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提交 f1f5353a 编写于 作者: C cuicheng01 提交者： ruri
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Update model (#3003) 

* add densenet, squeezenet, darknet, resnext, resnext101_wsl pretrained models
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PaddleCV/image_classification/README.md
浏览文件 @ f1f5353a
...@@ -166,16 +166,23 @@ The image classification models currently supported by PaddlePaddle are listed i ...@@ -166,16 +166,23 @@ The image classification models currently supported by PaddlePaddle are listed i
As the activation function ```swish``` and ```relu6``` which separately used in ShuffleNetV2_swish 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_swish 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 and Xception net is ```299x299```, others are ```224x224```. - Note2: The image resolution feeded in InceptionV4 and Xception net is ```299x299```, Fix_ResNeXt101_32x48d_wsl is ```320x320```, DarkNet is ```256x256```, others are ```224x224```.In test time, the resize_short_size of the DarkNet53 and Fix_ResNeXt101_32x48d_wsl series networks is the same as the width or height of the input image resolution, the InceptionV4 and Xception network resize_short_size is 320, and the other networks resize_short_size are 256.
- 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```
- Note4: The pretrained model of the ResNeXt101_wsl series network is converted from the pytorch model. Please go to [RESNEXT WSL](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/) for details.
### AlexNet ### 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 |
### SqueezeNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[SqueezeNet1_0](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_0_pretrained.tar) | 59.60%/81.66% | 2.740 | 1.688 |
|[SqueezeNet1_1](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_1_pretrained.tar) | 60.08%/81.85% | 2.751 | 1.270 |
### VGG ### VGG
|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) |
|- |:-: |:-: |:-: | |- |:-: |:-: |:-: |
...@@ -224,12 +231,23 @@ As the activation function ```swish``` and ```relu6``` which separately used in ...@@ -224,12 +231,23 @@ As the activation function ```swish``` and ```relu6``` which separately used in
|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) |
|- |:-: |:-: |:-: | |- |:-: |:-: |:-: |
|[ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar) | 77.75%/93.82% | 12.863 | 9.837 | |[ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar) | 77.75%/93.82% | 12.863 | 9.837 |
|[ResNeXt50_vd_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_32x4d_pretrained.tar) | 79.56%/94.62% | 13.673 | 9.991 |
|[ResNeXt50_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 28.162 | 18.271 | |[ResNeXt50_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 28.162 | 18.271 |
|[ResNeXt50_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_64x4d_pretrained.tar) | 80.12%/94.86% | 20.888 | 17.687 | |[ResNeXt50_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_64x4d_pretrained.tar) | 80.12%/94.86% | 20.888 | 17.687 |
|[ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x4d_pretrained.tar) | 78.65%/94.19% | 24.154 | 21.387 | |[ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x4d_pretrained.tar) | 78.65%/94.19% | 24.154 | 21.387 |
|[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 41.073 | 38.736 | |[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 |
|[ResNeXt152_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_32x4d_pretrained.tar) | 78.98%/94.33% | 37.007 | 31.301 | |[ResNeXt152_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_32x4d_pretrained.tar) | 78.98%/94.33% | 37.007 | 31.301 |
|[ResNeXt152_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_64x4d_pretrained.tar) | 79.51%/94.71% | 58.966 | 57.267 |
### DenseNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[DenseNet121](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet121_pretrained.tar) | 75.66%/92.58% | 12.437 | 5.813 |
|[DenseNet161](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet161_pretrained.tar) | 78.57%/94.14% | 27.717 | 12.861 |
|[DenseNet169](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet169_pretrained.tar) | 76.81%/93.31% | 18.941 | 8.146 |
|[DenseNet201](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet201_pretrained.tar) | 77.63%/93.66% | 26.583 | 10.549 |
|[DenseNet264](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet264_pretrained.tar) | 77.96%/93.85% | 41.495 | 15.574 |
### SENet ### SENet
|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) |
...@@ -245,8 +263,19 @@ As the activation function ```swish``` and ```relu6``` which separately used in ...@@ -245,8 +263,19 @@ As the activation function ```swish``` and ```relu6``` which separately used in
|[Xception_41](https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_pretrained.tar) | 79.30%/94.53% | 13.757 | 10.831 | |[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 |
### DarkNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[DarkNet53](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_ImageNet1k_pretrained.tar) | 78.04%/94.05% | 11.969 | 7.153 |
### ResNeXt101_wsl
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ResNeXt101_32x8d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x8d_wsl_pretrained.tar) | 82.55%/96.74% | 33.310 | 27.648 |
|[ResNeXt101_32x16d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x16d_wsl_pretrained.tar) | 84.24%/97.26% | 54.320 | 46.064 |
|[ResNeXt101_32x32d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x32d_wsl_pretrained.tar) | 84.97%/97.59% | 97.734 | 87.961 |
|[ResNeXt101_32x48d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x48d_wsl_pretrained.tar) | 85.37%/97.69% | 161.722 | |
|[Fix_ResNeXt101_32x48d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/Fix_ResNeXt101_32x48d_wsl_pretrained.tar) | 86.26%/97.97% | 236.091 | |
## FAQ ## FAQ
...@@ -272,7 +301,11 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100 ...@@ -272,7 +301,11 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100
- 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 - 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
- DarkNet: [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf), Joseph Redmon, Ali Farhadi
- DenseNet: [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993), Gao Huang, Zhuang Liu, Laurens van der Maaten
- SqueezeNet: [SQUEEZENET: ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE](https://arxiv.org/abs/1602.07360), Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
- ResNeXt101_wsl: [Exploring the Limits of Weakly Supervised Pretraining](https://arxiv.org/abs/1805.00932), Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten
- Fix_ResNeXt101_wsl: [Fixing the train-test resolution discrepancy](https://arxiv.org/abs/1906.06423), Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Herve ́ Je ́gou
## Update ## Update
...@@ -284,6 +317,7 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100 ...@@ -284,6 +317,7 @@ Enforce failed. Expected x_dims[1] == labels_dims[1], but received x_dims[1]:100
- 2019/06/22 Update ResNet50_vd_v2 - 2019/06/22 Update ResNet50_vd_v2
- 2019/07/02 **Stage5**: Update MobileNetV2_x0_5, ResNeXt50_32x4d, ResNeXt50_64x4d, Xception_41, ResNet101_vd - 2019/07/02 **Stage5**: Update MobileNetV2_x0_5, ResNeXt50_32x4d, ResNeXt50_64x4d, Xception_41, ResNet101_vd
- 2019/07/19 **Stage6**: Update ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, MobileNetV2_x0_25, MobileNetV2_x1_5, MobileNetV2_x2_0, ResNeXt50_vd_64x4d, ResNeXt101_32x4d, ResNeXt152_32x4d - 2019/07/19 **Stage6**: Update ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, MobileNetV2_x0_25, MobileNetV2_x1_5, MobileNetV2_x2_0, ResNeXt50_vd_64x4d, ResNeXt101_32x4d, ResNeXt152_32x4d
- 2019/08/01 **Stage7**: Update DarkNet53, DenseNet121. Densenet161, DenseNet169, DenseNet201, DenseNet264, SqueezeNet1_0, SqueezeNet1_1, ResNeXt50_vd_32x4d, ResNeXt152_64x4d, ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl
## Contribute ## Contribute
......
PaddleCV/image_classification/README_cn.md
浏览文件 @ f1f5353a
...@@ -154,17 +154,25 @@ python infer.py \ ...@@ -154,17 +154,25 @@ python infer.py \
试GPU型号为Tesla P4）。由于Paddle TensorRT对ShuffleNetV2_swish使用的激活函数swish，MobileNetV2使用的激活函数relu6不支持，因此预测加速不明显。可以通过点击相应模型的名称下载对应的预训练模型。 试GPU型号为Tesla P4）。由于Paddle TensorRT对ShuffleNetV2_swish使用的激活函数swish，MobileNetV2使用的激活函数relu6不支持，因此预测加速不明显。可以通过点击相应模型的名称下载对应的预训练模型。
- 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。 - 2：InceptionV4和Xception采用的输入图像的分辨率为299x299，DarkNet53为256x256，Fix_ResNeXt101_32x48d_wsl为320x320，其余模型使用的分辨率均为224x224。在预测时，DarkNet53与Fix_ResNeXt101_32x48d_wsl系列网络resize_short_size与输入的图像分辨率的宽或高相同,InceptionV4和Xception网络resize_short_size为320，其余网络resize_short_size均为256。
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```
- 4: ResNeXt101_wsl系列的预训练模型转自pytorch模型，详情请移步[RESNEXT WSL](https://pytorch.org/hub/facebookresearch_WSL-Images_resnext/)。
### AlexNet ### 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 |
### SqueezeNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[SqueezeNet1_0](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_0_pretrained.tar) | 59.60%/81.66% | 2.740 | 1.688 |
|[SqueezeNet1_1](https://paddle-imagenet-models-name.bj.bcebos.com/SqueezeNet1_1_pretrained.tar) | 60.08%/81.85% | 2.751 | 1.270 |
### VGG ### VGG
|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) |
|- |:-: |:-: |:-: | |- |:-: |:-: |:-: |
...@@ -213,12 +221,23 @@ python infer.py \ ...@@ -213,12 +221,23 @@ python infer.py \
|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) |
|- |:-: |:-: |:-: | |- |:-: |:-: |:-: |
|[ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar) | 77.75%/93.82% | 12.863 | 9.837 | |[ResNeXt50_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_32x4d_pretrained.tar) | 77.75%/93.82% | 12.863 | 9.837 |
|[ResNeXt50_vd_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_32x4d_pretrained.tar) | 79.56%/94.62% | 13.673 | 9.991 |
|[ResNeXt50_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 28.162 | 18.271 | |[ResNeXt50_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 28.162 | 18.271 |
|[ResNeXt50_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_64x4d_pretrained.tar) | 80.12%/94.86% | 20.888 | 17.687 | |[ResNeXt50_vd_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_vd_64x4d_pretrained.tar) | 80.12%/94.86% | 20.888 | 17.687 |
