add config module

configs/mmpretrain/classification_tensorrt-int8_dynamic-224x224-224x224.py

+_base_ = ['./classification_dynamic.py', '../_base_/backends/tensorrt-int8.py']
+
+onnx_config = dict(input_shape=[224, 224])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 224, 224],
+                    opt_shape=[4, 3, 224, 224],
+                    max_shape=[8, 3, 224, 224])))
+    ])

configs/mmpretrain/classification_tensorrt-int8_static-224x224.py

+_base_ = ['./classification_static.py', '../_base_/backends/tensorrt-int8.py']
+
+onnx_config = dict(input_shape=[224, 224])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 224, 224],
+                    opt_shape=[1, 3, 224, 224],
+                    max_shape=[1, 3, 224, 224])))
+    ])

configs/mmpretrain/classification_tensorrt_dynamic-224x224-224x224.py

+_base_ = ['./classification_dynamic.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=[224, 224])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 224, 224],
+                    opt_shape=[4, 3, 224, 224],
+                    max_shape=[8, 3, 224, 224])))
+    ])

configs/mmpretrain/classification_tensorrt_static-224x224.py

+_base_ = ['./classification_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=[224, 224])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 224, 224],
+                    opt_shape=[1, 3, 224, 224],
+                    max_shape=[1, 3, 224, 224])))
+    ])

configs/mmpretrain/classification_torchscript.py

+_base_ = [
+    '../_base_/torchscript_config.py', '../_base_/backends/torchscript.py'
+]
+
+ir_config = dict(input_shape=None)
+codebase_config = dict(type='mmpretrain', task='Classification')

configs/mmpretrain/classification_tvm-ansor_static-224x224.py

+_base_ = ['./classification_static.py', '../_base_/backends/tvm.py']
+
+onnx_config = dict(input_shape=[224, 224])
+backend_config = dict(model_inputs=[
+    dict(
+        shape=dict(input=[1, 3, 224, 224]),
+        dtype=dict(input='float32'),
+        tuner=dict(
+            type='AutoScheduleTuner',
+            log_file='tvm_tune_log.log',
+            num_measure_trials=2000))
+])

configs/mmpretrain/classification_tvm-autotvm-int8_static-224x224.py

+_base_ = ['./classification_tvm-autotvm_static-224x224.py']
+
+calib_config = dict(create_calib=True, calib_file='calib_data.h5')
+backend_config = dict(model_inputs=[
+    dict(
+        shape=dict(input=[1, 3, 224, 224]),
+        dtype=dict(input='float32'),
+        tuner=dict(
+            type='AutoTVMTuner',
+            log_file='tvm_tune_log.log',
+            n_trial=1000,
+            tuner=dict(type='XGBTuner'),
+        ),
+        qconfig=dict(calibrate_mode='kl_divergence', weight_scale='max'),
+    )
+])

configs/mmpretrain/classification_tvm-autotvm_static-224x224.py

+_base_ = ['./classification_static.py', '../_base_/backends/tvm.py']
+
+onnx_config = dict(input_shape=[224, 224])
+backend_config = dict(model_inputs=[
+    dict(
+        shape=dict(input=[1, 3, 224, 224]),
+        dtype=dict(input='float32'),
+        tuner=dict(
+            type='AutoTVMTuner',
+            log_file='tvm_tune_log.log',
+            n_trial=1000,
+            tuner=dict(type='XGBTuner'),
+        ))
+])

configs/mmpretrain/classification_vacc-fp16_static.py

+_base_ = ['./classification_static.py', '../_base_/backends/vacc.py']
+
+backend_config = dict(
+    common_config=dict(
+        vdsp_params_info=dict(
+            vdsp_op_type=300,
+            iimage_format=5000,
+            iimage_width=256,
+            iimage_height=256,
+            iimage_width_pitch=256,
+            iimage_height_pitch=256,
+            short_edge_threshold=256,
+            resize_type=1,
+            color_cvt_code=2,
+            color_space=0,
+            crop_size=224,
+            meanr=22459,
+            meang=22340,
+            meanb=22136,
+            stdr=21325,
+            stdg=21284,
+            stdb=21292,
+            norma_type=3)),
+    model_inputs=[
+        dict(shape=dict(input=[1, 3, 224, 224]), qconfig=dict(dtype='fp16'))
+    ])

