def prepareDatasetAndLogging(args):
# choose the dataset
if args.dataset == 'mnist':
DatasetClass = datasets.MNIST
elif args.dataset == 'fashion_mnist':
DatasetClass = datasets.FashionMNIST
else:
raise ValueError('unknown dataset: ' + args.dataset + ' try mnist or fashion_mnist')
training_run_name = timeStamped(args.dataset + '_' + args.name)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
# Create the dataset, mnist or fasion_mnist
dataset_dir = os.path.join(args.data_dir, args.dataset)
training_run_dir = os.path.join(args.data_dir, training_run_name)
train_dataset = DatasetClass(
dataset_dir, train=True, download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
train_loader = torch.utils.data.DataLoader(
train_dataset, batch_size=args.batch_size, shuffle=True, **kwargs)
test_dataset = DatasetClass(
dataset_dir, train=False, transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307,), (0.3081,))
]))
test_loader = torch.utils.data.DataLoader(
test_dataset, batch_size=args.test_batch_size, shuffle=True, **kwargs)
# Set up visualization and progress status update code
callback_params = {'epochs': args.epochs,
'samples': len(train_loader) * args.batch_size,
'steps': len(train_loader),
'metrics': {'acc': np.array([]),
'loss': np.array([]),
'val_acc': np.array([]),
'val_loss': np.array([])}}
if args.print_log:
output_on_train_end = os.sys.stdout
else:
output_on_train_end = None
callbacklist = callbacks.CallbackList(
[callbacks.BaseLogger(),
callbacks.TQDMCallback(),
callbacks.CSVLogger(filename=training_run_dir + training_run_name + '.csv',
output_on_train_end=output_on_train_end)])
callbacklist.set_params(callback_params)
tensorboard_writer = SummaryWriter(log_dir=training_run_dir, comment=args.dataset + '_embedding_training')
# show some image examples in tensorboard projector with inverted color
images = 255 - test_dataset.test_data[:100].float()
label = test_dataset.test_labels[:100]
features = images.view(100, 784)
tensorboard_writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
return tensorboard_writer, callbacklist, train_loader, test_loader
def __init_callback(self):
callback_params = {'epochs': self.args.epochs,
'samples': len(self.train_loader) * self.args.batch_size,
'steps': len(self.train_loader),
'metrics': {'acc': np.array([]),
'loss': np.array([]),
'val_acc': np.array([]),
'val_loss': np.array([])}}
callback_list = callbacks.CallbackList(
[callbacks.BaseLogger(),
callbacks.TQDMCallback(),
])
callback_list.set_params(callback_params)
callback_list.set_model(self.model)
return callback_list
def prepareDatasetAndLogging(args):
# choose the dataset
if args.dataset == 'mnist':
DatasetClass = datasets.MNIST
elif args.dataset == 'fashion_mnist':
DatasetClass = datasets.FashionMNIST
else:
raise ValueError('unknown dataset: ' + args.dataset +
' try mnist or fashion_mnist')
training_run_name = timeStamped(args.dataset + '_' + args.name)
kwargs = {'num_workers': 1, 'pin_memory': True} if args.cuda else {}
# Create the dataset, mnist or fasion_mnist
dataset_dir = os.path.join(args.data_dir, args.dataset)
training_run_dir = os.path.join(args.data_dir, training_run_name)
if args.transform:
print('Using Data Augmentation!')
train_dataset = DatasetClass(
dataset_dir,
train=True,
download=True,
transform=transforms.Compose([
transforms.RandomRotation(5),
transforms.ColorJitter(),
transforms.RandomResizedCrop(28, scale=(0.9, 1.0)),
transforms.ToTensor(),
transforms.Normalize((0.1307, ), (0.3081, ))
]))
else:
train_dataset = DatasetClass(dataset_dir,
train=True,
download=True,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
train_loader = torch.utils.data.DataLoader(train_dataset,
batch_size=args.batch_size,
shuffle=True,
**kwargs)
test_dataset = DatasetClass(dataset_dir,
train=False,
transform=transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.1307, ),
(0.3081, ))
]))
test_loader = torch.utils.data.DataLoader(test_dataset,
batch_size=args.test_batch_size,
shuffle=True,
**kwargs)
# Set up visualization and progress status update code
callback_params = {
'epochs': args.epochs,
'samples': len(train_loader) * args.batch_size,
'steps': len(train_loader),
'metrics': {
'acc': np.array([]),
'loss': np.array([]),
'val_acc': np.array([]),
'val_loss': np.array([])
}
}
if args.print_log:
output_on_train_end = os.sys.stdout
else:
output_on_train_end = None
callbacklist = callbacks.CallbackList([
callbacks.BaseLogger(),
callbacks.TQDMCallback(),
callbacks.CSVLogger(filename=training_run_dir + training_run_name +
'.csv',
output_on_train_end=output_on_train_end)
])
callbacklist.set_params(callback_params)
tensorboard_writer = SummaryWriter(log_dir=training_run_dir,
comment=args.dataset +
'_embedding_training')
return tensorboard_writer, callbacklist, train_loader, test_loader
# Set up visualization and progress status update code
callback_params = {'epochs': args.epochs,
'samples': len(train_loader) * args.batch_size,
'steps': len(train_loader),
'metrics': {'acc': np.array([]),
'loss': np.array([]),
'val_acc': np.array([]),
'val_loss': np.array([])}}
if args.print_log:
output_on_train_end = os.sys.stdout
else:
output_on_train_end = None
callbacklist = callbacks.CallbackList(
[callbacks.BaseLogger(),
callbacks.TQDMCallback(),
callbacks.CSVLogger(filename=training_run_dir + training_run_name + '.csv',
output_on_train_end=output_on_train_end)])
callbacklist.set_params(callback_params)
tensorboard_writer = SummaryWriter(log_dir=training_run_dir, comment=args.dataset + '_embedding_training')
# show some image examples in tensorboard projector with inverted color
images = 255 - test_dataset.test_data[:100].float()
label = test_dataset.test_labels[:100]
features = images.view(100, 784)
tensorboard_writer.add_embedding(features, metadata=label, label_img=images.unsqueeze(1))
return tensorboard_writer, callbacklist, train_loader, test_loader
# TODO Add classes for every option listed under the --model parser argument above.