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Add pretrained arg to reference scripts #935

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Merged
merged 1 commit into from
May 21, 2019
Merged

Add pretrained arg to reference scripts #935

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8 changes: 7 additions & 1 deletion references/classification/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -144,7 +144,7 @@ def main(args):
sampler=test_sampler, num_workers=args.workers, pin_memory=True)

print("Creating model")
model = torchvision.models.__dict__[args.model]()
model = torchvision.models.__dict__[args.model](pretrained=args.pretrained)
model.to(device)
if args.distributed and args.sync_bn:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
Expand Down Expand Up @@ -242,6 +242,12 @@ def parse_args():
help="Only test the model",
action="store_true",
)
parser.add_argument(
"--pretrained",
dest="pretrained",
help="Use pre-trained models from the modelzoo",
action="store_true",
)

# distributed training parameters
parser.add_argument('--world-size', default=1, type=int,
Expand Down
9 changes: 8 additions & 1 deletion references/detection/train.py
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Expand Up @@ -76,7 +76,8 @@ def main(args):
collate_fn=utils.collate_fn)

print("Creating model")
model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes)
model = torchvision.models.detection.__dict__[args.model](num_classes=num_classes,
pretrained=args.pretrained)
model.to(device)

model_without_ddp = model
Expand Down Expand Up @@ -156,6 +157,12 @@ def main(args):
help="Only test the model",
action="store_true",
)
parser.add_argument(
"--pretrained",
dest="pretrained",
help="Use pre-trained models from the modelzoo",
action="store_true",
)

# distributed training parameters
parser.add_argument('--world-size', default=1, type=int,
Expand Down
10 changes: 9 additions & 1 deletion references/segmentation/train.py
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Original file line number Diff line number Diff line change
Expand Up @@ -121,7 +121,9 @@ def main(args):
sampler=test_sampler, num_workers=args.workers,
collate_fn=utils.collate_fn)

model = torchvision.models.segmentation.__dict__[args.model](num_classes=num_classes, aux_loss=args.aux_loss)
model = torchvision.models.segmentation.__dict__[args.model](num_classes=num_classes,
aux_loss=args.aux_loss,
pretrained=args.pretrained)
model.to(device)
if args.distributed:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model)
Expand Down Expand Up @@ -205,6 +207,12 @@ def parse_args():
help="Only test the model",
action="store_true",
)
parser.add_argument(
"--pretrained",
dest="pretrained",
help="Use pre-trained models from the modelzoo",
action="store_true",
)
# distributed training parameters
parser.add_argument('--world-size', default=1, type=int,
help='number of distributed processes')
Expand Down