format = data_format
        self.conv1_logit = None  # for gscnn shape stream
        self.layers = layers
        supported_layers = [18, 34, 50, 101, 152, 200]
        assert layers in supported_layers, \
            "supported layers are {} but input layer is {}".format(
                supported_layers, layers)

        if layers == 18:
            depth = [2, 2, 2, 2]
        elif layers == 34 or layers == 50:
            depth = [3, 4, 6, 3]
        elif layers == 101:
            depth = [3, 4, 23, 3]
        elif layers == 152:
            depth = [3, 8, 36, 3]
        elif layers == 200:
            depth = [3, 12, 48, 3]
        num_channels = [64, 256, 512,
                        1024] if layers >= 50 else [64, 64, 128, 256]
        num_filters = [64, 128, 256, 512]

        # for channels of four returned stages
        self.feat_channels = [c * 4 for c in num_filters
                              ] if layers >= 50 else num_filters

        dilation_dict = None
        if output_stride == 8:
            dilation_dict = {2: 2, 3: 4}
        elif output_stride == 16:
            dilation_dict = {3: 2}

        self.conv1_1 = ConvBNLayer(
            in_channels=in_channels,
            out_channels=32,
            kernel_size=3,
            stride=2,
            act='relu',
            data_format=data_format)
        self.conv1_2 = ConvBNLayer(
            in_channels=32,
            out_channels=32,
            kernel_size=3,
            stride=1,
            act='relu',
            data_format=data_format)
        self.conv1_3 = ConvBNLayer(
            in_channels=32,
            out_channels=64,
            kernel_size=3,
            stride=1,
            act='relu',
            data_format=data_format)
        self.pool2d_max = nn.MaxPool2D(
            kernel_size=3, stride=2, padding=1, data_format=data_format)

        # self.block_list = []
        self.stage_list = []
        if layers >= 50:
            for block in range(len(depth)):
                shortcut = False
                block_list = []
                for i in range(depth[block]):
                    if layers in [101, 152] and block == 2:
                        if i == 0:
                            conv_name = "res" + str(block + 2) + "a"
                        else:
                            conv_name = "res" + str(block + 2) + "b" + str(i)
                    else:
                        conv_name = "res" + str(block + 2) + chr(97 + i)

                    ###############################################################################
                    # Add dilation rate for some segmentation tasks, if dilation_dict is not None.
                    dilation_rate = dilation_dict[
                        block] if dilation_dict and block in dilation_dict else 1

                    # Actually block here is 'stage', and i is 'block' in 'stage'
                    # At the stage 4, expand the the dilation_rate if given multi_grid
                    if block == 3:
                        dilation_rate = dilation_rate * multi_grid[i]
                    ###############################################################################

                    bottleneck_block = self.add_sublayer(
                        'bb_%d_%d' % (block, i),
                        BottleneckBlock(
                            in_channels=num_channels[block]
                            if i == 0 else num_filters[block] * 4,
                            out_channels=num_filters[block],
                            stride=2 if i == 0 and block != 0 and
                            dilation_rate == 1 else 1,
                            shortcut=shortcut,
                            if_first=block == i == 0,
                            dilation=dilation_rate,
                            data_format=data_format))

                    block_list.append(bottleneck_block)
                    shortcut = True
                self.stage_list.append(block_list)
        else:
            for block in range(len(depth)):
                shortcut = False
                block_list = []
                for i in range(depth[block]):
                    dilation_rate = dilation_dict[block] \
                        if dilation_dict and block in dilation_dict else 1
                    if block == 3:
                        dilation_rate = dilation_rate * multi_grid[i]

                    basic_block = self.add_sublayer(
                        'bb_%d_%d' % (block, i),
                        BasicBlock(
                            in_channels=num_channels[block]
                            if i == 0 else num_filters[block],
                            out_channels=num_filters[block],
                            stride=2 if i == 0 and block != 0 \
                                and dilation_rate == 1 else 1,
                            dilation=dilation_rate,
                            shortcut=shortcut,
                            if_first=block == i == 0,
                            data_format=data_format))
                    block_list.append(basic_block)
                    shortcut = True
                self.stage_list.append(block_list)

        self.pretrained = pretrained
        self.init_weight()

    def forward(self, inputs):
        y = self.conv1_1(inputs)
        y = self.conv1_2(y)
        y = self.conv1_3(y)
        self.conv1_logit = y.clone()
        y = self.pool2d_max(y)

        # A feature list saves the output feature map of each stage.
        feat_list = []
        for stage in self.stage_list:
            for block in stage:
                y = block(y)
            feat_list.append(y)

        return feat_list

    def init_weight(self):
        utils.load_pretrained_model(self, self.pretrained)


@manager.BACKBONES.add_component
def ResNet18_vd(**args):
    model = ResNet_vd(layers=18, **args)
    return model


def ResNet34_vd(**args):
    model = ResNet_vd(layers=34, **args)
    return model


@manager.BACKBONES.add_component
def ResNet50_vd(**args):
    model = ResNet_vd(layers=50, **args)
    return model


@manager.BACKBONES.add_component
def ResNet101_vd(**args):
    model = ResNet_vd(layers=101, **args)
    return model


def ResNet152_vd(**args):
    model = ResNet_vd(layers=152, **args)
    return model


def ResNet200_vd(**args):
    model = ResNet_vd(layers=200, **args)
    return model
