littletomatodonkey
GitHub
parent
d039691fce
commit
4003cdb7ad
deploy
configs/PULC
traffic_sign
vehicle_attr
images/PULC
traffic_sign
vehicle_attr
docs
images/PULC/docs
zh_CN/PULC
ppcls
arch/backbone
legendary_models
model_zoo
configs/PULC
data/postprocess
engine
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@ -0,0 +1,35 @@
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Global:
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infer_imgs: "./images/PULC/traffic_sign/99603_17806.jpg"
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inference_model_dir: "./models/traffic_sign_infer"
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batch_size: 1
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use_gpu: True
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enable_mkldnn: True
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cpu_num_threads: 10
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benchmark: False
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use_fp16: False
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ir_optim: True
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use_tensorrt: False
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gpu_mem: 8000
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enable_profile: False
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PreProcess:
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transform_ops:
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- ResizeImage:
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resize_short: 256
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- CropImage:
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size: 224
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- NormalizeImage:
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scale: 0.00392157
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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channel_num: 3
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- ToCHWImage:
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PostProcess:
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main_indicator: Topk
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Topk:
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topk: 5
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class_id_map_file: "../dataset/traffic_sign/label_name_id.txt"
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SavePreLabel:
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save_dir: ./pre_label/
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Global:
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infer_imgs: "./images/PULC/vehicle_attr/0002_c002_00030670_0.jpg"
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inference_model_dir: "./models/vehicle_attr_infer"
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batch_size: 1
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use_gpu: True
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enable_mkldnn: True
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cpu_num_threads: 10
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benchmark: False
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use_fp16: False
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ir_optim: True
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use_tensorrt: False
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gpu_mem: 8000
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enable_profile: False
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PreProcess:
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transform_ops:
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- ResizeImage:
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size: [256, 192]
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- NormalizeImage:
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scale: 1.0/255.0
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mean: [0.485, 0.456, 0.406]
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std: [0.229, 0.224, 0.225]
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order: ''
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channel_num: 3
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- ToCHWImage:
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PostProcess:
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main_indicator: VehicleAttribute
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VehicleAttribute:
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color_threshold: 0.5
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type_threshold: 0.5
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@ -280,3 +280,45 @@ class Attribute(object):
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batch_res.append([label_res, pred_res])
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return batch_res
