def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
transform = Compose([
LoadImaged(keys="image"),
AddChanneld(keys="image"),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
def _test_dataset(dataset):
self.assertEqual(len(dataset), 5986)
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue("image_meta_dict" in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 64, 64))
try: # will start downloading if testing_dir doesn't have the MedNIST files
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=True)
except (ContentTooShortError, HTTPError, RuntimeError) as e:
print(str(e))
if isinstance(e, RuntimeError):
# FIXME: skip MD5 check as current downloading method may fail
self.assertTrue(str(e).startswith("md5 check"))
return # skipping this test due the network connection errors
_test_dataset(data)
# testing from
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False)
data.get_num_classes()
_test_dataset(data)
data = MedNISTDataset(root_dir=testing_dir,
section="test",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (64, 64))
shutil.rmtree(os.path.join(testing_dir, "MedNIST"))
try:
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False)
except RuntimeError as e:
print(str(e))
self.assertTrue(str(e).startswith("Cannot find dataset directory"))
def test_lr_finder(self):
# 0.001 gives 54 examples
train_ds = MedNISTDataset(
root_dir=self.root_dir,
transform=self.transforms,
section="validation",
val_frac=0.001,
download=True,
num_workers=10,
)
train_loader = DataLoader(train_ds, batch_size=300, shuffle=True, num_workers=10)
num_classes = train_ds.get_num_classes()
model = DenseNet(
spatial_dims=2, in_channels=1, out_channels=num_classes, init_features=2, growth_rate=2, block_config=(2,)
)
loss_function = torch.nn.CrossEntropyLoss()
learning_rate = 1e-5
optimizer = torch.optim.Adam(model.parameters(), learning_rate)
lr_finder = LearningRateFinder(model, optimizer, loss_function, device=device)
lr_finder.range_test(train_loader, val_loader=train_loader, end_lr=10, num_iter=5)
print(lr_finder.get_steepest_gradient(0, 0)[0])
if has_matplotlib:
ax = plt.subplot()
plt.show(block=False)
lr_finder.plot(0, 0, ax=ax) # to inspect the loss-learning rate graph
plt.pause(3)
plt.close()
lr_finder.reset() # to reset the model and optimizer to their initial state
def test_values(self):
testing_dir = os.path.join(os.path.dirname(os.path.realpath(__file__)),
"testing_data")
transform = Compose([
LoadImaged(keys="image"),
AddChanneld(keys="image"),
ScaleIntensityd(keys="image"),
ToTensord(keys=["image", "label"]),
])
def _test_dataset(dataset):
self.assertEqual(
len(dataset),
int(MEDNIST_FULL_DATASET_LENGTH * dataset.test_frac))
self.assertTrue("image" in dataset[0])
self.assertTrue("label" in dataset[0])
self.assertTrue(PostFix.meta("image") in dataset[0])
self.assertTupleEqual(dataset[0]["image"].shape, (1, 64, 64))
with skip_if_downloading_fails():
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=True,
copy_cache=False)
_test_dataset(data)
# testing from
data = MedNISTDataset(root_dir=Path(testing_dir),
transform=transform,
section="test",
download=False)
self.assertEqual(data.get_num_classes(), 6)
_test_dataset(data)
data = MedNISTDataset(root_dir=testing_dir,
section="test",
download=False)
self.assertTupleEqual(data[0]["image"].shape, (64, 64))
# test same dataset length with different random seed
data = MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False,
seed=42)
_test_dataset(data)
self.assertEqual(data[0]["class_name"], "AbdomenCT")
self.assertEqual(data[0]["label"].cpu().item(), 0)
shutil.rmtree(os.path.join(testing_dir, "MedNIST"))
try:
MedNISTDataset(root_dir=testing_dir,
transform=transform,
section="test",
download=False)
except RuntimeError as e:
print(str(e))
self.assertTrue(str(e).startswith("Cannot find dataset directory"))