configs/mmpretrain/classification_vacc-int8_static.py

+_base_ = ['./classification_static.py', '../_base_/backends/vacc.py']
+
+backend_config = dict(model_inputs=[
+    dict(shape=dict(input=[1, 3, 224, 224]), qconfig=dict(dtype='int8'))
+])

configs/mmrotate/rotated-detection_onnxruntime-fp16_dynamic.py

+_base_ = [
+    './rotated-detection_static.py', '../_base_/backends/onnxruntime-fp16.py'
+]
+
+onnx_config = dict(
+    output_names=['dets', 'labels'],
+    input_shape=[1024, 1024],
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'dets': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'labels': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+    })
+
+backend_config = dict(
+    common_config=dict(op_block_list=['NMSRotated', 'Resize']))

configs/mmrotate/rotated-detection_onnxruntime_dynamic.py

+_base_ = ['./rotated-detection_onnxruntime_static.py']
+onnx_config = dict(
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'dets': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'labels': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+    }, )

configs/mmrotate/rotated-detection_onnxruntime_static.py

+_base_ = ['./rotated-detection_static.py', '../_base_/backends/onnxruntime.py']
+
+onnx_config = dict(output_names=['dets', 'labels'], input_shape=[1024, 1024])

configs/mmrotate/rotated-detection_sdk_dynamic.py

+_base_ = ['./rotated-detection_static.py', '../_base_/backends/sdk.py']
+
+codebase_config = dict(model_type='sdk')
+
+backend_config = dict(pipeline=[
+    dict(type='LoadImageFromFile'),
+    dict(type='Collect', keys=['img'], meta_keys=['filename', 'ori_shape'])
+])

configs/mmrotate/rotated-detection_static.py

+_base_ = ['../_base_/onnx_config.py']
+codebase_config = dict(
+    type='mmrotate',
+    task='RotatedDetection',
+    post_processing=dict(
+        score_threshold=0.05,
+        iou_threshold=0.1,
+        pre_top_k=3000,
+        keep_top_k=2000,
+        max_output_boxes_per_class=2000))

configs/mmrotate/rotated-detection_tensorrt-fp16_dynamic-320x320-1024x1024.py

+_base_ = ['./rotated-detection_tensorrt_dynamic-320x320-1024x1024.py']
+
+backend_config = dict(common_config=dict(fp16_mode=True))

configs/mmrotate/rotated-detection_tensorrt-fp16_static-1024x1024.py

+_base_ = [
+    './rotated-detection_static.py', '../_base_/backends/tensorrt-fp16.py'
+]
+
+onnx_config = dict(output_names=['dets', 'labels'], input_shape=(1024, 1024))
+
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 1024, 1024],
+                    opt_shape=[1, 3, 1024, 1024],
+                    max_shape=[1, 3, 1024, 1024])))
+    ])

configs/mmrotate/rotated-detection_tensorrt_dynamic-320x320-1024x1024.py

+_base_ = ['./rotated-detection_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(
+    output_names=['dets', 'labels'],
+    input_shape=None,
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            2: 'height',
+            3: 'width'
+        },
+        'dets': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+        'labels': {
+            0: 'batch',
+            1: 'num_dets',
+        },
+    },
+)
+
+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, 1024, 1024],
+                    max_shape=[1, 3, 1024, 1024])))
+    ])

configs/mmseg/segmentation_ascend_static-1024x2048.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/ascend.py']
+
+onnx_config = dict(input_shape=[2048, 1024])
+backend_config = dict(
+    model_inputs=[dict(input_shapes=dict(input=[1, 3, 1024, 2048]))])

configs/mmseg/segmentation_ascend_static-512x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/ascend.py']
+
+onnx_config = dict(input_shape=[1024, 512])
+backend_config = dict(
+    model_inputs=[dict(input_shapes=dict(input=[1, 3, 512, 1024]))])