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