###########################################################################
# Created by: Tramac
# Date: 2019-03-25
# Copyright (c) 2017
###########################################################################

"""Fast Segmentation Convolutional Neural Network"""
import os
import torch
import torch.nn as nn
import torch.nn.functional as F

__all__ = ['FastSCNN', 'get_fast_scnn']


class FastSCNN(nn.Module):
    def __init__(self, num_classes, aux=False, **kwargs):
        super(FastSCNN, self).__init__()
        self.aux = aux
        self.learning_to_downsample = LearningToDownsample(32, 48, 64)
        self.global_feature_extractor = GlobalFeatureExtractor(64, [64, 96, 128], 128, 6, [3, 3, 3])
        self.feature_fusion = FeatureFusionModule(64, 128, 128)
        self.classifier = Classifer(128, num_classes)
        if self.aux:
            self.auxlayer = nn.Sequential(
                nn.Conv2d(64, 32, 3, padding=1, bias=False),
                nn.BatchNorm2d(32),
                nn.ReLU(True),
                nn.Dropout(0.1),
                nn.Conv2d(32, num_classes, 1)
            )

    def forward(self, x):
        size = x.size()[2:]
        higher_res_features = self.learning_to_downsample(x)
        x = self.global_feature_extractor(higher_res_features)
        x = self.feature_fusion(higher_res_features, x)
        x = self.classifier(x)
        #outputs = []
        x = F.interpolate(x, size, mode='bilinear', align_corners=True)
        #outputs.append(x)
        if self.aux:
            auxout = self.auxlayer(higher_res_features)
            auxout = F.interpolate(auxout, size, mode='bilinear', align_corners=True)
            outputs.append(auxout)
        return x#tuple(outputs)


class _ConvBNReLU(nn.Module):
    """Conv-BN-ReLU"""

    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=0, **kwargs):
        super(_ConvBNReLU, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(in_channels, out_channels, kernel_size, stride, padding, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(True)
        )

    def forward(self, x):
        return self.conv(x)


class _DSConv(nn.Module):
    """Depthwise Separable Convolutions"""

    def __init__(self, dw_channels, out_channels, stride=1, **kwargs):
        super(_DSConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(dw_channels, dw_channels, 3, stride, 1, groups=dw_channels, bias=False),
            nn.BatchNorm2d(dw_channels),
            nn.ReLU(True),
            nn.Conv2d(dw_channels, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(True)
        )

    def forward(self, x):
        return self.conv(x)


class _DWConv(nn.Module):
    def __init__(self, dw_channels, out_channels, stride=1, **kwargs):
        super(_DWConv, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(dw_channels, out_channels, 3, stride, 1, groups=dw_channels, bias=False),
            nn.BatchNorm2d(out_channels),
            nn.ReLU(True)
        )

    def forward(self, x):
        return self.conv(x)


class LinearBottleneck(nn.Module):
    """LinearBottleneck used in MobileNetV2"""

    def __init__(self, in_channels, out_channels, t=6, stride=2, **kwargs):
        super(LinearBottleneck, self).__init__()
        self.use_shortcut = stride == 1 and in_channels == out_channels
        self.block = nn.Sequential(
            # pw
            _ConvBNReLU(in_channels, in_channels * t, 1),
            # dw
            _DWConv(in_channels * t, in_channels * t, stride),
            # pw-linear
            nn.Conv2d(in_channels * t, out_channels, 1, bias=False),
            nn.BatchNorm2d(out_channels)
        )

    def forward(self, x):
        out = self.block(x)
        if self.use_shortcut:
            out = x + out
        return out


class PyramidPooling(nn.Module):
    """Pyramid pooling module"""

    def __init__(self, in_channels, out_channels, **kwargs):
        super(PyramidPooling, self).__init__()
        inter_channels = int(in_channels / 4)
        self.conv1 = _ConvBNReLU(in_channels, inter_channels, 1, **kwargs)
        self.conv2 = _ConvBNReLU(in_channels, inter_channels, 1, **kwargs)
        self.conv3 = _ConvBNReLU(in_channels, inter_channels, 1, **kwargs)
        self.conv4 = _ConvBNReLU(in_channels, inter_channels, 1, **kwargs)
        self.out = _ConvBNReLU(in_channels * 2, out_channels, 1)

    def pool(self, x, size):
        avgpool = nn.AdaptiveAvgPool2d(size)
        return avgpool(x)

