fix bert bug and add export file for yolov3 and add test file for maskrcnn

model_zoo/official/cv/yolov3_darknet53/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.
+# ============================================================================
+import argparse
+import numpy as np
+
+import mindspore as ms
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
+
+from src.yolo import YOLOV3DarkNet53
+from src.config import ConfigYOLOV3DarkNet53
+
+parser = argparse.ArgumentParser(description='yolov3_darknet53 export')
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="yolov3_darknet53.air", help="output file name.")
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
+
+if __name__ == "__main__":
+    network = YOLOV3DarkNet53(is_training=False)
+
+    param_dict = load_checkpoint(args.ckpt_file)
+    load_param_into_net(network, param_dict)
+
+    config = ConfigYOLOV3DarkNet53()
+    network.set_train(False)
+
+    shape = [args.batch_size, 3] + config.test_img_shape
+    input_data = Tensor(np.zeros(shape), ms.float32)
+    input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
+
+    export(network, input_data, input_shape, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/cv/yolov3_darknet53_quant/export.py

@@ -0,0 +1,53 @@
+# 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.
+# ============================================================================
+import argparse
+import numpy as np
+
+import mindspore as ms
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
+from mindspore.compression.quant import QuantizationAwareTraining
+
+from src.yolo import YOLOV3DarkNet53
+from src.config import ConfigYOLOV3DarkNet53
+
+parser = argparse.ArgumentParser(description='yolov3_darknet53_quant export')
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="yolov3_darknet53_quant.mindir", help="output file name.")
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='MINDIR', help='file format')
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
+
+if __name__ == "__main__":
+    network = YOLOV3DarkNet53(is_training=False)
+    config = ConfigYOLOV3DarkNet53()
+
+    if config.quantization_aware:
+        quantizer = QuantizationAwareTraining(bn_fold=True, per_channel=[True, False], symmetric=[True, False])
+        network = quantizer.quantize(network)
+
+    param_dict = load_checkpoint(args.ckpt_file)
+    load_param_into_net(network, param_dict)
+
+    network.set_train(False)
+
+    shape = [args.batch_size, 3] + config.test_img_shape
+    input_data = Tensor(np.zeros(shape), ms.float32)
+    input_shape = Tensor(tuple(config.test_img_shape), ms.float32)
+
+    export(network, input_data, input_shape, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/cv/yolov3_resnet18/export.py

@@ -0,0 +1,47 @@
+# 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.
+# ============================================================================
+import argparse
+import numpy as np
+
+import mindspore as ms
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
+
+from src.yolov3 import yolov3_resnet18
+from src.config import ConfigYOLOV3ResNet18
+
+parser = argparse.ArgumentParser(description='yolov3_resnet18 export')
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=1, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="yolov3_resnet18.air", help="output file name.")
+parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
+args = parser.parse_args()
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend", device_id=args.device_id)
+
+if __name__ == "__main__":
+    config = ConfigYOLOV3ResNet18()
+    network = yolov3_resnet18(config)
+
+    param_dict = load_checkpoint(args.ckpt_file)
+    load_param_into_net(network, param_dict)
+
+    network.set_train(False)
+
+    shape = [args.batch_size, 3] + config.img_shape
+    input_data = Tensor(np.zeros(shape), ms.float32)
+
+    export(network, input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/nlp/bert/export.py

@@ -22,6 +22,7 @@ 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.bert_for_finetune import BertNER
 from src.utils import convert_labels_to_index
 
 context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
@@ -30,7 +31,7 @@ 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('--num_class', type=int, default=41, help='The number of class, default is 41.')
 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.')
@@ -55,6 +56,10 @@ else:
 
 if __name__ == '__main__':
     if args.downstream_task == "NER":
+        if args.use_crf.lower() == "true":
+            net = BertNER(bert_net_cfg, optimizer_cfg.batch_size, False, num_labels=number_labels,
+                          use_crf=True, tag_to_index=tag_to_index)
+        else:
             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)
@@ -69,6 +74,10 @@ if __name__ == '__main__':
     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)
+    label_ids = Tensor(np.zeros([optimizer_cfg.batch_size, bert_net_cfg.seq_length]), mstype.int32)
 
+    if args.downstream_task == "NER" and args.use_crf.lower() == "true":
+        input_data = [input_ids, input_mask, token_type_id, label_ids]
+    else:
         input_data = [input_ids, input_mask, token_type_id]
     export(net, *input_data, file_name=args.output_file, file_format=args.file_format)

model_zoo/official/nlp/bert/run_ner.py

@@ -155,7 +155,7 @@ def parse_args():
                         help="Use crf, default is false")
     parser.add_argument("--device_id", type=int, default=0, help="Device id, default is 0.")
     parser.add_argument("--epoch_num", type=int, default="1", help="Epoch number, default is 1.")
-    parser.add_argument("--num_class", type=int, default="2", help="The number of class, default is 2.")
+    parser.add_argument("--num_class", type=int, default="41", help="The number of class, default is 41.")
     parser.add_argument("--train_data_shuffle", type=str, default="true", choices=["true", "false"],
                         help="Enable train data shuffle, default is true")
     parser.add_argument("--eval_data_shuffle", type=str, default="false", choices=["true", "false"],

tests/st/model_zoo_tests/maskrcnn/test_maskrcnn.py

@@ -0,0 +1,56 @@
+# 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.
+# ============================================================================
+"""maskrcnn testing script."""
+
+import os
+import pytest
+import numpy as np
+from model_zoo.official.cv.maskrcnn.src.maskrcnn.mask_rcnn_r50 import Mask_Rcnn_Resnet50
+from model_zoo.official.cv.maskrcnn.src.config import config
+
+from mindspore import Tensor, context, export
+
+context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+
+@pytest.mark.level0
+@pytest.mark.platform_arm_ascend_training
+@pytest.mark.platform_x86_ascend_training
+@pytest.mark.env_onecard
+def test_maskrcnn_export():
+    """
+    export maskrcnn air.
+    """
+    net = Mask_Rcnn_Resnet50(config=config)
+    net.set_train(False)
+
+    bs = config.test_batch_size
+
+    img = Tensor(np.zeros([bs, 3, 768, 1280], np.float16))
+    img_metas = Tensor(np.zeros([bs, 4], np.float16))
+    gt_bboxes = Tensor(np.zeros([bs, 128, 4], np.float16))
+    gt_labels = Tensor(np.zeros([bs, 128], np.int32))
+    gt_num = Tensor(np.zeros([bs, 128], np.bool))
+    gt_mask = Tensor(np.zeros([bs, 128], np.bool))
+
+    input_data = [img, img_metas, gt_bboxes, gt_labels, gt_num, gt_mask]
+    file_name = "maskrcnn.air"
+
+    export(net, *input_data, file_name=file_name, file_format="AIR")
+
+    assert os.path.exists(file_name)
+    os.remove(file_name)
+
+if __name__ == '__main__':
+    test_maskrcnn_export()