diff --git a/README_ch.md b/README_ch.md
index 011e638c0ff2a065120915f62039a66b46937672..74b9c1318ea85839ad21b8da9463865ca0193e61 100644
--- a/README_ch.md
+++ b/README_ch.md
@@ -7,7 +7,7 @@
飞桨图像识别套件PaddleClas是飞桨为工业界和学术界所准备的一个图像识别任务的工具集，助力使用者训练出更好的视觉模型和应用落地。
**近期更新**
-
+- 2021.09.17 增加PaddleClas自研PP-LCNet系列模型, 这些模型在Intel CPU上有较强的竞争力。相关指标和预训练权重可以从 [这里](docs/zh_CN/ImageNet_models.md)下载。
- 2021.08.11 更新7个[FAQ](docs/zh_CN/faq_series/faq_2021_s2.md)。
- 2021.06.29 添加Swin-transformer系列模型，ImageNet1k数据集上Top1 acc最高精度可达87.2%；支持训练预测评估与whl包部署，预训练模型可以从[这里](docs/zh_CN/models/models_intro.md)下载。
- 2021.06.22,23,24 PaddleClas官方研发团队带来技术深入解读三日直播课。课程回放：[https://aistudio.baidu.com/aistudio/course/introduce/24519](https://aistudio.baidu.com/aistudio/course/introduce/24519)
diff --git a/README_en.md b/README_en.md
index bc4f59cf9cec7fc86f9182254e2c355dc1e76156..aa65ce3825ac9a2d5691be6a1dcc45aae1004626 100644
--- a/README_en.md
+++ b/README_en.md
@@ -8,6 +8,8 @@ PaddleClas is an image recognition toolset for industry and academia, helping us
**Recent updates**
+- 2021.09.17 Add PP-LCNet series model developed by PaddleClas, these models show strong competitiveness on Intel CPUs. The metrics and pretrained model are available [here](docs/en/ImageNet_models_en.md).
+
- 2021.06.29 Add Swin-transformer series model，Highest top1 acc on ImageNet1k dataset reaches 87.2%, training, evaluation and inference are all supported. Pretrained models can be downloaded [here](docs/en/models/models_intro_en.md).
- 2021.06.16 PaddleClas release/2.2. Add metric learning and vector search modules. Add product recognition, animation character recognition, vehicle recognition and logo recognition. Added 30 pretrained models of LeViT, Twins, TNT, DLA, HarDNet, and RedNet, and the accuracy is roughly the same as that of the paper.
- [more](./docs/en/update_history_en.md)
diff --git a/docs/en/ImageNet_models_en.md b/docs/en/ImageNet_models_en.md
index 743337e46aaa2516c6418b4c3e819c4bcf873a97..740fcd3828fd61f404de35bd737e077df3a38813 100644
--- a/docs/en/ImageNet_models_en.md
+++ b/docs/en/ImageNet_models_en.md
@@ -24,13 +24,13 @@ Accuracy and inference time of the prtrained models based on SSLD distillation a
* Server-side distillation pretrained models
| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | Download Address |
-|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
+|---------------------|-----------|-----------|---------------|----------------|----------|-----------|-----------------------------------|
| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.434 | 6.222 | 7.39 | 21.82 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) |
-| ResNet50_vd_
ssld | 0.830 | 0.792 | 0.039 | 3.531 | 8.090 | 8.67 | 25.58 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) |
-| ResNet101_vd_
ssld | 0.837 | 0.802 | 0.035 | 6.117 | 13.762 | 16.1 | 44.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) |
-| Res2Net50_vd_
26w_4s_ssld | 0.831 | 0.798 | 0.033 | 4.527 | 9.657 | 8.37 | 25.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) |
-| Res2Net101_vd_
26w_4s_ssld | 0.839 | 0.806 | 0.033 | 8.087 | 17.312 | 16.67 | 45.22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) |
-| Res2Net200_vd_
26w_4s_ssld | 0.851 | 0.812 | 0.049 | 14.678 | 32.350 | 31.49 | 76.21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
+| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 3.531 | 8.090 | 8.67 | 25.58 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) |
+| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 6.117 | 13.762 | 16.1 | 44.57 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) |
+| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 4.527 | 9.657 | 8.37 | 25.06 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) |
+| Res2Net101_vd_26w_4s_ssld | 0.839 | 0.806 | 0.033 | 8.087 | 17.312 | 16.67 | 45.22 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) |
+| Res2Net200_vd_26w_4s_ssld | 0.851 | 0.812 | 0.049 | 14.678 | 32.350 | 31.49 | 76.21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 7.406 | 13.297 | 4.14 | 21.29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) |
| HRNet_W48_C_ssld | 0.836 | 0.790 | 0.046 | 13.707 | 34.435 | 34.58 | 77.47 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W48_C_ssld_pretrained.pdparams) |
| SE_HRNet_W64_C_ssld | 0.848 | - | - | 31.697 | 94.995 | 57.83 | 128.97 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/SE_HRNet_W64_C_ssld_pretrained.pdparams) |
@@ -38,19 +38,44 @@ Accuracy and inference time of the prtrained models based on SSLD distillation a
* Mobile-side distillation pretrained models
-| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | SD855 time(ms)
