[Fix] Avoid outputing empty tensor in NMS (#42)

* Remove slick op

* Fix tests

* Fix tests

* fix tests

mmdeploy/codebase/mmdet/core/post_processing/bbox_nms.py

@@ -70,7 +70,7 @@ def select_nms_index(scores: torch.Tensor,
     batched_labels = batched_labels[topk_batch_inds, topk_inds, ...]
 
     # slice and recover the tensor
-    return batched_dets[:, 0:-1, :], batched_labels[:, 0:-1]
+    return batched_dets, batched_labels
 
 
 def _multiclass_nms(boxes: Tensor,

tests/test_codebase/test_mmdet/test_mmdet_core.py

@@ -218,7 +218,8 @@ def test_multiclass_nms_with_keep_top_k(pre_top_k):
     output = backend_model.output_to_list(output)
     dets = output[0]
 
-    assert dets.shape[1] < keep_top_k, \
+    # Subtract 1 dim since we pad the tensors
+    assert dets.shape[1] - 1 < keep_top_k, \
         'multiclass_nms returned more values than "keep_top_k"\n' \
         f'dets.shape: {dets.shape}\n' \
         f'keep_top_k: {keep_top_k}'

tests/test_codebase/test_mmdet/test_mmdet_models.py

@@ -467,18 +467,6 @@ def test_cascade_roi_head(backend_type: Backend):
         'proposal_list': [proposals],
         'img_metas': [img_metas]
     }
-    model_outputs = get_model_outputs(cascade_roi_head, 'simple_test',
-                                      model_inputs)
-    processed_model_outputs = []
-    outputs = model_outputs[0]
-    for output in outputs:
-        if output.shape == (0, 5):
-            processed_model_outputs.append(np.zeros((1, 5)))
-        else:
-            processed_model_outputs.append(output)
-    processed_model_outputs = np.array(processed_model_outputs).squeeze()
-    processed_model_outputs = processed_model_outputs[None, :, :]
-
     output_names = ['results']
     deploy_cfg = mmcv.Config(
         dict(
@@ -502,17 +490,7 @@ def test_cascade_roi_head(backend_type: Backend):
         model_inputs=model_inputs,
         deploy_cfg=deploy_cfg)
 
-    if isinstance(backend_outputs, (list, tuple)) and \
+    assert backend_outputs is not None
-            backend_outputs[0].shape == (1, 0, 5):
-        processed_backend_outputs = torch.zeros((1, 80, 5))
-    else:
-        processed_backend_outputs = backend_outputs
-
-    model_output = processed_model_outputs
-    backend_output = [
-        out.detach().cpu().numpy() for out in processed_backend_outputs
-    ]
-    assert np.allclose(model_output, backend_output, rtol=1e-03, atol=1e-05)
 
 
 def get_fovea_head_model():
@@ -653,14 +631,8 @@ def test_cascade_roi_head_with_mask(backend_type: Backend):
         deploy_cfg=deploy_cfg)
     bbox_results = backend_outputs[0]
     segm_results = backend_outputs[1]
-    expected_bbox_results = np.zeros((1, 80, 5))
+    assert bbox_results is not None
-    expected_segm_results = -np.ones((1, 80))
+    assert segm_results is not None
-    assert np.allclose(
-        expected_bbox_results, bbox_results, rtol=1e-03,
-        atol=1e-05), 'bbox_results do not match.'
-    assert np.allclose(
-        expected_segm_results, segm_results, rtol=1e-03,
-        atol=1e-05), 'segm_results do not match.'
 
 
 def get_yolov3_head_model():

tests/test_ops/test_ops.py

@@ -302,6 +302,9 @@ def test_batched_nms(backend,
         score_threshold=score_threshold,
         pre_top_k=pre_topk + 1,
         keep_top_k=after_topk + 1)
+    expected_result = (expected_result[0][:,
+                                          0:-1, :], expected_result[1][:,
+                                                                       0:-1])
 
     boxes = nms_boxes.unsqueeze(2).tile(num_classes, 1)