def main():
args = parse_args()
from gluon.utils import prepare_model as prepare_model_gl
prepare_model_gl(model_name=args.model,
use_pretrained=True,
pretrained_model_file_path="",
dtype=np.float32)
from pytorch.utils import prepare_model as prepare_model_pt
prepare_model_pt(model_name=args.model,
use_pretrained=True,
pretrained_model_file_path="",
use_cuda=False)
from chainer_.utils import prepare_model as prepare_model_ch
prepare_model_ch(model_name=args.model,
use_pretrained=True,
pretrained_model_file_path="")
from tensorflow2.utils import prepare_model as prepare_model_tf2
prepare_model_tf2(model_name=args.model,
use_pretrained=True,
pretrained_model_file_path="",
use_cuda=False)
def prepare_dst_model(dst_fwk, dst_model, src_fwk, ctx, use_cuda):
if dst_fwk == "gluon":
from gluon.utils import prepare_model as prepare_model_gl
dst_net = prepare_model_gl(model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="",
dtype=np.float32,
tune_layers="",
ctx=ctx)
dst_params = dst_net._collect_params_with_prefix()
dst_param_keys = list(dst_params.keys())
elif dst_fwk == "pytorch":
from pytorch.utils import prepare_model as prepare_model_pt
dst_net = prepare_model_pt(model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="",
use_cuda=use_cuda,
use_data_parallel=False)
dst_params = dst_net.state_dict()
dst_param_keys = list(dst_params.keys())
if src_fwk != "pytorch":
dst_param_keys = [
key for key in dst_param_keys
if not key.endswith("num_batches_tracked")
]
elif dst_fwk == "chainer":
from chainer_.utils import prepare_model as prepare_model_ch
dst_net = prepare_model_ch(model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="")
dst_params = {i[0]: i[1] for i in dst_net.namedparams()}
dst_param_keys = list(dst_params.keys())
elif dst_fwk == "keras":
from keras_.utils import prepare_model as prepare_model_ke
dst_net = prepare_model_ke(model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="")
dst_param_keys = list(dst_net._arg_names) + list(dst_net._aux_names)
dst_params = {}
for layer in dst_net.layers:
if layer.name:
for weight in layer.weights:
if weight.name:
dst_params.setdefault(weight.name, []).append(weight)
dst_params[weight.name] = (layer, weight)
elif dst_fwk == "tensorflow":
import tensorflow as tf
from tensorflow_.utils import prepare_model as prepare_model_tf
dst_net = prepare_model_tf(model_name=dst_model,
use_pretrained=False,
pretrained_model_file_path="")
dst_param_keys = [v.name for v in tf.global_variables()]
dst_params = {v.name: v for v in tf.global_variables()}
else:
raise ValueError("Unsupported dst fwk: {}".format(dst_fwk))
return dst_params, dst_param_keys, dst_net
def prepare_src_model(src_fwk, src_model, src_params_file_path, dst_fwk, ctx,
use_cuda):
ext_src_param_keys = None
ext_src_param_keys2 = None
if src_fwk == "gluon":
from gluon.utils import prepare_model as prepare_model_gl
src_net = prepare_model_gl(
model_name=src_model,
use_pretrained=False,
pretrained_model_file_path=src_params_file_path,
dtype=np.float32,
tune_layers="",
ctx=ctx)
src_params = src_net._collect_params_with_prefix()
src_param_keys = list(src_params.keys())
if src_model in ["resnet50_v1", "resnet101_v1", "resnet152_v1"]:
src_param_keys = [
key for key in src_param_keys
if not (key.startswith("features.") and key.endswith(".bias"))
]
if src_model in [
"resnet18_v2", "resnet34_v2", "resnet50_v2", "resnet101_v2",
"resnet152_v2"
]:
src_param_keys = src_param_keys[4:]
if dst_fwk == "chainer":
src_param_keys_ = src_param_keys.copy()
src_param_keys = [
key for key in src_param_keys_
if (not key.endswith(".running_mean")) and (
not key.endswith(".running_var"))
]
ext_src_param_keys = [
key for key in src_param_keys_
if (key.endswith(".running_mean")) or (
key.endswith(".running_var"))
]
if src_model in ["condensenet74_c4_g4", "condensenet74_c8_g8"]:
src_param_keys_ = src_param_keys.copy()
src_param_keys = [
key for key in src_param_keys_
if (not key.endswith(".index"))
]
ext_src_param_keys2 = [
key for key in src_param_keys_ if (key.endswith(".index"))
]
elif src_fwk == "pytorch":
from pytorch.utils import prepare_model as prepare_model_pt
src_net = prepare_model_pt(
model_name=src_model,
use_pretrained=False,
pretrained_model_file_path=src_params_file_path,
use_cuda=use_cuda,
use_data_parallel=False)
src_params = src_net.state_dict()
src_param_keys = list(src_params.keys())
if dst_fwk != "pytorch":
src_param_keys = [
key for key in src_param_keys
if not key.endswith("num_batches_tracked")
]
if src_model in ["oth_shufflenetv2_wd2"]:
src_param_keys = [
key for key in src_param_keys
if not key.startswith("network.0.")
]
elif src_fwk == "mxnet":
src_sym, src_arg_params, src_aux_params = mx.model.load_checkpoint(
prefix=src_params_file_path, epoch=0)
src_params = {}
src_params.update(src_arg_params)
src_params.update(src_aux_params)
src_param_keys = list(src_params.keys())
elif src_fwk == "tensorflow":
# import tensorflow as tf
# from tensorflow_.utils import prepare_model as prepare_model_tf
# src_net = prepare_model_tf(
# model_name=src_model,
# classes=num_classes,
# use_pretrained=False,
# pretrained_model_file_path=src_params_file_path)
# src_param_keys = [v.name for v in tf.global_variables()]
# src_params = {v.name: v for v in tf.global_variables()}
src_net = None
src_params = dict(np.load(src_params_file_path))
src_param_keys = list(src_params.keys())
else:
raise ValueError("Unsupported src fwk: {}".format(src_fwk))
return src_params, src_param_keys, ext_src_param_keys, ext_src_param_keys2
