add config module

configs/mmdet/_base_/base_tensorrt_static-300x300.py

+_base_ = ['./base_static.py', '../../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=(300, 300))
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 300, 300],
+                    opt_shape=[1, 3, 300, 300],
+                    max_shape=[1, 3, 300, 300])))
+    ])

configs/mmdet/_base_/base_tensorrt_static-800x1344.py

+_base_ = ['./base_static.py', '../../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=(1344, 800))
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 800, 1344],
+                    opt_shape=[1, 3, 800, 1344],
+                    max_shape=[1, 3, 800, 1344])))
+    ])

configs/mmdet/_base_/base_torchscript.py

+_base_ = ['../../_base_/torchscript_config.py']
+
+ir_config = dict(output_names=['dets', 'labels'])
+codebase_config = dict(
+    type='mmdet',
+    task='ObjectDetection',
+    model_type='end2end',
+    post_processing=dict(
+        score_threshold=0.05,
+        confidence_threshold=0.005,  # for YOLOv3
+        iou_threshold=0.5,
+        max_output_boxes_per_class=200,
+        pre_top_k=5000,
+        keep_top_k=100,
+        background_label_id=-1,
+    ))

configs/mmdet/detection/detection_ascend_dynamic-800x1344.py

+_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/ascend.py']
+
+onnx_config = dict(input_shape=[1344, 800])
+backend_config = dict(model_inputs=[
+    dict(
+        dynamic_image_size=[(800, 1344), (1344, 800)],
+        input_shapes=dict(input=[1, 3, -1, -1]))
+])

configs/mmdet/detection/detection_ascend_static-640x640.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/ascend.py']
+
+onnx_config = dict(input_shape=[640, 640])
+backend_config = dict(
+    model_inputs=[dict(input_shapes=dict(input=[1, 3, 640, 640]))])

configs/mmdet/detection/detection_coreml_static-800x1344.py

+_base_ = ['../_base_/base_coreml_static-800x1344.py']

configs/mmdet/detection/detection_onnxruntime-fp16_dynamic.py

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

configs/mmdet/detection/detection_onnxruntime_dynamic.py

+_base_ = ['../_base_/base_dynamic.py', '../../_base_/backends/onnxruntime.py']

configs/mmdet/detection/detection_onnxruntime_static.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/onnxruntime.py']

configs/mmdet/detection/detection_openvino_dynamic-300x300.py

+_base_ = ['../_base_/base_openvino_dynamic-300x300.py']

configs/mmdet/detection/detection_openvino_dynamic-640x640.py

+_base_ = ['../_base_/base_openvino_dynamic-640x640.py']

configs/mmdet/detection/detection_openvino_dynamic-800x1344.py

+_base_ = ['../_base_/base_openvino_dynamic-800x1344.py']

configs/mmdet/detection/detection_pplnn_dynamic-800x1344.py

+_base_ = ['../_base_/base_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/detection/detection_rknn-fp16_static-320x320.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/rknn.py']
+
+onnx_config = dict(input_shape=[320, 320])
+
+codebase_config = dict(model_type='rknn')
+
+backend_config = dict(
+    input_size_list=[[3, 320, 320]],
+    quantization_config=dict(do_quantization=False))
+
+# # yolov3, yolox for rknn-toolkit and rknn-toolkit2
+# partition_config = dict(
+#     type='rknn',  # the partition policy name
+#     apply_marks=True,  # should always be set to True
+#     partition_cfg=[
+#         dict(
+#             save_file='model.onnx',  # name to save the partitioned onnx
+#             start=['detector_forward:input'],  # [mark_name:input, ...]
+#             end=['yolo_head:input'],  # [mark_name:output, ...]
+#             output_names=[f'pred_maps.{i}' for i in range(3)])  # out names
+#     ])
+
+# # retinanet, ssd, fsaf for rknn-toolkit2
+# partition_config = dict(
+#     type='rknn',  # the partition policy name
+#     apply_marks=True,
+#     partition_cfg=[
+#         dict(
+#             save_file='model.onnx',
+#             start='detector_forward:input',
+#             end=['BaseDenseHead:output'],
+#             output_names=[f'BaseDenseHead.cls.{i}' for i in range(5)] +
+#             [f'BaseDenseHead.loc.{i}' for i in range(5)])
+#     ])

configs/mmdet/detection/detection_rknn-int8_static-320x320.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/rknn.py']
+
+onnx_config = dict(input_shape=[320, 320])
+
+codebase_config = dict(model_type='rknn')
+
+backend_config = dict(input_size_list=[[3, 320, 320]])
+
+# # yolov3, yolox for rknn-toolkit and rknn-toolkit2
+# partition_config = dict(
+#     type='rknn',  # the partition policy name
+#     apply_marks=True,  # should always be set to True
+#     partition_cfg=[
+#         dict(
+#             save_file='model.onnx',  # name to save the partitioned onnx
+#             start=['detector_forward:input'],  # [mark_name:input, ...]
+#             end=['yolo_head:input'],  # [mark_name:output, ...]
+#             output_names=[f'pred_maps.{i}' for i in range(3)])  # out names
+#     ])
+
+# # retinanet, ssd, fsaf for rknn-toolkit2
+# partition_config = dict(
+#     type='rknn',  # the partition policy name
+#     apply_marks=True,
+#     partition_cfg=[
+#         dict(
+#             save_file='model.onnx',
+#             start='detector_forward:input',
+#             end=['BaseDenseHead:output'],
+#             output_names=[f'BaseDenseHead.cls.{i}' for i in range(5)] +
+#             [f'BaseDenseHead.loc.{i}' for i in range(5)])
+#     ])

configs/mmdet/detection/detection_rknn-int8_static-640x640.py

+_base_ = ['../_base_/base_static.py', '../../_base_/backends/rknn.py']
+
+onnx_config = dict(input_shape=[640, 640])
+
+codebase_config = dict(model_type='rknn')
+
+backend_config = dict(input_size_list=[[3, 640, 640]])
+
+# rtmdet for rknn-toolkit and rknn-toolkit2
+# partition_config = dict(
+#     type='rknn',  # the partition policy name
+#     apply_marks=True,  # should always be set to True
+#     partition_cfg=[
+#         dict(
+#             save_file='model.onnx',  # name to save the partitioned onnx
+#             start=['detector_forward:input'],  # [mark_name:input, ...]
+#             end=['rtmdet_head:output'],  # [mark_name:output, ...]
+#             output_names=[f'pred_maps.{i}' for i in range(6)]) # output names
+#     ])

configs/mmdet/detection/detection_sdk_dynamic.py

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

configs/mmdet/detection/detection_tensorrt-fp16_dynamic-300x300-512x512.py

+_base_ = ['../_base_/base_tensorrt-fp16_dynamic-300x300-512x512.py']

configs/mmdet/detection/detection_tensorrt-fp16_dynamic-320x320-1344x1344.py

+_base_ = ['../_base_/base_tensorrt-fp16_dynamic-320x320-1344x1344.py']

configs/mmdet/detection/detection_tensorrt-fp16_dynamic-64x64-608x608.py

+_base_ = [
+    '../_base_/base_dynamic.py', '../../_base_/backends/tensorrt-fp16.py'
+]
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 64, 64],
+                    opt_shape=[1, 3, 608, 608],
+                    max_shape=[1, 3, 608, 608])))
+    ])