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

80 lines
2.4 KiB
Python

import warnings
from .basic_utils import basicOperator, deep_copy_dicts, overwrite_dicts
__all__ = ["tile"]
default_params = {
# op related
"op": "TILE",
"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,
"output_size": None,
"reps_size": None,
"reps": 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 tile(basicOperator):
rep_cnt = 0
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_h"],
self.params["input_w"],
self.params["input_c"],
)
self._add_output(
self.params["output_idx"],
self.params["output_dtype"],
self.params["output_h"],
self.params["output_w"],
self.params["output_c"],
)
self.params["output_size"] = self.params["output_c"] * self.params["output_h"] * self.params["output_w"]
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
if params["input_dtype"] == "float32":
string = (
f"tile_3D({self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])},"
+ f"{self.params['input_h']},{self.params['input_w']},{self.params['input_c']},"
+ f"{self._getBufferstrCast(params['output_buf_add'], params['output_buf_add_offset'])},"
+ f"{self.params['output_h']},"
+ f"{self.params['output_w']},{self.params['output_c']});\n"
)
tile.rep_cnt += 1
else:
raise NotImplementedError
return string