本文整理汇总了Python中maskrcnn_benchmark.data.datasets.evaluation.evaluate方法的典型用法代码示例。如果您正苦于以下问题：Python evaluation.evaluate方法的具体用法？Python evaluation.evaluate怎么用？Python evaluation.evaluate使用的例子？那么恭喜您, 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maskrcnn_benchmark.data.datasets.evaluation的用法示例。
在下文中一共展示了evaluation.evaluate方法的6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: evaluate_predictions_on_coco
# 需要导入模块: from maskrcnn_benchmark.data.datasets import evaluation [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets.evaluation import evaluate [as 别名]
def evaluate_predictions_on_coco(
coco_gt, coco_results, json_result_file, iou_type="bbox"
):
import json
with open(json_result_file, "w") as f:
json.dump(coco_results, f)
from pycocotools.cocoeval import COCOeval
coco_dt = coco_gt.loadRes(str(json_result_file))
# coco_dt = coco_gt.loadRes(coco_results)
coco_eval = COCOeval(coco_gt, coco_dt, iou_type)
coco_eval.evaluate()
coco_eval.accumulate()
coco_eval.summarize()
return coco_eval 示例2: inference
# 需要导入模块: from maskrcnn_benchmark.data.datasets import evaluation [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets.evaluation import evaluate [as 别名]
def inference(
model,
data_loader,
dataset_name,
iou_types=("bbox",),
box_only=False,
device="cuda",
expected_results=(),
expected_results_sigma_tol=4,
output_folder=None,
):
# convert to a torch.device for efficiency
device = torch.device(device)
num_devices = (
torch.distributed.get_world_size()
if torch.distributed.is_initialized()
else 1
)
logger = logging.getLogger("maskrcnn_benchmark.inference")
dataset = data_loader.dataset
logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
start_time = time.time()
predictions = compute_on_dataset(model, data_loader, device)
# wait for all processes to complete before measuring the time
synchronize()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=total_time))
logger.info(
"Total inference time: {} ({} s / img per device, on {} devices)".format(
total_time_str, total_time * num_devices / len(dataset), num_devices
)
)
predictions = _accumulate_predictions_from_multiple_gpus(predictions)
if not is_main_process():
return
if output_folder:
torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
extra_args = dict(
box_only=box_only,
iou_types=iou_types,
expected_results=expected_results,
expected_results_sigma_tol=expected_results_sigma_tol,
)
return evaluate(dataset=dataset,
predictions=predictions,
output_folder=output_folder,
**extra_args) 示例3: inference
# 需要导入模块: from maskrcnn_benchmark.data.datasets import evaluation [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets.evaluation import evaluate [as 别名]
def inference(
model,
data_loader,
dataset_name,
iou_types=("bbox",),
box_only=False,
device="cuda",
expected_results=(),
expected_results_sigma_tol=4,
output_folder=None,
):
# convert to a torch.device for efficiency
device = torch.device(device)
num_devices = get_world_size()
logger = logging.getLogger("maskrcnn_benchmark.inference")
dataset = data_loader.dataset
logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
total_timer = Timer()
inference_timer = Timer()
total_timer.tic()
predictions = compute_on_dataset(model, data_loader, device, inference_timer)
# wait for all processes to complete before measuring the time
synchronize()
total_time = total_timer.toc()
total_time_str = get_time_str(total_time)
logger.info(
"Total run time: {} ({} s / img per device, on {} devices)".format(
total_time_str, total_time * num_devices / len(dataset), num_devices
)
)
total_infer_time = get_time_str(inference_timer.total_time)
logger.info(
"Model inference time: {} ({} s / img per device, on {} devices)".format(
total_infer_time,
inference_timer.total_time * num_devices / len(dataset),
num_devices,
)
)
predictions = _accumulate_predictions_from_multiple_gpus(predictions)
if not is_main_process():
return
if output_folder:
torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
extra_args = dict(
box_only=box_only,
iou_types=iou_types,
expected_results=expected_results,
expected_results_sigma_tol=expected_results_sigma_tol,
)
return evaluate(dataset=dataset,
predictions=predictions,
output_folder=output_folder,
**extra_args) 示例4: inference
# 需要导入模块: from maskrcnn_benchmark.data.datasets import evaluation [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets.evaluation import evaluate [as 别名]
def inference(
model,
data_loader,
dataset_name,
iou_types=("bbox",),
box_only=False,
device="cuda",
