parser.add_argument('--eval', action='store_true')
parser.add_argument('--flops', action='store_true', help='print flops and exit')
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
if args.v2 and args.group != parser.get_default('group'):
logger.error("group= is not used in ShuffleNetV2!")
model = Model()
if args.eval:
batch = 128 # something that can run on one gpu
ds = get_data('val', batch)
eval_on_ILSVRC12(model, get_model_loader(args.load), ds)
elif args.flops:
# manually build the graph with batch=1
input_desc = [
InputDesc(tf.float32, [1, 224, 224, 3], 'input'),
InputDesc(tf.int32, [1], 'label')
]
input = PlaceholderInput()
input.setup(input_desc)
with TowerContext('', is_training=False):
model.build_graph(*input.get_input_tensors())
model_utils.describe_trainable_vars()
tf.profiler.profile(
tf.get_default_graph(),
cmd='op',
"Note that it's best to keep per-GPU batch size in [32, 64] to obtain the best accuracy."
"Pretrained models listed in README were trained with batch=32x8.")
args = parser.parse_args()
if args.gpu:
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
bit_actn, bit_weight = None, None
if args.quant:
bit_actn, bit_weight = args.quant_bit_actn, args.quant_bit_weight
model = Model(args.use_fp16, bit_actn, bit_weight)
model.data_format = args.data_format
if args.eval:
batch = 128 # something that can run on one gpu
ds = get_data('val', batch)
eval_on_ILSVRC12(model, get_model_loader(args.load), ds)
else:
if args.fake:
logger.set_logger_dir(os.path.join('train_log', 'tmp'), 'd')
else:
logger.set_logger_dir(
os.path.join('train_log',
'imagenet-darknet-batch{}'.format(args.batch)))
config = get_config(model, fake=args.fake)
if args.load:
config.session_init = get_model_loader(args.load)
trainer = SyncMultiGPUTrainerReplicated(max(get_num_gpu(), 1))
launch_train_with_config(config, trainer)
raise
logger.info("Name Transform: " + k + ' --> ' + newname)
resnet_param[newname] = v
return resnet_param
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--load', required=True,
help='.npy model file generated by tensorpack.utils.loadcaffe')
parser.add_argument('-d', '--depth', help='resnet depth', required=True, type=int, choices=[50, 101, 152])
parser.add_argument('--input', help='an input image')
parser.add_argument('--convert', help='npz output file to save the converted model')
parser.add_argument('--eval', help='ILSVRC dir to run validation on')
args = parser.parse_args()
DEPTH = args.depth
param = np.load(args.load, encoding='latin1').item()
param = convert_param_name(param)
if args.convert:
assert args.convert.endswith('.npz')
np.savez_compressed(args.convert, **param)
if args.eval:
ds = get_imagenet_dataflow(args.eval, 'val', 128, get_inference_augmentor())
eval_on_ILSVRC12(Model(), DictRestore(param), ds)
elif args.input:
run_test(param, args.input)
'--load',
required=True,
help='.npz model file generated by tensorpack.utils.loadcaffe')
parser.add_argument('-d',
'--depth',
help='resnet depth',
required=True,
type=int,
choices=[50, 101, 152])
parser.add_argument('--input', help='an input image')
parser.add_argument('--convert',
help='npz output file to save the converted model')
parser.add_argument('--eval', help='ILSVRC dir to run validation on')
args = parser.parse_args()
DEPTH = args.depth
param = dict(np.load(args.load))
param = convert_param_name(param)
if args.convert:
assert args.convert.endswith('.npz')
np.savez_compressed(args.convert, **param)
if args.eval:
ds = get_imagenet_dataflow(args.eval, 'val', 128,
get_inference_augmentor())
eval_on_ILSVRC12(Model(), DictRestore(param), ds)
elif args.input:
run_test(param, args.input)
