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PaddlePaddle / Paddle2ONNX

ONNX Model Exporter for PaddlePaddle
Apache License 2.0
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AttributeError: module 'paddle2onnx' has no attribute 'dygraph2onnx' #263

Closed cww97 closed 3 years ago

cww97 commented 3 years ago

https://github.com/PaddlePaddle/Paddle2ONNX/blob/release/0.6/README_zh.md 动态图，readme里面的example代码，“动态图模型导出”这一段，直接无法运行，有特殊的版本需求吗

cww97 commented 3 years ago 
import paddle
from paddle import nn
from paddle.static import InputSpec
import paddle2onnx as p2o

class LinearNet(nn.Layer):
    def __init__(self):
        super(LinearNet, self).__init__()
        self._linear = nn.Linear(784, 10)

    def forward(self, x):
        return self._linear(x)

layer = LinearNet()

# configure model inputs
x_spec = InputSpec([None, 784], 'float32', 'x')

# convert model to inference mode
layer.eval()

save_path = 'onnx.save/linear_net'
# p2o.dygraph2onnx(layer, save_path + '.onnx', input_spec=[x_spec])

# when you paddlepaddle>2.0.0, you can try:
paddle.onnx.export(layer, save_path, input_spec=[x_spec])

出来结果，直接copy的这一段

python tut.py 
Inference models that PaddleClas provides are listed as follows:
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------+
|       Series      |                                                                         Name                                                                         |
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------+
|      AlexNet      |                                                                       AlexNet                                                                        |
|      DarkNet      |                                                                      DarkNet53                                                                       |
|        DeiT       |   DeiT_base_distilled_patch16_224  DeiT_base_distilled_patch16_384  DeiT_base_patch16_224  DeiT_base_patch16_384  DeiT_small_distilled_patch16_224   |
|                   |                                    DeiT_small_patch16_224  DeiT_tiny_distilled_patch16_224  DeiT_tiny_patch16_224                                    |
|      DenseNet     |                                           DenseNet121  DenseNet161  DenseNet169  DenseNet201  DenseNet264                                            |
|        DPN        |                                                         DPN68  DPN92  DPN98  DPN107  DPN131                                                          |
|    EfficientNet   | EfficientNetB0  EfficientNetB0_small  EfficientNetB1  EfficientNetB2  EfficientNetB3  EfficientNetB4  EfficientNetB5  EfficientNetB6  EfficientNetB7 |
|      GhostNet     |                                           GhostNet_x0_5  GhostNet_x1_0  GhostNet_x1_3  GhostNet_x1_3_ssld                                            |
|       HRNet       |            HRNet_W18_C  HRNet_W30_C  HRNet_W32_C  HRNet_W40_C  HRNet_W44_C  HRNet_W48_C  HRNet_W64_C  HRNet_W18_C_ssld  HRNet_W48_C_ssld             |
|     Inception     |                                                         GoogLeNet  InceptionV3  InceptionV4                                                          |
|    MobileNetV1    |                                MobileNetV1_x0_25  MobileNetV1_x0_5  MobileNetV1_x0_75  MobileNetV1  MobileNetV1_ssld                                 |
|    MobileNetV2    |              MobileNetV2_x0_25  MobileNetV2_x0_5  MobileNetV2_x0_75  MobileNetV2  MobileNetV2_x1_5  MobileNetV2_x2_0  MobileNetV2_ssld               |
|    MobileNetV3    |  MobileNetV3_small_x0_35  MobileNetV3_small_x0_5  MobileNetV3_small_x0_75  MobileNetV3_small_x1_0  MobileNetV3_small_x1_25  MobileNetV3_large_x0_35  |
|                   |            MobileNetV3_large_x0_5  MobileNetV3_large_x0_75  MobileNetV3_large_x1_0  MobileNetV3_large_x1_25  MobileNetV3_small_x1_0_ssld             |
|                   |                                                             MobileNetV3_large_x1_0_ssld                                                              |
