def run_solver_loop(self):
solver_def = 'solver.prototxt'
solver_param = caffe_pb2.SolverParameter()
text_format.Merge(open(solver_def).read(), solver_param)
solver = caffe.get_solver_from_string(solver_param.SerializeToString())
for i in range(50):
solver.step(1)
def run_solver_loop(self):
solver_def='solver.prototxt'
solver_param = caffe_pb2.SolverParameter()
text_format.Merge(open(solver_def).read(), solver_param)
solver = caffe.get_solver_from_string(solver_param.SerializeToString())
for i in range(50):
solver.step(1)
def __init__(self,
prototxt,
final_file=None,
snap_file=None,
solver='Adam',
log_file=None,
**kwargs):
self.running = False
self.force_snapshot = False
self.last_interrupt = 0
self.final_file, self.snap_file = final_file, snap_file
if final_file is None:
print("Are you sure you dont want to save the model?")
solver_str = 'train_net: "%s"\n' % prototxt
if solver is not None:
# Get the cases right
if solver.upper() == "ADAM": solver = "Adam"
if solver.upper() == "ADADELTA": solver = "AdaDelta"
if solver.upper() == "ADAGRAD": solver = "AdaGrad"
if solver.upper() == "NESTEROV": solver = "Nesterov"
if solver.upper() == "RMSPROP": solver = "RMSProp"
if solver.upper() == "SGD": solver = "SGD"
solver_str += 'type: "%s"\n' % solver
if solver == "RMSProp":
if 'rms_decay' not in kwargs:
kwargs['rms_decay'] = 0.9
if solver == "SGD":
if 'momentum' not in kwargs:
kwargs['momentum'] = 0.9
if solver == "Adam":
if 'momentum' not in kwargs:
kwargs['momentum'] = 0.9
if 'momentum2' not in kwargs:
kwargs['momentum2'] = 0.99
if 'base_lr' not in kwargs: kwargs['base_lr'] = 0.001
if 'lr_policy' not in kwargs: kwargs['lr_policy'] = 'fixed'
for i in kwargs:
if isinstance(kwargs[i], str):
solver_str += '%s: "%s"\n' % (i, kwargs[i])
elif isinstance(kwargs[i], int):
solver_str += '%s: %d\n' % (i, kwargs[i])
else:
solver_str += '%s: %f\n' % (i, kwargs[i])
self.solver = caffe.get_solver_from_string(solver_str)
self.solver.add_callback(self.on_start, self.on_gradient)
self.log = None
if log_file:
self.log = open(log_file, 'w')
def __init__(self,
solver_def,
solver_state=None,
weights=None,
gpus=[0],
log_dir='log',
log_db_prefix='log_db'):
"""
Acts as a driver for training with smart logging.
Logs will be stored to a MySQL database.
All paths can be set relative to the location of the solver prototxt.
:param solver_def: prototxt that defines the solver
:param solver_state: optional: a .solverstate file from which to resume training \ NEVER SET THESE TWO
:param weights: optional: a .caffemodel file from which to begin finetuning / AT THE SAME TIME
:param gpus: optional: a list of GPU IDs to use for (multi-)GPU training
if set to None, caffe will operate in CPU mode
:param log_dir: optional: will log into this directory under solver.prototxt
:param log_db_prefix: prefix for both SQLite db names and table names
The following parameters should to be set in the solver prototxt file:
log_interval log per this number of iterations (simple log) [default = 20]
viz_interval log visualization per this number of iterations (net blobs snapshot) [default = 100]
test_iter: The number of iterations for each test net.
"""
if not os.path.isabs(solver_def):
if not os.path.isfile(os.path.join(os.getcwd(), solver_def)):
os.chdir('..')
solver_def = os.path.join(os.getcwd(), solver_def)
else:
solver_def = os.path.join(os.getcwd(), solver_def)
self.solver_dir = solver_def[:solver_def.rfind('/')]
os.chdir(self.solver_dir)
self.log_dir = os.path.join(self.solver_dir, log_dir)
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
self.logprint("Logging to {}".format(self.log_dir))
self.log_db_prefix = log_db_prefix # used for db name and as a prefix for tables
self.solver_param = caffe_pb2.SolverParameter()
text_format.Merge(open(solver_def).read(), self.solver_param)
# read params from solver definition
self.iterations = self.solver_param.max_iter
self.log_interval = self.solver_param.log_interval
self.viz_interval = self.solver_param.viz_interval
self.test_interval = self.solver_param.test_interval
self.se_index_blob = self.solver_param.se_index_blob
self.se_error_blob = self.solver_param.se_error_blob
self.se_interval = self.solver_param.se_interval
self.se_indices = []
self.se_errors = []
# make solver param for net fail safe
if not os.path.isabs(self.solver_param.net):
self.solver_param.net = os.path.join(self.solver_dir,
self.solver_param.net)
if not os.path.isfile(self.solver_param.net):
raise Exception(
'could not find net definition from solver prototxt!')
self.gpus = gpus
if gpus:
self.solver_param.device_id = gpus[0]
caffe.set_device(gpus[0])
caffe.set_mode_gpu()
caffe.set_solver_count(len(gpus))
self.solver = caffe.get_solver_from_string(
self.solver_param.SerializeToString())
if solver_state:
# check if this file is in the current (or parent) directory or if the solver path needs to be prepended
if not os.path.isfile(os.path.join('..', solver_state)):
if not os.path.isfile(solver_state):
solver_state = os.path.join(self.solver_dir, solver_state)
if not os.path.isfile(solver_state):
raise Exception(
'could not find solver state specified!')
else:
solver_state = os.path.join('..', solver_state)
self.solver.restore(solver_state)
if weights:
raise Exception(
'should not specify both solverstate and caffemodel! Preference will be given to solverstate.'
