cuicheng01
GitHub
commit
e8b99c7597
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@ -41,6 +41,8 @@ def main():
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'inference.pdmodel')) and os.path.exists(
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os.path.join(config["Global"]["save_inference_dir"],
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'inference.pdiparams'))
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if "Query" in config["DataLoader"]["Eval"]:
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config["DataLoader"]["Eval"] = config["DataLoader"]["Eval"]["Query"]
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config["DataLoader"]["Eval"]["sampler"]["batch_size"] = 1
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config["DataLoader"]["Eval"]["loader"]["num_workers"] = 0
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@ -541,9 +541,9 @@ The accuracy and speed indicators of MobileViT series models are shown in the fo
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| Model | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | Pretrained Model Download Address | Inference Model Download Address |
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| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| MobileViT_XXS | 0.6867 | 0.8878 | - | - | - | 1849.35 | 5.59 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
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| MobileViT_XXS | 0.6867 | 0.8878 | - | - | - | 337.24 | 1.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
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| MobileViT_XS | 0.7454 | 0.9227 | - | - | - | 930.75 | 2.33 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XS_infer.tar) |
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| MobileViT_S | 0.7814 | 0.9413 | - | - | - | 337.24 | 1.28 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
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| MobileViT_S | 0.7814 | 0.9413 | - | - | - | 1849.35 | 5.59 | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [Download link](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
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<a name="26"></a>
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@ -18,6 +18,6 @@ MobileViT is a lightweight visual Transformer network that can be used as a gene
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPs<br>(M) | Params<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| MobileViT_XXS | 0.6867 | 0.8878 | 0.690 | - | 1849.35 | 5.59 |
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| MobileViT_XXS | 0.6867 | 0.8878 | 0.690 | - | 337.24 | 1.28 |
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| MobileViT_XS | 0.7454 | 0.9227 | 0.747 | - | 930.75 | 2.33 |
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| MobileViT_S | 0.7814 | 0.9413 | 0.783 | - | 337.24 | 1.28 |
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| MobileViT_S | 0.7814 | 0.9413 | 0.783 | - | 1849.35 | 5.59 |
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Binary file not shown.
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Before Width: | Height: | Size: 104 KiB After Width: | Height: | Size: 406 KiB |
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@ -58,7 +58,7 @@
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从表中可以看出，backbone 为 Res2Net200_vd_26w_4s 时精度较高，但是推理速度较慢。将 backbone 替换为轻量级模型 MobileNetV3_small_x0_35 后，速度可以大幅提升，但是精度下降明显。将 backbone 替换为 PPLCNet_x1_0 时，精度提升 2 个百分点，同时速度也提升 23% 左右。在此基础上，使用 SSLD 预训练模型后，在不改变推理速度的前提下，精度可以提升约 0.5 个百分点，进一步地，当融合EDA策略后，精度可以再提升 0.52 个百分点，最后，在使用 SKL-UGI 知识蒸馏后，精度可以继续提升 0.23 个百分点。此时，PPLCNet_x1_0 的精度与 Res2Net200_vd_26w_4s 仅相差 0.55 个百分点，但是速度快 32 倍。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。
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**备注：**
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* 延时是基于 Intel(R) Xeon(R) Gold 6148 CPU @ 2.40GHz 测试得到，开启 MKLDNN 加速策略，线程数为10。
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* 关于PP-LCNet的介绍可以参考[PP-LCNet介绍](../models/PP-LCNet.md)，相关论文可以查阅[PP-LCNet paper](https://arxiv.org/abs/2109.15099)。
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@ -178,7 +178,7 @@ from xml.dom.minidom import parse
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vehicleids = []
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def convert_annotation(input_fp, output_fp):
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def convert_annotation(input_fp, output_fp, subdir):
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in_file = open(input_fp)
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list_file = open(output_fp, 'w')
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tree = parse(in_file)
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@ -201,12 +201,12 @@ def convert_annotation(input_fp, output_fp):
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typeid = int (item.getAttribute("typeID"))
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label[typeid+9] = '1'
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label = ','.join(label)
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list_file.write(os.path.join('image_train', name) + "\t" + label + "\n")
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list_file.write(os.path.join(subdir, name) + "\t" + label + "\n")
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list_file.close()
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convert_annotation('train_label.xml', 'train_list.txt') #imagename vehiclenum colorid typeid
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convert_annotation('test_label.xml', 'test_list.txt')
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convert_annotation('train_label.xml', 'train_list.txt', 'image_train') #imagename vehiclenum colorid typeid
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convert_annotation('test_label.xml', 'test_list.txt', 'image_test')
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```
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执行上述命令后，`VeRi`目录中具有以下数据：
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@ -568,9 +568,9 @@ ViT(Vision Transformer) 与 DeiT（Data-efficient Image Transformers）系列模
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| 模型 | Top-1 Acc | Top-5 Acc | time(ms)<br>bs=1 | time(ms)<br>bs=4 | time(ms)<br/>bs=8 | FLOPs(M) | Params(M) | 预训练模型下载地址 | inference模型下载地址 |
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| ---------- | --------- | --------- | ---------------- | ---------------- | -------- | --------- | ------------------------------------------------------------ | ------------------------------------------------------------ | ------------------------------------------------------------ |
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| MobileViT_XXS | 0.6867 | 0.8878 | - | - | - | 1849.35 | 5.59 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
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| MobileViT_XXS | 0.6867 | 0.8878 | - | - | - | 337.24 | 1.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XXS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XXS_infer.tar) |
