Python dataset.DatasetFromHdf5() Examples

The following are 4 code examples of dataset.DatasetFromHdf5(). You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example. You may also want to check out all available functions/classes of the module dataset , or try the search function .
Example #1
Source File: main_edsr.py    From pytorch-edsr with MIT License 4 votes vote down vote up 
def main():

    global opt, model 
    opt = parser.parse_args()
    print(opt)

    cuda = opt.cuda
    if cuda and not torch.cuda.is_available():
        raise Exception("No GPU found, 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")
    train_set = DatasetFromHdf5("path_to_dataset.h5")
    training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)

    print("===> Building model")
    model = Net()
    criterion = nn.L1Loss(size_average=False)

    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))

    print("===> Setting Optimizer")
    optimizer = optim.Adam(filter(lambda p: p.requires_grad, model.parameters()), lr=opt.lr, weight_decay=opt.weight_decay, betas = (0.9, 0.999), eps=1e-08)

    print("===> Training")
    for epoch in range(opt.start_epoch, opt.nEpochs + 1): 
        train(training_data_loader, optimizer, model, criterion, epoch)
        save_checkpoint(model, epoch)
Example #2
Source File: main.py    From DRRN-pytorch with MIT License 4 votes vote down vote up 
def main():
	global opt, model
	opt = parser.parse_args()
	print(opt)

	cuda = opt.cuda
	if cuda  and not torch.cuda.is_available():
		raise Exception("No GPU found, please run without --cuda")

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

	cudnn.benchmark = True

	print("===> Loading datasets")
	train_set = DatasetFromHdf5("data/train_291_32_x234.h5")
	training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)

	print("===> Building model")
	model = DRRN()
	criterion = nn.MSELoss(size_average=False)

	print("===> Setting GPU")
	if cuda:
		model = torch.nn.DataParallel(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("===> load 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)
		# os.system("python eval.py --cuda --model=model/model_epoch_{}.pth".format(epoch))
Example #3
Source File: main_lapsrn.py    From pytorch-LapSRN with MIT License 4 votes vote down vote up 
def main():

    global opt, model
    opt = parser.parse_args()
    print(opt)

    cuda = opt.cuda
    if cuda and not torch.cuda.is_available():
        raise Exception("No GPU found, 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")
    train_set = DatasetFromHdf5("data/lap_pry_x4_small.h5")
    training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)

    print("===> Building model")
    model = Net()
    criterion = L1_Charbonnier_loss()

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

    # 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.Adam(model.parameters(), lr=opt.lr)

    print("===> Training")
    for epoch in range(opt.start_epoch, opt.nEpochs + 1): 
        train(training_data_loader, optimizer, model, criterion, epoch)
        save_checkpoint(model, epoch)
Example #4
Source File: main.py    From pytorch-SRDenseNet with MIT License 4 votes vote down vote up 
def main():

    global opt, model 
    opt = parser.parse_args()
    print opt

    cuda = opt.cuda
    if cuda and not torch.cuda.is_available():
        raise Exception("No GPU found, 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")
    train_set = DatasetFromHdf5("/path/to/your/dataset/like/imagenet_50K.h5")
    training_data_loader = DataLoader(dataset=train_set, num_workers=opt.threads, batch_size=opt.batchSize, shuffle=True)

    print("===> Building model")
    model = Net()
    criterion = L1_Charbonnier_loss()

    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.Adam(model.parameters(), lr=opt.lr)

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