# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Bin Xiao (leoxiaobin@gmail.com)
# Modified by Bowen Cheng (bcheng9@illinois.edu)
# ------------------------------------------------------------------------------

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

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

import os
import logging

import torch
import torch.nn as nn

BN_MOMENTUM = 0.1


def conv3x3(in_planes, out_planes, stride=1):
    """3x3 convolution with padding"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,
        padding=1, bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
            padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes, momentum=BN_MOMENTUM)
        self.conv3 = nn.Conv2d(planes, planes * self.expansion, kernel_size=1,
            bias=False)
        self.bn3 = nn.BatchNorm2d(planes * self.expansion,
            momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class HighResolutionModule(nn.Module):
    def __init__(self, num_branches, blocks, num_blocks, num_inchannels,
                 num_channels, fuse_method, multi_scale_output=True):
        super(HighResolutionModule, self).__init__()
        self._check_branches(
            num_branches, blocks, num_blocks, num_inchannels, num_channels)

        self.num_inchannels = num_inchannels
        self.fuse_method = fuse_method
        self.num_branches = num_branches

        self.multi_scale_output = multi_scale_output

        self.branches = self._make_branches(
            num_branches, blocks, num_blocks, num_channels)
        self.fuse_layers = self._make_fuse_layers()
        self.relu = nn.ReLU(True)

    def _check_branches(self, num_branches, blocks, num_blocks,
                        num_inchannels, num_channels):
        if num_branches != len(num_blocks):
            error_msg = 'NUM_BRANCHES({}) <> NUM_BLOCKS({})'.format(
                num_branches, len(num_blocks))
            raise ValueError(error_msg)

        if num_branches != len(num_channels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_CHANNELS({})'.format(
                num_branches, len(num_channels))
            raise ValueError(error_msg)

        if num_branches != len(num_inchannels):
            error_msg = 'NUM_BRANCHES({}) <> NUM_INCHANNELS({})'.format(
                num_branches, len(num_inchannels))
            raise ValueError(error_msg)

    def _make_one_branch(self, branch_index, block, num_blocks, num_channels,
                         stride=1):
        downsample = None
        if stride != 1 or \
                self.num_inchannels[branch_index] != num_channels[branch_index] * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.num_inchannels[branch_index],
                    num_channels[branch_index] * block.expansion,
                    kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(num_channels[branch_index] * block.expansion,
                    momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(block(self.num_inchannels[branch_index],
            num_channels[branch_index], stride, downsample))
        self.num_inchannels[branch_index] = \
            num_channels[branch_index] * block.expansion
        for i in range(1, num_blocks[branch_index]):
            layers.append(block(self.num_inchannels[branch_index],
                num_channels[branch_index]))

        return nn.Sequential(*layers)

    def _make_branches(self, num_branches, block, num_blocks, num_channels):
        branches = []

        for i in range(num_branches):
            branches.append(
                self._make_one_branch(i, block, num_blocks, num_channels))

        return nn.ModuleList(branches)

    def _make_fuse_layers(self):
        if self.num_branches == 1:
            return None

        num_branches = self.num_branches
        num_inchannels = self.num_inchannels
        fuse_layers = []
        for i in range(num_branches if self.multi_scale_output else 1):
            fuse_layer = []
            for j in range(num_branches):
                if j > i:
                    fuse_layer.append(nn.Sequential(
                        nn.Conv2d(num_inchannels[j],
                            num_inchannels[i],
                            1,
                            1,
                            0,
                            bias=False),
                        nn.BatchNorm2d(num_inchannels[i]),
                        nn.Upsample(scale_factor=2 ** (j - i), mode='nearest')))
                elif j == i:
                    fuse_layer.append(None)
                else:
                    conv3x3s = []
                    for k in range(i - j):
                        if k == i - j - 1:
                            num_outchannels_conv3x3 = num_inchannels[i]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                    num_outchannels_conv3x3,
                                    3, 2, 1, bias=False),
                                nn.BatchNorm2d(num_outchannels_conv3x3)))
                        else:
                            num_outchannels_conv3x3 = num_inchannels[j]
                            conv3x3s.append(nn.Sequential(
                                nn.Conv2d(num_inchannels[j],
                                    num_outchannels_conv3x3,
                                    3, 2, 1, bias=False),
                                nn.BatchNorm2d(num_outchannels_conv3x3),
                                nn.ReLU(True)))
                    fuse_layer.append(nn.Sequential(*conv3x3s))
            fuse_layers.append(nn.ModuleList(fuse_layer))

