Consolidate test_forward and test_forward2

tests/test_forward.py

@@ -168,6 +168,111 @@ def test_retina_ghm_forward():
                 batch_results.append(result)
 
 
+def test_cascade_empty_forward():
+    try:
+        from torchvision import _C as C  # NOQA
+    except ImportError:
+        import pytest
+        raise pytest.skip('requires torchvision on cpu')
+
+    model, train_cfg, test_cfg = _get_detector_cfg(
+        'cascade_rcnn_r50_fpn_1x.py')
+    model['pretrained'] = None
+    # torchvision roi align supports CPU
+    model['bbox_roi_extractor']['roi_layer']['use_torchvision'] = True
+
+    from mmdet.models import build_detector
+    detector = build_detector(model, train_cfg=train_cfg, test_cfg=test_cfg)
+
+    input_shape = (1, 3, 256, 256)
+
+    # Test forward train with an empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    from mmdet.apis.train import parse_losses
+    total_loss = float(parse_losses(losses)[0].item())
+    assert total_loss > 0
+
+    # Test forward train with a non-empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[10])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    from mmdet.apis.train import parse_losses
+    total_loss = float(parse_losses(losses)[0].item())
+    assert total_loss > 0
+
+
+def test_faster_rcnn_empty_forward():
+    try:
+        from torchvision import _C as C  # NOQA
+    except ImportError:
+        import pytest
+        raise pytest.skip('requires torchvision on cpu')
+
+    model, train_cfg, test_cfg = _get_detector_cfg('faster_rcnn_r50_fpn_1x.py')
+    model['pretrained'] = None
+    # torchvision roi align supports CPU
+    model['bbox_roi_extractor']['roi_layer']['use_torchvision'] = True
+
+    from mmdet.models import build_detector
+    detector = build_detector(model, train_cfg=train_cfg, test_cfg=test_cfg)
+
+    input_shape = (1, 3, 256, 256)
+
+    # Test forward train with an empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[0])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    from mmdet.apis.train import parse_losses
+    total_loss = float(parse_losses(losses)[0].item())
+    assert total_loss > 0
+
+    # Test forward train with a non-empty truth batch
+    mm_inputs = _demo_mm_inputs(input_shape, num_items=[10])
+    imgs = mm_inputs.pop('imgs')
+    img_metas = mm_inputs.pop('img_metas')
+    gt_bboxes = mm_inputs['gt_bboxes']
+    gt_labels = mm_inputs['gt_labels']
+    losses = detector.forward(
+        imgs,
+        img_metas,
+        gt_bboxes=gt_bboxes,
+        gt_labels=gt_labels,
+        return_loss=True)
+    assert isinstance(losses, dict)
+    from mmdet.apis.train import parse_losses
+    total_loss = float(parse_losses(losses)[0].item())
+    assert total_loss > 0
+
+
 def _demo_mm_inputs(
         input_shape=(1, 3, 300, 300), num_items=None, num_classes=10):
     """