def _test_std(self):
current_dir = osp.dirname(osp.realpath(__file__))
cfg_file = osp.join(current_dir, '..', 'configs', 'R-50_1x.yaml')
merge_cfg_from_file(cfg_file)
cfg.TEST.WEIGHTS = osp.join(
current_dir, '..', 'outputs', 'train',
'coco_2014_train+coco_2014_valminusminival', 'R-50_1x', 'default',
'model_final.pkl')
cfg.RETINANET.INFERENCE_TH = 0.
dataset = JsonDataset('coco_2014_minival')
roidb = dataset.get_roidb()
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=0)
utils.net.initialize_gpu_from_weights_file(model,
cfg.TEST.WEIGHTS,
gpu_id=0)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
entry = roidb[0]
im = cv2.imread(entry['image'])
with utils.c2.NamedCudaScope(0):
cls_boxes, cls_preds, cls_probs, box_preds, anchors, im_info = im_detect_bbox(
model, im, debug=True)
workspace.ResetWorkspace()
return cls_preds, cls_probs, box_preds, anchors, im_info
def initialize_model_from_cfg(gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
net_utils.initialize_gpu_from_weights_file(
model,
cfg.TEST.WEIGHTS,
gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
workspace.CreateNet(model.keypoint_net)
graph = net_drawer.GetPydotGraphMinimal(model.net.Proto().op,
rankdir="LR",
minimal_dependency=True)
#display.Image(graph.create_png(), height=1800)
graph.write_png('graph.png')
with open(os.path.join("video/keypoint", "net.pbtxt"), 'w') as fid:
fid.write(str(model.net.Proto()))
return model
def test_retinanet(ind_range=None):
"""
Test RetinaNet model either on the entire dataset or the subset of dataset
specified by the index range
"""
assert cfg.RETINANET.RETINANET_ON, \
'RETINANET_ON must be set for testing RetinaNet model'
output_dir = get_output_dir(training=False)
dataset = JsonDataset(cfg.TEST.DATASET)
roidb = dataset.get_roidb()
if ind_range is not None:
start, end = ind_range
roidb = roidb[start:end]
# Create and load the model
model = model_builder.create(cfg.MODEL.TYPE, train=False)
if cfg.TEST.WEIGHTS:
nu.initialize_from_weights_file(
model, cfg.TEST.WEIGHTS, broadcast=False
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
# Compute the detections
all_boxes = im_list_detections(model, roidb)
# Save the detections
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'detection_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(all_boxes=all_boxes, cfg=cfg_yaml),
det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return all_boxes
def initialize_model_from_cfg(weights_file, gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
net_utils.initialize_gpu_from_weights_file(
model, weights_file, gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
return model
def initialize_model_from_cfg():
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False)
net_utils.initialize_from_weights_file(
model, cfg.TEST.WEIGHTS, broadcast=False
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
workspace.CreateNet(model.keypoint_net)
return model
def initialize_model_from_cfg(gpu_id=0):
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
net_utils.initialize_gpu_from_weights_file(
model, cfg.TEST.WEIGHTS, gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
workspace.CreateNet(model.keypoint_net)
return model
def generate_rpn_on_range(
weights_file,
dataset_name,
_proposal_file_ignored,
output_dir,
ind_range=None,
gpu_id=0
):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert cfg.MODEL.RPN_ONLY or cfg.MODEL.FASTER_RCNN
roidb, start_ind, end_ind, total_num_images = get_roidb(
dataset_name, ind_range
)
logger.info(
'Output will be saved to: {:s}'.format(os.path.abspath(output_dir))
)
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
nu.initialize_gpu_from_weights_file(
model, weights_file, gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
boxes, scores, ids = generate_proposals_on_roidb(
model,
roidb,
start_ind=start_ind,
end_ind=end_ind,
total_num_images=total_num_images,
gpu_id=gpu_id,
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
rpn_name = 'rpn_proposals_range_%s_%s.pkl' % tuple(ind_range)
else:
rpn_name = 'rpn_proposals.pkl'
rpn_file = os.path.join(output_dir, rpn_name)
save_object(
dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml), rpn_file
)
logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
return boxes, scores, ids, rpn_file
def generate_rpn_on_range(weights_file,
dataset_name,
_proposal_file_ignored,
output_dir,
ind_range=None,
gpu_id=0):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert cfg.MODEL.RPN_ONLY or cfg.MODEL.FASTER_RCNN
roidb, start_ind, end_ind, total_num_images = get_roidb(
dataset_name, ind_range)
logger.info('Output will be saved to: {:s}'.format(
os.path.abspath(output_dir)))
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
nu.initialize_gpu_from_weights_file(