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| MobileViT_XS | 0.7454 | 0.9227 | - | - | - | 930.75 | 2.33 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_XS_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_XS_infer.tar) |
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| MobileViT_S | 0.7814 | 0.9413 | - | - | - | 337.24 | 1.28 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
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| MobileViT_S | 0.7814 | 0.9413 | - | - | - | 1849.35 | 5.59 | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/MobileViT_S_pretrained.pdparams) | [下载链接](https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileViT_S_infer.tar) |
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<a name="Others"></a>
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@ -17,6 +17,6 @@ MobileViT 是一个轻量级的视觉 Transformer 网络，可以用作计算机
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| Models | Top1 | Top5 | Reference<br>top1 | Reference<br>top5 | FLOPs<br>(M) | Params<br>(M) |
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|:--:|:--:|:--:|:--:|:--:|:--:|:--:|
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| MobileViT_XXS | 0.6867 | 0.8878 | 0.690 | - | 1849.35 | 5.59 |
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| MobileViT_XXS | 0.6867 | 0.8878 | 0.690 | - | 337.24 | 1.28 |
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| MobileViT_XS | 0.7454 | 0.9227 | 0.747 | - | 930.75 | 2.33 |
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| MobileViT_S | 0.7814 | 0.9413 | 0.783 | - | 337.24 | 1.28 |
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| MobileViT_S | 0.7814 | 0.9413 | 0.783 | - | 1849.35 | 5.59 |
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@ -5,8 +5,9 @@
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- [1.2 模型细节](#1.2)
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- [1.3 实验结果](#1.3)
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- [2. 模型快速体验](#2)
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- [2.1 安装 paddleclas](#2.1)
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- [2.2 预测](#2.2)
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- [2.1 安装 paddlepaddle](#2.1)
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- [2.2 安装 paddleclas](#2.2)
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- [2.3 预测](#2.3)
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- [3. 模型训练、评估和预测](#3)
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- [3.1 环境配置](#3.1)
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- [3.2 数据准备](#3.2)
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@ -88,24 +89,43 @@ PP-HGNet 与其他模型的比较如下，其中测试机器为 NVIDIA® Tesla®
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| ResNeXt101_32x48d_wsl | 85.37 | 97.69 | 55.07 |
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| SwinTransformer_base | 85.2 | 97.5 | 13.53 |
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| <b>PPHGNet_base_ssld<b> | <b>85.00<b>| <b>97.35<b> | <b>5.97<b> |
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<a name="2"></a>
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<a name="2"></a>
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## 2. 模型快速体验
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<a name="2.1"></a>
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### 2.1 安装 paddleclas
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使用如下命令快速安装 paddlepaddle, paddleclas
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```
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pip3 install paddlepaddle paddleclas
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<a name="2.1"></a>
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### 2.1 安装 paddlepaddle
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- 您的机器安装的是 CUDA9 或 CUDA10，请运行以下命令安装
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```bash
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python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
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```
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<a name="2.2"></a>
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- 您的机器是CPU，请运行以下命令安装
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```bash
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python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
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```
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更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
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<a name="2.2"></a>
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### 2.2 安装 paddleclas
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使用如下命令快速安装 paddleclas
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```
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pip3 install paddleclas
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```
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<a name="2.3"></a>
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### 2.2 预测
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### 2.3 预测
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* 在命令行中使用 PPHGNet_small 的权重快速预测
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@ -16,8 +16,9 @@
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- [1.4.2 基于 V100 GPU 的预测速度](#1.4.2)
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- [1.4.3 基于 SD855 的预测速度](#1.4.3)
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- [2. 模型快速体验](#2)
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- [2.1 安装 paddleclas](#2.1)
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- [2.2 预测](#2.2)
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- [2.1 安装 paddlepaddle](#2.1)
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- [2.2 安装 paddleclas](#2.2)
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- [2.3 预测](#2.3)
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- [3. 模型训练、评估和预测](#3)
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- [3.1 环境配置](#3.1)
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- [3.2 数据准备](#3.2)
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@ -240,16 +241,35 @@ MobileNetV3_large_x0_75 | 64.53 | 151 |
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<a name="2.1"></a>
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### 2.1 安装 paddleclas
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使用如下命令快速安装 paddlepaddle, paddleclas
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```
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pip3 install paddlepaddle paddleclas
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### 2.1 安装 paddlepaddle
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- 您的机器安装的是 CUDA9 或 CUDA10，请运行以下命令安装
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```bash
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python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
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```
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- 您的机器是CPU，请运行以下命令安装
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```bash