|[ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x4d_pretrained.tar) | 78.65%/94.19% | 24.154 | 21.387 | |[ResNeXt101_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x4d_pretrained.tar) | 78.65%/94.19% | 24.154 | 21.387 |
|[ResNeXt101_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt50_64x4d_pretrained.tar) | 78.43%/94.13% | 41.073 | 38.736 | |[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 |
|[ResNeXt152_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_32x4d_pretrained.tar) | 78.98%/94.33% | 37.007 | 31.301 | |[ResNeXt152_32x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_32x4d_pretrained.tar) | 78.98%/94.33% | 37.007 | 31.301 |
|[ResNeXt152_64x4d](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt152_64x4d_pretrained.tar) | 79.51%/94.71% | 58.966 | 57.267 |
### DenseNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[DenseNet121](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet121_pretrained.tar) | 75.66%/92.58% | 12.437 | 5.813 |
|[DenseNet161](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet161_pretrained.tar) | 78.57%/94.14% | 27.717 | 12.861 |
|[DenseNet169](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet169_pretrained.tar) | 76.81%/93.31% | 18.941 | 8.146 |
|[DenseNet201](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet201_pretrained.tar) | 77.63%/93.66% | 26.583 | 10.549 |
|[DenseNet264](https://paddle-imagenet-models-name.bj.bcebos.com/DenseNet264_pretrained.tar) | 77.96%/93.85% | 41.495 | 15.574 |
### SENet ### SENet
|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) |
...@@ -234,7 +253,19 @@ python infer.py \ ...@@ -234,7 +253,19 @@ python infer.py \
|[Xception_41](https://paddle-imagenet-models-name.bj.bcebos.com/Xception41_pretrained.tar) | 79.30%/94.53% | 13.757 | 10.831 | |[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 |
### DarkNet
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[DarkNet53](https://paddle-imagenet-models-name.bj.bcebos.com/DarkNet53_ImageNet1k_pretrained.tar) | 78.04%/94.05% | 11.969 | 7.153 |
### ResNeXt101_wsl
|model | top-1/top-5 accuracy(CV2) | Paddle Fluid inference time(ms) | Paddle TensorRT inference time(ms) |
|- |:-: |:-: |:-: |
|[ResNeXt101_32x8d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x8d_wsl_pretrained.tar) | 82.55%/96.74% | 33.310 | 27.648 |
|[ResNeXt101_32x16d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x16d_wsl_pretrained.tar) | 84.24%/97.26% | 54.320 | 46.064 |
|[ResNeXt101_32x32d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x32d_wsl_pretrained.tar) | 84.97%/97.59% | 97.734 | 87.961 |
|[ResNeXt101_32x48d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/ResNeXt101_32x48d_wsl_pretrained.tar) | 85.37%/97.69% | 161.722 | |
|[Fix_ResNeXt101_32x48d_wsl](https://paddle-imagenet-models-name.bj.bcebos.com/Fix_ResNeXt101_32x48d_wsl_pretrained.tar) | 86.26%/97.97% | 236.091 | |
## FAQ ## FAQ
...@@ -256,6 +287,11 @@ python infer.py \ ...@@ -256,6 +287,11 @@ python infer.py \
- 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 - 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
- DarkNet: [YOLOv3: An Incremental Improvement](https://pjreddie.com/media/files/papers/YOLOv3.pdf), Joseph Redmon, Ali Farhadi
- DenseNet: [Densely Connected Convolutional Networks](https://arxiv.org/abs/1608.06993), Gao Huang, Zhuang Liu, Laurens van der Maaten
- SqueezeNet: [SQUEEZENET: ALEXNET-LEVEL ACCURACY WITH 50X FEWER PARAMETERS AND <0.5MB MODEL SIZE](https://arxiv.org/abs/1602.07360), Forrest N. Iandola, Song Han, Matthew W. Moskewicz, Khalid Ashraf, William J. Dally, Kurt Keutzer
- ResNeXt101_wsl: [Exploring the Limits of Weakly Supervised Pretraining](https://arxiv.org/abs/1805.00932), Dhruv Mahajan, Ross Girshick, Vignesh Ramanathan, Kaiming He, Manohar Paluri, Yixuan Li, Ashwin Bharambe, Laurens van der Maaten
- Fix_ResNeXt101_wsl: [Fixing the train-test resolution discrepancy](https://arxiv.org/abs/1906.06423), Hugo Touvron, Andrea Vedaldi, Matthijs Douze, Herve ́ Je ́gou
## 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
...@@ -266,6 +302,7 @@ python infer.py \ ...@@ -266,6 +302,7 @@ python infer.py \
- 2019/06/22 更新ResNet50_vd_v2 - 2019/06/22 更新ResNet50_vd_v2
- 2019/07/02 **Stage5**: 更新MobileNetV2_x0_5, ResNeXt50_32x4d, ResNeXt50_64x4d, Xception_41, ResNet101_vd - 2019/07/02 **Stage5**: 更新MobileNetV2_x0_5, ResNeXt50_32x4d, ResNeXt50_64x4d, Xception_41, ResNet101_vd
- 2019/07/19 **Stage6**: 更新ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, MobileNetV2_x0_25, MobileNetV2_x1_5, MobileNetV2_x2_0, ResNeXt50_vd_64x4d, ResNeXt101_32x4d, ResNeXt152_32x4d - 2019/07/19 **Stage6**: 更新ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0, MobileNetV2_x0_25, MobileNetV2_x1_5, MobileNetV2_x2_0, ResNeXt50_vd_64x4d, ResNeXt101_32x4d, ResNeXt152_32x4d
- 2019/08/01 **Stage7**: 更新DarkNet53, DenseNet121. Densenet161, DenseNet169, DenseNet201, DenseNet264, SqueezeNet1_0, SqueezeNet1_1, ResNeXt50_vd_32x4d, ResNeXt152_64x4d, ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl
## 如何贡献代码 ## 如何贡献代码
......