bs=1 | Flops(G) | Params(M) | 模型大小(M) | Download Address |
+| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | SD855 time(ms)
bs=1 | Flops(G) | Params(M) | Storage Size(M) | Download Address |
+|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
+| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 32.523 | 1.11 | 4.19 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) |
+| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 23.318 | 0.6 | 3.44 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) |
+| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.635 | 0.026 | 1.66 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) |
+| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 19.308 | 0.45 | 5.47 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) |
+| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 6.546 | 0.123 | 2.94 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) |
+| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.983 | 0.44 | 7.3 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams)
+
+* Intel-CPU-side distillation pretrained models
+
+| Model | Top-1 Acc | Reference
Top-1 Acc | Acc gain | Intel-Xeon-Gold-6148 time(ms)
bs=1 | Flops(M) | Params(M) | Download Address |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
-| MobileNetV1_
ssld | 0.779 | 0.710 | 0.069 | 32.523 | 1.11 | 4.19 | 16 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) |
-| MobileNetV2_
ssld | 0.767 | 0.722 | 0.045 | 23.318 | 0.6 | 3.44 | 14 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) |
-| MobileNetV3_
small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.635 | 0.026 | 1.66 | 6.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) |
-| MobileNetV3_
large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 19.308 | 0.45 | 5.47 | 21 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) |
-| MobileNetV3_small_
x1_0_ssld | 0.713 | 0.682 | 0.031 | 6.546 | 0.123 | 2.94 | 12 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) |
-| GhostNet_
x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.983 | 0.44 | 7.3 | 29 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams)
+| PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47 | 1.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) |
+| PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 161 | 3.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) |
+| PPLCNet_x2_5_ssld | 0.808 | 0.766 | 0.042 | 5.39 | 906 | 9.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) |
* Note: `Reference Top-1 Acc` means accuracy of pretrained models which are trained on ImageNet1k dataset.
+
+### PPLCNet_series
+
+Accuracy and inference time metrics of PPLCNet series models are shown as follows. More detailed information can be refered to [PPLCNet series tutorial](../en/models/PPLCNet_en.md).
+
+| Model | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)
bs=1 | FLOPs(M) | Params(M) | Download Address |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| PPLCNet_x0_25 |0.5186 | 0.7565 | 1.74 | 18 | 1.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams) |
+| PPLCNet_x0_35 |0.5809 | 0.8083 | 1.92 | 29 | 1.6 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams) |
+| PPLCNet_x0_5 |0.6314 | 0.8466 | 2.05 | 47 | 1.9 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams) |
+| PPLCNet_x0_75 |0.6818 | 0.8830 | 2.29 | 99 | 2.4 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams) |
+| PPLCNet_x1_0 |0.7132 | 0.9003 | 2.46 | 161 | 3.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams) |
+| PPLCNet_x1_5 |0.7371 | 0.9153 | 3.19 | 342 | 4.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams) |
+| PPLCNet_x2_0 |0.7518 | 0.9227 | 4.27 | 590 | 6.5 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) |
+| PPLCNet_x2_5 |0.7660 | 0.9300 | 5.39 | 906 | 9.0 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) |
+
+
### ResNet and Vd series
diff --git a/docs/en/models/PPLCNet_en.md b/docs/en/models/PPLCNet_en.md
new file mode 100644
index 0000000000000000000000000000000000000000..56a90c1266e6ed96ab832097c5851483ef43efa9
--- /dev/null
+++ b/docs/en/models/PPLCNet_en.md
@@ -0,0 +1,41 @@
+# PPLCNet series
+
+## Overview
+
+The PPLCNet series is a network that has excellent performance on Intel-CPU proposed by the Baidu PaddleCV team. The author summarizes some methods that can improve the accuracy of the model on Intel-CPU but hardly increase the inference time. The author combines these methods into a new network, namely PPLCNet. Compared with other lightweight networks, PPLCNet can achieve higher accuracy with the same inference time. PPLCNet has shown strong competitiveness in image classification, object detection, and semantic segmentation.
