def main():
c2_utils.import_detectron_ops()
parser = argparse.ArgumentParser(
description='Classification model testing')
parser.add_argument('--config_file',
type=str,
default=None,
required=True,
help='Optional config file for params')
parser.add_argument('opts',
help='see config.py for all options',
default=None,
nargs=argparse.REMAINDER)
if len(sys.argv) == 1:
parser.print_help()
sys.exit(1)
args = parser.parse_args()
if args.config_file is not None:
cfg_from_file(args.config_file)
if args.opts is not None:
cfg_from_list(args.opts)
assert_and_infer_cfg()
print_cfg()
test(args)
def build_graph(self):
c2_utils.import_detectron_ops()
# OpenCL may be enabled by default in OpenCV3; disable it because it's not
# thread safe and causes unwanted GPU memory allocations.
cv2.ocl.setUseOpenCL(False)
merge_cfg_from_file(self.config.args['config_path'])
# If this is a CPU kernel, tell Caffe2 that it should not use
# any GPUs for its graph operations
cpu_only = True
for handle in self.config.devices:
if handle.type == DeviceType.GPU.value:
cpu_only = False
if cpu_only:
cfg.NUM_GPUS = 0
else:
cfg.NUM_GPUS = 1
# TODO: wrap this in "with device"
weights_path = cache_url(self.config.args['weights_path'],
cfg.DOWNLOAD_CACHE)
assert_and_infer_cfg(cache_urls=False)
model = infer_engine.initialize_model_from_cfg(weights_path)
return model
import time
from caffe2.python import workspace
from core.config import assert_and_infer_cfg
from core.config import cfg
from core.config import merge_cfg_from_file
from utils.io import cache_url
from utils.timer import Timer
import core.test_engine as infer_engine
import datasets.dummy_datasets as dummy_datasets
import utils.c2 as c2_utils
import utils.logging
import utils.vis as vis_utils
c2_utils.import_detectron_ops()
# OpenCL may be enabled by default in OpenCV3; disable it because it's not
# thread safe and causes unwanted GPU memory allocations.
cv2.ocl.setUseOpenCL(False)
def parse_args():
parser = argparse.ArgumentParser(description='End-to-end inference')
parser.add_argument('--cfg',
dest='cfg',
help='cfg model file (/path/to/model_config.yaml)',
default=None,
type=str)
parser.add_argument('--wts',
dest='weights',
help='weights model file (/path/to/model_weights.pkl)',
from caffe2.proto import caffe2_pb2
from core.config import assert_and_infer_cfg
from core.config import cfg
from core.config import merge_cfg_from_file
from core.config import merge_cfg_from_list
from modeling import generate_anchors
import core.test_engine as test_engine
import utils.c2 as c2_utils
import utils.vis as vis_utils
import utils.logging
import utils.model_convert_utils as mutils
from utils.model_convert_utils import op_filter, convert_op_in_proto
c2_utils.import_contrib_ops()
c2_utils.import_detectron_ops()
logger = utils.logging.setup_logging(__name__)
def parse_args():
parser = argparse.ArgumentParser(
description='Convert a trained network to pb format'
)
parser.add_argument(
'--cfg', dest='cfg_file', help='optional config file', default=None,
type=str)
parser.add_argument(
'--net_name', dest='net_name', help='optional name for the net',
default="detectron", type=str)
parser.add_argument(