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dog_gans / model.py

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Create model.py

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	import torch
	import torch.nn as nn

	class Generator(nn.Module):
	def __init__(self, z_dim=100, img_channels=3):
	super(Generator, self).__init__()
	self.gen = nn.Sequential(
	# input is Z, going into a convolution
	nn.ConvTranspose2d(z_dim, 512, 4, 1, 0, bias=False),
	nn.BatchNorm2d(512),
	nn.ReLU(True),
	# state size. 512 x 4 x 4
	nn.ConvTranspose2d(512, 256, 4, 2, 1, bias=False),
	nn.BatchNorm2d(256),
	nn.ReLU(True),
	# state size. 256 x 8 x 8
	nn.ConvTranspose2d(256, 128, 4, 2, 1, bias=False),
	nn.BatchNorm2d(128),
	nn.ReLU(True),
	# state size. 128 x 16 x 16
	nn.ConvTranspose2d(128, 64, 4, 2, 1, bias=False),
	nn.BatchNorm2d(64),
	nn.ReLU(True),
	# state size. 64 x 32 x 32
	nn.ConvTranspose2d(64, img_channels, 4, 2, 1, bias=False),
	nn.Tanh()
	# state size. img_channels x 64 x 64
	)

	def forward(self, input):
	return self.gen(input)

	class Discriminator(nn.Module):
	def __init__(self, img_channels=3):
	super(Discriminator, self).__init__()
	self.disc = nn.Sequential(
	# input is img_channels x 64 x 64
	nn.Conv2d(img_channels, 64, 4, 2, 1, bias=False),
	nn.LeakyReLU(0.2, inplace=True),
	# state size. 64 x 32 x 32
	nn.Conv2d(64, 128, 4, 2, 1, bias=False),
	nn.BatchNorm2d(128),
	nn.LeakyReLU(0.2, inplace=True),
	# state size. 128 x 16 x 16
	nn.Conv2d(128, 256, 4, 2, 1, bias=False),
	nn.BatchNorm2d(256),
	nn.LeakyReLU(0.2, inplace=True),
	# state size. 256 x 8 x 8
	nn.Conv2d(256, 512, 4, 2, 1, bias=False),
	nn.BatchNorm2d(512),
	nn.LeakyReLU(0.2, inplace=True),
	# state size. 512 x 4 x 4
	nn.Conv2d(512, 1, 4, 1, 0, bias=False),
	nn.Sigmoid()
	)

	def forward(self, input):
	return self.disc(input).view(-1, 1).squeeze(1)

	batch_size = 32
	latent_vector_size = 100

	generator = Generator()
	discriminator = Discriminator()

	generator.load_state_dict(torch.load('netG.pth', map_location=torch.device('cpu') ))
	discriminator.load_state_dict(torch.load('netD.pth', map_location=torch.device('cpu') ))