PaddleCV/image_classification/models/__init__.py
浏览文件 @ f1f5353a
...@@ -17,3 +17,8 @@ from .shufflenet_v2_swish import ShuffleNetV2, ShuffleNetV2_x0_5_swish, ShuffleN ...@@ -17,3 +17,8 @@ from .shufflenet_v2_swish import ShuffleNetV2, ShuffleNetV2_x0_5_swish, ShuffleN
from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0 from .shufflenet_v2 import ShuffleNetV2_x0_25, ShuffleNetV2_x0_33, ShuffleNetV2_x0_5, ShuffleNetV2_x1_0, ShuffleNetV2_x1_5, ShuffleNetV2_x2_0
from .fast_imagenet import FastImageNet from .fast_imagenet import FastImageNet
from .xception import Xception_41, Xception_65, Xception_71 from .xception import Xception_41, Xception_65, Xception_71
from .densenet import DenseNet121, DenseNet161, DenseNet169, DenseNet201, DenseNet264
from .squeezenet import SqueezeNet1_0, SqueezeNet1_1
from .darknet import DarkNet53
from .resnext101_wsl import ResNeXt101_32x8d_wsl, ResNeXt101_32x16d_wsl, ResNeXt101_32x32d_wsl, ResNeXt101_32x48d_wsl, Fix_ResNeXt101_32x48d_wsl
PaddleCV/image_classification/models/darknet.py 0 → 100644
浏览文件 @ f1f5353a
import paddle.fluid as fluid
from paddle.fluid.param_attr import ParamAttr
import math
__all__ = ["DarkNet53"]
train_parameters = {
"input_size": [3, 224, 224],
"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 DarkNet53():
def __init__(self):
self.params = train_parameters
def net(self, input, class_dim=1000):
DarkNet_cfg = {53: ([1, 2, 8, 8, 4], self.basicblock)}
stages, block_func = DarkNet_cfg[53]
stages = stages[0:5]
conv1 = self.conv_bn_layer(
input,
ch_out=32,
filter_size=3,
stride=1,
padding=1,
name="yolo_input")
conv = self.downsample(
conv1,
ch_out=conv1.shape[1] * 2,
name="yolo_input.downsample")
for i, stage in enumerate(stages):
conv = self.layer_warp(
block_func,
conv,
32 * (2**i),
stage,
name="stage.{}".format(i))
if i < len(stages) - 1: # do not downsaple in the last stage
conv = self.downsample(
conv,
ch_out=conv.shape[1] * 2,
name="stage.{}.downsample".format(i))
pool = fluid.layers.pool2d(
input=conv, 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,
param_attr=ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),name='fc_weights'),
bias_attr=ParamAttr(name='fc_offset'))
return out
def conv_bn_layer(self, input, ch_out, filter_size, stride, padding, name=None):
conv = fluid.layers.conv2d(
input=input,
num_filters=ch_out,
filter_size=filter_size,
stride=stride,
padding=padding,
act=None,
param_attr=ParamAttr(name=name + ".conv.weights"),
bias_attr=False)
bn_name = name + ".bn"
out = fluid.layers.batch_norm(
input=conv,
act='relu',
param_attr=ParamAttr(name=bn_name + '.scale'),
bias_attr=ParamAttr(name=bn_name + '.offset'),
moving_mean_name=bn_name + '.mean',
moving_variance_name=bn_name + '.var')
return out
def downsample(self, input, ch_out, filter_size=3, stride=2, padding=1, name=None):
return self.conv_bn_layer(
input,
ch_out=ch_out,
filter_size=filter_size,
stride=stride,
padding=padding,
name=name)
def basicblock(self, input, ch_out, name=None):
conv1 = self.conv_bn_layer(
input, ch_out, 1, 1, 0, name=name + ".0")
conv2 = self.conv_bn_layer(
conv1, ch_out * 2, 3, 1, 1, name=name + ".1")
out = fluid.layers.elementwise_add(x=input, y=conv2, act=None)
return out
def layer_warp(self, block_func, input, ch_out, count, name=None):
res_out = block_func(
input, ch_out, name='{}.0'.format(name))
for j in range(1, count):
res_out = block_func(
res_out, ch_out, name='{}.{}'.format(name, j))
return res_out
PaddleCV/image_classification/models/densenet.py 0 → 100644
浏览文件 @ f1f5353a
import paddle
import paddle.fluid as fluid
import math
from paddle.fluid.param_attr import ParamAttr
__all__ = ["DenseNet", "DenseNet121", "DenseNet161", "DenseNet169", "DenseNet201", "DenseNet264"]
train_parameters = {
"input_size": [3, 224, 224],
"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 DenseNet():
def __init__(self, layers=121):
self.params = train_parameters
self.layers = layers
def net(self, input, bn_size=4, dropout=0, class_dim=1000):
layers = self.layers
supported_layers = [121, 161, 169, 201, 264]
assert layers in supported_layers, \
"supported layers are {} but input layer is {}".format(supported_layers, layers)
densenet_spec = {121: (64, 32, [6, 12, 24, 16]),
161: (96, 48, [6, 12, 36, 24]),
169: (64, 32, [6, 12, 32, 32]),
201: (64, 32, [6, 12, 48, 32]),
