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Update models (#2683) 

* add mobilenet_v2_x0_5, resnext50_32x4d, resnext50_64x4d, xception41 pretrained models
* Update README_cn.md
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PaddleCV/image_classification/README.md
浏览文件 @ 97121cde
...@@ -166,20 +166,39 @@ The image classification models currently supported by PaddlePaddle are listed i ...@@ -166,20 +166,39 @@ The image classification models currently supported by PaddlePaddle are listed i
As the activation function ```swish``` and ```relu6``` which separately used in ShuffleNetV2 and MobileNetV2 net are not supported by Paddle TensorRT, inference acceleration performance of them doesn't significient improve. Pretrained models can be downloaded by clicking related model names. As the activation function ```swish``` and ```relu6``` which separately used in ShuffleNetV2 and MobileNetV2 net are not supported by Paddle TensorRT, inference acceleration performance of them doesn't significient improve. Pretrained models can be downloaded by clicking related model names.
- Note1: ResNet50_vd_v2 is the distilled version of ResNet50_vd. - Note1: ResNet50_vd_v2 is the distilled version of ResNet50_vd.
- Note2: In addition to the image resolution feeded in InceptionV4 net is ```299x299```, others are ```224x224```. - Note2: In addition to the image resolution feeded in InceptionV4 and Xception net is ```299x299```, others are ```224x224```.
- Note3: It's necessary to convert the train model to a binary model when appling dynamic link library to infer, One can do it by running following command: - Note3: It's necessary to convert the train model to a binary model when appling dynamic link library to infer, One can do it by running following command:
```python infer.py --save_inference=True``` ```python infer.py --save_inference=True```
### AlexNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) | |model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: | |- |:-: |:-: |:-: |
|[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar) | 56.72%/79.17% | 3.083 | 2.728 | |[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar) | 56.72%/79.17% | 3.083 | 2.728 |
### VGG
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.tar) | 69.28%/89.09% | 8.223 | 6.821 | |[VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.tar) | 69.28%/89.09% | 8.223 | 6.821 |
|[VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.tar) | 70.02%/89.42% | 9.512 | 7.783 | |[VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.tar) | 70.02%/89.42% | 9.512 | 7.783 |
|[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar) | 72.00%/90.69% | 11.315 | 9.067 | |[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar) | 72.00%/90.69% | 11.315 | 9.067 |
|[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.tar) | 72.56%/90.93% | 13.096 | 10.388 | |[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.tar) | 72.56%/90.93% | 13.096 | 10.388 |
|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | 70.99%/89.68% | 2.609 | 1.615 |
|[MobileNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | 72.15%/90.65% | 4.546 | 5.278 | ### MobileNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | 70.99%/89.68% | 2.609 |1.615 |
|[MobileNetV2_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_5_pretrained.tar) | 65.03%/85.72% | 4.514 | 4.150 |
|[MobileNetV2_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | 72.15%/90.65% | 4.546 | 5.278 |
### ShuffleNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ShuffleNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | 70.03%/89.17% | 6.078 | 6.282 |
### ResNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar) | 70.98%/89.92% | 3.456 | 2.484 | |[ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar) | 70.98%/89.92% | 3.456 | 2.484 |
|[ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) | 74.57%/92.14% | 5.668 | 3.767 | |[ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) | 74.57%/92.14% | 5.668 | 3.767 |
|[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | 76.50%/93.00% | 8.787 | 5.434 | |[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | 76.50%/93.00% | 8.787 | 5.434 |
...@@ -187,19 +206,36 @@ As the activation function ```swish``` and ```relu6``` which separately used in ...@@ -187,19 +206,36 @@ As the activation function ```swish``` and ```relu6``` which separately used in
|[ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 79.12%/94.44% | 9.058 | 5.510 | |[ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 79.12%/94.44% | 9.058 | 5.510 |
|[ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_v2_pretrained.tar) | 79.84%/94.93% | 9.058 | 5.510 | |[ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_v2_pretrained.tar) | 79.84%/94.93% | 9.058 | 5.510 |
|[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | 77.56%/93.64% | 15.447 | 8.779 | |[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | 77.56%/93.64% | 15.447 | 8.779 |
|[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 79.44%/94.47% | 15.685 | 8.878 | |[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 80.17%/94.97% | 15.685 | 8.878 |
|[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% | 21.816 | 12.148 | |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% | 21.816 | 12.148 |
|[ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.59%/95.30% | 22.041 | 12.259 | |[ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.59%/95.30% | 22.041 | 12.259 |
|[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar) | 80.93%/95.33% | 28.015 | 15.278 | |[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar) | 80.93%/95.33% | 28.015 | 15.278 |
|[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_64x4d_pretrained.tar) | 79.35%/94.52% | 41.073 | 38.736 |
### ResNeXt
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar) | 77.75%/93.82% | 12.863 | 9.837 |
|[ResNeXt50_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 28.162 | 18.271 |
|[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 41.073 | 38.736 |
|[ResNeXt101_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar) | 80.78%/95.20% | 42.277 | 40.929 | |[ResNeXt101_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar) | 80.78%/95.20% | 42.277 | 40.929 |
### SENet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44%/93.96% | 14.916 | 12.126 | |[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44%/93.96% | 14.916 | 12.126 |
|[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12%/94.20% | 30.085 | 24.110 | |[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12%/94.20% | 30.085 | 24.110 |
|[SE154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.40%/95.48% | 71.892 | 64.855 | |[SENet154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.40%/95.48% | 71.892 | 64.855 |
### Inception
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/GoogleNet_pretrained.tar) | 70.70%/89.66% | 6.528 | 3.076 | |[GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/GoogleNet_pretrained.tar) | 70.70%/89.66% | 6.528 | 3.076 |
|[ShuffleNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | 70.03%/89.17% | 6.078 | 6.282 | |[Xception_41](https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_pretrained.tar) | 79.30%/94.53% | 13.757 | 10.831 |
|[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.77%/95.26% | 32.413 | 18.154 | |[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.77%/95.26% | 32.413 | 18.154 |
## FAQ ## FAQ
**Q:** How to solve this problem when I try to train a 6-classes dataset with indicating pretrained_model parameter ? **Q:** How to solve this problem when I try to train a 6-classes dataset with indicating pretrained_model parameter ?
