add config module

configs/mmdet/instance-seg/instance-seg_tensorrt-fp16_dynamic-320x320-1344x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_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, 320, 320],
+                    opt_shape=[1, 3, 800, 1344],
+                    max_shape=[1, 3, 1344, 1344])))
+    ])

configs/mmdet/instance-seg/instance-seg_tensorrt-fp16_static-800x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_static.py',
+    '../../_base_/backends/tensorrt-fp16.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/instance-seg/instance-seg_tensorrt-int8_dynamic-320x320-1344x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_dynamic.py',
+    '../../_base_/backends/tensorrt-int8.py'
+]
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 320, 320],
+                    opt_shape=[1, 3, 800, 1344],
+                    max_shape=[1, 3, 1344, 1344])))
+    ])

configs/mmdet/instance-seg/instance-seg_tensorrt-int8_static-800x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_static.py',
+    '../../_base_/backends/tensorrt-int8.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/instance-seg/instance-seg_tensorrt_dynamic-320x320-1344x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_dynamic.py',
+    '../../_base_/backends/tensorrt.py'
+]
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 320, 320],
+                    opt_shape=[1, 3, 800, 1344],
+                    max_shape=[1, 3, 1344, 1344])))
+    ])

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

+_base_ = [
+    '../_base_/base_instance-seg_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/instance-seg/instance-seg_torchscript.py

+_base_ = [
+    '../_base_/base_instance-seg_torchscript.py',
+    '../../_base_/backends/torchscript.py'
+]

configs/mmdet/instance-seg/instance-seg_tvm-ansor_static-800x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_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='AutoScheduleTuner',
+            log_file='tvm_tune_log.log',
+            num_measure_trials=20000))
+])

configs/mmdet/instance-seg/instance-seg_tvm-autotvm_static-800x1344.py

+_base_ = [
+    '../_base_/base_instance-seg_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=10000,
+            tuner=dict(type='XGBTuner'),
+        ))
+])

configs/mmdet/panoptic-seg/panoptic-seg_maskformer_onnxruntime_dynamic.py

+_base_ = [
+    './panoptic-seg_maskformer_onnxruntime_static-800x1344.py',
+]
+onnx_config = dict(
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'cls_logits': {
+            0: 'batch',
+        },
+        'mask_logits': {
+            0: 'batch',
+            2: 'h',
+            3: 'w',
+        },
+    },
+    input_shape=None)

configs/mmdet/panoptic-seg/panoptic-seg_maskformer_onnxruntime_static-800x1344.py

+_base_ = [
+    '../_base_/base_panoptic-seg_static.py',
+    '../../_base_/backends/onnxruntime.py'
+]
+onnx_config = dict(
+    opset_version=13,
+    output_names=['cls_logits', 'mask_logits'],
+    input_shape=[1344, 800])

configs/mmdet/panoptic-seg/panoptic-seg_maskformer_tensorrt_dynamic-320x512-1344x1344.py

+_base_ = ['./panoptic-seg_maskformer_tensorrt_static-800x1344.py']
+onnx_config = dict(
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'cls_logits': {
+            0: 'batch',
+        },
+        'mask_logits': {
+            0: 'batch',
+            2: 'h',
+            3: 'w',
+        },
+    },
+    input_shape=None)
+
+backend_config = dict(model_inputs=[
+    dict(
+        input_shapes=dict(
+            input=dict(
+                min_shape=[1, 3, 320, 512],
+                opt_shape=[1, 3, 800, 1344],
+                max_shape=[1, 3, 1344, 1344])))
+])

configs/mmdet/panoptic-seg/panoptic-seg_maskformer_tensorrt_static-800x1344.py

+_base_ = [
+    '../_base_/base_panoptic-seg_static.py',
+    '../../_base_/backends/tensorrt.py'
+]
+onnx_config = dict(
+    opset_version=13,
+    output_names=['cls_logits', 'mask_logits'],
+    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/panoptic-seg/panoptic-seg_panoptic-fpn_onnxruntime_dynamic.py

+_base_ = [
+    '../_base_/base_panoptic-seg_static.py',
+    '../../_base_/backends/onnxruntime.py'
+]
+onnx_config = dict(
+    input_shape=None,
+    output_names=['dets', 'labels', 'masks', 'semseg'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'dets': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'labels': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'masks': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'semseg': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+    },
+)

configs/mmdet/panoptic-seg/panoptic-seg_panoptic-fpn_tensorrt_dynamic-352x512-1344x1344.py

+_base_ = [
+    '../_base_/base_panoptic-seg_static.py',
+    '../../_base_/backends/tensorrt.py'
+]
+onnx_config = dict(
+    input_shape=None,
+    output_names=['dets', 'labels', 'masks', 'semseg'],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'dets': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'labels': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'masks': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'semseg': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+    },
+)
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 352, 512],
+                    opt_shape=[1, 3, 800, 1344],
+                    max_shape=[1, 3, 1344, 1344])))
+    ])

configs/mmdet3d/mono-detection/mono-detection_dynamic.py

+_base_ = ['./mono-detection_static.py']
+
+onnx_config = dict(
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'cls_score': {
+            0: 'batch',
+        },
+        'bbox_pred': {
+            0: 'batch',
+        },
+    },
+    input_shape=None)

configs/mmdet3d/mono-detection/mono-detection_onnxruntime-fp16_dynamic.py

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

configs/mmdet3d/mono-detection/mono-detection_onnxruntime_dynamic.py

+_base_ = [
+    './mono-detection_dynamic.py', '../../_base_/backends/onnxruntime.py'
+]

configs/mmdet3d/mono-detection/mono-detection_static.py

+_base_ = ['../../_base_/onnx_config.py']
+codebase_config = dict(
+    type='mmdet3d', task='MonoDetection', model_type='end2end')
+onnx_config = dict(
+    input_names=['input'], output_names=['cls_score', 'bbox_pred'])

configs/mmdet3d/mono-detection/mono-detection_tensorrt_dynamic-320x320-1344x1344.py

+_base_ = ['./mono-detection_dynamic.py', '../../_base_/backends/tensorrt.py']
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 320, 320],
+                    opt_shape=[1, 3, 384, 1280],
+                    max_shape=[1, 3, 1344, 1344]), ))
+    ])