import torch
import torch.nn as nn
import torch.nn.functional as F


class FCDiscriminator(nn.Module):

	def __init__(self, num_classes, ndf = 64):
		super(FCDiscriminator, self).__init__()

		self.conv1 = nn.Conv2d(num_classes, ndf, kernel_size=4, stride=2, padding=1)
		self.conv2 = nn.Conv2d(ndf, ndf*2, kernel_size=4, stride=2, padding=1)
		self.conv3 = nn.Conv2d(ndf*2, ndf*4, kernel_size=4, stride=2, padding=1)
		self.conv4 = nn.Conv2d(ndf*4, ndf*8, kernel_size=4, stride=2, padding=1)
		self.classifier = nn.Conv2d(ndf*8, 1, kernel_size=4, stride=2, padding=1)

		self.leaky_relu = nn.LeakyReLU(negative_slope=0.2, inplace=True)
		#self.up_sample = nn.Upsample(scale_factor=32, mode='bilinear')
		#self.sigmoid = nn.Sigmoid()


	def forward(self, x):
		x = self.conv1(x)
		x = self.leaky_relu(x)
		x = self.conv2(x)
		x = self.leaky_relu(x)
		x = self.conv3(x)
		x = self.leaky_relu(x)
		x = self.conv4(x)
		x = self.leaky_relu(x)
		x = self.classifier(x)
		#x = self.up_sample(x)
		# x = self.sigmoid(x) 

		return x


class PixelDiscriminator(nn.Module):
    def __init__(self, input_nc, ndf=512, num_classes=1):
        super(PixelDiscriminator, self).__init__()

        self.D = nn.Sequential(
            nn.Conv2d(input_nc, ndf, kernel_size=3, stride=1, padding=1),
            nn.LeakyReLU(negative_slope=0.2, inplace=True),
            nn.Conv2d(ndf, ndf//2, kernel_size=3, stride=1, padding=1),
            nn.LeakyReLU(negative_slope=0.2, inplace=True)
		)
        self.cls1 = nn.Conv2d(ndf//2, num_classes, kernel_size=3, stride=1, padding=1)
        self.cls2 = nn.Conv2d(ndf//2, num_classes, kernel_size=3, stride=1, padding=1)

    def forward(self, x, size=None):
        out = self.D(x)
        src_out = self.cls1(out)
        tgt_out = self.cls2(out)
        out = torch.cat((src_out, tgt_out), dim=1)
        if size is not None:
            out = F.interpolate(out, size=size, mode='bilinear', align_corners=True)
        return out