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Add more unit tests (Project-MONAI#483)
Fixes # . ### Description This PR is used to add unit tests on the following bundles: - [x] endoscopic_tool_segmentation - [x] pathology_nuclei_segmentation_classification - [x] wholeBody_ct_segmentation - [x] pathology_nuclei_classification - [x] pathology_nuclick_annotation - [x] lung_nodule_ct_detection ### Status **Ready/Work in progress/Hold** ### Please ensure all the checkboxes: <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Codeformat tests passed locally by running `./runtests.sh --codeformat`. - [ ] In-line docstrings updated. - [ ] Update `version` and `changelog` in `metadata.json` if changing an existing bundle. - [ ] Please ensure the naming rules in config files meet our requirements (please refer to: `CONTRIBUTING.md`). - [ ] Ensure versions of packages such as `monai`, `pytorch` and `numpy` are correct in `metadata.json`. - [ ] Descriptions should be consistent with the content, such as `eval_metrics` of the provided weights and TorchScript modules. - [ ] Files larger than 25MB are excluded and replaced by providing download links in `large_file.yml`. - [ ] Avoid using path that contains personal information within config files (such as use `/home/your_name/` for `"bundle_root"`). --------- Signed-off-by: KumoLiu <[email protected]> Signed-off-by: Yiheng Wang <[email protected]> Co-authored-by: Yiheng Wang <[email protected]> Co-authored-by: Yiheng Wang <[email protected]> Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
1 parent 0f70ac3 commit 0420991

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‎.pre-commit-config.yaml‎

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autoupdate_schedule: quarterly
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# submodules: true
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exclude: '.*\.ipynb$'
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repos:
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- repo: https://github.com/pre-commit/pre-commit-hooks
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rev: v4.4.0

‎ci/run_premerge_cpu.sh‎

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@@ -39,6 +39,8 @@ remove_pipenv() {
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echo "removing pip environment"
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pipenv --rm
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rm Pipfile Pipfile.lock
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pipenv --clear
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df -h
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}
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verify_bundle() {

‎ci/unit_tests/test_endoscopic_tool_segmentation.py‎

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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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from monai.bundle import ConfigWorkflow
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from monai.data import PILWriter
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from parameterized import parameterized
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from utils import check_workflow
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TEST_CASE_1 = [ # train, evaluate
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{
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"bundle_root": "models/endoscopic_tool_segmentation",
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"train#trainer#max_epochs": 2,
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"train#dataloader#num_workers": 1,
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"validate#dataloader#num_workers": 1,
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"train#deterministic_transforms#3#spatial_size": [32, 32],
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}
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]
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TEST_CASE_2 = [ # inference
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{
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"bundle_root": "models/endoscopic_tool_segmentation",
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"handlers#0#_disabled_": True,
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"preprocessing#transforms#2#spatial_size": [32, 32],
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}
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]
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class TestEndoscopicSeg(unittest.TestCase):
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def setUp(self):
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self.dataset_dir = tempfile.mkdtemp()
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dataset_size = 10
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writer = PILWriter(np.uint8)
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shape = (736, 480)
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for mode in ["train", "val", "test"]:
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for sub_folder in ["inbody", "outbody"]:
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sample_dir = os.path.join(self.dataset_dir, f"{mode}/{sub_folder}")
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os.makedirs(sample_dir)
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for s in range(dataset_size):
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image = np.random.randint(low=0, high=5, size=(3, *shape)).astype(np.int8)
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image_filename = os.path.join(sample_dir, f"{sub_folder}_{s}.jpg")
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writer.set_data_array(image, channel_dim=0)
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writer.write(image_filename, verbose=True)
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if mode != "test":
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label = np.random.randint(low=0, high=5, size=shape).astype(np.int8)
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label_filename = os.path.join(sample_dir, f"{sub_folder}_{s}_seg.jpg")
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writer.set_data_array(label, channel_dim=None)
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writer.write(label_filename, verbose=True)
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def tearDown(self):
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shutil.rmtree(self.dataset_dir)
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@parameterized.expand([TEST_CASE_1])
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def test_train_eval_config(self, override):
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override["dataset_dir"] = self.dataset_dir
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bundle_root = override["bundle_root"]
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train_file = os.path.join(bundle_root, "configs/train.json")
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eval_file = os.path.join(bundle_root, "configs/evaluate.json")
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trainer = ConfigWorkflow(
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workflow="train",
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config_file=train_file,
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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**override,
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)
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check_workflow(trainer, check_properties=True)
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validator = ConfigWorkflow(
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# override train.json, thus set the workflow to "train" rather than "eval"
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workflow="train",
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config_file=[train_file, eval_file],
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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**override,
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)
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check_workflow(validator, check_properties=True)
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@parameterized.expand([TEST_CASE_2])
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def test_infer_config(self, override):
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override["dataset_dir"] = self.dataset_dir
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bundle_root = override["bundle_root"]
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inferrer = ConfigWorkflow(
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workflow="infer",
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config_file=os.path.join(bundle_root, "configs/inference.json"),
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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**override,
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)
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check_workflow(inferrer, check_properties=True)
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if __name__ == "__main__":
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unittest.main()