...@@ -222,6 +258,7 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100 ...@@ -222,6 +258,7 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100
- MobileNetV2: [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/pdf/1801.04381v4.pdf), Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen - MobileNetV2: [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/pdf/1801.04381v4.pdf), Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
- VGG: [Very Deep Convolutional Networks for Large-scale Image Recognition](https://arxiv.org/pdf/1409.1556), Karen Simonyan, Andrew Zisserman - VGG: [Very Deep Convolutional Networks for Large-scale Image Recognition](https://arxiv.org/pdf/1409.1556), Karen Simonyan, Andrew Zisserman
- GoogLeNet: [Going Deeper with Convolutions](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf), Christian Szegedy1, Wei Liu2, Yangqing Jia - GoogLeNet: [Going Deeper with Convolutions](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf), Christian Szegedy1, Wei Liu2, Yangqing Jia
- Xception: [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357), Franc ̧ois Chollet
- InceptionV4: [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261), Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi - InceptionV4: [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261), Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
...@@ -229,11 +266,12 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100 ...@@ -229,11 +266,12 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100
- 2018/12/03 **Stage1**: Update AlexNet, ResNet50, ResNet101, MobileNetV1 - 2018/12/03 **Stage1**: Update AlexNet, ResNet50, ResNet101, MobileNetV1
- 2018/12/23 **Stage2**: Update VGG Series, SeResNeXt50_32x4d, SeResNeXt101_32x4d, ResNet152 - 2018/12/23 **Stage2**: Update VGG Series, SeResNeXt50_32x4d, SeResNeXt101_32x4d, ResNet152
- 2019/01/31 Update MobileNetV2 - 2019/01/31 Update MobileNetV2_x1_0
- 2019/04/01 **Stage3**: Update ResNet18, ResNet34, GoogLeNet, ShuffleNetV2 - 2019/04/01 **Stage3**: Update ResNet18, ResNet34, GoogLeNet, ShuffleNetV2
- 2019/06/12 **Stage4**:Update ResNet50_vc, ResNet50_vd, ResNet101_vd, ResNet152_vd, ResNet200_vd, SE154_vd InceptionV4, ResNeXt101_64x4d, ResNeXt101_vd_64x4d - 2019/06/12 **Stage4**:Update ResNet50_vc, ResNet50_vd, ResNet101_vd, ResNet152_vd, ResNet200_vd, SE154_vd InceptionV4, ResNeXt101_64x4d, ResNeXt101_vd_64x4d
- 2019/06/22 Update ResNet50_vd_v2 - 2019/06/22 Update ResNet50_vd_v2
- 2019/07/02 Update MobileNetV2_x0_5, ResNeXt50_32x4d, ResNeXt50_64x4d, Xception_41, ResNet101_vd
## Contribute ## Contribute
If you can fix an issue or add a new feature, please open a PR to us. If your PR is accepted, you can get scores according to the quality and difficulty of your PR(0~5), while you got 10 scores, you can contact us for interview or recommendation letter. If you can fix an issue or add a new feature, please open a PR to us. If your PR is accepted, you can get scores according to the quality and difficulty of your PR(0~5), while you got 10 scores, you can contact us for interview or recommendation letter.