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python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
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```
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更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
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<a name="2.2"></a>
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### 2.2 安装 paddleclas
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使用如下命令快速安装 paddleclas
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```
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pip3 install paddleclas
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```
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<a name="2.2"></a>
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### 2.2 预测
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<a name="2.3"></a>
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### 2.3 预测
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* 在命令行中使用 PPLCNet_x1_0 的权重快速预测
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@ -14,8 +14,9 @@
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- [1.2.5 SE 模块](#1.2.5)
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- [1.3 实验结果](#1.3)
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- [2. 模型快速体验](#2)
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- [2.1 安装 paddleclas](#2.1)
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- [2.2 预测](#2.2)
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- [2.1 安装 paddlepaddle](#2.1)
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- [2.2 安装 paddleclas](#2.2)
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- [2.3 预测](#2.3)
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- [3. 模型训练、评估和预测](#3)
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- [3.1 环境配置](#3.1)
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- [3.2 数据准备](#3.2)
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@ -114,22 +115,41 @@ PPLCNetV2 目前提供的模型的精度、速度指标及预训练权重链接
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| <b>PPLCNetV2_base_ssld<b> | <b>6.6<b> | <b>604<b> | <b>80.07<b> | <b>94.87<b> | <b>4.32<b> |
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<a name="2"></a>
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<a name="2"></a>
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## 2. 模型快速体验
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<a name="2.1"></a>
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### 2.1 安装 paddleclas
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使用如下命令快速安装 paddlepaddle, paddleclas
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```
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pip3 install paddlepaddle paddleclas
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### 2.1 安装 paddlepaddle
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- 您的机器安装的是 CUDA9 或 CUDA10，请运行以下命令安装
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```bash
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python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
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```
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||||
|
||||
- 您的机器是CPU，请运行以下命令安装
|
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|
||||
```bash
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python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
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```
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更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
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<a name="2.2"></a>
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### 2.2 安装 paddleclas
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使用如下命令快速安装 paddleclas
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```
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pip3 install paddleclas
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```
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<a name="2.2"></a>
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### 2.2 预测
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<a name="2.3"></a>
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### 2.3 预测
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* 在命令行中使用 PPLCNetV2_base 的权重快速预测
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@ -9,8 +9,9 @@
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- [1.3.1 基于 V100 GPU 的预测速度](#1.3.1)
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- [1.3.2 基于 T4 GPU 的预测速度](#1.3.2)
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- [2. 模型快速体验](#2)
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- [2.1 安装 paddleclas](#2.1)
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- [2.2 预测](#2.2)
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- [2.1 安装 paddlepaddle](#2.1)
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- [2.2 安装 paddleclas](#2.2)
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- [2.3 预测](#2.3)
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- [3. 模型训练、评估和预测](#3)
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- [3.1 环境配置](#3.1)
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- [3.2 数据准备](#3.2)
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@ -131,16 +132,34 @@ PaddleClas 提供的 ResNet 系列的模型包括 ResNet50，ResNet50_vd，ResNe
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<a name="2.1"></a>
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### 2.1 安装 paddleclas
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|
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使用如下命令快速安装 paddlepaddle, paddleclas
|
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|
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```
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pip3 install paddlepaddle paddleclas
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### 2.1 安装 paddlepaddle
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||||
|
||||
- 您的机器安装的是 CUDA9 或 CUDA10，请运行以下命令安装
|
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|
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```bash
|
||||
python3 -m pip install paddlepaddle-gpu -i https://mirror.baidu.com/pypi/simple
|
||||
```
|
||||
<a name="2.2"></a>
|
||||
|
||||
- 您的机器是CPU，请运行以下命令安装
|
||||
|
||||
```bash
|
||||
python3 -m pip install paddlepaddle -i https://mirror.baidu.com/pypi/simple
|
||||
```
|
||||
|
||||
更多的版本需求，请参照[飞桨官网安装文档](https://www.paddlepaddle.org.cn/install/quick)中的说明进行操作。
|
||||
|
||||
<a name="2.2"></a>
|
||||
|
||||
### 2.2 安装 paddleclas
|
||||
|
||||
使用如下命令快速安装 paddleclas
|
||||
|
||||
```
|
||||
pip3 install paddleclas
|
||||
```
|
||||
<a name="2.3"></a>
|
||||
|
||||
### 2.2 预测
|
||||
### 2.3 预测
|
||||
|
||||
* 在命令行中使用 ResNet50 的权重快速预测
|
||||
|
||||
|
|
|
|||
|
|
@ -340,6 +340,7 @@ def print_info():
|
|||
first_width = 30
|
||||
second_width = total_width - first_width if total_width > 50 else 10
|
||||
except OSError:
|
||||
total_width = 100
|
||||
second_width = 100
|
||||
for series in IMN_MODEL_SERIES:
|
||||
names = textwrap.fill(
|
||||
|
|
@ -452,7 +453,9 @@ class PaddleClas(object):
|
|||
"""PaddleClas.
