From 1a2dc3af28a373e694c468e534dd4b2b747718af Mon Sep 17 00:00:00 2001
From: tanghongyan <1349905607@qq.com>
Date: Thu, 24 Mar 2022 15:18:16 +0800
Subject: [PATCH] =?UTF-8?q?=E5=9B=9E=E5=BD=92=E6=B5=8B=E8=AF=95=E4=BF=AE?=
 =?UTF-8?q?=E6=94=B9?=
MIME-Version: 1.0
Content-Type: text/plain; charset=UTF-8
Content-Transfer-Encoding: 8bit

.

.
---
 .../MnasNet/mnasnet_pthtar2onnx.py            | 42 +++++++++++++++++++
 .../MnasNet/modelArts/pth2onnx.py             | 23 +++++-----
 2 files changed, 53 insertions(+), 12 deletions(-)
 create mode 100644 PyTorch/contrib/cv/classification/MnasNet/mnasnet_pthtar2onnx.py

diff --git a/PyTorch/contrib/cv/classification/MnasNet/mnasnet_pthtar2onnx.py b/PyTorch/contrib/cv/classification/MnasNet/mnasnet_pthtar2onnx.py
new file mode 100644
index 0000000000..92d41e71aa
--- /dev/null
+++ b/PyTorch/contrib/cv/classification/MnasNet/mnasnet_pthtar2onnx.py
@@ -0,0 +1,42 @@
+# Copyright 2022 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 sys
+import torch
+import mnasnet
+import torch.onnx
+
+from collections import OrderedDict
+
+
+def convert():
+    checkpoint = torch.load(input_file, map_location=None)
+    model = mnasnet.mnasnet1_0()
+    model.load_state_dict(checkpoint)
+    model.eval()
+    print(model)
+
+    input_names = ["image"]
+    output_names = ["class"]
+    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
+    dummy_input = torch.randn(1, 3, 224, 224)
+
+    torch.onnx.export(model, dummy_input, "mnasnet1_0.onnx", input_names=input_names, output_names=output_names,
+                      opset_version=11)
+
+
+if __name__ == "__main__":
+    input_file = sys.argv[1]
+    convert()
diff --git a/PyTorch/contrib/cv/classification/MnasNet/modelArts/pth2onnx.py b/PyTorch/contrib/cv/classification/MnasNet/modelArts/pth2onnx.py
index 34178af7c3..5b66c0830c 100644
--- a/PyTorch/contrib/cv/classification/MnasNet/modelArts/pth2onnx.py
+++ b/PyTorch/contrib/cv/classification/MnasNet/modelArts/pth2onnx.py
@@ -49,21 +49,20 @@ def proc_node_module(checkpoint, AttrName):
     return new_state_dict
 
 
-def convert(pth_file, onnx_path, class_num, train_url, npu):
+def convert(pth_file, onnx_path, class_num, train_url):
 
-    loc = 'npu:{}'.format(npu)
-    checkpoint = torch.load(pth_file, map_location=loc)
-
-    checkpoint['state_dict'] = proc_node_module(checkpoint, 'state_dict')
+    checkpoint = torch.load(pth_file, map_location=None)
+    
     model = mnasnet.mnasnet1_0(num_classes=class_num)
+    model.load_state_dict(checkpoint)
 
-    model.to(loc)
-    model.load_state_dict(checkpoint['state_dict'])
     model.eval()
-    input_names = ["actual_input_1"]
-    output_names = ["output1"]
-    dummy_input = torch.randn(16, 3, 224, 224)
-    dummy_input = dummy_input.to(loc, non_blocking=False)
+    
+    input_names = ["image"]
+    output_names = ["class"]
+    dynamic_axes = {'image': {0: '-1'}, 'class': {0: '-1'}}
+    dummy_input = torch.randn(1, 3, 224, 224)
+
     torch.onnx.export(model, dummy_input, onnx_path, input_names=input_names, output_names=output_names,  opset_version=11)
     mox.file.copy_parallel(onnx_path, train_url + 'model.onnx')
 
@@ -76,7 +75,7 @@ def convert_pth_to_onnx(config_args):
         return
     pth_file = pth_file_list[0]
     onnx_path = pth_file.split(".")[0] + '.onnx'
-    convert(pth_file, onnx_path, config_args.class_num, config_args.train_url, config_args.npu)
+    convert(pth_file, onnx_path, config_args.class_num, config_args.train_url)
 if __name__ == '__main__':
     parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
     # modelarts
-- 
Gitee