        return nn.ModuleList(fuse_layers)

    def get_num_inchannels(self):
        return self.num_inchannels

    def forward(self, x):
        if self.num_branches == 1:
            return [self.branches[0](x[0])]

        for i in range(self.num_branches):
            x[i] = self.branches[i](x[i])

        x_fuse = []

        for i in range(len(self.fuse_layers)):
            y = x[0] if i == 0 else self.fuse_layers[i][0](x[0])
            for j in range(1, self.num_branches):
                if i == j:
                    y = y + x[j]
                else:
                    y = y + self.fuse_layers[i][j](x[j])
            x_fuse.append(self.relu(y))

        return x_fuse


blocks_dict = {
    'BASIC': BasicBlock,
    'BOTTLENECK': Bottleneck
}


class PoseHigherResolutionNet(nn.Module):

    def __init__(self):
        self.inplanes = 64
        extra = {
            'FINAL_CONV_KERNEL': 1,
            'PRETRAINED_LAYERS': ['*'],
            'STEM_INPLANES': 64,
            'STAGE2': {
                'NUM_MODULES': 1,
                'NUM_BRANCHES': 2,
                'BLOCK': 'BASIC',
                'NUM_BLOCKS': [4, 4],
                'NUM_CHANNELS': [32, 64],
                'FUSE_METHOD': 'SUM',
            },
            'STAGE3': {
                'NUM_MODULES': 4,
                'NUM_BRANCHES': 3,
                'BLOCK': 'BASIC',
                'NUM_BLOCKS': [4, 4, 4],
                'NUM_CHANNELS': [32, 64, 128],
                'FUSE_METHOD': 'SUM',
            },
            'STAGE4': {
                'NUM_MODULES': 3,
                'NUM_BRANCHES': 4,
                'BLOCK': 'BASIC',
                'NUM_BLOCKS': [4, 4, 4, 4],
                'NUM_CHANNELS': [32, 64, 128, 256],
                'FUSE_METHOD': 'SUM',
            },
            'DECONV': {
                'NUM_DECONVS': 1,
                'NUM_CHANNELS': [32],
                'KERNEL_SIZE': [4],
                'NUM_BASIC_BLOCKS': 4,
                'CAT_OUTPUT': [True]
            }
        }
        self.extra = extra
        self.WITH_AE_LOSS = [True, False]
        # extra = cfg.MODEL.EXTRA
        super(PoseHigherResolutionNet, self).__init__()

        # stem net
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=2, padding=1,
            bias=False)
        self.bn1 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=2, padding=1,
            bias=False)
        self.bn2 = nn.BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=True)
        self.layer1 = self._make_layer(Bottleneck, 64, 4)

        self.stage2_cfg = extra['STAGE2']
        num_channels = self.stage2_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage2_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))
        ]
        self.transition1 = self._make_transition_layer([256], num_channels)
        self.stage2, pre_stage_channels = self._make_stage(
            self.stage2_cfg, num_channels)

        self.stage3_cfg = extra['STAGE3']
        num_channels = self.stage3_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage3_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))
        ]
        self.transition2 = self._make_transition_layer(
            pre_stage_channels, num_channels)
        self.stage3, pre_stage_channels = self._make_stage(
            self.stage3_cfg, num_channels)

        self.stage4_cfg = extra['STAGE4']
        num_channels = self.stage4_cfg['NUM_CHANNELS']
        block = blocks_dict[self.stage4_cfg['BLOCK']]
        num_channels = [
            num_channels[i] * block.expansion for i in range(len(num_channels))
        ]
        self.transition3 = self._make_transition_layer(
            pre_stage_channels, num_channels)
        self.stage4, pre_stage_channels = self._make_stage(
            self.stage4_cfg, num_channels, multi_scale_output=False)

        self.final_layers = self._make_final_layers(pre_stage_channels[0])
        self.deconv_layers = self._make_deconv_layers(pre_stage_channels[0])