|
||||
"""
|
||||
|
||||
print_info()
|
||||
if not os.environ.get('ppcls', False):
|
||||
os.environ.setdefault('ppcls', 'True')
|
||||
print_info()
|
||||
|
||||
def __init__(self,
|
||||
model_name: str=None,
|
||||
|
|
|
|||
|
|
@ -62,7 +62,7 @@ def drop_path(x, drop_prob=0., training=False):
|
|||
return x
|
||||
keep_prob = paddle.to_tensor(1 - drop_prob)
|
||||
shape = (paddle.shape(x)[0], ) + (1, ) * (x.ndim - 1)
|
||||
random_tensor = keep_prob + paddle.rand(shape, dtype=x.dtype)
|
||||
random_tensor = keep_prob + paddle.rand(shape).astype(x.dtype)
|
||||
random_tensor = paddle.floor(random_tensor) # binarize
|
||||
output = x.divide(keep_prob) * random_tensor
|
||||
return output
|
||||
|
|
|
|||
|
|
@ -48,6 +48,12 @@ def quantize_model(config, model, mode="train"):
|
|||
QUANT_CONFIG["activation_preprocess_type"] = "PACT"
|
||||
if mode in ["infer", "export"]:
|
||||
QUANT_CONFIG['activation_preprocess_type'] = None
|
||||
|
||||
# for rep nets, convert to reparameterized model first
|
||||
for layer in model.sublayers():
|
||||
if hasattr(layer, "rep"):
|
||||
layer.rep()
|
||||
|
||||
model.quanter = QAT(config=QUANT_CONFIG)
|
||||
model.quanter.quantize(model)
|
||||
logger.info("QAT model summary:")
|
||||
|
|
|
|||
|
|
@ -430,7 +430,7 @@ class RandCropImageV2(object):
|
|||
|
||||
def __call__(self, img):
|
||||
if isinstance(img, np.ndarray):
|
||||
img_h, img_w = img.shap[0], img.shap[1]
|
||||
img_h, img_w = img.shape[0], img.shape[1]
|
||||
else:
|
||||
img_w, img_h = img.size
|
||||
tw, th = self.size
|
||||
|
|
|
|||
|
|
@ -466,7 +466,7 @@ class Engine(object):
|
|||
|
||||
# for rep nets
|
||||
for layer in self.model.sublayers():
|
||||
if hasattr(layer, "rep"):
|
||||
if hasattr(layer, "rep") and not getattr(layer, "is_repped"):
|
||||
layer.rep()
|
||||
|
||||
save_path = os.path.join(self.config["Global"]["save_inference_dir"],
|
||||
|
|
|
|||
|
|
@ -159,7 +159,15 @@ def cal_feature(engine, name='gallery'):
|
|||
if len(batch) == 3:
|
||||
has_unique_id = True
|
||||
batch[2] = batch[2].reshape([-1, 1]).astype("int64")
|
||||
out = engine.model(batch[0], batch[1])
|
||||
if engine.amp and engine.amp_eval:
|
||||
with paddle.amp.auto_cast(
|
||||
custom_black_list={
|
||||
"flatten_contiguous_range", "greater_than"
|
||||
},
|
||||
level=engine.amp_level):
|
||||
out = engine.model(batch[0], batch[1])
|
||||
else:
|
||||
out = engine.model(batch[0], batch[1])
|
||||
if "Student" in out:
|
||||
out = out["Student"]
|
||||
|
||||
|
|
|
|||
|
|
@ -236,8 +236,13 @@ class DistillationDKDLoss(DKDLoss):
|
|||
temperature=1.0,
|
||||
alpha=1.0,
|
||||
beta=1.0,
|
||||
use_target_as_gt=False,
|
||||
name="loss_dkd"):
|
||||
super().__init__(temperature=temperature, alpha=alpha, beta=beta)
|
||||
super().__init__(
|
||||
temperature=temperature,
|
||||
alpha=alpha,
|
||||
beta=beta,
|
||||
use_target_as_gt=use_target_as_gt)
|
||||
self.key = key
|
||||
self.model_name_pairs = model_name_pairs
|
||||
self.name = name
|
||||
|
|
|
|||
|
|
@ -10,13 +10,20 @@ class DKDLoss(nn.Layer):
|
|||
Code was heavily based on https://github.com/megvii-research/mdistiller
|
||||
"""
|
||||
|
||||
def __init__(self, temperature=1.0, alpha=1.0, beta=1.0):
|
||||
def __init__(self,
|
||||
temperature=1.0,
|
||||
alpha=1.0,
|
||||
beta=1.0,
|
||||
use_target_as_gt=False):
|
||||
super().__init__()
|
||||
self.temperature = temperature
|
||||
self.alpha = alpha
|
||||
self.beta = beta
|
||||
self.use_target_as_gt = use_target_as_gt
|
||||
|
||||
def forward(self, logits_student, logits_teacher, target):
|
||||
def forward(self, logits_student, logits_teacher, target=None):
|
||||
if target is None or self.use_target_as_gt:
|
||||
target = logits_teacher.argmax(axis=-1)
|
||||
gt_mask = _get_gt_mask(logits_student, target)
|
||||
other_mask = 1 - gt_mask
|
||||
pred_student = F.softmax(logits_student / self.temperature, axis=1)
|
||||
|
|
|
|||
|
|
@ -16,9 +16,9 @@ from __future__ import absolute_import
|
|||
from __future__ import division
|
||||
from __future__ import print_function
|
||||
|
||||
from paddle import optimizer as optim
|
||||
import paddle
|
||||
import inspect
|
||||
|
||||
from paddle import optimizer as optim
|
||||
from ppcls.utils import logger
|
||||
|
||||
|
||||
|
|
@ -49,21 +49,32 @@ class SGD(object):
|
|||
learning_rate=0.001,
|
||||
weight_decay=None,
|
||||
grad_clip=None,
|
||||
multi_precision=False,
|
||||
name=None):