+
+
+
+## Accuracy, FLOPS and Parameters
+
+| Models | Top1 | Top5 | FLOPs
(M) | Parameters
(M) |
+|:--:|:--:|:--:|:--:|:--:|
+| PPLCNet_x0_25 |0.5186 | 0.7565 | 18 | 1.5 |
+| PPLCNet_x0_35 |0.5809 | 0.8083 | 29 | 1.6 |
+| PPLCNet_x0_5 |0.6314 | 0.8466 | 47 | 1.9 |
+| PPLCNet_x0_75 |0.6818 | 0.8830 | 99 | 2.4 |
+| PPLCNet_x1_0 |0.7132 | 0.9003 | 161 | 3.0 |
+| PPLCNet_x1_5 |0.7371 | 0.9153 | 342 | 4.5 |
+| PPLCNet_x2_0 |0.7518 | 0.9227 | 590 | 6.5 |
+| PPLCNet_x2_5 |0.7660 | 0.9300 | 906 | 9.0 |
+| PPLCNet_x0_5_ssld |0.6610 | 0.8646 | 47 | 1.9 |
+| PPLCNet_x1_0_ssld |0.7439 | 0.9209 | 161 | 3.0 |
+| PPLCNet_x2_5_ssld |0.8082 | 0.9533 | 906 | 9.0 |
+
+
+
+## Inference speed based on Intel(R)-Xeon(R)-Gold-6148-CPU
+
+| Models | Crop Size | Resize Short Size | FP32
Batch Size=1
(ms) |
+|------------------|-----------|-------------------|--------------------------|
+| PPLCNet_x0_25 | 224 | 256 | 1.74 |
+| PPLCNet_x0_35 | 224 | 256 | 1.92 |
+| PPLCNet_x0_5 | 224 | 256 | 2.05 |
+| PPLCNet_x0_75 | 224 | 256 | 2.29 |
+| PPLCNet_x1_0 | 224 | 256 | 2.46 |
+| PPLCNet_x1_5 | 224 | 256 | 3.19 |
+| PPLCNet_x2_0 | 224 | 256 | 4.27 |
+| PPLCNet_x2_5 | 224 | 256 | 5.39 |
+| PPLCNet_x0_5_ssld | 224 | 256 | 2.05 |
+| PPLCNet_x1_0_ssld | 224 | 256 | 2.46 |
+| PPLCNet_x2_5_ssld | 224 | 256 | 5.39 |
diff --git a/docs/zh_CN/ImageNet_models_cn.md b/docs/zh_CN/ImageNet_models_cn.md
index 3732959923540ac8169dead5b29d646d181e6fd0..c5b97cfa8f71cd5356782e793c30e840937661fc 100644
--- a/docs/zh_CN/ImageNet_models_cn.md
+++ b/docs/zh_CN/ImageNet_models_cn.md
@@ -31,9 +31,9 @@
| 模型 | Top-1 Acc | Reference
Top-1 Acc | Acc gain | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
| ResNet34_vd_ssld | 0.797 | 0.760 | 0.037 | 2.434 | 6.222 | 7.39 | 21.82 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet34_vd_ssld_pretrained.pdparams) |
-| ResNet50_vd_
ssld | 0.830 | 0.792 | 0.039 | 3.531 | 8.090 | 8.67 | 25.58 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) |
-| ResNet101_vd_
ssld | 0.837 | 0.802 | 0.035 | 6.117 | 13.762 | 16.1 | 44.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) |
-| Res2Net50_vd_
26w_4s_ssld | 0.831 | 0.798 | 0.033 | 4.527 | 9.657 | 8.37 | 25.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) |
+| ResNet50_vd_ssld | 0.830 | 0.792 | 0.039 | 3.531 | 8.090 | 8.67 | 25.58 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_vd_ssld_pretrained.pdparams) |
+| ResNet101_vd_ssld | 0.837 | 0.802 | 0.035 | 6.117 | 13.762 | 16.1 | 44.57 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet101_vd_ssld_pretrained.pdparams) |
+| Res2Net50_vd_26w_4s_ssld | 0.831 | 0.798 | 0.033 | 4.527 | 9.657 | 8.37 | 25.06 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net50_vd_26w_4s_ssld_pretrained.pdparams) |
| Res2Net101_vd_
26w_4s_ssld | 0.839 | 0.806 | 0.033 | 8.087 | 17.312 | 16.67 | 45.22 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net101_vd_26w_4s_ssld_pretrained.pdparams) |
| Res2Net200_vd_
26w_4s_ssld | 0.851 | 0.812 | 0.049 | 14.678 | 32.350 | 31.49 | 76.21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/Res2Net200_vd_26w_4s_ssld_pretrained.pdparams) |
| HRNet_W18_C_ssld | 0.812 | 0.769 | 0.043 | 7.406 | 13.297 | 4.14 | 21.29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/HRNet_W18_C_ssld_pretrained.pdparams) |
@@ -45,16 +45,44 @@
| 模型 | Top-1 Acc | Reference
Top-1 Acc | Acc gain | SD855 time(ms)
bs=1 | Flops(G) | Params(M) | 模型大小(M) | 下载地址 |
|---------------------|-----------|-----------|---------------|----------------|-----------|----------|-----------|-----------------------------------|
-| MobileNetV1_
ssld | 0.779 | 0.710 | 0.069 | 32.523 | 1.11 | 4.19 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) |
-| MobileNetV2_
ssld | 0.767 | 0.722 | 0.045 | 23.318 | 0.6 | 3.44 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) |
-| MobileNetV3_
small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.635 | 0.026 | 1.66 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) |