|       RegNet      |                                                                     RegNetX_4GF                                                                      |
|      Res2Net      |            Res2Net50_14w_8s  Res2Net50_26w_4s  Res2Net50_vd_26w_4s  Res2Net200_vd_26w_4s  Res2Net101_vd_26w_4s  Res2Net50_vd_26w_4s_ssld             |
|                   |                                                 Res2Net101_vd_26w_4s_ssld  Res2Net200_vd_26w_4s_ssld                                                 |
|      ResNeSt      |                                                          ResNeSt50  ResNeSt50_fast_1s1x64d                                                           |
|       ResNet      |  ResNet18  ResNet18_vd  ResNet34  ResNet34_vd  ResNet50  ResNet50_vc  ResNet50_vd  ResNet50_vd_v2  ResNet101  ResNet101_vd  ResNet152  ResNet152_vd  |
|                   |       ResNet200_vd  ResNet34_vd_ssld  ResNet50_vd_ssld  ResNet50_vd_ssld_v2  ResNet101_vd_ssld  Fix_ResNet50_vd_ssld_v2  ResNet50_ACNet_deploy       |
|      ResNeXt      |        ResNeXt50_32x4d  ResNeXt50_vd_32x4d  ResNeXt50_64x4d  ResNeXt50_vd_64x4d  ResNeXt101_32x4d  ResNeXt101_vd_32x4d  ResNeXt101_32x8d_wsl         |
|                   |        ResNeXt101_32x16d_wsl  ResNeXt101_32x32d_wsl  ResNeXt101_32x48d_wsl  Fix_ResNeXt101_32x48d_wsl  ResNeXt101_64x4d  ResNeXt101_vd_64x4d         |
|                   |                                     ResNeXt152_32x4d  ResNeXt152_vd_32x4d  ResNeXt152_64x4d  ResNeXt152_vd_64x4d                                     |
|       SENet       |   SENet154_vd  SE_HRNet_W64_C_ssld  SE_ResNet18_vd  SE_ResNet34_vd  SE_ResNet50_vd  SE_ResNeXt50_32x4d  SE_ResNeXt50_vd_32x4d  SE_ResNeXt101_32x4d   |
|    ShuffleNetV2   |        ShuffleNetV2_swish  ShuffleNetV2_x0_25  ShuffleNetV2_x0_33  ShuffleNetV2_x0_5  ShuffleNetV2_x1_0  ShuffleNetV2_x1_5  ShuffleNetV2_x2_0        |
|     SqueezeNet    |                                                             SqueezeNet1_0  SqueezeNet1_1                                                             |
|  SwinTransformer  | SwinTransformer_large_patch4_window7_224_22kto1k  SwinTransformer_large_patch4_window12_384_22kto1k  SwinTransformer_base_patch4_window7_224_22kto1k |
|                   |         SwinTransformer_base_patch4_window12_384_22kto1k  SwinTransformer_base_patch4_window12_384  SwinTransformer_base_patch4_window7_224          |
|                   |                                  SwinTransformer_small_patch4_window7_224  SwinTransformer_tiny_patch4_window7_224                                   |
|        VGG        |                                                              VGG11  VGG13  VGG16  VGG19                                                              |
| VisionTransformer |        ViT_base_patch16_224  ViT_base_patch16_384  ViT_base_patch32_384  ViT_large_patch16_224  ViT_large_patch16_384  ViT_large_patch32_384         |
|                   |                                                                ViT_small_patch16_224                                                                 |
|      Xception     |                                      Xception41  Xception41_deeplab  Xception65  Xception65_deeplab  Xception71                                      |
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------+
Traceback (most recent call last):
  File "tut.py", line 26, in <module>
    paddle.onnx.export(layer, save_path, input_spec=[x_spec])
  File "/root/anaconda3/lib/python3.8/site-packages/paddle/onnx/export.py", line 100, in export
    p2o.dygraph2onnx(
AttributeError: module 'paddle2onnx' has no attribute 'dygraph2onnx'
jiangjiajun commented 3 years ago

请问你的paddle2onnx版本是多少，目前最新版本为v0.6

pip install paddle2onnx==0.6
cww97 commented 3 years ago

请问你的paddle2onnx版本是多少，目前最新版本为v0.6

pip install paddle2onnx==0.6
❯ pip install paddle2onnx==0.6
Requirement already satisfied: paddle2onnx==0.6 in /root/anaconda3/lib/python3.8/site-packages (0.6)

