# ------------------------------------------------------------------------------
# Copyright (c) Microsoft
# Licensed under the MIT License.
# Written by Ke Sun (sunk@mail.ustc.edu.cn), Jingyi Xie (hsfzxjy@gmail.com)
# ------------------------------------------------------------------------------

from __future__ import absolute_import, division, print_function

import os

import numpy as np
import torch
import torch._utils
import torch.nn as nn
import torch.nn.functional as F
from magnet.model.base import BN_MOMENTUM, BatchNorm2d

from .config import blocks_dict, config_hrnet_w18_ocr, config_hrnet_w48_ocr
from .module import ALIGN_CORNERS, HighResolutionModule, SpatialGather_Module, SpatialOCR_Module, relu_inplace


class HighResolutionNet(nn.Module):
    config = None

    def __init__(self, n_classes, full_os=True):

        extra = self.config["EXTRA"]
        super(HighResolutionNet, self).__init__()

        # stem net
        self.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1 if full_os else 2, padding=1, bias=False)
        self.bn1 = BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.conv2 = nn.Conv2d(64, 64, kernel_size=3, stride=1 if full_os else 2, padding=1, bias=False)
        self.bn2 = BatchNorm2d(64, momentum=BN_MOMENTUM)
        self.relu = nn.ReLU(inplace=relu_inplace)

        self.stage1_cfg = extra["STAGE1"]
        num_channels = self.stage1_cfg["NUM_CHANNELS"][0]
        block = blocks_dict[self.stage1_cfg["BLOCK"]]
        num_blocks = self.stage1_cfg["NUM_BLOCKS"][0]
        self.layer1 = self._make_layer(block, 64, num_channels, num_blocks)
        stage1_out_channel = block.expansion * num_channels

        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([stage1_out_channel], 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=True)

        last_inp_channels = np.int(np.sum(pre_stage_channels))
        ocr_mid_channels = self.config["OCR"]["MID_CHANNELS"]
        ocr_key_channels = self.config["OCR"]["KEY_CHANNELS"]

        self.conv3x3_ocr = nn.Sequential(
            nn.Conv2d(last_inp_channels, ocr_mid_channels, kernel_size=3, stride=1, padding=1),
            BatchNorm2d(ocr_mid_channels),
            nn.ReLU(inplace=relu_inplace),
        )
        self.ocr_gather_head = SpatialGather_Module(n_classes)

        self.ocr_distri_head = SpatialOCR_Module(
            in_channels=ocr_mid_channels,
            key_channels=ocr_key_channels,
            out_channels=ocr_mid_channels,
            scale=1,
            dropout=0.05,
        )
        self.cls_head = nn.Conv2d(ocr_mid_channels, n_classes, kernel_size=1, stride=1, padding=0, bias=True)

        self.aux_head = nn.Sequential(
            nn.Conv2d(last_inp_channels, last_inp_channels, kernel_size=1, stride=1, padding=0),
            BatchNorm2d(last_inp_channels),
            nn.ReLU(inplace=relu_inplace),
            nn.Conv2d(last_inp_channels, n_classes, kernel_size=1, stride=1, padding=0, bias=True),
        )

    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),
                            BatchNorm2d(num_channels_cur_layer[i], momentum=BN_MOMENTUM),
                            nn.ReLU(inplace=relu_inplace),
                        )
                    )
                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),
                            BatchNorm2d(outchannels, momentum=BN_MOMENTUM),
                            nn.ReLU(inplace=relu_inplace),
                        )
                    )
                transition_layers.append(nn.Sequential(*conv3x3s))

        return nn.ModuleList(transition_layers)

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

        layers = []
        layers.append(block(inplanes, planes, stride, downsample))
        inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(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, return_auxilary=False):
        _, _, H, W = x.shape
        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:
                if i < self.stage2_cfg["NUM_BRANCHES"]:
                    x_list.append(self.transition2[i](y_list[i]))
                else:
                    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:
                if i < self.stage3_cfg["NUM_BRANCHES"]:
                    x_list.append(self.transition3[i](y_list[i]))
                else:
                    x_list.append(self.transition3[i](y_list[-1]))
            else:
                x_list.append(y_list[i])
        x = self.stage4(x_list)

        # Upsampling
        x0_h, x0_w = x[0].size(2), x[0].size(3)
        x1 = F.interpolate(x[1], size=(x0_h, x0_w), mode="bilinear", align_corners=ALIGN_CORNERS)
        x2 = F.interpolate(x[2], size=(x0_h, x0_w), mode="bilinear", align_corners=ALIGN_CORNERS)
        x3 = F.interpolate(x[3], size=(x0_h, x0_w), mode="bilinear", align_corners=ALIGN_CORNERS)

        feats = torch.cat([x[0], x1, x2, x3], 1)

        # ocr
        out_aux = self.aux_head(feats)
        out_aux = F.interpolate(out_aux, size=(H, W), mode="bilinear", align_corners=ALIGN_CORNERS)
        # compute contrast feature
        feats = self.conv3x3_ocr(feats)

        context = self.ocr_gather_head(feats, out_aux)
        feats = self.ocr_distri_head(feats, context)

        out = self.cls_head(feats)
        out = F.interpolate(out, size=(H, W), mode="bilinear", align_corners=ALIGN_CORNERS)

        if return_auxilary:
            return out, out_aux
        return (out + 0.4 * out_aux) / 1.4

    def init_weights(
        self,
        pretrained="",
    ):
        print("=> init weights from normal distribution")
        for name, m in self.named_modules():
            if any(part in name for part in {"cls", "aux", "ocr"}):
                # print('skipped', name)
                continue
            if isinstance(m, nn.Conv2d):
                nn.init.normal_(m.weight, std=0.001)
            elif isinstance(m, BatchNorm2d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)
        if os.path.isfile(pretrained):
            pretrained_dict = torch.load(pretrained, map_location={"cuda:0": "cpu"})
            print("=> loading pretrained model {}".format(pretrained))
            model_dict = self.state_dict()
            pretrained_dict = {
                k.replace("last_layer", "aux_head").replace("model.", ""): v for k, v in pretrained_dict.items()
            }
            pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys()}
            for k in pretrained_dict:
                print("Update weight for layer", k)
            model_dict.update(pretrained_dict)
            self.load_state_dict(model_dict)

    def load_state_dict(self, state_dict):
        state_dict = {k.replace("model.", ""): v for k, v in state_dict.items()}
        super().load_state_dict(state_dict, strict=False)


class HRNetW18_OCR(HighResolutionNet):
    config = config_hrnet_w18_ocr


class HRNetW48_OCR(HighResolutionNet):
    config = config_hrnet_w48_ocr
