def __init__(self, prototxt):
"""Construct a Solver.
Parameters
----------
prototxt : str
The path of ``.prototxt`` file.
Returns
-------
Solver
The solver.
Examples
--------
>>> solver = Solver('solver.prototxt')
"""
self._param = pb.SolverParameter()
Parse(open(prototxt, 'r').read(), self._param)
self.ParseUpdateParam()
self._net = None
self._test_nets = []
self._layer_blobs = []
self._iter = self._current_step = 0
self._optimizer = None
self.scalar_writer = sw.ScalarSummary() if root_solver() else None
self.InitTrainNet()
self.InitTestNets()
self.BuildNets()
def __init__(self, proto_txt):
"""Construct a Solver.
Parameters
----------
proto_txt : str
The path of ``.prototxt`` file.
Returns
-------
Solver
The solver.
Examples
--------
>>> solver = Solver('solver.prototxt')
"""
self._param = _proto_def.SolverParameter()
_parse_text_proto(open(proto_txt, 'r').read(), self._param)
if self._param.iter_size > 1:
raise NotImplementedError('Gradients accumulating is deprecated.')
self._net = None
self._test_nets = []
self._layer_blobs = []
self._iter = self._current_step = 0
self.optimizer = None
self.InitTrainNet()
self.InitTestNets()
self.BuildNets()
self.ParseOptimizerArguments()
def __init__(self, prototxt):
self._param = pb.SolverParameter()
Parse(open(prototxt, 'r').read(), self._param)
self._net = None; self._test_nets = []
self._iter = self._current_step = 0
self.train = self.tests = self.update = self._updater = None
self.scalar_writer = sw.ScalarSummary() if root_solver() else None
self._lr_blobs = []
self.InitTrainNet()
self.InitTestNets()
self.CheckUpdateParam()
def __init__(self, solver_prototxt, output_dir, pretrained_model=None):
self.output_dir = output_dir
self.solver = caffe.SGDSolver(solver_prototxt)
if pretrained_model is not None:
print('Loading pretrained model '
'weights from {:s}').format(pretrained_model)
self.solver.net.copy_from(pretrained_model)
self.solver_param = caffe_pb2.SolverParameter()
with open(solver_prototxt, 'rt') as f:
pb2.text_format.Merge(f.read(), self.solver_param)