add config module

configs/_base_/backends/ncnn-int8.py

+backend_config = dict(type='ncnn', precision='INT8', use_vulkan=False)

configs/_base_/backends/ncnn.py

+backend_config = dict(type='ncnn', precision='FP32', use_vulkan=False)

configs/_base_/backends/onnxruntime-fp16.py

+backend_config = dict(
+    type='onnxruntime',
+    precision='fp16',
+    common_config=dict(
+        min_positive_val=1e-7,
+        max_finite_val=1e4,
+        keep_io_types=False,
+        disable_shape_infer=False,
+        op_block_list=None,
+        node_block_list=None))

configs/_base_/backends/onnxruntime.py

+backend_config = dict(type='onnxruntime')

configs/_base_/backends/openvino.py

+backend_config = dict(type='openvino')

configs/_base_/backends/pplnn.py

+backend_config = dict(type='pplnn')

configs/_base_/backends/rknn.py

+backend_config = dict(
+    type='rknn',
+    common_config=dict(
+        target_platform='rv1126',  # 'rk3588'
+        optimization_level=1),
+    quantization_config=dict(
+        do_quantization=True,
+        dataset=None,
+        pre_compile=False,
+        rknn_batch_size=-1))

configs/_base_/backends/sdk.py

+backend_config = dict(type='sdk')

configs/_base_/backends/snpe.py

+backend_config = dict(type='snpe')

configs/_base_/backends/tensorrt-fp16.py

+backend_config = dict(
+    type='tensorrt', common_config=dict(fp16_mode=True, max_workspace_size=0))

configs/_base_/backends/tensorrt-int8.py

+_base_ = ['./tensorrt.py']
+
+backend_config = dict(common_config=dict(fp16_mode=True, int8_mode=True))
+
+calib_config = dict(create_calib=True, calib_file='calib_data.h5')

configs/_base_/backends/tensorrt.py

+backend_config = dict(
+    type='tensorrt', common_config=dict(fp16_mode=False, max_workspace_size=0))

configs/_base_/backends/torchscript.py

+backend_config = dict(type='torchscript')

configs/_base_/backends/tvm.py

+backend_config = dict(type='tvm')

configs/_base_/backends/vacc.py

+backend_config = dict(
+    type='vacc',
+    common_config=dict(name='end2end'),
+    model_inputs=[
+        dict(
+            shape=dict(input=[1, 3, 224, 224]),
+            qconfig=dict(
+                dtype='fp16',
+                calibrate_mode='percentile',
+                weight_scale='max',
+                data_transmode=1,
+                per_channel=False,
+                cluster_mode=0,
+                skip_conv_layers=[],
+                calib_num=1000,
+            ))
+    ])

configs/_base_/onnx_config.py

+onnx_config = dict(
+    type='onnx',
+    export_params=True,
+    keep_initializers_as_inputs=False,
+    opset_version=11,
+    save_file='end2end.onnx',
+    input_names=['input'],
+    output_names=['output'],
+    input_shape=None,
+    optimize=True)

configs/_base_/torchscript_config.py

+ir_config = dict(
+    type='torchscript',
+    save_file='end2end.pt',
+    input_names=['input'],
+    output_names=['output'],
+    input_shape=None)

configs/mmaction/video-recognition/video-recognition_2d_dynamic.py

+_base_ = ['./video-recognition_static.py']
+
+onnx_config = dict(
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            1: 'num_crops * num_segs',
+            3: 'height',
+            4: 'width'
+        },
+        'output': {
+            0: 'batch',
+        }
+    },
+    input_shape=None)

configs/mmaction/video-recognition/video-recognition_2d_tensorrt_static-224x224.py

+_base_ = ['./video-recognition_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, 250, 3, 224, 224],
+                    opt_shape=[1, 250, 3, 224, 224],
+                    max_shape=[1, 250, 3, 224, 224])))
+    ])

configs/mmaction/video-recognition/video-recognition_3d_dynamic.py

+_base_ = ['./video-recognition_static.py']
+
+onnx_config = dict(
+    dynamic_axes={
+        'input': {
+            0: 'batch',
+            1: 'num_crops * num_segs',
+            3: 'time',
+            4: 'height',
+            5: 'width'
+        },
+        'output': {
+            0: 'batch',
+        }
+    },
+    input_shape=None)