\ No newline at end of file
PaddleCV/image_classification/README_cn.md
浏览文件 @ 97121cde
...@@ -155,20 +155,39 @@ python infer.py \ ...@@ -155,20 +155,39 @@ python infer.py \
- 注意 - 注意
1：ResNet50_vd_v2是ResNet50_vd蒸馏版本。 1：ResNet50_vd_v2是ResNet50_vd蒸馏版本。
2：除了InceptionV4采用的输入图像的分辨率为299x299，其余模型测试时使用的分辨率均为224x224。 2：除了InceptionV4和Xception采用的输入图像的分辨率为299x299，其余模型测试时使用的分辨率均为224x224。
3：调用动态链接库预测时需要将训练模型转换为二进制模型 3：调用动态链接库预测时需要将训练模型转换为二进制模型
```python infer.py --save_inference=True``` ```python infer.py --save_inference=True```
### AlexNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) | |model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: | |- |:-: |:-: |:-: |
|[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar) | 56.72%/79.17% | 3.083 | 2.728 | |[AlexNet](http://paddle-imagenet-models-name.bj.bcebos.com/AlexNet_pretrained.tar) | 56.72%/79.17% | 3.083 | 2.728 |
### VGG
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.tar) | 69.28%/89.09% | 8.223 | 6.821 | |[VGG11](https://paddle-imagenet-models-name.bj.bcebos.com/VGG11_pretrained.tar) | 69.28%/89.09% | 8.223 | 6.821 |
|[VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.tar) | 70.02%/89.42% | 9.512 | 7.783 | |[VGG13](https://paddle-imagenet-models-name.bj.bcebos.com/VGG13_pretrained.tar) | 70.02%/89.42% | 9.512 | 7.783 |
|[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar) | 72.00%/90.69% | 11.315 | 9.067 | |[VGG16](https://paddle-imagenet-models-name.bj.bcebos.com/VGG16_pretrained.tar) | 72.00%/90.69% | 11.315 | 9.067 |
|[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.tar) | 72.56%/90.93% | 13.096 | 10.388 | |[VGG19](https://paddle-imagenet-models-name.bj.bcebos.com/VGG19_pretrained.tar) | 72.56%/90.93% | 13.096 | 10.388 |
|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | 70.99%/89.68% | 2.609 | 1.615 |
|[MobileNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | 72.15%/90.65% | 4.546 | 5.278 | ### MobileNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[MobileNetV1](http://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV1_pretrained.tar) | 70.99%/89.68% | 2.609 |1.615 |
|[MobileNetV2_x0_5](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_x0_5_pretrained.tar) | 65.03%/85.72% | 4.514 | 4.150 |
|[MobileNetV2_x1_0](https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV2_pretrained.tar) | 72.15%/90.65% | 4.546 | 5.278 |
### ShuffleNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ShuffleNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | 70.03%/89.17% | 6.078 | 6.282 |
### ResNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar) | 70.98%/89.92% | 3.456 | 2.484 | |[ResNet18](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet18_pretrained.tar) | 70.98%/89.92% | 3.456 | 2.484 |
|[ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) | 74.57%/92.14% | 5.668 | 3.767 | |[ResNet34](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet34_pretrained.tar) | 74.57%/92.14% | 5.668 | 3.767 |
|[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | 76.50%/93.00% | 8.787 | 5.434 | |[ResNet50](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_pretrained.tar) | 76.50%/93.00% | 8.787 | 5.434 |
...@@ -176,20 +195,36 @@ python infer.py \ ...@@ -176,20 +195,36 @@ python infer.py \
|[ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 79.12%/94.44% | 9.058 | 5.510 | |[ResNet50_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_pretrained.tar) | 79.12%/94.44% | 9.058 | 5.510 |
|[ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_v2_pretrained.tar) | 79.84%/94.93% | 9.058 | 5.510 | |[ResNet50_vd_v2](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet50_vd_v2_pretrained.tar) | 79.84%/94.93% | 9.058 | 5.510 |
|[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | 77.56%/93.64% | 15.447 | 8.779 | |[ResNet101](http://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_pretrained.tar) | 77.56%/93.64% | 15.447 | 8.779 |
|[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 79.44%/94.47% | 15.685 | 8.878 | |[ResNet101_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet101_vd_pretrained.tar) | 80.17%/94.97% | 15.685 | 8.878 |
|[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% | 21.816 | 12.148 | |[ResNet152](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_pretrained.tar) | 78.26%/93.96% | 21.816 | 12.148 |
|[ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.59%/95.30% | 22.041 | 12.259 | |[ResNet152_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet152_vd_pretrained.tar) | 80.59%/95.30% | 22.041 | 12.259 |
|[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar) | 80.93%/95.33% | 28.015 | 15.278 | |[ResNet200_vd](https://paddle-imagenet-models-name.bj.bcebos.com/ResNet200_vd_pretrained.tar) | 80.93%/95.33% | 28.015 | 15.278 |
