# copyright (c) 2021 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.

from __future__ import absolute_import
from __future__ import division
from __future__ import print_function

import os
import math
import paddle
from paddle import ParamAttr
import paddle.nn as nn
import paddle.nn.functional as F
from paddle.nn.initializer import Normal

__all__ = ["ResNet18", "ResNet34", "ResNet50", "ResNet101", "ResNet152"]


class ConvBNLayer(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 filter_size,
                 stride=1,
                 dilation=1,
                 groups=1,
                 act=None,
                 lr_mult=1.0,
                 name=None,
                 data_format="NCHW"):
        super(ConvBNLayer, self).__init__()
        conv_stdv = filter_size * filter_size * num_filters
        self._conv = nn.Conv2D(
            in_channels=num_channels,
            out_channels=num_filters,
            kernel_size=filter_size,
            stride=stride,
            padding=(filter_size - 1) // 2,
            dilation=dilation,
            groups=groups,
            weight_attr=ParamAttr(
                learning_rate=lr_mult,
                initializer=Normal(0, math.sqrt(2. / conv_stdv))),
            bias_attr=False,
            data_format=data_format)

        self._batch_norm = nn.BatchNorm(
            num_filters, act=act, data_layout=data_format)

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


class BottleneckBlock(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True,
                 name=None,
                 lr_mult=1.0,
                 dilation=1,
                 data_format="NCHW"):
        super(BottleneckBlock, self).__init__()
        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=1,
            dilation=dilation,
            act="relu",
            lr_mult=lr_mult,
            name=name + "_branch2a",
            data_format=data_format)
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            dilation=dilation,
            stride=stride,
            act="relu",
            lr_mult=lr_mult,
            name=name + "_branch2b",
            data_format=data_format)
        self.conv2 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters * 4,
            filter_size=1,
            dilation=dilation,
            act=None,
            lr_mult=lr_mult,
            name=name + "_branch2c",
            data_format=data_format)
        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters * 4,
                filter_size=1,
                dilation=dilation,
                stride=stride,
                lr_mult=lr_mult,
                name=name + "_branch1",
                data_format=data_format)
        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 = paddle.add(x=short, y=conv2)
        y = F.relu(y)
        return y


class BasicBlock(nn.Layer):
    def __init__(self,
                 num_channels,
                 num_filters,
                 stride,
                 shortcut=True,
                 name=None,
                 data_format="NCHW"):
        super(BasicBlock, self).__init__()
        self.stride = stride
        self.conv0 = ConvBNLayer(
            num_channels=num_channels,
            num_filters=num_filters,
            filter_size=3,
            stride=stride,
            act="relu",
            name=name + "_branch2a",
            data_format=data_format)
        self.conv1 = ConvBNLayer(
            num_channels=num_filters,
            num_filters=num_filters,
            filter_size=3,
            act=None,
            name=name + "_branch2b",
            data_format=data_format)
        if not shortcut:
            self.short = ConvBNLayer(
                num_channels=num_channels,
                num_filters=num_filters,
                filter_size=1,
                stride=stride,
                name=name + "_branch1",
                data_format=data_format)
        self.shortcut = shortcut

    def forward(self, inputs):
        y = self.conv0(inputs)
        conv1 = self.conv1(y)
        if self.shortcut:
            short = inputs
        else:
            short = self.short(inputs)
        y = paddle.add(x=short, y=conv1)
        y = F.relu(y)
        return y


class ResNet(nn.Layer):
    def __init__(self,
                 layers=50,
                 lr_mult=1.0,
                 last_conv_stride=2,
                 last_conv_dilation=1):
        super(ResNet, self).__init__()
        self.layers = layers
        self.data_format = "NCHW"
        self.input_image_channel = 3
        supported_layers = [18, 34, 50, 101, 152]
        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]
        num_channels = [64, 256, 512,
                        1024] if layers >= 50 else [64, 64, 128, 256]
        num_filters = [64, 128, 256, 512]
        self.conv = ConvBNLayer(
            num_channels=self.input_image_channel,
            num_filters=64,
            filter_size=7,
            stride=2,
            act="relu",
            lr_mult=lr_mult,
            name="conv1",
            data_format=self.data_format)
        self.pool2d_max = nn.MaxPool2D(
            kernel_size=3, stride=2, padding=1, data_format=self.data_format)
        self.block_list = []
        if layers >= 50:
            for block in range(len(depth)):
                shortcut = False
                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)
                    if i != 0 or block == 0:
                        stride = 1
                    elif block == len(depth) - 1:
                        stride = last_conv_stride
                    else:
                        stride = 2
                    bottleneck_block = self.add_sublayer(
                        conv_name,
                        BottleneckBlock(
                            num_channels=num_channels[block]
                            if i == 0 else num_filters[block] * 4,
                            num_filters=num_filters[block],
                            stride=stride,
                            shortcut=shortcut,
                            name=conv_name,
                            lr_mult=lr_mult,
                            dilation=last_conv_dilation
                            if block == len(depth) - 1 else 1,
                            data_format=self.data_format))
                    self.block_list.append(bottleneck_block)
                    shortcut = True
        else:
            for block in range(len(depth)):
                shortcut = False
                for i in range(depth[block]):
                    conv_name = "res" + str(block + 2) + chr(97 + i)
                    basic_block = self.add_sublayer(
                        conv_name,
                        BasicBlock(
                            num_channels=num_channels[block]
                            if i == 0 else num_filters[block],
                            num_filters=num_filters[block],
                            stride=2 if i == 0 and block != 0 else 1,
                            shortcut=shortcut,
                            name=conv_name,
                            data_format=self.data_format))
                    self.block_list.append(basic_block)
                    shortcut = True

    def forward(self, inputs):
        y = self.conv(inputs)
        y = self.pool2d_max(y)
        for block in self.block_list:
            y = block(y)
        return y


def ResNet18(**args):
    model = ResNet(layers=18, **args)
    return model


def ResNet34(**args):
    model = ResNet(layers=34, **args)
    return model


def ResNet50(pretrained=None, **args):
    model = ResNet(layers=50, **args)
    if pretrained is not None:
        if not (os.path.isdir(pretrained) or
                os.path.exists(pretrained + '.pdparams')):
            raise ValueError("Model pretrain path {} does not "
                             "exists.".format(pretrained))
        param_state_dict = paddle.load(pretrained + '.pdparams')
        model.set_dict(param_state_dict)
    return model


def ResNet101(pretrained=None, **args):
    model = ResNet(layers=101, **args)
    if pretrained is not None:
        if not (os.path.isdir(pretrained) or
                os.path.exists(pretrained + '.pdparams')):
            raise ValueError("Model pretrain path {} does not "
                             "exists.".format(pretrained))
        param_state_dict = paddle.load(pretrained + '.pdparams')
        model.set_dict(param_state_dict)
    return model


def ResNet152(**args):
    model = ResNet(layers=152, **args)
    return model