def test_assert(func, params, expected):
gpu_test = ("torch_gpu",) if torch.cuda.is_available() else ()
for b in ("torch", "numpy") + gpu_test:
if b == "torch_gpu":
m = func(*params, device="cuda:0", backend="torch")
else:
m = func(*params, backend=b)
assert_allclose(m, expected, type_test=False, rtol=1e-2 if is_tf32_env() else 1e-5, atol=1e-5)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from parameterized import parameterized
from monai.networks import normalize_transform, to_norm_affine
from monai.networks.layers import AffineTransform
from tests.utils import is_tf32_env
_rtol = 1e-4 if not is_tf32_env() else 5e-3
TEST_NORM_CASES = [
[(4, 5), True, [[[0.666667, 0, -1], [0, 0.5, -1], [0, 0, 1]]]],
[
(2, 4, 5),
True,
[[[2.0, 0.0, 0.0, -1.0], [0.0, 0.6666667, 0.0, -1.0],
[0.0, 0.0, 0.5, -1.0], [0.0, 0.0, 0.0, 1.0]]],
],
[(4, 5), False, [[[0.5, 0.0, -0.75], [0.0, 0.4, -0.8], [0.0, 0.0, 1.0]]]],
[(2, 4, 5), False,
[[[1.0, 0.0, 0.0, -0.5], [0.0, 0.5, 0.0, -0.75], [0.0, 0.0, 0.4, -0.8],
[0.0, 0.0, 0.0, 1.0]]]],
]
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import numpy as np
import torch
from parameterized import parameterized
from monai.transforms import RandAffineGrid
from tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env
_rtol = 1e-1 if is_tf32_env() else 1e-4
TESTS = []
for p in TEST_NDARRAYS:
for device in [None, "cpu", "cuda"
] if torch.cuda.is_available() else [None, "cpu"]:
TESTS.append([{
"device": device
}, {
"grid": p(torch.ones((3, 3, 3)))
},
p(np.ones((3, 3, 3)))])
TESTS.append([
{
"rotate_range": (1, 2),
"translate_range": (3, 3, 3)
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
from itertools import product
import numpy as np
import torch
from parameterized import parameterized
from monai.transforms import RandSmoothDeformd, RandSmoothFieldAdjustContrastd, RandSmoothFieldAdjustIntensityd
from tests.utils import TEST_NDARRAYS, assert_allclose, is_tf32_env
_rtol = 5e-3 if is_tf32_env() else 1e-4
INPUT_SHAPES = ((1, 8, 8), (2, 8, 8), (1, 8, 8, 8))
TESTS_CONTRAST = []
TESTS_INTENSITY = []
TESTS_DEFORM = []
KEY = "test"
for arr_type, shape in product(TEST_NDARRAYS, INPUT_SHAPES):
in_arr = arr_type(np.ones(shape, np.float32))
exp_arr = arr_type(np.ones(shape, np.float32))
rand_size = (4, ) * (len(shape) - 1)
device = torch.device("cpu")
TEST_CASE_2 = [{
"spatial_size": 6,
"mode": "trilinear",
"align_corners": True
}, (2, 4, 6)]
TEST_CASE_3 = [{"spatial_size": 15, "anti_aliasing": True}, (6, 10, 15)]
TEST_CASE_4 = [{
"spatial_size": 6,
"anti_aliasing": True,
"anti_aliasing_sigma": 2.0
}, (2, 4, 6)]
diff_t = 0.3 if is_tf32_env() else 0.2
class TestResize(NumpyImageTestCase2D):
def test_invalid_inputs(self):
with self.assertRaises(ValueError):
resize = Resize(spatial_size=(128, 128, 3), mode="order")
resize(self.imt[0])
with self.assertRaises(ValueError):
resize = Resize(spatial_size=(128, ), mode="order")
resize(self.imt[0])
@parameterized.expand([
((32, -1), "area", True),
((32, 32), "area", False),
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import unittest
import torch
from parameterized import parameterized
from monai.networks.layers import LLTM
from tests.utils import SkipIfNoModule, is_tf32_env
_rtol = 0.001 if is_tf32_env() else 0.0001
TEST_CASE_1 = [
{
"input_features": 32,
"state_size": 2
},
torch.tensor([[-0.1622, 0.1663], [0.5465, 0.0459], [-0.1436, 0.6171],
[0.3632, -0.0111]]),
torch.tensor([[-1.3773, 0.3348], [0.8353, 1.3064], [-0.2179, 4.1739],
[1.3045, -0.1444]]),
]
class TestLLTM(unittest.TestCase):
@parameterized.expand([TEST_CASE_1])
