diff --git a/README_zh-CN.md b/README_zh-CN.md index 969da6132..e834d65f5 100644 --- a/README_zh-CN.md +++ b/README_zh-CN.md @@ -53,9 +53,9 @@ MMDeploy 是 [OpenMMLab](https://openmmlab.com/) 模型部署工具箱，**为 ### 支持多种推理后端 -| ONNX Runtime | TensorRT | ppl.nn | ncnn | OpenVINO | LibTorch | snpe | Ascend | Core ML | RKNN | more | -| ------------ | -------- | ------ | ---- | -------- | -------- | ---- | ------ | ------- | ---- | ---------------------------------------------- | -| ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | [benchmark](docs/en/03-benchmark/benchmark.md) | +| ONNX Runtime | TensorRT | ppl.nn | ncnn | OpenVINO | LibTorch | snpe | Ascend | Core ML | RKNN | more | +| ------------ | -------- | ------ | ---- | -------- | -------- | ---- | ------ | ------- | ---- | ------------------------------------------------- | +| ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | ✔️ | [benchmark](docs/zh_cn/03-benchmark/benchmark.md) | ### SDK 可高度定制化 diff --git a/mmdeploy/codebase/mmocr/deploy/mmocr.py b/mmdeploy/codebase/mmocr/deploy/mmocr.py index 285c6e1f7..f63034cd6 100644 --- a/mmdeploy/codebase/mmocr/deploy/mmocr.py +++ b/mmdeploy/codebase/mmocr/deploy/mmocr.py @@ -19,5 +19,5 @@ class MMOCR(MMCodebase): register_all_modules as register_all_modules_mmdet from mmocr.utils.setup_env import \ register_all_modules as register_all_modules_mmocr - register_all_modules_mmocr(False) - register_all_modules_mmdet(True) + register_all_modules_mmocr(True) + register_all_modules_mmdet(False) diff --git a/tests/test_codebase/test_mmdet/test_mmdet_models.py b/tests/test_codebase/test_mmdet/test_mmdet_models.py index 03df47a11..b3b94d28a 100644 --- a/tests/test_codebase/test_mmdet/test_mmdet_models.py +++ b/tests/test_codebase/test_mmdet/test_mmdet_models.py @@ -1659,107 +1659,6 @@ def test_yolox_head_predict_by_feat_ncnn(): assert rewrite_outputs.shape[-1] == 6 -def get_vfnet_head_model(): - """VFNet Head Config.""" - test_cfg = Config( - dict( - deploy_nms_pre=0, - min_bbox_size=0, - score_thr=0.05, - nms=dict(type='nms', iou_threshold=0.5), - max_per_img=100)) - from mmdet.models.dense_heads import VFNetHead - model = VFNetHead(num_classes=4, in_channels=1, test_cfg=test_cfg) - - model.requires_grad_(False) - model.cpu().eval() - return model - - -@pytest.mark.parametrize('backend_type', - [Backend.OPENVINO, Backend.ONNXRUNTIME]) -def test_predict_by_feat_of_vfnet_head(backend_type: Backend): - """Test predict_by_feat rewrite of VFNet head.""" - check_backend(backend_type) - vfnet_head = get_vfnet_head_model() - vfnet_head.cpu().eval() - s = 16 - batch_img_metas = [{ - 'scale_factor': np.ones(4), - 'pad_shape': (s, s, 3), - 'img_shape': (s, s, 3) - }] - output_names = ['dets', 'labels'] - deploy_cfg = Config( - dict( - backend_config=dict(type=backend_type.value), - onnx_config=dict(output_names=output_names, input_shape=None), - codebase_config=dict( - type='mmdet', - task='ObjectDetection', - post_processing=dict( - score_threshold=0.05, - iou_threshold=0.5, - max_output_boxes_per_class=200, - pre_top_k=-1, - keep_top_k=100, - background_label_id=-1, - )))) - - seed_everything(1234) - cls_score = [ - torch.rand(1, vfnet_head.num_classes, pow(2, i), pow(2, i)) - for i in range(5, 0, -1) - ] - seed_everything(5678) - bboxes = [torch.rand(1, 4, pow(2, i), pow(2, i)) for i in range(5, 0, -1)] - seed_everything(9101) - - model_inputs = { - 'cls_scores': cls_score, - 'bbox_preds': bboxes, - 'batch_img_metas': batch_img_metas - } - model_outputs = get_model_outputs(vfnet_head, 'predict_by_feat', - model_inputs) - - batch_img_metas[0]['img_shape'] = torch.Tensor([s, s]) - wrapped_model = WrapModel( - vfnet_head, - 'predict_by_feat', - batch_img_metas=batch_img_metas, - with_nms=True) - rewrite_inputs = {'cls_scores': cls_score, 'bbox_preds': bboxes} - rewrite_outputs, is_backend_output = get_rewrite_outputs( - wrapped_model=wrapped_model, - model_inputs=rewrite_inputs, - deploy_cfg=deploy_cfg) - - if is_backend_output: - # hard code to make two tensors with the same shape - # rewrite and original codes applied different nms strategy - min_shape = min(model_outputs[0].bboxes.shape[0], - rewrite_outputs[0].shape[1], 1) - for i in range(len(model_outputs)): - assert np.allclose( - model_outputs[i].bboxes[:min_shape], - rewrite_outputs[0][i, :min_shape, :4], - rtol=1e-03, - atol=1e-05) - assert np.allclose( - model_outputs[i].scores[:min_shape], - rewrite_outputs[0][i, :min_shape, 4], - rtol=1e-03, - atol=1e-05) - assert np.allclose( - model_outputs[i].labels[:min_shape], - rewrite_outputs[1][i, :min_shape], - rtol=1e-03, - atol=1e-05) - else: - assert rewrite_outputs is not None - - def get_deploy_cfg(backend_type: Backend, ir_type: str): return Config( dict( diff --git a/tests/test_codebase/test_mmpose/test_mmpose_models.py b/tests/test_codebase/test_mmpose/test_mmpose_models.py index c9525db08..2f544a5dd 100644 --- a/tests/test_codebase/test_mmpose/test_mmpose_models.py +++ b/tests/test_codebase/test_mmpose/test_mmpose_models.py @@ -49,11 +49,7 @@ def test_heatmaphead_predict(backend_type: Backend): def get_msmu_head(): from mmpose.models.heads import MSPNHead model = MSPNHead( - num_stages=1, - num_units=1, - out_shape=(32, 48), - unit_channels=16, - level_indices=[0]) + num_stages=1, num_units=1, out_shape=(32, 48), unit_channels=16) model.requires_grad_(False) return model