-| MobileNetV3_
large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 19.308 | 0.45 | 5.47 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) |
-| MobileNetV3_small_
x1_0_ssld | 0.713 | 0.682 | 0.031 | 6.546 | 0.123 | 2.94 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) |
-| GhostNet_
x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.983 | 0.44 | 7.3 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) |
+| MobileNetV1_ssld | 0.779 | 0.710 | 0.069 | 32.523 | 1.11 | 4.19 | 16 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV1_ssld_pretrained.pdparams) |
+| MobileNetV2_ssld | 0.767 | 0.722 | 0.045 | 23.318 | 0.6 | 3.44 | 14 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileNetV2_ssld_pretrained.pdparams) |
+| MobileNetV3_small_x0_35_ssld | 0.556 | 0.530 | 0.026 | 2.635 | 0.026 | 1.66 | 6.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x0_35_ssld_pretrained.pdparams) |
+| MobileNetV3_large_x1_0_ssld | 0.790 | 0.753 | 0.036 | 19.308 | 0.45 | 5.47 | 21 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_ssld_pretrained.pdparams) |
+| MobileNetV3_small_x1_0_ssld | 0.713 | 0.682 | 0.031 | 6.546 | 0.123 | 2.94 | 12 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_small_x1_0_ssld_pretrained.pdparams) |
+| GhostNet_x1_3_ssld | 0.794 | 0.757 | 0.037 | 19.983 | 0.44 | 7.3 | 29 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/GhostNet_x1_3_ssld_pretrained.pdparams) |
+
+
+* Intel CPU端知识蒸馏模型
+
+| 模型 | Top-1 Acc | Reference
Top-1 Acc | Acc gain | Intel-Xeon-Gold-6148 time(ms)
bs=1 | Flops(M) | Params(M) | 下载地址 |
+|---------------------|-----------|-----------|---------------|----------------|----------|-----------|-----------------------------------|
+| PPLCNet_x0_5_ssld | 0.661 | 0.631 | 0.030 | 2.05 | 47 | 1.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_ssld_pretrained.pdparams) |
+| PPLCNet_x1_0_ssld | 0.744 | 0.713 | 0.033 | 2.46 | 161 | 3.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_ssld_pretrained.pdparams) |
+| PPLCNet_x2_5_ssld | 0.808 | 0.766 | 0.042 | 5.39 | 906 | 9.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_ssld_pretrained.pdparams) |
+
+
* 注: `Reference Top-1 Acc`表示PaddleClas基于ImageNet1k数据集训练得到的预训练模型精度。
+
+### PPLCNet系列
+
+PPLCNet系列模型的精度、速度指标如下表所示，更多关于该系列的模型介绍可以参考：[PPLCNet系列模型文档](./models/PPLCNet.md)。
+
+| 模型 | Top-1 Acc | Top-5 Acc | Intel-Xeon-Gold-6148 time(ms)
bs=1 | FLOPs(M) | Params(M) | 下载地址 |
+|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
+| PPLCNet_x0_25 |0.5186 | 0.7565 | 1.74 | 18 | 1.5 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams) |
+| PPLCNet_x0_35 |0.5809 | 0.8083 | 1.92 | 29 | 1.6 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams) |
+| PPLCNet_x0_5 |0.6314 | 0.8466 | 2.05 | 47 | 1.9 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams) |
+| PPLCNet_x0_75 |0.6818 | 0.8830 | 2.29 | 99 | 2.4 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams) |
+| PPLCNet_x1_0 |0.7132 | 0.9003 | 2.46 | 161 | 3.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams) |
+| PPLCNet_x1_5 |0.7371 | 0.9153 | 3.19 | 342 | 4.5 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams) |
+| PPLCNet_x2_0 |0.7518 | 0.9227 | 4.27 | 590 | 6.5 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams) |
+| PPLCNet_x2_5 |0.7660 | 0.9300 | 5.39 | 906 | 9.0 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams) |
+
+
### ResNet及其Vd系列
@@ -429,7 +457,7 @@ ViT（Vision Transformer）与DeiT（Data-efficient Image Transformers）系列
| 模型 | Top-1 Acc | Top-5 Acc | time(ms)
bs=1 | time(ms)
bs=4 | Flops(G) | Params(M) | 下载地址 |
| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ |
-| TNT_small | 0.8121 |0.9563 | | | 5.2 | 23.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams) | |
+| TNT_small | 0.8121 |0.9563 | | | 5.2 | 23.8 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/TNT_small_pretrained.pdparams) | |
