Merge pull request atong01#80 from atong01/add_tests

Add tests on lambda_t, low sigma values and guided functions

Author: kilianFatras

.github/workflows/test.yaml

@@ -62,11 +62,10 @@ jobs:
           pip install -e .
 
       - name: Run tests and collect coverage
-        run: pytest . --cov torchcfm --ignore=runner --ignore=examples --ignore=torchcfm/models/
+        run: pytest . --cov torchcfm --ignore=runner --ignore=examples --ignore=torchcfm/models/ --cov-fail-under=30
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3
         with:
           name: codecov-torchcfm
           verbose: true
-          fail_ci_if_error: true

.github/workflows/test_runner.yaml

@@ -64,11 +64,10 @@ jobs:
           pip install -e .
 
       - name: Run tests and collect coverage
-        run: pytest runner --cov runner # NEEDS TO BE UPDATED WHEN CHANGING THE NAME OF "src" FOLDER
+        run: pytest runner --cov runner --cov-fail-under=30 # NEEDS TO BE UPDATED WHEN CHANGING THE NAME OF "src" FOLDER
 
       - name: Upload coverage to Codecov
         uses: codecov/codecov-action@v3
         with:
           name: codecov-runner
           verbose: true
-          fail_ci_if_error: true

tests/test_conditional_flow_matcher.py

@@ -92,7 +92,7 @@ def sample_plan(method, x0, x1, sigma):
 
 @pytest.mark.parametrize("method", ["vp_cfm", "t_cfm", "sb_cfm", "exact_ot_cfm", "i_cfm"])
 # Test both integer and floating sigma
-@pytest.mark.parametrize("sigma", [0.0, 0.5, 1.5, 0, 1])
+@pytest.mark.parametrize("sigma", [0.0, 5e-4, 0.5, 1.5, 0, 1])
 @pytest.mark.parametrize("shape", [[1], [2], [1, 2], [3, 4, 5]])
 def test_fm(method, sigma, shape):
     batch_size = TEST_BATCH_SIZE
@@ -107,6 +107,7 @@ def test_fm(method, sigma, shape):
     torch.manual_seed(TEST_SEED)
     np.random.seed(TEST_SEED)
     t, xt, ut, eps = FM.sample_location_and_conditional_flow(x0, x1, return_noise=True)
+    _ = FM.compute_lambda(t)
 
     if method in ["sb_cfm", "exact_ot_cfm"]:
         torch.manual_seed(TEST_SEED)

tests/test_time_t.py

@@ -49,7 +49,8 @@ def test_random_Tensor_t(FM):
         SchrodingerBridgeConditionalFlowMatcher(sigma=0.1),
     ],
 )
-def test_guided_random_Tensor_t(FM):
+@pytest.mark.parametrize("return_noise", [True, False])
+def test_guided_random_Tensor_t(FM, return_noise):
     # Test guided_sample_location_and_conditional_flow functions
     x0 = torch.randn(batch_size, 2)
     y0 = torch.randint(high=10, size=(batch_size, 1))
@@ -58,13 +59,13 @@ def test_guided_random_Tensor_t(FM):
 
     torch.manual_seed(seed)
     t_given = torch.rand(batch_size)
-    t_given, xt, ut, y0, y1 = FM.guided_sample_location_and_conditional_flow(
-        x0, x1, y0=y0, y1=y1, t=t_given
-    )
+    t_given = FM.guided_sample_location_and_conditional_flow(
+        x0, x1, y0=y0, y1=y1, t=t_given, return_noise=return_noise
+    )[0]
 
     torch.manual_seed(seed)
-    t_random, xt, ut, y0, y1 = FM.guided_sample_location_and_conditional_flow(
-        x0, x1, y0=y0, y1=y1, t=None
-    )
+    t_random = FM.guided_sample_location_and_conditional_flow(
+        x0, x1, y0=y0, y1=y1, t=None, return_noise=return_noise
+    )[0]
 
     assert any(t_given == t_random)