add config module

configs/mmdet/detection/detection_tvm-autotvm_static-800x1344.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/tvm.py']
+
+onnx_config = dict(input_shape=[1344, 800])
+backend_config = dict(model_inputs=[
+    dict(
+        use_vm=True,
+        shape=dict(input=[1, 3, 800, 1344]),
+        dtype=dict(input='float32'),
+        tuner=dict(
+            type='AutoTVMTuner',
+            log_file='tvm_tune_log.log',
+            n_trial=1000,
+            tuner=dict(type='XGBTuner'),
+        ))
+])

configs/mmdet/detection/detection_vacc-fp16_static_416x416.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/vacc.py']
+
+onnx_config = dict(input_shape=[416, 416])
+
+backend_config = dict(
+    common_config=dict(
+        vdsp_params_info=dict(
+            vdsp_op_type=303,
+            iimage_format=5000,
+            iimage_width=640,
+            iimage_height=640,
+            oimage_width=416,
+            oimage_height=416,
+            iimage_width_pitch=640,
+            iimage_height_pitch=640,
+            resize_type=1,
+            color_cvt_code=2,
+            color_space=0,
+            padding_value_r=114,
+            padding_value_g=114,
+            padding_value_b=114,
+            edge_padding_type=0,
+            meanr=0,
+            meang=0,
+            meanb=0,
+            stdr=23544,
+            stdg=23544,
+            stdb=23544,
+            norma_type=3)),
+    model_inputs=[
+        dict(shape=dict(input=[1, 3, 416, 416]), qconfig=dict(dtype='fp16'))
+    ])
+
+partition_config = dict(
+    type='vacc_det',
+    apply_marks=True,
+    partition_cfg=[
+        dict(
+            save_file='model.onnx',
+            start=['detector_forward:input'],
+            end=['yolo_head:input'],
+            output_names=[f'pred_maps.{i}' for i in range(3)])
+    ])

configs/mmdet/detection/detection_vacc-int8_static_416x416.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/vacc.py']
+
+onnx_config = dict(input_shape=[416, 416])
+
+backend_config = dict(model_inputs=[
+    dict(shape=dict(input=[1, 3, 416, 416]), qconfig=dict(dtype='int8'))
+])
+
+partition_config = dict(
+    type='vacc_det',
+    apply_marks=True,
+    partition_cfg=[
+        dict(
+            save_file='model.onnx',
+            start=['detector_forward:input'],
+            end=['yolo_head:input'],
+            output_names=[f'pred_maps.{i}' for i in range(3)])
+    ])

configs/mmdet/detection/single-stage_ncnn-fp16_static-320x320.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ncnn.py']
+
+backend_config = dict(precision='FP16')
+codebase_config = dict(model_type='ncnn_end2end')
+onnx_config = dict(output_names=['detection_output'], input_shape=[320, 320])

configs/mmdet/detection/single-stage_ncnn_dynamic.py

+_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/ncnn.py']
+
+codebase_config = dict(model_type='ncnn_end2end')
+onnx_config = dict(output_names=['detection_output'], input_shape=None)

configs/mmdet/detection/single-stage_ncnn_static-300x300.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ncnn.py']
+
+codebase_config = dict(model_type='ncnn_end2end')
+onnx_config = dict(output_names=['detection_output'], input_shape=[300, 300])

configs/mmdet/detection/single-stage_ncnn_static-320x320.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ncnn.py']
+
+codebase_config = dict(model_type='ncnn_end2end')
+onnx_config = dict(output_names=['detection_output'], input_shape=[320, 320])

configs/mmdet/detection/single-stage_ncnn_static-416x416.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ncnn.py']
+
+codebase_config = dict(model_type='ncnn_end2end')
+onnx_config = dict(output_names=['detection_output'], input_shape=[416, 416])

configs/mmdet/detection/single-stage_ncnn_static-640x640.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ncnn.py']
+
+codebase_config = dict(model_type='ncnn_end2end')
+onnx_config = dict(output_names=['detection_output'], input_shape=[640, 640])

