add export script for vgg16,bert. fix faster-rcnn and inception bug

model_zoo/official/cv/faster_rcnn/export.py

@@ -23,20 +23,22 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net,
 from src.FasterRcnn.faster_rcnn_r50 import Faster_Rcnn_Resnet50
 from src.config import config
 
-if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='fasterrcnn_export')
-    parser.add_argument('--ckpt_file', type=str, default='', help='fasterrcnn ckpt file.')
-    parser.add_argument('--output_file', type=str, default='', help='fasterrcnn output air name.')
-    args_opt = parser.parse_args()
+parser = argparse.ArgumentParser(description='fasterrcnn_export')
+parser.add_argument('--ckpt_file', type=str, default='', help='fasterrcnn ckpt file.')
+parser.add_argument('--output_file', type=str, default='', help='fasterrcnn output air name.')
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+args = parser.parse_args()
 
+if __name__ == '__main__':
     net = Faster_Rcnn_Resnet50(config=config)
 
-    param_dict = load_checkpoint(args_opt.ckpt_file)
+    param_dict = load_checkpoint(args.ckpt_file)
     load_param_into_net(net, param_dict)
 
-    img = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, 768, 1280]), ms.float16)
-    img_shape = Tensor(np.random.uniform(0.0, 1.0, size=[768, 1280, 1]), ms.float16)
-    gt_bboxes = Tensor(np.random.uniform(0.0, 1.0, size=[1, 128]), ms.float16)
-    gt_label = Tensor(np.random.uniform(0.0, 1.0, size=[1, 128]), ms.int32)
-    gt_num = Tensor(np.random.uniform(0.0, 1.0, size=[1, 128]), ms.bool)
-    export(net, img, img_shape, gt_bboxes, gt_label, gt_num, file_name=args_opt.output_file, file_format="AIR")
+    img = Tensor(np.zeros([config.test_batch_size, 3, config.img_height, config.img_width]), ms.float16)
+    img_metas = Tensor(np.random.uniform(0.0, 1.0, size=[config.test_batch_size, 4]), ms.float16)
+    gt_bboxes = Tensor(np.random.uniform(0.0, 1.0, size=[config.test_batch_size, config.num_gts]), ms.float16)
+    gt_label = Tensor(np.random.uniform(0.0, 1.0, size=[config.test_batch_size, config.num_gts]), ms.int32)
+    gt_num = Tensor(np.random.uniform(0.0, 1.0, size=[config.test_batch_size, config.num_gts]), ms.bool_)
+
+    export(net, img, img_metas, gt_bboxes, gt_label, gt_num, file_name=args.output_file, file_format=args.file_format)

model_zoo/official/cv/inceptionv3/export.py

@@ -13,7 +13,7 @@
 # limitations under the License.
 # ============================================================================
 """
-##############export checkpoint file into air and onnx models#################
+export checkpoint file into models
 """
 import argparse
 import numpy as np
@@ -25,16 +25,19 @@ from mindspore.train.serialization import load_checkpoint, load_param_into_net,
 from src.config import config_gpu as cfg
 from src.inception_v3 import InceptionV3
 
+parser = argparse.ArgumentParser(description='inceptionv3 export')
+parser.add_argument('--ckpt_file', type=str, required=True, help='inceptionv3 ckpt file.')
+parser.add_argument('--output_file', type=str, default='inceptionv3.air', help='inceptionv3 output air name.')
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "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')
+args = parser.parse_args()
 
 if __name__ == '__main__':
-    parser = argparse.ArgumentParser(description='checkpoint export')
-    parser.add_argument('--checkpoint', type=str, default='', help='checkpoint of inception-v3 (Default: None)')
-    args_opt = parser.parse_args()
-
     net = InceptionV3(num_classes=cfg.num_classes, is_training=False)
-    param_dict = load_checkpoint(args_opt.checkpoint)
+    param_dict = load_checkpoint(args.ckpt_file)
     load_param_into_net(net, param_dict)
 
