add tests part code

tests/regression/mmaction.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmaction2/tree/main
+  codebase_dir: ../mmaction2
+  checkpoint_force_download: False
+  images:
+    video: &video ../mmaction2/demo/demo.mp4
+
+  metric_info: &metric_info
+    Top 1 Accuracy:
+      metric_key: acc/top1
+      tolerance: 1
+      multi_value: 100
+      dataset: Kinetics-400
+    Top 5 Accuracy:
+      metric_key: acc/top5
+      tolerance: 1
+      multi_value: 100
+      dataset: Kinetics-400
+  convert_image: &convert_image
+    input_img: *video
+    test_img: *video
+  backend_test: &default_backend_test False
+  sdk:
+    sdk_dynamic: &sdk_dynamic ""
+
+onnxruntime:
+  pipeline_ort_static_fp32: &pipeline_ort_static_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmaction/video-recognition/video-recognition_onnxruntime_static.py
+    backend_test: *default_backend_test
+  pipeline_ort_static_fp16: &pipeline_ort_static_fp16
+    convert_image: *convert_image
+    deploy_config: configs/mmaction/video-recognition/video-recognition_onnxruntime-fp16_static.py
+    backend_test: *default_backend_test
+
+torchscript:
+  pipeline_torchscript_fp32: &pipeline_torchscript_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmaction/video-recognition/video-recognition_torchscript.py
+    backend_test: *default_backend_test
+
+tensorrt:
+  pipeline_trt_2d_static_fp32: &pipeline_trt_2d_static_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmaction/video-recognition/video-recognition_2d_tensorrt_static-224x224.py
+    backend_test: *default_backend_test
+  pipeline_trt_3d_static_fp32: &pipeline_trt_3d_static_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmaction/video-recognition/video-recognition_3d_tensorrt_static-256x256.py
+    backend_test: *default_backend_test
+
+models:
+  - name: TSN
+    metafile: configs/recognition/tsn/metafile.yml
+    model_configs:
+      - configs/recognition/tsn/tsn_imagenet-pretrained-r50_8xb32-1x1x3-100e_kinetics400-rgb.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_2d_static_fp32
+      - *pipeline_torchscript_fp32
+
+  - name: SlowFast
+    metafile: configs/recognition/slowfast/metafile.yml
+    model_configs:
+      - configs/recognition/slowfast/slowfast_r50_8xb8-4x16x1-256e_kinetics400-rgb.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_3d_static_fp32
+      - *pipeline_torchscript_fp32
+
+  - name: TSM
+    metafile: configs/recognition/tsm/metafile.yml
+    model_configs:
+      - configs/recognition/tsm/tsm_imagenet-pretrained-mobilenetv2_8xb16-1x1x8-100e_kinetics400-rgb.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_torchscript_fp32
+
+  - name: X3D
+    metafile: configs/recognition/x3d/metafile.yml
+    model_configs:
+      - configs/recognition/x3d/x3d_m_16x5x1_facebook-kinetics400-rgb.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_torchscript_fp32

tests/regression/mmagic.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmagic/tree/main
+  codebase_dir: ../mmagic
+  checkpoint_force_download: False
+  images:
+    img_face: &img_face ../mmagic/tests/data/image/face/000001.png
+    img_bg: &img_bg ../mmagic/tests/data/image/gt/baboon.png
+  metric_info: &metric_info
+    Set14 PSNR: # named after metafile.Results.Metrics
+      metric_key: Set14/PSNR # eval log key name
+      tolerance: 4 # metric ±n%
+    Set14 SSIM:
+      metric_key: Set14/SSIM
+      tolerance: 0.02 # metric ±n
+    Set5 PSNR: # named after metafile.Results.Metrics
+      metric_key: Set5/PSNR # eval log key name
+      tolerance: 4 # metric ±n%
+    Set5 SSIM:
+      metric_key: Set5/SSIM
+      tolerance: 0.02 # metric ±n
+    DIV2K PSNR: # named after metafile.Results.Metrics
+      metric_key: DIV2K/PSNR # eval log key name
+      tolerance: 4 # metric ±n%
+    DIV2K SSIM:
+      metric_key: DIV2K/SSIM
+      tolerance: 0.02 # metric ±n
+    PSNR: # named after metafile.Results.Metrics
+      metric_key: PSNR # eval log key name
+      tolerance: 4 # metric ±n%
+    SSIM:
+      metric_key: SSIM
+      tolerance: 0.02 # metric ±n
+  convert_image: &convert_image
+    input_img: *img_face
+    test_img: *img_bg
+  backend_test: &default_backend_test True
+  sdk:
+    sdk_dynamic: &sdk_dynamic configs/mmagic/super-resolution/super-resolution_sdk_dynamic.py
+
+onnxruntime:
+  pipeline_ort_static_fp32: &pipeline_ort_static_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmagic/super-resolution/super-resolution_onnxruntime_static.py
+
+  pipeline_ort_dynamic_fp32: &pipeline_ort_dynamic_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmagic/super-resolution/super-resolution_onnxruntime_dynamic.py
+
+  pipeline_ort_dynamic_fp16: &pipeline_ort_dynamic_fp16
+    convert_image: *convert_image
+    deploy_config: configs/mmagic/super-resolution/super-resolution_onnxruntime-fp16_dynamic.py
+
+
+tensorrt:
+  pipeline_trt_static_fp32: &pipeline_trt_static_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmagic/super-resolution/super-resolution_tensorrt_static-256x256.py
+
+  pipeline_trt_static_fp16: &pipeline_trt_static_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmagic/super-resolution/super-resolution_tensorrt-fp16_static-256x256.py
+
+  pipeline_trt_static_int8: &pipeline_trt_static_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmagic/super-resolution/super-resolution_tensorrt-int8_static-256x256.py
+
+  pipeline_trt_dynamic_fp32: &pipeline_trt_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmagic/super-resolution/super-resolution_tensorrt_dynamic-32x32-512x512.py
+
+  pipeline_trt_dynamic_fp16: &pipeline_trt_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmagic/super-resolution/super-resolution_tensorrt-fp16_dynamic-32x32-512x512.py
+
+  pipeline_trt_dynamic_int8: &pipeline_trt_dynamic_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmagic/super-resolution/super-resolution_tensorrt-int8_dynamic-32x32-512x512.py
+
+openvino:
+  pipeline_openvino_dynamic_fp32: &pipeline_openvino_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmagic/super-resolution/super-resolution_openvino_dynamic-256x256.py
+
+ncnn:
+  pipeline_ncnn_dynamic_fp32: &pipeline_ncnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmagic/super-resolution/super-resolution_ncnn_dynamic.py
+
+pplnn:
+  pipeline_pplnn_dynamic_fp32: &pipeline_pplnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmagic/super-resolution/super-resolution_pplnn_dynamic-32x32.py
+
+torchscript:
+  pipeline_ts_fp32: &pipeline_ts_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmagic/super-resolution/super-resolution_torchscript.py
+
+models:
+  - name: SRCNN
+    metafile: configs/srcnn/metafile.yml
+    model_configs:
+      - configs/srcnn/srcnn_x4k915_1xb16-1000k_div2k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+#      - *pipeline_trt_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+#      - *pipeline_trt_dynamic_int8
+      - *pipeline_ncnn_dynamic_fp32
+      # - *pipeline_pplnn_dynamic_fp32
+      # - *pipeline_openvino_dynamic_fp32
+
+  - name: ESRGAN
+    metafile: configs/esrgan/metafile.yml
+    model_configs:
+      - configs/esrgan/esrgan_x4c64b23g32_1xb16-400k_div2k.py
+      - configs/esrgan/esrgan_psnr-x4c64b23g32_1xb16-1000k_div2k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_dynamic_fp32
+      # - *pipeline_pplnn_dynamic_fp32
+      # - *pipeline_openvino_dynamic_fp32
+
+  - name: SRGAN
+    metafile: configs/srgan_resnet/metafile.yml
+    model_configs:
+      - configs/srgan_resnet/srgan_x4c64b16_1xb16-1000k_div2k.py
+      - configs/srgan_resnet/msrresnet_x4c64b16_1xb16-1000k_div2k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_dynamic_fp32
+      # - *pipeline_pplnn_dynamic_fp32
+      # - *pipeline_openvino_dynamic_fp32
+
+  - name: Real-ESRGAN
+    metafile: configs/real_esrgan/metafile.yml
+    model_configs:
+      - configs/real_esrgan/realesrgan_c64b23g32_4xb12-lr1e-4-400k_df2k-ost.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_dynamic_fp32
+      # - *pipeline_pplnn_dynamic_fp32
+      # - *pipeline_openvino_dynamic_fp32
+
+  - name: EDSR
+    metafile: configs/edsr/metafile.yml
+    model_configs:
+      - configs/edsr/edsr_x2c64b16_1xb16-300k_div2k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      # - *pipeline_ncnn_dynamic_fp32
+      # - *pipeline_openvino_dynamic_fp32
+
+  - name: RDN
+    metafile: configs/rdn/metafile.yml
+    model_configs:
+      - configs/rdn/rdn_x2c64b16_1xb16-1000k_div2k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      # - *pipeline_ncnn_dynamic_fp32
+      # - *pipeline_openvino_dynamic_fp32