)
if weights and not solver_state:
self.solver.net.copy_from(weights)
self.sync = None
self.viz_thread = None
self.log_thread = None
self.test_input = None
self.test_out_blobs = None
self.test_start_layer = None
self.iteration = 0
# check if blobs update_sample_errors exist
if self.se_interval and not self.se_index_blob in self.solver.net.blobs:
self.logprint(
"WARNING: index_blob not found in net! Won't send errors to Net."
)
self.se_interval = 0
if self.se_interval and not self.se_error_blob in self.solver.net.blobs:
self.logprint(
"WARNING: error_blob not found in net! Won't send errors to Net."
)
self.se_interval = 0
def __init__(self, solver_def, solver_state=None, weights=None, gpus=[0], log_dir='log', log_db_prefix='log_db'):
"""
Acts as a driver for training with smart logging.
Logs will be stored to a MySQL database.
All paths can be set relative to the location of the solver prototxt.
:param solver_def: prototxt that defines the solver
:param solver_state: optional: a .solverstate file from which to resume training \ NEVER SET THESE TWO
:param weights: optional: a .caffemodel file from which to begin finetuning / AT THE SAME TIME
:param gpus: optional: a list of GPU IDs to use for (multi-)GPU training
if set to None, caffe will operate in CPU mode
:param log_dir: optional: will log into this directory under solver.prototxt
:param log_db_prefix: prefix for both SQLite db names and table names
The following parameters should to be set in the solver prototxt file:
log_interval log per this number of iterations (simple log) [default = 20]
viz_interval log visualization per this number of iterations (net blobs snapshot) [default = 100]
test_iter: The number of iterations for each test net.
"""
if not os.path.isabs(solver_def):
if not os.path.isfile(os.path.join(os.getcwd(), solver_def)):
os.chdir('..')
solver_def = os.path.join(os.getcwd(), solver_def)
else:
solver_def = os.path.join(os.getcwd(), solver_def)
self.solver_dir = solver_def[:solver_def.rfind('/')]
os.chdir(self.solver_dir)
self.log_dir = os.path.join(self.solver_dir, log_dir)
if not os.path.exists(self.log_dir):
os.mkdir(self.log_dir)
self.logprint("Logging to {}".format(self.log_dir))
self.log_db_prefix = log_db_prefix # used for db name and as a prefix for tables
self.solver_param = caffe_pb2.SolverParameter()
text_format.Merge(open(solver_def).read(), self.solver_param)
# read params from solver definition
self.iterations = self.solver_param.max_iter
self.log_interval = self.solver_param.log_interval
self.viz_interval = self.solver_param.viz_interval
self.test_interval = self.solver_param.test_interval
self.se_index_blob = self.solver_param.se_index_blob
self.se_error_blob = self.solver_param.se_error_blob
self.se_interval = self.solver_param.se_interval
self.se_indices = []
self.se_errors = []
# make solver param for net fail safe
if not os.path.isabs(self.solver_param.net):
self.solver_param.net = os.path.join(self.solver_dir, self.solver_param.net)
if not os.path.isfile(self.solver_param.net):
raise Exception('could not find net definition from solver prototxt!')
self.gpus = gpus
if gpus:
self.solver_param.device_id = gpus[0]
caffe.set_device(gpus[0])
caffe.set_mode_gpu()
caffe.set_solver_count(len(gpus))
self.solver = caffe.get_solver_from_string(self.solver_param.SerializeToString())
if solver_state:
# check if this file is in the current (or parent) directory or if the solver path needs to be prepended
if not os.path.isfile(os.path.join('..', solver_state)):
if not os.path.isfile(solver_state):
solver_state = os.path.join(self.solver_dir, solver_state)
if not os.path.isfile(solver_state):
raise Exception('could not find solver state specified!')
else:
solver_state = os.path.join('..', solver_state)
self.solver.restore(solver_state)
if weights:
raise Exception(
'should not specify both solverstate and caffemodel! Preference will be given to solverstate.')
if weights and not solver_state:
self.solver.net.copy_from(weights)
self.sync = None
self.viz_thread = None
self.log_thread = None
self.test_input = None
self.test_out_blobs = None
self.test_start_layer = None
self.iteration = 0
# check if blobs update_sample_errors exist
if self.se_interval and not self.se_index_blob in self.solver.net.blobs:
self.logprint("WARNING: index_blob not found in net! Won't send errors to Net.")
self.se_interval = 0
if self.se_interval and not self.se_error_blob in self.solver.net.blobs:
self.logprint("WARNING: error_blob not found in net! Won't send errors to Net.")
self.se_interval = 0