其他版本， paddlepaddle 2.1.0 python3.8 centos 7

jiangjiajun commented 3 years ago

麻烦你把上面的代码加几行重新执行一下

import paddle
from paddle import nn
from paddle.static import InputSpec
import paddle2onnx as p2o

# 加这几行
print("======", p2o.__path__)
print("======", p2o.__version__)

class LinearNet(nn.Layer):
    def __init__(self):
        super(LinearNet, self).__init__()
        self._linear = nn.Linear(784, 10)

    def forward(self, x):
        return self._linear(x)

layer = LinearNet()

# configure model inputs
x_spec = InputSpec([None, 784], 'float32', 'x')

# convert model to inference mode
layer.eval()

save_path = 'onnx.save/linear_net'
# p2o.dygraph2onnx(layer, save_path + '.onnx', input_spec=[x_spec])

# when you paddlepaddle>2.0.0, you can try:
paddle.onnx.export(layer, save_path, input_spec=[x_spec])

然后贴一下输出的日志看下

cww97 commented 3 years ago 
❯ python tut.py 
Inference models that PaddleClas provides are listed as follows:
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------+
|       Series      |                                                                         Name                                                                         |
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------+
|      AlexNet      |                                                                       AlexNet                                                                        |
|      DarkNet      |                                                                      DarkNet53                                                                       |
|        DeiT       |   DeiT_base_distilled_patch16_224  DeiT_base_distilled_patch16_384  DeiT_base_patch16_224  DeiT_base_patch16_384  DeiT_small_distilled_patch16_224   |
|                   |                                    DeiT_small_patch16_224  DeiT_tiny_distilled_patch16_224  DeiT_tiny_patch16_224                                    |
|      DenseNet     |                                           DenseNet121  DenseNet161  DenseNet169  DenseNet201  DenseNet264                                            |
|        DPN        |                                                         DPN68  DPN92  DPN98  DPN107  DPN131                                                          |
|    EfficientNet   | EfficientNetB0  EfficientNetB0_small  EfficientNetB1  EfficientNetB2  EfficientNetB3  EfficientNetB4  EfficientNetB5  EfficientNetB6  EfficientNetB7 |
|      GhostNet     |                                           GhostNet_x0_5  GhostNet_x1_0  GhostNet_x1_3  GhostNet_x1_3_ssld                                            |
|       HRNet       |            HRNet_W18_C  HRNet_W30_C  HRNet_W32_C  HRNet_W40_C  HRNet_W44_C  HRNet_W48_C  HRNet_W64_C  HRNet_W18_C_ssld  HRNet_W48_C_ssld             |
|     Inception     |                                                         GoogLeNet  InceptionV3  InceptionV4                                                          |
|    MobileNetV1    |                                MobileNetV1_x0_25  MobileNetV1_x0_5  MobileNetV1_x0_75  MobileNetV1  MobileNetV1_ssld                                 |
|    MobileNetV2    |              MobileNetV2_x0_25  MobileNetV2_x0_5  MobileNetV2_x0_75  MobileNetV2  MobileNetV2_x1_5  MobileNetV2_x2_0  MobileNetV2_ssld               |
|    MobileNetV3    |  MobileNetV3_small_x0_35  MobileNetV3_small_x0_5  MobileNetV3_small_x0_75  MobileNetV3_small_x1_0  MobileNetV3_small_x1_25  MobileNetV3_large_x0_35  |
|                   |            MobileNetV3_large_x0_5  MobileNetV3_large_x0_75  MobileNetV3_large_x1_0  MobileNetV3_large_x1_25  MobileNetV3_small_x1_0_ssld             |
|                   |                                                             MobileNetV3_large_x1_0_ssld                                                              |
