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Add --use-train-size flag to force use of train input_size (over test input size) for validation. Default test-time pooling to use train input size (fixes issues).

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Ross Wightman 2022-07-07 22:01:24 -07:00
parent ce65a7b29f
commit 7c7ecd2492
2 changed files with 10 additions and 3 deletions
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2
timm/models/layers/test_time_pool.py
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@ -36,7 +36,7 @@ class TestTimePoolHead(nn.Module):
return x.view(x.size(0), -1) return x.view(x.size(0), -1)
def apply_test_time_pool(model, config, use_test_size=True): def apply_test_time_pool(model, config, use_test_size=False):
test_time_pool = False test_time_pool = False
if not hasattr(model, 'default_cfg') or not model.default_cfg: if not hasattr(model, 'default_cfg') or not model.default_cfg:
return model, False return model, False

11
validate.py
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@ -67,6 +67,8 @@ parser.add_argument('--img-size', default=None, type=int,
metavar='N', help='Input image dimension, uses model default if empty') metavar='N', help='Input image dimension, uses model default if empty')
parser.add_argument('--input-size', default=None, nargs=3, type=int, parser.add_argument('--input-size', default=None, nargs=3, type=int,
metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty') metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
parser.add_argument('--use-train-size', action='store_true', default=False,
help='force use of train input size, even when test size is specified in pretrained cfg')
parser.add_argument('--crop-pct', default=None, type=float, parser.add_argument('--crop-pct', default=None, type=float,
metavar='N', help='Input image center crop pct') metavar='N', help='Input image center crop pct')
parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN', parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
@ -164,10 +166,15 @@ def validate(args):
param_count = sum([m.numel() for m in model.parameters()]) param_count = sum([m.numel() for m in model.parameters()])
_logger.info('Model %s created, param count: %d' % (args.model, param_count)) _logger.info('Model %s created, param count: %d' % (args.model, param_count))
data_config = resolve_data_config(vars(args), model=model, use_test_size=True, verbose=True) data_config = resolve_data_config(
vars(args),
model=model,
use_test_size=not args.use_train_size,
verbose=True
)
test_time_pool = False test_time_pool = False
if args.test_pool: if args.test_pool:
model, test_time_pool = apply_test_time_pool(model, data_config, use_test_size=True) model, test_time_pool = apply_test_time_pool(model, data_config)
if args.torchscript: if args.torchscript:
torch.jit.optimized_execution(True) torch.jit.optimized_execution(True)