264: (64, 32, [6, 12, 64, 48])}
num_init_features, growth_rate, block_config = densenet_spec[layers]
conv = fluid.layers.conv2d(
input=input,
num_filters=num_init_features,
filter_size=7,
stride=2,
padding=3,
act=None,
param_attr=ParamAttr(name="conv1_weights"),
bias_attr=False)
conv = fluid.layers.batch_norm(input=conv,
act='relu',
param_attr=ParamAttr(name='conv1_bn_scale'),
bias_attr=ParamAttr(name='conv1_bn_offset'),
moving_mean_name='conv1_bn_mean',
moving_variance_name='conv1_bn_variance')
conv = fluid.layers.pool2d(input=conv, pool_size=3, pool_stride=2, pool_padding=1, pool_type='max')
num_features = num_init_features
for i, num_layers in enumerate(block_config):
conv = self.make_dense_block(conv, num_layers, bn_size, growth_rate, dropout, name='conv'+str(i+2))
num_features = num_features + num_layers * growth_rate
if i != len(block_config) - 1:
conv = self.make_transition(conv, num_features // 2, name='conv'+str(i+2)+'_blk')
num_features = num_features // 2
conv = fluid.layers.batch_norm(input=conv,
act='relu',
param_attr=ParamAttr(name='conv5_blk_bn_scale'),
bias_attr=ParamAttr(name='conv5_blk_bn_offset'),
moving_mean_name='conv5_blk_bn_mean',
moving_variance_name='conv5_blk_bn_variance')
conv = fluid.layers.pool2d(input=conv, pool_type='avg', global_pooling=True)
stdv = 1.0 / math.sqrt(conv.shape[1] * 1.0)
out = fluid.layers.fc(input=conv,
size=class_dim,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv), name="fc_weights"),
bias_attr=ParamAttr(name='fc_offset'))
return out
def make_transition(self, input, num_output_features, name=None):
bn_ac = fluid.layers.batch_norm(input,
act='relu',
param_attr=ParamAttr(name=name + '_bn_scale'),
bias_attr=ParamAttr(name + '_bn_offset'),
moving_mean_name=name + '_bn_mean',
moving_variance_name=name + '_bn_variance'
)
bn_ac_conv = fluid.layers.conv2d(
input=bn_ac,
num_filters=num_output_features,
filter_size=1,
stride=1,
act=None,
bias_attr=False,
param_attr=ParamAttr(name=name + "_weights")
)
pool = fluid.layers.pool2d(input=bn_ac_conv, pool_size=2, pool_stride=2, pool_type='avg')
return pool
def make_dense_block(self, input, num_layers, bn_size, growth_rate, dropout, name=None):
conv = input
for layer in range(num_layers):
conv = self.make_dense_layer(conv, growth_rate, bn_size, dropout, name=name + '_' + str(layer+1))
return conv
def make_dense_layer(self, input, growth_rate, bn_size, dropout, name=None):
bn_ac = fluid.layers.batch_norm(input,
act='relu',
param_attr=ParamAttr(name=name + '_x1_bn_scale'),
bias_attr=ParamAttr(name + '_x1_bn_offset'),
moving_mean_name=name + '_x1_bn_mean',
moving_variance_name=name + '_x1_bn_variance')
bn_ac_conv = fluid.layers.conv2d(
input=bn_ac,
num_filters=bn_size * growth_rate,
filter_size=1,
stride=1,
act=None,
bias_attr=False,
param_attr=ParamAttr(name=name + "_x1_weights"))
bn_ac = fluid.layers.batch_norm(bn_ac_conv,
act='relu',
param_attr=ParamAttr(name=name + '_x2_bn_scale'),
bias_attr=ParamAttr(name + '_x2_bn_offset'),
moving_mean_name=name + '_x2_bn_mean',
moving_variance_name=name + '_x2_bn_variance')
bn_ac_conv = fluid.layers.conv2d(
input=bn_ac,
num_filters=growth_rate,
filter_size=3,
stride=1,
padding=1,
act=None,
bias_attr=False,
param_attr=ParamAttr(name=name + "_x2_weights"))
if dropout:
bn_ac_conv = fluid.layers.dropout(x=bn_ac_conv, dropout_prob=dropout)
bn_ac_conv = fluid.layers.concat([input, bn_ac_conv], axis=1)
return bn_ac_conv
def DenseNet121():
model=DenseNet(layers=121)
return model
def DenseNet161():
model=DenseNet(layers=161)
return model
def DenseNet169():
model=DenseNet(layers=169)
return model
def DenseNet201():
model=DenseNet(layers=201)
return model
def DenseNet264():
model=DenseNet(layers=264)
return model
PaddleCV/image_classification/models/resnext101_wsl.py 0 → 100644
浏览文件 @ f1f5353a
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.fluid as fluid
import math
from paddle.fluid.param_attr import ParamAttr
__all__ = ["ResNeXt101_32x8d_wsl", "ResNeXt101_32x16d_wsl", "ResNeXt101_32x32d_wsl", "ResNeXt101_32x48d_wsl", "Fix_ResNeXt101_32x48d_wsl"]
train_parameters = {
"input_size": [3, 224, 224],
"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 ResNeXt101_wsl():
def __init__(self, layers=101, cardinality=32, width=48):
self.params = train_parameters
self.layers = layers
self.cardinality = cardinality