|
||||
self.learning_rate = learning_rate
|
||||
self.weight_decay = weight_decay
|
||||
self.grad_clip = grad_clip
|
||||
self.multi_precision = multi_precision
|
||||
self.name = name
|
||||
|
||||
def __call__(self, model_list):
|
||||
# model_list is None in static graph
|
||||
parameters = sum([m.parameters() for m in model_list],
|
||||
[]) if model_list else None
|
||||
opt = optim.SGD(learning_rate=self.learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
name=self.name)
|
||||
argspec = inspect.getargspec(optim.SGD.__init__).args
|
||||
if 'multi_precision' in argspec:
|
||||
opt = optim.SGD(learning_rate=self.learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
multi_precision=self.multi_precision,
|
||||
name=self.name)
|
||||
else:
|
||||
opt = optim.SGD(learning_rate=self.learning_rate,
|
||||
parameters=parameters,
|
||||
weight_decay=self.weight_decay,
|
||||
grad_clip=self.grad_clip,
|
||||
name=self.name)
|
||||
return opt
|
||||
|
||||
|
||||
|
|
@ -242,8 +253,9 @@ class AdamW(object):
|
|||
|
||||
if self.one_dim_param_no_weight_decay:
|
||||
self.no_weight_decay_param_name_list += [
|
||||
p.name for model in model_list
|
||||
for n, p in model.named_parameters() if len(p.shape) == 1
|
||||
p.name
|
||||
for model in model_list for n, p in model.named_parameters()
|
||||
if len(p.shape) == 1
|
||||
] if model_list else []
|
||||
|
||||
opt = optim.AdamW(
|
||||
|
|
|
|||
|
|
@ -12,11 +12,11 @@
|
|||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import datetime
|
||||
import logging
|
||||
import os
|
||||
import sys
|
||||
|
||||
import logging
|
||||
import datetime
|
||||
import paddle.distributed as dist
|
||||
|
||||
_logger = None
|
||||
|
|
@ -39,8 +39,12 @@ def init_logger(name='ppcls', log_file=None, log_level=logging.INFO):
|
|||
logging.Logger: The expected logger.
|
||||
"""
|
||||
global _logger
|
||||
assert _logger is None, "logger should not be initialized twice or more."
|
||||
_logger = logging.getLogger(name)
|
||||
|
||||
# solve mutiple init issue when using paddleclas.py and engin.engin
|
||||
init_flag = False
|
||||
if _logger is None:
|
||||
_logger = logging.getLogger(name)
|
||||
init_flag = True
|
||||
|
||||
formatter = logging.Formatter(
|
||||
'[%(asctime)s] %(name)s %(levelname)s: %(message)s',
|
||||
|
|
@ -48,13 +52,32 @@ def init_logger(name='ppcls', log_file=None, log_level=logging.INFO):
|
|||
|
||||
stream_handler = logging.StreamHandler(stream=sys.stdout)
|
||||
stream_handler.setFormatter(formatter)
|
||||
_logger.addHandler(stream_handler)
|
||||
stream_handler._name = 'stream_handler'
|
||||
|
||||
# add stream_handler when _logger dose not contain stream_handler
|
||||
for i, h in enumerate(_logger.handlers):
|
||||
if h.get_name() == stream_handler.get_name():
|
||||
break
|
||||
if i == len(_logger.handlers) - 1:
|
||||
_logger.addHandler(stream_handler)
|
||||
if init_flag:
|
||||
_logger.addHandler(stream_handler)
|
||||
|
||||
if log_file is not None and dist.get_rank() == 0:
|
||||
log_file_folder = os.path.split(log_file)[0]
|
||||
os.makedirs(log_file_folder, exist_ok=True)
|
||||
file_handler = logging.FileHandler(log_file, 'a')
|
||||
file_handler.setFormatter(formatter)
|
||||
_logger.addHandler(file_handler)
|
||||
file_handler._name = 'file_handler'
|
||||
|
||||
# add file_handler when _logger dose not contain same file_handler
|
||||
for i, h in enumerate(_logger.handlers):
|
||||
if h.get_name() == file_handler.get_name() and \
|
||||
h.baseFilename == file_handler.baseFilename:
|
||||
break
|
||||
if i == len(_logger.handlers) - 1:
|
||||
_logger.addHandler(file_handler)
|
||||
|
||||
if dist.get_rank() == 0:
|
||||
_logger.setLevel(log_level)
|
||||
else:
|
||||
|
|
|
|||
|
|
@ -107,6 +107,7 @@ bash test_tipc/test_train_inference_python.sh ./test_tipc/configs/MobileNetV3/Mo
|
|||
各功能测试中涉及混合精度、裁剪、量化等训练相关，及mkldnn、Tensorrt等多种预测相关参数配置，请点击下方相应链接了解更多细节和使用教程：
|
||||
|
||||
- [test_train_inference_python 使用](docs/test_train_inference_python.md)：测试基于Python的模型训练、评估、推理等基本功能，包括裁剪、量化、蒸馏。
|
||||
- [test_train_pact_inference_python 使用](docs/test_train_pact_inference_python.md)：测试基于Python的模型PACT在线量化等基本功能。
|
||||
- [test_inference_cpp 使用](docs/test_inference_cpp.md) ：测试基于C++的模型推理。
|
||||
- [test_serving 使用](docs/test_serving.md) ：测试基于Paddle Serving的服务化部署功能。
|
||||
- [test_lite_arm_cpu_cpp 使用](docs/test_lite_arm_cpu_cpp.md): 测试基于Paddle-Lite的ARM CPU端c++预测部署功能.
