ADDA.PyTorch-resnet/core/experiment.py

import os
import sys
sys.path.append(os.path.abspath('.'))
import torch
from torch import nn, optim
from torch.utils.data import DataLoader
from torchvision.datasets import SVHN, MNIST
from torchvision import transforms
from models.models import CNN, Discriminator
from core.trainer import train_target_cnn
from utils.utils import get_logger


def run(args):
    if not os.path.exists(args.logdir):
        os.makedirs(args.logdir)
    logger = get_logger(os.path.join(args.logdir, 'main.log'))
    logger.info(args)

    dataset_root = os.environ["DATASETDIR"]

    # data
    source_transform = transforms.Compose([
        # transforms.Grayscale(),
        transforms.ToTensor()]
    )
    target_transform = transforms.Compose([
        transforms.Resize(32),
        transforms.ToTensor(),
        transforms.Lambda(lambda x: x.repeat(3, 1, 1))
    ])
    source_dataset_train = SVHN(
        'input/', 'train', transform=source_transform, download=True)
    target_dataset_train = MNIST(
        'input/', 'train', transform=target_transform, download=True)
    target_dataset_test = MNIST(
        'input/', 'test', transform=target_transform, download=True)
    source_train_loader = DataLoader(
        source_dataset_train, args.batch_size, shuffle=True,
        drop_last=True,
        num_workers=args.n_workers)
    target_train_loader = DataLoader(
        target_dataset_train, args.batch_size, shuffle=True,
        drop_last=True,
        num_workers=args.n_workers)
    target_test_loader = DataLoader(
        target_dataset_test, args.batch_size, shuffle=False,
        num_workers=args.n_workers)

    # train source CNN
    source_cnn = CNN(in_channels=args.in_channels).to(args.device)
    if os.path.isfile(args.trained):
        c = torch.load(args.trained)
        source_cnn.load_state_dict(c['model'])
        logger.info('Loaded `{}`'.format(args.trained))

    # train target CNN
    target_cnn = CNN(in_channels=args.in_channels, target=True).to(args.device)
    target_cnn.load_state_dict(source_cnn.state_dict())
    discriminator = Discriminator(args=args).to(args.device)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(
        target_cnn.encoder.parameters(),
        lr=args.lr, betas=args.betas, weight_decay=args.weight_decay)
    d_optimizer = optim.Adam(
        discriminator.parameters(),
        lr=args.lr, betas=args.betas, weight_decay=args.weight_decay)
    train_target_cnn(
        source_cnn, target_cnn, discriminator,
        criterion, optimizer, d_optimizer,
        source_train_loader, target_train_loader, target_test_loader,
        args=args)