self.width = width
def net(self, input, class_dim=1000):
layers = self.layers
cardinality = self.cardinality
width = self.width
depth = [3, 4, 23, 3]
base_width = cardinality * width
num_filters = [base_width * i for i in [1, 2, 4, 8]]
conv = self.conv_bn_layer(
input=input,
num_filters=64,
filter_size=7,
stride=2,
act='relu',
name="conv1") #debug
conv = fluid.layers.pool2d(
input=conv,
pool_size=3,
pool_stride=2,
pool_padding=1,
pool_type='max')
for block in range(len(depth)):
for i in range(depth[block]):
conv_name = 'layer' + str(block+1) + "." + str(i)
conv = self.bottleneck_block(
input=conv,
num_filters=num_filters[block],
stride=2 if i == 0 and block != 0 else 1,
cardinality=cardinality,
name=conv_name)
pool = fluid.layers.pool2d(
input=conv, pool_size=7, 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,
param_attr=fluid.param_attr.ParamAttr(
initializer=fluid.initializer.Uniform(-stdv, stdv),name='fc.weight'),
bias_attr=fluid.param_attr.ParamAttr(name='fc.bias'))
return out
def conv_bn_layer(self,
input,
num_filters,
filter_size,
stride=1,
groups=1,
act=None,
name=None):
if "downsample" in name:
conv_name = name + '.0'
else:
conv_name = name
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=conv_name + ".weight"),
bias_attr=False)
if "downsample" in name:
bn_name = name[:9] + 'downsample' + '.1'
else:
if "conv1" == name:
bn_name = 'bn' + name[-1]
else:
bn_name = (name[:10] if name[7:9].isdigit() else name[:9]) + 'bn' + name[-1]
return fluid.layers.batch_norm(
input=conv,
act=act,
param_attr=ParamAttr(name=bn_name + '.weight'),
bias_attr=ParamAttr(bn_name + '.bias'),
moving_mean_name=bn_name + '.running_mean',
moving_variance_name=bn_name + '.running_var', )
def shortcut(self, input, ch_out, stride, name):
ch_in = input.shape[1]
if ch_in != ch_out or stride != 1:
return self.conv_bn_layer(input, ch_out, 1, stride, name=name)
else:
return input
def bottleneck_block(self, input, num_filters, stride, cardinality, name):
cardinality = self.cardinality
width = self.width
conv0 = self.conv_bn_layer(
input=input,
num_filters=num_filters,
filter_size=1,
act='relu',
name=name + ".conv1")
conv1 = self.conv_bn_layer(
input=conv0,
num_filters=num_filters,
filter_size=3,
stride=stride,
groups=cardinality,
act='relu',
name=name + ".conv2")
conv2 = self.conv_bn_layer(
input=conv1,
num_filters=num_filters//(width//8),
filter_size=1,
act=None,
name=name + ".conv3")
short = self.shortcut(
input, num_filters//(width//8), stride, name=name + ".downsample")
return fluid.layers.elementwise_add(
x=short, y=conv2, act='relu')
def ResNeXt101_32x8d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=8)
return model
def ResNeXt101_32x16d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=16)
return model
def ResNeXt101_32x32d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=32)
return model
def ResNeXt101_32x48d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=48)
return model
def Fix_ResNeXt101_32x48d_wsl():
model = ResNeXt101_wsl(cardinality=32, width=48)
return model
PaddleCV/image_classification/models/resnext_vd.py
浏览文件 @ f1f5353a
...@@ -198,7 +198,7 @@ def ResNeXt101_vd_64x4d(): ...@@ -198,7 +198,7 @@ def ResNeXt101_vd_64x4d():
return model return model
def ResNeXt101_vd_32x4d(): def ResNeXt101_vd_32x4d():
model = ResNeXt(layers=50, cardinality=32, is_3x3 = True) model = ResNeXt(layers=101, cardinality=32, is_3x3 = True)
return model return model
def ResNeXt152_vd_64x4d(): def ResNeXt152_vd_64x4d():
...@@ -206,6 +206,6 @@ def ResNeXt152_vd_64x4d(): ...@@ -206,6 +206,6 @@ def ResNeXt152_vd_64x4d():
return model return model
def ResNeXt152_vd_32x4d(): def ResNeXt152_vd_32x4d():
model = ResNeXt(layers=50, cardinality=32, is_3x3 = True) model = ResNeXt(layers=152, cardinality=32, is_3x3 = True)
return model return model
PaddleCV/image_classification/models/shufflenet_v2.py
浏览文件 @ f1f5353a
...@@ -46,7 +46,7 @@ class ShuffleNetV2(): ...@@ -46,7 +46,7 @@ class ShuffleNetV2():
scale = self.scale scale = self.scale
stage_repeats = [4, 8, 4] stage_repeats = [4, 8, 4]
if scale == 0.25: if scale == 0.25:
stage_out_channels = [-1, 24, 24, 48, 96, 512] stage_out_channels = [-1, 24, 24, 48, 96, 512]
elif scale == 0.33: elif scale == 0.33:
stage_out_channels = [-1, 24, 32, 64, 128, 512] stage_out_channels = [-1, 24, 32, 64, 128, 512]
......