tests/regression/mmdet.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmdetection/tree/main
+  codebase_dir: ../mmdetection
+  checkpoint_force_download: False
+  images:
+    input_img: &input_img ../mmdetection/demo/demo.jpg
+    test_img: &test_img ./tests/data/tiger.jpeg
+  metric_info: &metric_info
+    box AP: # named after metafile.Results.Metrics
+      metric_key: coco/bbox_mAP # eval OrderedDict key name
+      tolerance: 0.2 # metric ±n%
+      multi_value: 100
+    mask AP:
+      metric_key: coco/segm_mAP
+      tolerance: 1 # metric ±n%
+      multi_value: 100
+    PQ:
+      metric_key: 'coco_panoptic/PQ'
+      tolerance: 0.5 # metric ±n%
+  convert_image: &convert_image
+    input_img: *input_img
+    test_img: *test_img
+  backend_test: &default_backend_test True
+  sdk:
+    sdk_static: &sdk_static configs/mmdet/detection/detection_sdk_static.py
+    sdk_dynamic: &sdk_dynamic configs/mmdet/detection/detection_sdk_dynamic.py
+#    sdk_seg_static: &sdk_seg_static configs/mmdet/instance-seg/instance-seg_sdk_static.py
+    sdk_seg_dynamic: &sdk_seg_dynamic configs/mmdet/instance-seg/instance-seg_sdk_dynamic.py
+
+onnxruntime:
+  pipeline_ort_static_fp32: &pipeline_ort_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/detection_onnxruntime_static.py
+
+  pipeline_ort_dynamic_fp32: &pipeline_ort_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/detection_onnxruntime_dynamic.py
+
+  pipeline_ort_dynamic_fp16: &pipeline_ort_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/detection_onnxruntime-fp16_dynamic.py
+
+  pipeline_seg_ort_static_fp32: &pipeline_seg_ort_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/instance-seg/instance-seg_onnxruntime_static.py
+
+  pipeline_seg_ort_dynamic_fp32: &pipeline_seg_ort_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/instance-seg/instance-seg_onnxruntime_dynamic.py
+
+  pipeline_seg_ort_dynamic_fp16: &pipeline_seg_ort_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/instance-seg/instance-seg_onnxruntime-fp16_dynamic.py
+
+tensorrt:
+  pipeline_trt_static_fp32: &pipeline_trt_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/detection_tensorrt_static-800x1344.py
+
+  pipeline_trt_static_fp16: &pipeline_trt_static_fp16
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/detection_tensorrt-fp16_static-800x1344.py
+
+  pipeline_trt_static_int8: &pipeline_trt_static_int8
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/detection_tensorrt-int8_static-800x1344.py
+
+  pipeline_trt_dynamic_fp32: &pipeline_trt_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    # sdk_config: *sdk_dynamic
+    deploy_config: configs/mmdet/detection/detection_tensorrt_dynamic-320x320-1344x1344.py
+
+  pipeline_trt_dynamic_fp16: &pipeline_trt_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    # sdk_config: *sdk_dynamic
+    deploy_config: configs/mmdet/detection/detection_tensorrt-fp16_dynamic-320x320-1344x1344.py
+
+  pipeline_trt_dynamic_int8: &pipeline_trt_dynamic_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    # sdk_config: *sdk_dynamic
+    deploy_config: configs/mmdet/detection/detection_tensorrt-int8_dynamic-320x320-1344x1344.py
+
+  # ============= seg ================
+  pipeline_seg_trt_static_fp32: &pipeline_seg_trt_static_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    # sdk_config: *sdk_seg_dynamic
+    deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt_static-800x1344.py
+
+  pipeline_seg_trt_static_fp16: &pipeline_seg_trt_static_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    # sdk_config: *sdk_seg_dynamic
+    deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt-fp16_static-800x1344.py
+
+  pipeline_seg_trt_static_int8: &pipeline_seg_trt_static_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    # sdk_config: *sdk_seg_dynamic
+    deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt-int8_static-800x1344.py
+
+  pipeline_seg_trt_dynamic_fp32: &pipeline_seg_trt_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_seg_dynamic
+    deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt_dynamic-320x320-1344x1344.py
+
+  pipeline_seg_trt_dynamic_fp16: &pipeline_seg_trt_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_seg_dynamic
+    deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt-fp16_dynamic-320x320-1344x1344.py
+
+  pipeline_seg_trt_dynamic_int8: &pipeline_seg_trt_dynamic_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    # sdk_config: *sdk_seg_dynamic
+    deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt-int8_dynamic-320x320-1344x1344.py
+
+openvino:
+  pipeline_openvino_dynamic_fp32: &pipeline_openvino_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/detection_openvino_dynamic-800x1344.py
+
+  # ============= seg ================
+  pipeline_seg_openvino_dynamic_fp32: &pipeline_seg_openvino_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/instance-seg/instance-seg_openvino_dynamic-800x1344.py
+
+ncnn:
+  pipeline_ncnn_static_fp32: &pipeline_ncnn_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/single-stage_ncnn_static-800x1344.py
+
+  pipeline_ncnn_dynamic_fp32: &pipeline_ncnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet/detection/single-stage_ncnn_dynamic.py
+
+pplnn:
+  pipeline_pplnn_dynamic_fp32: &pipeline_pplnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet/detection/detection_pplnn_dynamic-800x1344.py
+
+  # ============= seg ================
+  pipeline_seg_pplnn_dynamic_fp32: &pipeline_seg_pplnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet/instance-seg/instance-seg_pplnn_dynamic-800x1344.py
+
+torchscript:
+  pipeline_ts_fp32: &pipeline_ts_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet/detection/detection_torchscript.py
+
+  # ============= seg ================
+  pipeline_seg_ts_fp32: &pipeline_seg_ts_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet/instance-seg/instance-seg_torchscript.py
+
+models:
+  - name: YOLOV3
+    metafile: configs/yolo/metafile.yml
+    model_configs:
+      - configs/yolo/yolov3_d53_8xb8-320-273e_coco.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - deploy_config: configs/mmdet/detection/detection_tensorrt-fp16_dynamic-64x64-608x608.py
+        convert_image: *convert_image
+        backend_test: *default_backend_test
+        sdk_config: *sdk_dynamic
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+
+  - name: SSD
+    metafile: configs/ssd/metafile.yml
+    model_configs:
+      - configs/ssd/ssd300_coco.py
+    pipelines: # special cases
+      - *pipeline_ts_fp32
+      - *pipeline_ort_static_fp32
+      - deploy_config: configs/mmdet/detection/detection_tensorrt_dynamic-300x300-512x512.py
+        convert_image: *convert_image
+        backend_test: *default_backend_test
+        sdk_config: *sdk_dynamic
+      - deploy_config: configs/mmdet/detection/single-stage_ncnn_static-300x300.py
+        convert_image: *convert_image
+        backend_test: False
+
+  - name: RetinaNet
+    metafile: configs/retinanet/metafile.yml
+    model_configs:
+      - configs/retinanet/retinanet_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: Cascade Mask R-CNN
+    metafile: configs/cascade_rcnn/metafile.yml
+    model_configs:
+      - configs/cascade_rcnn/cascade_mask_rcnn_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_ts_fp32
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_trt_dynamic_fp32
+      - *pipeline_seg_openvino_dynamic_fp32
+
+  - name: FCOS
+    metafile: configs/fcos/metafile.yml
+    model_configs:
+      - configs/fcos/fcos_r50_caffe_fpn_gn-head_1x_coco.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: FSAF
+    metafile: configs/fsaf/metafile.yml
+    model_configs:
+      - configs/fsaf/fsaf_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: YOLOX
+    metafile: configs/yolox/metafile.yml
+    model_configs:
+      - configs/yolox/yolox_s_8xb8-300e_coco.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - deploy_config: configs/mmdet/detection/detection_openvino_dynamic-640x640.py
+        convert_image: *convert_image
+        backend_test: False
+
+  - name: Faster R-CNN
+    metafile: configs/faster_rcnn/metafile.yml
+    model_configs:
+      - configs/faster_rcnn/faster_rcnn_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: ATSS
+    metafile: configs/atss/metafile.yml
+    model_configs:
+      - configs/atss/atss_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: Cascade R-CNN
+    metafile: configs/cascade_rcnn/metafile.yml
+    model_configs:
+      - configs/cascade_rcnn/cascade_rcnn_r50_caffe_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: GFL
+    metafile: configs/gfl/metafile.yml
+    model_configs:
+      - configs/gfl/gfl_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: DETR
+    metafile: configs/detr/metafile.yml
+    model_configs:
+      - configs/detr/detr_r50_8xb2-150e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+
+  - name: CenterNet
+    metafile: configs/centernet/metafile.yml
+    model_configs:
+      - configs/centernet/centernet_r18_8xb16-crop512-140e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - deploy_config: configs/mmdet/detection/detection_tensorrt-fp16_dynamic-64x64-800x800.py
+        convert_image: *convert_image
+        backend_test: *default_backend_test
+        sdk_config: *sdk_dynamic
+
+  - name: Mask R-CNN
+    metafile: configs/mask_rcnn/metafile.yml
+    model_configs:
+      - configs/mask_rcnn/mask-rcnn_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_ts_fp32
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_trt_dynamic_fp32
+      - *pipeline_seg_openvino_dynamic_fp32
+
+  - name: Swin Transformer
+    metafile: configs/swin/metafile.yml
+    model_configs:
+      - configs/swin/mask_rcnn_swin-t-p4-w7_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_trt_dynamic_fp32
+
+  - name: RTMDet
+    metafile: configs/rtmdet/metafile.yml
+    model_configs:
+      - configs/rtmdet/rtmdet_s_8xb32-300e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - deploy_config: configs/mmdet/detection/detection_tensorrt_static-640x640.py
+        convert_image: *convert_image
+        backend_test: *default_backend_test
+      - deploy_config: configs/mmdet/detection/single-stage_ncnn_static-640x640.py
+        convert_image: *convert_image
+
+  - name: SOLO
+    metafile: configs/solo/metafile.yml
+    model_configs:
+      - configs/solo/solo_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_openvino_dynamic_fp32
+
+  - name: SOLOV2
+    metafile: configs/solov2/metafile.yml
+    model_configs:
+      - configs/solov2/solov2_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_openvino_dynamic_fp32
+
+  - name: RTMDetInst
+    metafile: configs/rtmdet/metafile.yml
+    model_configs:
+      - configs/rtmdet/rtmdet-ins_s_8xb32-300e_coco.py
+    pipelines:
+      - *pipeline_seg_trt_dynamic_fp32
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_openvino_dynamic_fp32
+
+  - name: PanopticFPN
+    metafile: configs/panoptic_fpn/metafile.yml
+    model_configs:
+      - configs/panoptic_fpn/panoptic-fpn_r50_fpn_1x_coco.py
+    pipelines:
+      - deploy_config: configs/mmdet/panoptic-seg/panoptic-seg_panoptic-fpn_onnxruntime_dynamic.py
+        convert_image: *convert_image
+        backend_test: False
+      - deploy_config: configs/mmdet/panoptic-seg/panoptic-seg_panoptic-fpn_tensorrt_dynamic-352x512-1344x1344.py
+        convert_image: *convert_image
+        backend_test: True
+
+  - name: MaskFormer
+    metafile: configs/maskformer/metafile.yml
+    model_configs:
+      - configs/maskformer/maskformer_r50_ms-16xb1-75e_coco.py
+    pipelines:
+      - deploy_config: configs/mmdet/panoptic-seg/panoptic-seg_maskformer_onnxruntime_dynamic.py
+        convert_image: *convert_image
+        backend_test: False
+      - deploy_config: configs/mmdet/panoptic-seg/panoptic-seg_maskformer_tensorrt_dynamic-320x512-1344x1344.py
+        convert_image: *convert_image
+        backend_test: True
+
+  - name: Mask2Former
+    metafile: configs/mask2former/metafile.yml
+    model_configs:
+      - configs/mask2former/mask2former_r50_8xb2-lsj-50e_coco-panoptic.py
+    pipelines:
+      - deploy_config: configs/mmdet/panoptic-seg/panoptic-seg_maskformer_onnxruntime_dynamic.py
+        convert_image: *convert_image
+        backend_test: *default_backend_test
+      - deploy_config: configs/mmdet/panoptic-seg/panoptic-seg_maskformer_tensorrt_static-800x1344.py
+        convert_image: *convert_image
+        backend_test: False
+
+  - name: DINO
+    metafile: configs/dino/metafile.yml
+    model_configs:
+      - configs/dino/dino-4scale_r50_8xb2-12e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+
+  - name: ConditionalDETR
+    metafile: configs/conditional_detr/metafile.yml
+    model_configs:
+      - configs/conditional_detr/conditional-detr_r50_8xb2-50e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+
+  - name: DAB-DETR
+    metafile: configs/dab_detr/metafile.yml
+    model_configs:
+      - configs/dab_detr/dab-detr_r50_8xb2-50e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+
+  - name: DeformableDETR
+    metafile: configs/deformable_detr/metafile.yml
+    model_configs:
+      - configs/deformable_detr/deformable-detr_r50_16xb2-50e_coco.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+
+  - name: CondInst
+    metafile: configs/condinst/metafile.yml
+    model_configs:
+      - configs/condinst/condinst_r50_fpn_ms-poly-90k_coco_instance.py
+    pipelines:
+      - deploy_config: configs/mmdet/instance-seg/instance-seg_onnxruntime_dynamic.py
+        backend_test: *default_backend_test
+      - deploy_config: configs/mmdet/instance-seg/instance-seg_tensorrt_dynamic-320x320-1344x1344.py
+        backend_test: *default_backend_test
+
+  - name: HTC
+    metafile: configs/htc/metafile.yml
+    model_configs:
+      - configs/htc/htc_r50_fpn_1x_coco.py
+    pipelines:
+      - *pipeline_seg_ort_dynamic_fp32
+      - *pipeline_seg_trt_dynamic_fp32

tests/regression/mmdet3d.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmdetection3d/tree/main
+  codebase_dir: ../mmdetection3d
+  checkpoint_force_download: False
+  images:
+    kitti_input: &kitti_input ../mmdetection3d/demo/data/kitti/000008.bin
+    nus_input: &nus_input tests/data/n008-2018-08-01-15-16-36-0400__LIDAR_TOP__1533151612397179.pcd.bin
+    nus_pkl_input: &nus_pkl_input tests/test_codebase/test_mmdet3d/data/nuscenes/n015-2018-07-24-11-22-45+0800.pkl
+
+  metric_info: &metric_info
+    AP: # named after metafile.Results.Metrics
+      eval_name: bbox # test.py --metrics args
+      metric_key: bbox_mAP # eval OrderedDict key name
+      tolerance: 1 # metric ±n%
+      task_name: 3D Object Detection # metafile.Results.Task
+      dataset: KITTI # metafile.Results.Dataset
+    mAP:
+      eval_name: bbox
+      metric_key: bbox_mAP
+      tolerance: 1 # metric ±n%
+      task_name: 3D Object Detection
+      dataset: nuScenes
+    NDS:
+      eval_name: bbox
+      metric_key: bbox_mAP
+      tolerance: 1 # metric ±n%
+      task_name: 3D Object Detection
+      dataset: nuScenes
+  backend_test: &default_backend_test False
+
+  convert_image: &convert_image
+    input_img: *kitti_input
+    test_img: *kitti_input
+
+  convert_image_nus: &convert_image_nus
+    input_img: *nus_input
+    test_img: *nus_input
+
+  convert_image_nus_pkl: &convert_image_nus_pkl
+    input_img: *nus_pkl_input
+    test_img: *nus_pkl_input
+
+onnxruntime:
+  # ======= voxel-detection =======
+  pipeline_ort_voxel_dynamic_kitti_fp32: &pipeline_ort_voxel_dynamic_kitti_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_onnxruntime_dynamic.py
+
+  pipeline_ort_voxel_dynamic_kitti_fp16: &pipeline_ort_voxel_dynamic_kitti_fp16
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_onnxruntime-fp16_dynamic.py
+
+  pipeline_ort_voxel_dynamic_nus_fp32: &pipeline_ort_voxel_dynamic_nus_fp32
+    convert_image: *convert_image_nus
+    backend_test: False
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_onnxruntime_dynamic.py
+
+  # ======= mono-detection =======
+  pipeline_ort_mono_dynamic_nus_fp32: &pipeline_ort_mono_dynamic_nus_fp32
+    convert_image: *convert_image_nus_pkl
+    backend_test: False
+    deploy_config: configs/mmdet3d/mono-detection/mono-detection_onnxruntime_dynamic.py
+
+  pipeline_ort_mono_dynamic_nus_fp16: &pipeline_ort_mono_dynamic_nus_fp16
+    convert_image: *convert_image_nus_pkl
+    backend_test: False
+    deploy_config: configs/mmdet3d/mono-detection/mono-detection_onnxruntime-fp16_dynamic.py
+
+tensorrt:
+  # ======= voxel-detection =======
+  pipeline_trt_voxel_dynamic_nus_fp32_64x4: &pipeline_trt_voxel_dynamic_nus_fp32_64x4
+    convert_image: *convert_image_nus
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_tensorrt_dynamic-nus-64x4.py
+
+  pipeline_trt_voxel_dynamic_nus_fp32_20x5: &pipeline_trt_voxel_dynamic_nus_fp32_20x5
+    convert_image: *convert_image_nus
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_tensorrt_dynamic-nus-20x5.py
+
+  pipeline_trt_voxel_dynamic_kitti_fp32: &pipeline_trt_voxel_dynamic_kitti_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_tensorrt_dynamic-kitti-32x4.py
+
+  # ======= mono-detection =======
+  pipeline_trt_mono_dynamic_nus_fp32: &pipeline_trt_mono_dynamic_nus_fp32
+    convert_image: *convert_image_nus_pkl
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet3d/mono-detection/mono-detection_tensorrt_dynamic-320x320-1344x1344.py
+
+openvino:
+  # ======= voxel-detection =======
+  pipeline_openvino_voxel_dynamic_kitti_fp32: &pipeline_openvino_voxel_dynamic_kitti_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_openvino_dynamic-kitti-32x4.py
+
+  pipeline_openvino_voxel_dynamic_nus_fp32_64x4: &pipeline_openvino_voxel_dynamic_nus_fp32_64x4
+    convert_image: *convert_image_nus
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_openvino_dynamic-nus-64x4.py
+
+  pipeline_openvino_voxel_dynamic_nus_fp32_20x5: &pipeline_openvino_voxel_dynamic_nus_fp32_20x5
+    convert_image: *convert_image_nus
+    backend_test: *default_backend_test
+    deploy_config: configs/mmdet3d/voxel-detection/voxel-detection_openvino_dynamic-nus-20x5.py
+
+models:
+  - name: PointPillars
+    metafile: configs/pointpillars/metafile.yml
+    model_configs:
+      - configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-3class.py
+      - configs/pointpillars/pointpillars_hv_secfpn_8xb6-160e_kitti-3d-car.py
+    pipelines:
+      - *pipeline_ort_voxel_dynamic_kitti_fp16
+      - *pipeline_openvino_voxel_dynamic_kitti_fp32
+      - *pipeline_trt_voxel_dynamic_kitti_fp32
+  - name: PointPillars
+    metafile: configs/pointpillars/metafile.yml
+    model_configs:
+      - configs/pointpillars/pointpillars_hv_secfpn_sbn-all_8xb4-2x_nus-3d.py
+    pipelines:
+      - *pipeline_ort_voxel_dynamic_nus_fp32
+      - *pipeline_openvino_voxel_dynamic_nus_fp32_64x4
+      - *pipeline_trt_voxel_dynamic_nus_fp32_64x4
+  - name: CenterPoint
+    metafile: configs/centerpoint/metafile.yml
+    model_configs:
+      - configs/centerpoint/centerpoint_pillar02_second_secfpn_head-circlenms_8xb4-cyclic-20e_nus-3d.py
+    pipelines:
+      - *pipeline_ort_voxel_dynamic_nus_fp32
+      - *pipeline_openvino_voxel_dynamic_nus_fp32_20x5
+      - *pipeline_trt_voxel_dynamic_nus_fp32_20x5
+  - name: SMOKE
+    metafile: configs/smoke/metafile.yml
+    model_configs:
+      - configs/smoke/smoke_dla34_dlaneck_gn-all_4xb8-6x_kitti-mono3d.py
+    pipelines:
+      - *pipeline_ort_mono_dynamic_nus_fp32
+      - *pipeline_trt_mono_dynamic_nus_fp32

