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December 15, 2019 12:15
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import argparse, json, gzip | |
from attrdict import AttrDict as D | |
import modules | |
from modules.manager.train import TrainManager | |
parser = argparse.ArgumentParser('cifar10/100 supervised classification') | |
parser.add_argument('--name', type=str, default='test') | |
parser.add_argument('--exp_id', type=str, default=None) | |
parser.add_argument('--run_id', type=str, default='0') | |
parser.add_argument('--depth', type=int, default=28) | |
parser.add_argument('--width', type=int, default=2) | |
parser.add_argument('--epoch', type=int, default=200) | |
parser.add_argument('--pre-trained-dataset', default=None, type=str) | |
parser.add_argument('--train-dataset', required=True, type=str) | |
parser.add_argument('--test-dataset', required=True, type=str) | |
parser.add_argument('--extractor-checkpoint', type=str, default=None) | |
parser.add_argument('--fix-extractor', action='store_true') | |
parser.add_argument('--keep-freq', type=int, default=None) | |
parser.add_argument('--debug', action='store_true') | |
args = parser.parse_args() | |
if args.fix_extractor: | |
assert args.extractor_checkpoint is not None | |
modules.setup() | |
def dataset(dataset_file: str, train: bool): | |
return D( | |
dataset='dataset.ClassDataset', | |
dataset_arg=D( | |
dataset_file=dataset_file, | |
transform='cifar.cifar_aug_transform', | |
transform_arg=D(train=train, dataset=args.pre_trained_dataset), | |
), | |
) | |
TrainManager( | |
suffix=args.name, | |
exp_id=args.exp_id, | |
run_id=args.run_id, | |
keep_freq=args.keep_freq, | |
auto_resume_strict=True, | |
model='classification.ClassificationModel', | |
model_arg=D( | |
extractor='cifar_wideresnet.WideResNet', | |
extractor_arg=D( | |
depth=args.depth, | |
width=args.width, | |
base_model_checkpoint_path=args.extractor_checkpoint, | |
), | |
classifier='linear.Linear', | |
classifier_arg=D(class_dim=train_dataset_info.num_class), | |
fix_extractor=args.fix_extractor, | |
), | |
trainer='iterative.IterativeTrainer', | |
trainer_arg=D( | |
loss='ce.CrossEntropy', | |
loss_arg=D(), | |
metrics=['accuracy.Accuracy'], | |
metrics_arg=[D()], | |
debug_max_itr=2 if args.debug else None, | |
epoch=args.epoch, | |
optimizer='SGD', | |
optimizer_arg=D(lr=0.1, momentum=0.9, weight_decay=0.0005, nesterov=True), | |
data_loader='dataloader.DataLoader', | |
data_loader_arg=D( | |
batchsize=128, | |
shuffle=True, | |
num_workers=4, | |
**dataset(args.train_dataset, train=True), | |
), | |
lr_scheduler='MultiStepLR', | |
lr_scheduler_arg=D(milestones=[60, 120, 160], gamma=0.2), | |
print_freq_itr=200, | |
), | |
evaluators=['iterative.IterativeEvaluator'], | |
evaluators_arg=[ | |
D( | |
name='SeenClassification', | |
eval_freq=1, | |
metrics=['ce.CrossEntropy', 'accuracy.Accuracy'], | |
metrics_arg=[D(), D()], | |
debug_max_itr=2 if args.debug else None, | |
print_freq_itr=100, | |
data_loader='dataloader.DataLoader', | |
data_loader_arg=D( | |
batchsize=128, | |
shuffle=False, | |
num_workers=4, | |
**dataset(args.test_dataset, train=False), | |
), | |
), | |
], | |
).run_train() |
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