|[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_64x4d_pretrained.tar) | 79.35%/94.52% | 41.073 | 38.736 |
### ResNeXt
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar) | 77.75%/93.82% | 12.863 | 9.837 |
|[ResNeXt50_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 28.162 | 18.271 |
|[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 41.073 | 38.736 |
|[ResNeXt101_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar) | 80.78%/95.20% | 42.277 | 40.929 | |[ResNeXt101_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_vd_64x4d_pretrained.tar) | 80.78%/95.20% | 42.277 | 40.929 |
### SENet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44%/93.96% | 14.916 | 12.126 | |[SE_ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt50_32x4d_pretrained.tar) | 78.44%/93.96% | 14.916 | 12.126 |
|[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12%/94.20% | 30.085 | 24.110 | |[SE_ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/SE_ResNeXt101_32x4d_pretrained.tar) | 79.12%/94.20% | 30.085 | 24.110 |
|[SE154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.40%/95.48% | 71.892 | 64.855 | |[SENet154_vd](https://paddle-imagenet-models-name.bj.bcebos.com/SE154_vd_pretrained.tar) | 81.40%/95.48% | 71.892 | 64.855 |
### Inception
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/GoogleNet_pretrained.tar) | 70.70%/89.66% | 6.528 | 3.076 | |[GoogLeNet](https://paddle-imagenet-models-name.bj.bcebos.com/GoogleNet_pretrained.tar) | 70.70%/89.66% | 6.528 | 3.076 |
|[ShuffleNetV2](https://paddle-imagenet-models-name.bj.bcebos.com/ShuffleNetV2_pretrained.tar) | 70.03%/89.17% | 6.078 | 6.282 | |[Xception_41](https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_pretrained.tar) | 79.30%/94.53% | 13.757 | 10.831 |
|[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.77%/95.26% | 32.413 | 18.154 | |[InceptionV4](https://paddle-imagenet-models-name.bj.bcebos.com/InceptionV4_pretrained.tar) | 80.77%/95.26% | 32.413 | 18.154 |
## FAQ ## FAQ
**Q:** 加载预训练模型报错，Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:1000 != labels_dims[1]:6. **Q:** 加载预训练模型报错，Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:1000 != labels_dims[1]:6.
...@@ -207,15 +242,18 @@ python infer.py \ ...@@ -207,15 +242,18 @@ python infer.py \
- MobileNetV2: [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/pdf/1801.04381v4.pdf), Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen - MobileNetV2: [MobileNetV2: Inverted Residuals and Linear Bottlenecks](https://arxiv.org/pdf/1801.04381v4.pdf), Mark Sandler, Andrew Howard, Menglong Zhu, Andrey Zhmoginov, Liang-Chieh Chen
- VGG: [Very Deep Convolutional Networks for Large-scale Image Recognition](https://arxiv.org/pdf/1409.1556), Karen Simonyan, Andrew Zisserman - VGG: [Very Deep Convolutional Networks for Large-scale Image Recognition](https://arxiv.org/pdf/1409.1556), Karen Simonyan, Andrew Zisserman
- GoogLeNet: [Going Deeper with Convolutions](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf), Christian Szegedy1, Wei Liu2, Yangqing Jia - GoogLeNet: [Going Deeper with Convolutions](https://www.cs.unc.edu/~wliu/papers/GoogLeNet.pdf), Christian Szegedy1, Wei Liu2, Yangqing Jia
- Xception: [Xception: Deep Learning with Depthwise Separable Convolutions](https://arxiv.org/abs/1610.02357), Franc ̧ois Chollet
- InceptionV4: [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261), Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi - InceptionV4: [Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning](https://arxiv.org/abs/1602.07261), Christian Szegedy, Sergey Ioffe, Vincent Vanhoucke, Alex Alemi
## 版本更新 ## 版本更新
- 2018/12/03 **Stage1**: 更新AlexNet，ResNet50，ResNet101，MobileNetV1 - 2018/12/03 **Stage1**: 更新AlexNet，ResNet50，ResNet101，MobileNetV1
- 2018/12/23 **Stage2**: 更新VGG系列 SeResNeXt50_32x4d，SeResNeXt101_32x4d，ResNet152 - 2018/12/23 **Stage2**: 更新VGG系列 SeResNeXt50_32x4d，SeResNeXt101_32x4d，ResNet152
- 2019/01/31 更新MobileNetV2 - 2019/01/31 更新MobileNetV2_x1_0
- 2019/04/01 **Stage3**: 更新ResNet18，ResNet34，GoogLeNet，ShuffleNetV2 - 2019/04/01 **Stage3**: 更新ResNet18，ResNet34，GoogLeNet，ShuffleNetV2
- 2019/06/12 **Stage4**: 更新ResNet50_vc，ResNet50_vd，ResNet101_vd，ResNet152_vd，ResNet200_vd，SE154_vd InceptionV4，ResNeXt101_64x4d，ResNeXt101_vd_64x4d - 2019/06/12 **Stage4**: 更新ResNet50_vc，ResNet50_vd，ResNet101_vd，ResNet152_vd，ResNet200_vd，SE154_vd InceptionV4，ResNeXt101_64x4d，ResNeXt101_vd_64x4d
- 2019/06/22 更新ResNet50_vd_v2 - 2019/06/22 更新ResNet50_vd_v2
- 2019/07/02 更新MobileNetV2_x0_5，ResNeXt50_32x4d，ResNeXt50_64x4d，Xception_41，ResNet101_vd
## 如何贡献代码 ## 如何贡献代码
......