tests/regression/mmocr.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmocr/tree/main
+  codebase_dir: ../mmocr
+  checkpoint_force_download: False
+  images:
+    img_densetext_det: &img_densetext_det ../mmocr/demo/demo_densetext_det.jpg
+    img_demo_text_det: &img_demo_text_det ../mmocr/demo/demo_text_det.jpg
+    img_demo_text_recog: &img_demo_text_recog ../mmocr/demo/demo_text_recog.jpg
+  metric_info: &metric_info
+    hmean-iou: # named after metafile.Results.Metrics
+      metric_key: icdar/hmean # eval key name
+      tolerance: 0.15 # metric ±n%
+    IIIT5K word_acc:
+      metric_key: IIIT5K/recog/word_acc_ignore_case_symbol
+      tolerance: 0.05 # metric ±n%
+      dataset: IIIT5K
+    SVT word_acc:
+      metric_key: SVT/recog/word_acc_ignore_case_symbol
+      tolerance: 0.05 # metric ±n%
+      dataset: SVT
+    SVTP word_acc:
+      metric_key: SVTP/recog/word_acc_ignore_case_symbol
+      tolerance: 0.05 # metric ±n%
+      dataset: SVTP
+  convert_image_det: &convert_image_det
+    input_img: *img_densetext_det
+    test_img: *img_demo_text_det
+  convert_image_rec: &convert_image_rec
+    input_img: *img_demo_text_recog
+    test_img: *img_demo_text_recog
+  backend_test: &default_backend_test True
+  sdk:
+    sdk_detection_dynamic: &sdk_detection_dynamic configs/mmocr/text-detection/text-detection_sdk_dynamic.py
+    sdk_recognition_dynamic: &sdk_recognition_dynamic configs/mmocr/text-recognition/text-recognition_sdk_dynamic.py
+
+onnxruntime:
+  # ======= detection =======
+  pipeline_ort_detection_static_fp32: &pipeline_ort_detection_static_fp32
+    convert_image: *convert_image_det
+    deploy_config: configs/mmocr/text-detection/text-detection_onnxruntime_static.py
+
+  pipeline_ort_detection_dynamic_fp32: &pipeline_ort_detection_dynamic_fp32
+    convert_image: *convert_image_det
+    deploy_config: configs/mmocr/text-detection/text-detection_onnxruntime_dynamic.py
+
+  pipeline_ort_detection_dynamic_fp16: &pipeline_ort_detection_dynamic_fp16
+    convert_image: *convert_image_det
+    deploy_config: configs/mmocr/text-detection/text-detection_onnxruntime-fp16_dynamic.py
+
+  pipeline_ort_detection_mrcnn_dynamic_fp32: &pipeline_ort_detection_mrcnn_dynamic_fp32
+    convert_image: *convert_image_det
+    deploy_config: configs/mmocr/text-detection/text-detection_mrcnn_onnxruntime_dynamic.py
+
+  # ======= recognition =======
+  pipeline_ort_recognition_static_fp32: &pipeline_ort_recognition_static_fp32
+    convert_image: *convert_image_rec
+    deploy_config: configs/mmocr/text-recognition/text-recognition_onnxruntime_static.py
+
+  pipeline_ort_recognition_dynamic_fp32: &pipeline_ort_recognition_dynamic_fp32
+    convert_image: *convert_image_rec
+    deploy_config: configs/mmocr/text-recognition/text-recognition_onnxruntime_dynamic.py
+
+  pipeline_ort_recognition_dynamic_fp16: &pipeline_ort_recognition_dynamic_fp16
+    convert_image: *convert_image_rec
+    deploy_config: configs/mmocr/text-recognition/text-recognition_onnxruntime-fp16_dynamic.py
+
+
+tensorrt:
+  # ======= detection =======
+  pipeline_trt_detection_static_fp32: &pipeline_trt_detection_static_fp32
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    sdk_config: *sdk_detection_dynamic
+    deploy_config: configs/mmocr/text-detection/text-detection_tensorrt_static-512x512.py
+
+  pipeline_trt_detection_static_fp16: &pipeline_trt_detection_static_fp16
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    sdk_config: *sdk_detection_dynamic
+    deploy_config: configs/mmocr/text-detection/text-detection_tensorrt-fp16_static-512x512.py
+
+  pipeline_trt_detection_static_int8: &pipeline_trt_detection_static_int8
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    sdk_config: *sdk_detection_dynamic
+    deploy_config: configs/mmocr/text-detection/text-detection_tensorrt-int8_static-512x512.py
+
+  pipeline_trt_detection_dynamic_fp32: &pipeline_trt_detection_dynamic_fp32
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    deploy_config: configs/mmocr/text-detection/text-detection_tensorrt_dynamic-320x320-2240x2240.py
+
+  pipeline_trt_detection_mrcnn_dynamic_fp32: &pipeline_trt_detection_mrcnn_dynamic_fp32
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    sdk_config: *sdk_detection_dynamic
+    deploy_config: configs/mmocr/text-detection/text-detection_mrcnn_tensorrt_dynamic-320x320-2240x2240.py
+
+  pipeline_trt_detection_dynamic_fp16: &pipeline_trt_detection_dynamic_fp16
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    sdk_config: *sdk_detection_dynamic
+    deploy_config: configs/mmocr/text-detection/text-detection_tensorrt-fp16_dynamic-320x320-2240x2240.py
+
+  pipeline_trt_detection_mrcnn_dynamic_fp16: &pipeline_trt_detection_mrcnn_dynamic_fp16
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    sdk_config: *sdk_detection_dynamic
+    deploy_config: configs/mmocr/text-detection/text-detection_mrcnn_tensorrt-fp16_dynamic-320x320-2240x2240.py
+
+  pipeline_trt_detection_dynamic_int8: &pipeline_trt_detection_dynamic_int8
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    sdk_config: *sdk_detection_dynamic
+    deploy_config: configs/mmocr/text-detection/text-detection_tensorrt-int8_dynamic-320x320-2240x2240.py
+
+  # ======= recognition =======
+  pipeline_trt_recognition_static_fp32_C1: &pipeline_trt_recognition_static_fp32_C1
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt_static-1x32x32.py
+
+  # ABINet models with static shape 32x128
+  pipeline_trt_recognition_static_fp32_C3: &pipeline_trt_recognition_static_fp32_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt_static-32x128.py
+
+  pipeline_trt_recognition_static_fp16_C3: &pipeline_trt_recognition_static_fp16_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-fp16_static-32x128.py
+
+  # SAR models with height 48 and channel 3
+  pipeline_trt_recognition_dynamic_fp32_H48_C3: &pipeline_trt_recognition_dynamic_fp32_H48_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt_dynamic-48x64-48x640.py
+
+  pipeline_trt_recognition_dynamic_fp16_H48_C3: &pipeline_trt_recognition_dynamic_fp16_H48_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-fp16_dynamic-48x64-48x640.py
+
+  pipeline_trt_recognition_dynamic_int8_H48_C3: &pipeline_trt_recognition_dynamic_int8_H48_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-int8_dynamic-48x64-48x640.py
+
+  # CRNN models with height 32 and channel 1
+  pipeline_trt_recognition_dynamic_fp32_H32_C1: &pipeline_trt_recognition_dynamic_fp32_H32_C1
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt_dynamic-1x32x32-1x32x640.py
+
+  pipeline_trt_recognition_dynamic_fp16_H32_C1: &pipeline_trt_recognition_dynamic_fp16_H32_C1
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-fp16_dynamic-1x32x32-1x32x640.py
+
+  pipeline_trt_recognition_dynamic_int8_H32_C1: &pipeline_trt_recognition_dynamic_int8_H32_C1
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-int8_dynamic-1x32x32-1x32x640.py
+
+  # SATRN models with height 32 and channel 3
+  pipeline_trt_recognition_dynamic_fp32_H32_C3: &pipeline_trt_recognition_dynamic_fp32_H32_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt_dynamic-32x32-32x640.py
+
+  pipeline_trt_recognition_dynamic_fp16_H32_C3: &pipeline_trt_recognition_dynamic_fp16_H32_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-fp16_dynamic-32x32-32x640.py
+
+  pipeline_trt_recognition_dynamic_int8_H32_C3: &pipeline_trt_recognition_dynamic_int8_H32_C3
+    convert_image: *convert_image_rec
+    backend_test: *default_backend_test
+    sdk_config: *sdk_recognition_dynamic
+    deploy_config: configs/mmocr/text-recognition/text-recognition_tensorrt-int8_dynamic-32x32-32x640.py
+
+openvino:
+  pipeline_openvino_detection_dynamic_fp32: &pipeline_openvino_detection_dynamic_fp32
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    deploy_config: configs/mmocr/text-detection/text-detection_openvino_dynamic-640x640.py
+
+#  pipeline_openvino_recognition_dynamic_fp32: &pipeline_openvino_recognition_dynamic_fp32
+#    convert_image: *convert_image_rec
+#    backend_test: *default_backend_test
+#    deploy_config:
+
+ncnn:
+  pipeline_ncnn_detection_static_fp32: &pipeline_ncnn_detection_static_fp32
+    convert_image: *convert_image_det
+    backend_test: False
+    deploy_config: configs/mmocr/text-detection/text-detection_ncnn_static.py
+
+  pipeline_ncnn_recognition_static_fp32: &pipeline_ncnn_recognition_static_fp32
+    convert_image: *convert_image_rec
+    backend_test: False
+    deploy_config: configs/mmocr/text-recognition/text-recognition_ncnn_static.py
+
+pplnn:
+  pipeline_pplnn_detection_dynamic_fp32: &pipeline_pplnn_detection_dynamic_fp32
+    convert_image: *convert_image_det
+    backend_test: False
+    deploy_config: configs/mmocr/text-detection/text-detection_pplnn_dynamic-640x640.py
+
+  pipeline_pplnn_recognition_dynamic_fp32: &pipeline_pplnn_recognition_dynamic_fp32
+    convert_image: *convert_image_rec
+    backend_test: False
+    deploy_config: configs/mmocr/text-recognition/text-recognition_pplnn_dynamic-1x32x32.py
+
+torchscript:
+  pipeline_ts_detection_fp32: &pipeline_ts_detection_fp32
+    convert_image: *convert_image_det
+    backend_test: False
+    deploy_config: configs/mmocr/text-detection/text-detection_torchscript.py
+
+  pipeline_ts_detection_mrcnn_fp32: &pipeline_ts_detection_mrcnn_fp32
+    convert_image: *convert_image_det
+    backend_test: False
+    deploy_config: configs/mmocr/text-detection/text-detection_mrcnn_torchscript.py
+
+  pipeline_ts_recognition_fp32: &pipeline_ts_recognition_fp32
+    convert_image: *convert_image_rec
+    backend_test: False
+    deploy_config: configs/mmocr/text-recognition/text-recognition_torchscript.py
+
+
+models:
+  - name: DBNet
+    metafile: configs/textdet/dbnet/metafile.yml
+    model_configs:
+      - configs/textdet/dbnet/dbnet_resnet18_fpnc_1200e_icdar2015.py
+    pipelines:
+      - *pipeline_ts_detection_fp32
+      - *pipeline_ort_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16
+      - *pipeline_ncnn_detection_static_fp32
+      - *pipeline_pplnn_detection_dynamic_fp32
+      - *pipeline_openvino_detection_dynamic_fp32
+
+  - name: DBNetpp
+    metafile: configs/textdet/dbnetpp/metafile.yml
+    model_configs:
+      - configs/textdet/dbnetpp/dbnetpp_resnet50_fpnc_1200e_icdar2015.py
+    pipelines:
+      - *pipeline_ort_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16
+      - *pipeline_ncnn_detection_static_fp32
+      - *pipeline_openvino_detection_dynamic_fp32
+
+  - name: PANet
+    metafile: configs/textdet/panet/metafile.yml
+    model_configs:
+      - configs/textdet/panet/panet_resnet18_fpem-ffm_600e_icdar2015.py
+    pipelines:
+      - *pipeline_ts_detection_fp32
+      - *pipeline_ort_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16
+      - *pipeline_ncnn_detection_static_fp32
+      - *pipeline_pplnn_detection_dynamic_fp32
+      - *pipeline_openvino_detection_dynamic_fp32
+
+  - name: PSENet
+    metafile: configs/textdet/psenet/metafile.yml
+    model_configs:
+      - configs/textdet/psenet/psenet_resnet50_fpnf_600e_icdar2015.py
+    pipelines:
+      - *pipeline_ts_detection_fp32
+      - *pipeline_ort_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16
+      - *pipeline_ncnn_detection_static_fp32
+      - *pipeline_pplnn_detection_dynamic_fp32
+      - *pipeline_openvino_detection_dynamic_fp32
+
+  - name: TextSnake
+    metafile: configs/textdet/textsnake/metafile.yml
+    model_configs:
+      - configs/textdet/textsnake/textsnake_resnet50_fpn-unet_1200e_ctw1500.py
+    pipelines:
+      - *pipeline_ts_detection_fp32
+      - *pipeline_ort_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp32
+
+  - name: MaskRCNN
+    metafile: configs/textdet/maskrcnn/metafile.yml
+    model_configs:
+      - configs/textdet/maskrcnn/mask-rcnn_resnet50_fpn_160e_icdar2015.py
+    pipelines:
+      - *pipeline_ts_detection_mrcnn_fp32
+      - *pipeline_ort_detection_mrcnn_dynamic_fp32
+      - *pipeline_trt_detection_mrcnn_dynamic_fp32
+
+  - name: CRNN
+    metafile: configs/textrecog/crnn/metafile.yml
+    model_configs:
+      - configs/textrecog/crnn/crnn_mini-vgg_5e_mj.py
+    pipelines:
+      - *pipeline_ts_recognition_fp32
+      - *pipeline_ort_recognition_dynamic_fp32
+      - *pipeline_trt_recognition_dynamic_fp16_H32_C1
+      - *pipeline_ncnn_recognition_static_fp32
+      - *pipeline_pplnn_recognition_dynamic_fp32
+
+  - name: SAR
+    metafile: configs/textrecog/sar/metafile.yml
+    model_configs:
+      - configs/textrecog/sar/sar_resnet31_parallel-decoder_5e_st-sub_mj-sub_sa_real.py
+    pipelines:
+      - *pipeline_ts_recognition_fp32
+      - *pipeline_ort_recognition_dynamic_fp32
+
+  - name: SATRN
+    metafile: configs/textrecog/satrn/metafile.yml
+    model_configs:
+      - configs/textrecog/satrn/satrn_shallow-small_5e_st_mj.py
+    pipelines:
+      - *pipeline_ts_recognition_fp32
+      - *pipeline_ort_recognition_dynamic_fp32
+      - *pipeline_trt_recognition_dynamic_fp32_H32_C3
+
+  - name: ABINet
+    metafile: configs/textrecog/abinet/metafile.yml
+    model_configs:
+      - configs/textrecog/abinet/abinet_20e_st-an_mj.py
+    pipelines:
+      - *pipeline_ts_recognition_fp32
+      - *pipeline_ort_recognition_static_fp32
+      - *pipeline_trt_recognition_static_fp16_C3

