35 lines
1.2 KiB
Python
35 lines
1.2 KiB
Python
_base_ = ['../_base_/base_static.py', '../../_base_/backends/rknn.py']
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onnx_config = dict(input_shape=[320, 320])
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codebase_config = dict(model_type='rknn')
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backend_config = dict(
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input_size_list=[[3, 320, 320]],
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quantization_config=dict(do_quantization=False))
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# # yolov3, yolox for rknn-toolkit and rknn-toolkit2
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# partition_config = dict(
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# type='rknn', # the partition policy name
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# apply_marks=True, # should always be set to True
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# partition_cfg=[
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# dict(
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# save_file='model.onnx', # name to save the partitioned onnx
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# start=['detector_forward:input'], # [mark_name:input, ...]
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# end=['yolo_head:input'], # [mark_name:output, ...]
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# output_names=[f'pred_maps.{i}' for i in range(3)]) # out names
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# ])
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# # retinanet, ssd, fsaf for rknn-toolkit2
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# partition_config = dict(
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# type='rknn', # the partition policy name
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# apply_marks=True,
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# partition_cfg=[
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# dict(
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# save_file='model.onnx',
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# start='detector_forward:input',
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# end=['BaseDenseHead:output'],
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# output_names=[f'BaseDenseHead.cls.{i}' for i in range(5)] +
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# [f'BaseDenseHead.loc.{i}' for i in range(5)])
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# ])
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