|
||||
|
|
|
|||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,14 +39,14 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_rec.py -c configs/inference_rec.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.rec_inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
null:null
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
===========================train_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=100
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.004 -o Global.pretrained_model="pretrained_model/general_PPLCNet_x2_5_pretrained_v1.0"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/pretrain/general_PPLCNet_x2_5_pretrained_v1.0.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_rec.py -c configs/inference_rec.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.rec_inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,54 @@
|
|||
===========================train_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=100
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/GeneralRecognition/GeneralRecognition_PPLCNet_x2_5.yaml -o Global.save_inference_dir=./general_PPLCNet_x2_5_lite_v1.0_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/rec/models/inference/general_PPLCNet_x2_5_lite_v1.0_infer.tar
|
||||
infer_model:./general_PPLCNet_x2_5_lite_v1.0_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_rec.py -c configs/inference_rec.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.rec_inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/Aliproduct/demo_test/
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:MobileNetV3_large_x1_0_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./MobileNetV3_large_x1_0_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/MobileNetV3_large_x1_0_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:MobileNetV3_large_x1_0_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/MobileNetV3_large_x1_0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:MobileNetV3_large_x1_0_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/MobileNetV3_large_x1_0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/MobileNetV3_large_x1_0_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -3,7 +3,7 @@ model_name:MobileNetV3_large_x1_0
|
|||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:amp
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
|
|
@ -12,16 +12,16 @@ train_model_name:latest
|
|||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train|pact_train|fpgm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
fpgm_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -33,20 +33,20 @@ fpgm_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_p
|
|||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./inference
|
||||
export2:null
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/whole_chain/MobileNetV3_large_x1_0_inference.tar
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:null
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
null:null
|
||||
|
|
|
|||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:MobileNetV3_large_x1_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/MobileNetV3_large_x1_0_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/MobileNetV3_large_x1_0_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:256|640
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:MobileNetV3_large_x1_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
fpgm_train:tools/train.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
distill_train:null
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml
|
||||
quant_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_quantization.yaml
|
||||
fpgm_export:tools/export_model.py -c ppcls/configs/slim/MobileNetV3_large_x1_0_prune.yaml
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/MobileNetV3/MobileNetV3_large_x1_0.yaml -o Global.save_inference_dir=./MobileNetV3_large_x1_0_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/MobileNetV3_large_x1_0_infer.tar
|
||||
infer_model:./MobileNetV3_large_x1_0_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:256|640
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5_KL
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./general_PPLCNet_x2_5_lite_v1.0_kl_quant_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_kl_quant_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/general_PPLCNet_x2_5_lite_v1.0_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/general_PPLCNet_x2_5_lite_v1.0_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./general_PPLCNet_x2_5_lite_v1.0_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/general_PPLCNet_x2_5_lite_v1.0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:GeneralRecognition_PPLCNet_x2_5_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/general_PPLCNet_x2_5_lite_v1.0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/general_PPLCNet_x2_5_lite_v1.0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/GeneralRecognition_PPLCNet_x2_5_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPHGNet_small_KL
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPHGNet_small_kl_quant_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_kl_quant_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPHGNet_small_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPHGNet_small_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPHGNet_small_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/PPHGNet_small_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPHGNet_small_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPHGNet_small_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPHGNet_small_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/PPHGNet_small_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPHGNet_small_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPHGNet_small_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPHGNet_small_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPHGNet_small_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPHGNet_small_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPHGNet_small_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPHGNet_small_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPHGNet_small_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPHGNet_small_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPHGNet_small_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPHGNet_small_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_small
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_small
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/PPHGNet_small_pretrained" -o AMP=None
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_small_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_small
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_small.yaml -o Global.save_inference_dir=./PPHGNet_small_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_small_infer.tar
|
||||
infer_model:./PPHGNet_small_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_tiny
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_tiny
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/PPHGNet_tiny_pretrained" -o AMP=None
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPHGNet_tiny_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPHGNet_tiny