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class VehicleAttribute(object):
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def __init__(self, color_threshold=0.5, type_threshold=0.5):
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self.color_threshold = color_threshold
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self.type_threshold = type_threshold
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self.color_list = [
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"yellow", "orange", "green", "gray", "red", "blue", "white",
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"golden", "brown", "black"
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]
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self.type_list = [
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"sedan", "suv", "van", "hatchback", "mpv", "pickup", "bus",
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"truck", "estate"
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]
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def __call__(self, batch_preds, file_names=None):
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# postprocess output of predictor
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batch_res = []
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for res in batch_preds:
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res = res.tolist()
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label_res = []
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color_idx = np.argmax(res[:10])
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type_idx = np.argmax(res[10:])
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if res[color_idx] >= self.color_threshold:
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color_info = f"Color: ({self.color_list[color_idx]}, prob: {res[color_idx]})"
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else:
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color_info = "Color unknown"
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if res[type_idx + 10] >= self.type_threshold:
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type_info = f"Type: ({self.type_list[type_idx]}, prob: {res[type_idx + 10]})"
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else:
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type_info = "Type unknown"
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label_res = f"{color_info}, {type_info}"
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threshold_list = [self.color_threshold
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] * 10 + [self.type_threshold] * 9
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pred_res = (np.array(res) > np.array(threshold_list)
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).astype(np.int8).tolist()
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batch_res.append([label_res, pred_res])
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return batch_res
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@ -138,7 +138,9 @@ def main(config):
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continue
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batch_results = cls_predictor.predict(batch_imgs)
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for number, result_dict in enumerate(batch_results):
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if "Attribute" in config["PostProcess"]:
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if "Attribute" in config[
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"PostProcess"] or "VehicleAttribute" in config[
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"PostProcess"]:
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filename = batch_names[number]
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attr_message = result_dict[0]
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pred_res = result_dict[1]
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@ -0,0 +1,420 @@
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# PULC 交通标志分类模型
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------
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## 目录
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- [1. 模型和应用场景介绍](#1)
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- [2. 模型快速体验](#2)
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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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- [3.2.1 数据集来源](#3.2.1)
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- [3.2.2 数据集获取](#3.2.2)
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- [3.3 模型训练](#3.3)
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- [3.4 模型评估](#3.4)
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- [3.5 模型预测](#3.5)
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- [4. 模型压缩](#4)
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- [4.1 SKL-UGI 知识蒸馏](#4.1)