tests/regression/mmpose.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmpose/tree/main
+  codebase_dir: ../mmpose
+  checkpoint_force_download: False
+  images:
+    img_human_pose: &img_human_pose ../mmpose/tests/data/coco/000000000785.jpg
+    img_human_pose_256x192: &img_human_pose_256x192 ./demo/resources/human-pose.jpg
+  metric_info: &metric_info
+    AP: # named after metafile.Results.Metrics
+      metric_key: coco/AP # eval key name
+      tolerance: 0.02 # metric ±n
+    AR:
+      metric_key: coco/AR
+      tolerance: 0.02 # metric ±n
+  convert_image: &convert_image
+    input_img: *img_human_pose
+    test_img: *img_human_pose_256x192
+  backend_test: &default_backend_test True
+  sdk:
+    sdk_static: &sdk_static configs/mmpose/pose-detection_sdk_static-256x192.py
+
+onnxruntime:
+  pipeline_ort_static_fp32: &pipeline_ort_static_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmpose/pose-detection_onnxruntime_static.py
+  pipeline_ort_static_fp16: &pipeline_ort_static_fp16
+    convert_image: *convert_image
+    deploy_config: configs/mmpose/pose-detection_onnxruntime-fp16_static.py
+
+tensorrt:
+  pipeline_trt_static_fp32: &pipeline_trt_static_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_static
+    deploy_config: configs/mmpose/pose-detection_tensorrt_static-256x192.py
+  pipeline_trt_static_fp32_256x256: &pipeline_trt_static_fp32_256x256
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_static
+    deploy_config: configs/mmpose/pose-detection_tensorrt_static-256x256.py
+  pipeline_trt_static_fp16: &pipeline_trt_static_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_static
+    deploy_config: configs/mmpose/pose-detection_tensorrt-fp16_static-256x192.py
+  pipeline_trt_static_int8: &pipeline_trt_static_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_static
+    deploy_config: configs/mmpose/pose-detection_tensorrt-int8_static-256x192.py
+
+openvino:
+  pipeline_openvino_static_fp32: &pipeline_openvino_static_fp32
+    convert_image: *convert_image
+    deploy_config: configs/mmpose/pose-detection_openvino_static-256x192.py
+  pipeline_openvino_static_fp32_256x256: &pipeline_openvino_static_fp32_256x256
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpose/pose-detection_openvino_static-256x256.py
+
+ncnn:
+  pipeline_ncnn_static_fp32: &pipeline_ncnn_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpose/pose-detection_ncnn_static-256x192.py
+  pipeline_ncnn_static_fp32_256x256: &pipeline_ncnn_static_fp32_256x256
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpose/pose-detection_ncnn_static-256x256.py
+
+pplnn:
+  pipeline_pplnn_static_fp32: &pipeline_pplnn_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpose/pose-detection_pplnn_static-256x192.py
+
+torchscript:
+  pipeline_ts_static_fp32: &pipeline_ts_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmpose/pose-detection_torchscript.py
+
+models:
+  - name: HRNET
+    metafile: configs/body_2d_keypoint/topdown_heatmap/coco/hrnet_coco.yml
+    model_configs:
+      - configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hrnet-w48_8xb32-210e_coco-256x192.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_static_fp32
+      - *pipeline_ts_fp32
+      - *pipeline_pplnn_static_fp32
+
+  - name: LiteHRNet
+    metafile: configs/body_2d_keypoint/topdown_heatmap/coco/litehrnet_coco.yml
+    model_configs:
+      - configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_litehrnet-30_8xb64-210e_coco-256x192.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_static_fp32
+      - *pipeline_ts_fp32
+      - *pipeline_pplnn_static_fp32
+
+  - name: MSPN
+    metafile: configs/body_2d_keypoint/topdown_heatmap/coco/mspn_coco.yml
+    model_configs:
+      - configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_4xmspn50_8xb32-210e_coco-256x192.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_static_fp32
+      - *pipeline_ts_fp32
+      - *pipeline_pplnn_static_fp32
+
+  - name: Hourglass
+    metafile: configs/body_2d_keypoint/topdown_heatmap/coco/hourglass_coco.yml
+    model_configs:
+      - configs/body_2d_keypoint/topdown_heatmap/coco/td-hm_hourglass52_8xb32-210e_coco-256x256.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp32_256x256
+      - *pipeline_ncnn_static_fp32_256x256
+      - *pipeline_openvino_static_fp32_256x256
+
+  - name: SimCC
+    metafile: configs/body_2d_keypoint/simcc/coco/mobilenetv2_coco.yml
+    model_configs:
+      - configs/body_2d_keypoint/simcc/coco/simcc_mobilenetv2_wo-deconv-8xb64-210e_coco-256x192.py
+    pipelines:
+      - convert_image: *convert_image
+        deploy_config: configs/mmpose/pose-detection_simcc_onnxruntime_dynamic.py
+      - convert_image: *convert_image
+        deploy_config: configs/mmpose/pose-detection_simcc_tensorrt_dynamic-256x192.py
+        backend_test: *default_backend_test
+        sdk_config: configs/mmpose/pose-detection_simcc_sdk_static-256x192.py
+      - convert_image: *convert_image
+        deploy_config: configs/mmpose/pose-detection_simcc_ncnn_static-256x192.py
+
+  - name: YOLOX-Pose
+    metafile: configs/body_2d_keypoint/yoloxpose/coco/yoloxpose_coco.yml
+    model_configs:
+      - configs/body_2d_keypoint/yoloxpose/coco/yoloxpose_s_8xb32-300e_coco-640.py
+    pipelines:
+      - convert_image:
+          input_img: *img_human_pose
+          test_img: *img_human_pose
+        deploy_config: configs/mmpose/pose-detection_yolox-pose_onnxruntime_dynamic.py
+
+  - name: RTMO
+    metafile: configs/body_2d_keypoint/rtmo/body7/rtmo_body7.yml
+    model_configs:
+      - configs/body_2d_keypoint/rtmo/body7/rtmo-s_8xb32-600e_body7-640x640.py
+    pipelines:
+      - convert_image:
+          input_img: *img_human_pose
+          test_img: *img_human_pose
+        deploy_config: configs/mmpose/pose-detection_rtmo_onnxruntime_dynamic.py
+      - convert_image:
+          input_img: *img_human_pose
+          test_img: *img_human_pose
+        deploy_config: configs/mmpose/pose-detection_rtmo_tensorrt-fp16_dynamic-640x640.py

