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update mmar tests #4091

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Merged
merged 3 commits into from
Apr 8, 2022
Merged

update mmar tests #4091

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ae46a03
update mmar tests
wyli Apr 8, 2022
625ec09
[pre-commit.ci] auto fixes from pre-commit.com hooks
pre-commit-ci[bot] Apr 8, 2022
d9418a7
fixes pylint error
wyli Apr 8, 2022
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29 changes: 27 additions & 2 deletions tests/ngc_mmar_loading.py
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Original file line number Diff line number Diff line change
Expand Up @@ -10,13 +10,15 @@
# limitations under the License.

import os
import sys
import unittest

import torch
from parameterized import parameterized

from monai.apps.mmars import MODEL_DESC, load_from_mmar
from monai.config import print_debug_info
from monai.networks.utils import copy_model_state


class TestAllDownloadingMMAR(unittest.TestCase):
Expand All @@ -26,10 +28,33 @@ def setUp(self):

@parameterized.expand((item,) for item in MODEL_DESC)
def test_loading_mmar(self, item):
if item["name"] == "clara_pt_self_supervised_learning_segmentation": # test the byow model
default_model_file = os.path.join("ssl_models_2gpu", "best_metric_model.pt")
pretrained_weights = load_from_mmar(
item=item["name"],
mmar_dir="./",
map_location="cpu",
api=True,
model_file=default_model_file,
weights_only=True,
)
pretrained_weights = {k.split(".", 1)[1]: v for k, v in pretrained_weights["state_dict"].items()}
sys.path.append(os.path.join(f"{item['name']}", "custom")) # custom model folder
from vit_network import ViTAutoEnc # pylint: disable=E0401

model = ViTAutoEnc(
in_channels=1,
img_size=(96, 96, 96),
patch_size=(16, 16, 16),
pos_embed="conv",
hidden_size=768,
mlp_dim=3072,
)
_, loaded, not_loaded = copy_model_state(model, pretrained_weights)
self.assertTrue(len(loaded) > 0 and len(not_loaded) == 0)
return
if item["name"] == "clara_pt_fed_learning_brain_tumor_mri_segmentation":
default_model_file = os.path.join("models", "server", "best_FL_global_model.pt")
elif item["name"] == "clara_pt_self_supervised_learning_segmentation":
default_model_file = os.path.join("models_2gpu", "best_metric_model.pt")
else:
default_model_file = None
pretrained_model = load_from_mmar(
Expand Down