add config module

configs/mmseg/segmentation_tensorrt-fp16_dynamic-512x1024-2048x2048.py

+_base_ = ['./segmentation_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, 512, 1024],
+                    opt_shape=[1, 3, 1024, 2048],
+                    max_shape=[1, 3, 2048, 2048])))
+    ])

configs/mmseg/segmentation_tensorrt-fp16_static-1024x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-fp16.py']
+
+onnx_config = dict(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/mmseg/segmentation_tensorrt-fp16_static-1024x2048.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-fp16.py']
+
+onnx_config = dict(input_shape=[2048, 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, 2048],
+                    opt_shape=[1, 3, 1024, 2048],
+                    max_shape=[1, 3, 1024, 2048])))
+    ])

configs/mmseg/segmentation_tensorrt-fp16_static-512x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-fp16.py']
+
+onnx_config = dict(input_shape=[1024, 512])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 512, 1024],
+                    opt_shape=[1, 3, 512, 1024],
+                    max_shape=[1, 3, 512, 1024])))
+    ])

configs/mmseg/segmentation_tensorrt-fp16_static-512x512.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-fp16.py']
+
+onnx_config = dict(input_shape=[512, 512])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 512, 512],
+                    opt_shape=[1, 3, 512, 512],
+                    max_shape=[1, 3, 512, 512])))
+    ])

configs/mmseg/segmentation_tensorrt-int8_dynamic-512x1024-2048x2048.py

+_base_ = ['./segmentation_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, 512, 1024],
+                    opt_shape=[1, 3, 1024, 2048],
+                    max_shape=[1, 3, 2048, 2048])))
+    ])

configs/mmseg/segmentation_tensorrt-int8_static-1024x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-int8.py']
+
+onnx_config = dict(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/mmseg/segmentation_tensorrt-int8_static-1024x2048.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-int8.py']
+
+onnx_config = dict(input_shape=[2048, 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, 2048],
+                    opt_shape=[1, 3, 1024, 2048],
+                    max_shape=[1, 3, 1024, 2048])))
+    ])

configs/mmseg/segmentation_tensorrt-int8_static-512x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-int8.py']
+
+onnx_config = dict(input_shape=[1024, 512])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 512, 1024],
+                    opt_shape=[1, 3, 512, 1024],
+                    max_shape=[1, 3, 512, 1024])))
+    ])

configs/mmseg/segmentation_tensorrt-int8_static-512x512.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt-int8.py']
+
+onnx_config = dict(input_shape=[512, 512])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 512, 512],
+                    opt_shape=[1, 3, 512, 512],
+                    max_shape=[1, 3, 512, 512])))
+    ])

configs/mmseg/segmentation_tensorrt_dynamic-512x1024-2048x2048.py

+_base_ = ['./segmentation_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, 512, 1024],
+                    opt_shape=[1, 3, 1024, 2048],
+                    max_shape=[1, 3, 2048, 2048])))
+    ])

configs/mmseg/segmentation_tensorrt_static-1024x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(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/mmseg/segmentation_tensorrt_static-1024x2048.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=[2048, 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, 2048],
+                    opt_shape=[1, 3, 1024, 2048],
+                    max_shape=[1, 3, 1024, 2048])))
+    ])

configs/mmseg/segmentation_tensorrt_static-512x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=[1024, 512])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 512, 1024],
+                    opt_shape=[1, 3, 512, 1024],
+                    max_shape=[1, 3, 512, 1024])))
+    ])

configs/mmseg/segmentation_tensorrt_static-512x512.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tensorrt.py']
+
+onnx_config = dict(input_shape=[512, 512])
+backend_config = dict(
+    common_config=dict(max_workspace_size=1 << 30),
+    model_inputs=[
+        dict(
+            input_shapes=dict(
+                input=dict(
+                    min_shape=[1, 3, 512, 512],
+                    opt_shape=[1, 3, 512, 512],
+                    max_shape=[1, 3, 512, 512])))
+    ])

configs/mmseg/segmentation_torchscript.py

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

configs/mmseg/segmentation_tvm-ansor_static-512x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tvm.py']
+
+onnx_config = dict(input_shape=[1024, 512])
+backend_config = dict(model_inputs=[
+    dict(
+        shape=dict(input=[1, 3, 512, 1024]),
+        dtype=dict(input='float32'),
+        tuner=dict(
+            type='AutoScheduleTuner',
+            log_file='tvm_tune_log.log',
+            num_measure_trials=2000))
+])

configs/mmseg/segmentation_tvm-autotvm_static-512x1024.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/tvm.py']
+
+onnx_config = dict(input_shape=[1024, 512])
+backend_config = dict(model_inputs=[
+    dict(
+        shape=dict(input=[1, 3, 512, 1024]),
+        dtype=dict(input='float32'),
+        tuner=dict(
+            type='AutoTVMTuner',
+            log_file='tvm_tune_log.log',
+            n_trial=1000,
+            tuner=dict(type='XGBTuner')))
+])

configs/mmseg/segmentation_vacc-fp16_static_512x512.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/vacc.py']
+
+onnx_config = dict(input_shape=[512, 512])
+
+backend_config = dict(
+    common_config=dict(
+        vdsp_params_info=dict(
+            vdsp_op_type=301,
+            iimage_format=5000,
+            iimage_width=512,
+            iimage_height=512,
+            oimage_width=512,
+            oimage_height=512,
+            iimage_width_pitch=512,
+            iimage_height_pitch=512,
+            resize_type=1,
+            color_cvt_code=2,
+            color_space=0,
+            meanr=22459,
+            meang=22340,
+            meanb=22136,
+            stdr=21325,
+            stdg=21284,
+            stdb=21292,
+            norma_type=3)),
+    model_inputs=[dict(shape=dict(input=[1, 3, 512, 512]))])
+
+codebase_config = dict(model_type='vacc_seg')
+
+partition_config = dict(
+    type='vacc_seg',
+    apply_marks=True,
+    partition_cfg=[
+        dict(
+            save_file='model.onnx',
+            start=['segmentor_forward:output'],
+            # 'decode_head' will skip `ArgMax`
+            # 'seg_maps' will skip `Resize` and `ArgMax`
+            end=['decode_head:input'],
+            output_names=['feat'])
+    ])

configs/mmseg/segmentation_vacc-int8_static_512x512.py

+_base_ = ['./segmentation_static.py', '../_base_/backends/vacc.py']
+
+onnx_config = dict(input_shape=[512, 512])
+
+backend_config = dict(model_inputs=[
+    dict(shape=dict(input=[1, 3, 512, 512]), qconfig=dict(dtype='int8'))
+])
+
+partition_config = dict(
+    type='vacc_seg',
+    apply_marks=True,
+    partition_cfg=[
+        dict(
+            save_file='model.onnx',
+            start=['segmentor_forward:output'],
+            # 'decode_head' will skip `ArgMax`
+            # 'seg_maps' will skip `Resize` and `ArgMax`
+            end=['decode_head:input'],
+            output_names=['feat'])
+    ])