def convert_from_yaml_config(yamlConfig):
model = None
if 'OnnxModel' in yamlConfig:
if __onnx_enabled__:
model = onnx_to_hls(yamlConfig)
else:
raise Exception("ONNX not found. Please install ONNX.")
elif 'PytorchModel' in yamlConfig:
if __pytorch_enabled__:
model = pytorch_to_hls(yamlConfig)
else:
raise Exception("PyTorch not found. Please install PyTorch.")
elif 'TensorFlowModel' in yamlConfig:
if __tensorflow_enabled__:
model = tf_to_hls(yamlConfig)
else:
raise Exception("TensorFlow not found. Please install TensorFlow.")
else:
model = keras_to_hls(yamlConfig)
return model
def convert_from_config(config):
"""Convert to hls4ml model based on the provided configuration.
Arguments:
config: A string containing the path to the YAML configuration file on
the filesystem or a dict containig the parsed configuration.
Returns:
HLSModel: hls4ml model.
"""
if isinstance(config, str):
yamlConfig = parse_yaml_config(config)
else:
yamlConfig = config
model = None
if 'OnnxModel' in yamlConfig:
if __onnx_enabled__:
model = onnx_to_hls(yamlConfig)
else:
raise Exception("ONNX not found. Please install ONNX.")
elif 'PytorchModel' in yamlConfig:
if __pytorch_enabled__:
model = pytorch_to_hls(yamlConfig)
else:
raise Exception("PyTorch not found. Please install PyTorch.")
elif 'TensorFlowModel' in yamlConfig:
if __tensorflow_enabled__:
model = tf_to_hls(yamlConfig)
else:
raise Exception("TensorFlow not found. Please install TensorFlow.")
else:
model = keras_to_hls(yamlConfig)
return model
def convert_from_onnx_model(model,
output_dir='my-hls-test',
project_name='myproject',
input_data_tb=None,
output_data_tb=None,
backend='Vivado',
board=None,
part=None,
clock_period=5,
io_type='io_parallel',
hls_config={},
**kwargs):
"""
Convert an ONNX model to a hls model.
Parameters
----------
model : ONNX model object.
Model to be converted to hls model object.
output_dir (str, optional): Output directory of the generated HLS
project. Defaults to 'my-hls-test'.
project_name (str, optional): Name of the HLS project.
Defaults to 'myproject'.
input_data_tb (str, optional): String representing the path of input data in .npy or .dat format that will be
used during csim and cosim.
output_data_tb (str, optional): String representing the path of output data in .npy or .dat format that will be
used during csim and cosim.
backend (str, optional): Name of the backend to use, e.g., 'Vivado'
or 'Quartus'.
board (str, optional): One of target boards specified in `supported_board.json` file. If set to `None` a default
device of a backend will be used. See documentation of the backend used.
part (str, optional): The FPGA part. If set to `None` a default part of a backend will be used.
See documentation of the backend used. Note that if `board` is specified, the part associated to that board
will overwrite any part passed as a parameter.
clock_period (int, optional): Clock period of the design.
Defaults to 5.
io_type (str, optional): Type of implementation used. One of
'io_parallel' or 'io_serial'. Defaults to 'io_parallel'.
hls_config (dict, optional): The HLS config.
kwargs** (dict, optional): Additional parameters that will be used to create the config of the specified backend
Returns
-------
hls_model : hls4ml model object.
See Also
--------
hls4ml.convert_from_keras_model, hls4ml.convert_from_pytorch_model
Examples
--------
>>> import hls4ml
>>> config = hls4ml.utils.config_from_onnx_model(model, granularity='model')
>>> hls_model = hls4ml.converters.convert_from_onnx_model(model, hls_config=config)
"""
config = create_config(output_dir=output_dir,
project_name=project_name,
board=board,
part=part,
clock_period=clock_period,
io_type=io_type,
backend=backend,
**kwargs)
config['OnnxModel'] = model
config['InputData'] = input_data_tb
config['OutputPredictions'] = output_data_tb
config['HLSConfig'] = {}
model_config = hls_config.get('Model', None)
config['HLSConfig']['Model'] = _check_model_config(model_config)
_check_hls_config(config, hls_config)
return onnx_to_hls(config)