PaddleCV/image_classification/models/__init__.py
浏览文件 @ 97121cde
from .alexnet import AlexNet from .alexnet import AlexNet
from .mobilenet import MobileNet from .mobilenet import MobileNet
from .mobilenet_v2 import MobileNetV2 from .mobilenet_v2 import MobileNetV2_x0_25, MobileNetV2_x0_5, MobileNetV2_x1_0, MobileNetV2_x1_5, MobileNetV2_x2_0, MobileNetV2_scale
from .googlenet import GoogleNet from .googlenet import GoogleNet
from .vgg import VGG11, VGG13, VGG16, VGG19 from .vgg import VGG11, VGG13, VGG16, VGG19
from .resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152 from .resnet import ResNet18, ResNet34, ResNet50, ResNet101, ResNet152
from .resnet_vc import ResNet50_vc, ResNet101_vc, ResNet152_vc from .resnet_vc import ResNet50_vc, ResNet101_vc, ResNet152_vc
from .resnet_vd import ResNet50_vd, ResNet101_vd, ResNet152_vd, ResNet200_vd from .resnet_vd import ResNet50_vd, ResNet101_vd, ResNet152_vd, ResNet200_vd
from .resnext import ResNeXt50_64x4d, ResNeXt101_64x4d, ResNeXt152_64x4d from .resnext import ResNeXt50_64x4d, ResNeXt101_64x4d, ResNeXt152_64x4d, ResNeXt50_32x4d, ResNeXt101_32x4d, ResNeXt152_32x4d
from .resnext_vd import ResNeXt50_vd_64x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_64x4d from .resnext_vd import ResNeXt50_vd_64x4d, ResNeXt101_vd_64x4d, ResNeXt152_vd_64x4d
from .resnet_dist import DistResNet from .resnet_dist import DistResNet
from .inception_v4 import InceptionV4 from .inception_v4 import InceptionV4
from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_32x4d from .se_resnext import SE_ResNeXt50_32x4d, SE_ResNeXt101_32x4d, SE_ResNeXt152_32x4d
...@@ -16,3 +15,4 @@ from .se_resnext_vd import SE_ResNeXt50_32x4d_vd, SE_ResNeXt101_32x4d_vd, SE154_ ...@@ -16,3 +15,4 @@ from .se_resnext_vd import SE_ResNeXt50_32x4d_vd, SE_ResNeXt101_32x4d_vd, SE154_
from .dpn import DPN68, DPN92, DPN98, DPN107, DPN131 from .dpn import DPN68, DPN92, DPN98, DPN107, DPN131
from .shufflenet_v2 import ShuffleNetV2, ShuffleNetV2_x0_5_swish, ShuffleNetV2_x1_0_swish, ShuffleNetV2_x1_5_swish, ShuffleNetV2_x2_0_swish, ShuffleNetV2_x8_0_swish from .shufflenet_v2 import ShuffleNetV2, ShuffleNetV2_x0_5_swish, ShuffleNetV2_x1_0_swish, ShuffleNetV2_x1_5_swish, ShuffleNetV2_x2_0_swish, ShuffleNetV2_x8_0_swish
from .fast_imagenet import FastImageNet from .fast_imagenet import FastImageNet
from .xception import Xception_41, Xception_65, Xception_71
\ No newline at end of file
PaddleCV/image_classification/models/mobilenet_v2.py
浏览文件 @ 97121cde
#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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.
from __future__ import absolute_import from __future__ import absolute_import
from __future__ import division from __future__ import division
from __future__ import print_function from __future__ import print_function
...@@ -5,7 +19,8 @@ import paddle.fluid as fluid ...@@ -5,7 +19,8 @@ import paddle.fluid as fluid
from paddle.fluid.initializer import MSRA from paddle.fluid.initializer import MSRA
from paddle.fluid.param_attr import ParamAttr from paddle.fluid.param_attr import ParamAttr
__all__ = ['MobileNetV2'] __all__ = ['MobileNetV2', 'MobileNetV2_x0_25, ''MobileNetV2_x0_5', 'MobileNetV2_x1_0', 'MobileNetV2_x1_5', 'MobileNetV2_x2_0',
'MobileNetV2_scale']
train_parameters = { train_parameters = {
"input_size": [3, 224, 224], "input_size": [3, 224, 224],
...@@ -21,11 +36,16 @@ train_parameters = { ...@@ -21,11 +36,16 @@ train_parameters = {
class MobileNetV2(): class MobileNetV2():
def __init__(self): def __init__(self, scale=1.0, change_depth=False):
self.params = train_parameters self.params = train_parameters
self.scale = scale
def net(self, input, class_dim=1000, scale=1.0): self.change_depth=change_depth
def net(self, input, class_dim=1000):
scale = self.scale
change_depth = self.change_depth
#if change_depth is True, the new depth is 1.4 times as deep as before.