tests/regression/mmpretrain.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmpretrain/tree/main
+  codebase_dir: ../mmpretrain
+  checkpoint_force_download: False
+  images:
+    img_snake: &img_snake ../mmpretrain/demo/demo.JPEG
+    img_color_cat: &img_color_cat ../mmpretrain/tests/data/color.jpg
+
+  metric_info: &metric_info
+    Top 1 Accuracy: # named after metafile.Results.Metrics
+      metric_key: accuracy/top1 # key name in output json
+      tolerance: 1 # metric ±n%
+    Top 5 Accuracy:
+      metric_key: accuracy/top5
+      tolerance: 1 # metric ±n%
+  convert_image: &convert_image
+    input_img: *img_snake
+    test_img: *img_color_cat
+  backend_test: &default_backend_test True
+  sdk:
+    sdk_dynamic: &sdk_dynamic configs/mmpretrain/classification_sdk_dynamic.py
+
+
+onnxruntime:
+  pipeline_ort_static_fp32: &pipeline_ort_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpretrain/classification_onnxruntime_static.py
+
+  pipeline_ort_dynamic_fp32: &pipeline_ort_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpretrain/classification_onnxruntime_dynamic.py
+
+  pipeline_ort_dynamic_fp16: &pipeline_ort_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpretrain/classification_onnxruntime-fp16_dynamic.py
+
+tensorrt:
+  pipeline_trt_static_fp32: &pipeline_trt_static_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmpretrain/classification_tensorrt_static-224x224.py
+
+  pipeline_trt_static_fp16: &pipeline_trt_static_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmpretrain/classification_tensorrt-fp16_static-224x224.py
+
+  pipeline_trt_static_fp16_384x384: &pipeline_trt_static_fp16_384x384
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmpretrain/classification_tensorrt-fp16_static-384x384.py
+
+  pipeline_trt_static_int8: &pipeline_trt_static_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    deploy_config: configs/mmpretrain/classification_tensorrt-int8_static-224x224.py
+
+  pipeline_trt_dynamic_fp32: &pipeline_trt_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmpretrain/classification_tensorrt_dynamic-224x224-224x224.py
+
+  pipeline_trt_dynamic_fp16: &pipeline_trt_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmpretrain/classification_tensorrt-fp16_dynamic-224x224-224x224.py
+
+  pipeline_trt_dynamic_int8: &pipeline_trt_dynamic_int8
+    convert_image: *convert_image
+    calib_dataset_cfg:
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmpretrain/classification_tensorrt-int8_dynamic-224x224-224x224.py
+
+
+openvino:
+  pipeline_openvino_dynamic_fp32: &pipeline_openvino_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpretrain/classification_openvino_dynamic-224x224.py
+
+
+ncnn:
+  pipeline_ncnn_static_fp32: &pipeline_ncnn_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpretrain/classification_ncnn_static.py
+
+  pipeline_ncnn_dynamic_fp32: &pipeline_ncnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpretrain/classification_ncnn_dynamic.py
+
+
+pplnn:
+  pipeline_pplnn_dynamic_fp32: &pipeline_pplnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmpretrain/classification_pplnn_dynamic-224x224.py
+
+
+torchscript:
+  pipeline_ts_fp32: &pipeline_ts_fp32
+    convert_image: *convert_image
+    backend_test: True
+    deploy_config: configs/mmpretrain/classification_torchscript.py
+
+
+models:
+  - name: ResNet
+    metafile: configs/resnet/metafile.yml
+    model_configs:
+      - configs/resnet/resnet18_8xb32_in1k.py # TODO Not benchmark config
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+#      - *pipeline_trt_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+#      - *pipeline_trt_dynamic_int8
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: ResNeXt
+    metafile: configs/resnext/metafile.yml
+    model_configs:
+      - configs/resnext/resnext50-32x4d_8xb32_in1k.py # TODO Not benchmark config
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: DenseNet
+    metafile: configs/densenet/metafile.yml
+    model_configs:
+      - configs/densenet/densenet121_4xb256_in1k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: SE-ResNet
+    metafile: configs/seresnet/metafile.yml
+    model_configs:
+      - configs/seresnet/seresnet50_8xb32_in1k.py # TODO Not benchmark config
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: MobileNetV2
+    metafile: configs/mobilenet_v2/metafile.yml
+    model_configs:
+      - configs/mobilenet_v2/mobilenet-v2_8xb32_in1k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: ShuffleNetV1
+    metafile: configs/shufflenet_v1/metafile.yml
+    model_configs:
+      - configs/shufflenet_v1/shufflenet-v1-1x_16xb64_in1k.py
+    pipelines:
+      - *pipeline_ts_fp32
+#      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_ncnn_static_fp32
+#      - *pipeline_pplnn_dynamic_fp32
+#      - *pipeline_openvino_dynamic_fp32
+
+  - name: ShuffleNetV2
+    metafile: configs/shufflenet_v2/metafile.yml
+    model_configs:
+      - configs/shufflenet_v2/shufflenet-v2-1x_16xb64_in1k.py
+    pipelines:
+      - *pipeline_ts_fp32
+#      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_ncnn_static_fp32
+#      - *pipeline_pplnn_dynamic_fp32
+#      - *pipeline_openvino_dynamic_fp32
+
+  - name: VisionTransformer
+    metafile: configs/vision_transformer/metafile.yml
+    model_configs:
+      - configs/vision_transformer/vit-base-p16_64xb64_in1k-384px.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_static_fp16_384x384
+      - *pipeline_ncnn_static_fp32
+
+  - name: SwinTransformer
+    metafile: configs/swin_transformer/metafile.yml
+    model_configs:
+      - configs/swin_transformer/swin-tiny_16xb64_in1k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+
+  - name: MobileOne
+    metafile: configs/mobileone/metafile.yml
+    model_configs:
+      - configs/mobileone/mobileone-s0_8xb32_in1k.py
+    pipelines:
+      - *pipeline_trt_static_fp16
+      - *pipeline_ort_dynamic_fp32
+
+  - name: EfficientNet
+    metafile: configs/efficientnet/metafile.yml
+    model_configs:
+      - configs/efficientnet/efficientnet-b0_8xb32_in1k.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - convert_image: *convert_image
+        deploy_config: configs/mmpretrain/classification_tensorrt_dynamic-224x224-224x224.py
+
+  - name: Conformer
+    metafile: configs/conformer/metafile.yml
+    model_configs:
+      - configs/conformer/conformer-tiny-p16_8xb128_in1k.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+
+  - name: EfficientFormer
+    metafile: configs/efficientformer/metafile.yml
+    model_configs:
+      - configs/efficientformer/efficientformer-l1_8xb128_in1k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: MobileNetV3
+    metafile: configs/mobilenet_v3/metafile.yml
+    model_configs:
+      - configs/mobilenet_v3/mobilenet-v3-small_8xb128_in1k.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32

tests/regression/mmrotate.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmrotate/tree/main
+  codebase_dir: ../mmrotate
+  checkpoint_force_download: False
+  images:
+    img_demo: &img_demo ../mmrotate/demo/demo.jpg
+    img_dota_demo: &img_dota_demo ../mmrotate/demo/dota_demo.jpg
+  metric_info: &metric_info
+    mAP: # named after metafile.Results.Metrics
+      metric_key: AP # eval key name
+      tolerance: 0.10 # metric ±n%
+  convert_image_det: &convert_image_det
+    input_img: *img_demo
+    test_img: *img_dota_demo
+  backend_test: &default_backend_test False
+
+onnxruntime:
+  # ======= detection =======
+  pipeline_ort_detection_static_fp32: &pipeline_ort_detection_static_fp32
+    convert_image: *convert_image_det
+    backend_test: False
+    deploy_config: configs/mmrotate/rotated-detection_onnxruntime_static.py
+
+  pipeline_ort_detection_dynamic_fp32: &pipeline_ort_detection_dynamic_fp32
+    convert_image: *convert_image_det
+    deploy_config: configs/mmrotate/rotated-detection_onnxruntime_dynamic.py
+
+  pipeline_ort_detection_dynamic_fp16: &pipeline_ort_detection_dynamic_fp16
+    convert_image: *convert_image_det
+    deploy_config: configs/mmrotate/rotated-detection_onnxruntime-fp16_dynamic.py
+
+tensorrt:
+  # ======= detection =======
+  pipeline_trt_detection_dynamic_fp32: &pipeline_trt_detection_dynamic_fp32
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    deploy_config: configs/mmrotate/rotated-detection_tensorrt_dynamic-320x320-1024x1024.py
+
+  pipeline_trt_detection_dynamic_fp16: &pipeline_trt_detection_dynamic_fp16
+    convert_image: *convert_image_det
+    backend_test: *default_backend_test
+    deploy_config: configs/mmrotate/rotated-detection_tensorrt-fp16_dynamic-320x320-1024x1024.py
+
+models:
+  - name: RotatedRetinanet
+    metafile: configs/rotated_retinanet/metafile.yml
+    model_configs:
+      - configs/rotated_retinanet/rotated-retinanet-hbox-oc_r50_fpn_1x_dota.py
+    pipelines:
+      - *pipeline_ort_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16
+
+  - name: oriented_rcnn
+    metafile: configs/oriented_rcnn/metafile.yml
+    model_configs:
+      - configs/oriented_rcnn/oriented-rcnn-le90_r50_fpn_1x_dota.py
+    pipelines:
+      - *pipeline_ort_detection_static_fp32
+      - *pipeline_trt_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16
+
+  - name: gliding_vertex
+    metafile: configs/gliding_vertex/metafile.yml
+    model_configs:
+      - configs/gliding_vertex/gliding-vertex-rbox_r50_fpn_1x_dota.py
+    pipelines:
+      - *pipeline_trt_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16
+
+  - name: RoITransformer
+    metafile: configs/roi_trans/metafile.yml
+    model_configs:
+      - configs/roi_trans/roi-trans-le90_r50_fpn_1x_dota.py
+    pipelines:
+      - *pipeline_ort_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp32
+      - *pipeline_trt_detection_dynamic_fp16

tests/regression/mmseg.yml

+globals:
+  repo_url: https://github.com/open-mmlab/mmsegmentation/tree/main
+  codebase_dir: ../mmsegmentation
+  checkpoint_force_download: False
+  images:
+    img_leftImg8bit: &img_leftImg8bit ../mmsegmentation/tests/data/pseudo_cityscapes_dataset/leftImg8bit/val/frankfurt/frankfurt_000000_000294_leftImg8bit.png
+    img_loveda_0: &img_loveda_0 ../mmsegmentation/tests/data/pseudo_loveda_dataset/img_dir/0.png
+
+  metric_info: &metric_info
+    mIoU: # named after metafile.Results.Metrics
+      metric_key: mIoU # eval OrderedDict key name
+      tolerance: 1 # metric ±n%
+  convert_image: &convert_image
+    input_img: *img_leftImg8bit
+    test_img: *img_loveda_0
+  backend_test: &default_backend_test True
+  sdk:
+    sdk_dynamic: &sdk_dynamic configs/mmseg/segmentation_sdk_dynamic.py
+
+onnxruntime:
+  pipeline_ort_static_fp32: &pipeline_ort_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_onnxruntime_static-1024x2048.py
+
+  pipeline_ort_static_fp32_512x512: &pipeline_ort_static_fp32_512x512
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_onnxruntime_static-512x512.py
+
+  pipeline_ort_dynamic_fp32: &pipeline_ort_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_onnxruntime_dynamic.py
+
+  pipeline_ort_dynamic_fp16: &pipeline_ort_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_onnxruntime-fp16_dynamic.py
+
+tensorrt:
+  pipeline_trt_static_fp32: &pipeline_trt_static_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmseg/segmentation_tensorrt_static-1024x2048.py
+
+  pipeline_trt_static_fp32_512x512: &pipeline_trt_static_fp32_512x512
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_tensorrt_static-512x512.py
+
+  pipeline_trt_static_fp16: &pipeline_trt_static_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmseg/segmentation_tensorrt-fp16_static-1024x2048.py
+
+  pipeline_trt_static_int8: &pipeline_trt_static_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmseg/segmentation_tensorrt-int8_static-1024x2048.py
+
+  pipeline_trt_static_fp16_512x512: &pipeline_trt_static_fp16_512x512
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_tensorrt-fp16_static-512x512.py
+
+  pipeline_trt_dynamic_fp32: &pipeline_trt_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmseg/segmentation_tensorrt_dynamic-512x1024-2048x2048.py
+
+  pipeline_trt_dynamic_fp16: &pipeline_trt_dynamic_fp16
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmseg/segmentation_tensorrt-fp16_dynamic-512x1024-2048x2048.py
+
+  pipeline_trt_dynamic_int8: &pipeline_trt_dynamic_int8
+    convert_image: *convert_image
+    backend_test: *default_backend_test
+    sdk_config: *sdk_dynamic
+    deploy_config: configs/mmseg/segmentation_tensorrt-int8_dynamic-512x1024-2048x2048.py
+
+openvino:
+  pipeline_openvino_dynamic_fp32: &pipeline_openvino_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_openvino_dynamic-1024x2048.py
+
+  pipeline_openvino_static_fp32: &pipeline_openvino_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_openvino_static-1024x2048.py
+
+  pipeline_openvino_static_fp32_512x512: &pipeline_openvino_static_fp32_512x512
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_openvino_static-512x512.py
+
+ncnn:
+  pipeline_ncnn_static_fp32: &pipeline_ncnn_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_ncnn_static-512x512.py
+
+pplnn:
+  pipeline_pplnn_dynamic_fp32: &pipeline_pplnn_dynamic_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_pplnn_dynamic-1024x2048.py
+
+  pipeline_pplnn_static_fp32: &pipeline_pplnn_static_fp32
+    convert_image: *convert_image
+    backend_test: False
+    deploy_config: configs/mmseg/segmentation_pplnn_static-1024x2048.py
+
+torchscript:
+  pipeline_ts_fp32: &pipeline_ts_fp32
+    convert_image: *convert_image
+    backend_test: True
+    deploy_config: configs/mmseg/segmentation_torchscript.py
+
+
+models:
+  - name: FCN
+    metafile: configs/fcn/metafile.yaml
+    model_configs:
+      - configs/fcn/fcn_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: PSPNet
+    metafile: configs/pspnet/metafile.yaml
+    model_configs:
+      - configs/pspnet/pspnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_static_fp32
+      - *pipeline_openvino_static_fp32
+
+  - name: deeplabv3
+    metafile: configs/deeplabv3/metafile.yaml
+    model_configs:
+      - configs/deeplabv3/deeplabv3_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: deeplabv3+
+    metafile: configs/deeplabv3plus/metafile.yaml
+    model_configs:
+      - configs/deeplabv3plus/deeplabv3plus_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+      - *pipeline_openvino_dynamic_fp32
+
+  - name: Fast-SCNN
+    metafile: configs/fastscnn/metafile.yaml
+    model_configs:
+      - configs/fastscnn/fast_scnn_8xb4-160k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_pplnn_static_fp32
+      - *pipeline_openvino_static_fp32
+
+  - name: UNet
+    metafile: configs/unet/metafile.yaml
+    model_configs:
+      - configs/unet/unet-s5-d16_fcn_4xb4-160k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_pplnn_dynamic_fp32
+
+  - name: ANN
+    metafile: configs/ann/metafile.yaml
+    model_configs:
+      - configs/ann/ann_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_ts_fp32
+
+  - name: APCNet
+    metafile: configs/apcnet/metafile.yaml
+    model_configs:
+      - configs/apcnet/apcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_ts_fp32
+
+  - name: BiSeNetV1
+    metafile: configs/bisenetv1/metafile.yaml
+    model_configs:
+      - configs/bisenetv1/bisenetv1_r18-d32_4xb4-160k_cityscapes-1024x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: BiSeNetV2
+    metafile: configs/bisenetv2/metafile.yaml
+    model_configs:
+      - configs/bisenetv2/bisenetv2_fcn_4xb4-160k_cityscapes-1024x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: CGNet
+    metafile: configs/cgnet/metafile.yaml
+    model_configs:
+      - configs/cgnet/cgnet_fcn_4xb8-60k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: EMANet
+    metafile: configs/emanet/metafile.yaml
+    model_configs:
+      - configs/emanet/emanet_r50-d8_4xb2-80k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: EncNet
+    metafile: configs/encnet/metafile.yaml
+    model_configs:
+      - configs/encnet/encnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: ERFNet
+    metafile: configs/erfnet/metafile.yaml
+    model_configs:
+      - configs/erfnet/erfnet_fcn_4xb4-160k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: FastFCN
+    metafile: configs/fastfcn/metafile.yaml
+    model_configs:
+      - configs/fastfcn/fastfcn_r50-d32_jpu_aspp_4xb2-80k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: GCNet
+    metafile: configs/gcnet/metafile.yaml
+    model_configs:
+      - configs/gcnet/gcnet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ts_fp32
+
+  - name: ICNet
+    metafile: configs/icnet/metafile.yaml
+    model_configs:
+      - configs/icnet/icnet_r18-d8_4xb2-80k_cityscapes-832x832.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+      - *pipeline_openvino_static_fp32
+      - *pipeline_ts_fp32
+
+  - name: ISANet
+    metafile: configs/isanet/metafile.yaml
+    model_configs:
+      - configs/isanet/isanet_r50-d8_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_static_fp32_512x512
+      - *pipeline_trt_static_fp16_512x512
+      - *pipeline_openvino_static_fp32_512x512
+
+  - name: OCRNet
+    metafile: configs/ocrnet/metafile.yaml
+    model_configs:
+      - configs/ocrnet/ocrnet_hr18s_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: PointRend
+    metafile: configs/point_rend/metafile.yaml
+    model_configs:
+      - configs/point_rend/pointrend_r50_4xb2-80k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_static_fp32_512x512
+      - *pipeline_trt_static_fp16
+      - *pipeline_ts_fp32
+
+  - name: Semantic FPN
+    metafile: configs/sem_fpn/metafile.yaml
+    model_configs:
+      - configs/sem_fpn/fpn_r50_4xb2-80k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp32
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: STDC
+    metafile: configs/stdc/metafile.yaml
+    model_configs:
+      - configs/stdc/stdc1_in1k-pre_4xb12-80k_cityscapes-512x1024.py
+      - configs/stdc/stdc2_in1k-pre_4xb12-80k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_dynamic_fp32
+      - *pipeline_trt_dynamic_fp16
+      - *pipeline_ncnn_static_fp32
+      - *pipeline_openvino_dynamic_fp32
+      - *pipeline_ts_fp32
+
+  - name: UPerNet
+    metafile: configs/upernet/metafile.yaml
+    model_configs:
+      - configs/upernet/upernet_r50_4xb2-40k_cityscapes-512x1024.py
+    pipelines:
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp16
+  - name: Segmenter
+    metafile: configs/segmenter/metafile.yaml
+    model_configs:
+      - configs/segmenter/segmenter_vit-s_fcn_8xb1-160k_ade20k-512x512.py
+    pipelines:
+      - *pipeline_ort_static_fp32_512x512
+      - *pipeline_trt_static_fp32_512x512
+      - *pipeline_openvino_static_fp32_512x512
+      - *pipeline_ncnn_static_fp32
+  - name: SegFormer
+    metafile: configs/segformer/metafile.yaml
+    model_configs:
+      - configs/segformer/segformer_mit-b0_8xb1-160k_cityscapes-1024x1024.py
+    pipelines:
+      - *pipeline_ts_fp32
+      - *pipeline_ort_static_fp32
+      - *pipeline_trt_static_fp32
+      - *pipeline_openvino_static_fp32