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- [4.1.1 教师模型训练](#4.1.1)
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- [4.1.2 蒸馏训练](#4.1.2)
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- [5. 超参搜索](#5)
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- [6. 模型推理部署](#6)
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- [6.1 推理模型准备](#6.1)
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- [6.1.1 基于训练得到的权重导出 inference 模型](#6.1.1)
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- [6.1.2 直接下载 inference 模型](#6.1.2)
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- [6.2 基于 Python 预测引擎推理](#6.2)
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- [6.2.1 预测单张图像](#6.2.1)
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- [6.2.2 基于文件夹的批量预测](#6.2.2)
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- [6.3 基于 C++ 预测引擎推理](#6.3)
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- [6.4 服务化部署](#6.4)
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- [6.5 端侧部署](#6.5)
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- [6.6 Paddle2ONNX 模型转换与预测](#6.6)
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<a name="1"></a>
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## 1. 模型和应用场景介绍
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该案例提供了用户使用 PaddleClas 的超轻量图像分类方案（PULC，Practical Ultra Lightweight Classification）快速构建轻量级、高精度、可落地的交通标志分类模型。该模型可以广泛应用于自动驾驶、道路监控等场景。
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下表列出了不同交通标志分类模型的相关指标，前两行展现了使用 SwinTranformer_tiny 和 MobileNetV3_large_x1_0 作为 backbone 训练得到的模型的相关指标，第三行至第六行依次展现了替换 backbone 为 PPLCNet_x1_0、使用 SSLD 预训练模型、使用 SSLD 预训练模型 + EDA 策略、使用 SSLD 预训练模型 + EDA 策略 + SKL-UGI 知识蒸馏策略训练得到的模型的相关指标。
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| 模型 | Top-1 Acc（%） | 延时（ms） | 存储（M） | 策略 |
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|-------|-----------|----------|---------------|---------------|
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| SwinTranformer_tiny | 98.11 | 87.19 | 111 | 使用ImageNet预训练模型 |
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| MobileNetV3_large_x1_0 | 97.79 | 5.59 | 23 | 使用ImageNet预训练模型 |
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| PPLCNet_x1_0 | 97.78 | 2.67 | 8.2 | 使用ImageNet预训练模型 |
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| PPLCNet_x1_0 | 97.84 | 2.67 | 8.2 | 使用SSLD预训练模型 |
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| PPLCNet_x1_0 | 98.14 | 2.67 | 8.2 | 使用SSLD预训练模型+EDA策略|
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| <b>PPLCNet_x1_0<b> | <b>98.35<b> | <b>2.67<b> | <b>8.2<b> | 使用SSLD预训练模型+EDA策略+SKL-UGI知识蒸馏策略|
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从表中可以看出，backbone 为 SwinTranformer_tiny 时精度较高，但是推理速度较慢。将 backbone 替换为轻量级模型 MobileNetV3_large_x1_0 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，精度低0.01%，但是速度提升 2 倍左右。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 0.06%，进一步地，当融合EDA策略后，精度可以再提升 0.3%，最后，在使用 SKL-UGI 知识蒸馏后，精度可以继续提升 0.21%。此时，PPLCNet_x1_0 的精度超越了SwinTranformer_tiny，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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* 关于PPLCNet的介绍可以参考[PPLCNet介绍](../models/PP-LCNet.md)，相关论文可以查阅[PPLCNet paper](https://arxiv.org/abs/2109.15099)。
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<a name="2"></a>
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## 2. 模型快速体验
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（pip方式，待补充）
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<a name="3"></a>
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## 3. 模型训练、评估和预测
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<a name="3.1"></a>
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### 3.1 环境配置
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* 安装：请先参考 [Paddle 安装教程](../installation/install_paddle.md) 以及 [PaddleClas 安装教程](../installation/install_paddleclas.md) 配置 PaddleClas 运行环境。
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<a name="3.2"></a>
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### 3.2 数据准备
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<a name="3.2.1"></a>
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#### 3.2.1 数据集来源
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本案例中所使用的数据为[Tsinghua-Tencent 100K dataset (CC-BY-NC license)](https://cg.cs.tsinghua.edu.cn/traffic-sign/)，在使用的过程中，对交通标志检测框进行随机扩充与裁剪，从而得到用于训练与测试的图像，下面简称该数据集为`TT100K`数据集。
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<a name="3.2.2"></a>
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#### 3.2.2 数据集获取
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在TT00K数据集上，对交通标志检测框进行随机扩充与裁剪，从而得到用于训练与测试的图像。随机扩充检测框的逻辑如下所示。
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```python
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def get_random_crop_box(xmin, ymin, xmax, ymax, img_height, img_width, ratio=1.0):
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h = ymax - ymin
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w = ymax - ymin
