本文整理汇总了Python中detectron.utils.env.get_runtime_dir方法的典型用法代码示例。如果您正苦于以下问题：Python env.get_runtime_dir方法的具体用法？Python env.get_runtime_dir怎么用？Python env.get_runtime_dir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detectron.utils.env的用法示例。
在下文中一共展示了env.get_runtime_dir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: multi_gpu_generate_rpn_on_dataset
# 需要导入模块: from detectron.utils import env [as 别名]
# 或者: from detectron.utils.env import get_runtime_dir [as 别名]
def multi_gpu_generate_rpn_on_dataset(
weights_file, dataset_name, _proposal_file_ignored, num_images, output_dir
):
"""Multi-gpu inference on a dataset."""
# Retrieve the test_net binary path
binary_dir = envu.get_runtime_dir()
binary_ext = envu.get_py_bin_ext()
binary = os.path.join(binary_dir, 'test_net' + binary_ext)
assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)
# Pass the target dataset via the command line
opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
opts += ['TEST.WEIGHTS', weights_file]
# Run inference in parallel in subprocesses
outputs = subprocess_utils.process_in_parallel(
'rpn_proposals', num_images, binary, output_dir, opts
)
# Collate the results from each subprocess
boxes, scores, ids = [], [], []
for rpn_data in outputs:
boxes += rpn_data['boxes']
scores += rpn_data['scores']
ids += rpn_data['ids']
rpn_file = os.path.join(output_dir, 'rpn_proposals.pkl')
cfg_yaml = envu.yaml_dump(cfg)
save_object(
dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml), rpn_file
)
logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
return boxes, scores, ids, rpn_file 示例2: multi_gpu_generate_rpn_on_dataset
# 需要导入模块: from detectron.utils import env [as 别名]
# 或者: from detectron.utils.env import get_runtime_dir [as 别名]
def multi_gpu_generate_rpn_on_dataset(
weights_file, dataset_name, _proposal_file_ignored, num_images, output_dir
):
"""Multi-gpu inference on a dataset."""
# Retrieve the test_net binary path
binary_dir = envu.get_runtime_dir()
binary_ext = envu.get_py_bin_ext()
binary = os.path.join(binary_dir, 'test_net' + binary_ext)
assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)
# Pass the target dataset via the command line
opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
opts += ['TEST.WEIGHTS', weights_file]
# Run inference in parallel in subprocesses
outputs = subprocess_utils.process_in_parallel(
'rpn_proposals', num_images, binary, output_dir, opts
)
# Collate the results from each subprocess
boxes, scores, ids = [], [], []
for rpn_data in outputs:
boxes += rpn_data['boxes']
scores += rpn_data['scores']
ids += rpn_data['ids']
rpn_file = os.path.join(output_dir, 'rpn_proposals.pkl')
cfg_yaml = yaml.dump(cfg)
save_object(
dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml), rpn_file
)
logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
return boxes, scores, ids, rpn_file 示例3: multi_gpu_test_net_on_dataset
# 需要导入模块: from detectron.utils import env [as 别名]
# 或者: from detectron.utils.env import get_runtime_dir [as 别名]
def multi_gpu_test_net_on_dataset(
weights_file, dataset_name, proposal_file, num_images, output_dir
):
"""Multi-gpu inference on a dataset."""
binary_dir = envu.get_runtime_dir()
binary_ext = envu.get_py_bin_ext()
binary = os.path.join(binary_dir, 'test_net' + binary_ext)
assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)
# Pass the target dataset and proposal file (if any) via the command line
opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
opts += ['TEST.WEIGHTS', weights_file]
if proposal_file:
opts += ['TEST.PROPOSAL_FILES', '("{}",)'.format(proposal_file)]
# Run inference in parallel in subprocesses
# Outputs will be a list of outputs from each subprocess, where the output
# of each subprocess is the dictionary saved by test_net().
outputs = subprocess_utils.process_in_parallel(
'detection', num_images, binary, output_dir, opts
)
# Collate the results from each subprocess
all_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
all_segms = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
all_keyps = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
for det_data in outputs:
all_boxes_batch = det_data['all_boxes']
all_segms_batch = det_data['all_segms']
all_keyps_batch = det_data['all_keyps']
for cls_idx in range(1, cfg.MODEL.NUM_CLASSES):
all_boxes[cls_idx] += all_boxes_batch[cls_idx]
all_segms[cls_idx] += all_segms_batch[cls_idx]
all_keyps[cls_idx] += all_keyps_batch[cls_idx]
det_file = os.path.join(output_dir, 'detections.pkl')
cfg_yaml = envu.yaml_dump(cfg)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps 示例4: multi_gpu_test_net_on_dataset
# 需要导入模块: from detectron.utils import env [as 别名]
# 或者: from detectron.utils.env import get_runtime_dir [as 别名]
def multi_gpu_test_net_on_dataset(
weights_file, dataset_name, proposal_file, num_images, output_dir
):
"""Multi-gpu inference on a dataset."""
binary_dir = envu.get_runtime_dir()
binary_ext = envu.get_py_bin_ext()
binary = os.path.join(binary_dir, 'test_net' + binary_ext)
assert os.path.exists(binary), 'Binary \'{}\' not found'.format(binary)
# Pass the target dataset and proposal file (if any) via the command line
opts = ['TEST.DATASETS', '("{}",)'.format(dataset_name)]
opts += ['TEST.WEIGHTS', weights_file]
if proposal_file:
opts += ['TEST.PROPOSAL_FILES', '("{}",)'.format(proposal_file)]
# Run inference in parallel in subprocesses
# Outputs will be a list of outputs from each subprocess, where the output
# of each subprocess is the dictionary saved by test_net().
outputs = subprocess_utils.process_in_parallel(
'detection', num_images, binary, output_dir, opts
)
# Collate the results from each subprocess
all_boxes = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
all_segms = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
all_keyps = [[] for _ in range(cfg.MODEL.NUM_CLASSES)]
for det_data in outputs:
all_boxes_batch = det_data['all_boxes']
all_segms_batch = det_data['all_segms']
all_keyps_batch = det_data['all_keyps']
for cls_idx in range(1, cfg.MODEL.NUM_CLASSES):
all_boxes[cls_idx] += all_boxes_batch[cls_idx]
all_segms[cls_idx] += all_segms_batch[cls_idx]
all_keyps[cls_idx] += all_keyps_batch[cls_idx]
det_file = os.path.join(output_dir, 'detections.pkl')
cfg_yaml = yaml.dump(cfg)
save_object(
dict(
all_boxes=all_boxes,
all_segms=all_segms,
all_keyps=all_keyps,
cfg=cfg_yaml
), det_file
)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes, all_segms, all_keyps