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)
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(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)
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 = envu.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 initialize_mixed_model_from_cfg(weights_list, preffix_list, gpu_id=0):
"""Initialize a model from the global cfg. Loads and combinds multiple test-time weights
and creates the networks in the Caffe2 workspace.
"""
model = model_builder.create(cfg.MODEL.TYPE, train=False, gpu_id=gpu_id)
assert len(weights_list) == len(preffix_list)
for i, weights_file in enumerate(weights_list):
net_utils.initialize_gpu_from_weights_file(model,
weights_file,
gpu_id=gpu_id,
preffix=preffix_list[i])
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)
if cfg.MODEL.TRACKING_ON:
workspace.CreateNet(model.track_net)
return model
def initialize_model_from_cfg(weights_file, gpu_id=0, int8=True):
"""Initialize a model from the global cfg. Loads test-time weights and
creates the networks in the Caffe2 workspace.
"""
ob = None
ob_mask = None
ob_keypoint = None
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)
int8_path = os.environ.get('INT8PATH')
def LoadModuleFile(fname):
with open(fname) as f:
from caffe2.proto import caffe2_pb2
net_def = caffe2_pb2.NetDef()
if os.environ.get('INT8PTXT') == "1":
import google.protobuf.text_format as ptxt
net_def = ptxt.Parse(f.read(), caffe2_pb2.NetDef())
else:
net_def.ParseFromString(f.read())
if gpu_id == -2:
device_opts = caffe2_pb2.DeviceOption()
device_opts.device_type = caffe2_pb2.IDEEP
for op in net_def.op:
op.device_option.CopyFrom(device_opts)
return net_def
return None
def CreateNet(net):
int8_file_path = int8_path if int8_path else ''
if os.environ.get('INT8PTXT') == "1":
int8_predict_file = int8_file_path + '/' + net.Proto(
).name + '_predict_int8.pbtxt'
int8_init_file = int8_file_path + '/' + net.Proto(
).name + '_init_int8.pbtxt'
else:
int8_predict_file = int8_file_path + '/' + net.Proto(
).name + '_predict_int8.pb'
int8_init_file = int8_file_path + '/' + net.Proto(
).name + '_init_int8.pb'
if os.path.isfile(int8_init_file):
logging.warning('Loading Int8 init file for module {}'.format(
net.Proto().name))
workspace.RunNetOnce(LoadModuleFile(int8_init_file))
if os.path.isfile(int8_predict_file):
logging.warning('Loading Int8 predict file for module {}'.format(
net.Proto().name))
net.Proto().CopyFrom(LoadModuleFile(int8_predict_file))
if os.environ.get('DEBUGMODE') == "1":
for i, op in enumerate(net.Proto().op):
if len(op.name) == 0:
op.name = op.type.lower() + str(i)
if gpu_id == -2 and os.environ.get('DNOOPT') != "1":
logging.warning('Optimize module {}....................'.format(
net.Proto().name))
tf.optimizeForIDEEP(net)
if os.environ.get('DEBUGMODE') == "1":
with open("{}_opt_predict_net.pb".format(net.Proto().name),
"w") as fid:
fid.write(net.Proto().SerializeToString())
with open("{}_opt_predict_net.pbtxt".format(net.Proto().name),
"w") as fid:
fid.write(str(net.Proto()))
workspace.CreateNet(net)
if os.environ.get('COSIM') and int8 == False:
int8_path = None
CreateNet(model.net)
if os.environ.get('DPROFILE') == "1":
logging.warning('need profile, add observer....................')
ob = model.net.AddObserver("TimeObserver")
workspace.CreateNet(model.conv_body_net)
if cfg.MODEL.MASK_ON:
CreateNet(model.mask_net)
if os.environ.get('DPROFILE') == "1":
ob_mask = model.mask_net.AddObserver("TimeObserver")
if cfg.MODEL.KEYPOINTS_ON:
CreateNet(model.keypoint_net)
if os.environ.get('DPROFILE') == "1":
ob_keypoint = model.keypoint_net.AddObserver("TimeObserver")
return model, ob, ob_mask, ob_keypoint
