本文整理汇总了Python中caffe2.python.workspace.GlobalInit方法的典型用法代码示例。如果您正苦于以下问题:Python workspace.GlobalInit方法的具体用法?Python workspace.GlobalInit怎么用?Python workspace.GlobalInit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caffe2.python.workspace
的用法示例。
在下文中一共展示了workspace.GlobalInit方法的9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: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def main():
args = parse_args()
num_threads = 8 if not args.parallel else 1
workspace.GlobalInit(
[
"caffe2",
"--caffe2_log_level=2",
"--caffe2_omp_num_threads={}".format(num_threads),
"--caffe2_mkl_num_threads={}".format(num_threads),
]
)
net = load_model(args)
eval_segm_cpu(args, net)
示例2: main
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
)
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
logger.info("cuda version : {}".format(cuda_ver))
logger.info("cudnn version: {}".format(cudnn_ver))
logger.info("nvidia-smi output:\n{}".format(smi_output))
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# Execute the training run
checkpoints = detectron.utils.train.train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.single_gpu_testing, args.opts)
示例3: main
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def main():
args = parse_args()
num_threads = 8
workspace.GlobalInit(
[
"caffe2",
"--caffe2_log_level=2",
"--caffe2_omp_num_threads={}".format(num_threads),
"--caffe2_mkl_num_threads={}".format(num_threads),
]
)
net = load_model(args)
eval_segm_cpu(args, net)
示例4: main
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
)
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('detectron.roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
smi_output, cuda_ver, cudnn_ver = c2_utils.get_nvidia_info()
logger.info("cuda version : {}".format(cuda_ver))
logger.info("cudnn version: {}".format(cudnn_ver))
logger.info("nvidia-smi output:\n{}".format(smi_output))
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# Execute the training run
checkpoints = detectron.utils.train.train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
示例5: main
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
)
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# Execute the training run
checkpoints = utils.train.train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
示例6: main
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def main():
# Initialize C2
workspace.GlobalInit(
['caffe2', '--caffe2_log_level=0', '--caffe2_gpu_memory_tracking=1']
)
# Set up logging and load config options
logger = setup_logging(__name__)
logging.getLogger('roi_data.loader').setLevel(logging.INFO)
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
assert_and_infer_cfg()
logger.info('Training with config:')
logger.info(pprint.pformat(cfg))
# Note that while we set the numpy random seed network training will not be
# deterministic in general. There are sources of non-determinism that cannot
# be removed with a reasonble execution-speed tradeoff (such as certain
# non-deterministic cudnn functions).
np.random.seed(cfg.RNG_SEED)
# Execute the training run
checkpoints = train_model()
# Test the trained model
if not args.skip_test:
test_model(checkpoints['final'], args.multi_gpu_testing, args.opts)
示例7: __init__
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def __init__(
self,
m_spa,
ln_emb,
ln_bot,
ln_top,
arch_interaction_op,
arch_interaction_itself=False,
sigmoid_bot=-1,
sigmoid_top=-1,
save_onnx=False,
model=None,
tag=None,
ndevices=-1,
forward_ops=True,
enable_prof=False,
):
super(DLRM_Net, self).__init__()
# init model
if model is None:
global_init_opt = ["caffe2", "--caffe2_log_level=0"]
if enable_prof:
global_init_opt += [
"--logtostderr=0",
"--log_dir=$HOME",
"--caffe2_logging_print_net_summary=1",
]
workspace.GlobalInit(global_init_opt)
self.set_tags()
self.model = model_helper.ModelHelper(name="DLRM", init_params=True)
else:
# WARNING: assume that workspace and tags have been initialized elsewhere
self.set_tags(tag[0], tag[1], tag[2], tag[3], tag[4], tag[5], tag[6],
