本文整理汇总了Python中models.load_model方法的典型用法代码示例。如果您正苦于以下问题:Python models.load_model方法的具体用法?Python models.load_model怎么用?Python models.load_model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models
的用法示例。
在下文中一共展示了models.load_model方法的4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: main
# 需要导入模块: import models [as 别名]
# 或者: from models import load_model [as 别名]
def main():
'''Main demo function'''
# Save prediction into a file named 'prediction.obj' or the given argument
pred_file_name = sys.argv[1] if len(sys.argv) > 1 else 'prediction.obj'
# load images
demo_imgs = load_demo_images()
# Download and load pretrained weights
download_model(DEFAULT_WEIGHTS)
# Use the default network model
NetClass = load_model('ResidualGRUNet')
# Define a network and a solver. Solver provides a wrapper for the test function.
net = NetClass(compute_grad=False) # instantiate a network
net.load(DEFAULT_WEIGHTS) # load downloaded weights
solver = Solver(net) # instantiate a solver
# Run the network
voxel_prediction, _ = solver.test_output(demo_imgs)
# Save the prediction to an OBJ file (mesh file).
voxel2obj(pred_file_name, voxel_prediction[0, :, 1, :, :] > cfg.TEST.VOXEL_THRESH)
# Use meshlab or other mesh viewers to visualize the prediction.
# For Ubuntu>=14.04, you can install meshlab using
# `sudo apt-get install meshlab`
if cmd_exists('meshlab'):
call(['meshlab', pred_file_name])
else:
print('Meshlab not found: please use visualization of your choice to view %s' %
pred_file_name)
示例2: main
# 需要导入模块: import models [as 别名]
# 或者: from models import load_model [as 别名]
def main():
'''Main demo function'''
# Save prediction into a file named 'prediction.obj' or the given argument
pred_file_name = sys.argv[1] if len(sys.argv) > 1 else 'prediction.obj'
# load images
demo_imgs = load_demo_images()
# Use the default network model
NetClass = load_model('ResidualGRUNet')
# Define a network and a solver. Solver provides a wrapper for the test function.
net = NetClass() # instantiate a network
if torch.cuda.is_available():
net.cuda()
net.eval()
solver = Solver(net) # instantiate a solver
solver.load(DEFAULT_WEIGHTS)
# Run the network
voxel_prediction, _ = solver.test_output(demo_imgs)
voxel_prediction = voxel_prediction.detach().cpu().numpy()
# Save the prediction to an OBJ file (mesh file).
voxel2obj(pred_file_name, voxel_prediction[0, 1] > cfg.TEST.VOXEL_THRESH)
# Use meshlab or other mesh viewers to visualize the prediction.
# For Ubuntu>=14.04, you can install meshlab using
# `sudo apt-get install meshlab`
if cmd_exists('meshlab'):
call(['meshlab', pred_file_name])
else:
print('Meshlab not found: please use visualization of your choice to view %s' %
pred_file_name)
示例3: train_net
# 需要导入模块: import models [as 别名]
# 或者: from models import load_model [as 别名]
def train_net():
'''Main training function'''
# Set up the model and the solver
NetClass = load_model(cfg.CONST.NETWORK_CLASS)
print('Network definition: \n')
print(inspect.getsource(NetClass.network_definition))
net = NetClass()
# Check that single view reconstruction net is not used for multi view
# reconstruction.
if net.is_x_tensor4 and cfg.CONST.N_VIEWS > 1:
raise ValueError('Do not set the config.CONST.N_VIEWS > 1 when using' \
'single-view reconstruction network')
# Generate the solver
solver = Solver(net)
# Prefetching data processes
#
# Create worker and data queue for data processing. For training data, use
# multiple processes to speed up the loading. For validation data, use 1
# since the queue will be popped every TRAIN.NUM_VALIDATION_ITERATIONS.
global train_queue, val_queue, train_processes, val_processes
train_queue = Queue(cfg.QUEUE_SIZE)
val_queue = Queue(cfg.QUEUE_SIZE)
train_processes = make_data_processes(
train_queue,
category_model_id_pair(dataset_portion=cfg.TRAIN.DATASET_PORTION),
cfg.TRAIN.NUM_WORKER,
repeat=True)
val_processes = make_data_processes(
val_queue,
category_model_id_pair(dataset_portion=cfg.TEST.DATASET_PORTION),
1,
repeat=True,
train=False)
# Train the network
solver.train(train_queue, val_queue)
# Cleanup the processes and the queue.
kill_processes(train_queue, train_processes)
kill_processes(val_queue, val_processes)
示例4: train_net
# 需要导入模块: import models [as 别名]
# 或者: from models import load_model [as 别名]
def train_net():
'''Main training function'''
# Set up the model and the solver
NetClass = load_model(cfg.CONST.NETWORK_CLASS)
net = NetClass()
print('\nNetwork definition: ')
print(net)
# Check that single view reconstruction net is not used for multi view
# reconstruction.
if net.is_x_tensor4 and cfg.CONST.N_VIEWS > 1:
raise ValueError('Do not set the config.CONST.N_VIEWS > 1 when using' \
'single-view reconstruction network')
# Prefetching data processes
#
# Create worker and data queue for data processing. For training data, use
# multiple processes to speed up the loading. For validation data, use 1
# since the queue will be popped every TRAIN.NUM_VALIDATION_ITERATIONS.
train_dataset = ShapeNetDataset(cfg.TRAIN.DATASET_PORTION)
train_collate_fn = ShapeNetCollateFn()
train_loader = DataLoader(
dataset=train_dataset,
batch_size=cfg.CONST.BATCH_SIZE,
shuffle=True,
num_workers=cfg.TRAIN.NUM_WORKER,
collate_fn=train_collate_fn,
pin_memory=True
)
val_dataset = ShapeNetDataset(cfg.TEST.DATASET_PORTION)
val_collate_fn = ShapeNetCollateFn(train=False)
val_loader = DataLoader(
dataset=val_dataset,
batch_size=cfg.CONST.BATCH_SIZE,
shuffle=True,
num_workers=1,
collate_fn=val_collate_fn,
pin_memory=True
)
net.cuda()
# Generate the solver
solver = Solver(net)
# Train the network
solver.train(train_loader, val_loader)