bottleneck_params_list = [ bottleneck_params_list = [
(1, 16, 1, 1), (1, 16, 1, 1),
(6, 24, 2, 2), (6, 24, 2, 2),
...@@ -34,6 +54,14 @@ class MobileNetV2(): ...@@ -34,6 +54,14 @@ class MobileNetV2():
(6, 96, 3, 1), (6, 96, 3, 1),
(6, 160, 3, 2), (6, 160, 3, 2),
(6, 320, 1, 1), (6, 320, 1, 1),
] if change_depth == False else [
(1, 16, 1, 1),
(6, 24, 2, 2),
(6, 32, 5, 2),
(6, 64, 7, 2),
(6, 96, 5, 1),
(6, 160, 3, 2),
(6, 320, 1, 1),
] ]
#conv1 #conv1
...@@ -196,3 +224,29 @@ class MobileNetV2(): ...@@ -196,3 +224,29 @@ class MobileNetV2():
expansion_factor=t, expansion_factor=t,
name=name + '_' + str(i + 1)) name=name + '_' + str(i + 1))
return last_residual_block return last_residual_block
def MobileNetV2_x0_25():
model = MobileNetV2(scale=0.25)
return model
def MobileNetV2_x0_5():
model = MobileNetV2(scale=0.5)
return model
def MobileNetV2_x1_0():
model = MobileNetV2(scale=1.0)
return model
def MobileNetV2_x1_5():
model = MobileNetV2(scale=1.5)
return model
def MobileNetV2_x2_0():
model = MobileNetV2(scale=2.0)
return model
def MobileNetV2_scale():
model = MobileNetV2(scale=1.2, change_depth=True)
return model
\ No newline at end of file
PaddleCV/image_classification/models/resnext.py
浏览文件 @ 97121cde
...@@ -22,7 +22,8 @@ import paddle ...@@ -22,7 +22,8 @@ import paddle
import paddle.fluid as fluid import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr from paddle.fluid.param_attr import ParamAttr
__all__ = ["ResNeXt", "ResNeXt50_64x4d", "ResNeXt101_64x4d", "ResNeXt152_64x4d"] __all__ = ["ResNeXt", "ResNeXt50_64x4d", "ResNeXt101_64x4d", "ResNeXt152_64x4d", "ResNeXt50_32x4d", "ResNeXt101_32x4d",
"ResNeXt152_32x4d"]
train_parameters = { train_parameters = {
"input_size": [3, 224, 224], "input_size": [3, 224, 224],
...@@ -38,12 +39,14 @@ train_parameters = { ...@@ -38,12 +39,14 @@ train_parameters = {
class ResNeXt(): class ResNeXt():
def __init__(self, layers=50): def __init__(self, layers=50, cardinality=64):
self.params = train_parameters self.params = train_parameters
self.layers = layers self.layers = layers
self.cardinality = cardinality
def net(self, input, class_dim=1000): def net(self, input, class_dim=1000):
layers = self.layers layers = self.layers
cardinality = self.cardinality
supported_layers = [50, 101, 152] supported_layers = [50, 101, 152]
assert layers in supported_layers, \ assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers) "supported layers are {} but input layer is {}".format(supported_layers, layers)
...@@ -54,8 +57,9 @@ class ResNeXt(): ...@@ -54,8 +57,9 @@ class ResNeXt():
depth = [3, 4, 23, 3] depth = [3, 4, 23, 3]
elif layers == 152: elif layers == 152:
depth = [3, 8, 36, 3] depth = [3, 8, 36, 3]
num_filters = [256, 512, 1024, 2048]
cardinality = 64 num_filters1 = [256, 512, 1024, 2048]
num_filters2 = [128, 256, 512, 1024]
conv = self.conv_bn_layer( conv = self.conv_bn_layer(
input=input, input=input,
...@@ -82,7 +86,7 @@ class ResNeXt(): ...@@ -82,7 +86,7 @@ class ResNeXt():
conv_name = "res" + str(block + 2) + chr(97 + i) conv_name = "res" + str(block + 2) + chr(97 + i)
conv = self.bottleneck_block( conv = self.bottleneck_block(
input=conv, input=conv,
num_filters=num_filters[block], num_filters=num_filters1[block] if cardinality == 64 else num_filters2[block],
stride=2 if i == 0 and block != 0 else 1, stride=2 if i == 0 and block != 0 else 1,
cardinality=cardinality, cardinality=cardinality,
name=conv_name) name=conv_name)
...@@ -137,6 +141,7 @@ class ResNeXt(): ...@@ -137,6 +141,7 @@ class ResNeXt():
return input return input
def bottleneck_block(self, input, num_filters, stride, cardinality, name): def bottleneck_block(self, input, num_filters, stride, cardinality, name):
cardinality = self.cardinality
conv0 = self.conv_bn_layer( conv0 = self.conv_bn_layer(
input=input, input=input,
num_filters=num_filters, num_filters=num_filters,
...@@ -153,28 +158,40 @@ class ResNeXt(): ...@@ -153,28 +158,40 @@ class ResNeXt():