**注**：TNT模型的数据预处理部分`NormalizeImage`中的`mean`与`std`均为0.5。
diff --git a/docs/zh_CN/models/LCNet.md b/docs/zh_CN/models/LCNet.md
new file mode 100644
index 0000000000000000000000000000000000000000..3077085454b6b9e40db6df1eddd433f0ceefac2b
--- /dev/null
+++ b/docs/zh_CN/models/LCNet.md
@@ -0,0 +1,41 @@
+# PPLCNet系列
+
+## 概述
+
+PPLCNet系列是百度PaddleCV团队提出的一种在Intel-CPU上表现优异的网络，作者总结了一些在Intel-CPU上可以提升模型精度但几乎不增加推理耗时的方法，将这些方法组合成了一个新的网络，即PPLCNet。与其他轻量级网络相比，PPLCNet可以在相同延时下取得更高的精度。PPLCNet已在图像分类、目标检测、语义分割上表现出了强大的竞争力。
+
+
+
+## 精度、FLOPS和参数量
+
+| Models | Top1 | Top5 | FLOPs
(M) | Parameters
(M) |
+|:--:|:--:|:--:|:--:|:--:|
+| PPLCNet_x0_25 |0.5186 | 0.7565 | 18 | 1.5 |
+| PPLCNet_x0_35 |0.5809 | 0.8083 | 29 | 1.6 |
+| PPLCNet_x0_5 |0.6314 | 0.8466 | 47 | 1.9 |
+| PPLCNet_x0_75 |0.6818 | 0.8830 | 99 | 2.4 |
+| PPLCNet_x1_0 |0.7132 | 0.9003 | 161 | 3.0 |
+| PPLCNet_x1_5 |0.7371 | 0.9153 | 342 | 4.5 |
+| PPLCNet_x2_0 |0.7518 | 0.9227 | 590 | 6.5 |
+| PPLCNet_x2_5 |0.7660 | 0.9300 | 906 | 9.0 |
+| PPLCNet_x0_5_ssld |0.6610 | 0.8646 | 47 | 1.9 |
+| PPLCNet_x1_0_ssld |0.7439 | 0.9209 | 161 | 3.0 |
+| PPLCNet_x2_5_ssld |0.8082 | 0.9533 | 906 | 9.0 |
+
+
+
+## 基于Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz的预测速度
+
+| Models | Crop Size | Resize Short Size | FP32
Batch Size=1
(ms) |
+|------------------|-----------|-------------------|--------------------------|
+| PPLCNet_x0_25 | 224 | 256 | 1.74 |
+| PPLCNet_x0_35 | 224 | 256 | 1.92 |
+| PPLCNet_x0_5 | 224 | 256 | 2.05 |
+| PPLCNet_x0_75 | 224 | 256 | 2.29 |
+| PPLCNet_x1_0 | 224 | 256 | 2.46 |
+| PPLCNet_x1_5 | 224 | 256 | 3.19 |
+| PPLCNet_x2_0 | 224 | 256 | 4.27 |
+| PPLCNet_x2_5 | 224 | 256 | 5.39 |
+| PPLCNet_x0_5_ssld | 224 | 256 | 2.05 |
+| PPLCNet_x1_0_ssld | 224 | 256 | 2.46 |
+| PPLCNet_x2_5_ssld | 224 | 256 | 5.39 |
diff --git a/docs/zh_CN/models/PPLCNet.md b/docs/zh_CN/models/PPLCNet.md
new file mode 100644
index 0000000000000000000000000000000000000000..3077085454b6b9e40db6df1eddd433f0ceefac2b
--- /dev/null
+++ b/docs/zh_CN/models/PPLCNet.md
@@ -0,0 +1,41 @@
+# PPLCNet系列
+
+## 概述
+
+PPLCNet系列是百度PaddleCV团队提出的一种在Intel-CPU上表现优异的网络，作者总结了一些在Intel-CPU上可以提升模型精度但几乎不增加推理耗时的方法，将这些方法组合成了一个新的网络，即PPLCNet。与其他轻量级网络相比，PPLCNet可以在相同延时下取得更高的精度。PPLCNet已在图像分类、目标检测、语义分割上表现出了强大的竞争力。
+
+
+
+## 精度、FLOPS和参数量
+
+| Models | Top1 | Top5 | FLOPs
(M) | Parameters
(M) |
+|:--:|:--:|:--:|:--:|:--:|
+| PPLCNet_x0_25 |0.5186 | 0.7565 | 18 | 1.5 |
+| PPLCNet_x0_35 |0.5809 | 0.8083 | 29 | 1.6 |
+| PPLCNet_x0_5 |0.6314 | 0.8466 | 47 | 1.9 |
+| PPLCNet_x0_75 |0.6818 | 0.8830 | 99 | 2.4 |
+| PPLCNet_x1_0 |0.7132 | 0.9003 | 161 | 3.0 |
+| PPLCNet_x1_5 |0.7371 | 0.9153 | 342 | 4.5 |
+| PPLCNet_x2_0 |0.7518 | 0.9227 | 590 | 6.5 |
+| PPLCNet_x2_5 |0.7660 | 0.9300 | 906 | 9.0 |
+| PPLCNet_x0_5_ssld |0.6610 | 0.8646 | 47 | 1.9 |
+| PPLCNet_x1_0_ssld |0.7439 | 0.9209 | 161 | 3.0 |
+| PPLCNet_x2_5_ssld |0.8082 | 0.9533 | 906 | 9.0 |
+
+
+
+## 基于Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz的预测速度
+
+| Models | Crop Size | Resize Short Size | FP32
Batch Size=1
(ms) |
+|------------------|-----------|-------------------|--------------------------|
+| PPLCNet_x0_25 | 224 | 256 | 1.74 |
+| PPLCNet_x0_35 | 224 | 256 | 1.92 |
+| PPLCNet_x0_5 | 224 | 256 | 2.05 |
+| PPLCNet_x0_75 | 224 | 256 | 2.29 |
+| PPLCNet_x1_0 | 224 | 256 | 2.46 |
+| PPLCNet_x1_5 | 224 | 256 | 3.19 |
+| PPLCNet_x2_0 | 224 | 256 | 4.27 |
+| PPLCNet_x2_5 | 224 | 256 | 5.39 |
+| PPLCNet_x0_5_ssld | 224 | 256 | 2.05 |
+| PPLCNet_x1_0_ssld | 224 | 256 | 2.46 |
+| PPLCNet_x2_5_ssld | 224 | 256 | 5.39 |
diff --git a/ppcls/arch/backbone/__init__.py b/ppcls/arch/backbone/__init__.py
index a1727613a6eef3f167d74f371bf4f25639c85b95..d2efcdc073a91a1571d528d7cb414d14fb251f6b 100644
--- a/ppcls/arch/backbone/__init__.py
+++ b/ppcls/arch/backbone/__init__.py
@@ -21,6 +21,7 @@ from ppcls.arch.backbone.legendary_models.resnet import ResNet18, ResNet18_vd, R
from ppcls.arch.backbone.legendary_models.vgg import VGG11, VGG13, VGG16, VGG19