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xmin_diff = random.random() * ratio * min(w, xmin/ratio)
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ymin_diff = random.random() * ratio * min(h, ymin/ratio)
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xmax_diff = random.random() * ratio * min(w, (img_width-xmin-1)/ratio)
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ymax_diff = random.random() * ratio * min(h, (img_height-ymin-1)/ratio)
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new_xmin = round(xmin - xmin_diff)
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new_ymin = round(ymin - ymin_diff)
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new_xmax = round(xmax + xmax_diff)
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new_ymax = round(ymax + ymax_diff)
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return new_xmin, new_ymin, new_xmax, new_ymax
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```
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完整的预处理逻辑，可以参考下载好的数据集文件夹中的`deal.py`文件。
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处理后的数据集部分数据可视化如下。
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<div align="center">
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<img src="../../images/PULC/docs/traffic_sign_data_demo.png" width = "500" />
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</div>
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此处提供了经过上述方法处理好的数据，可以直接下载得到。
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进入 PaddleClas 目录。
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```
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cd path_to_PaddleClas
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```
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进入 `dataset/` 目录，下载并解压交通标志分类场景的数据。
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```shell
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cd dataset
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wget https://paddleclas.bj.bcebos.com/data/cls_demo/traffic_sign.tar
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tar -xf traffic_sign.tar
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cd ../
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```
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执行上述命令后，`dataset/`下存在`traffic_sign`目录，该目录中具有以下数据：
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```
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traffic_sign
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├── train
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│ ├── 0_62627.jpg
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│ ├── 100000_89031.jpg
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│ ├── 100001_89031.jpg
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...
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├── test
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│ ├── 100423_2315.jpg
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│ ├── 100424_2315.jpg
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│ ├── 100425_2315.jpg
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...
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├── other
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│ ├── 100603_3422.jpg
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│ ├── 100604_3422.jpg
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...
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├── label_list_train.txt
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├── label_list_test.txt
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├── label_list_other.txt
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├── label_list_train_for_distillation.txt
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├── label_list_train.txt.debug
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├── label_list_test.txt.debug
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├── label_name_id.txt
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├── deal.py
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```
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其中`train/`和`test/`分别为训练集和验证集。`label_list_train.txt`和`label_list_test.txt`分别为训练集和验证集的标签文件，`label_list_train.txt.debug`和`label_list_test.txt.debug`分别为训练集和验证集的`debug`标签文件，其分别是`label_list_train.txt`和`label_list_test.txt`的子集，用该文件可以快速体验本案例的流程。`train`与`other`的混合数据用于本案例的`SKL-UGI知识蒸馏策略`，对应的训练标签文件为`label_list_train_for_distillation.txt`。
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**备注：**
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* 关于 `label_list_train.txt`、`label_list_test.txt`的格式说明，可以参考[PaddleClas分类数据集格式说明](../data_preparation/classification_dataset.md#1-数据集格式说明) 。
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* 关于如何得到蒸馏的标签文件可以参考[知识蒸馏标签获得方法](@ruoyu)。
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<a name="3.3"></a>