|       RegNet      |                                                                     RegNetX_4GF                                                                      |
|      Res2Net      |            Res2Net50_14w_8s  Res2Net50_26w_4s  Res2Net50_vd_26w_4s  Res2Net200_vd_26w_4s  Res2Net101_vd_26w_4s  Res2Net50_vd_26w_4s_ssld             |
|                   |                                                 Res2Net101_vd_26w_4s_ssld  Res2Net200_vd_26w_4s_ssld                                                 |
|      ResNeSt      |                                                          ResNeSt50  ResNeSt50_fast_1s1x64d                                                           |
|       ResNet      |  ResNet18  ResNet18_vd  ResNet34  ResNet34_vd  ResNet50  ResNet50_vc  ResNet50_vd  ResNet50_vd_v2  ResNet101  ResNet101_vd  ResNet152  ResNet152_vd  |
|                   |       ResNet200_vd  ResNet34_vd_ssld  ResNet50_vd_ssld  ResNet50_vd_ssld_v2  ResNet101_vd_ssld  Fix_ResNet50_vd_ssld_v2  ResNet50_ACNet_deploy       |
|      ResNeXt      |        ResNeXt50_32x4d  ResNeXt50_vd_32x4d  ResNeXt50_64x4d  ResNeXt50_vd_64x4d  ResNeXt101_32x4d  ResNeXt101_vd_32x4d  ResNeXt101_32x8d_wsl         |
|                   |        ResNeXt101_32x16d_wsl  ResNeXt101_32x32d_wsl  ResNeXt101_32x48d_wsl  Fix_ResNeXt101_32x48d_wsl  ResNeXt101_64x4d  ResNeXt101_vd_64x4d         |
|                   |                                     ResNeXt152_32x4d  ResNeXt152_vd_32x4d  ResNeXt152_64x4d  ResNeXt152_vd_64x4d                                     |
|       SENet       |   SENet154_vd  SE_HRNet_W64_C_ssld  SE_ResNet18_vd  SE_ResNet34_vd  SE_ResNet50_vd  SE_ResNeXt50_32x4d  SE_ResNeXt50_vd_32x4d  SE_ResNeXt101_32x4d   |
|    ShuffleNetV2   |        ShuffleNetV2_swish  ShuffleNetV2_x0_25  ShuffleNetV2_x0_33  ShuffleNetV2_x0_5  ShuffleNetV2_x1_0  ShuffleNetV2_x1_5  ShuffleNetV2_x2_0        |
|     SqueezeNet    |                                                             SqueezeNet1_0  SqueezeNet1_1                                                             |
|  SwinTransformer  | SwinTransformer_large_patch4_window7_224_22kto1k  SwinTransformer_large_patch4_window12_384_22kto1k  SwinTransformer_base_patch4_window7_224_22kto1k |
|                   |         SwinTransformer_base_patch4_window12_384_22kto1k  SwinTransformer_base_patch4_window12_384  SwinTransformer_base_patch4_window7_224          |
|                   |                                  SwinTransformer_small_patch4_window7_224  SwinTransformer_tiny_patch4_window7_224                                   |
|        VGG        |                                                              VGG11  VGG13  VGG16  VGG19                                                              |
| VisionTransformer |        ViT_base_patch16_224  ViT_base_patch16_384  ViT_base_patch32_384  ViT_large_patch16_224  ViT_large_patch16_384  ViT_large_patch32_384         |
|                   |                                                                ViT_small_patch16_224                                                                 |
|      Xception     |                                      Xception41  Xception41_deeplab  Xception65  Xception65_deeplab  Xception71                                      |
+-------------------+------------------------------------------------------------------------------------------------------------------------------------------------------+
Traceback (most recent call last):
  File "tut.py", line 7, in <module>
    print("======", p2o.__path__)
AttributeError: module 'paddle2onnx' has no attribute '__path__'

我是不是装了个假的p2o

jiangjiajun commented 3 years ago

检查下你当前目录下，是不是有名称为paddle2onnx.py的文件，或者是paddle2onnx的目录

jiangjiajun commented 3 years ago

如若没有，则先卸载paddle2onnx后，重新安装试试

cww97 commented 3 years ago

哈哈哈哈哈哈，没事了

我一开始建的python文件的文件名就叫paddle2onnx.py，甚至还有个paddle2onnx.sh

我是呆子