Port test_models_detection_negative_samples.py to pytest

test/test_models_detection_negative_samples.py

@@ -6,10 +6,11 @@
 from torchvision.models.detection.roi_heads import RoIHeads
 from torchvision.models.detection.faster_rcnn import FastRCNNPredictor, TwoMLPHead
 
-import unittest
+import pytest
+from _assert_utils import assert_equal
 
 
-class Tester(unittest.TestCase):
+class TestModelsDetectionNegativeSamples:
 
     def _make_empty_sample(self, add_masks=False, add_keypoints=False):
         images = [torch.rand((3, 100, 100), dtype=torch.float32)]
@@ -48,13 +49,13 @@ def test_targets_to_anchors(self):
 
         labels, matched_gt_boxes = head.assign_targets_to_anchors(anchors, targets)
 
-        self.assertEqual(labels[0].sum(), 0)
-        self.assertEqual(labels[0].shape, torch.Size([anchors[0].shape[0]]))
-        self.assertEqual(labels[0].dtype, torch.float32)
+        assert labels[0].sum() == 0
+        assert labels[0].shape == torch.Size([anchors[0].shape[0]])
+        assert labels[0].dtype == torch.float32
 
-        self.assertEqual(matched_gt_boxes[0].sum(), 0)
-        self.assertEqual(matched_gt_boxes[0].shape, anchors[0].shape)
-        self.assertEqual(matched_gt_boxes[0].dtype, torch.float32)
+        assert matched_gt_boxes[0].sum() == 0
+        assert matched_gt_boxes[0].shape == anchors[0].shape
+        assert matched_gt_boxes[0].dtype == torch.float32
 
     def test_assign_targets_to_proposals(self):
 
@@ -88,25 +89,28 @@ def test_assign_targets_to_proposals(self):
 
         matched_idxs, labels = roi_heads.assign_targets_to_proposals(proposals, gt_boxes, gt_labels)
 
-        self.assertEqual(matched_idxs[0].sum(), 0)
-        self.assertEqual(matched_idxs[0].shape, torch.Size([proposals[0].shape[0]]))
-        self.assertEqual(matched_idxs[0].dtype, torch.int64)
-
-        self.assertEqual(labels[0].sum(), 0)
-        self.assertEqual(labels[0].shape, torch.Size([proposals[0].shape[0]]))
-        self.assertEqual(labels[0].dtype, torch.int64)
-
-    def test_forward_negative_sample_frcnn(self):
-        for name in ["fasterrcnn_resnet50_fpn", "fasterrcnn_mobilenet_v3_large_fpn",
-                     "fasterrcnn_mobilenet_v3_large_320_fpn"]:
-            model = torchvision.models.detection.__dict__[name](
-                num_classes=2, min_size=100, max_size=100)
+        assert matched_idxs[0].sum() == 0
+        assert matched_idxs[0].shape == torch.Size([proposals[0].shape[0]])
+        assert matched_idxs[0].dtype == torch.int64
+
+        assert labels[0].sum() == 0
+        assert labels[0].shape == torch.Size([proposals[0].shape[0]])
+        assert labels[0].dtype == torch.int64
+
+    @pytest.mark.parametrize('name', [
+        "fasterrcnn_resnet50_fpn",
+        "fasterrcnn_mobilenet_v3_large_fpn",
+        "fasterrcnn_mobilenet_v3_large_320_fpn",
+    ])
+    def test_forward_negative_sample_frcnn(self, name):
+        model = torchvision.models.detection.__dict__[name](
+            num_classes=2, min_size=100, max_size=100)
 
-            images, targets = self._make_empty_sample()
-            loss_dict = model(images, targets)
+        images, targets = self._make_empty_sample()
+        loss_dict = model(images, targets)
 
-            self.assertEqual(loss_dict["loss_box_reg"], torch.tensor(0.))
-            self.assertEqual(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_box_reg"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
 
     def test_forward_negative_sample_mrcnn(self):
         model = torchvision.models.detection.maskrcnn_resnet50_fpn(
@@ -115,9 +119,9 @@ def test_forward_negative_sample_mrcnn(self):
         images, targets = self._make_empty_sample(add_masks=True)
         loss_dict = model(images, targets)
 
-        self.assertEqual(loss_dict["loss_box_reg"], torch.tensor(0.))
-        self.assertEqual(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
-        self.assertEqual(loss_dict["loss_mask"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_box_reg"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_mask"], torch.tensor(0.))
 
     def test_forward_negative_sample_krcnn(self):
         model = torchvision.models.detection.keypointrcnn_resnet50_fpn(
@@ -126,9 +130,9 @@ def test_forward_negative_sample_krcnn(self):
         images, targets = self._make_empty_sample(add_keypoints=True)
         loss_dict = model(images, targets)
 
-        self.assertEqual(loss_dict["loss_box_reg"], torch.tensor(0.))
-        self.assertEqual(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
-        self.assertEqual(loss_dict["loss_keypoint"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_box_reg"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_rpn_box_reg"], torch.tensor(0.))
+        assert_equal(loss_dict["loss_keypoint"], torch.tensor(0.))
 
     def test_forward_negative_sample_retinanet(self):
         model = torchvision.models.detection.retinanet_resnet50_fpn(
@@ -137,7 +141,7 @@ def test_forward_negative_sample_retinanet(self):
         images, targets = self._make_empty_sample()
         loss_dict = model(images, targets)
 
-        self.assertEqual(loss_dict["bbox_regression"], torch.tensor(0.))
+        assert_equal(loss_dict["bbox_regression"], torch.tensor(0.))
 
     def test_forward_negative_sample_ssd(self):
         model = torchvision.models.detection.ssd300_vgg16(
@@ -146,8 +150,8 @@ def test_forward_negative_sample_ssd(self):
         images, targets = self._make_empty_sample()
         loss_dict = model(images, targets)
 
-        self.assertEqual(loss_dict["bbox_regression"], torch.tensor(0.))
+        assert_equal(loss_dict["bbox_regression"], torch.tensor(0.))
 
 
 if __name__ == '__main__':
-    unittest.main()
+    pytest.main([__file__])