PaddleCV/image_classification/models/squeezenet.py 0 → 100644
浏览文件 @ f1f5353a
import paddle
import paddle.fluid as fluid
import math
from paddle.fluid.param_attr import ParamAttr
__all__ = ["SqueezeNet", "SqueezeNet1_0", "SqueezeNet1_1"]
train_parameters = {
"input_size": [3, 224, 224],
"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 SqueezeNet():
def __init__(self, version='1.0'):
self.params = train_parameters
self.version = version
def net(self, input, class_dim=1000):
version = self.version
assert version in ['1.0', '1.1'], \
"supported version are {} but input version is {}".format(['1.0', '1.1'], version)
if version == '1.0':
conv = fluid.layers.conv2d(input,
num_filters=96,
filter_size=7,
stride=2,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2,pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
else:
conv = fluid.layers.conv2d(input,
num_filters=64,
filter_size=3,
stride=2,
padding=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv1_weights"),
bias_attr=ParamAttr(name='conv1_offset'))
conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 16, 64, 64, name='fire2')
conv = self.make_fire(conv, 16, 64, 64, name='fire3')
conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 32, 128, 128, name='fire4')
conv = self.make_fire(conv, 32, 128, 128, name='fire5')
conv = fluid.layers.pool2d(conv, pool_size=3, pool_stride=2, pool_type='max')
conv = self.make_fire(conv, 48, 192, 192, name='fire6')
conv = self.make_fire(conv, 48, 192, 192, name='fire7')
conv = self.make_fire(conv, 64, 256, 256, name='fire8')
conv = self.make_fire(conv, 64, 256, 256, name='fire9')
conv = fluid.layers.dropout(conv, dropout_prob=0.5)
conv = fluid.layers.conv2d(conv,
num_filters=class_dim,
filter_size=1,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name="conv10_weights"),
bias_attr=ParamAttr(name='conv10_offset'))
conv = fluid.layers.pool2d(conv, pool_type='avg', global_pooling=True)
out = fluid.layers.flatten(conv)
return out
def make_fire_conv(self, input, num_filters, filter_size, padding=0, name=None):
conv = fluid.layers.conv2d(input,
num_filters=num_filters,
filter_size=filter_size,
padding=padding,
act='relu',
param_attr=fluid.param_attr.ParamAttr(name=name + "_weights"),
bias_attr=ParamAttr(name=name + '_offset'))
return conv
def make_fire(self, input, squeeze_channels, expand1x1_channels, expand3x3_channels, name=None):
conv = self.make_fire_conv(input, squeeze_channels, 1, name=name+'_squeeze1x1')
conv_path1 = self.make_fire_conv(conv, expand1x1_channels, 1, name=name+'_expand1x1')
conv_path2 = self.make_fire_conv(conv, expand3x3_channels, 3, 1, name=name+'_expand3x3')
out = fluid.layers.concat([conv_path1, conv_path2], axis=1)
return out
def SqueezeNet1_0():
model = SqueezeNet(version='1.0')
return model
def SqueezeNet1_1():
model = SqueezeNet(version='1.1')
return model
PaddleCV/image_classification/models/xception.py
浏览文件 @ f1f5353a
...@@ -227,7 +227,7 @@ class Xception(object): ...@@ -227,7 +227,7 @@ class Xception(object):
num_filters=num_filters, num_filters=num_filters,
filter_size=filter_size, filter_size=filter_size,
stride=stride, stride=stride,
padding=(filter_size - 1) / 2, padding=(filter_size - 1) // 2,
groups=groups, groups=groups,
act=None, act=None,
param_attr=ParamAttr(name=name + "_weights"), param_attr=ParamAttr(name=name + "_weights"),
......