‎ci/unit_tests/test_endoscopic_tool_segmentation_dist.py‎

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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import torch
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from monai.data import PILWriter
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from parameterized import parameterized
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from utils import export_config_and_run_mgpu_cmd
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TEST_CASE_1 = [
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{
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"bundle_root": "models/endoscopic_tool_segmentation",
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"train#trainer#max_epochs": 1,
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"train#dataloader#num_workers": 1,
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"validate#dataloader#num_workers": 1,
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"train#deterministic_transforms#3#spatial_size": [32, 32],
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}
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]
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TEST_CASE_2 = [
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{
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"bundle_root": "models/endoscopic_tool_segmentation",
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"validate#dataloader#num_workers": 4,
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"train#deterministic_transforms#3#spatial_size": [32, 32],
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}
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]
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def test_order(test_name1, test_name2):
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def get_order(name):
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if "train" in name:
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return 1
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if "eval" in name:
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return 2
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if "infer" in name:
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return 3
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return 4
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return get_order(test_name1) - get_order(test_name2)
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class TestEndoscopicSegMGPU(unittest.TestCase):
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def setUp(self):
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self.dataset_dir = tempfile.mkdtemp()
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dataset_size = 10
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writer = PILWriter(np.uint8)
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shape = (3, 256, 256)
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for mode in ["train", "val"]:
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for sub_folder in ["inbody", "outbody"]:
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sample_dir = os.path.join(self.dataset_dir, f"{mode}/{sub_folder}")
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os.makedirs(sample_dir)
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for s in range(dataset_size):
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image = np.random.randint(low=0, high=5, size=(3, *shape)).astype(np.int8)
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image_filename = os.path.join(sample_dir, f"{sub_folder}_{s}.jpg")
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writer.set_data_array(image, channel_dim=0)
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writer.write(image_filename, verbose=True)
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label = np.random.randint(low=0, high=5, size=shape).astype(np.int8)
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label_filename = os.path.join(sample_dir, f"{sub_folder}_{s}_seg.jpg")
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writer.set_data_array(label, channel_dim=None)
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writer.write(label_filename, verbose=True)
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def tearDown(self):
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shutil.rmtree(self.dataset_dir)
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@parameterized.expand([TEST_CASE_1])
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def test_train_mgpu_config(self, override):
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override["dataset_dir"] = self.dataset_dir
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bundle_root = override["bundle_root"]
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train_file = os.path.join(bundle_root, "configs/train.json")
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mgpu_train_file = os.path.join(bundle_root, "configs/multi_gpu_train.json")
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output_path = os.path.join(bundle_root, "configs/train_override.json")
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n_gpu = torch.cuda.device_count()
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export_config_and_run_mgpu_cmd(
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config_file=[train_file, mgpu_train_file],
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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override_dict=override,
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output_path=output_path,
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ngpu=n_gpu,
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check_config=True,
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)
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@parameterized.expand([TEST_CASE_2])
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def test_evaluate_mgpu_config(self, override):
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override["dataset_dir"] = self.dataset_dir
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bundle_root = override["bundle_root"]
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train_file = os.path.join(bundle_root, "configs/train.json")
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evaluate_file = os.path.join(bundle_root, "configs/evaluate.json")
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mgpu_evaluate_file = os.path.join(bundle_root, "configs/multi_gpu_evaluate.json")
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output_path = os.path.join(bundle_root, "configs/evaluate_override.json")
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n_gpu = torch.cuda.device_count()
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export_config_and_run_mgpu_cmd(
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config_file=[train_file, evaluate_file, mgpu_evaluate_file],
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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override_dict=override,
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output_path=output_path,
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ngpu=n_gpu,
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check_config=True,
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)
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if __name__ == "__main__":
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loader = unittest.TestLoader()
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loader.sortTestMethodsUsing = test_order
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unittest.main(testLoader=loader)