configs/mmdet/detection/single-stage_ncnn_static-800x1344.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ncnn.py']
+
+codebase_config = dict(model_type='ncnn_end2end')
+onnx_config = dict(output_names=['detection_output'], input_shape=[1344, 800])

configs/mmdet/detection/two-stage_partition_ncnn_static.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ncnn.py']
+
+partition_config = dict(type='two_stage', apply_marks=True)

configs/mmdet/detection/yolov3_partition_onnxruntime_static.py

+_base_ = ['./detection_onnxruntime_static.py']
+
+onnx_config = dict(input_shape=[608, 608])
+partition_config = dict(
+    type='yolov3_partition',
+    apply_marks=True,
+    partition_cfg=[
+        dict(
+            save_file='yolov3.onnx',
+            start=['detector_forward:input'],
+            end=['yolo_head:input'],
+            output_names=[f'pred_maps.{i}' for i in range(3)])
+    ])

configs/mmdet/instance-seg/instance-seg_onnxruntime-fp16_dynamic.py

+_base_ = [
+    '../_base_/base_instance-seg_dynamic.py',
+    '../../_base_/backends/onnxruntime-fp16.py'
+]

configs/mmdet/instance-seg/instance-seg_onnxruntime_dynamic.py

+_base_ = [
+    '../_base_/base_instance-seg_dynamic.py',
+    '../../_base_/backends/onnxruntime.py'
+]

configs/mmdet/instance-seg/instance-seg_onnxruntime_static.py

+_base_ = [
+    '../_base_/base_instance-seg_static.py',
+    '../../_base_/backends/onnxruntime.py'
+]

configs/mmdet/instance-seg/instance-seg_openvino_dynamic-800x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_dynamic.py',
+    '../../_base_/backends/openvino.py'
+]
+
+onnx_config = dict(input_shape=None)
+backend_config = dict(
+    model_inputs=[dict(opt_shapes=dict(input=[1, 3, 800, 1344]))])
+codebase_config = dict(post_processing=dict(export_postprocess_mask=False))

configs/mmdet/instance-seg/instance-seg_pplnn_dynamic-800x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_dynamic.py', '../../_base_/backends/pplnn.py'
+]
+
+onnx_config = dict(input_shape=None)
+
+backend_config = dict(model_inputs=dict(opt_shape=[1, 3, 800, 1344]))

configs/mmdet/instance-seg/instance-seg_rtmdet-ins_onnxruntime_static-640x640.py

+_base_ = [
+    '../_base_/base_instance-seg_static.py',
+    '../../_base_/backends/onnxruntime.py'
+]
+
+# Notice: Do not set input_shape in onnx_config!
+# This will result in an incorrect scale_factor!
+# The input shape will be automatically inferred
+# from the model's test_pipeline config.
+onnx_config = dict(input_shape=None)
+codebase_config = dict(post_processing=dict(export_postprocess_mask=True))

configs/mmdet/instance-seg/instance-seg_rtmdet-ins_tensorrt_static-640x640.py

+_base_ = [
+    '../_base_/base_instance-seg_static.py',
+    '../../_base_/backends/tensorrt.py'
+]
+
+# Notice: Do not set input_shape in onnx_config!
+# This will result in an incorrect scale_factor!
+# The input shape will be automatically inferred
+# from the model's test_pipeline config.
+onnx_config = dict(input_shape=None)
+codebase_config = dict(post_processing=dict(export_postprocess_mask=True))
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 640, 640],
+                    opt_shape=[1, 3, 640, 640],
+                    max_shape=[1, 3, 640, 640])))
+    ])

configs/mmdet/instance-seg/instance-seg_sdk_dynamic.py

+_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/sdk.py']
+
+codebase_config = dict(model_type='sdk', has_mask=True)
+
+backend_config = dict(pipeline=[
+    dict(type='LoadImageFromFile'),
+    dict(type='LoadAnnotations', with_bbox=True, with_mask=True),
+    dict(
+        type='PackDetInputs',
+        meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'))
+])