PaddleCV/image_classification/reader_cv2.py
浏览文件 @ f1f5353a
...@@ -192,7 +192,6 @@ def process_image(sample, ...@@ -192,7 +192,6 @@ def process_image(sample,
if crop_size > 0: if crop_size > 0:
target_size = settings.resize_short_size target_size = settings.resize_short_size
img = resize_short(img, target_size) img = resize_short(img, target_size)
img = crop_image(img, target_size=crop_size, center=True) img = crop_image(img, target_size=crop_size, center=True)
img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255 img = img[:, :, ::-1].astype('float32').transpose((2, 0, 1)) / 255
...@@ -209,10 +208,11 @@ def process_image(sample, ...@@ -209,10 +208,11 @@ def process_image(sample,
def process_batch_data(input_data, settings, mode, color_jitter, rotate): def process_batch_data(input_data, settings, mode, color_jitter, rotate):
batch_data = [] batch_data = []
crop_size = int(settings.image_shape.split(',')[-1])
for sample in input_data: for sample in input_data:
if os.path.isfile(sample[0]): if os.path.isfile(sample[0]):
batch_data.append( batch_data.append(
process_image(sample, settings, mode, color_jitter, rotate)) process_image(sample, settings, mode, color_jitter, rotate, crop_size))
else: else:
print("File not exist : %s" % sample[0]) print("File not exist : %s" % sample[0])
return batch_data return batch_data
......
PaddleCV/image_classification/run.sh
浏览文件 @ f1f5353a
...@@ -28,6 +28,34 @@ python train.py \ ...@@ -28,6 +28,34 @@ python train.py \
# --lr=0.01 \ # --lr=0.01 \
# --l2_decay=1e-4 # --l2_decay=1e-4
#SqueezeNet1_0
#python train.py \
# --model=SqueezeNet1_0 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --class_dim=1000 \
# --model_save_dir=output/ \
# --lr=0.02 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --l2_decay=1e-4
#SqueezeNet1_1
#python train.py \
# --model=SqueezeNet1_1 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --lr_strategy=cosine_decay \
# --class_dim=1000 \
# --model_save_dir=output/ \
# --lr=0.02 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --l2_decay=1e-4
#VGG11: #VGG11:
#python train.py \ #python train.py \
# --model=VGG11 \ # --model=VGG11 \
...@@ -448,6 +476,22 @@ python train.py \ ...@@ -448,6 +476,22 @@ python train.py \
# --model_save_dir=output/ \ # --model_save_dir=output/ \
# --l2_decay=1e-4 # --l2_decay=1e-4
#ResNeXt50_vd_32x4d
#python train.py \
# --model=ResNeXt50_vd_32x4d \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --class_dim=1000 \
# --lr_strategy=cosine_decay \
# --lr=0.1 \
# --num_epochs=200 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=1e-4 \
# --use_mixup=True \
# --use_label_smoothing=True \
# --label_smoothing_epsilon=0.1 \
#ResNeXt50_64x4d #ResNeXt50_64x4d
#python train.py \ #python train.py \
...@@ -539,6 +583,90 @@ python train.py \ ...@@ -539,6 +583,90 @@ python train.py \
# --model_save_dir=output/ \ # --model_save_dir=output/ \
# --l2_decay=1e-4 # --l2_decay=1e-4
#ResNeXt152_64x4d
#python train.py \
# --model=ResNeXt152_64x4d \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --class_dim=1000 \
# --lr_strategy=piecewise_decay \
# --lr=0.1 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=18e-5
# DenseNet121
# python train.py \
# --model=DenseNet121 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --class_dim=1000 \
# --lr_strategy=piecewise_decay \
# --lr=0.1 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=1e-4
# DenseNet161
# python train.py \
# --model=DenseNet161 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --class_dim=1000 \
# --lr_strategy=piecewise_decay \
# --lr=0.1 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=1e-4
# DenseNet169
# python train.py \
# --model=DenseNet169 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --class_dim=1000 \
# --lr_strategy=piecewise_decay \
# --lr=0.1 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=1e-4
# DenseNet201
# python train.py \
# --model=DenseNet201 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --class_dim=1000 \
# --lr_strategy=piecewise_decay \
# --lr=0.1 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=1e-4
# DenseNet264
# python train.py \
# --model=DenseNet264 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,224,224 \
# --class_dim=1000 \
# --lr_strategy=piecewise_decay \
# --lr=0.1 \
# --num_epochs=120 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=1e-4
#SE_ResNeXt50_32x4d: #SE_ResNeXt50_32x4d:
#python train.py \ #python train.py \
# --model=SE_ResNeXt50_32x4d \ # --model=SE_ResNeXt50_32x4d \
...@@ -631,6 +759,24 @@ python train.py \ ...@@ -631,6 +759,24 @@ python train.py \
# --use_label_smoothing=True \ # --use_label_smoothing=True \
# --label_smoothing_epsilon=0.1 \ # --label_smoothing_epsilon=0.1 \
#DarkNet53
python train.py
# --model=DarkNet53 \
# --batch_size=256 \
# --total_images=1281167 \
# --image_shape=3,256,256 \
# --class_dim=1000 \
# --lr_strategy=cosine_decay \
# --lr=0.1 \
# --num_epochs=200 \
# --with_mem_opt=True \
# --model_save_dir=output/ \
# --l2_decay=1e-4 \
# --use_mixup=True \
# --resize_short_size=256 \
# --use_label_smoothing=True \
# --label_smoothing_epsilon=0.1 \
#ResNet50 nGraph: #ResNet50 nGraph:
# Training: # Training:
#OMP_NUM_THREADS=`nproc` FLAGS_use_ngraph=true python train.py \ #OMP_NUM_THREADS=`nproc` FLAGS_use_ngraph=true python train.py \
......
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