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Image2Image.py
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Image2Image.py
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import torch
from torch.utils.tensorboard import SummaryWriter
import os
from torch.utils.data import DataLoader, Subset
import training.TrainingOptions
import training.AdversarialTraining
import Utils
from datasets.GeneratorInputDataset import GeneratorInputDataset
from datasets.InfiniteDataSampler import InfiniteDataSampler
from datasets.TransformDataSampler import TransformDataSampler
from datasets.image2image import get_aligned_dataset
from eval.Cityscapes import get_L2, get_pixel_acc
from eval.Visualisation import generate_images
from models.generators.Unet import Unet
from training.DiscriminatorTraining import DiscriminatorSetup, DependencyDiscriminatorSetup, DependencyDiscriminatorPair
from models.discriminators.ConvDiscriminator import ConvDiscriminator
from datasets.CropDataset import CropDataset
def set_paths(opt):
# Set up paths and create folders
opt.experiment_path = os.path.join(opt.out_path, "Image2Image_" + str(opt.dataset), opt.experiment_name)
opt.gen_path = os.path.join(opt.experiment_path, "gen")
opt.log_path = os.path.join(opt.experiment_path, "logs")
Utils.make_dirs([opt.experiment_path, opt.gen_path, opt.log_path])
def train(opt):
Utils.set_seeds(opt)
device = Utils.get_device(opt.cuda)
set_paths(opt)
# DATA
dataset = get_aligned_dataset(opt, "train")
nc = dataset.A_nc + dataset.B_nc
if opt.num_joint_samples > len(dataset):
print("WARNING: Cannot train with " + str(opt.num_joint_samples) + " samples, dataset has only size of " + str(len(dataset))+ ". Using full dataset!")
opt.num_joint_samples = len(dataset)
# Joint samples
dataset_train = Subset(dataset, range(opt.num_joint_samples))
train_joint = InfiniteDataSampler(
DataLoader(dataset_train, num_workers=int(opt.workers), batch_size=opt.batchSize, shuffle=True, drop_last=True))
if opt.factorGAN == 1:
# For marginals, take full dataset and crop
a_dataset = CropDataset(dataset, lambda x : x[0:dataset.A_nc, :, :])
b_dataset = CropDataset(dataset, lambda x : x[dataset.A_nc:, :, :])
train_b = InfiniteDataSampler(DataLoader(b_dataset, num_workers=int(opt.workers), batch_size=opt.batchSize, shuffle=True, drop_last=True))
generator_input_data = a_dataset
else:
generator_input_data = CropDataset(dataset_train, lambda x : x[0:dataset.A_nc, :, :])
# SETUP GENERATOR MODEL
G = Unet(opt, opt.generator_channels, dataset.A_nc, dataset.B_nc).to(device)
G_noise = torch.distributions.uniform.Uniform(torch.Tensor([-1] * opt.nz), torch.Tensor([1] * opt.nz))
G_opt = Utils.create_optim(G.parameters(), opt)
# Prepare data sources that are a combination of real data and generator network, or purely from the generator network
G_input_data = DataLoader(GeneratorInputDataset(generator_input_data, G_noise), num_workers=int(opt.workers),
batch_size=opt.batchSize, shuffle=True, drop_last=True)
G_inputs = InfiniteDataSampler(G_input_data)
G_filled_outputs = TransformDataSampler(InfiniteDataSampler(G_input_data), G, device)
# SETUP DISCRIMINATOR(S)
if opt.factorGAN == 1:
# Setup disc networks
D2 = ConvDiscriminator(opt.loadSize, opt.loadSize, dataset.B_nc).to(device)
# If our dep discriminators are only defined on classifier probabilites, integrate classification into discriminator network as first step
if opt.use_real_dep_disc == 1:
DP = ConvDiscriminator(opt.loadSize, opt.loadSize, nc, spectral_norm=(opt.lipschitz_p == 1)).to(device)
else:
DP = lambda x : 0
DQ = ConvDiscriminator(opt.loadSize, opt.loadSize, nc).to(device)
