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Merged
merged 4 commits into from
May 29, 2020
Merged

447 change Zoom default to keep_shape=True #448

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68fcd09
[DLMED] change Zoom default to keep_shape=True
Nic-Ma May 28, 2020
440dfd6
Merge branch 'master' into 447-change-zoom-default-size
Nic-Ma May 28, 2020
674a9c0
[DLMED] update tests and examples
Nic-Ma May 29, 2020
c3e2361
[DLMED] fix integration test
Nic-Ma May 29, 2020
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5 changes: 2 additions & 3 deletions examples/notebooks/mednist_tutorial.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -80,7 +80,7 @@
"from torch.utils.data import Dataset, DataLoader\n",
"\n",
"from monai.transforms import \\\n",
" Compose, LoadPNG, AddChannel, ScaleIntensity, ToTensor, RandRotate, RandFlip, RandZoom, Resize\n",
" Compose, LoadPNG, AddChannel, ScaleIntensity, ToTensor, RandRotate, RandFlip, RandZoom\n",
"from monai.networks.nets import densenet121\n",
"from monai.metrics import compute_roc_auc\n",
"\n",
Expand Down Expand Up @@ -235,10 +235,9 @@
" LoadPNG(image_only=True),\n",
" AddChannel(),\n",
" ScaleIntensity(),\n",
" RandRotate(degrees=15, prob=0.5),\n",
" RandRotate(degrees=15, prob=0.5, reshape=False),\n",
" RandFlip(spatial_axis=0, prob=0.5),\n",
" RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),\n",
" Resize(spatial_size=(64, 64), mode='constant'),\n",
" ToTensor()\n",
"])\n",
"\n",
Expand Down
8 changes: 4 additions & 4 deletions monai/transforms/spatial/array.py
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Original file line number Diff line number Diff line change
Expand Up @@ -333,7 +333,7 @@ class Zoom(Transform):
prefilter (bool): Apply spline_filter before interpolation. Default: True.
use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes
'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found.
keep_size (bool): Should keep original size (pad if needed).
keep_size (bool): Should keep original size (pad if needed), default is True.
"""

def __init__(
Expand All @@ -344,7 +344,7 @@ def __init__(
cval=0,
prefilter=True,
use_gpu=False,
keep_size=False,
keep_size=True,
):
self.zoom = zoom
self.interp_order = interp_order
Expand Down Expand Up @@ -585,7 +585,7 @@ class RandZoom(Randomizable, Transform):
prefilter (bool): Apply spline_filter before interpolation. Default: True.
use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes
'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found.
keep_size (bool): Should keep original size (pad if needed).
keep_size (bool): Should keep original size (pad if needed), default is True.
"""

def __init__(
Expand All @@ -598,7 +598,7 @@ def __init__(
cval=0,
prefilter=True,
use_gpu=False,
keep_size=False,
keep_size=True,
):
if hasattr(min_zoom, "__iter__") and hasattr(max_zoom, "__iter__"):
assert len(min_zoom) == len(max_zoom), "min_zoom and max_zoom must have same length."
Expand Down
8 changes: 4 additions & 4 deletions monai/transforms/spatial/dictionary.py
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Original file line number Diff line number Diff line change
Expand Up @@ -714,7 +714,7 @@ class Zoomd(MapTransform):
prefilter (bool or sequence of bool): Apply spline_filter before interpolation. Default: True.
use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes
'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found.
keep_size (bool): Should keep original size (pad if needed).
keep_size (bool): Should keep original size (pad if needed), default is True.
"""

def __init__(
Expand All @@ -726,7 +726,7 @@ def __init__(
cval=0,
prefilter=True,
use_gpu=False,
keep_size=False,
keep_size=True,
):
super().__init__(keys)
self.zoomer = Zoom(zoom=zoom, use_gpu=use_gpu, keep_size=keep_size)
Expand Down Expand Up @@ -768,7 +768,7 @@ class RandZoomd(Randomizable, MapTransform):
prefilter (bool or sequence of bool): Apply spline_filter before interpolation. Default: True.
use_gpu (bool): Should use cpu or gpu. Uses cupyx which doesn't support order > 1 and modes
'wrap' and 'reflect'. Defaults to cpu for these cases or if cupyx not found.
keep_size (bool): Should keep original size (pad if needed).
keep_size (bool): Should keep original size (pad if needed), default is True.
"""

def __init__(
Expand All @@ -782,7 +782,7 @@ def __init__(
cval=0,
prefilter=True,
use_gpu=False,
keep_size=False,
keep_size=True,
):
super().__init__(keys)
if hasattr(min_zoom, "__iter__") and hasattr(max_zoom, "__iter__"):
Expand Down
10 changes: 4 additions & 6 deletions tests/test_integration_classification_2d.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,7 +30,6 @@
RandFlip,
RandRotate,
RandZoom,
Resize,
ScaleIntensity,
ToTensor,
)
Expand Down Expand Up @@ -62,10 +61,9 @@ def run_training_test(root_dir, train_x, train_y, val_x, val_y, device=torch.dev
LoadPNG(image_only=True),
AddChannel(),
ScaleIntensity(),
RandRotate(degrees=15, prob=0.5),
RandRotate(degrees=15, prob=0.5, reshape=False),
RandFlip(spatial_axis=0, prob=0.5),
RandZoom(min_zoom=0.9, max_zoom=1.1, prob=0.5),
Resize(spatial_size=(64, 64), mode="constant"),
ToTensor(),
]
)
Expand Down Expand Up @@ -223,11 +221,11 @@ def test_training(self):

# check training properties
np.testing.assert_allclose(
losses, [0.8501208358129878, 0.18469145818121113, 0.08108749352158255, 0.04965383692342005], rtol=1e-3
losses, [0.8009556981788319, 0.16794362251356149, 0.07708252014912617, 0.04769148252856959], rtol=1e-3
)
repeated[i].extend(losses)
print("best metric", best_metric)
np.testing.assert_allclose(best_metric, 0.9999480167572079, rtol=1e-4)
np.testing.assert_allclose(best_metric, 0.9999460507942981, rtol=1e-4)
repeated[i].append(best_metric)
np.testing.assert_allclose(best_metric_epoch, 4)
model_file = os.path.join(self.data_dir, "best_metric_model.pth")
Expand All @@ -236,7 +234,7 @@ def test_training(self):
infer_metric = run_inference_test(self.data_dir, self.test_x, self.test_y, device=self.device)

# check inference properties
np.testing.assert_allclose(np.asarray(infer_metric), [1036, 895, 982, 1033, 958, 1047], atol=1)
np.testing.assert_allclose(np.asarray(infer_metric), [1034, 895, 982, 1033, 961, 1047], atol=1)
repeated[i].extend(infer_metric)

np.testing.assert_allclose(repeated[0], repeated[1])
Expand Down