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPHGNet/PPHGNet_tiny.yaml -o Global.save_inference_dir=./PPHGNet_tiny_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPHGNet_tiny_infer.tar
|
||||
infer_model:./PPHGNet_tiny_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml -o PreProcess.transform_ops.0.ResizeImage.resize_short=236
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_25
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_25_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_25_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_25
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_25.yaml -o Global.save_inference_dir=./PPLCNet_x0_25_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_25_infer.tar
|
||||
infer_model:./PPLCNet_x0_25_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_35
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_35_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_35_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_35
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_35.yaml -o Global.save_inference_dir=./PPLCNet_x0_35_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_35_infer.tar
|
||||
infer_model:./PPLCNet_x0_35_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_5_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_5_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_5.yaml -o Global.save_inference_dir=./PPLCNet_x0_5_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_5_infer.tar
|
||||
infer_model:./PPLCNet_x0_5_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_75
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x0_75_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x0_75_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x0_75
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x0_75.yaml -o Global.save_inference_dir=./PPLCNet_x0_75_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x0_75_infer.tar
|
||||
infer_model:./PPLCNet_x0_75_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPLCNet_x1_0_KL
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPLCNet_x1_0_kl_quant_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_kl_quant_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNet_x1_0_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNet_x1_0_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNet_x1_0_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNet_x1_0_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPLCNet_x1_0_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPLCNet_x1_0_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNet_x1_0_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNet_x1_0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNet_x1_0_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNet_x1_0_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNet_x1_0_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNet_x1_0_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNet_x1_0_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x1_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x1_0_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_0_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x1_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_0.yaml -o Global.save_inference_dir=./PPLCNet_x1_0_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_0_infer.tar
|
||||
infer_model:./PPLCNet_x1_0_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x1_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x1_5_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x1_5_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x1_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x1_5.yaml -o Global.save_inference_dir=./PPLCNet_x1_5_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x1_5_infer.tar
|
||||
infer_model:./PPLCNet_x1_5_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x2_0_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_0_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_0
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_0.yaml -o Global.save_inference_dir=./PPLCNet_x2_0_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_0_infer.tar
|
||||
infer_model:./PPLCNet_x2_0_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -3,7 +3,7 @@ model_name:PPLCNet_x2_5
|
|||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:amp
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
|
|
@ -12,16 +12,16 @@ train_model_name:latest
|
|||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,13 +39,13 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
|
|||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNet_x2_5_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNet_x2_5_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNet_x2_5
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNet/PPLCNet_x2_5.yaml -o Global.save_inference_dir=./PPLCNet_x2_5_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNet_x2_5_infer.tar
|
||||
infer_model:./PPLCNet_x2_5_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPLCNetV2_base_KL
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPLCNetV2_base_kl_quant_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_kl_quant_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNetV2_base_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNetV2_base_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNetV2_base_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNetV2_base_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNetV2_base_KL
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_kl_quant_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNetV2_base_kl_quant_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNetV2_base_kl_quant_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNetV2_base_kl_quant_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:PPLCNetV2_base_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./PPLCNetV2_base_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNetV2_base_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNetV2_base_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNetV2_base_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNetV2_base_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:PPLCNetV2_base_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/PPLCNetV2_base_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/PPLCNetV2_base_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/PPLCNetV2_base_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/PPLCNetV2_base_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
|
|
@ -0,0 +1,51 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNetV2_base
|
||||
python:python3.7
|
||||
gpu_list:0|0,1
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:null
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNetV2_base
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.first_bs:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.08 -o Global.pretrained_model="pretrained_model/PPLCNetV2_base_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/PPLCNetV2_base_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,53 @@
|
|||
===========================train_params===========================
|
||||