# Prepare discriminators for training method
# Marginal discriminator
D_setups = [DiscriminatorSetup("D2", D2, Utils.create_optim(D2.parameters(), opt),
train_b, G_filled_outputs, crop_fake=lambda x: x[:, dataset.A_nc:, :, :])]
# Dependency discriminators
shuffle_batch_func = lambda x: Utils.shuffle_batch_dims(x, [dataset.A_nc])
if opt.use_real_dep_disc:
DP_setup = DependencyDiscriminatorSetup("DP", DP, Utils.create_optim(DP.parameters(), opt),
train_joint, shuffle_batch_func)
else:
DP_setup = None
DQ_setup = DependencyDiscriminatorSetup("DQ", DQ, Utils.create_optim(DQ.parameters(), opt),
G_filled_outputs, shuffle_batch_func)
D_dep_setups = [DependencyDiscriminatorPair(DP_setup, DQ_setup)]
else:
D = ConvDiscriminator(opt.loadSize, opt.loadSize, nc).to(device)
print(sum(p.numel() for p in D.parameters()))
D_setup = DiscriminatorSetup("D", D, Utils.create_optim(D.parameters(), opt),
train_joint, G_filled_outputs)
D_setups = [D_setup]
D_dep_setups = []
# RUN TRAINING
training.AdversarialTraining.train(opt, G, G_inputs, G_opt, D_setups, D_dep_setups, device, opt.log_path)
torch.save(G.state_dict(), os.path.join(opt.experiment_path, "G"))
def eval(opt):
Utils.set_seeds(opt)
device = Utils.get_device(opt.cuda)
set_paths(opt)
# DATASET
dataset = get_aligned_dataset(opt, "val")
input_dataset = CropDataset(dataset, lambda x: x[0:dataset.A_nc, :, :])
# GENERATOR
G = Unet(opt, opt.generator_channels, dataset.A_nc, dataset.B_nc).to(device)
G_noise = torch.distributions.uniform.Uniform(torch.Tensor([-1] * opt.nz), torch.Tensor([1] * opt.nz))
G.load_state_dict(torch.load(os.path.join(opt.experiment_path, opt.eval_model)))
G.eval()
# EVALUATE: Generate some images using test set and noise as conditional input
G_input_data = DataLoader(GeneratorInputDataset(input_dataset, G_noise), num_workers=int(opt.workers),
batch_size=opt.batchSize, shuffle=False)
G_inputs = InfiniteDataSampler(G_input_data)
generate_images(G, G_inputs, opt.gen_path, 100, device, lambda x : Utils.create_image_pair(x, dataset.A_nc, dataset.B_nc))
# EVALUATE for Cityscapes
if opt.dataset == "cityscapes":
writer = SummaryWriter(opt.log_path)
val_input_data = DataLoader(dataset, num_workers=int(opt.workers),batch_size=opt.batchSize)
pixel_error = get_pixel_acc(opt, device, G, val_input_data, G_noise)
print("VALIDATION PERFORMANCE Pixel: " + str(pixel_error))
writer.add_scalar("val_pix", pixel_error)
L2_error = get_L2(opt, device, G, val_input_data, G_noise)
print("VALIDATION PERFORMANCE L2: " + str(L2_error))
writer.add_scalar("val_L2", L2_error)
def get_opt():
# COLLECT ALL CMD ARGUMENTS
parser = training.TrainingOptions.get_parser()
parser.add_argument('--dataset', type=str, default="cityscapes",
help="Dataset to train on - can be cityscapes or edges2shoes (but other img2img datasets can be integrated easily")
parser.add_argument('--num_joint_samples', type=int, default=1000,
help="Number of joint observations available for training normal gan/dependency discriminators")
parser.add_argument('--loadSize', type=int, default=128,
help="Dimensions (no. of pixels) the dataset images are resampled to")
parser.add_argument('--generator_channels', type=int, default=32,
help="Number of intial feature channels used in G. 64 was used in the paper")
opt = parser.parse_args()
print(opt)
# Set generator to sigmoid output
opt.generator_activation = "sigmoid"
# Square images => loadSize determines width and height
opt.input_width = opt.loadSize
opt.input_height = opt.loadSize
return opt
if __name__ == "__main__":
opt = get_opt()
if not opt.eval:
train(opt)
eval(opt)