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Fix integration test in 24.10 (#8480)
Fixes #8479 ### Description A few sentences describing the changes proposed in this pull request. ### Types of changes <!--- Put an `x` in all the boxes that apply, and remove the not applicable items --> - [x] Non-breaking change (fix or new feature that would not break existing functionality). - [ ] Breaking change (fix or new feature that would cause existing functionality to change). - [ ] New tests added to cover the changes. - [ ] Integration tests passed locally by running `./runtests.sh -f -u --net --coverage`. - [ ] Quick tests passed locally by running `./runtests.sh --quick --unittests --disttests`. - [ ] In-line docstrings updated. - [ ] Documentation updated, tested `make html` command in the `docs/` folder. --------- Signed-off-by: YunLiu <55491388+KumoLiu@users.noreply.github.com>
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‎tests/integration/test_integration_classification_2d.py

Lines changed: 2 additions & 2 deletions
Original file line numberDiff line numberDiff line change
@@ -256,7 +256,7 @@ def train_and_infer(self, idx=0):
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# check training properties
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self.assertTrue(test_integration_value(TASK, key="losses", data=losses, rtol=1e-2))
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self.assertTrue(test_integration_value(TASK, key="best_metric", data=best_metric, rtol=1e-4))
259-
np.testing.assert_allclose(best_metric_epoch, 4)
259+
np.testing.assert_allclose(best_metric_epoch, 1)
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model_file = os.path.join(self.data_dir, "best_metric_model.pth")
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self.assertTrue(os.path.exists(model_file))
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# check inference properties
@@ -268,7 +268,7 @@ def train_and_infer(self, idx=0):
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def test_training(self):
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repeated = []
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for i in range(1):
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for i in range(2):
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results = self.train_and_infer(i)
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repeated.append(results)
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np.testing.assert_allclose(repeated[0], repeated[1])

‎tests/integration/test_integration_workflows_adversarial.py

Lines changed: 1 addition & 1 deletion
Original file line numberDiff line numberDiff line change
@@ -158,7 +158,7 @@ def tearDown(self):
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set_determinism(seed=None)
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shutil.rmtree(self.data_dir)
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@TimedCall(seconds=200, daemon=False)
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@TimedCall(seconds=300, daemon=False)
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def test_training(self):
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torch.manual_seed(0)
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‎tests/testing_data/integration_answers.py

Lines changed: 97 additions & 58 deletions
Original file line numberDiff line numberDiff line change
@@ -70,64 +70,6 @@
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],
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}
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},
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{ # test answers for PyTorch 1.13
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"integration_workflows": {
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"output_sums_2": [
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0.14264830205979873,
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84-
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85-
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0.11437829790254125,
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0.19424317961257148,
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0.18121074825540173,
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0.19064286213535897,
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0.0747544243069024,
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]
117-
},
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"integration_segmentation_3d": { # for the mixed readers
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"losses": [
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0.5451162219047546,
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0.4709601759910583,
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0.45201429128646853,
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0.4443251401185989,
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0.4341257899999619,
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0.4350819975137711,
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],
127-
"best_metric": 0.9316844940185547,
128-
"infer_metric": 0.9316383600234985,
129-
},
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},
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{ # test answers for cuda 12
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"integration_segmentation_3d": {
13375
"losses": [
@@ -296,6 +238,103 @@
296238
],
297239
}
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},
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{ # test answers for 24.10
242+
"integration_classification_2d": {
243+
"losses": 0.7806512035761669,
244+
"best_metric": 0.9977695200407783,
245+
"infer_prop": [805, 727, 955, 1033, 321, 993],
246+
},
247+
"integration_workflows": {
248+
"best_metric": 0.9207136034965515,
249+
"best_metric_2": 0.9216295480728149,
250+
"infer_metric": 0.920440673828125,
251+
"infer_metric_2": 0.9203161001205444,
252+
"output_sums": [
253+
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254+
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],
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"output_sums_2": [
295+
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297+
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335+
],
336+
},
337+
},
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]
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