def _deprecated_build_caffe2_model(runner, predict_net, init_net):
if hasattr(runner, "_deprecated_build_caffe2_model"):
return runner._deprecated_build_caffe2_model(predict_net, init_net)
pb_model = ProtobufDetectionModel(predict_net, init_net)
pb_model.validate_cfg = partial(update_cfg_from_pb_model, model=pb_model)
return pb_model
def build_caffe2_model(self, predict_net, init_net):
pb_model = ProtobufDetectionModel(predict_net, init_net)
pb_model.validate_cfg = partial(update_cfg_from_pb_model,
model=pb_model)
return pb_model
img, img_tensor = load_img(IMAGE_LOCATION, transform_gen)
device_opts = core.DeviceOption(caffe2_pb2.CPU)
# Read the contents of the input protobufs into local variables
init_net = caffe2_pb2.NetDef()
with open(INIT_NET, 'rb') as f:
init_net.ParseFromString(f.read())
init_net.device_option.CopyFrom(device_opts)
predict_net = caffe2_pb2.NetDef()
with open(PREDICT_NET, "rb") as f:
predict_net.ParseFromString(f.read())
predict_net.device_option.CopyFrom(device_opts)
c2det = ProtobufDetectionModel(predict_net, init_net)
results = c2det([{
"image": img_tensor,
"height": img.shape[0],
"width": img.shape[1]
}])
# Filtering by confidence
# Indices -> N x 1 matrix
indices = (results[0]['instances'].scores >
args.confidence_threshold).nonzero()
# Indices -> N dim vector
indices = indices.squeeze()
# Boxes