main_spatio.py

import argparse, os, glob
import torch
import random
import torch.backends.cudnn as cudnn
import torch.nn as nn
import torch.optim as optim
from torch.autograd import Variable
from torch.utils.data import DataLoader, ConcatDataset
import torchvision.transforms as trans
from models import Net, DVDModel, spatioModel
from dataset import DatasetFromHdf5, DVDTrainingDataset

# Training settings
parser = argparse.ArgumentParser(description="PyTorch VDSR")
parser.add_argument("--batchSize", type=int, default=32, help="Training batch size")
parser.add_argument("--nEpochs", type=int, default=50, help="Number of epochs to train for")
parser.add_argument("--lr", type=float, default=0.01, help="Learning Rate. Default=0.1")
parser.add_argument("--step", type=int, default=10, help="Sets the learning rate to the initial LR decayed by momentum every n epochs, Default: n=10")
parser.add_argument("--cuda", action="store_true", help="Use cuda?")
parser.add_argument("--resume", default="", type=str, help="Path to checkpoint (default: none)")
parser.add_argument("--start-epoch", default=1, type=int, help="Manual epoch number (useful on restarts)")
# parser.add_argument("--clip", type=float, default=0.4, help="Clipping Gradients. Default=0.4")
parser.add_argument("--threads", type=int, default=1, help="Number of threads for data loader to use, Default: 1")
parser.add_argument("--momentum", default=0.9, type=float, help="Momentum, Default: 0.9")
parser.add_argument("--weight-decay", "--wd", default=1e-4, type=float, help="Weight decay, Default: 1e-4")
parser.add_argument('--pretrained', default='', type=str, help='path to pretrained model (default: none)')
parser.add_argument("--gpus", default="0", type=str, help="gpu ids (default: 0)")
parser.add_argument("--dataset", default="../data/DeepVideoDeblurring_Dataset/quantitative_datasets/", type=str, help="the folder where dataset can be found")
parser.add_argument("--model", default="spatio", type=str, help="the model to be trained. Default: spatio temporal transformer set  by \"spatio\". Other options are \"dvd\" and \"vdsr\" \
for deep video deblurring and very deep super resolution method")

def main():
    global opt, model
    opt = parser.parse_args()
    print(opt)

    cuda = opt.cuda
    if cuda:
        print("=> use gpu id: '{}'".format(opt.gpus))
        os.environ["CUDA_VISIBLE_DEVICES"] = opt.gpus
        if not torch.cuda.is_available():
                raise Exception("No GPU found or Wrong gpu id, please run without --cuda")

    opt.seed = random.randint(1, 10000)
    print("Random Seed: ", opt.seed)
    torch.manual_seed(opt.seed)
    if cuda:
        torch.cuda.manual_seed(opt.seed)

    cudnn.benchmark = True

    print("===> Loading datasets")

    # list all possible datasets
    dataset_names = glob.glob(opt.dataset + "*")

    # construct the traning by concatenating the available datasets
    train_set_list = [DVDTrainingDataset(dataset_name) for dataset_name in dataset_names]
    train_set = ConcatDataset(train_set_list)
    training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)

    print("===> Building model")

    if opt.model == "spatio":
        model = spatioModel()
    elif opt.model == "vdsr":
        model = Net()
    elif opt.model == "dvd":
        model = DVDModel()
    else:
        model = Net()

    criterion = nn.MSELoss(reduction='mean')

    print("===> Setting GPU")
    if cuda:
        model = model.cuda()
        criterion = criterion.cuda()

    # optionally resume from a checkpoint
    if opt.resume:
        if os.path.isfile(opt.resume):
            print("=> loading checkpoint '{}'".format(opt.resume))
            checkpoint = torch.load(opt.resume)
            opt.start_epoch = checkpoint["epoch"] + 1
            model.load_state_dict(checkpoint["model"].state_dict())
        else:
            print("=> no checkpoint found at '{}'".format(opt.resume))

    # optionally copy weights from a checkpoint
    if opt.pretrained:
        if os.path.isfile(opt.pretrained):
            print("=> loading model '{}'".format(opt.pretrained))
            weights = torch.load(opt.pretrained)
            model.load_state_dict(weights['model'].state_dict())
        else:
            print("=> no model found at '{}'".format(opt.pretrained))

    print("===> Setting Optimizer")
    optimizer = optim.SGD(model.parameters(), lr=opt.lr, momentum=opt.momentum, weight_decay=opt.weight_decay)

    print("===> Training")
    for epoch in range(opt.start_epoch, opt.nEpochs + 1):
        train(training_data_loader, optimizer, model, criterion, epoch)
        save_checkpoint(model, epoch)

def adjust_learning_rate(optimizer, epoch):
    """Sets the learning rate to the initial LR decayed by 10 every 10 epochs"""
    lr = opt.lr * (0.1 ** (epoch // opt.step))
    return lr

def train(training_data_loader, optimizer, model, criterion, epoch):
    # lr = adjust_learning_rate(optimizer, epoch-1)
    lr = opt.lr

    for param_group in optimizer.param_groups:
        param_group["lr"] = lr

    print("Epoch = {}, lr = {}".format(epoch, optimizer.param_groups[0]["lr"]))

    model.train()

    for iteration, batch in enumerate(training_data_loader, 1):
        input, target = Variable(batch[0]), Variable(batch[1], requires_grad=False)

        if opt.cuda:
            input = input.cuda()
            target = target.cuda()

        # print("Input size is ", input.size())
        # print("Label size is ", target.size())
        # print("Output size is ", model(input).size())
        # print("Iteration is ", iteration)
        loss = criterion(model(input), target)
        loss_in_out = criterion(input[:,6:9,:,:], target)
        optimizer.zero_grad()
        loss.backward()
        # nn.utils.clip_grad_norm_(model.parameters(),opt.clip)
        optimizer.step()

        # if iteration == 0:
        print("===> Epoch[{}]({}/{}): Loss: {:.10f}, Loss_in_out: {:.10f}".format(epoch, iteration, len(training_data_loader), loss.data, loss_in_out.data))

def save_checkpoint(model, epoch):
    model_out_path = "checkpoint/" + "model_epoch_{}.pth".format(epoch)
    state = {"epoch": epoch ,"model": model}
    if not os.path.exists("checkpoint/"):
        os.makedirs("checkpoint/")

    torch.save(state, model_out_path)

    print("Checkpoint saved to {}".format(model_out_path))

def data_transformation():
    transforms = trans.Compose([
    trans.Resize((224,224)),
    trans.ColorJitter(hue=.05, saturation=.05),
    trans.RandomHorizontalFlip(),
    trans.RandomRotation(20, resample=PIL.Image.BILINEAR)
    ])
    return transforms

if __name__ == "__main__":
    main()