def dump_infer_config(FLAGS, config):
from ppdet.core.config.yaml_helpers import setup_orderdict
setup_orderdict()
infer_cfg = OrderedDict({
'use_python_inference': False,
'mode': 'fluid',
'draw_threshold': 0.5,
'metric': config['metric']
})
trt_min_subgraph = {
'YOLO': 3,
'SSD': 3,
'RCNN': 40,
'RetinaNet': 40,
'Face': 3,
'TTFNet': 3,
}
infer_arch = config['architecture']
for arch, min_subgraph_size in trt_min_subgraph.items():
if arch in infer_arch:
infer_cfg['arch'] = arch
infer_cfg['min_subgraph_size'] = min_subgraph_size
break
if 'Mask' in config['architecture']:
infer_cfg['mask_resolution'] = config['MaskHead']['resolution']
infer_cfg['with_background'], infer_cfg['Preprocess'], infer_cfg[
'label_list'] = parse_reader(config['TestReader'], config['metric'],
infer_cfg['arch'])
yaml.dump(infer_cfg,
open(os.path.join(FLAGS.output_dir, 'infer_cfg.yml'), 'w'))
logger.info("Export inference config file to {}".format(
os.path.join(FLAGS.output_dir, 'infer_cfg.yml')))
def dump_infer_config(FLAGS, config):
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(FLAGS.output_dir, cfg_name)
from ppdet.core.config.yaml_helpers import setup_orderdict
setup_orderdict()
infer_cfg = OrderedDict({
'use_python_inference': False,
'mode': 'fluid',
'draw_threshold': 0.5,
'metric': config['metric']
})
infer_arch = config['architecture']
for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
if arch in infer_arch:
infer_cfg['arch'] = arch
infer_cfg['min_subgraph_size'] = min_subgraph_size
break
if 'Mask' in config['architecture']:
infer_cfg['mask_resolution'] = config['MaskHead']['resolution']
infer_cfg['with_background'], infer_cfg['Preprocess'], infer_cfg[
'label_list'] = parse_reader(config['TestReader'], config['metric'],
infer_cfg['arch'])
yaml.dump(infer_cfg, open(os.path.join(save_dir, 'infer_cfg.yml'), 'w'))
logger.info("Export inference config file to {}".format(
os.path.join(save_dir, 'infer_cfg.yml')))
def _dump_infer_config(config, path, image_shape, model):
arch_state = False
from ppdet.core.config.yaml_helpers import setup_orderdict
setup_orderdict()
infer_cfg = OrderedDict({
'mode': 'fluid',
'draw_threshold': 0.5,
'metric': config['metric'],
'image_shape': image_shape
})
infer_arch = config['architecture']
for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
if arch in infer_arch:
infer_cfg['arch'] = arch
infer_cfg['min_subgraph_size'] = min_subgraph_size
arch_state = True
break
if not arch_state:
logger.error(
'Architecture: {} is not supported for exporting model now'.format(
infer_arch))
os._exit(0)
if 'mask_post_process' in model.__dict__ and model.__dict__[
'mask_post_process']:
infer_cfg['mask_resolution'] = model.mask_post_process.mask_resolution
infer_cfg['with_background'], infer_cfg['Preprocess'], infer_cfg[
'label_list'], image_shape = _parse_reader(
config['TestReader'], config['TestDataset'], config['metric'],
infer_cfg['arch'], image_shape)
yaml.dump(infer_cfg, open(path, 'w'))
logger.info("Export inference config file to {}".format(os.path.join(path)))
return image_shape
def _dump_infer_config(config, path, image_shape, model):
arch_state = False
from ppdet.core.config.yaml_helpers import setup_orderdict
setup_orderdict()
use_dynamic_shape = True if image_shape[1] == -1 else False
infer_cfg = OrderedDict({
'mode': 'fluid',
'draw_threshold': 0.5,
'metric': config['metric'],
'use_dynamic_shape': use_dynamic_shape
})
infer_arch = config['architecture']
if infer_arch in MOT_ARCH:
if infer_arch == 'DeepSORT':
tracker_cfg = config['DeepSORTTracker']
else:
tracker_cfg = config['JDETracker']
infer_cfg['tracker'] = _parse_tracker(tracker_cfg)
for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
if arch in infer_arch:
infer_cfg['arch'] = arch
infer_cfg['min_subgraph_size'] = min_subgraph_size
arch_state = True
break
if not arch_state:
logger.error(
'Architecture: {} is not supported for exporting model now'.format(
infer_arch))
os._exit(0)
if 'mask_head' in config[config['architecture']] and config[
config['architecture']]['mask_head']:
infer_cfg['mask'] = True
label_arch = 'detection_arch'
if infer_arch in KEYPOINT_ARCH:
label_arch = 'keypoint_arch'
if infer_arch in MOT_ARCH:
label_arch = 'mot_arch'
reader_cfg = config['TestMOTReader']
dataset_cfg = config['TestMOTDataset']
else:
reader_cfg = config['TestReader']
dataset_cfg = config['TestDataset']
infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader(
reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape)
yaml.dump(infer_cfg, open(path, 'w'))
