def write_solver_file(solver_file, train_model, test_models, type, base_lr, momentum, weight_decay,
lr_policy, gamma, power, random_seed, max_iter, clip_gradients, snapshot_prefix,display=0):
'''Writes a solver prototxt file with parameters set to the
corresponding argument values. In particular, the train_net
parameter is set to train_model, and a test_net parameter is
added for each of test_models, which should be a list.'''
param = SolverParameter()
param.train_net = train_model
for test_model in test_models:
param.test_net.append(test_model)
param.test_iter.append(0) #don't test automatically
param.test_interval = max_iter
param.type = type
param.base_lr = base_lr
param.momentum = momentum
param.weight_decay = weight_decay
param.lr_policy = lr_policy
param.gamma = gamma
param.power = power
param.display = display #don't print solver iterations unless requested
param.random_seed = random_seed
param.max_iter = max_iter
if clip_gradients > 0:
param.clip_gradients = clip_gradients
param.snapshot_prefix = snapshot_prefix
print "WRITING",solver_file
with open(solver_file,'w') as f:
f.write(str(param))
def write_solver_file(solver_file, train_model, test_models, type, base_lr,
momentum, weight_decay, lr_policy, gamma, power,
random_seed, max_iter, clip_gradients, snapshot_prefix):
'''Writes a solver prototxt file with parameters set to the
corresponding argument values. In particular, the train_net
parameter is set to train_model, and a test_net parameter is
added for each of test_models, which should be a list.'''
param = SolverParameter()
param.train_net = train_model
for test_model in test_models:
param.test_net.append(test_model)
param.test_iter.append(0) #don't test automatically
param.test_interval = max_iter
param.type = type
param.base_lr = base_lr
param.momentum = momentum
param.weight_decay = weight_decay
param.lr_policy = lr_policy
param.gamma = gamma
param.power = power
param.display = 0 #don't print solver iterations
param.random_seed = random_seed
param.max_iter = max_iter
if clip_gradients > 0:
param.clip_gradients = clip_gradients
param.snapshot_prefix = snapshot_prefix
print "WRITING", solver_file
with open(solver_file, 'w') as f:
f.write(str(param))
def lenet_solver():
"""A simple version of LeNet's solver proto"""
parser = argparse.ArgumentParser()
parser.add_argument(
'--train_net',
default='../../Lenet/lenet_auto_train.prototxt',
help=
'path to train net prototxt. [DEFAULT=../../Section4/caffenet_train.prototxt]'
)
parser.add_argument(
'--test_net',
default='../../Lenet/lenet_auto_test.prototxt',
help=
'path to validation net prototxt. [DEFAULT=../../Section4/caffenet_valid.prototxt]'
)
parser.add_argument('--solver_target_folder',
default='../../Lenet/',
help='solver target FOLDER. [DEFAULT=../../Section5/]')
parser.add_argument(
'--solver_filename',
default='Lenet_solver.prototxt',
help='solver prototxt NAME. [DEFAULT=caffenet_solver.prototxt]')
parser.add_argument(
'--snapshot_target_folder',
default='../../Lenet/',
help='snapshot target FOLDER. [DEFAULT=../../Section6/')
parser.add_argument('--snapshot_prefix',
default='Lenet',
help='snapshot NAME prefix, [DEFAULT=caffenet]')
args = parser.parse_args()
SOLVER_FULL_PATH = args.solver_target_folder + args.solver_filename
SNAPSHOT_FULL_PATH = args.snapshot_target_folder + args.snapshot_prefix
os.system('rm -rf ' + SOLVER_FULL_PATH)
os.system('rm -rf ' + SNAPSHOT_FULL_PATH + '*')
solver = SolverParameter()
solver.train_net = 'lenet_auto_train.prototxt'
solver.test_net.append('lenet_auto_test.prototxt')
solver.test_iter.append(100)
solver.test_interval = 500
solver.base_lr = 0.01
solver.momentum = 0.9
solver.weight_decay = 0.0005
solver.lr_policy = 'inv'
solver.gamma = 0.0001
solver.power = 0.75
# solver.stepsize = 2500
solver.display = 100
solver.max_iter = 10000
solver.snapshot = 5000
solver.snapshot_prefix = SNAPSHOT_FULL_PATH
solver.solver_mode = SolverParameter.GPU
with open(args.solver_filename, 'w') as f: # generating prototxt
f.write(str(solver))
def new_solver(learning_rate):
param = SolverParameter()
param.solver_type = SolverParameter.ADAM
param.momentum = 0.95
param.base_lr = learning_rate
param.lr_policy = "step"
param.gamma = 0.1
param.stepsize = 10000000
param.max_iter = 10000000
param.display = 0
param.clip_gradients = 10
return param
def lenet_solver_simple():
"""A simple version of LeNet's solver proto"""
solver = SolverParameter()
solver.train_net = 'lenet_auto_train.prototxt'
solver.test_net.append('lenet_auto_test.prototxt')
solver.test_iter.append(100)
solver.test_interval = 500
solver.base_lr = 0.01
solver.momentum = 0.9
solver.weight_decay = 0.0005
solver.lr_policy = 'inv'
solver.gamma = 0.0001
solver.power = 0.75
# solver.stepsize = 2500
solver.display = 100
solver.max_iter = 10000
solver.snapshot = 5000
solver.snapshot_prefix = 'SNAPSHOT_FULL_PATH'
solver.solver_mode = SolverParameter.GPU
with open('SOLVER_FULL_PATH', 'w') as f: # generating prototxt
f.write(str(solver))