add config module

configs/mmpose/pose-detection_simcc_ncnn_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/ncnn.py']
+
+onnx_config = dict(input_shape=[192, 256], output_names=['simcc_x', 'simcc_y'])

configs/mmpose/pose-detection_simcc_onnxruntime-fp16_dynamic.py

+_base_ = [
+    './pose-detection_static.py', '../_base_/backends/onnxruntime-fp16.py'
+]
+
+onnx_config = dict(
+    input_shape=[192, 256],
+    output_names=['simcc_x', 'simcc_y'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'simcc_x': {
+            0: 'batch'
+        },
+        'simcc_y': {
+            0: 'batch'
+        }
+    })

configs/mmpose/pose-detection_simcc_onnxruntime_dynamic.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/onnxruntime.py']
+
+onnx_config = dict(
+    input_shape=[192, 256],
+    output_names=['simcc_x', 'simcc_y'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'simcc_x': {
+            0: 'batch'
+        },
+        'simcc_y': {
+            0: 'batch'
+        }
+    })
+
+codebase_config = dict(
+    export_postprocess=False  # do not export get_simcc_maximum
+)

configs/mmpose/pose-detection_simcc_openvino_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/openvino.py']
+
+backend_config = dict(
+    model_inputs=[dict(opt_shapes=dict(input=[1, 3, 256, 192]))])
+onnx_config = dict(
+    input_shape=[192, 256],
+    output_names=['simcc_x', 'simcc_y'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'simcc_x': {
+            0: 'batch'
+        },
+        'simcc_y': {
+            0: 'batch'
+        }
+    })

configs/mmpose/pose-detection_simcc_rknn-fp16_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/rknn.py']
+
+onnx_config = dict(input_shape=[192, 256], output_names=['simcc_x', 'simcc_y'])
+
+backend_config = dict(
+    input_size_list=[[3, 256, 192]],
+    quantization_config=dict(do_quantization=False))

configs/mmpose/pose-detection_simcc_rknn-int8_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/rknn.py']
+
+onnx_config = dict(input_shape=[192, 256], output_names=['simcc_x', 'simcc_y'])
+
+backend_config = dict(input_size_list=[[3, 256, 192]])

configs/mmpose/pose-detection_simcc_sdk_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/sdk.py']
+
+codebase_config = dict(model_type='sdk')
+onnx_config = dict(output_names=['simcc_x', 'simcc_y'])
+
+backend_config = dict(pipeline=[
+    dict(type='LoadImageFromFile'),
+    dict(type='GetBBoxCenterScale'),
+    dict(type='PackPoseInputs')
+])
+
+ext_info = dict(image_size=[192, 256], padding=1.25)

configs/mmpose/pose-detection_simcc_tensorrt-fp16_dynamic-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt-fp16.py']
+
+onnx_config = dict(
+    input_shape=[192, 256],
+    output_names=['simcc_x', 'simcc_y'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'simcc_x': {
+            0: 'batch'
+        },
+        'simcc_y': {
+            0: 'batch'
+        }
+    })
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 256, 192],
+                    opt_shape=[2, 3, 256, 192],
+                    max_shape=[4, 3, 256, 192])))
+    ])

configs/mmpose/pose-detection_simcc_tensorrt_dynamic-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(
+    input_shape=[192, 256],
+    output_names=['simcc_x', 'simcc_y'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'simcc_x': {
+            0: 'batch'
+        },
+        'simcc_y': {
+            0: 'batch'
+        }
+    })
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 256, 192],
+                    opt_shape=[2, 3, 256, 192],
+                    max_shape=[4, 3, 256, 192])))
+    ])

configs/mmpose/pose-detection_snpe_static-256x256.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/snpe.py']
+
+onnx_config = dict(input_shape=[256, 256])

configs/mmpose/pose-detection_static.py

+_base_ = ['../_base_/onnx_config.py']
+
+codebase_config = dict(type='mmpose', task='PoseDetection')

configs/mmpose/pose-detection_tensorrt-fp16_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt-fp16.py']
+
+onnx_config = dict(input_shape=[192, 256])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 256, 192],
+                    opt_shape=[1, 3, 256, 192],
+                    max_shape=[1, 3, 256, 192])))
+    ])

configs/mmpose/pose-detection_tensorrt-int8_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt-int8.py']
+
+onnx_config = dict(input_shape=[192, 256])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 256, 192],
+                    opt_shape=[1, 3, 256, 192],
+                    max_shape=[1, 3, 256, 192])))
+    ])

configs/mmpose/pose-detection_tensorrt_dynamic-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(
+    input_shape=[192, 256],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'output': {
+            0: 'batch'
+        }
+    })
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 256, 192],
+                    opt_shape=[2, 3, 256, 192],
+                    max_shape=[4, 3, 256, 192])))
+    ])

configs/mmpose/pose-detection_tensorrt_static-256x192.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=[192, 256])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 256, 192],
+                    opt_shape=[1, 3, 256, 192],
+                    max_shape=[1, 3, 256, 192])))
+    ])

configs/mmpose/pose-detection_tensorrt_static-256x256.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=[256, 256])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 256, 256],
+                    opt_shape=[1, 3, 256, 256],
+                    max_shape=[1, 3, 256, 256])))
+    ])

configs/mmpose/pose-detection_torchscript.py

+_base_ = [
+    '../_base_/torchscript_config.py', '../_base_/backends/torchscript.py'
+]
+
+codebase_config = dict(type='mmpose', task='PoseDetection')
+ir_config = dict(input_shape=[192, 256])

configs/mmpose/pose-detection_yolox-pose_onnxruntime_dynamic.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/onnxruntime.py']
+
+onnx_config = dict(
+    output_names=['dets', 'keypoints'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'dets': {
+            0: 'batch',
+        },
+        'keypoints': {
+            0: 'batch'
+        }
+    })
+
+codebase_config = dict(
+    post_processing=dict(
+        score_threshold=0.05,
+        iou_threshold=0.5,
+        max_output_boxes_per_class=200,
+        pre_top_k=5000,
+        keep_top_k=100,
+        background_label_id=-1,
+    ))

configs/mmpose/pose-detection_yolox-pose_openvino_dynamic-640x640.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/openvino.py']
+
+onnx_config = dict(
+    output_names=['dets', 'keypoints'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'dets': {
+            0: 'batch',
+        },
+        'keypoints': {
+            0: 'batch'
+        }
+    })
+backend_config = dict(
+    model_inputs=[dict(opt_shapes=dict(input=[1, 3, 640, 640]))])
+
+codebase_config = dict(
+    post_processing=dict(
+        score_threshold=0.05,
+        iou_threshold=0.5,
+        max_output_boxes_per_class=200,
+        pre_top_k=5000,
+        keep_top_k=100,
+        background_label_id=-1,
+    ))

configs/mmpose/pose-detection_yolox-pose_tensorrt_dynamic-640x640.py

+_base_ = ['./pose-detection_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(
+    output_names=['dets', 'keypoints'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+        },
+        'dets': {
+            0: 'batch',
+        },
+        'keypoints': {
+            0: 'batch'
+        }
+    })
+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])))
+    ])
+
+codebase_config = dict(
+    post_processing=dict(
+        score_threshold=0.05,
+        iou_threshold=0.5,
+        max_output_boxes_per_class=200,
+        pre_top_k=5000,
+        keep_top_k=100,
+        background_label_id=-1,
+    ))