tests/test_apis/test_calibration.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+from multiprocessing import Process
+
+from mmengine import Config
+
+from mmdeploy.apis import create_calib_input_data
+
+calib_file = tempfile.NamedTemporaryFile(suffix='.h5').name
+ann_file = 'tests/data/annotation.json'
+
+
+def get_end2end_deploy_cfg():
+    deploy_cfg = Config(
+        dict(
+            onnx_config=dict(
+                dynamic_axes={
+                    'input': {
+                        0: 'batch',
+                        2: 'height',
+                        3: 'width'
+                    },
+                    'dets': {
+                        0: 'batch',
+                        1: 'num_dets',
+                    },
+                    'labels': {
+                        0: 'batch',
+                        1: 'num_dets',
+                    },
+                },
+                type='onnx',
+                export_params=True,
+                keep_initializers_as_inputs=False,
+                opset_version=11,
+                save_file='end2end.onnx',
+                input_names=['input'],
+                output_names=['dets', 'labels'],
+                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,
+                )),
+            backend_config=dict(type='onnxruntime')))
+    return deploy_cfg
+
+
+def get_partition_deploy_cfg():
+    deploy_cfg = get_end2end_deploy_cfg()
+    deploy_cfg._cfg_dict['partition_config'] = dict(
+        type='two_stage', apply_marks=True)
+    return deploy_cfg
+
+
+def get_model_cfg():
+    dataset_type = 'CustomDataset'
+    data_root = 'tests/data/'
+    img_norm_cfg = dict(
+        mean=[123.675, 116.28, 103.53],
+        std=[58.395, 57.12, 57.375],
+        to_rgb=True)
+    test_pipeline = [
+        dict(type='LoadImageFromFile'),
+        dict(
+            type='MultiScaleFlipAug',
+            img_scale=(1, 1),
+            flip=False,
+            transforms=[
+                dict(type='Resize', keep_ratio=True),
+                dict(type='RandomFlip'),
+                dict(type='Normalize', **img_norm_cfg),
+                dict(type='Pad', size_divisor=32),
+                dict(type='ImageToTensor', keys=['img']),
+                dict(type='Collect', keys=['img']),
+            ])
+    ]
+
+    model_cfg = Config(
+        dict(
+            data=dict(
+                samples_per_gpu=1,
+                workers_per_gpu=1,
+                val=dict(
+                    type=dataset_type,
+                    ann_file=ann_file,
+                    img_prefix=data_root,
+                    pipeline=test_pipeline)),
+
+            # model settings
+            model=dict(
+                type='FasterRCNN',
+                backbone=dict(
+                    type='ResNet',
+                    depth=50,
+                    num_stages=4,
+                    out_indices=(0, 1, 2, 3),
+                    frozen_stages=1,
+                    norm_cfg=dict(type='BN', requires_grad=True),
+                    norm_eval=True,
+                    style='pytorch',
+                    init_cfg=dict(type='Pretrained')),
+                neck=dict(
+                    type='FPN',
+                    in_channels=[256, 512, 1024, 2048],
+                    out_channels=256,
+                    num_outs=5),
+                rpn_head=dict(
+                    type='RPNHead',
+                    in_channels=256,
+                    feat_channels=256,
+                    anchor_generator=dict(
+                        type='AnchorGenerator',
+                        scales=[8],
+                        ratios=[0.5, 1.0, 2.0],
+                        strides=[4, 8, 16, 32, 64]),
+                    bbox_coder=dict(
+                        type='DeltaXYWHBBoxCoder',
+                        target_means=[.0, .0, .0, .0],
+                        target_stds=[1.0, 1.0, 1.0, 1.0]),
+                    loss_cls=dict(
+                        type='CrossEntropyLoss',
+                        use_sigmoid=True,
+                        loss_weight=1.0),
+                    loss_bbox=dict(type='L1Loss', loss_weight=1.0)),
+                roi_head=dict(
+                    type='StandardRoIHead',
+                    bbox_roi_extractor=dict(
+                        type='SingleRoIExtractor',
+                        roi_layer=dict(
+                            type='RoIAlign', output_size=7, sampling_ratio=0),
+                        out_channels=256,
+                        featmap_strides=[4, 8, 16, 32]),
+                    bbox_head=dict(
+                        type='Shared2FCBBoxHead',
+                        in_channels=256,
+                        fc_out_channels=1024,
+                        roi_feat_size=7,
+                        num_classes=80,
+                        bbox_coder=dict(
+                            type='DeltaXYWHBBoxCoder',
+                            target_means=[0., 0., 0., 0.],
+                            target_stds=[0.1, 0.1, 0.2, 0.2]),
+                        reg_class_agnostic=False,
+                        loss_cls=dict(
+                            type='CrossEntropyLoss',
+                            use_sigmoid=False,
+                            loss_weight=1.0),
+                        loss_bbox=dict(type='L1Loss', loss_weight=1.0))),
+                # model testing settings
+                test_cfg=dict(
+                    rpn=dict(
+                        nms_pre=1000,
+                        max_per_img=1000,
+                        nms=dict(type='nms', iou_threshold=0.7),
+                        min_bbox_size=0),
+                    rcnn=dict(
+                        score_thr=0.05,
+                        nms=dict(type='nms', iou_threshold=0.5),
+                        max_per_img=100)))))
+
+    return model_cfg
+
+
+def run_test_create_calib_end2end():
+    import h5py
+    model_cfg = get_model_cfg()
+    deploy_cfg = get_end2end_deploy_cfg()
+    create_calib_input_data(
+        calib_file,
+        deploy_cfg,
+        model_cfg,
+        None,
+        dataset_cfg=model_cfg,
+        dataset_type='val',
+        device='cpu')
+    assert osp.exists(calib_file)
+
+    with h5py.File(calib_file, mode='r') as calibrator:
+        assert calibrator['calib_data'] is not None
+        assert calibrator['calib_data']['end2end'] is not None
+        assert calibrator['calib_data']['end2end']['input'] is not None
+        assert calibrator['calib_data']['end2end']['input']['0'] is not None
+
+
+# Because Faster-RCNN needs too much memory on GPU, we need to run tests in a
+# new process.
+
+
+def test_create_calib_end2end():
+    p = Process(target=run_test_create_calib_end2end)
+    try:
+        p.start()
+    finally:
+        p.join()
+
+
+def run_test_create_calib_parittion():
+    import h5py
+    model_cfg = get_model_cfg()
+    deploy_cfg = get_partition_deploy_cfg()
+    create_calib_input_data(
+        calib_file,
+        deploy_cfg,
+        model_cfg,
+        None,
+        dataset_cfg=model_cfg,
+        dataset_type='val',
+        device='cpu')
+    assert osp.exists(calib_file)
+
+    input_names = ['input', 'bbox_feats']
+    with h5py.File(calib_file, mode='r') as calibrator:
+        assert calibrator['calib_data'] is not None
+        calib_data = calibrator['calib_data']
+        for i in range(2):
+            partition_name = f'partition{i}'
+            assert calib_data[partition_name] is not None
+            assert calib_data[partition_name][input_names[i]] is not None
+            assert calib_data[partition_name][input_names[i]]['0'] is not None
+
+
+def test_create_calib_parittion():
+    p = Process(target=run_test_create_calib_parittion)
+    try:
+        p.start()
+    finally:
+        p.join()

tests/test_apis/test_extract.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+
+import onnx
+import torch
+
+from mmdeploy.apis.onnx import extract_partition
+from mmdeploy.core import mark
+
+output_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+
+
+def test_extract():
+
+    @mark('add', outputs='z')
+    def add(x, y):
+        return torch.add(x, y)
+
+    class TestModel(torch.nn.Module):
+
+        def __init__(self):
+            super().__init__()
+
+        def forward(self, x, y):
+            return add(x, y)
+
+    model = TestModel().eval()
+
+    # dummy input
+    x = torch.rand(2, 3, 4)
+    y = torch.rand(2, 3, 4)
+
+    torch.onnx.export(model, (x, y), output_file)
+    onnx_model = onnx.load(output_file)
+
+    extracted = extract_partition(onnx_model, 'add:input', 'add:output')
+
+    assert extracted.graph.input[0].name == 'x'
+    assert extracted.graph.input[1].name == 'y'
+    assert extracted.graph.output[0].name == 'z'
+    assert extracted.graph.node[0].op_type == 'Add'

tests/test_apis/test_onnx2ascend.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import mmengine
+import pytest
+import torch
+import torch.nn as nn
+
+from mmdeploy.utils import Backend
+from mmdeploy.utils.test import backend_checker
+
+onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+test_img = torch.rand([1, 3, 8, 8])
+
+
+@pytest.mark.skip(reason='This a not test class but a utility class.')
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x * 0.5
+
+
+test_model = TestModel().eval()
+
+
+def generate_onnx_file(model):
+    with torch.no_grad():
+        dynamic_axes = {
+            'input': {
+                0: 'batch',
+                2: 'width',
+                3: 'height'
+            },
+            'output': {
+                0: 'batch'
+            }
+        }
+        torch.onnx.export(
+            model,
+            test_img,
+            onnx_file,
+            output_names=['output'],
+            input_names=['input'],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11,
+            dynamic_axes=dynamic_axes)
+        assert osp.exists(onnx_file)
+
+
+@backend_checker(Backend.ASCEND)
+def test_onnx2ascend():
+    from mmdeploy.apis.ascend import from_onnx
+    model = test_model
+    generate_onnx_file(model)
+
+    work_dir, _ = osp.split(onnx_file)
+    file_name = osp.splitext(onnx_file)[0]
+    om_path = osp.join(work_dir, file_name + '.om')
+    model_inputs = mmengine.Config(
+        dict(
+            dynamic_batch_size=[1, 2, 4],
+            input_shapes=dict(input=[-1, 3, 224, 224])))
+    from_onnx(onnx_file, work_dir, model_inputs)
+    assert osp.exists(work_dir)
+    assert osp.exists(om_path)