name=name + "_branch2b") name=name + "_branch2b")
conv2 = self.conv_bn_layer( conv2 = self.conv_bn_layer(
input=conv1, input=conv1,
num_filters=num_filters, num_filters=num_filters if cardinality == 64 else num_filters*2,
filter_size=1, filter_size=1,
act=None, act=None,
name=name + "_branch2c") name=name + "_branch2c")
short = self.shortcut( short = self.shortcut(
input, num_filters, stride, name=name + "_branch1") input, num_filters if cardinality == 64 else num_filters*2, stride, name=name + "_branch1")
return fluid.layers.elementwise_add( return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu', name=name + ".add.output.5") x=short, y=conv2, act='relu', name=name + ".add.output.5")
def ResNeXt50_64x4d(): def ResNeXt50_64x4d():
model = ResNeXt(layers=50) model = ResNeXt(layers=50, cardinality=64)
return model
def ResNeXt50_32x4d():
model = ResNeXt(layers=50, cardinality=32)
return model return model
def ResNeXt101_64x4d(): def ResNeXt101_64x4d():
model = ResNeXt(layers=101) model = ResNeXt(layers=101, cardinality=64)
return model
def ResNeXt101_32x4d():
model = ResNeXt(layers=101, cardinality=32)
return model return model
def ResNeXt152_64x4d(): def ResNeXt152_64x4d():
model = ResNeXt(layers=152) model = ResNeXt(layers=152, cardinality=64)
return model
def ResNeXt152_32x4d():
model = ResNeXt(layers=152, cardinality=32)
return model return model
PaddleCV/image_classification/models/xception.py 0 → 100644
浏览文件 @ 97121cde
#copyright (c) 2019 PaddlePaddle Authors. All Rights Reserve.
#
#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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import math
import sys
from paddle.fluid.param_attr import ParamAttr
__all__ = ['Xception', 'Xception_41', 'Xception_65', 'Xception_71']
train_parameters = {
"input_size": [3, 299, 299],
"input_mean": [0.485, 0.456, 0.406],
"input_std": [0.229, 0.224, 0.225],
"learning_strategy": {
"name": "piecewise_decay",
"batch_size": 256,
"epochs": [30, 60, 90],
"steps": [0.1, 0.01, 0.001, 0.0001]
}
}
class Xception(object):
"""Xception"""
def __init__(self, entry_flow_block_num=3, middle_flow_block_num=8):
self.params = train_parameters
self.entry_flow_block_num = entry_flow_block_num
self.middle_flow_block_num = middle_flow_block_num
return
def net(self, input, class_dim=1000):
conv = self.entry_flow(input, self.entry_flow_block_num)
conv = self.middle_flow(conv, self.middle_flow_block_num)
conv = self.exit_flow(conv, class_dim)
return conv
def entry_flow(self, input, block_num=3):
'''xception entry_flow'''
name = "entry_flow"
conv = self.conv_bn_layer(
input=input, num_filters=32, filter_size=3, stride=2, act='relu', name=name+"_conv1")
conv = self.conv_bn_layer(
input=conv, num_filters=64, filter_size=3, stride=1, act='relu', name=name+"_conv2")
if block_num == 3:
relu_first = [False, True, True]
num_filters = [128, 256, 728]
stride=[2,2,2]
elif block_num == 5:
relu_first = [False, True, True, True, True]
num_filters = [128, 256, 256, 728, 728]
stride=[2,1,2,1,2]
else:
sys.exit(-1)
for block in range(block_num):
curr_name = "{}_{}".format( name, block )
conv = self.entry_flow_bottleneck_block(conv,
num_filters=num_filters[block],
name=curr_name,
stride=stride[block],
relu_first=relu_first[block])
return conv
def entry_flow_bottleneck_block(self, input, num_filters, name, stride=2, relu_first=False):
'''entry_flow_bottleneck_block'''
short = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=1,
stride=stride,
padding=0,
act=None,
param_attr=ParamAttr(name+"_branch1_weights"),
bias_attr=False
)
conv0 = input
if relu_first:
conv0 = fluid.layers.relu( conv0 )
conv1 = self.separable_conv( conv0, num_filters, stride=1, name=name+"_branch2a_weights" )
conv2 = fluid.layers.relu( conv1 )
conv2 = self.separable_conv( conv2, num_filters, stride=1, name=name+"_branch2b_weights" )
pool = fluid.layers.pool2d(
input=conv2,
