###########################################################################

# 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