-    input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[1, 3, 299, 299]), ms.float32)
-    export(net, input_arr, file_name=cfg.onnx_filename, file_format="ONNX")
-    export(net, input_arr, file_name=cfg.air_filename, file_format="AIR")
+    input_arr = Tensor(np.random.uniform(0.0, 1.0, size=[cfg.batch_size, 3, args.width, args.height]), ms.float32)
+
+    export(net, input_arr, file_name=args.output_file, file_format=args.file_format)

model_zoo/official/cv/vgg16/export.py

@@ -0,0 +1,61 @@
+# 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 models"""
+import argparse
+import numpy as np
+
+from mindspore import Tensor, context
+import mindspore.common.dtype as mstype
+from mindspore.train.serialization import load_checkpoint, export
+
+from src.vgg import vgg16
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+parser = argparse.ArgumentParser(description='VGG16 export')
+parser.add_argument('--dataset', type=str, choices=["cifar10", "imagenet2012"], default="cifar10", help='ckpt file')
+parser.add_argument('--ckpt_file', type=str, required=True, help='vgg16 ckpt file.')
+parser.add_argument('--output_file', type=str, default='vgg16.air', help='vgg16 output air name.')
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+args = parser.parse_args()
+
+if args.dataset == "cifar10":
+    from src.config import cifar_cfg as cfg
+else:
+    from src.config import imagenet_cfg as cfg
+
+args.num_classes = cfg.num_classes
+args.pad_mode = cfg.pad_mode
+args.padding = cfg.padding
+args.has_bias = cfg.has_bias
+args.initialize_mode = cfg.initialize_mode
+args.batch_norm = cfg.batch_norm
+args.has_dropout = cfg.has_dropout
+args.image_size = list(map(int, cfg.image_size.split(',')))
+
+
+if __name__ == '__main__':
+    if args.dataset == "cifar10":
+        net = vgg16(num_classes=args.num_classes, args=args)
+    else:
+        net = vgg16(args.num_classes, args, phase="test")
+        net.add_flags_recursive(fp16=True)
+
+    load_checkpoint(args.ckpt_file, net=net)
+    net.set_train(False)
+
+    input_data = Tensor(np.zeros([cfg.batch_size, 3, args.image_size[0], args.image_size[1]]), mstype.float32)
+
+    export(net, input_data, file_name=args.output_file, file_format=args.file_format)

model_zoo/official/nlp/bert/export.py

@@ -0,0 +1,74 @@
+# 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 models"""
+import argparse
+import numpy as np
+
+from mindspore import Tensor, context
+import mindspore.common.dtype as mstype
+from mindspore.train.serialization import load_checkpoint, export
+
+from src.finetune_eval_model import BertCLSModel, BertSquadModel, BertNERModel
+from src.finetune_eval_config import optimizer_cfg, bert_net_cfg
+from src.utils import convert_labels_to_index
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+parser = argparse.ArgumentParser(description='Bert export')
+parser.add_argument('--use_crf', type=str, default="false", help='Use cfg, default is false.')
+parser.add_argument('--downstream_task', type=str, choices=["NER", "CLS", "SQUAD"], default="NER",
+                    help='at present，support NER only')
+parser.add_argument('--num_class', type=int, default=2, help='The number of class, default is 2.')
+parser.add_argument('--label_file_path', type=str, default="", help='label file path, used in clue benchmark.')
+parser.add_argument('--ckpt_file', type=str, required=True, help='Bert ckpt file.')
+parser.add_argument('--output_file', type=str, default='Bert.air', help='bert output air name.')
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+args = parser.parse_args()
+
+label_list = []
+with open(args.label_file_path) as f:
+    for label in f:
+        label_list.append(label.strip())
+
+tag_to_index = convert_labels_to_index(label_list)
+
+if args.use_crf.lower() == "true":
+    max_val = max(tag_to_index.values())
+    tag_to_index["<START>"] = max_val + 1
+    tag_to_index["<STOP>"] = max_val + 2
+    number_labels = len(tag_to_index)
+else:
+    number_labels = args.num_class
+
+
+if __name__ == '__main__':
+    if args.downstream_task == "NER":
+        net = BertNERModel(bert_net_cfg, False, number_labels, use_crf=(args.use_crf.lower() == "true"))
+    elif args.downstream_task == "CLS":
+        net = BertCLSModel(bert_net_cfg, False, num_labels=number_labels)
+    elif args.downstream_task == "SQUAD":
+        net = BertSquadModel(bert_net_cfg, False)
+    else:
+        raise ValueError("unsupported downstream task")
+
+    load_checkpoint(args.ckpt_file, net=net)
+    net.set_train(False)
+
+    input_ids = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
+    input_mask = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
+    token_type_id = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
+
+    input_data = [input_ids, input_mask, token_type_id]
+    export(net, *input_data, file_name=args.output_file, file_format=args.file_format)