本文整理汇总了Python中datasets.json_dataset.JsonDataset方法的典型用法代码示例。如果您正苦于以下问题:Python json_dataset.JsonDataset方法的具体用法?Python json_dataset.JsonDataset怎么用?Python json_dataset.JsonDataset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datasets.json_dataset
的用法示例。
在下文中一共展示了json_dataset.JsonDataset方法的15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: get_roidb_and_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def get_roidb_and_dataset(dataset_name, proposal_file, ind_range):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
dataset = JsonDataset(dataset_name)
if cfg.TEST.PRECOMPUTED_PROPOSALS:
assert proposal_file, 'No proposal file given'
roidb = dataset.get_roidb(
proposal_file=proposal_file,
proposal_limit=cfg.TEST.PROPOSAL_LIMIT
)
else:
roidb = dataset.get_roidb()
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, dataset, start, end, total_num_images
示例2: get_roidb
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def get_roidb(dataset_name, ind_range):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
dataset = JsonDataset(dataset_name)
roidb = dataset.get_roidb(gt=cfg.TEST.USE_GT_PROPOSALS)
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, start, end, total_num_images
示例3: get_roidb
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def get_roidb(dataset_name, ind_range):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
dataset = JsonDataset(dataset_name)
roidb = dataset.get_roidb()
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, start, end, total_num_images
示例4: get_roidb_and_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def get_roidb_and_dataset(dataset_name, proposal_file, ind_range):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
dataset = JsonDataset(dataset_name)
if cfg.TEST.PRECOMPUTED_PROPOSALS:
assert proposal_file, 'No proposal file given'
roidb = dataset.get_roidb(
proposal_file=proposal_file,
proposal_limit=cfg.TEST.PROPOSAL_LIMIT
)
else:
roidb = dataset.get_roidb(gt=cfg.DEBUG_TEST_WITH_GT)
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, dataset, start, end, total_num_images
示例5: generate_rpn_on_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def generate_rpn_on_dataset(multi_gpu=False):
"""Run inference on a dataset."""
output_dir = get_output_dir(training=False)
dataset = JsonDataset(cfg.TEST.DATASET)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb())
_boxes, _scores, _ids, rpn_file = multi_gpu_generate_rpn_on_dataset(
num_images, output_dir
)
else:
# Processes entire dataset range by default
_boxes, _scores, _ids, rpn_file = generate_rpn_on_range()
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(test_timer.average_time))
return evaluate_proposal_file(dataset, rpn_file, output_dir)
示例6: get_roidb
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def get_roidb(ind_range):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
dataset = JsonDataset(cfg.TEST.DATASET)
roidb = dataset.get_roidb()
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, start, end, total_num_images
示例7: load_all_roidb
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def load_all_roidb():
roidb = []
ds = ('nuclei_stage_1_local_train_split',
'nuclei_stage_1_local_val_split',
# 'nuclei_stage_1_test',
'nucleisegmentationbenchmark',
'cluster_nuclei',
'BBBC006',
'BBBC007',
'BBBC018',
'BBBC020',
'2009_ISBI_2DNuclei',
# 'nuclei_partial_annotations',
'TNBC_NucleiSegmentation',
)
for d in ds:
roidb.extend(JsonDataset(d).get_roidb(gt=True))
return roidb
示例8: test_net_on_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def test_net_on_dataset(output_dir, multi_gpu=False, gpu_id=0):
"""Run inference on a dataset."""
dataset = JsonDataset(cfg.TEST.DATASET)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb())
all_boxes, all_segms, all_keyps = multi_gpu_test_net_on_dataset(
num_images, output_dir
)
else:
all_boxes, all_segms, all_keyps = test_net(output_dir, gpu_id=gpu_id)
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(test_timer.average_time))
results = task_evaluation.evaluate_all(
dataset, all_boxes, all_segms, all_keyps, output_dir
)
return results
示例9: get_roidb_and_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def get_roidb_and_dataset(ind_range):
"""Get the roidb for the dataset specified in the global cfg. Optionally
restrict it to a range of indices if ind_range is a pair of integers.
"""
dataset = JsonDataset(cfg.TEST.DATASET)
if cfg.TEST.PRECOMPUTED_PROPOSALS:
roidb = dataset.get_roidb(
proposal_file=cfg.TEST.PROPOSAL_FILE,
proposal_limit=cfg.TEST.PROPOSAL_LIMIT
)
else:
roidb = dataset.get_roidb()
if ind_range is not None:
total_num_images = len(roidb)
start, end = ind_range
roidb = roidb[start:end]
else:
start = 0
end = len(roidb)
total_num_images = end
return roidb, dataset, start, end, total_num_images
示例10: generate_rpn_on_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def generate_rpn_on_dataset(output_dir, multi_gpu=False, gpu_id=0):
"""Run inference on a dataset."""
