!9264 export script for resnet50_quant and densenet121，fix centerface and transformaer bug

From: @yuzhenhua666
Reviewed-by: @linqingke,@yingjy
Signed-off-by: @linqingke

model_zoo/official/cv/centerface/export.py

@@ -20,14 +20,14 @@ import mindspore
 from mindspore import context, Tensor
 from mindspore.train.serialization import load_checkpoint, load_param_into_net, export
 
-from src.centerface import CenterfaceMobilev2
+from src.centerface import CenterfaceMobilev2, CenterFaceWithNms
 from src.config import ConfigCenterface
 
 parser = argparse.ArgumentParser(description='centerface 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="centerface.air", help="output file name.")
+parser.add_argument("--file_name", type=str, default="centerface", help="output file name.")
 parser.add_argument('--file_format', type=str, choices=["AIR", "ONNX", "MINDIR"], default='AIR', help='file format')
 args = parser.parse_args()
 
@@ -48,6 +48,7 @@ if __name__ == '__main__':
             param_dict_new[key] = values
 
     load_param_into_net(net, param_dict_new)
+    net = CenterFaceWithNms(net)
     net.set_train(False)
 
     input_data = Tensor(np.zeros([args.batch_size, 3, config.input_h, config.input_w]), mindspore.float32)

model_zoo/official/cv/densenet121/export.py

@@ -0,0 +1,57 @@
+# 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
+
+from mindspore.common import dtype as mstype
+from mindspore import context, Tensor
+from mindspore.train.serialization import export, load_checkpoint, load_param_into_net
+
+from src.network import DenseNet121
+from src.config import config
+
+parser = argparse.ArgumentParser(description="densenet121 export")
+parser.add_argument("--device_id", type=int, default=0, help="Device id")
+parser.add_argument("--batch_size", type=int, default=32, help="batch size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="densenet121", 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 = DenseNet121(config.num_classes)
+
+    param_dict = load_checkpoint(args.ckpt_file)
+
+    param_dict_new = {}
+    for key, value in param_dict.items():
+        if key.startswith("moments."):
+            continue
+        elif key.startswith("network."):
+            param_dict_new[key[8:]] = value
+        else:
+            param_dict_new[key] = value
+
+    load_param_into_net(network, param_dict_new)
+
+    network.add_flags_recursive(fp16=True)
+    network.set_train(False)
+
+    shape = [int(args.batch_size), 3] + [int(config.image_size.split(",")[0]), int(config.image_size.split(",")[1])]
+    input_data = Tensor(np.zeros(shape), mstype.float32)
+
+    export(network, input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/cv/resnet50_quant/export.py

@@ -0,0 +1,51 @@
+# 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
+
+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.config import config_quant
+from modelsresnet_quant_manual import resnet50_quant
+
+parser = argparse.ArgumentParser(description='resnet50_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("--img_size", type=int, default=224, help="image size")
+parser.add_argument("--ckpt_file", type=str, required=True, help="Checkpoint file path.")
+parser.add_argument("--file_name", type=str, default="resnet50_quant", 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__":
+    config = config_quant
+
+    network = resnet50_quant(class_num=config.class_num)
+
+    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 = [config.batch_size, 3] + [args.img_size, args.img_size]
+    input_data = Tensor(np.zeros(shape).astype(np.float32))
+
+    export(network, input_data, file_name=args.file_name, file_format=args.file_format)

model_zoo/official/nlp/transformer/export.py

@@ -12,8 +12,9 @@
 # See the License for the specific language governing permissions and
 # limitations under the License.
 # ============================================================================
-"""export checkpoint file into air models"""
+""" export checkpoint file into models"""
 
+import argparse
 import numpy as np
 
 from mindspore import Tensor, context
@@ -23,7 +24,14 @@ from src.transformer_model import TransformerModel
 from src.eval_config import cfg, transformer_net_cfg
 from eval import load_weights
 
-context.set_context(mode=context.GRAPH_MODE, device_target="Ascend")
+parser = argparse.ArgumentParser(description='transformer 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("--file_name", type=str, default="transformer", 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__':
     tfm_model = TransformerModel(config=transformer_net_cfg, is_training=False, use_one_hot_embeddings=False)
@@ -31,9 +39,9 @@ if __name__ == '__main__':
     parameter_dict = load_weights(cfg.model_file)
     load_param_into_net(tfm_model, parameter_dict)
 
-    source_ids = Tensor(np.ones((1, 128)).astype(np.int32))
-    source_mask = Tensor(np.ones((1, 128)).astype(np.int32))
+    source_ids = Tensor(np.ones((args.batch_size, transformer_net_cfg.seq_length)).astype(np.int32))
+    source_mask = Tensor(np.ones((args.batch_size, transformer_net_cfg.seq_length)).astype(np.int32))
 
     dec_len = transformer_net_cfg.max_decode_length
 
-    export(tfm_model, source_ids, source_mask, file_name="len" + str(dec_len) + ".air", file_format="AIR")
+    export(tfm_model, source_ids, source_mask, file_name=args.file_name + str(dec_len), file_format=args.file_format)