‎ci/unit_tests/test_lung_nodule_ct_detection.py‎

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# Copyright (c) MONAI Consortium
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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# http://www.apache.org/licenses/LICENSE-2.0
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import json
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import os
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import shutil
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import sys
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import tempfile
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import unittest
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import nibabel as nib
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import numpy as np
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from monai.bundle import ConfigWorkflow
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from monai.data import create_test_image_3d
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from monai.utils import set_determinism
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from parameterized import parameterized
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from utils import check_workflow
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set_determinism(123)
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TEST_CASE_1 = [ # train, evaluate
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{
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"bundle_root": "models/lung_nodule_ct_detection",
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"epochs": 3,
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"batch_size": 1,
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"val_interval": 2,
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"train#dataloader#num_workers": 1,
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"validate#dataloader#num_workers": 1,
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}
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]
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TEST_CASE_2 = [{"bundle_root": "models/lung_nodule_ct_detection"}] # inference
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def test_order(test_name1, test_name2):
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def get_order(name):
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if "train" in name:
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return 1
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if "eval" in name:
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return 2
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if "infer" in name:
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return 3
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return 4
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return get_order(test_name1) - get_order(test_name2)
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class TestLungNoduleDetection(unittest.TestCase):
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def setUp(self):
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self.dataset_dir = tempfile.mkdtemp()
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dataset_size = 3
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train_patch_size = (192, 192, 80)
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dataset_json = {}
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img, _ = create_test_image_3d(train_patch_size[0], train_patch_size[1], train_patch_size[2], 2)
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image_filename = os.path.join(self.dataset_dir, "image.nii.gz")
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nib.save(nib.Nifti1Image(img, np.eye(4)), image_filename)
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label = [0, 0]
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box = [[108, 119, 131, 142, 26, 37], [132, 147, 149, 164, 25, 40]]
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data = {"box": box, "image": image_filename, "label": label}
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dataset_json["training"] = [data for _ in range(dataset_size)]
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dataset_json["validation"] = [data for _ in range(dataset_size)]
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self.ds_file = os.path.join(self.dataset_dir, "dataset.json")
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with open(self.ds_file, "w") as fp:
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json.dump(dataset_json, fp, indent=2)
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def tearDown(self):
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shutil.rmtree(self.dataset_dir)
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@parameterized.expand([TEST_CASE_1])
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def test_train_eval_config(self, override):
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override["dataset_dir"] = self.dataset_dir
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override["data_list_file_path"] = self.ds_file
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bundle_root = override["bundle_root"]
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train_file = os.path.join(bundle_root, "configs/train.json")
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eval_file = os.path.join(bundle_root, "configs/evaluate.json")
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sys.path.append(bundle_root)
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trainer = ConfigWorkflow(
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workflow="train",
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config_file=train_file,
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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**override,
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)
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check_workflow(trainer, check_properties=False)
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validator = ConfigWorkflow(
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# override train.json, thus set the workflow to "train" rather than "eval"
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workflow="train",
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config_file=[train_file, eval_file],
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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**override,
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)
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check_workflow(validator, check_properties=False)
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@parameterized.expand([TEST_CASE_2])
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def test_infer_config(self, override):
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override["dataset_dir"] = self.dataset_dir
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override["data_list_file_path"] = self.ds_file
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bundle_root = override["bundle_root"]
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inferrer = ConfigWorkflow(
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workflow="infer",
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config_file=os.path.join(bundle_root, "configs/inference.json"),
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logging_file=os.path.join(bundle_root, "configs/logging.conf"),
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meta_file=os.path.join(bundle_root, "configs/metadata.json"),
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**override,
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)
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check_workflow(inferrer, check_properties=True)
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if __name__ == "__main__":
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loader = unittest.TestLoader()
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loader.sortTestMethodsUsing = test_order
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unittest.main(testLoader=loader)

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