quark.onnx.auto_search#
Module Contents#
Classes#
Functions#
- quark.onnx.auto_search.l2_metric(base_input: Any, ref_input: Any) Any#
Calculate the L2 metric between baseline and reference inputs.
- Args:
base_input: Baseline input as a numpy array of float32. ref_input: Reference input as a numpy array of float32.
- Returns:
The L2 metric as a float32 value.
- Note:
Only np.ndarray datatype is accepted as input.
- quark.onnx.auto_search.l1_metric(base_input: Any, ref_input: Any) Any#
Calculate the L1 metric between baseline and reference inputs.
- Args:
base_input: Baseline input as a numpy array of float32. ref_input: Reference input as a numpy array of float32.
- Returns:
The L1 metric as a float32 value.
- Note:
Only np.ndarray datatype is accepted as input.
- quark.onnx.auto_search.cos_metric(base_input: Any, ref_input: Any) Any#
Calculate the cosine metric between baseline and reference inputs.
- Args:
base_input: Baseline input as a numpy array of float32. ref_input: Reference input as a numpy array of float32.
- Returns:
The cosine metric as a float32 value. Value range: [0.0, 1.0]
- Note:
Only np.ndarray datatype is accepted as input.
- quark.onnx.auto_search.psnr_metric(base_input: Any, ref_input: Any) Any#
Calculate the psnr metric between baseline and reference inputs.
- Args:
base_input: Baseline input as a numpy array of float32. ref_input: Reference input as a numpy array of float32.
- Returns:
The psnr metric as a float32 value.
- Note:
Only np.ndarray datatype is accepted as input.
- quark.onnx.auto_search.ssim_metric(base_input: Any, ref_input: Any) Any#
Calculate the ssim metric between baseline and reference inputs.
- Args:
base_input: Baseline input as a numpy array of float32. ref_input: Reference input as a numpy array of float32.
- Returns:
The ssim metric as a float32 value.
- Note:
Only np.ndarray datatype is accepted as input.
- quark.onnx.auto_search.buildin_eval_func(onnx_path: str, data_reader: Any, save_path: str = '', save_prefix: str = 'iter_x_') str#
Buildin evalation function using data_reader
- Args:
onnx_path: onnx model path that will excute evalution, it can be either float porint or quantized onnx model data_reader: user defined data_reader save_path: path used to save the output result save_prefix: prefix string used to name the saved output
- Note: Data reader here should be defined as dataloader.Because the raw data reader is iterator, it’s
not convient for evaluation.
- class quark.onnx.auto_search.AssembleIdxs(values_idxs: Any)#
List all the combination of one list. Example:
input_idxs: [[1,2,], [3,4]] output: [[1,3], [1,4], [2,3], [2,4]]
- Args:
values_idxs
- Note:
Only list the item in the input[i] list.
- search_forward(item_forward: list[Any]) None#
Recresively find the next item until the last one.
- Args:
item_forward: searched item collection.
- run() list[int | list[int]]#
Excute the assemble process and return the result
- class quark.onnx.auto_search.SearchSpace(conf: Dict[str, Any])#
Build the all possible search space from the input. # TODO remove the invalid config generated by the search space # TODO give the config priority # TODO validate the space dict right
- Args:
config: config which includes the search space defined by the list
- Note:
Because the search space is in difference levels, so we need to split the level of the search space.
- three_level_spaces() list[Any]#
According to the user defined search space, list all the possible configs. There several situation we need to tell it apart(splited spaces): leve11 + level2 level1 + level3 level1 + level3 level1