tests/test_apis/test_onnx2ncnn.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import pytest
+import torch
+import torch.nn as nn
+
+from mmdeploy.backend.ncnn.onnx2ncnn import get_output_model_file
+from mmdeploy.utils import Backend
+from mmdeploy.utils.test import backend_checker
+
+onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+test_img = torch.rand([1, 3, 8, 8])
+
+
+@pytest.mark.skip(reason='This a not test class but a utility class.')
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x * 0.5
+
+
+test_model = TestModel().eval()
+
+
+def generate_onnx_file(model):
+    with torch.no_grad():
+        dynamic_axes = {
+            'input': {
+                0: 'batch',
+                2: 'width',
+                3: 'height'
+            },
+            'output': {
+                0: 'batch'
+            }
+        }
+        torch.onnx.export(
+            model,
+            test_img,
+            onnx_file,
+            output_names=['output'],
+            input_names=['input'],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11,
+            dynamic_axes=dynamic_axes)
+        assert osp.exists(onnx_file)
+
+
+@backend_checker(Backend.NCNN)
+def test_onnx2ncnn():
+    from mmdeploy.apis.ncnn import from_onnx
+    model = test_model
+    generate_onnx_file(model)
+
+    work_dir, _ = osp.split(onnx_file)
+    save_param, save_bin = get_output_model_file(onnx_file, work_dir=work_dir)
+    file_name = osp.splitext(onnx_file)[0]
+    from_onnx(onnx_file, osp.join(work_dir, file_name))
+    assert osp.exists(work_dir)
+    assert osp.exists(save_param)
+    assert osp.exists(save_bin)

tests/test_apis/test_onnx2openvino.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import numpy as np
+import pytest
+import torch
+import torch.nn as nn
+from mmengine import Config
+
+from mmdeploy.utils import Backend
+from mmdeploy.utils.test import backend_checker, get_random_name
+
+
+@pytest.mark.skip(reason='This a not test class but a utility class.')
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x * 0.5
+
+
+def generate_onnx_file(model, export_img, onnx_file, input_name, output_name):
+    with torch.no_grad():
+        dynamic_axes = {
+            input_name: {
+                0: 'batch',
+                2: 'width',
+                3: 'height'
+            },
+            output_name: {
+                0: 'batch'
+            }
+        }
+        torch.onnx.export(
+            model,
+            export_img,
+            onnx_file,
+            output_names=[output_name],
+            input_names=[input_name],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11,
+            dynamic_axes=dynamic_axes)
+        assert osp.exists(onnx_file)
+
+
+def get_outputs(pytorch_model, openvino_model_path, input, input_name,
+                output_name):
+    output_pytorch = pytorch_model(input).numpy()
+
+    from mmdeploy.backend.openvino import OpenVINOWrapper
+    openvino_model = OpenVINOWrapper(openvino_model_path)
+    openvino_output = openvino_model({input_name: input})[output_name]
+
+    return output_pytorch, openvino_output
+
+
+def get_base_deploy_cfg():
+    deploy_cfg = Config(dict(backend_config=dict(type='openvino')))
+    return deploy_cfg
+
+
+def get_deploy_cfg_with_mo_args():
+    deploy_cfg = Config(dict(backend_config=dict(type='openvino')))
+    return deploy_cfg
+
+
+@pytest.mark.parametrize('get_deploy_cfg',
+                         [get_base_deploy_cfg, get_deploy_cfg_with_mo_args])
+@backend_checker(Backend.OPENVINO)
+def test_onnx2openvino(get_deploy_cfg):
+    from mmdeploy.apis.openvino import (from_onnx, get_mo_options_from_cfg,
+                                        get_output_model_file)
+    pytorch_model = TestModel().eval()
+    export_img = torch.rand([1, 3, 8, 8])
+    onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+    input_name = get_random_name()
+    output_name = get_random_name()
+    generate_onnx_file(pytorch_model, export_img, onnx_file, input_name,
+                       output_name)
+
+    input_info = {input_name: export_img.shape}
+    output_names = [output_name]
+    openvino_dir = tempfile.TemporaryDirectory().name
+    deploy_cfg = get_deploy_cfg()
+    mo_options = get_mo_options_from_cfg(deploy_cfg)
+    from_onnx(onnx_file, openvino_dir, input_info, output_names, mo_options)
+    openvino_model_path = get_output_model_file(onnx_file, openvino_dir)
+    assert osp.exists(openvino_model_path), \
+        'The file (.xml) for OpenVINO IR has not been created.'
+
+    test_img = torch.rand([1, 3, 16, 16])
+    output_pytorch, openvino_output = get_outputs(pytorch_model,
+                                                  openvino_model_path,
+                                                  test_img, input_name,
+                                                  output_name)
+    assert np.allclose(output_pytorch, openvino_output), \
+        'OpenVINO and PyTorch outputs are not the same.'
+
+
+@backend_checker(Backend.OPENVINO)
+def test_get_input_info_from_cfg():
+    from mmdeploy.apis.openvino import get_input_info_from_cfg
+
+    # Test 1
+    deploy_cfg = Config()
+    with pytest.raises(KeyError):
+        get_input_info_from_cfg(deploy_cfg)
+
+    # Test 2
+    input_name = 'input'
+    height, width = 600, 1000
+    shape = [1, 3, height, width]
+    expected_input_info = {input_name: shape}
+    deploy_cfg = Config({
+        'backend_config': {
+            'model_inputs': [{
+                'opt_shapes': expected_input_info
+            }]
+        }
+    })
+    input_info = get_input_info_from_cfg(deploy_cfg)
+    assert input_info == expected_input_info, 'Test 2: ' \
+        'The expected value of \'input_info\' does not match the received one.'
+
+    # Test 3
+    # The case where the input name in 'onnx_config'
+    # is different from 'backend_config'.
+    onnx_config_input_name = get_random_name(1234)
+    deploy_cfg.merge_from_dict(
+        {'onnx_config': {
+            'input_names': [onnx_config_input_name]
+        }})
+    expected_input_info = {onnx_config_input_name: shape}
+    input_info = get_input_info_from_cfg(deploy_cfg)
+    assert input_info == expected_input_info, 'Test 3: ' \
+        'The expected value of \'input_info\' does not match the received one.'
+
+    # Test 4
+    # The case where 'backend_config.model_inputs.opt_shapes'
+    # is given by a list, not a dictionary.
+    deploy_cfg.merge_from_dict(
+        {'backend_config': {
+            'model_inputs': [{
+                'opt_shapes': [shape]
+            }]
+        }})
+    input_info = get_input_info_from_cfg(deploy_cfg)
+    assert input_info == expected_input_info, 'Test 4: ' \
+        'The expected value of \'input_info\' does not match the received one.'

tests/test_apis/test_onnx2rknn.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import mmengine
+import pytest
+import torch
+import torch.nn as nn
+
+from mmdeploy.utils import Backend
+from mmdeploy.utils.test import backend_checker
+
+onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+test_img = torch.rand([1, 3, 8, 8])
+
+
+@pytest.mark.skip(reason='This a not test class but a utility class.')
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x * 0.5
+
+
+test_model = TestModel().eval()
+
+
+def generate_onnx_file(model):
+    with torch.no_grad():
+        torch.onnx.export(
+            model,
+            test_img,
+            onnx_file,
+            output_names=['output'],
+            input_names=['input'],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11)
+        assert osp.exists(onnx_file)
+
+
+def get_deploy_cfg():
+    deploy_cfg = mmengine.Config(
+        dict(
+            backend_config=dict(
+                type='rknn',
+                common_config=dict(),
+                quantization_config=dict(do_quantization=False, dataset=None),
+                input_size_list=[[3, 8, 8]])))
+    return deploy_cfg
+
+
+@backend_checker(Backend.RKNN)
+def test_onnx2rknn():
+    from mmdeploy.backend.rknn.onnx2rknn import onnx2rknn
+    model = test_model
+    generate_onnx_file(model)
+
+    work_dir, _ = osp.split(onnx_file)
+    rknn_file = onnx_file.replace('.onnx', '.rknn')
+    deploy_cfg = get_deploy_cfg()
+    onnx2rknn(onnx_file, rknn_file, deploy_cfg)
+    assert osp.exists(work_dir)
+    assert osp.exists(rknn_file)

tests/test_apis/test_onnx2tensorrt.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import pytest
+import torch
+import torch.nn as nn
+from mmengine import Config
+
+from mmdeploy.utils import Backend
+from mmdeploy.utils.test import backend_checker
+
+onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+engine_file = tempfile.NamedTemporaryFile(suffix='.engine').name
+test_img = torch.rand([1, 3, 8, 8])
+
+
+@pytest.mark.skip(reason='This a not test class but a utility class.')
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x * 0.5
+
+
+test_model = TestModel().eval().cuda()
+
+
+def get_deploy_cfg():
+    deploy_cfg = Config(
+        dict(
+            backend_config=dict(
+                type='tensorrt',
+                common_config=dict(
+                    fp16_mode=False, max_workspace_size=1 << 20),
+                model_inputs=[
+                    dict(
+                        input_shapes=dict(
+                            input=dict(
+                                min_shape=[1, 3, 8, 8],
+                                opt_shape=[1, 3, 8, 8],
+                                max_shape=[1, 3, 8, 8])))
+                ])))
+    return deploy_cfg
+
+
+def generate_onnx_file(model):
+    with torch.no_grad():
+        dynamic_axes = {
+            'input': {
+                0: 'batch',
+                2: 'width',
+                3: 'height'
+            },
+            'output': {
+                0: 'batch'
+            }
+        }
+        torch.onnx.export(
+            model,
+            test_img,
+            onnx_file,
+            output_names=['output'],
+            input_names=['input'],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11,
+            dynamic_axes=dynamic_axes)
+        assert osp.exists(onnx_file)
+
+
+@backend_checker(Backend.TENSORRT)
+def test_onnx2tensorrt():
+    from mmdeploy.apis.tensorrt import onnx2tensorrt
+    from mmdeploy.backend.tensorrt import load
+    model = test_model
+    generate_onnx_file(model)
+    deploy_cfg = get_deploy_cfg()
+
+    work_dir, save_file = osp.split(engine_file)
+
+    onnx2tensorrt(work_dir, save_file, 0, deploy_cfg, onnx_file)
+    assert osp.exists(work_dir)
+    assert osp.exists(engine_file)
+    engine = load(engine_file)
+    assert engine is not None

tests/test_apis/test_onnx2tvm.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import pytest
+import torch
+import torch.nn as nn
+
+from mmdeploy.utils import Backend
+from mmdeploy.utils.test import backend_checker
+
+onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+test_img = torch.rand([1, 3, 8, 8])
+
+
+@pytest.mark.skip(reason='This a not test class but a utility class.')
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        self.conv = torch.nn.Conv2d(3, 8, 3, 1, 1)
+
+    def forward(self, x):
+        return self.conv(x)
+
+
+test_model = TestModel().eval()
+
+
+def generate_onnx_file(model):
+    with torch.no_grad():
+        torch.onnx.export(
+            model,
+            test_img,
+            onnx_file,
+            output_names=['output'],
+            input_names=['input'],
+            keep_initializers_as_inputs=True,
+            do_constant_folding=True,
+            verbose=False,
+            opset_version=11)
+        assert osp.exists(onnx_file)
+
+
+@backend_checker(Backend.TVM)
+def test_onnx2tvm():
+    from mmdeploy.apis.tvm import from_onnx, get_library_ext
+    model = test_model
+    generate_onnx_file(model)
+
+    work_dir, _ = osp.split(onnx_file)
+    file_name = osp.splitext(onnx_file)[0]
+    ext = get_library_ext()
+    lib_path = osp.join(work_dir, file_name + ext)
+    bytecode_path = osp.join(work_dir, file_name + '.code')
+    log_file = osp.join(work_dir, file_name + '.log')
+    shape = {'input': test_img.shape}
+    dtype = {'input': 'float32'}
+    target = 'llvm'
+
+    # test default tuner
+    tuner_dict = dict(type='DefaultTuner', target=target)
+    from_onnx(onnx_file, lib_path, shape=shape, dtype=dtype, tuner=tuner_dict)
+    assert osp.exists(lib_path)
+
+    # test autotvm
+    lib_path = osp.join(work_dir, file_name + '_autotvm' + ext)
+    bytecode_path = osp.join(work_dir, file_name + '_autotvm.code')
+    log_file = osp.join(work_dir, file_name + '_autotvm.log')
+    tuner_dict = dict(
+        type='AutoTVMTuner',
+        target=target,
+        log_file=log_file,
+        n_trial=1,
+        tuner=dict(type='XGBTuner'))
+    from_onnx(
+        onnx_file,
+        lib_path,
+        use_vm=True,
+        bytecode_file=bytecode_path,
+        shape=shape,
+        dtype=dtype,
+        tuner=tuner_dict)
+    assert osp.exists(lib_path)
+    assert osp.exists(bytecode_path)
+
+    # test ansor
+    lib_path = osp.join(work_dir, file_name + '_ansor' + ext)
+    bytecode_path = osp.join(work_dir, file_name + '_ansor.code')
+    log_file = osp.join(work_dir, file_name + '_ansor.log')
+    tuner_dict = dict(
+        type='AutoScheduleTuner',
+        target=target,
+        log_file=log_file,
+        num_measure_trials=2)
+    from_onnx(
+        onnx_file,
+        lib_path,
+        use_vm=True,
+        bytecode_file=bytecode_path,
+        shape=shape,
+        dtype=dtype,
+        tuner=tuner_dict)
+    assert osp.exists(lib_path)
+    assert osp.exists(bytecode_path)