    def upsample(self, x, size):
        return F.interpolate(x, size, mode='bilinear', align_corners=True)

    def forward(self, x):
        size = x.size()[2:]
        feat1 = self.upsample(self.conv1(self.pool(x, 1)), size)
        feat2 = self.upsample(self.conv2(self.pool(x, 2)), size)
        feat3 = self.upsample(self.conv3(self.pool(x, 3)), size)
        feat4 = self.upsample(self.conv4(self.pool(x, 6)), size)
        x = torch.cat([x, feat1, feat2, feat3, feat4], dim=1)
        x = self.out(x)
        return x


class LearningToDownsample(nn.Module):
    """Learning to downsample module"""

    def __init__(self, dw_channels1=32, dw_channels2=48, out_channels=64, **kwargs):
        super(LearningToDownsample, self).__init__()
        self.conv = _ConvBNReLU(3, dw_channels1, 3, 2)
        self.dsconv1 = _DSConv(dw_channels1, dw_channels2, 2)
        self.dsconv2 = _DSConv(dw_channels2, out_channels, 2)

    def forward(self, x):
        x = self.conv(x)
        x = self.dsconv1(x)
        x = self.dsconv2(x)
        return x


class GlobalFeatureExtractor(nn.Module):
    """Global feature extractor module"""

    def __init__(self, in_channels=64, block_channels=(64, 96, 128),
                 out_channels=128, t=6, num_blocks=(3, 3, 3), **kwargs):
        super(GlobalFeatureExtractor, self).__init__()
        self.bottleneck1 = self._make_layer(LinearBottleneck, in_channels, block_channels[0], num_blocks[0], t, 2)
        self.bottleneck2 = self._make_layer(LinearBottleneck, block_channels[0], block_channels[1], num_blocks[1], t, 2)
        self.bottleneck3 = self._make_layer(LinearBottleneck, block_channels[1], block_channels[2], num_blocks[2], t, 1)
        self.ppm = PyramidPooling(block_channels[2], out_channels)

    def _make_layer(self, block, inplanes, planes, blocks, t=6, stride=1):
        layers = []
        layers.append(block(inplanes, planes, t, stride))
        for i in range(1, blocks):
            layers.append(block(planes, planes, t, 1))
        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.bottleneck1(x)
        x = self.bottleneck2(x)
        x = self.bottleneck3(x)
        x = self.ppm(x)
        return x


class FeatureFusionModule(nn.Module):
    """Feature fusion module"""

    def __init__(self, highter_in_channels, lower_in_channels, out_channels, scale_factor=4, **kwargs):
        super(FeatureFusionModule, self).__init__()
        self.scale_factor = scale_factor
        self.dwconv = _DWConv(lower_in_channels, out_channels, 1)
        self.conv_lower_res = nn.Sequential(
            nn.Conv2d(out_channels, out_channels, 1),
            nn.BatchNorm2d(out_channels)
        )
        self.conv_higher_res = nn.Sequential(
            nn.Conv2d(highter_in_channels, out_channels, 1),
            nn.BatchNorm2d(out_channels)
        )
        self.relu = nn.ReLU(True)

    def forward(self, higher_res_feature, lower_res_feature):
        lower_res_feature = F.interpolate(lower_res_feature, scale_factor=4, mode='bilinear', align_corners=True)
        lower_res_feature = self.dwconv(lower_res_feature)
        lower_res_feature = self.conv_lower_res(lower_res_feature)

        higher_res_feature = self.conv_higher_res(higher_res_feature)
        out = higher_res_feature + lower_res_feature
        return self.relu(out)


class Classifer(nn.Module):
    """Classifer"""

    def __init__(self, dw_channels, num_classes, stride=1, **kwargs):
        super(Classifer, self).__init__()
        self.dsconv1 = _DSConv(dw_channels, dw_channels, stride)
        self.dsconv2 = _DSConv(dw_channels, dw_channels, stride)
        self.conv = nn.Sequential(
            nn.Dropout(0.1),
            nn.Conv2d(dw_channels, num_classes, 1)
        )

    def forward(self, x):
        x = self.dsconv1(x)
        x = self.dsconv2(x)
        x = self.conv(x)
        return x


def get_fast_scnn(n_classes, **kwargs):

    model = FastSCNN(n_classes, **kwargs)

    return model