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.
98 lines
4.0 KiB
Python
98 lines
4.0 KiB
Python
#!/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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import onnx
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from cvi_toolkit.transform.BaseConverter import TensorType
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from cvi_toolkit.transform.onnx_converter import OnnxConverter
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from cvi_toolkit.transform.tflite_converter_int8 import TFLiteConverter
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from cvi_toolkit.transform.tensorflow_converter import TFConverter
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from cvi_toolkit.utils.log_setting import setup_logger
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from cvi_toolkit.data.preprocess import add_preprocess_parser, preprocess
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logger = setup_logger('root', log_level="INFO")
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class MyOnnxConverter(OnnxConverter):
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def __init__(self, model_name, onnx_model, mlir_file_path, batch_size=1, preprocessor=None):
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super().__init__(model_name, onnx_model, mlir_file_path, batch_size, preprocessor.to_dict())
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self.onnxop_factory['LeakyRelu'] = lambda node: self.convert_leaky_relu(node);
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def convert_graph(self):
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"""convert all to mlir"""
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# add input op
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# add input op
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for idx, input in enumerate(self.input_nodes):
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input_shape = list()
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for i, dim in enumerate(input.type.tensor_type.shape.dim):
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# batch size
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# dim is zero, mean mutli batch
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if i == 0 and dim.dim_value <= 0:
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input_shape.append(self.batch_size)
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else:
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input_shape.append(dim.dim_value)
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if not self.preprocess_args:
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input_op = self.CVI.add_input_op(input.name, idx, **{})
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else:
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preprocess_hint = {
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'mean': self.preprocess_args['perchannel_mean'],
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'scale': self.preprocess_args['perchannel_scale'],
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'pixel_format': self.preprocess_args["pixel_format"],
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'channel_order': self.preprocess_args["channel_order"],
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'aligned': self.preprocess_args["aligned"],
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'resize_dims': self.preprocess_args['resize_dims'],
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'keep_aspect_ratio': self.preprocess_args['keep_aspect_ratio']
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}
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# add input op
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input_op = self.CVI.add_input_op(input.name, idx, **preprocess_hint)
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self.addOperand(input.name, input_op, input_shape, TensorType.ACTIVATION)
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def NoneAndRaise(node):
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raise RuntimeError("{} Op not support now".format(node.op_type))
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# add node op
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for n in self.converted_nodes:
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self.onnxop_factory.get(n.op_type, lambda x: NoneAndRaise(x))(n)
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self.add_softmax_op()
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# add return op
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return_op = list()
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# Set output
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op, _, _ = self.getOperand("prob")
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return_op.append(op)
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self.CVI.add_return_op(return_op)
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mlir_txt = self.CVI.print_module()
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with open(self.mlir_file_path, "w") as f:
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f.write(mlir_txt)
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def add_softmax_op(self):
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softmax_op_param = {
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'tpu': False,
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'do_quant': False,
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'operation_name': 'mysoftmax',
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'threshold_overwrite': 'none',
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'param': {
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'axis': 1
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}
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}
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op, input_shape, tensor_type = self.getOperand('output')
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operands = list()
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operands.append(op)
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output_shape = input_shape
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custom_op = self.CVI.add_custom_op("prob_softmax", operands, output_shape, **softmax_op_param)
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self.addOperand("prob", custom_op, output_shape, TensorType.ACTIVATION)
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if __name__ == "__main__":
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onnx_model = onnx.load('model/resnet18.onnx')
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preprocessor = preprocess()
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preprocessor.config(net_input_dims="224,224",
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resize_dims="256,256", crop_method='center', keep_aspect_ratio=False,
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raw_scale=1.0, mean='0.406,0.456,0.485', std='0.225,0.224,0.229', input_scale=1.0,
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channel_order='bgr', pixel_format=None, data_format='nchw',
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aligned=False, gray=False)
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c = MyOnnxConverter('resnet18', 'model/resnet18.onnx',
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'resnet18.mlir', batch_size=1, preprocessor=preprocessor)
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c.run()
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