def initialize_model_from_cfg():
def create_input_blobs(net_def):
for op in net_def.op:
for blob_in in op.input:
if not workspace.HasBlob(blob_in):
workspace.CreateBlob(blob_in)
model = model_builder.create(
cfg.MODEL.TYPE,
train=False,
init_params=cfg.TEST.INIT_RANDOM_VARS_BEFORE_LOADING)
model_builder.add_inputs(model)
if cfg.TEST.INIT_RANDOM_VARS_BEFORE_LOADING:
workspace.RunNetOnce(model.param_init_net)
net_utils.initialize_from_weights_file(model,
cfg.TEST.WEIGHTS,
broadcast=False)
create_input_blobs(model.net.Proto())
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
create_input_blobs(model.mask_net.Proto())
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
create_input_blobs(model.keypoint_net.Proto())
workspace.CreateNet(model.keypoint_net)
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():
def create_input_blobs(net_def):
for op in net_def.op:
for blob_in in op.input:
if not workspace.HasBlob(blob_in):
workspace.CreateBlob(blob_in)
model = model_builder.create(
cfg.MODEL.TYPE, train=False,
init_params=cfg.TEST.INIT_RANDOM_VARS_BEFORE_LOADING)
model_builder.add_inputs(model)
if cfg.TEST.INIT_RANDOM_VARS_BEFORE_LOADING:
workspace.RunNetOnce(model.param_init_net)
net_utils.initialize_from_weights_file(
model, cfg.TEST.WEIGHTS, broadcast=False)
create_input_blobs(model.net.Proto())
workspace.CreateNet(model.net)
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
create_input_blobs(model.mask_net.Proto())
workspace.CreateNet(model.mask_net)
if cfg.MODEL.KEYPOINTS_ON:
create_input_blobs(model.keypoint_net.Proto())
workspace.CreateNet(model.keypoint_net)
return model
def generate_rpn_on_range(ind_range=None):
assert cfg.TEST.WEIGHTS != '', \
'TEST.WEIGHTS must be set to the model file to test'
assert cfg.TEST.DATASET != '', \
'TEST.DATASET must be set to the dataset name to test'
assert cfg.MODEL.RPN_ONLY or cfg.MODEL.FASTER_RCNN
im_list, start_ind, end_ind, total_num_images = get_image_list(ind_range)
output_dir = get_output_dir(training=False)
logger.info(
'Output will be saved to: {:s}'.format(os.path.abspath(output_dir)))
model = model_builder.create(cfg.MODEL.TYPE, train=False)
model_builder.add_inputs(model)
nu.initialize_from_weights_file(model, cfg.TEST.WEIGHTS)
workspace.CreateNet(model.net)
boxes, scores, ids = im_list_proposals(
model,
im_list,
start_ind=start_ind,
end_ind=end_ind,
total_num_images=total_num_images)
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)
robust_pickle_dump(
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 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():
"""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 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_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 generate_rpn_on_range(ind_range=None):
"""Run inference on all images in a dataset or over an index range of images
in a dataset using a single GPU.
"""
assert cfg.TEST.WEIGHTS != '', \
'TEST.WEIGHTS must be set to the model file to test'
assert cfg.TEST.DATASET != '', \
'TEST.DATASET must be set to the dataset name to test'
assert cfg.MODEL.RPN_ONLY or cfg.MODEL.FASTER_RCNN
roidb, start_ind, end_ind, total_num_images = get_roidb(ind_range)
output_dir = get_output_dir(training=False)
logger.info(
'Output will be saved to: {:s}'.format(os.path.abspath(output_dir))
)
model = model_builder.create(cfg.MODEL.TYPE, train=False)
nu.initialize_from_weights_file(model, cfg.TEST.WEIGHTS)
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
)
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