def resnet(n=3, num_output = 16):
"""6n+2, n=3 9 18 coresponds to 20 56 110 layers"""
net_name = "resnet-"
pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
name = net_name+str(6*n+2)+'-cifar10'
if n > 18:
# warm up
solver = Solver(solver_name="solver_warm.prototxt", folder=pt_folder, lr_policy=Solver.policy.fixed)
solver.p.base_lr = 0.01
solver.set_max_iter(500)
solver.write()
del solver
solver = Solver(folder=pt_folder)
solver.write()
del solver
builder = Net(name)
builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
builder.Data('cifar-10-batches-py/test', phase='TEST')
builder.resnet_cifar(n, num_output=num_output)
builder.write(folder=pt_folder)
python类TEST的实例源码
def resnet_orth(n=3):
"""6n+2, n=3 9 18 coresponds to 20 56 110 layers"""
net_name = "resnet-orth-"
pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
name = net_name+str(6*n+2)+'-cifar10'
if n > 18:
# warm up
solver = Solver(solver_name="solver_warm.prototxt", folder=pt_folder, lr_policy=Solver.policy.fixed)
solver.p.base_lr = 0.01
solver.set_max_iter(500)
solver.write()
del solver
solver = Solver(folder=pt_folder)
solver.write()
del solver
builder = Net(name)
builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
builder.Data('cifar-10-batches-py/test', phase='TEST')
builder.resnet_cifar(n, orth=True)
builder.write(folder=pt_folder)
def _get_include(phase):
inc = caffe_pb2.NetStateRule()
if phase == 'train':
inc.phase = caffe_pb2.TRAIN
elif phase == 'test':
inc.phase = caffe_pb2.TEST
else:
raise ValueError("Unknown phase {}".format(phase))
return inc
def transform_param(self, mean_value=128, batch_size=128, scale=.0078125, mirror=1, crop_size=None, mean_file_size=None, phase=None):
new_transform_param = self.this.transform_param
new_transform_param.scale = scale
new_transform_param.mean_value.extend([mean_value])
if phase is not None and phase == 'TEST':
return
new_transform_param.mirror = mirror
if crop_size is not None:
new_transform_param.crop_size = crop_size
def include(self, phase='TRAIN'):
if phase is not None:
includes = self.this.include.add()
if phase == 'TRAIN':
includes.phase = caffe_pb2.TRAIN
elif phase == 'TEST':
includes.phase = caffe_pb2.TEST
else:
NotImplementedError
#************************** inplace **************************
def transform_param(self, mean_value=128, batch_size=128, scale=.0078125, mirror=1, crop_size=None, mean_file_size=None, phase=None):
new_transform_param = self.this.transform_param
if scale != 1:
new_transform_param.scale = scale
if isinstance(mean_value, list):
new_transform_param.mean_value.extend(mean_value)
else:
new_transform_param.mean_value.extend([mean_value])
if phase is not None and phase == 'TEST':
return
new_transform_param.mirror = mirror
if crop_size is not None:
new_transform_param.crop_size = crop_size
def include(self, phase='TRAIN'):
if phase is not None:
includes = self.this.include.add()
if phase == 'TRAIN':
includes.phase = caffe_pb2.TRAIN
elif phase == 'TEST':
includes.phase = caffe_pb2.TEST
else:
NotImplementedError
#************************** inplace **************************
def BatchNorm(self, name=None, inplace=True,eps=1e-5):
moving_average_fraction = 0
if not inplace:
bottom = self.this.name
# train
bn_name = self.suffix('bn', name)
self.setup(bn_name, 'BatchNorm', inplace=inplace)
# self.include()
self.param(lr_mult=0, decay_mult=0)
self.param(lr_mult=0, decay_mult=0)
self.param(lr_mult=0, decay_mult=0)
batch_norm_param = self.this.batch_norm_param
if eps != 1e-5:
batch_norm_param.eps = eps
return bn_name
# batch_norm_param.use_global_stats = False
#batch_norm_param.moving_average_fraction = moving_average_fraction
# test
# if not inplace:
# self.setup(bn_name, 'BatchNorm', inplace=inplace, bottom=[bottom])
# else:
# self.setup(bn_name, 'BatchNorm', inplace=inplace)
# self.include(phase='TEST')
# self.param(lr_mult=0, decay_mult=0)
# self.param(lr_mult=0, decay_mult=0)
# self.param(lr_mult=0, decay_mult=0)
# batch_norm_param = self.this.batch_norm_param
# batch_norm_param.use_global_stats = True
# batch_norm_param.moving_average_fraction = moving_average_fraction
def plain(n=3):
"""6n+2, n=3 9 18 coresponds to 20 56 110 layers"""
net_name = "plain"
pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
name = net_name+str(6*n+2)+'-cifar10'
solver = Solver(folder=pt_folder)
solver.write()
del solver
builder = Net(name)
builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
builder.Data('cifar-10-batches-py/test', phase='TEST')
builder.plain_cifar(n, num_output = 16)
builder.write(folder=pt_folder)
def plain_orth(n=3):
"""6n+2, n=3 5 7 9 18 coresponds to 20 56 110 layers"""
net_name = "plain-orth"
pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
name = net_name+str(6*n+2)+'-cifar10'
solver = Solver(folder=pt_folder)
solver.write()
del solver
builder = Net(name)
builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
builder.Data('cifar-10-batches-py/test', phase='TEST')
builder.plain_cifar(n, orth=True)
builder.write(folder=pt_folder)
def plain_orth_v1(n=3):
"""6n+2, n=3 5 7 9 18 coresponds to 20 32 44 56 110 layers"""
net_name = "plain-orth-v1-"
pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
name = net_name+str(6*n+2)+'-cifar10'
solver = Solver(folder=pt_folder)
solver.write()
del solver
builder = Net(name)
builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
builder.Data('cifar-10-batches-py/test', phase='TEST')
builder.plain_cifar(n, orth=True, inplace=False, num_output = 16)
builder.write(folder=pt_folder)
def acc(n=3):
"""6n+2, n=3 9 18 coresponds to 20 56 110 layers"""
net_name = "plain"
pt_folder = osp.join(osp.abspath(osp.curdir), net_name +str(6*n+2))
name = net_name+str(6*n+2)+'-cifar10'
solver = Solver(folder=pt_folder)
solver.write()
del solver
builder = Net(name)
builder.Data('cifar-10-batches-py/train', phase='TRAIN', crop_size=32)
builder.Data('cifar-10-batches-py/test', phase='TEST')
builder.plain_cifar(n, num_output = 16, inplace=False)
builder.write(folder=pt_folder)