expected_results=(),
expected_results_sigma_tol=4,
output_folder=None,
):
# convert to a torch.device for efficiency
device = torch.device(device)
num_devices = (
torch.distributed.get_world_size()
if torch.distributed.is_initialized()
else 1
)
logger = logging.getLogger("maskrcnn_benchmark.inference")
dataset = data_loader.dataset
logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
start_time = time.time()
predictions = compute_on_dataset(model, data_loader, device)
# wait for all processes to complete before measuring the time
synchronize()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=total_time))
logger.info(
"Total inference time: {} ({} s / img per device, on {} devices)".format(
total_time_str, total_time * num_devices / len(dataset), num_devices
)
)
predictions = _accumulate_predictions_from_multiple_gpus(predictions)
if not is_main_process():
return
if output_folder:
torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
extra_args = dict(
box_only=box_only,
iou_types=iou_types,
expected_results=expected_results,
expected_results_sigma_tol=expected_results_sigma_tol,
)
return predictions
# return evaluate(dataset=dataset,
# predictions=predictions,
# output_folder=output_folder,
# **extra_args) 示例5: inference
# 需要导入模块: from maskrcnn_benchmark.data.datasets import evaluation [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets.evaluation import evaluate [as 别名]
def inference(
model,
data_loader,
dataset_name,
iou_types=("bbox",),
box_only=False,
bbox_aug=False,
device="cuda",
expected_results=(),
expected_results_sigma_tol=4,
output_folder=None,
):
# convert to a torch.device for efficiency
device = torch.device(device)
num_devices = get_world_size()
logger = logging.getLogger("maskrcnn_benchmark.inference")
dataset = data_loader.dataset
logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
total_timer = Timer()
inference_timer = Timer()
total_timer.tic()
predictions = compute_on_dataset(model, data_loader, device, bbox_aug, inference_timer)
# wait for all processes to complete before measuring the time
synchronize()
total_time = total_timer.toc()
total_time_str = get_time_str(total_time)
logger.info(
"Total run time: {} ({} s / img per device, on {} devices)".format(
total_time_str, total_time * num_devices / len(dataset), num_devices
)
)
total_infer_time = get_time_str(inference_timer.total_time)
logger.info(
"Model inference time: {} ({} s / img per device, on {} devices)".format(
total_infer_time,
inference_timer.total_time * num_devices / len(dataset),
num_devices,
)
)
predictions = _accumulate_predictions_from_multiple_gpus(predictions)
if not is_main_process():
return
if output_folder:
torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
extra_args = dict(
box_only=box_only,
iou_types=iou_types,
expected_results=expected_results,
expected_results_sigma_tol=expected_results_sigma_tol,
)
return evaluate(dataset=dataset,
predictions=predictions,
output_folder=output_folder,
**extra_args) 示例6: inference
# 需要导入模块: from maskrcnn_benchmark.data.datasets import evaluation [as 别名]
# 或者: from maskrcnn_benchmark.data.datasets.evaluation import evaluate [as 别名]
def inference(
model,
data_loader,
dataset_name,
iou_types=("bbox",),
box_only=False,
device="cuda",
expected_results=(),
expected_results_sigma_tol=4,
output_folder=None,
):
# convert to a torch.device for efficiency
device = torch.device(device)
num_devices = get_world_size()
logger = logging.getLogger("maskrcnn_benchmark.inference")
dataset = data_loader.dataset
logger.info("Start evaluation on {} dataset({} images).".format(dataset_name, len(dataset)))
start_time = time.time()
predictions = compute_on_dataset(model, data_loader, device)
# wait for all processes to complete before measuring the time
synchronize()
total_time = time.time() - start_time
total_time_str = str(datetime.timedelta(seconds=total_time))
logger.info(
"Total inference time: {} ({} s / img per device, on {} devices)".format(
total_time_str, total_time * num_devices / len(dataset), num_devices
)
)
predictions = _accumulate_predictions_from_multiple_gpus(predictions)
if not is_main_process():
return
if output_folder:
torch.save(predictions, os.path.join(output_folder, "predictions.pth"))
extra_args = dict(
box_only=box_only,
iou_types=iou_types,
expected_results=expected_results,
expected_results_sigma_tol=expected_results_sigma_tol,
)
return evaluate(dataset=dataset,
predictions=predictions,
output_folder=output_folder,
**extra_args)