model,
weights_file,
gpu_id=gpu_id,
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
boxes, scores, ids = generate_proposals_on_roidb(
model,
roidb,
start_ind=start_ind,
end_ind=end_ind,
total_num_images=total_num_images,
gpu_id=gpu_id,
)
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
rpn_name = 'rpn_proposals_range_%s_%s.pkl' % tuple(ind_range)
else:
rpn_name = 'rpn_proposals.pkl'
rpn_file = os.path.join(output_dir, rpn_name)
save_object(dict(boxes=boxes, scores=scores, ids=ids, cfg=cfg_yaml),
rpn_file)
logger.info('Wrote RPN proposals to {}'.format(os.path.abspath(rpn_file)))
return boxes, scores, ids, rpn_file
def test_retinanet(ind_range=None):
"""
Test RetinaNet model either on the entire dataset or the subset of dataset
specified by the index range
"""
assert cfg.RETINANET.RETINANET_ON, \
'RETINANET_ON must be set for testing RetinaNet model'
output_dir = get_output_dir(training=False)
dataset = JsonDataset(cfg.TEST.DATASET)
im_list = dataset.get_roidb()
if ind_range is not None:
start, end = ind_range
im_list = im_list[start:end]
logger.info('Testing on roidb range: {}-{}'.format(start, end))
else:
# if testing over the whole dataset, use the NUM_TEST_IMAGES setting
# the NUM_TEST_IMAGES could be over a small set of images for quick
# debugging purposes
im_list = im_list[0:cfg.TEST.NUM_TEST_IMAGES]
model = model_builder.create(cfg.MODEL.TYPE, train=False)
if cfg.TEST.WEIGHTS:
nu.initialize_from_weights_file(model,
cfg.TEST.WEIGHTS,
broadcast=False)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
boxes, scores, classes, image_ids = im_list_detections(
model, im_list[0:cfg.TEST.NUM_TEST_IMAGES])
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'retinanet_detections_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'retinanet_detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(boxes=boxes,
scores=scores,
classes=classes,
ids=image_ids,
cfg=cfg_yaml), det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return boxes, scores, classes, image_ids
def _test_std(self):
current_dir = osp.dirname(osp.realpath(__file__))
cfg_file = osp.join(current_dir, '..', 'configs', 'R-50_1x.yaml')
merge_cfg_from_file(cfg_file)
cfg.TEST.WEIGHTS = osp.join(
current_dir, '..', 'outputs', 'train',
'coco_2014_train+coco_2014_valminusminival', 'R-50_1x', 'default',
'model_final.pkl')
cfg.RETINANET.INFERENCE_TH = 0.
dataset = JsonDataset('coco_2014_minival')
roidb = dataset.get_roidb()
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=0)
utils.net.initialize_gpu_from_weights_file(model,
cfg.TEST.WEIGHTS,
gpu_id=0)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
num_images = len(roidb)
num_classes = cfg.MODEL.NUM_CLASSES
entry = roidb[5]
im = cv2.imread(entry['image'])
with utils.c2.NamedCudaScope(0):
cls_boxes, cls_preds, cls_probs, box_preds, anchors, im_info = im_detect_bbox(
model, im, debug=True)
cls_boxes = cls_boxes[:, :5]
im_name = osp.splitext(osp.basename(entry['image']))[0]
# utils.vis.vis_one_image(im[:, :, ::-1],
# '{:s}-std-output'.format(im_name),
# current_dir,
# cls_boxes,
# segms=None,
# keypoints=None,
# thresh=0.,
# box_alpha=0.8,
# dataset=dataset,
# show_class=False)
workspace.ResetWorkspace()
return cls_preds, cls_probs, box_preds, anchors, im_info, im, im_name, current_dir, dataset
def test_retinanet(ind_range=None):
"""
Test RetinaNet model either on the entire dataset or the subset of dataset
specified by the index range
"""
assert cfg.RETINANET.RETINANET_ON, \
'RETINANET_ON must be set for testing RetinaNet model'
output_dir = get_output_dir(training=False)
dataset = JsonDataset(cfg.TEST.DATASET)
im_list = dataset.get_roidb()
if ind_range is not None:
start, end = ind_range
im_list = im_list[start:end]
logger.info('Testing on roidb range: {}-{}'.format(start, end))
else:
# if testing over the whole dataset, use the NUM_TEST_IMAGES setting
# the NUM_TEST_IMAGES could be over a small set of images for quick
# debugging purposes
im_list = im_list[0:cfg.TEST.NUM_TEST_IMAGES]
model = model_builder.create(cfg.MODEL.TYPE, train=False)
if cfg.TEST.WEIGHTS:
nu.initialize_from_weights_file(
model, cfg.TEST.WEIGHTS, broadcast=False
)
model_builder.add_inference_inputs(model)
workspace.CreateNet(model.net)
boxes, scores, classes, image_ids = im_list_detections(
model, im_list[0:cfg.TEST.NUM_TEST_IMAGES])
cfg_yaml = yaml.dump(cfg)
if ind_range is not None:
det_name = 'retinanet_detections_range_%s_%s.pkl' % tuple(ind_range)
else:
det_name = 'retinanet_detections.pkl'
det_file = os.path.join(output_dir, det_name)
save_object(
dict(boxes=boxes, scores=scores, classes=classes, ids=image_ids, cfg=cfg_yaml),
det_file)
logger.info('Wrote detections to: {}'.format(os.path.abspath(det_file)))
return boxes, scores, classes, image_ids