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### 3.3 模型训练
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在 `ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0.yaml` 中提供了基于该场景的训练配置，可以通过如下脚本启动训练：
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```shell
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export CUDA_VISIBLE_DEVICES=0,1,2,3
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python3 -m paddle.distributed.launch \
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--gpus="0,1,2,3" \
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tools/train.py \
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-c ./ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0.yaml
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```
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验证集的最佳指标在 `98.14%` 左右（数据集较小，一般有0.1%左右的波动）。
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<a name="3.4"></a>
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### 3.4 模型评估
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训练好模型之后，可以通过以下命令实现对模型指标的评估。
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```bash
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python3 tools/eval.py \
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-c ./ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0.yaml \
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-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"
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```
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其中 `-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` 指定了当前最佳权重所在的路径，如果指定其他权重，只需替换对应的路径即可。
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<a name="3.5"></a>
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### 3.5 模型预测
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模型训练完成之后，可以加载训练得到的预训练模型，进行模型预测。在模型库的 `tools/infer.py` 中提供了完整的示例，只需执行下述命令即可完成模型预测：
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```bash
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python3 tools/infer.py \
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-c ./ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0.yaml \
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-o Global.pretrained_model=output/DistillationModel/best_model
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```
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输出结果如下：
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```
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99603_17806.jpg: class id(s): [216, 145, 49, 207, 169], score(s): [1.00, 0.00, 0.00, 0.00, 0.00], label_name(s): ['pm20', 'pm30', 'pm40', 'pl25', 'pm15']
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```
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**备注：**
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|
||||
* 这里`-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` 指定了当前最佳权重所在的路径，如果指定其他权重，只需替换对应的路径即可。
|
||||
|
||||
* 默认是对 `deploy/images/PULC/traffic_sign/99603_17806.jpg` 进行预测，此处也可以通过增加字段 `-o Infer.infer_imgs=xxx` 对其他图片预测。
|
||||
|
||||
<a name="4"></a>
|
||||
|
||||
## 4. 模型压缩
|
||||
|
||||
<a name="4.1"></a>
|
||||
|
||||
### 4.1 SKL-UGI 知识蒸馏
|
||||
|
||||
SKL-UGI 知识蒸馏是 PaddleClas 提出的一种简单有效的知识蒸馏方法，关于该方法的介绍，可以参考[SKL-UGI 知识蒸馏](@ruoyu)。
|
||||
|
||||
<a name="4.1.1"></a>
|
||||
|
||||
#### 4.1.1 教师模型训练
|
||||
|
||||
复用 `ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0.yaml` 中的超参数，训练教师模型，训练脚本如下：
|
||||
|
||||
```shell
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0.yaml \
|
||||
-o Arch.name=ResNet101_vd
|
||||
```
|
||||
|
||||
验证集的最佳指标为 `98.59%` 左右，当前教师模型最好的权重保存在 `output/ResNet101_vd/best_model.pdparams`。
|
||||
|
||||
<a name="4.1.2"></a>
|
||||
|
||||
#### 4.1.2 蒸馏训练
|
||||
|
||||
配置文件`ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0_distillation.yaml`提供了`SKL-UGI知识蒸馏策略`的配置。该配置将`ResNet101_vd`当作教师模型，`PPLCNet_x1_0`当作学生模型，使用ImageNet数据集的验证集作为新增的无标签数据。训练脚本如下：
|
||||
|
||||
```shell
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0_distillation.yaml \
|
||||
-o Arch.models.0.Teacher.pretrained=output/ResNet101_vd/best_model
|
||||
```
|
||||
|
||||
验证集的最佳指标为 `98.35%` 左右，当前模型最好的权重保存在 `output/DistillationModel/best_model_student.pdparams`。
|
||||
|
||||
|
||||
<a name="5"></a>
|
||||
|
||||
## 5. 超参搜索
|
||||
|
||||
在 [3.2 节](#3.2)和 [4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的，如果希望在自己的数据集上得到更好的结果，可以参考[SHAS 超参数搜索策略](#TODO)来获得更好的训练超参数。
|
||||
|
||||
**备注：** 此部分内容是可选内容，搜索过程需要较长的时间，您可以根据自己的硬件情况来选择执行。如果没有更换数据集，可以忽略此节内容。
|
||||
|
||||
<a name="6"></a>
|
||||
|
||||
## 6. 模型推理部署
|
||||
|
||||
<a name="6.1"></a>
|
||||
|
||||
### 6.1 推理模型准备
|
||||
|
||||
Paddle Inference 是飞桨的原生推理库， 作用于服务器端和云端，提供高性能的推理能力。相比于直接基于预训练模型进行预测，Paddle Inference可使用MKLDNN、CUDNN、TensorRT 进行预测加速，从而实现更优的推理性能。更多关于Paddle Inference推理引擎的介绍，可以参考[Paddle Inference官网教程](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/infer/inference/inference_cn.html)。
|
||||
|
||||