        self.num_deconvs = extra['DECONV']['NUM_DECONVS']
        self.deconv_config = extra['DECONV']

        self.pretrained_layers = extra['PRETRAINED_LAYERS']

    def _make_final_layers(self, input_channels):
        dim_tag = 17
        extra = self.extra

        final_layers = []
        output_channels = 17 + dim_tag if self.WITH_AE_LOSS[0] else 17
        final_layers.append(nn.Conv2d(
            in_channels=input_channels,
            out_channels=output_channels,
            kernel_size=extra['FINAL_CONV_KERNEL'],
            stride=1,
            padding=1 if extra['FINAL_CONV_KERNEL'] == 3 else 0
        ))

        deconv_cfg = extra['DECONV']
        for i in range(deconv_cfg['NUM_DECONVS']):
            input_channels = deconv_cfg['NUM_CHANNELS'][i]
            output_channels = 17 + dim_tag if self.WITH_AE_LOSS[i + 1] else 17
            final_layers.append(nn.Conv2d(
                in_channels=input_channels,
                out_channels=output_channels,
                kernel_size=extra['FINAL_CONV_KERNEL'],
                stride=1,
                padding=1 if extra['FINAL_CONV_KERNEL'] == 3 else 0
            ))

        return nn.ModuleList(final_layers)

    def _make_deconv_layers(self, input_channels):
        dim_tag = 17
        extra = self.extra
        deconv_cfg = extra['DECONV']

        deconv_layers = []
        for i in range(deconv_cfg['NUM_DECONVS']):
            if deconv_cfg['CAT_OUTPUT'][i]:
                final_output_channels = 17 + dim_tag if self.WITH_AE_LOSS[i] else 17
                input_channels += final_output_channels
            output_channels = deconv_cfg['NUM_CHANNELS'][i]
            deconv_kernel, padding, output_padding = \
                self._get_deconv_cfg(deconv_cfg['KERNEL_SIZE'][i])

            layers = []
            layers.append(nn.Sequential(
                nn.ConvTranspose2d(
                    in_channels=input_channels,
                    out_channels=output_channels,
                    kernel_size=deconv_kernel,
                    stride=2,
                    padding=padding,
                    output_padding=output_padding,
                    bias=False),
                nn.BatchNorm2d(output_channels, momentum=BN_MOMENTUM),
                nn.ReLU(inplace=True)
            ))
            for _ in range(deconv_cfg['NUM_BASIC_BLOCKS']):
                layers.append(nn.Sequential(
                    BasicBlock(output_channels, output_channels),
                ))
            deconv_layers.append(nn.Sequential(*layers))
            input_channels = output_channels

        return nn.ModuleList(deconv_layers)

    def _get_deconv_cfg(self, deconv_kernel):
        if deconv_kernel == 4:
            padding = 1
            output_padding = 0
        elif deconv_kernel == 3:
            padding = 1
            output_padding = 1
        elif deconv_kernel == 2:
            padding = 0
            output_padding = 0

        return deconv_kernel, padding, output_padding

    def _make_transition_layer(
            self, num_channels_pre_layer, num_channels_cur_layer):
        num_branches_cur = len(num_channels_cur_layer)
        num_branches_pre = len(num_channels_pre_layer)

        transition_layers = []
        for i in range(num_branches_cur):
            if i < num_branches_pre:
                if num_channels_cur_layer[i] != num_channels_pre_layer[i]:
                    transition_layers.append(nn.Sequential(
                        nn.Conv2d(num_channels_pre_layer[i],
                            num_channels_cur_layer[i],
                            3,
                            1,
                            1,
                            bias=False),
                        nn.BatchNorm2d(num_channels_cur_layer[i]),
                        nn.ReLU(inplace=True)))
                else:
                    transition_layers.append(None)
            else:
                conv3x3s = []
                for j in range(i + 1 - num_branches_pre):
                    inchannels = num_channels_pre_layer[-1]
                    outchannels = num_channels_cur_layer[i] \
                        if j == i - num_branches_pre else inchannels
                    conv3x3s.append(nn.Sequential(
                        nn.Conv2d(
                            inchannels, outchannels, 3, 2, 1, bias=False),
                        nn.BatchNorm2d(outchannels),
                        nn.ReLU(inplace=True)))
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,
                    kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion, momentum=BN_MOMENTUM),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    def _make_stage(self, layer_config, num_inchannels,
                    multi_scale_output=True):
        num_modules = layer_config['NUM_MODULES']
        num_branches = layer_config['NUM_BRANCHES']
        num_blocks = layer_config['NUM_BLOCKS']
        num_channels = layer_config['NUM_CHANNELS']
        block = blocks_dict[layer_config['BLOCK']]
        fuse_method = layer_config['FUSE_METHOD']

