def test_export(self, key_in_ckpt):
meta_file = os.path.join(os.path.dirname(__file__), "testing_data",
"metadata.json")
config_file = os.path.join(os.path.dirname(__file__), "testing_data",
"inference.json")
with tempfile.TemporaryDirectory() as tempdir:
def_args = {"meta_file": "will be replaced by `meta_file` arg"}
def_args_file = os.path.join(tempdir, "def_args.json")
ckpt_file = os.path.join(tempdir, "model.pt")
ts_file = os.path.join(tempdir, "model.ts")
parser = ConfigParser()
parser.export_config_file(config=def_args, filepath=def_args_file)
parser.read_config(config_file)
net = parser.get_parsed_content("network_def")
save_state(src=net if key_in_ckpt == "" else {key_in_ckpt: net},
path=ckpt_file)
cmd = [
"coverage", "run", "-m", "monai.bundle", "ckpt_export",
"network_def", "--filepath", ts_file
]
cmd += [
"--meta_file", meta_file, "--config_file", config_file,
"--ckpt_file", ckpt_file
]
cmd += ["--key_in_ckpt", key_in_ckpt, "--args_file", def_args_file]
subprocess.check_call(cmd)
self.assertTrue(os.path.exists(ts_file))
def test_transforms(self, case_id):
set_determinism(2022)
config = ConfigParser()
config.read_config(TEST_CASES)
config["input_keys"] = keys
test_case = config.get_parsed_content(id=case_id, instantiate=True) # transform instance
dataset = CacheDataset(self.files, transform=test_case)
loader = DataLoader(dataset, batch_size=3, shuffle=True)
for x in loader:
self.assertIsInstance(x[keys[0]], MetaTensor)
self.assertIsInstance(x[keys[1]], MetaTensor)
out = decollate_batch(x) # decollate every batch should work
# test forward patches
loaded = out[0]
self.assertEqual(len(loaded), len(keys))
img, seg = loaded[keys[0]], loaded[keys[1]]
expected = config.get_parsed_content(id=f"{case_id}_answer", instantiate=True) # expected results
self.assertEqual(expected["load_shape"], list(x[keys[0]].shape))
assert_allclose(expected["affine"], img.affine, type_test=False, atol=TINY_DIFF, rtol=TINY_DIFF)
assert_allclose(expected["affine"], seg.affine, type_test=False, atol=TINY_DIFF, rtol=TINY_DIFF)
test_cls = [type(x).__name__ for x in test_case.transforms]
tracked_cls = [x[TraceKeys.CLASS_NAME] for x in img.applied_operations]
self.assertTrue(len(tracked_cls) <= len(test_cls)) # tracked items should be no more than the compose items.
with tempfile.TemporaryDirectory() as tempdir: # test writer
SaveImageD(keys, resample=False, output_dir=tempdir, output_postfix=case_id)(loaded)
# test inverse
inv = InvertD(keys, orig_keys=keys, transform=test_case, nearest_interp=True)
out = inv(loaded)
img, seg = out[keys[0]], out[keys[1]]
assert_allclose(expected["inv_affine"], img.affine, type_test=False, atol=TINY_DIFF, rtol=TINY_DIFF)
assert_allclose(expected["inv_affine"], seg.affine, type_test=False, atol=TINY_DIFF, rtol=TINY_DIFF)
self.assertFalse(img.applied_operations)
self.assertFalse(seg.applied_operations)
assert_allclose(expected["inv_shape"], img.shape, type_test=False, atol=TINY_DIFF, rtol=TINY_DIFF)
assert_allclose(expected["inv_shape"], seg.shape, type_test=False, atol=TINY_DIFF, rtol=TINY_DIFF)
with tempfile.TemporaryDirectory() as tempdir: # test writer
SaveImageD(keys, resample=False, output_dir=tempdir, output_postfix=case_id)(out)
seg_file = os.path.join(tempdir, key_1, f"{key_1}_{case_id}.nii.gz")
segout = nib.load(seg_file).get_fdata()
segin = nib.load(FILE_PATH_1).get_fdata()
ndiff = np.sum(np.abs(segout - segin) > 0)
total = np.prod(segout.shape)
self.assertTrue(ndiff / total < 0.4, f"{ndiff / total}")
