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train.py
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import os
import argparse
import torch
import mixed_precision
from augmentation.rand_augment import augmentation_dicts_depth
from stats import StatTracker
from datasets import Dataset, build_dataset, get_dataset, get_encoder_size
from model import Model
from checkpoint import Checkpointer
from task_self_supervised import train_self_supervised
from task_classifiers import train_classifiers
parser = argparse.ArgumentParser(description='Infomax Representations - Training Script')
# parameters for general training stuff
parser.add_argument('--dataset', type=str, default='STL10')
parser.add_argument('--batch_size', type=int, default=200,
help='input batch size (default: 200)')
parser.add_argument('--learning_rate', type=float, default=0.0002,
help='learning rate')
parser.add_argument('--seed', type=int, default=1,
help='random seed (default: 1)')
parser.add_argument('--amp', action='store_true', default=False,
help='Enables automatic mixed precision')
# parameters for model and training objective
parser.add_argument('--classifiers', action='store_true', default=False,
help="Wether to run self-supervised encoder or"
"classifier training task")
parser.add_argument('--ndf', type=int, default=128,
help='feature width for encoder')
parser.add_argument('--n_rkhs', type=int, default=1024,
help='number of dimensions in fake RKHS embeddings')
parser.add_argument('--tclip', type=float, default=20.0,
help='soft clipping range for NCE scores')
parser.add_argument('--n_depth', type=int, default=3)
parser.add_argument('--use_bn', type=int, default=0)
# parameters for output, logging, checkpointing, etc
parser.add_argument('--output_dir', type=str, default='./runs',
help='directory where tensorboard events and checkpoints will be stored')
parser.add_argument('--input_dir', type=str, default='/mnt/imagenet',
help="Input directory for the dataset. Not needed For C10,"
" C100 or STL10 as the data will be automatically downloaded.")
parser.add_argument('--cpt_load_path', type=str, default=None,
help='path from which to load checkpoint (if available)')
parser.add_argument('--cpt_name', type=str, default='amdim_cpt.pth',
help='name to use for storing checkpoints during training')
parser.add_argument('--run_name', type=str, default='default_run',
help='name to use for the tensorbaord summary for this run')
parser.add_argument('--modality', type=str, default='dual', choices=['dual', 'rgb', 'depth'])
parser.add_argument('--modality_to_test', type=str, default='random', choices=['random', 'rgb', 'depth'])
parser.add_argument('--baseline', action='store_true', default=False,
help='Indicates whether the whole model should be trained.'
'Needs to be combined with classifiers=True')
parser.add_argument('--label_proportion', type=float, default=None,
help='Give the label proportion')
parser.add_argument('--use_randaugment', action='store_true', default=False,
help='Use rand augmentation')
parser.add_argument('--selected_randaugment', type=str, default=None)
parser.add_argument('--depth_augmentation_set', type=str, default=None)
parser.add_argument('--loss_predictions', type=str, default=None)
parser.add_argument('--rkhs_conv_depth', type=int, default=0)
parser.add_argument('--epochs', type=int, default=None, help='Number of epochs')
# ...
args = parser.parse_args()
def main():
# create target output dir if it doesn't exist yet
if not os.path.isdir(args.output_dir):
os.mkdir(args.output_dir)
# enable mixed-precision computation if desired
if args.amp:
mixed_precision.enable_mixed_precision()
if args.baseline and args.classifiers:
print('Mode active which trains classifiers and baseline - without classification loss')
elif args.baseline:
print('Mode active which trains classifiers and feature extract at the same time')
if args.loss_predictions is not None:
split_loss_terms = args.loss_predictions.split(',')
if len(split_loss_terms) == 1 and split_loss_terms[0] == 'all':
loss_predictions = 'all'
else:
# make sure we only have valid loss terms
assert all([t in ['1t5', '1t7', '5t5'] for t in split_loss_terms])
loss_predictions = split_loss_terms
else:
loss_predictions = None
if args.baseline and args.classifiers:
args.cpt_name = 'amdim_baseline_cpt.pth'
if args.modality != 'dual':
if args.modality_to_test != args.modality:
raise BaseException('Modality for testing should be the same as for testing {} != {}'.format(
args.modality_to_test,
args.modality
))
# set the RNG seeds (probably more hidden elsewhere...)
torch.manual_seed(args.seed)
torch.cuda.manual_seed(args.seed)
# get the dataset
dataset = get_dataset(args.dataset)
encoder_size = get_encoder_size(dataset)
# get a helper object for tensorboard logging
log_dir = os.path.join(args.output_dir, args.run_name)
stat_tracker = StatTracker(log_dir=log_dir)
assert(args.selected_randaugment is None or
args.selected_randaugment in list(augmentation_dicts_depth().keys()))
# get dataloaders for training and testing
train_loader, test_loader, num_classes = \
build_dataset(dataset=dataset,
batch_size=args.batch_size,
input_dir=args.input_dir,
labeled_only=args.classifiers,
modality=args.modality,
label_proportion=args.label_proportion,
use_randaugment=args.use_randaugment,
selected_randaugment=args.selected_randaugment,
depth_augmentation_type_set=args.depth_augmentation_set
)
torch_device = torch.device('cuda')
checkpointer = Checkpointer(args.output_dir, args.cpt_name)
if args.cpt_load_path:
model = checkpointer.restore_model_from_checkpoint(
args.cpt_load_path,
training_classifier=args.classifiers)
else:
# create new model with random parameters
model = Model(ndf=args.ndf, n_classes=num_classes, n_rkhs=args.n_rkhs,
tclip=args.tclip, n_depth=args.n_depth, encoder_size=encoder_size,
use_bn=(args.use_bn == 1), loss_predictions=loss_predictions,
rkhs_conv_depth=args.rkhs_conv_depth)
model.init_weights(init_scale=1.0)
checkpointer.track_new_model(model)
model = model.to(torch_device)
# select which type of training to do
task = train_classifiers if args.classifiers else train_self_supervised
task(model, args.learning_rate, dataset, train_loader,
test_loader, stat_tracker, checkpointer,
log_dir=args.output_dir,
device=torch_device,
modality_to_test=args.modality_to_test,
baseline_training=args.baseline,
overwrite_epochs=args.epochs,
label_proportion=args.label_proportion
)
if __name__ == "__main__":
print(args)
main()