add export.py

model_zoo/official/cv/alexnet/export.py

@@ -0,0 +1,55 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+##############export checkpoint file into air and onnx models#################
+python export.py
+"""
+import argparse
+import numpy as np
+
+import mindspore as ms
+from mindspore import Tensor
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+
+from src.config import alexnet_cifar10_cfg, alexnet_imagenet_cfg
+from src.alexnet import AlexNet
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser(description='Classification')
+    parser.add_argument('--dataset_name', type=str, default='cifar10', choices=['imagenet', 'cifar10'],
+                        help='please choose dataset: imagenet or cifar10.')
+    parser.add_argument('--device_target', type=str, default="Ascend",
+                        choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--ckpt_path', type=str, default="./ckpt", help='if is test, must provide\
+                                path where the trained ckpt file')
+    args_opt = parser.parse_args()
+    context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target)
+
+    if args_opt.dataset_name == 'cifar10':
+        cfg = alexnet_cifar10_cfg
+    elif args_opt.dataset_name == 'imagenet':
+        cfg = alexnet_imagenet_cfg
+    else:
+        raise ValueError("dataset is not support.")
+
+    net = AlexNet(num_classes=cfg.num_classes)
+
+    param_dict = load_checkpoint(args_opt.ckpt_path)
+    load_param_into_net(net, param_dict)
+
+    input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, cfg.image_height, cfg.image_width]), ms.float32)
+    export(net, input_arr, file_name=cfg.air_name, file_format="AIR")

model_zoo/official/cv/alexnet/src/config.py

@@ -29,6 +29,7 @@ alexnet_cifar10_cfg = edict({
     'image_width': 227,
     'save_checkpoint_steps': 1562,
     'keep_checkpoint_max': 10,
+    'air_name': "alexnet.air",
 })
 
 alexnet_imagenet_cfg = edict({
@@ -42,6 +43,7 @@ alexnet_imagenet_cfg = edict({
     'image_width': 227,
     'save_checkpoint_steps': 625,
     'keep_checkpoint_max': 10,
+    'air_name': "alexnet.air",
 
     # opt
     'weight_decay': 0.0001,

model_zoo/official/cv/lenet/export.py

@@ -0,0 +1,48 @@
+# Copyright 2020 Huawei Technologies Co., Ltd
+#
+# 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.
+# ============================================================================
+"""
+export quantization aware training network to infer `AIR` backend.
+"""
+
+import argparse
+import numpy as np
+
+import mindspore
+from mindspore import Tensor
+from mindspore import context
+from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
+
+from src.config import mnist_cfg as cfg
+from src.lenet import LeNet5
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description='MindSpore MNIST Example')
+    parser.add_argument('--device_target', type=str, default="Ascend",
+                        choices=['Ascend', 'GPU'],
+                        help='device where the code will be implemented (default: Ascend)')
+    parser.add_argument('--ckpt_path', type=str, default="",
+                        help='if mode is test, must provide path where the trained ckpt file')
+    args = parser.parse_args()
+    context.set_context(mode=context.GRAPH_MODE, device_target=args.device_target)
+
+    # define fusion network
+    network = LeNet5(cfg.num_classes)
+    # load network checkpoint
+    param_dict = load_checkpoint(args.ckpt_path)
+    load_param_into_net(network, param_dict)
+
+    # export network
+    inputs = Tensor(np.ones([1, 1, cfg.image_height, cfg.image_width]), mindspore.float32)
+    export(network, inputs, file_name=cfg.air_name, file_format='AIR')

model_zoo/official/cv/lenet/src/config.py

@@ -29,4 +29,5 @@ mnist_cfg = edict({
     'image_width': 32,
     'save_checkpoint_steps': 1875,
     'keep_checkpoint_max': 10,
+    'air_name': "lenet.air",
 })