from ppcls.arch.backbone.legendary_models.inception_v3 import InceptionV3
from ppcls.arch.backbone.legendary_models.hrnet import HRNet_W18_C, HRNet_W30_C, HRNet_W32_C, HRNet_W40_C, HRNet_W44_C, HRNet_W48_C, HRNet_W60_C, HRNet_W64_C, SE_HRNet_W64_C
+from ppcls.arch.backbone.legendary_models.pp_lcnet import PPLCNet_x0_25, PPLCNet_x0_35, PPLCNet_x0_5, PPLCNet_x0_75, PPLCNet_x1_0, PPLCNet_x1_5, PPLCNet_x2_0, PPLCNet_x2_5
from ppcls.arch.backbone.model_zoo.resnet_vc import ResNet50_vc
from ppcls.arch.backbone.model_zoo.resnext import ResNeXt50_32x4d, ResNeXt50_64x4d, ResNeXt101_32x4d, ResNeXt101_64x4d, ResNeXt152_32x4d, ResNeXt152_64x4d
diff --git a/ppcls/arch/backbone/legendary_models/pp_lcnet.py b/ppcls/arch/backbone/legendary_models/pp_lcnet.py
new file mode 100644
index 0000000000000000000000000000000000000000..05bbccd3554f5c32f993619035298581f3e669cd
--- /dev/null
+++ b/ppcls/arch/backbone/legendary_models/pp_lcnet.py
@@ -0,0 +1,399 @@
+# copyright (c) 2021 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, division, print_function
+
+import paddle
+import paddle.nn as nn
+from paddle import ParamAttr
+from paddle.nn import AdaptiveAvgPool2D, BatchNorm, Conv2D, Dropout, Linear
+from paddle.regularizer import L2Decay
+from paddle.nn.initializer import KaimingNormal
+from ppcls.arch.backbone.base.theseus_layer import TheseusLayer
+from ppcls.utils.save_load import load_dygraph_pretrain, load_dygraph_pretrain_from_url
+
+MODEL_URLS = {
+ "PPLCNet_x0_25":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams",
+ "PPLCNet_x0_35":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams",
+ "PPLCNet_x0_5":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams",
+ "PPLCNet_x0_75":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams",
+ "PPLCNet_x1_0":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams",
+ "PPLCNet_x1_5":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams",
+ "PPLCNet_x2_0":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams",
+ "PPLCNet_x2_5":
+ "https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams"
+}
+
+__all__ = list(MODEL_URLS.keys())
+
+# Each element(list) represents a depthwise block, which is composed of k, in_c, out_c, s, use_se.
+# k: kernel_size
+# in_c: input channel number in depthwise block
+# out_c: output channel number in depthwise block
+# s: stride in depthwise block
+# use_se: whether to use SE block
+
+NET_CONFIG = {
+ "blocks2":
+ #k, in_c, out_c, s, use_se
+ [[3, 16, 32, 1, False]],
+ "blocks3": [[3, 32, 64, 2, False], [3, 64, 64, 1, False]],
+ "blocks4": [[3, 64, 128, 2, False], [3, 128, 128, 1, False]],
+ "blocks5": [[3, 128, 256, 2, False], [5, 256, 256, 1, False],
+ [5, 256, 256, 1, False], [5, 256, 256, 1, False],
+ [5, 256, 256, 1, False], [5, 256, 256, 1, False]],
+ "blocks6": [[5, 256, 512, 2, True], [5, 512, 512, 1, True]]
+}
+
+
+def make_divisible(v, divisor=8, min_value=None):
+ if min_value is None:
+ min_value = divisor
+ new_v = max(min_value, int(v + divisor / 2) // divisor * divisor)
+ if new_v < 0.9 * v:
+ new_v += divisor
+ return new_v
+
+
+class ConvBNLayer(TheseusLayer):
+ def __init__(self,
+ num_channels,
+ filter_size,
+ num_filters,
+ stride,
+ num_groups=1):
+ super().__init__()
+
+ self.conv = Conv2D(
+ in_channels=num_channels,
+ out_channels=num_filters,
+ kernel_size=filter_size,
+ stride=stride,
+ padding=(filter_size - 1) // 2,
+ groups=num_groups,
+ weight_attr=ParamAttr(initializer=KaimingNormal()),
+ bias_attr=False)
+
+ self.bn = BatchNorm(
+ num_filters,
+ param_attr=ParamAttr(regularizer=L2Decay(0.0)),
+ bias_attr=ParamAttr(regularizer=L2Decay(0.0)))
+ self.hardswish = nn.Hardswish()
+
+ def forward(self, x):
+ x = self.conv(x)
+ x = self.bn(x)
+ x = self.hardswish(x)
+ return x
+
+
+class DepthwiseSeparable(TheseusLayer):
+ def __init__(self,
+ num_channels,
+ num_filters,
+ stride,
+ dw_size=3,
+ use_se=False):
+ super().__init__()
+ self.use_se = use_se
+ self.dw_conv = ConvBNLayer(
+ num_channels=num_channels,
