def do_reval(dataset_name, output_dir, args):
dataset = JsonDataset(dataset_name)
dets = load_object(os.path.join(output_dir, 'detections.pkl'))
# Override config with the one saved in the detections file
if args.cfg_file is not None:
core_config.merge_cfg_from_cfg(core_config.load_cfg(dets['cfg']))
else:
core_config._merge_a_into_b(core_config.load_cfg(dets['cfg']), cfg)
# re-filter on score threshold:
dets['all_boxes'] = \
[
[
im[im[:,4] > cfg.TEST.SCORE_THRESH,:] if len(im) != 0 else []
for im in cls
]
for cls in dets['all_boxes']
]
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)
def run_inference(
weights_file,
ind_range=None,
multi_gpu_testing=False,
gpu_id=0,
check_expected_results=False,
):
parent_func, child_func = get_eval_functions()
is_parent = ind_range is None
def result_getter():
if is_parent:
# Parent case:
# In this case we're either running inference on the entire dataset in a
# single process or (if multi_gpu_testing is True) using this process to
# launch subprocesses that each run inference on a range of the dataset
all_results = {}
subset_pointer = None
if cfg.VOC_SUBSET != '':
subset_pointer = result_getter #any dummy object could be used that is more advanced than 'object()' or similar builtins.
subset_pointer.subset = np.load(cfg.VOC_SUBSET)
print('loading subset')
for i in range(len(cfg.TEST.DATASETS)):
dataset_name, proposal_file = get_inference_dataset(i)
output_dir = get_output_dir(dataset_name, training=False)
print('len before', len(subset_pointer.subset))
results = parent_func(weights_file,
dataset_name,
proposal_file,
output_dir,
multi_gpu=multi_gpu_testing,
subset_pointer=subset_pointer)
all_results.update(results)
return all_results
else:
# Subprocess child case:
# In this case test_net was called via subprocess.Popen to execute on a
# range of inputs on a single dataset
dataset_name, proposal_file = get_inference_dataset(
0, is_parent=False)
output_dir = get_output_dir(dataset_name, training=False)
return child_func(weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=ind_range,
gpu_id=gpu_id)
all_results = result_getter()
if check_expected_results and is_parent:
task_evaluation.check_expected_results(all_results,
atol=cfg.EXPECTED_RESULTS_ATOL,
rtol=cfg.EXPECTED_RESULTS_RTOL)
task_evaluation.log_copy_paste_friendly_results(all_results)
return all_results
def run_inference(
weights_file, ind_range=None,
multi_gpu_testing=False, gpu_id=0,
check_expected_results=False,
evaluation = True
):
parent_func, child_func = get_eval_functions()
is_parent = ind_range is None
def result_getter():
if is_parent:
# Parent case:
# In this case we're either running inference on the entire dataset in a
# single process or (if multi_gpu_testing is True) using this process to
# launch subprocesses that each run inference on a range of the dataset
all_results = {}
for i in range(len(cfg.TEST.DATASETS)):
dataset_name, proposal_file = get_inference_dataset(i)
output_dir = get_output_dir(dataset_name, training=False)
results = parent_func(
weights_file,
dataset_name,
proposal_file,
output_dir,
multi_gpu=multi_gpu_testing,
evaluation = evaluation
)
all_results.update(results)
return all_results
else:
# Subprocess child case:
# In this case test_net was called via subprocess.Popen to execute on a
# range of inputs on a single dataset
dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
output_dir = get_output_dir(dataset_name, training=False)
return child_func(
weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=ind_range,
gpu_id=gpu_id
)
all_results = result_getter()
if check_expected_results and is_parent:
task_evaluation.check_expected_results(
all_results,
atol=cfg.EXPECTED_RESULTS_ATOL,
rtol=cfg.EXPECTED_RESULTS_RTOL
)
if not cfg.MODEL.CLASSIFICATION:
task_evaluation.log_copy_paste_friendly_results(all_results)
return all_results
def run_inference(
weights_file, ind_range=None,
multi_gpu_testing=False, gpu_id=0,
check_expected_results=False,
):
parent_func, child_func = get_eval_functions()
is_parent = ind_range is None
def result_getter():