tag[7], tag[8], tag[9])
self.model = model
# save arguments
self.m_spa = m_spa
self.ln_emb = ln_emb
self.ln_bot = ln_bot
self.ln_top = ln_top
self.arch_interaction_op = arch_interaction_op
self.arch_interaction_itself = arch_interaction_itself
self.sigmoid_bot = sigmoid_bot
self.sigmoid_top = sigmoid_top
self.save_onnx = save_onnx
self.ndevices = ndevices
# onnx types and shapes dictionary
if self.save_onnx:
self.onnx_tsd = {}
# create forward operators
if forward_ops:
if self.ndevices <= 1:
return self.create_sequential_forward_ops()
else:
return self.create_parallel_forward_ops()
示例8: __init__
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def __init__(
self,
m_spa,
ln_emb,
ln_bot,
ln_top,
arch_interaction_op,
arch_interaction_itself=False,
sigmoid_bot=-1,
sigmoid_top=-1,
save_onnx=False,
model=None,
test_net=None,
tag=None,
ndevices=-1,
forward_ops=True,
enable_prof=False,
):
super(DLRM_Net, self).__init__()
# init model
if model is None:
global_init_opt = ["caffe2", "--caffe2_log_level=0"]
if enable_prof:
global_init_opt += [
"--logtostderr=0",
"--log_dir=$HOME",
"--caffe2_logging_print_net_summary=1",
]
workspace.GlobalInit(global_init_opt)
self.set_tags()
self.model = model_helper.ModelHelper(name="DLRM", init_params=True)
self.test_net = None
else:
# WARNING: assume that workspace and tags have been initialized elsewhere
self.set_tags(tag[0], tag[1], tag[2], tag[3], tag[4], tag[5], tag[6],
tag[7], tag[8], tag[9])
self.model = model
self.test_net = test_net
# save arguments
self.m_spa = m_spa
self.ln_emb = ln_emb
self.ln_bot = ln_bot
self.ln_top = ln_top
self.arch_interaction_op = arch_interaction_op
self.arch_interaction_itself = arch_interaction_itself
self.sigmoid_bot = sigmoid_bot
self.sigmoid_top = sigmoid_top
self.save_onnx = save_onnx
self.ndevices = ndevices
# onnx types and shapes dictionary
if self.save_onnx:
self.onnx_tsd = {}
# create forward operators
if forward_ops:
if self.ndevices <= 1:
return self.create_sequential_forward_ops()
else:
return self.create_parallel_forward_ops()
示例9: main
# 需要导入模块: from caffe2.python import workspace [as 别名]
# 或者: from caffe2.python.workspace import GlobalInit [as 别名]
def main():
workspace.GlobalInit(['caffe2', '--caffe2_log_level=0'])
args = parse_args()
logger.info('Called with args:')
logger.info(args)
if args.cfg_file is not None:
merge_cfg_from_file(args.cfg_file)
if args.opts is not None:
merge_cfg_from_list(args.opts)
cfg.NUM_GPUS = 1
assert_and_infer_cfg()
logger.info('Conerting model with config:')
logger.info(pprint.pformat(cfg))
assert not cfg.MODEL.KEYPOINTS_ON, "Keypoint model not supported."
assert not cfg.MODEL.MASK_ON, "Mask model not supported."
assert not cfg.FPN.FPN_ON, "FPN not supported."
assert not cfg.RETINANET.RETINANET_ON, "RetinaNet model not supported."
# load model from cfg
model, blobs = load_model(args)
net = core.Net('')
net.Proto().op.extend(copy.deepcopy(model.net.Proto().op))
net.Proto().external_input.extend(
copy.deepcopy(model.net.Proto().external_input))
net.Proto().external_output.extend(
copy.deepcopy(model.net.Proto().external_output))
net.Proto().type = args.net_execution_type
net.Proto().num_workers = 1 if args.net_execution_type == 'simple' else 4
# Reset the device_option, change to unscope name and replace python operators
convert_net(args, net.Proto(), blobs)
# add operators for bbox
add_bbox_ops(args, net, blobs)
if args.fuse_af:
print('Fusing affine channel...')
net, blobs = mutils.fuse_net_affine(
net, blobs)
if args.use_nnpack:
mutils.update_mobile_engines(net.Proto())
# generate init net
empty_blobs = ['data', 'im_info']
init_net = gen_init_net(net, blobs, empty_blobs)
if args.device == 'gpu':
[net, init_net] = convert_model_gpu(args, net, init_net)
net.Proto().name = args.net_name
init_net.Proto().name = args.net_name + "_init"
if args.test_img is not None:
verify_model(args, [net, init_net], args.test_img)
_save_models(net, init_net, args)