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

79 lines
2.3 KiB
Python

import warnings
from .basic_utils import basicOperator, deep_copy_dicts, overwrite_dicts
default_params = {
# op related
"op": "BIAS_ADD",
"input_idx": None,
"output_idx": None,
# tensor related
"input_dim": None,
"input_h": None,
"input_w": None,
"input_c": None,
"output_dim": None,
"output_h": None,
"output_w": None,
"output_c": None,
"input_dtype": "float32",
"output_dtype": "float32",
# quantization related
"bias": None,
"bias_name": None,
"input_zero_point": None,
"output_zero_point": None,
"input_scale": None,
"output_scale": None,
"multiplier": None,
"shift": None,
}
class biasAdd(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_c"],
self.params["input_w"],
self.params["input_h"],
)
self._add_output(
self.params["output_idx"],
self.params["output_dtype"],
self.params["output_c"],
self.params["output_w"],
self.params["output_h"],
)
if None in default_params:
warnings.warn(f"parameters are not all set for op {self.params['op']}")
def get_macs(self) -> int:
p = self.params
return p["output_h"] * p["output_w"] * p["output_c"]
def get_bias_size(self) -> int:
p = self.params
return 4 * p["output_c"]
def generate_inference_str(self):
params = self.params
if params["input_dtype"] == "float32":
string = (
f"bias_add_3D({self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])},"
+ f"{str(params['input_h'])},{str(params['input_w'])},"
+ f"{str(params['input_c'])},bias_fp{params['parsed_trainable']},"
+ f"{self._getBufferstrCast(params['output_buf_add'], params['output_buf_add_offset'])});\n"
)
else:
raise NotImplementedError
return string