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import glob
import time
import numpy as np
from PIL import Image
from pathlib import Path
from tqdm.notebook import tqdm
import matplotlib.pyplot as plt
from skimage.color import rgb2lab, lab2rgb
import torch
from torch import nn, optim
from torchvision import transforms
from torchvision.utils import make_grid
from torch.utils.data import Dataset, DataLoader
from .Generator import UnetBlock, Unet
from .Discriminator import PatchDiscriminator
from .weights import init_weights
from .loss import GANLoss
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
def init_model(model, device):
model = model.to(device)
model = init_weights(model)
return model
class MainModel(nn.Module):
def __init__(
self, net_G=None, lr_G=2e-4, lr_D=2e-4, beta1=0.5, beta2=0.999, lambda_L1=100.0
):
super().__init__()
self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
self.lambda_L1 = lambda_L1
if net_G is None:
self.net_G = init_model(
Unet(input_c=1, output_c=2, n_down=8, num_filters=64), self.device
)
else:
self.net_G = net_G.to(self.device)
self.net_D = init_model(
PatchDiscriminator(input_c=3, n_down=3, num_filters=64), self.device
)
self.GANcriterion = GANLoss(gan_mode="vanilla").to(self.device)
self.L1criterion = nn.L1Loss()
self.opt_G = optim.Adam(self.net_G.parameters(), lr=lr_G, betas=(beta1, beta2))
self.opt_D = optim.Adam(self.net_D.parameters(), lr=lr_D, betas=(beta1, beta2))
def set_requires_grad(self, model, requires_grad=True):
for p in model.parameters():
p.requires_grad = requires_grad
def setup_input(self, data):
self.L = data["L"].to(self.device)
self.ab = data["ab"].to(self.device)
def forward(self):
self.fake_color = self.net_G(self.L)
def backward_D(self):
fake_image = torch.cat([self.L, self.fake_color], dim=1)
fake_preds = self.net_D(fake_image.detach())
self.loss_D_fake = self.GANcriterion(fake_preds, False)
real_image = torch.cat([self.L, self.ab], dim=1)
real_preds = self.net_D(real_image)
self.loss_D_real = self.GANcriterion(real_preds, True)
self.loss_D = (self.loss_D_fake + self.loss_D_real) * 0.5
self.loss_D.backward()
def backward_G(self):
fake_image = torch.cat([self.L, self.fake_color], dim=1)
fake_preds = self.net_D(fake_image)
self.loss_G_GAN = self.GANcriterion(fake_preds, True)
self.loss_G_L1 = self.L1criterion(self.fake_color, self.ab) * self.lambda_L1
self.loss_G = self.loss_G_GAN + self.loss_G_L1
self.loss_G.backward()
def optimize(self):
self.forward()
self.net_D.train()
self.set_requires_grad(self.net_D, True)
self.opt_D.zero_grad()
self.backward_D()
self.opt_D.step()
self.net_G.train()
self.set_requires_grad(self.net_D, False)
self.opt_G.zero_grad()
self.backward_G()
self.opt_G.step()
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