dataset = JsonDataset(cfg.TEST.DATASET)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb())
_boxes, _scores, _ids, rpn_file = multi_gpu_generate_rpn_on_dataset(
num_images, output_dir
)
else:
# Processes entire dataset range by default
_boxes, _scores, _ids, rpn_file = generate_rpn_on_range(
output_dir, gpu_id=gpu_id
)
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(test_timer.average_time))
return evaluate_proposal_file(dataset, rpn_file, output_dir)
示例11: do_reval
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def do_reval(dataset_name, output_dir, args):
dataset = JsonDataset(dataset_name)
with open(os.path.join(output_dir, 'detections.pkl'), 'rb') as f:
dets = pickle.load(f)
# Override config with the one saved in the detections file
if args.cfg_file is not None:
core.config.merge_cfg_from_cfg(yaml.load(dets['cfg']))
else:
core.config._merge_a_into_b(yaml.load(dets['cfg']), cfg)
results = task_evaluation.evaluate_all(
dataset,
dets['all_boxes'],
dets['all_segms'],
dets['all_keyps'],
output_dir,
use_matlab=args.matlab_eval
)
task_evaluation.log_copy_paste_friendly_results(results)
示例12: test_net_on_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def test_net_on_dataset(multi_gpu=False):
output_dir = get_output_dir(training=False)
dataset = JsonDataset(cfg.TEST.DATASET)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb())
all_boxes, all_segms, all_keyps = multi_gpu_test_net_on_dataset(
num_images, output_dir)
else:
all_boxes, all_segms, all_keyps = test_net()
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(
test_timer.average_time))
# Run tracking and eval for posetrack datasets
if dataset.name.startswith('posetrack') or dataset.name.startswith('kinetics'):
roidb, dataset, _, _, _ = get_roidb_and_dataset(None)
run_posetrack_tracking(output_dir, roidb)
try:
evaluate_all(dataset, all_boxes, all_segms, all_keyps, output_dir)
except Exception as e:
# Typically would crash as we don't have evaluators for each dataset
logger.error('Evaluation crashed with exception {}'.format(e))
示例13: generate_rpn_on_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def generate_rpn_on_dataset(multi_gpu=False):
output_dir = get_output_dir(training=False)
dataset = JsonDataset(cfg.TEST.DATASET)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb())
_boxes, _scores, _ids, rpn_file = multi_gpu_generate_rpn_on_dataset(
num_images, output_dir)
else:
# Processes entire dataset range by default
_boxes, _scores, _ids, rpn_file = generate_rpn_on_range()
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(
test_timer.average_time))
evaluate_proposal_file(dataset, rpn_file, output_dir)
示例14: test_net_on_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def test_net_on_dataset(
args,
dataset_name,
proposal_file,
output_dir,
multi_gpu=False,
gpu_id=0):
"""Run inference on a dataset."""
dataset = JsonDataset(dataset_name)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb())
all_boxes, all_segms, all_keyps = multi_gpu_test_net_on_dataset(
args, dataset_name, proposal_file, num_images, output_dir
)
else:
all_boxes, all_segms, all_keyps = test_net(
args, dataset_name, proposal_file, output_dir, gpu_id=gpu_id
)
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(test_timer.average_time))
results = task_evaluation.evaluate_all(
dataset, all_boxes, all_segms, all_keyps, output_dir
)
return results
示例15: test_net_on_dataset
# 需要导入模块: from datasets import json_dataset [as 别名]
# 或者: from datasets.json_dataset import JsonDataset [as 别名]
def test_net_on_dataset(
args,
dataset_name,
proposal_file,
output_dir,
multi_gpu=False,
gpu_id=0):
"""Run inference on a dataset."""
dataset = JsonDataset(dataset_name)
test_timer = Timer()
test_timer.tic()
if multi_gpu:
num_images = len(dataset.get_roidb())
all_boxes = multi_gpu_test_net_on_dataset(
args, dataset_name, proposal_file, num_images, output_dir
)
else:
all_boxes = test_net(
args, dataset_name, proposal_file, output_dir, gpu_id=gpu_id
)
test_timer.toc()
logger.info('Total inference time: {:.3f}s'.format(test_timer.average_time))
roidb = dataset.get_roidb()
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES + 1
final_boxes = empty_results(num_classes, num_images)
test_corloc = 'train' in dataset_name
for i, entry in enumerate(roidb):
boxes = all_boxes[entry['image']]
if test_corloc:
_, _, cls_boxes_i = box_results_for_corloc(boxes['scores'], boxes['boxes'])
else:
_, _, cls_boxes_i = box_results_with_nms_and_limit(boxes['scores'],
boxes['boxes'])
extend_results(i, final_boxes, cls_boxes_i)
results = task_evaluation.evaluate_all(
dataset, final_boxes, output_dir, test_corloc
)
return results