logger.info("Export inference config file to {}".format(
os.path.join(path)))
def dump_infer_config(FLAGS, config):
arch_state = 0
cfg_name = os.path.basename(FLAGS.config).split('.')[0]
save_dir = os.path.join(FLAGS.output_dir, cfg_name)
if not os.path.exists(save_dir):
os.makedirs(save_dir)
from ppdet.core.config.yaml_helpers import setup_orderdict
setup_orderdict()
infer_cfg = OrderedDict({
'use_python_inference': False,
'mode': 'fluid',
'draw_threshold': 0.5,
'metric': config['metric']
})
infer_arch = config['architecture']
for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
if arch in infer_arch:
infer_cfg['arch'] = arch
infer_cfg['min_subgraph_size'] = min_subgraph_size
arch_state = 1
break
if not arch_state:
logger.error(
'Architecture: {} is not supported for exporting model now'.format(
infer_arch))
os._exit(0)
# support land mark output
if 'with_lmk' in config and config['with_lmk'] == True:
infer_cfg['with_lmk'] = True
if 'Mask' in config['architecture']:
infer_cfg['mask_resolution'] = config['MaskHead']['resolution']
infer_cfg['with_background'], infer_cfg['Preprocess'], infer_cfg[
'label_list'] = parse_reader(config['TestReader'], config['metric'],
infer_cfg['arch'])
yaml.dump(infer_cfg, open(os.path.join(save_dir, 'infer_cfg.yml'), 'w'))
logger.info("Export inference config file to {}".format(
os.path.join(save_dir, 'infer_cfg.yml')))
def _dump_infer_config(config, path, image_shape, model):
arch_state = False
from ppdet.core.config.yaml_helpers import setup_orderdict
setup_orderdict()
infer_cfg = OrderedDict({
'mode': 'fluid',
'draw_threshold': 0.5,
'metric': config['metric'],
})
infer_arch = config['architecture']
for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
if arch in infer_arch:
infer_cfg['arch'] = arch
infer_cfg['min_subgraph_size'] = min_subgraph_size
arch_state = True
break
if not arch_state:
logger.error(
'Architecture: {} is not supported for exporting model now'.format(
infer_arch))
os._exit(0)
if 'Mask' in infer_arch:
infer_cfg['mask'] = True
label_arch = 'detection_arch'
if infer_arch in KEYPOINT_ARCH:
label_arch = 'keypoint_arch'
infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader(
config['TestReader'], config['TestDataset'], config['metric'],
label_arch, image_shape)
if infer_arch == 'S2ANet':
# TODO: move background to num_classes
if infer_cfg['label_list'][0] != 'background':
infer_cfg['label_list'].insert(0, 'background')
yaml.dump(infer_cfg, open(path, 'w'))
logger.info("Export inference config file to {}".format(
os.path.join(path)))
def _dump_infer_config(config, path, image_shape, model):
arch_state = False
from ppdet.core.config.yaml_helpers import setup_orderdict
setup_orderdict()
use_dynamic_shape = True if image_shape[2] == -1 else False
infer_cfg = OrderedDict({
'mode': 'fluid',
'draw_threshold': 0.5,
'metric': config['metric'],
'use_dynamic_shape': use_dynamic_shape
})
infer_arch = config['architecture']
if infer_arch in MOT_ARCH:
if infer_arch == 'DeepSORT':
tracker_cfg = config['DeepSORTTracker']
else:
tracker_cfg = config['JDETracker']
infer_cfg['tracker'] = _parse_tracker(tracker_cfg)
for arch, min_subgraph_size in TRT_MIN_SUBGRAPH.items():
if arch in infer_arch:
infer_cfg['arch'] = arch
infer_cfg['min_subgraph_size'] = min_subgraph_size
arch_state = True
break
if not arch_state:
logger.error(
'Architecture: {} is not supported for exporting model now.\n'.
format(infer_arch) +
'Please set TRT_MIN_SUBGRAPH in ppdet/engine/export_utils.py')
os._exit(0)
if 'mask_head' in config[config['architecture']] and config[
config['architecture']]['mask_head']:
infer_cfg['mask'] = True
label_arch = 'detection_arch'
if infer_arch in KEYPOINT_ARCH:
label_arch = 'keypoint_arch'
if infer_arch in MOT_ARCH:
label_arch = 'mot_arch'
reader_cfg = config['TestMOTReader']
dataset_cfg = config['TestMOTDataset']
else:
reader_cfg = config['TestReader']
dataset_cfg = config['TestDataset']
infer_cfg['Preprocess'], infer_cfg['label_list'] = _parse_reader(
reader_cfg, dataset_cfg, config['metric'], label_arch, image_shape[1:])
if infer_arch == 'PicoDet':
infer_cfg['NMS'] = config['PicoHead']['nms']
# In order to speed up the prediction, the threshold of nms
# is adjusted here, which can be changed in infer_cfg.yml
config['PicoHead']['nms']["score_threshold"] = 0.3
config['PicoHead']['nms']["nms_threshold"] = 0.5
infer_cfg['fpn_stride'] = config['PicoHead']['fpn_stride']
yaml.dump(infer_cfg, open(path, 'w'))
logger.info("Export inference config file to {}".format(
os.path.join(path)))
