test_utils Namespace Reference

Data Structures
class	TestCase
class	TestRunner

Functions
	_dump_npy_included_json (str output_dir, dict json_content)
	_str_to_npy_dtype (str dtype_str)
dict	gen_random_tensor (str dtype_str, typing.Tuple[int] scale_shape, typing.Tuple[int] zerop_shape, int quantized_dimension, typing.Optional[typing.Tuple[int]] value_shape=None)

Function Documentation

_dump_npy_included_json()

test_utils._dump_npy_included_json ( str  output_dir, dict  json_content  )

protected

Dump json and npy files to output_dir

Definition at line 7 of file test_utils.py.

def _dump_npy_included_json(output_dir: str, json_content: dict):
    """
    Dump json and npy files to output_dir
    """
    # Create output_dir if not exists
    if not os.path.exists(output_dir):
        os.makedirs(output_dir)
 
    # file name for npy data (ex: 0.npy, 1.npy, ...)
    _index = 0
    _index_to_value = dict()
 
    # Replace npy to the path to the npy file
    for tensor_name, qparam in json_content.items():
        assert type(tensor_name) == str
        assert type(qparam) == dict
        for field, value in qparam.items():
            if isinstance(value, np.ndarray):
                npy_name = str(_index) + '.npy'
 
                # Save npy file
                np.save(os.path.join(output_dir, npy_name), value)
 
                # Replace to the path to the npy file
                json_content[tensor_name][field] = npy_name
 
                # Save the mapping from index to tensor name
                _index_to_value[_index] = tensor_name + "_" + field
                _index += 1
 
    # Dump json
    with open(os.path.join(output_dir, 'qparam.json'), 'w') as f:
        json.dump(json_content, f, indent=2)
 
 

References type.

Referenced by test_utils.TestRunner.run().

_str_to_npy_dtype()

test_utils._str_to_npy_dtype ( str  dtype_str )

protected

Definition at line 42 of file test_utils.py.

def _str_to_npy_dtype(dtype_str: str):
    if dtype_str == "uint8":
        return np.uint8
    if dtype_str == "int16":
        return np.int16
    if dtype_str == "int32":
        return np.int32
    if dtype_str == "int64":
        return np.int64
    raise SystemExit("Unsupported npy dtype", dtype_str)
 
 

Referenced by gen_random_tensor().

gen_random_tensor()

dict test_utils.gen_random_tensor	(	str	dtype_str,
		typing.Tuple[int]	scale_shape,
		typing.Tuple[int]	zerop_shape,
		int	quantized_dimension,
		typing.Optional[typing.Tuple[int]]	value_shape = None
	)

Definition at line 54 of file test_utils.py.

                      value_shape: typing.Optional[typing.Tuple[int]] = None) -> dict:
    content = dict()
    content['dtype'] = dtype_str
    content['scale'] = np.random.rand(scale_shape).astype(np.float32)
    # Why 256? To ensure the smallest dtype (uint8) range [0, 256)
    content['zerop'] = np.random.randint(256, size=zerop_shape, dtype=np.int64)
    content['quantized_dimension'] = quantized_dimension
 
    if value_shape != None:
        dtype = _str_to_npy_dtype(dtype_str)
        content['value'] = np.random.randint(256, size=value_shape, dtype=dtype)
    return content
 
 

References _str_to_npy_dtype().