model_name:PPLCNetV2_base
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.first_bs:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.seed=1234 -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
null:null
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/PPLCNetV2/PPLCNetV2_base.yaml -o Global.save_inference_dir=./PPLCNetV2_base_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/PPLCNetV2_base_infer.tar
|
||||
infer_model:./PPLCNetV2_base_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -13,15 +13,15 @@ train_infer_img_dir:./dataset/ILSVRC2012/val
|
|||
null:null
|
||||
##
|
||||
trainer:amp_train
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50_amp_O1.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=128 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
amp_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2 -o Optimizer.multi_precision=True
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o AMP.scale_loss=65536 -o AMP.use_dynamic_loss_scaling=True -o AMP.level=O2
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
|
|
@ -39,15 +39,15 @@ infer_export:True
|
|||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:True|False
|
||||
-o Global.cpu_num_threads:1|6
|
||||
-o Global.batch_size:1|16
|
||||
-o Global.use_tensorrt:True|False
|
||||
-o Global.use_fp16:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:6
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:ResNet50
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=120
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:pact_train
|
||||
norm_train:null
|
||||
pact_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False -o Slim.quant.name=pact -o Optimizer.lr.learning_rate=0.01 -o Global.pretrained_model="pretrained_model/ResNet50_pretrained"
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Slim.quant.name=pact
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:null
|
||||
quant_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Slim.quant.name=pact
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:null
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/legendary_models/ResNet50_pretrained.pdparams
|
||||
infer_model:../inference/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../dataset/ILSVRC2012/val
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:True
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:128
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,60 @@
|
|||
===========================train_params===========================
|
||||
model_name:ResNet50
|
||||
python:python3.7
|
||||
gpu_list:0
|
||||
-o Global.device:gpu
|
||||
-o Global.auto_cast:null
|
||||
-o Global.epochs:lite_train_lite_infer=2|whole_train_whole_infer=200
|
||||
-o Global.output_dir:./output/
|
||||
-o DataLoader.Train.sampler.batch_size:8
|
||||
-o Global.pretrained_model:null
|
||||
train_model_name:latest
|
||||
train_infer_img_dir:./dataset/ILSVRC2012/val
|
||||
null:null
|
||||
##
|
||||
trainer:norm_train
|
||||
norm_train:tools/train.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.seed=1234 -o DataLoader.Train.sampler.shuffle=False -o DataLoader.Train.loader.num_workers=0 -o DataLoader.Train.loader.use_shared_memory=False
|
||||
pact_train:null
|
||||
fpgm_train:null
|
||||
distill_train:null
|
||||
to_static_train:-o Global.to_static=True
|
||||
null:null
|
||||
##
|
||||
===========================eval_params===========================
|
||||
eval:tools/eval.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml
|
||||
null:null
|
||||
##
|
||||
===========================infer_params==========================
|
||||
-o Global.save_inference_dir:./inference
|
||||
-o Global.pretrained_model:
|
||||
norm_export:tools/export_model.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml
|
||||
quant_export:null
|
||||
fpgm_export:null
|
||||
distill_export:null
|
||||
kl_quant:deploy/slim/quant_post_static.py -c ppcls/configs/ImageNet/ResNet/ResNet50.yaml -o Global.save_inference_dir=./ResNet50_infer
|
||||
export2:null
|
||||
pretrained_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/ResNet50_infer.tar
|
||||
infer_model:./ResNet50_infer/
|
||||
infer_export:True
|
||||
infer_quant:Fasle
|
||||
inference:python/predict_cls.py -c configs/inference_cls.yaml
|
||||
-o Global.use_gpu:True|False
|
||||
-o Global.enable_mkldnn:False
|
||||
-o Global.cpu_num_threads:1
|
||||
-o Global.batch_size:1
|
||||
-o Global.use_tensorrt:False
|
||||
-o Global.use_fp16:False
|
||||
-o Global.inference_model_dir:../inference
|
||||
-o Global.infer_imgs:../deploy/images/ImageNet/ILSVRC2012_val_00000010.jpeg
|
||||
-o Global.save_log_path:null
|
||||
-o Global.benchmark:False
|
||||
null:null
|
||||
null:null
|
||||
===========================train_benchmark_params==========================
|
||||
batch_size:128
|
||||
fp_items:fp32
|
||||
epoch:1
|
||||
--profiler_options:batch_range=[10,20];state=GPU;tracer_option=Default;profile_path=model.profile
|
||||
flags:FLAGS_eager_delete_tensor_gb=0.0;FLAGS_fraction_of_gpu_memory_to_use=0.98;FLAGS_conv_workspace_size_limit=4096
|
||||
===========================infer_benchmark_params==========================
|
||||
random_infer_input:[{float32,[3,224,224]}]
|
||||
|
|
@ -0,0 +1,18 @@
|
|||
===========================cpp_infer_params===========================
|
||||
model_name:ResNet50_vd_PACT
|
||||
cpp_infer_type:cls
|
||||
cls_inference_model_dir:./ResNet50_vd_pact_infer/
|
||||
det_inference_model_dir:
|
||||
cls_inference_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/ResNet50_vd_pact_infer.tar
|
||||
det_inference_url:
|
||||
infer_quant:False
|
||||
inference_cmd:./deploy/cpp/build/clas_system -c inference_cls.yaml
|
||||
use_gpu:True|False
|
||||
enable_mkldnn:False
|
||||
cpu_threads:1
|
||||
batch_size:1
|
||||
use_tensorrt:False
|
||||
precision:fp32
|
||||
image_dir:./dataset/ILSVRC2012/val/ILSVRC2012_val_00000001.JPEG
|
||||
benchmark:False
|
||||
generate_yaml_cmd:python3.7 test_tipc/generate_cpp_yaml.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:ResNet50_vd_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/ResNet50_vd_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/ResNet50_vd_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/ResNet50_vd_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/ResNet50_vd_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:null
|
||||
--use_gpu:0|null
|
||||
pipline:test_cpp_serving_client.py
|
||||
|
|
@ -0,0 +1,14 @@
|
|||
===========================serving_params===========================
|
||||
model_name:ResNet50_vd_PACT
|
||||
python:python3.7
|
||||
inference_model_url:https://paddle-imagenet-models-name.bj.bcebos.com/dygraph/inference/slim_model/ResNet50_vd_pact_infer.tar
|
||||
trans_model:-m paddle_serving_client.convert
|
||||
--dirname:./deploy/paddleserving/ResNet50_vd_pact_infer/
|
||||
--model_filename:inference.pdmodel
|
||||
--params_filename:inference.pdiparams
|
||||
--serving_server:./deploy/paddleserving/ResNet50_vd_pact_serving/
|
||||
--serving_client:./deploy/paddleserving/ResNet50_vd_pact_client/
|
||||
serving_dir:./deploy/paddleserving
|
||||
web_service:classification_web_service.py
|
||||
--use_gpu:0|null
|
||||
pipline:pipeline_http_client.py
|
||||
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Reference in New Issue