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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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@ -36,7 +36,7 @@ class TestTimePoolHead(nn.Module):
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return x.view(x.size(0), -1)
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def apply_test_time_pool(model, config, use_test_size=True):
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def apply_test_time_pool(model, config, use_test_size=False):
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test_time_pool = False
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if not hasattr(model, 'default_cfg') or not model.default_cfg:
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return model, False
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11
validate.py
11
validate.py
@ -67,6 +67,8 @@ parser.add_argument('--img-size', default=None, type=int,
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metavar='N', help='Input image dimension, uses model default if empty')
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parser.add_argument('--input-size', default=None, nargs=3, type=int,
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metavar='N N N', help='Input all image dimensions (d h w, e.g. --input-size 3 224 224), uses model default if empty')
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parser.add_argument('--use-train-size', action='store_true', default=False,
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help='force use of train input size, even when test size is specified in pretrained cfg')
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parser.add_argument('--crop-pct', default=None, type=float,
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metavar='N', help='Input image center crop pct')
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parser.add_argument('--mean', type=float, nargs='+', default=None, metavar='MEAN',
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@ -164,10 +166,15 @@ def validate(args):
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param_count = sum([m.numel() for m in model.parameters()])
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_logger.info('Model %s created, param count: %d' % (args.model, param_count))
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data_config = resolve_data_config(vars(args), model=model, use_test_size=True, verbose=True)
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data_config = resolve_data_config(
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vars(args),
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model=model,
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use_test_size=not args.use_train_size,
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verbose=True
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)
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test_time_pool = False
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if args.test_pool:
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model, test_time_pool = apply_test_time_pool(model, data_config, use_test_size=True)
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model, test_time_pool = apply_test_time_pool(model, data_config)
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if args.torchscript:
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torch.jit.optimized_execution(True)
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