96 lines
4.7 KiB
Python
96 lines
4.7 KiB
Python
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import numpy as np
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import data_providers as data_providers
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from arg_extractor import get_args
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from data_augmentations import Cutout
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from experiment_builder import ExperimentBuilder
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from model_architectures import ConvolutionalNetwork
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args, device = get_args() # get arguments from command line
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rng = np.random.RandomState(seed=args.seed) # set the seeds for the experiment
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from torchvision import transforms
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import torch
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torch.manual_seed(seed=args.seed) # sets pytorch's seed
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if args.dataset_name == 'emnist':
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train_data = data_providers.EMNISTDataProvider('train', batch_size=args.batch_size,
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rng=rng,
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flatten=False) # initialize our rngs using the argument set seed
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val_data = data_providers.EMNISTDataProvider('valid', batch_size=args.batch_size,
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rng=rng,
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flatten=False) # initialize our rngs using the argument set seed
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test_data = data_providers.EMNISTDataProvider('test', batch_size=args.batch_size,
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rng=rng,
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flatten=False) # initialize our rngs using the argument set seed
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num_output_classes = train_data.num_classes
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elif args.dataset_name == 'cifar10':
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transform_train = transforms.Compose([
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transforms.RandomCrop(32, padding=4),
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Cutout(n_holes=1, length=14),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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transform_test = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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trainset = data_providers.CIFAR10(root='data', set_name='train', download=True, transform=transform_train)
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train_data = torch.utils.data.DataLoader(trainset, batch_size=100, shuffle=True, num_workers=2)
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valset = data_providers.CIFAR10(root='data', set_name='val', download=True, transform=transform_test)
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val_data = torch.utils.data.DataLoader(valset, batch_size=100, shuffle=False, num_workers=2)
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testset = data_providers.CIFAR10(root='data', set_name='test', download=True, transform=transform_test)
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test_data = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
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classes = ('plane', 'car', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck')
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num_output_classes = 10
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elif args.dataset_name == 'cifar100':
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transform_train = transforms.Compose([
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transforms.RandomCrop(32, padding=4),
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Cutout(n_holes=1, length=14),
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transforms.RandomHorizontalFlip(),
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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transform_test = transforms.Compose([
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transforms.ToTensor(),
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transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
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])
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trainset = data_providers.CIFAR100(root='data', set_name='train', download=True, transform=transform_train)
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train_data = torch.utils.data.DataLoader(trainset, batch_size=100, shuffle=True, num_workers=2)
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valset = data_providers.CIFAR100(root='data', set_name='val', download=True, transform=transform_test)
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val_data = torch.utils.data.DataLoader(valset, batch_size=100, shuffle=False, num_workers=2)
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testset = data_providers.CIFAR100(root='data', set_name='test', download=True, transform=transform_test)
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test_data = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
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num_output_classes = 100
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custom_conv_net = ConvolutionalNetwork( # initialize our network object, in this case a ConvNet
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input_shape=(args.batch_size, args.image_num_channels, args.image_height, args.image_height),
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dim_reduction_type=args.dim_reduction_type, num_filters=args.num_filters, num_layers=args.num_layers,
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use_bias=False,
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num_output_classes=num_output_classes)
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conv_experiment = ExperimentBuilder(network_model=custom_conv_net, use_gpu=args.use_gpu,
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experiment_name=args.experiment_name,
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num_epochs=args.num_epochs,
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weight_decay_coefficient=args.weight_decay_coefficient,
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continue_from_epoch=args.continue_from_epoch,
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train_data=train_data, val_data=val_data,
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test_data=test_data) # build an experiment object
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experiment_metrics, test_metrics = conv_experiment.run_experiment() # run experiment and return experiment metrics
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