pool_size=3,
pool_stride=stride,
pool_padding=1,
pool_type='max')
return fluid.layers.elementwise_add(x=short, y=pool)
def middle_flow(self, input, block_num=8):
'''xception middle_flow'''
num_filters=728
conv = input
for block in range(block_num):
name = "middle_flow_{}".format( block )
conv =self.middle_flow_bottleneck_block(conv, num_filters, name)
return conv
def middle_flow_bottleneck_block(self, input, num_filters, name):
'''middle_flow_bottleneck_block'''
conv0 = fluid.layers.relu( input )
conv0 = self.separable_conv( conv0, num_filters=num_filters, stride=1, name=name+"_branch2a_weights" )
conv1 = fluid.layers.relu( conv0 )
conv1 = self.separable_conv( conv1, num_filters=num_filters, stride=1, name=name+"_branch2b_weights" )
conv2 = fluid.layers.relu( conv1 )
conv2 = self.separable_conv( conv2, num_filters=num_filters, stride=1, name=name+"_branch2c_weights" )
return fluid.layers.elementwise_add(x=input, y=conv2)
def exit_flow(self, input, class_dim):
'''xception exit flow'''
name = "exit_flow"
num_filters1 = 728
num_filters2 = 1024
conv0 = self.exit_flow_bottleneck_block( input, num_filters1, num_filters2, name=name+"_1" )
conv1 = self.separable_conv( conv0, num_filters=1536, stride=1, name=name+"_2" )
conv1 = fluid.layers.relu( conv1 )
conv2 = self.separable_conv( conv1, num_filters=2048, stride=1, name=name+"_3" )
conv2 = fluid.layers.relu( conv2 )
pool = fluid.layers.pool2d(
input=conv2, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(pool.shape[1] * 1.0)
out = fluid.layers.fc(input=pool,
size=class_dim,
act='softmax',
param_attr=fluid.param_attr.ParamAttr(
name='fc_weights',
initializer=fluid.initializer.Uniform(-stdv, stdv)),
bias_attr=fluid.param_attr.ParamAttr(name='fc_offset'))
return out
def exit_flow_bottleneck_block(self, input, num_filters1, num_filters2, name):
'''entry_flow_bottleneck_block'''
short = fluid.layers.conv2d(
input=input,
num_filters=num_filters2,
filter_size=1,
stride=2,
padding=0,
act=None,
param_attr=ParamAttr(name+"_branch1_weights"),
bias_attr=False
)
conv0 = fluid.layers.relu( input )
conv1 = self.separable_conv( conv0, num_filters1, stride=1, name=name+"_branch2a_weights" )
conv2 = fluid.layers.relu( conv1 )
conv2 = self.separable_conv( conv2, num_filters2, stride=1, name=name+"_branch2b_weights" )
pool = fluid.layers.pool2d(
input=conv2,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
return fluid.layers.elementwise_add(x=short, y=pool)
def separable_conv(self, input, num_filters, stride=1, name=None):
"""separable_conv"""
pointwise_conv = self.conv_bn_layer(
input=input,
filter_size=1,
num_filters=num_filters,
stride=1,
name=name + "_sep")
depthwise_conv = self.conv_bn_layer(
input=pointwise_conv,
filter_size=3,
num_filters=num_filters,
stride=stride,
groups=num_filters,
use_cudnn=False,
name=name + "_dw")
return depthwise_conv
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
use_cudnn=True,
name=None):
"""conv_bn_layer"""
conv = fluid.layers.conv2d(
input=input,
num_filters=num_filters,
filter_size=filter_size,
stride=stride,
padding=(filter_size - 1) / 2,
groups=groups,
act=None,
param_attr=ParamAttr(name=name + "_weights"),
bias_attr=False,
use_cudnn=use_cudnn,
name=name + '.conv2d.output.1')
bn_name = "bn_" + name
return fluid.layers.batch_norm(input=conv,
act=act,
name=bn_name + '.output.1',
param_attr=ParamAttr(name=bn_name + '_scale'),
bias_attr=ParamAttr(bn_name + '_offset'),
moving_mean_name=bn_name + '_mean',
moving_variance_name=bn_name + '_variance')
def Xception_41():
"""Xception_41"""
model = Xception(entry_flow_block_num=3, middle_flow_block_num=8)
return model
def Xception_65():
"""Xception_65"""
model = Xception(entry_flow_block_num=3, middle_flow_block_num=16)
return model
def Xception_71():
"""Xception_71"""
model = Xception(entry_flow_block_num=5, middle_flow_block_num=16)
return model
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