Use pytest parametrize cross product (#1504)

* Use pytest parametrize cross product

* Bug fixes

tests/models/test_changestar.py

@@ -1,8 +1,6 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-import itertools
-
 import pytest
 import torch
 import torch.nn as nn
@@ -56,9 +54,10 @@ class TestChangeStar:
             ChangeStarFarSeg(classes=4, backbone="anynet", backbone_pretrained=False)
 
     @torch.no_grad()
-    @pytest.mark.parametrize(
-        "inc,innerc,nc,sf", list(itertools.product(IN_CHANNELS, INNNR_CHANNELS, NC, SF))
-    )
+    @pytest.mark.parametrize("inc", IN_CHANNELS)
+    @pytest.mark.parametrize("innerc", INNNR_CHANNELS)
+    @pytest.mark.parametrize("nc", NC)
+    @pytest.mark.parametrize("sf", SF)
     def test_changemixin_output_size(
         self, inc: int, innerc: int, nc: int, sf: int
     ) -> None:

tests/models/test_fcsiam.py

@@ -1,8 +1,6 @@
 # Copyright (c) Microsoft Corporation. All rights reserved.
 # Licensed under the MIT License.
 
-import itertools
-
 import pytest
 import torch
 
@@ -15,7 +13,8 @@ CLASSES = [1, 2]
 
 class TestFCSiamConc:
     @torch.no_grad()
-    @pytest.mark.parametrize("b, c", list(itertools.product(BATCH_SIZE, CHANNELS)))
+    @pytest.mark.parametrize("b", BATCH_SIZE)
+    @pytest.mark.parametrize("c", CHANNELS)
     def test_in_channels(self, b: int, c: int) -> None:
         classes = 2
         t, h, w = 2, 64, 64
@@ -25,7 +24,8 @@ class TestFCSiamConc:
         assert y.shape == (b, classes, h, w)
 
     @torch.no_grad()
-    @pytest.mark.parametrize("b, classes", list(itertools.product(BATCH_SIZE, CLASSES)))
+    @pytest.mark.parametrize("b", BATCH_SIZE)
+    @pytest.mark.parametrize("classes", CLASSES)
     def test_classes(self, b: int, classes: int) -> None:
         t, c, h, w = 2, 3, 64, 64
         model = FCSiamConc(in_channels=3, classes=classes, encoder_weights=None)
@@ -36,7 +36,8 @@ class TestFCSiamConc:
 
 class TestFCSiamDiff:
     @torch.no_grad()
-    @pytest.mark.parametrize("b, c", list(itertools.product(BATCH_SIZE, CHANNELS)))
+    @pytest.mark.parametrize("b", BATCH_SIZE)
+    @pytest.mark.parametrize("c", CHANNELS)
     def test_in_channels(self, b: int, c: int) -> None:
         classes = 2
         t, h, w = 2, 64, 64
@@ -46,7 +47,8 @@ class TestFCSiamDiff:
         assert y.shape == (b, classes, h, w)
 
     @torch.no_grad()
-    @pytest.mark.parametrize("b, classes", list(itertools.product(BATCH_SIZE, CLASSES)))
+    @pytest.mark.parametrize("b", BATCH_SIZE)
+    @pytest.mark.parametrize("classes", CLASSES)
     def test_classes(self, b: int, classes: int) -> None:
         t, c, h, w = 2, 3, 64, 64
         model = FCSiamDiff(in_channels=3, classes=classes, encoder_weights=None)