def convert_from_keras_model(model, backend, output_dir='my-hls-test', project_name='myproject',
fpga_part='xcku115-flvb2104-2-i', clock_period=5, hls_config={}):
config = create_config(output_dir=output_dir,
project_name=project_name, backend=backend.name, fpga_part=fpga_part, clock_period=clock_period)
config['KerasModel'] = model
model_config = hls_config.get('Model', None)
if model_config is not None:
if not all(k in model_config for k in ('Precision', 'ReuseFactor')):
raise Exception('Precision and ReuseFactor must be provided in the hls_config')
else:
model_config = {}
model_config['Precision'] = backend.get_pstring(16,6)
model_config['ReuseFactor'] = '1'
config['HLSConfig']['Model'] = model_config
if 'LayerName' in hls_config:
config['HLSConfig']['LayerName'] = hls_config['LayerName']
if 'LayerType' in hls_config:
config['HLSConfig']['LayerType'] = hls_config['LayerType']
if 'Optimizers' in hls_config:
config['HLSConfig']['Optimizers'] = hls_config['Optimizers']
return keras_to_hls(config)
def convert_from_keras_model(model,
output_dir='my-hls-test',
project_name='myproject',
input_data_tb=None,
output_data_tb=None,
backend='Vivado',
hls_config={},
**kwargs):
"""Convert to hls4ml model based on the provided configuration.
Args:
model: Keras model to convert
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
Raises:
Exception: If precision and reuse factor are not present in 'hls_config'
Returns:
ModelGraph: hls4ml model.
"""
config = create_config(output_dir=output_dir,
project_name=project_name,
backend=backend,
**kwargs)
config['KerasModel'] = 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 keras_to_hls(config)
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)