mirror of
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63 lines
1.9 KiB
Python
63 lines
1.9 KiB
Python
import warnings
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from .basic_utils import basicOperator, deep_copy_dicts, overwrite_dicts
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default_params = {
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# op related
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"op": "RESHAPE_LIKE",
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"input_idx": None,
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"output_idx": None,
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# tensor related
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"input_h": None,
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"input_w": None,
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"input_c": None,
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"input2_h": None,
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"input2_w": None,
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"input2_c": None,
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"output_dim": None,
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"output_h": None,
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"output_w": None,
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"output_c": None,
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"input_dtype": "float32",
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"output_dtype": "float32",
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}
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class reshape_like(basicOperator):
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def __init__(self, params: dict) -> None:
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self.params = deep_copy_dicts(default_params)
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overwrite_dicts(self.params, params)
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super().__init__()
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# handle input/output tensors in HWC format
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self._add_input(
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self.params["input_idx"],
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self.params["input_dtype"],
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self.params["input_c"],
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self.params["input_w"],
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self.params["input_h"],
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)
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self._add_output(
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self.params["output_idx"],
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self.params["output_dtype"],
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self.params["output_c"],
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self.params["output_w"],
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self.params["output_h"],
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)
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if None in default_params:
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warnings.warn(f"parameters are not all set for op {self.params['op']}")
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def generate_inference_str(self):
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params = self.params
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if params["input_dtype"] == "float32":
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if params["input_w"] == params["input_h"] == 1:
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string = (
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"reshape_like_1dto4d("
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+ f"{self._getBufferstrCast(params['input_buf_add'], params['input_buf_add_offset'])},"
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+ f"{params['input2_h']},{params['input2_w']},{params['input2_c']},"
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+ f"{self._getBufferstrCast(params['output_buf_add'], params['output_buf_add_offset'])});\n"
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)
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else:
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raise NotImplementedError
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return string
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