        modules = []
        for i in range(num_modules):
            # multi_scale_output is only used last module
            if not multi_scale_output and i == num_modules - 1:
                reset_multi_scale_output = False
            else:
                reset_multi_scale_output = True

            modules.append(
                HighResolutionModule(
                    num_branches,
                    block,
                    num_blocks,
                    num_inchannels,
                    num_channels,
                    fuse_method,
                    reset_multi_scale_output)
            )
            num_inchannels = modules[-1].get_num_inchannels()

        return nn.Sequential(*modules), num_inchannels

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        x = self.conv2(x)
        x = self.bn2(x)
        x = self.relu(x)
        x = self.layer1(x)

        x_list = []
        for i in range(self.stage2_cfg['NUM_BRANCHES']):
            if self.transition1[i] is not None:
                x_list.append(self.transition1[i](x))
            else:
                x_list.append(x)
        y_list = self.stage2(x_list)

        x_list = []
        for i in range(self.stage3_cfg['NUM_BRANCHES']):
            if self.transition2[i] is not None:
                x_list.append(self.transition2[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        y_list = self.stage3(x_list)

        x_list = []
        for i in range(self.stage4_cfg['NUM_BRANCHES']):
            if self.transition3[i] is not None:
                x_list.append(self.transition3[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        y_list = self.stage4(x_list)

        final_outputs = []
        x = y_list[0]
        y = self.final_layers[0](x)
        final_outputs.append(y)

        for i in range(self.num_deconvs):
            if self.deconv_config['CAT_OUTPUT'][i]:
                x = torch.cat((x, y), 1)
            x = self.deconv_layers[i](x)
            y = self.final_layers[i + 1](x)
            final_outputs.append(y)

        return final_outputs

    def init_weights(self, pretrained='', verbose=False):
        print('=> init weights from normal distribution')
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, std=0.001)
                for name, _ in m.named_parameters():
                    if name in ['bias']:
                        nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
            elif isinstance(m, nn.ConvTranspose2d):
                nn.init.normal_(m.weight, std=0.001)
                for name, _ in m.named_parameters():
                    if name in ['bias']:
                        nn.init.constant_(m.bias, 0)

        parameters_names = set()
        for name, _ in self.named_parameters():
            parameters_names.add(name)

        buffers_names = set()
        for name, _ in self.named_buffers():
            buffers_names.add(name)

        if os.path.isfile(pretrained):
            pretrained_state_dict = torch.load(pretrained, map_location=lambda storage, loc: storage)
            print('=> loading pretrained model {}'.format(pretrained))

            need_init_state_dict = {}
            for name, m in pretrained_state_dict.items():
                if name.split('.')[0] in self.pretrained_layers \
                        or self.pretrained_layers[0] is '*':
                    if name in parameters_names or name in buffers_names:
                        if verbose:
                            print(
                                '=> init {} from {}'.format(name, pretrained)
                            )
                        need_init_state_dict[name] = m
            self.load_state_dict(need_init_state_dict, strict=False)


def get_pose_net():
    model = PoseHigherResolutionNet()
    model.init_weights('models/pytorch/hrnet_w32-36af842e.pth')
    return model


if __name__ == '__main__':
    from misc import summary
    # import os
    # os.environ['CUDA_VISIBLE_DEVICES'] = '2'  # if u.se this, net and inputs need add .cuda()
    net = get_pose_net()
    # summary.summary(net, torch.ones(2, 3, 192, 256))
    summary.summary2(net, torch.ones(2, 3, 512, 512))
    summary.summary3(net, (torch.ones(2, 3, 512, 512),))
