本文整理汇总了Python中caffe.get_solver方法的典型用法代码示例。如果您正苦于以下问题:Python caffe.get_solver方法的具体用法?Python caffe.get_solver怎么用?Python caffe.get_solver使用的例子?那么, 这里精选的方法代码示例或许可以为您提供帮助。您也可以进一步了解该方法所在类caffe
的用法示例。
在下文中一共展示了caffe.get_solver方法的5个代码示例,这些例子默认根据受欢迎程度排序。您可以为喜欢或者感觉有用的代码点赞,您的评价将有助于系统推荐出更棒的Python代码示例。
示例1: train
# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import get_solver [as 别名]
def train(solver_proto_path, snapshot_solver_path=None, init_weights=None, GPU_ID=0):
"""
Train the defined net. While we did not use this function for our final net, we used the caffe executable for multi-gpu use, this was used for prototyping
"""
import time
t0 = time.time()
caffe.set_mode_gpu()
caffe.set_device(GPU_ID)
solver = caffe.get_solver(solver_proto_path)
if snapshot_solver_path is not None:
solver.solve(snapshot_solver_path) # train from previous solverstate
else:
if init_weights is not None:
solver.net.copy_from(init_weights) # for copying weights from a model without solverstate
solver.solve() # train form scratch
t1 = time.time()
print 'Total training time: ', t1-t0, ' sec'
model_dir = "calc_" + time.strftime("%d-%m-%Y_%I%M%S")
moveModel(model_dir=model_dir) # move all the model files to a directory
print "Moved model to model/"+model_dir
示例2: setUp
# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import get_solver [as 别名]
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(delete=False)
f.write("""net: '""" + net_f + """'
test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
display: 100 max_iter: 100 snapshot_after_train: false""")
f.close()
self.solver = caffe.SGDSolver(f.name)
# also make sure get_solver runs
caffe.get_solver(f.name)
caffe.set_mode_cpu()
# fill in valid labels
self.solver.net.blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.net.blobs['label'].data.shape)
self.solver.test_nets[0].blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.test_nets[0].blobs['label'].data.shape)
os.remove(f.name)
os.remove(net_f)
示例3: setUp
# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import get_solver [as 别名]
def setUp(self):
self.num_output = 13
net_f = simple_net_file(self.num_output)
f = tempfile.NamedTemporaryFile(mode='w+', delete=False)
f.write("""net: '""" + net_f + """'
test_iter: 10 test_interval: 10 base_lr: 0.01 momentum: 0.9
weight_decay: 0.0005 lr_policy: 'inv' gamma: 0.0001 power: 0.75
display: 100 max_iter: 100 snapshot_after_train: false
snapshot_prefix: "model" """)
f.close()
self.solver = caffe.SGDSolver(f.name)
# also make sure get_solver runs
caffe.get_solver(f.name)
caffe.set_mode_cpu()
# fill in valid labels
self.solver.net.blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.net.blobs['label'].data.shape)
self.solver.test_nets[0].blobs['label'].data[...] = \
np.random.randint(self.num_output,
size=self.solver.test_nets[0].blobs['label'].data.shape)
os.remove(f.name)
os.remove(net_f)
示例4: train_network
# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import get_solver [as 别名]
def train_network(solver_file, num_classes, batch_size, num_iterations, use_gpu=True) :
if use_gpu :
caffe.set_mode_gpu()
else :
caffe.set_mode_cpu()
solver = caffe.get_solver(solver_file)
solver.net.blobs['data'].reshape(batch_size, solver.net.blobs['data'].shape[1], solver.net.blobs['data'].shape[2], solver.net.blobs['data'].shape[3])
solver.net.blobs['target'].reshape(batch_size, solver.net.blobs['target'].shape[1], solver.net.blobs['target'].shape[2], solver.net.blobs['target'].shape[3])
solver.net.reshape()
for i in range(num_iterations):
data, target = get_data(batch_size, numclasses=num_classes)
solver.net.blobs['data'].data[...] = data
solver.net.blobs['target'].data[...] = target
solver.step(1)
output = solver.net.blobs['argmax'].data[...]
fig, sub = plt.subplots(ncols=3, figsize=(15, 5))
sub[0].set_title('Input')
sub[0].imshow(data[0, 0, :, :])
sub[1].set_title('Ground Truth')
sub[1].imshow(np.argmax(target[0, :, :, :], axis=0))
sub[2].set_title('Segmentation')
sub[2].imshow(output[0, 0, :, :])
plt.show()
示例5: train
# 需要导入模块: import caffe [as 别名]
# 或者: from caffe import get_solver [as 别名]
def train(self, train_hdf5_fname, val_hdf5_fname=None, solverstate_fname=None, solver_param=None, batch_size=32, visualize_response_maps=False):
hdf5_txt_fnames = []
for hdf5_fname in [train_hdf5_fname, val_hdf5_fname]:
if hdf5_fname is not None:
head, tail = os.path.split(hdf5_fname)
root, _ = os.path.splitext(tail)
hdf5_txt_fname = os.path.join(head, '.' + root + '.txt')
if not os.path.isfile(hdf5_txt_fname):
with open(hdf5_txt_fname, 'w') as f:
f.write(hdf5_fname + '\n')
hdf5_txt_fnames.append(hdf5_txt_fname)
else:
hdf5_txt_fnames.append(None)
train_hdf5_txt_fname, val_hdf5_txt_fname = hdf5_txt_fnames
input_shapes = (self.x_shape, self.u_shape)
train_net_param, weight_fillers = self.net_func(input_shapes, train_hdf5_txt_fname, batch_size, self.net_name, phase=caffe.TRAIN)
if val_hdf5_fname is not None:
val_net_param, _ = self.net_func(input_shapes, val_hdf5_txt_fname, batch_size, self.net_name, phase=caffe.TEST)
self.train_val_net_param = train_net_param
if val_hdf5_fname is not None:
layers = [layer for layer in self.train_val_net_param.layer]
# remove layers except for data layers
for layer in layers:
if 'Data' not in layer.type:
self.train_val_net_param.layer.remove(layer)
# add data layers from validation net_caffe
self.train_val_net_param.layer.extend([layer for layer in val_net_param.layer if 'Data' in layer.type])
# add back the layers that are not data layers
self.train_val_net_param.layer.extend([layer for layer in layers if 'Data' not in layer.type])
self.train_val_net_param = net_caffe.train_val_net(self.train_val_net_param)
train_val_fname = self.get_model_fname('train_val')
with open(train_val_fname, 'w') as f:
f.write(str(self.train_val_net_param))
if solver_param is None:
solver_param = pb2.SolverParameter()
self.add_default_parameters(solver_param, val_net=val_hdf5_fname is not None)
solver_fname = self.get_model_fname('solver')
with open(solver_fname, 'w') as f:
f.write(str(solver_param))
solver = caffe.get_solver(solver_fname)
self.set_weight_fillers(solver.net.params, weight_fillers)
for param_name, param in self.params.items():
for blob, solver_blob in zip(param, solver.net.params[param_name]):
solver_blob.data[...] = blob.data
if solverstate_fname is not None:
if not solverstate_fname.endswith('.solverstate'):
solverstate_fname = self.get_snapshot_prefix() + '_iter_' + solverstate_fname + '.solverstate'
solver.restore(solverstate_fname)
self.solve(solver, solver_param, visualize_response_maps=visualize_response_maps)
for param_name, param in self.params.items():
for blob, solver_blob in zip(param, solver.net.params[param_name]):
blob.data[...] = solver_blob.data
self.train_net = solver.net
if val_hdf5_fname is not None:
self.val_net = solver.test_nets[0]