+ num_filters=num_channels,
+ filter_size=dw_size,
+ stride=stride,
+ num_groups=num_channels)
+ if use_se:
+ self.se = SEModule(num_channels)
+ self.pw_conv = ConvBNLayer(
+ num_channels=num_channels,
+ filter_size=1,
+ num_filters=num_filters,
+ stride=1)
+
+ def forward(self, x):
+ x = self.dw_conv(x)
+ if self.use_se:
+ x = self.se(x)
+ x = self.pw_conv(x)
+ return x
+
+
+class SEModule(TheseusLayer):
+ def __init__(self, channel, reduction=4):
+ super().__init__()
+ self.avg_pool = AdaptiveAvgPool2D(1)
+ self.conv1 = Conv2D(
+ in_channels=channel,
+ out_channels=channel // reduction,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.relu = nn.ReLU()
+ self.conv2 = Conv2D(
+ in_channels=channel // reduction,
+ out_channels=channel,
+ kernel_size=1,
+ stride=1,
+ padding=0)
+ self.hardsigmoid = nn.Hardsigmoid()
+
+ def forward(self, x):
+ identity = x
+ x = self.avg_pool(x)
+ x = self.conv1(x)
+ x = self.relu(x)
+ x = self.conv2(x)
+ x = self.hardsigmoid(x)
+ x = paddle.multiply(x=identity, y=x)
+ return x
+
+
+class PPLCNet(TheseusLayer):
+ def __init__(self,
+ scale=1.0,
+ class_num=1000,
+ dropout_prob=0.2,
+ class_expand=1280):
+ super().__init__()
+ self.scale = scale
+ self.class_expand = class_expand
+
+ self.conv1 = ConvBNLayer(
+ num_channels=3,
+ filter_size=3,
+ num_filters=make_divisible(16 * scale),
+ stride=2)
+
+ self.blocks2 = nn.Sequential(*[
+ DepthwiseSeparable(
+ num_channels=make_divisible(in_c * scale),
+ num_filters=make_divisible(out_c * scale),
+ dw_size=k,
+ stride=s,
+ use_se=se)
+ for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks2"])
+ ])
+
+ self.blocks3 = nn.Sequential(*[
+ DepthwiseSeparable(
+ num_channels=make_divisible(in_c * scale),
+ num_filters=make_divisible(out_c * scale),
+ dw_size=k,
+ stride=s,
+ use_se=se)
+ for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks3"])
+ ])
+
+ self.blocks4 = nn.Sequential(*[
+ DepthwiseSeparable(
+ num_channels=make_divisible(in_c * scale),
+ num_filters=make_divisible(out_c * scale),
+ dw_size=k,
+ stride=s,
+ use_se=se)
+ for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks4"])
+ ])
+
+ self.blocks5 = nn.Sequential(*[
+ DepthwiseSeparable(
+ num_channels=make_divisible(in_c * scale),
+ num_filters=make_divisible(out_c * scale),
+ dw_size=k,
+ stride=s,
+ use_se=se)
+ for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks5"])
+ ])
+
+ self.blocks6 = nn.Sequential(*[
+ DepthwiseSeparable(
+ num_channels=make_divisible(in_c * scale),
+ num_filters=make_divisible(out_c * scale),
+ dw_size=k,
+ stride=s,
+ use_se=se)
+ for i, (k, in_c, out_c, s, se) in enumerate(NET_CONFIG["blocks6"])
+ ])
+
+ self.avg_pool = AdaptiveAvgPool2D(1)
+
+ self.last_conv = Conv2D(
+ in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] * scale),
+ out_channels=self.class_expand,
+ kernel_size=1,
+ stride=1,
+ padding=0,
+ bias_attr=False)
+
+ self.hardswish = nn.Hardswish()
+ self.dropout = Dropout(p=dropout_prob, mode="downscale_in_infer")
+ self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
+
+ self.fc = Linear(self.class_expand, class_num)
+
+ def forward(self, x):
+ x = self.conv1(x)
+
+ x = self.blocks2(x)
+ x = self.blocks3(x)
+ x = self.blocks4(x)
+ x = self.blocks5(x)
+ x = self.blocks6(x)
+
+ x = self.avg_pool(x)
+ x = self.last_conv(x)
+ x = self.hardswish(x)
+ x = self.dropout(x)
+ x = self.flatten(x)
+ x = self.fc(x)
+ return x
+
+
+def _load_pretrained(pretrained, model, model_url, use_ssld):
+ if pretrained is False:
+ pass
+ elif pretrained is True:
+ load_dygraph_pretrain_from_url(model, model_url, use_ssld=use_ssld)
+ elif isinstance(pretrained, str):
+ load_dygraph_pretrain(model, pretrained)
+ else:
+ raise RuntimeError(
+ "pretrained type is not available. Please use `string` or `boolean` type."
+ )
+
+
+def PPLCNet_x0_25(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x0_25
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x0_25` model depends on args.