if is_parent:
# Parent case:
# In this case we're either running inference on the entire dataset in a
# single process or (if multi_gpu_testing is True) using this process to
# launch subprocesses that each run inference on a range of the dataset
all_results = {}
dataset_info, proposal_file = get_inference_dataset()
output_dir = osp.join(get_output_dir(dataset_info, training=False), weights_file.strip().split('/')[-1].split('.')[0])
if not osp.exists(output_dir):
os.makedirs(output_dir)
results = parent_func(
weights_file,
dataset_info,
proposal_file,
output_dir,
multi_gpu=multi_gpu_testing
)
all_results.update(results)
return all_results
else:
# Subprocess child case:
# In this case test_net was called via subprocess.Popen to execute on a
# range of inputs on a single dataset
dataset_info, proposal_file = get_inference_dataset(is_parent=False)
output_dir = osp.join(get_output_dir(dataset_info, training=False), weights_file.strip().split('/')[-1].split('.')[0])
if not osp.exists(output_dir):
os.makedirs(output_dir)
return child_func(
weights_file,
dataset_info,
proposal_file,
output_dir,
ind_range=ind_range,
gpu_id=gpu_id
)
all_results = result_getter()
if check_expected_results and is_parent:
task_evaluation.check_expected_results(
all_results,
atol=cfg.EXPECTED_RESULTS_ATOL,
rtol=cfg.EXPECTED_RESULTS_RTOL
)
task_evaluation.log_copy_paste_friendly_results(all_results)
return all_results
def run_inference(
weights_file, ind_range=None,
multi_gpu_testing=False, gpu_id=0,
check_expected_results=False,
):
parent_func, child_func = get_eval_functions()
is_parent = ind_range is None
def result_getter():
if is_parent:
# Parent case:
# In this case we're either running inference on the entire dataset in a
# single process or (if multi_gpu_testing is True) using this process to
# launch subprocesses that each run inference on a range of the dataset
all_results = {}
for i in range(len(cfg.TEST.DATASETS)):
dataset_name, proposal_file = get_inference_dataset(i)
output_dir = get_output_dir(dataset_name, training=False)
results = parent_func(
weights_file,
dataset_name,
proposal_file,
output_dir,
multi_gpu=multi_gpu_testing
)
all_results.update(results)
return all_results
else:
# Subprocess child case:
# In this case test_net was called via subprocess.Popen to execute on a
# range of inputs on a single dataset
dataset_name, proposal_file = get_inference_dataset(0, is_parent=False)
output_dir = get_output_dir(dataset_name, training=False)
return child_func(
weights_file,
dataset_name,
proposal_file,
output_dir,
ind_range=ind_range,
gpu_id=gpu_id
)
all_results = result_getter()
if check_expected_results and is_parent:
task_evaluation.check_expected_results(
all_results,
atol=cfg.EXPECTED_RESULTS_ATOL,
rtol=cfg.EXPECTED_RESULTS_RTOL
)
task_evaluation.log_copy_paste_friendly_results(all_results)
return all_results
def do_reval(dataset_name, output_dir, args):
dataset = JsonDataset(dataset_name)
dets = load_object(os.path.join(output_dir, 'detections.pkl'))
# Override config with the one saved in the detections file
if args.cfg_file is not None:
core_config.merge_cfg_from_cfg(core_config.load_cfg(dets['cfg']))
else:
core_config._merge_a_into_b(core_config.load_cfg(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)
def do_reval(dataset_name, output_dir, args):
dataset = JsonDataset(dataset_name)
dets = load_object(os.path.join(output_dir, 'detections.pkl'))
# Override config with the one saved in the detections file
if args.cfg_file is not None:
core_config.merge_cfg_from_cfg(core_config.load_cfg(dets['cfg']))
else:
core_config._merge_a_into_b(core_config.load_cfg(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)
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:
# bug: loads only already stored cfg
# core_config.merge_cfg_from_cfg(core_config.load_cfg(dets['cfg']))
# merge config from passed config file!!
core_config.merge_cfg_from_file(args.cfg_file)
else:
core_config._merge_a_into_b(core_config.load_cfg(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)
