2022-11-20 16:52:33 -05:00

49 lines
1.6 KiB
Python

import warnings
from .basic_utils import basicOperator, deep_copy_dicts, overwrite_dicts
__all__ = ["negative"]
default_params = {
# op related
"op": "NEGATIVE",
"input_idx": None,
"output_idx": None,
# tensor related
"input_size": None,
"output_size": None,
"input_dtype": "float32",
"output_dtype": "float32",
# quantization related
"weight_value": None,
"bias": None,
"input_zero_point": None,
"output_zero_point": None,
"input_scale": None,
"output_scale": None,
"multiplier": None,
"shift": None,
}
class negative(basicOperator):
def __init__(self, params: dict) -> None:
self.params = deep_copy_dicts(default_params)
overwrite_dicts(self.params, params)
super().__init__()
# handle input/output tensors in HWC format
self._add_input(self.params["input_idx"], self.params["input_dtype"], self.params["input_size"], 1, 1)
self._add_output(self.params["output_idx"], self.params["output_dtype"], self.params["input_size"], 1, 1)
if None in default_params:
warnings.warn(f"parameters are not all set for op {self.params['op']}")
def generate_inference_str(self):
params = self.params
size = params["input_size"]
string = f"fptr = (float*){self._getBufferstr(params['input_buf_add'], params['input_buf_add_offset'])};"
string += f"fptr2 = (float*){self._getBufferstr(params['output_buf_add'], params['output_buf_add_offset'])};"
string += f"for(int i = 0; i < {size}; i++) fptr2[i] = fptr[i] * -1.0f;\n"
return string