Python detectron.datasets.dummy_datasets.get_coco_dataset() Examples
The following are 13
code examples of detectron.datasets.dummy_datasets.get_coco_dataset().
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detectron.datasets.dummy_datasets
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Example #1
| Source File: model.py From nuke-ML-server with Apache License 2.0 | 5 votes |
|
def __init__(self):
super(Model, self).__init__()
self.name = 'Mask RCNN'
# Configuration and weights options
# By default, we use ResNet50 backbone architecture, you can switch to
# ResNet101 to increase quality if your GPU memory is higher than 8GB.
# To do so, you will need to download both .yaml and .pkl ResNet101 files
# then replace the below 'cfg_file' with the following:
# self.cfg_file = 'models/mrcnn/e2e_mask_rcnn_X-101-64x4d-FPN_2x.yaml'
self.cfg_file = 'models/mrcnn/e2e_mask_rcnn_R-50-FPN_2x.yaml'
self.weights = 'models/mrcnn/model_final.pkl'
self.default_cfg = copy.deepcopy(AttrDict(cfg)) # cfg from detectron.core.config
self.mrcnn_cfg = AttrDict()
self.dummy_coco_dataset = dummy_datasets.get_coco_dataset()
# Inference options
self.show_box = True
self.show_class = True
self.thresh = 0.7
self.alpha = 0.4
self.show_border = True
self.border_thick = 1
self.bbox_thick = 1
self.font_scale = 0.35
self.binary_masks = False
# Define exposed options
self.options = (
'show_box', 'show_class', 'thresh', 'alpha', 'show_border',
'border_thick', 'bbox_thick', 'font_scale', 'binary_masks',
)
# Define inputs/outputs
self.inputs = {'input': 3}
self.outputs = {'output': 3}
Example #2
| Source File: infer_simple.py From KL-Loss with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
Example #3
| Source File: infer.py From KL-Loss with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(envu.yaml_dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
)
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
Example #4
| Source File: infer_simple.py From Clustered-Object-Detection-in-Aerial-Image with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
Example #5
| Source File: infer.py From Clustered-Object-Detection-in-Aerial-Image with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(yaml.dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
)
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
Example #6
| Source File: infer_simple.py From Detectron-Cascade-RCNN with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
Example #7
| Source File: infer.py From Detectron-Cascade-RCNN with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(yaml.dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
)
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
Example #8
| Source File: infer_simple.py From Detectron with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
Example #9
| Source File: infer.py From Detectron with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(envu.yaml_dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
)
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
Example #10
| Source File: infer_simple.py From Detectron-DA-Faster-RCNN with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=args.thresh,
kp_thresh=args.kp_thresh,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
Example #11
| Source File: infer.py From Detectron-DA-Faster-RCNN with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(envu.yaml_dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
)
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
Example #12
| Source File: infer_simple.py From CBNet with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
merge_cfg_from_file(args.cfg)
cfg.NUM_GPUS = 1
args.weights = cache_url(args.weights, cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
assert not cfg.MODEL.RPN_ONLY, \
'RPN models are not supported'
assert not cfg.TEST.PRECOMPUTED_PROPOSALS, \
'Models that require precomputed proposals are not supported'
model = infer_engine.initialize_model_from_cfg(args.weights)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
if os.path.isdir(args.im_or_folder):
im_list = glob.iglob(args.im_or_folder + '/*.' + args.image_ext)
else:
im_list = [args.im_or_folder]
for i, im_name in enumerate(im_list):
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(im_name) + '.' + args.output_ext)
)
logger.info('Processing {} -> {}'.format(im_name, out_name))
im = cv2.imread(im_name)
timers = defaultdict(Timer)
t = time.time()
with c2_utils.NamedCudaScope(0):
cls_boxes, cls_segms, cls_keyps = infer_engine.im_detect_all(
model, im, None, timers=timers
)
logger.info('Inference time: {:.3f}s'.format(time.time() - t))
for k, v in timers.items():
logger.info(' | {}: {:.3f}s'.format(k, v.average_time))
if i == 0:
logger.info(
' \ Note: inference on the first image will be slower than the '
'rest (caches and auto-tuning need to warm up)'
)
vis_utils.vis_one_image(
im[:, :, ::-1], # BGR -> RGB for visualization
im_name,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2,
ext=args.output_ext,
out_when_no_box=args.out_when_no_box
)
Example #13
| Source File: infer.py From CBNet with Apache License 2.0 | 4 votes |
|
def main(args):
logger = logging.getLogger(__name__)
dummy_coco_dataset = dummy_datasets.get_coco_dataset()
cfg_orig = load_cfg(yaml.dump(cfg))
im = cv2.imread(args.im_file)
if args.rpn_pkl is not None:
proposal_boxes, _proposal_scores = get_rpn_box_proposals(im, args)
workspace.ResetWorkspace()
else:
proposal_boxes = None
cls_boxes, cls_segms, cls_keyps = None, None, None
for i in range(0, len(args.models_to_run), 2):
pkl = args.models_to_run[i]
yml = args.models_to_run[i + 1]
cfg.immutable(False)
merge_cfg_from_cfg(cfg_orig)
merge_cfg_from_file(yml)
if len(pkl) > 0:
weights_file = pkl
else:
weights_file = cfg.TEST.WEIGHTS
cfg.NUM_GPUS = 1
assert_and_infer_cfg(cache_urls=False)
model = model_engine.initialize_model_from_cfg(weights_file)
with c2_utils.NamedCudaScope(0):
cls_boxes_, cls_segms_, cls_keyps_ = \
model_engine.im_detect_all(model, im, proposal_boxes)
cls_boxes = cls_boxes_ if cls_boxes_ is not None else cls_boxes
cls_segms = cls_segms_ if cls_segms_ is not None else cls_segms
cls_keyps = cls_keyps_ if cls_keyps_ is not None else cls_keyps
workspace.ResetWorkspace()
out_name = os.path.join(
args.output_dir, '{}'.format(os.path.basename(args.im_file) + '.pdf')
)
logger.info('Processing {} -> {}'.format(args.im_file, out_name))
vis_utils.vis_one_image(
im[:, :, ::-1],
args.im_file,
args.output_dir,
cls_boxes,
cls_segms,
cls_keyps,
dataset=dummy_coco_dataset,
box_alpha=0.3,
show_class=True,
thresh=0.7,
kp_thresh=2
)