+ """
+ model = PPLCNet(scale=0.25, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_25"], use_ssld)
+ return model
+
+
+def PPLCNet_x0_35(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x0_35
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x0_35` model depends on args.
+ """
+ model = PPLCNet(scale=0.35, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_35"], use_ssld)
+ return model
+
+
+def PPLCNet_x0_5(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x0_5
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x0_5` model depends on args.
+ """
+ model = PPLCNet(scale=0.5, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_5"], use_ssld)
+ return model
+
+
+def PPLCNet_x0_75(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x0_75
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x0_75` model depends on args.
+ """
+ model = PPLCNet(scale=0.75, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x0_75"], use_ssld)
+ return model
+
+
+def PPLCNet_x1_0(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x1_0
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x1_0` model depends on args.
+ """
+ model = PPLCNet(scale=1.0, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x1_0"], use_ssld)
+ return model
+
+
+def PPLCNet_x1_5(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x1_5
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x1_5` model depends on args.
+ """
+ model = PPLCNet(scale=1.5, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x1_5"], use_ssld)
+ return model
+
+
+def PPLCNet_x2_0(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x2_0
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x2_0` model depends on args.
+ """
+ model = PPLCNet(scale=2.0, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x2_0"], use_ssld)
+ return model
+
+
+def PPLCNet_x2_5(pretrained=False, use_ssld=False, **kwargs):
+ """
+ PPLCNet_x2_5
+ Args:
+ pretrained: bool=False or str. If `True` load pretrained parameters, `False` otherwise.
+ If str, means the path of the pretrained model.
+ use_ssld: bool=False. Whether using distillation pretrained model when pretrained=True.
+ Returns:
+ model: nn.Layer. Specific `PPLCNet_x2_5` model depends on args.
+ """
+ model = PPLCNet(scale=2.5, **kwargs)
+ _load_pretrained(pretrained, model, MODEL_URLS["PPLCNet_x2_5"], use_ssld)
+ return model
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..6773af797e05013eea1bf676876e91ebc1322fee
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x0_25
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..36aa3dcb0b269b456550aa8dce6cb94866661303
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x0_35
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..3ffc493851076f8ddd692585de966647a5bc3f57
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x0_5
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..24ad8f3733b2b4fca7f43fc142f539c4d6d31520
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x0_75
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..0921db540c26caf075d57cb520397a08c932f49b
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x1_0
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00003
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..290d1c0e69812d43d76167c48ee6d5357baa2b96
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x1_5
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00004
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..c8f7a3cdc40c0d6a307c30b2167588e02ea892d7
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
@@ -0,0 +1,129 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x2_0
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00004
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]
diff --git a/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
new file mode 100644
index 0000000000000000000000000000000000000000..2ea69e30d0a45385cb6113418c4fc244055de2ce
--- /dev/null
+++ b/ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
@@ -0,0 +1,130 @@
+# global configs
+Global:
+ checkpoints: null
+ pretrained_model: null
+ output_dir: ./output/
+ device: gpu
+ class_num: 1000
+ save_interval: 1
+ eval_during_train: True
+ eval_interval: 1
+ epochs: 360
+ print_batch_step: 10
+ use_visualdl: False
+ # used for static mode and model export
+ image_shape: [3, 224, 224]
+ save_inference_dir: ./inference
+# model architecture
+Arch:
+ name: PPLCNet_x2_5
+
+# loss function config for traing/eval process
+Loss:
+ Train:
+ - CELoss:
+ weight: 1.0
+ epsilon: 0.1
+ Eval:
+ - CELoss:
+ weight: 1.0
+
+
+Optimizer:
+ name: Momentum
+ momentum: 0.9
+ lr:
+ name: Cosine
+ learning_rate: 0.8
+ warmup_epoch: 5
+ regularizer:
+ name: 'L2'
+ coeff: 0.00004
+
+
+# data loader for train and eval
+DataLoader:
+ Train:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/train_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - RandCropImage:
+ size: 224
+ - RandFlipImage:
+ flip_code: 1
+ - AutoAugment:
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 512
+ drop_last: False
+ shuffle: True
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+ Eval:
+ dataset:
+ name: ImageNetDataset
+ image_root: ./dataset/ILSVRC2012/
+ cls_label_path: ./dataset/ILSVRC2012/val_list.txt
+ transform_ops:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ sampler:
+ name: DistributedBatchSampler
+ batch_size: 64
+ drop_last: False
+ shuffle: False
+ loader:
+ num_workers: 4
+ use_shared_memory: True
+
+Infer:
+ infer_imgs: docs/images/whl/demo.jpg
+ batch_size: 10
+ transforms:
+ - DecodeImage:
+ to_rgb: True
+ channel_first: False
+ - ResizeImage:
+ resize_short: 256
+ - CropImage:
+ size: 224
+ - NormalizeImage:
+ scale: 1.0/255.0
+ mean: [0.485, 0.456, 0.406]
+ std: [0.229, 0.224, 0.225]
+ order: ''
+ - ToCHWImage:
+ PostProcess:
+ name: Topk
+ topk: 5
+ class_id_map_file: ppcls/utils/imagenet1k_label_list.txt
+
+Metric:
+ Train:
+ - TopkAcc:
+ topk: [1, 5]
+ Eval:
+ - TopkAcc:
+ topk: [1, 5]