tests/test_apis/test_onnx_passes.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import tempfile
+from typing import Any, List, Tuple
+
+import onnx
+import pytest
+import torch
+import torch.nn as nn
+from packaging import version
+
+from mmdeploy.apis.onnx.optimizer import \
+    model_to_graph__custom_optimizer  # noqa
+from mmdeploy.core import RewriterContext
+
+onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+
+ort_cfg = dict(
+    backend_config=dict(type='onnxruntime'), onnx_config=dict(type='onnx'))
+
+
+def _find_next_node(start: int, nodes: List, op_type: str) -> Tuple[Any, int]:
+    for idx, n in enumerate(nodes[start:]):
+        if n.op_type == op_type:
+            return n, idx
+    return None, -1
+
+
+def test_merge_shape_concate():
+    pytest.importorskip('mmdeploy.backend.torchscript.ts_optimizer.onnx')
+
+    try:
+        from mmdeploy.backend.torchscript import ts_optimizer
+        opt_pass = ts_optimizer.onnx._jit_pass_merge_shape_concate
+    except ImportError:
+        pytest.skip('pass not found.')
+
+    def _optimize_onnx(ctx, graph, params_dict, torch_out):
+        opt_pass(graph)
+        return graph, params_dict, torch_out
+
+    class TestModel(nn.Module):
+
+        def __init__(self):
+            super().__init__()
+
+        def forward(self, x):
+            return x.new_zeros(x.shape[-2:])
+
+    model = TestModel()
+    x = torch.rand(1, 3, 4, 8)
+
+    with RewriterContext({}, onnx_custom_passes=_optimize_onnx):
+        torch.onnx.export(
+            model,
+            x,
+            onnx_file,
+            input_names=['input'],
+            output_names=['output'],
+            dynamic_axes=dict(input={
+                2: 'h',
+                3: 'w'
+            }),
+            opset_version=11)
+
+    onnx_model = onnx.load(onnx_file)
+    graph = onnx_model.graph
+    nodes = graph.node
+    shape_idx = 0
+    for n in nodes:
+        if n.op_type != 'Shape':
+            shape_idx += 1
+        else:
+            break
+
+    assert shape_idx < len(nodes)
+    assert nodes[shape_idx + 1].op_type == 'Gather'
+    assert nodes[shape_idx + 2].op_type == 'ConstantOfShape'
+
+
+def test_peephole():
+    pytest.importorskip('mmdeploy.backend.torchscript.ts_optimizer.onnx')
+
+    try:
+        from mmdeploy.backend.torchscript import ts_optimizer
+        opt_pass = ts_optimizer.onnx._jit_pass_onnx_peephole
+    except ImportError:
+        pytest.skip('pass not found.')
+
+    def _optimize_onnx(ctx, graph, params_dict, torch_out):
+        opt_pass(graph)
+        return graph, params_dict, torch_out
+
+    class TestModel(torch.nn.Module):
+
+        def __init__(self):
+            super().__init__()
+
+        def forward(self, x):
+
+            x = x.int()
+            x = x.int()
+            x = x.float()
+
+            x = x.view(10, -1)
+            y = x.view(2, -1)
+            z = x.view(3, -1)
+
+            return y, z
+
+    model = TestModel()
+    x = torch.rand(2, 3, 5)
+
+    with RewriterContext({}, onnx_custom_passes=_optimize_onnx):
+        torch.onnx.export(
+            model,
+            x,
+            onnx_file,
+            input_names=['input'],
+            output_names=['output1', 'output2'],
+            dynamic_axes=dict(input={
+                0: 'b',
+                1: 'c',
+                2: 'w'
+            }),
+            opset_version=11)
+
+    onnx_model = onnx.load(onnx_file)
+    graph = onnx_model.graph
+    nodes = graph.node
+
+    node, idx = _find_next_node(0, nodes, 'Cast')
+    assert node is not None
+    assert node.attribute[0].i == 6
+
+    node, idx = _find_next_node(idx + 1, nodes, 'Cast')
+    assert node is not None
+    assert node.attribute[0].i == 1
+
+    node, idx = _find_next_node(idx + 1, nodes, 'Reshape')
+    assert node is not None
+
+    node, idx = _find_next_node(idx + 1, nodes, 'Reshape')
+    assert node is not None
+
+
+def test_flatten_cls_head():
+    pytest.importorskip('mmdeploy.backend.torchscript.ts_optimizer.onnx')
+
+    try:
+        from mmdeploy.backend.torchscript import ts_optimizer
+        opt_pass = ts_optimizer.onnx._jit_pass_flatten_cls_head
+    except ImportError:
+        pytest.skip('pass not found.')
+
+    def _optimize_onnx(ctx, graph, params_dict, torch_out):
+        opt_pass(graph)
+        return graph, params_dict, torch_out
+
+    class TestModel(torch.nn.Module):
+
+        def __init__(self) -> None:
+            super().__init__()
+
+        def forward(self, x):
+            batch = x.size(0)
+            gap = nn.functional.adaptive_avg_pool2d(x, (1, 1))
+            gap = gap.reshape(batch, -1)
+            return gap + 1  # gap should not be the output
+
+    model = TestModel()
+    x = torch.rand(1, 4, 8, 8)
+
+    with RewriterContext(ort_cfg, onnx_custom_passes=_optimize_onnx):
+        torch.onnx.export(
+            model,
+            x,
+            onnx_file,
+            input_names=['input'],
+            output_names=['output'],
+            dynamic_axes=dict(input={
+                2: 'h',
+                3: 'w'
+            }),
+            opset_version=11)
+
+    onnx_model = onnx.load(onnx_file)
+    graph = onnx_model.graph
+    nodes = graph.node
+
+    node, idx = _find_next_node(0, nodes, 'GlobalAveragePool')
+    assert node is not None
+
+    node, idx = _find_next_node(idx + 1, nodes, 'Flatten')
+    assert node is not None
+
+
+def test_fuse_select_assign():
+    pytest.importorskip('mmdeploy.backend.torchscript.ts_optimizer.onnx')
+    # TODO fix later
+    if version.parse(torch.__version__) >= version.parse('2.0.0'):
+        pytest.skip('ignore torch>=2.0.0')
+    try:
+        from mmdeploy.backend.torchscript import ts_optimizer
+        opt_pass = ts_optimizer.onnx._jit_pass_fuse_select_assign
+    except ImportError:
+        pytest.skip('pass not found.')
+
+    def _optimize_onnx(ctx, graph, params_dict, torch_out):
+        opt_pass(graph, params_dict)
+        return graph, params_dict, torch_out
+
+    class TestModel(torch.nn.Module):
+
+        def __init__(self) -> None:
+            super().__init__()
+
+        def forward(self, x):
+            z = x / 2
+            y = torch.zeros_like(x)
+            y[x < 0.5] = z[x < 0.5]
+            return y
+
+    model = TestModel()
+    x = torch.rand(1, 4, 8, 8)
+
+    with RewriterContext({}, onnx_custom_passes=_optimize_onnx):
+        torch.onnx.export(
+            model,
+            x,
+            onnx_file,
+            input_names=['input'],
+            output_names=['output'],
+            dynamic_axes=dict(input={
+                2: 'h',
+                3: 'w'
+            }),
+            opset_version=11)
+
+    onnx_model = onnx.load(onnx_file)
+    graph = onnx_model.graph
+    nodes = graph.node
+
+    node, _ = _find_next_node(0, nodes, 'Where')
+    assert node is not None
+
+
+def test_common_subgraph_elimination():
+    pytest.importorskip('mmdeploy.backend.torchscript.ts_optimizer.onnx')
+
+    try:
+        from mmdeploy.backend.torchscript import ts_optimizer
+        opt_pass = ts_optimizer.onnx._jit_pass_common_subgraph_elimination
+    except ImportError:
+        pytest.skip('pass not found.')
+
+    def _optimize_onnx(ctx, graph, params_dict, torch_out):
+        opt_pass(graph, params_dict)
+        return graph, params_dict, torch_out
+
+    class TestModel(torch.nn.Module):
+
+        def __init__(self) -> None:
+            super().__init__()
+
+        def forward(self, x):
+            y = x.unsqueeze(1)
+            z = x.unsqueeze(1)
+            return y + z
+
+    model = TestModel()
+    x = torch.rand(1, 2, 3)
+
+    with RewriterContext({}, onnx_custom_passes=_optimize_onnx):
+        torch.onnx.export(
+            model,
+            x,
+            onnx_file,
+            input_names=['input'],
+            output_names=['output'],
+            dynamic_axes=dict(input={
+                1: 'h',
+                2: 'w'
+            }),
+            opset_version=11)
+
+    onnx_model = onnx.load(onnx_file)
+    graph = onnx_model.graph
+    nodes = graph.node
+
+    unsqueeze_count = 0
+    for n in nodes:
+        if n.op_type == 'Unsqueeze':
+            unsqueeze_count += 1
+    assert unsqueeze_count == 1

tests/test_apis/test_torch2onnx.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import os.path as osp
+import tempfile
+
+import onnx
+import pytest
+import torch
+import torch.nn as nn
+from mmengine import Config
+
+from mmdeploy.apis.onnx import export
+from mmdeploy.utils.config_utils import (get_backend, get_dynamic_axes,
+                                         get_onnx_config)
+from mmdeploy.utils.test import get_random_name
+
+onnx_file = tempfile.NamedTemporaryFile(suffix='.onnx').name
+
+
+@pytest.mark.skip(reason='This a not test class but a utility class.')
+class TestModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        return x * 0.5
+
+
+test_model = TestModel().eval().cuda()
+test_img = torch.rand([1, 3, 8, 8])
+input_name = get_random_name()
+output_name = get_random_name()
+dynamic_axes_dict = {
+    input_name: {
+        0: 'batch',
+        2: 'height',
+        3: 'width'
+    },
+    output_name: {
+        0: 'batch'
+    }
+}
+dynamic_axes_list = [[0, 2, 3], [0]]
+
+
+def get_deploy_cfg(input_name, output_name, dynamic_axes):
+    return Config(
+        dict(
+            onnx_config=dict(
+                dynamic_axes=dynamic_axes,
+                type='onnx',
+                export_params=True,
+                keep_initializers_as_inputs=False,
+                opset_version=11,
+                input_names=[input_name],
+                output_names=[output_name],
+                input_shape=None),
+            codebase_config=dict(type='mmagic', task=''),
+            backend_config=dict(type='onnxruntime')))
+
+
+@pytest.mark.parametrize('input_name', [input_name])
+@pytest.mark.parametrize('output_name', [output_name])
+@pytest.mark.parametrize('dynamic_axes',
+                         [dynamic_axes_dict, dynamic_axes_list])
+def test_torch2onnx(input_name, output_name, dynamic_axes):
+    deploy_cfg = get_deploy_cfg(input_name, output_name, dynamic_axes)
+
+    output_prefix = osp.splitext(onnx_file)[0]
+    context_info = dict(cfg=deploy_cfg)
+    backend = get_backend(deploy_cfg).value
+    onnx_cfg = get_onnx_config(deploy_cfg)
+    opset_version = onnx_cfg.get('opset_version', 11)
+
+    input_names = onnx_cfg['input_names']
+    output_names = onnx_cfg['output_names']
+    axis_names = input_names + output_names
+    dynamic_axes = get_dynamic_axes(deploy_cfg, axis_names)
+    verbose = not onnx_cfg.get('strip_doc_string', True) or onnx_cfg.get(
+        'verbose', False)
+    keep_initializers_as_inputs = onnx_cfg.get('keep_initializers_as_inputs',
+                                               True)
+    export(
+        test_model,
+        test_img,
+        context_info=context_info,
+        output_path_prefix=output_prefix,
+        backend=backend,
+        input_names=input_names,
+        output_names=output_names,
+        opset_version=opset_version,
+        dynamic_axes=dynamic_axes,
+        verbose=verbose,
+        keep_initializers_as_inputs=keep_initializers_as_inputs)
+
+    assert osp.exists(onnx_file)
+
+    model = onnx.load(onnx_file)
+    assert model is not None
+    try:
+        onnx.checker.check_model(model)
+    except onnx.checker.ValidationError:
+        assert False

tests/test_apis/test_torch2torchscript.py

+# Copyright (c) OpenMMLab. All rights reserved.
+import importlib
+import os.path as osp
+import tempfile
+
+import pytest
+from mmengine import Config
+
+from mmdeploy.apis import torch2torchscript
+from mmdeploy.utils import IR, Backend
+from mmdeploy.utils.test import get_random_name
+
+ts_file = tempfile.NamedTemporaryFile(suffix='.pt').name
+input_name = get_random_name()
+output_name = get_random_name()
+
+
+def get_deploy_cfg(input_name, output_name):
+    return Config(
+        dict(
+            ir_config=dict(
+                type=IR.TORCHSCRIPT.value,
+                input_names=[input_name],
+                output_names=[output_name],
+                input_shape=None),
+            codebase_config=dict(type='mmagic', task='SuperResolution'),
+            backend_config=dict(type=Backend.TORCHSCRIPT.value)))
+
+
+def get_model_cfg():
+    import mmengine
+    file = 'tests/test_codebase/test_mmagic/data/model.py'
+    model_cfg = mmengine.Config.fromfile(file)
+    return model_cfg
+
+
+@pytest.mark.parametrize('input_name', [input_name])
+@pytest.mark.parametrize('output_name', [output_name])
+@pytest.mark.skipif(
+    not importlib.util.find_spec('mmagic'), reason='requires mmagic')
+def test_torch2torchscript(input_name, output_name):
+    import numpy as np
+    deploy_cfg = get_deploy_cfg(input_name, output_name)
+    torch2torchscript(
+        np.random.randint(0, 255, (8, 8, 3)),
+        '',
+        ts_file,
+        deploy_cfg,
+        model_cfg=get_model_cfg(),
+        device='cpu')
+
+    assert osp.exists(ts_file)