)
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--data', help='ILSVRC dataset dir')
parser.add_argument('--load', help='load model')
parser.add_argument('-d', '--depth', help='resnet depth',
type=int, default=50, choices=[50, 101])
parser.add_argument('--eval', action='store_true')
args = parser.parse_args()
DEPTH = args.depth
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.eval:
BATCH_SIZE = 128 # something that can run on one gpu
ds = get_data('val')
eval_on_ILSVRC12(Model(), get_model_loader(args.load), ds)
sys.exit()
logger.set_logger_dir(
os.path.join('train_log', 'imagenet-resnet-se-d' + str(DEPTH)))
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
SyncMultiGPUTrainerParameterServer(config).train()
parser = argparse.ArgumentParser()
parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.')
parser.add_argument('--data', help='ILSVRC dataset dir')
parser.add_argument('--load', help='load model')
parser.add_argument('-d',
'--depth',
help='resnet depth',
type=int,
default=18,
choices=[18, 34, 50, 101])
parser.add_argument('--eval', action='store_true')
args = parser.parse_args()
DEPTH = args.depth
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.eval:
BATCH_SIZE = 64 # something that can run on one gpu
ds = get_data('val')
eval_on_ILSVRC12(Model(), args.load, ds)
sys.exit()
logger.set_logger_dir(
os.path.join('train_log', 'imagenet-resnet-se-d' + str(DEPTH)))
config = get_config()
if args.load:
config.session_init = SaverRestore(args.load)
SyncMultiGPUTrainerParameterServer(config).train()
type=int,
choices=[50, 101, 152])
parser.add_argument('--input', help='an input image')
parser.add_argument('--eval', help='ILSVRC dir to run validation on')
args = parser.parse_args()
assert args.input or args.eval, "Choose either input or eval!"
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
MODEL_DEPTH = args.depth
param = np.load(args.load, encoding='latin1').item()
resnet_param = {}
for k, v in six.iteritems(param):
try:
newname = name_conversion(k)
except:
logger.error("Exception when processing caffe layer {}".format(k))
raise
logger.info("Name Transform: " + k + ' --> ' + newname)
resnet_param[newname] = v
if args.eval:
ds = ILSVRC12(args.eval, 'val', shuffle=False, dir_structure='train')
ds = AugmentImageComponent(ds, get_inference_augmentor())
ds = BatchData(ds, 128, remainder=True)
ds = PrefetchDataZMQ(ds, 1)
eval_on_ILSVRC12(Model(), DictRestore(resnet_param), ds)
else:
run_test(resnet_param, args.input)