e25f20f7a3
commit 3f4938648950a7f3bf9a19c320ca9fae7c52de20 Author: sophgo-forum-service <forum_service@sophgo.com> Date: Mon May 13 13:44:23 2024 +0800 [feat] cviruntime opensource for cv18xx soc. - a4b6a3, add cumsum and gatherelements_pt.
71 lines
2.5 KiB
Python
Executable File
71 lines
2.5 KiB
Python
Executable File
#!/usr/bin/python3
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"""
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Copyright (C) Cvitek Co., Ltd. 2019-2020. All rights reserved.
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"""
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from argparse import ArgumentParser
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from cvi_toolkit.transform.BaseConverter import TensorType
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from cvi_toolkit.transform.caffe_converter import CaffeConverter
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from cvi_toolkit.utils.log_setting import setup_logger
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from cvi_toolkit.data.preprocess import preprocess
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logger = setup_logger('root', log_level="INFO")
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class MyCaffeConverter(CaffeConverter):
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def __init__(self, model_name, prototxt, caffe_model, mlir_file_path, batch_size=1):
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super().__init__(model_name, prototxt, caffe_model, mlir_file_path, batch_size)
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self.caffeop_factory['Upsample'] = lambda layer: self.convert_unpooling_op(layer);
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def convert_unpooling_op(self, layer):
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assert(self.layerType(layer) == "Upsample")
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data, data_shape, _ = self.getOperand(layer.bottom[0])
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mask, mask_shape, _ = self.getOperand(layer.bottom[1])
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operands = list()
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operands.append(data)
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operands.append(mask)
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p = layer.upsample_param
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scale = p.scale
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if p.HasField("upsample_h"):
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unpool_h = p.upsample_h
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else:
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unpool_h = mask_shape[2]
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if p.HasField("upsample_w"):
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unpool_w = p.upsample_w
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else:
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unpool_w = mask_shape[3]
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output_shape = [data_shape[0], data_shape[1], unpool_h, unpool_w]
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custom_op_param = {
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'tpu': True,
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'do_quant': True,
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'operation_name': 'unpooling',
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'threshold_overwrite': 'backward',
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'param': {
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'unpool_h': unpool_h,
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'unpool_w': unpool_w,
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'scale': scale
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}
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}
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print("layer name: {}, top name: {}\n".format(layer.name, layer.top[0]))
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custom_op = self.CVI.add_custom_op(layer.name,
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operands, output_shape, **custom_op_param)
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self.addOperand(layer.top[0], custom_op, output_shape, TensorType.ACTIVATION)
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if __name__ == "__main__":
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parser = ArgumentParser()
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parser.add_argument("--model_path", type=str)
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parser.add_argument("--model_dat", type=str)
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parser.add_argument("--mlir_file_path", type=str)
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args = parser.parse_args()
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#preprocessor = preprocess()
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#preprocessor.config(net_input_dims="360,480",
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# resize_dims="360,480")
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c = MyCaffeConverter('segnet', args.model_path, args.model_dat,
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args.mlir_file_path, batch_size=1)
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c.run()
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