#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import numpy as np
from resnet_torch import resnet50
from mindspore import Tensor
from mindspore.train.serialization import context, export
context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
def test_resnet50_export(batch_size=1, num_classes=5):
input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224,
224]).astype(np.float32)
net = resnet50(batch_size, num_classes)
export(net,
Tensor(input_np),
file_name="./me_resnet50.pb",
file_format="GEIR")
help='inceptionv4 output air name.')
parser.add_argument('--file_format',
type=str,
choices=["AIR", "MINDIR"],
default='AIR',
help='file format')
parser.add_argument('--width', type=int, default=299, help='input width')
parser.add_argument('--height', type=int, default=299, help='input height')
parser.add_argument("--device_target",
type=str,
choices=["Ascend", "GPU", "CPU"],
default="Ascend",
help="device target")
args = parser.parse_args()
context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
if args.device_target == "Ascend":
context.set_context(device_id=args.device_id)
if __name__ == '__main__':
net = Inceptionv4(classes=config.num_classes)
param_dict = load_checkpoint(args.ckpt_file)
load_param_into_net(net, param_dict)
input_arr = Tensor(np.ones([args.batch_size, 3, args.width, args.height]),
ms.float32)
export(net,
input_arr,
file_name=args.file_name,
file_format=args.file_format)
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ============================================================================
import os
import numpy as np
from resnet_torch import resnet50
from mindspore import Tensor
from mindspore.train.serialization import save, load, _check_filedir_or_create, _chg_model_file_name_if_same_exist, \
_read_file_last_line, context, export
context.set_context(mode=context.GRAPH_MODE,
device_target="Ascend",
enable_loop_sink=True)
def test_resnet50_export(batch_size=1, num_classes=5):
input_np = np.random.uniform(0.0, 1.0, size=[batch_size, 3, 224,
224]).astype(np.float32)
net = resnet50(batch_size, num_classes)
# param_dict = load_checkpoint("./resnet50-1_103.ckpt")
# load_param_into_net(net, param_dict)
export(net,
Tensor(input_np),
file_name="./me_resnet50.pb",
file_format="GEIR")