def load_model(path):
if os.path.isfile(path):
return _nnef.parse_file(path)
graph_filename = os.path.join(path, 'graph.nnef')
quant_filename = os.path.join(path, 'graph.quant')
graph = _nnef.parse_file(graph_filename, quant_filename if os.path.isfile(quant_filename) else None)
for operation in graph.operations:
if operation.name == 'variable':
variable_filename = os.path.join(path, operation.attribs['label'] + '.dat')
tensor_name = operation.outputs['output']
with open(variable_filename) as variable_file:
data, compression = read_tensor(variable_file)
tensor = graph.tensors[tensor_name]
if list(tensor.shape) != list(data.shape):
raise Error('shape {} in variable file does not match shape {} defined in network structure'.format(data.shape, tensor.shape))
graph.tensors[tensor_name] = Tensor(tensor.name, tensor.dtype, tensor.shape, data, compression, tensor.quantization)
return graph
def parse_file(input, quantization=None, atomics=StandardOperations):
return _nnef.parse_file(input, quantization=quantization, atomics=atomics)
def parse_file(graph_fn, quant_fn=None, stdlib=None, lowered=[]):
return _nnef.parse_file(graph_fn,
quantization=quant_fn,
stdlib=stdlib,
lowered=lowered)
def parse_file(string,
flat=False,
layers=False,
atomics=StandardOperations,
shape_prop={}):
return _nnef.parse_file(string, flat, layers, atomics, shape_prop)