当使用 Paddle Inference 推理时，加载的模型类型为 inference 模型。本案例提供了两种获得 inference 模型的方法，如果希望得到和文档相同的结果，请选择[直接下载 inference 模型](#6.1.2)的方式。
|
||||
|
||||
<a name="6.1.1"></a>
|
||||
|
||||
### 6.1.1 基于训练得到的权重导出 inference 模型
|
||||
|
||||
此处，我们提供了将权重和模型转换的脚本，执行该脚本可以得到对应的 inference 模型：
|
||||
|
||||
```bash
|
||||
python3 tools/export_model.py \
|
||||
-c ./ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0.yaml \
|
||||
-o Global.pretrained_model=output/DistillationModel/best_model_student \
|
||||
-o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_traffic_sign_infer
|
||||
```
|
||||
执行完该脚本后会在 `deploy/models/` 下生成 `PPLCNet_x1_0_traffic_sign_infer` 文件夹，`models` 文件夹下应有如下文件结构：
|
||||
|
||||
```
|
||||
├── PPLCNet_x1_0_traffic_sign_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
```
|
||||
|
||||
**备注：** 此处的最佳权重是经过知识蒸馏后的权重路径，如果没有执行知识蒸馏的步骤，最佳模型保存在`output/PPLCNet_x1_0/best_model.pdparams`中。
|
||||
|
||||
<a name="6.1.2"></a>
|
||||
|
||||
### 6.1.2 直接下载 inference 模型
|
||||
|
||||
[6.1.1 小节](#6.1.1)提供了导出 inference 模型的方法，此处也提供了该场景可以下载的 inference 模型，可以直接下载体验。
|
||||
|
||||
```
|
||||
cd deploy/models
|
||||
# 下载 inference 模型并解压
|
||||
wget https://paddleclas.bj.bcebos.com/models/PULC/traffic_sign_infer.tar && tar -xf traffic_sign_infer.tar
|
||||
```
|
||||
|
||||
解压完毕后，`models` 文件夹下应有如下文件结构：
|
||||
|
||||
```
|
||||
├── traffic_sign_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
```
|
||||
|
||||
<a name="6.2"></a>
|
||||
|
||||
### 6.2 基于 Python 预测引擎推理
|
||||
|
||||
|
||||
<a name="6.2.1"></a>
|
||||
|
||||
#### 6.2.1 预测单张图像
|
||||
|
||||
返回 `deploy` 目录：
|
||||
|
||||
```
|
||||
cd ../
|
||||
```
|
||||
|
||||
运行下面的命令，对图像 `./images/PULC/traffic_sign/99603_17806.jpg` 进行交通标志分类。
|
||||
|
||||
```shell
|
||||
# 使用下面的命令使用 GPU 进行预测
|
||||
python3.7 python/predict_cls.py -c configs/PULC/traffic_sign/inference_traffic_sign.yaml
|
||||
# 使用下面的命令使用 CPU 进行预测
|
||||
python3.7 python/predict_cls.py -c configs/PULC/traffic_sign/inference_traffic_sign.yaml -o Global.use_gpu=False
|
||||
```
|
||||
|
||||
输出结果如下。
|
||||
|
||||
```
|
||||
99603_17806.jpg: class id(s): [216, 145, 49, 207, 169], score(s): [1.00, 0.00, 0.00, 0.00, 0.00], label_name(s): ['pm20', 'pm30', 'pm40', 'pl25', 'pm15']
|
||||
```
|
||||
|
||||
<a name="6.2.2"></a>
|
||||
|
||||
#### 6.2.2 基于文件夹的批量预测
|
||||
|
||||
如果希望预测文件夹内的图像，可以直接修改配置文件中的 `Global.infer_imgs` 字段，也可以通过下面的 `-o` 参数修改对应的配置。
|
||||
|
||||
```shell
|
||||
# 使用下面的命令使用 GPU 进行预测，如果希望使用 CPU 预测，可以在命令后面添加 -o Global.use_gpu=False
|
||||
python3.7 python/predict_cls.py -c configs/PULC/traffic_sign/inference_traffic_sign.yaml -o Global.infer_imgs="./images/PULC/traffic_sign/"
|
||||
```
|
||||
|
||||
终端中会输出该文件夹内所有图像的分类结果，如下所示。
|
||||
|
||||
```
|
||||
100999_83928.jpg: class id(s): [182, 179, 162, 128, 24], score(s): [0.99, 0.01, 0.00, 0.00, 0.00], label_name(s): ['pl110', 'pl100', 'pl120', 'p26', 'pm10']
|
||||
99603_17806.jpg: class id(s): [216, 145, 49, 24, 169], score(s): [1.00, 0.00, 0.00, 0.00, 0.00], label_name(s): ['pm20', 'pm30', 'pm40', 'pm10', 'pm15']
|
||||
```
|
||||
|
||||
输出的 `label_name`可以从`dataset/traffic_sign/report.pdf`文件中查阅对应的图片。
|
||||
|
||||
<a name="6.3"></a>
|
||||
|
||||
### 6.3 基于 C++ 预测引擎推理
|
||||
|
||||
PaddleClas 提供了基于 C++ 预测引擎推理的示例，您可以参考[服务器端 C++ 预测](../inference_deployment/cpp_deploy.md)来完成相应的推理部署。如果您使用的是 Windows 平台，可以参考[基于 Visual Studio 2019 Community CMake 编译指南](../inference_deployment/cpp_deploy_on_windows.md)完成相应的预测库编译和模型预测工作。
|
||||
|
||||
<a name="6.4"></a>
|
||||
|
||||
### 6.4 服务化部署
|
||||
|
||||
Paddle Serving 提供高性能、灵活易用的工业级在线推理服务。Paddle Serving 支持 RESTful、gRPC、bRPC 等多种协议，提供多种异构硬件和多种操作系统环境下推理解决方案。更多关于Paddle Serving 的介绍，可以参考[Paddle Serving 代码仓库](https://github.com/PaddlePaddle/Serving)。
|
||||
|
||||
PaddleClas 提供了基于 Paddle Serving 来完成模型服务化部署的示例，您可以参考[模型服务化部署](../inference_deployment/paddle_serving_deploy.md)来完成相应的部署工作。
|
||||
|
||||
<a name="6.5"></a>
|
||||
|
||||
### 6.5 端侧部署
|
||||
|
||||
Paddle Lite 是一个高性能、轻量级、灵活性强且易于扩展的深度学习推理框架，定位于支持包括移动端、嵌入式以及服务器端在内的多硬件平台。更多关于 Paddle Lite 的介绍，可以参考[Paddle Lite 代码仓库](https://github.com/PaddlePaddle/Paddle-Lite)。
|
||||
|
||||
PaddleClas 提供了基于 Paddle Lite 来完成模型端侧部署的示例，您可以参考[端侧部署](../inference_deployment/paddle_lite_deploy.md)来完成相应的部署工作。
|
||||
|
||||
<a name="6.6"></a>
|
||||
|
||||
### 6.6 Paddle2ONNX 模型转换与预测
|
||||
|
||||
Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式。通过 ONNX 可以完成将 Paddle 模型到多种推理引擎的部署，包括TensorRT/OpenVINO/MNN/TNN/NCNN，以及其它对 ONNX 开源格式进行支持的推理引擎或硬件。更多关于 Paddle2ONNX 的介绍，可以参考[Paddle2ONNX 代码仓库](https://github.com/PaddlePaddle/Paddle2ONNX)。
|
||||
|
||||
PaddleClas 提供了基于 Paddle2ONNX 来完成 inference 模型转换 ONNX 模型并作推理预测的示例，您可以参考[Paddle2ONNX 模型转换与预测](@shuilong)来完成相应的部署工作。
|
||||
|
|
@ -0,0 +1,414 @@
|
|||
# PULC 车辆属性识别模型
|
||||
|
||||
------
|
||||
|
||||
|
||||
## 目录
|
||||
|
||||
- [1. 模型和应用场景介绍](#1)
|
||||
- [2. 模型快速体验](#2)
|
||||
- [3. 模型训练、评估和预测](#3)
|
||||
- [3.1 环境配置](#3.1)
|
||||
- [3.2 数据准备](#3.2)
|
||||
- [3.2.1 数据集来源](#3.2.1)
|
||||
- [3.2.2 数据集获取](#3.2.2)
|
||||
- [3.3 模型训练](#3.3)
|
||||
- [3.4 模型评估](#3.4)
|
||||
- [3.5 模型预测](#3.5)
|
||||
- [4. 模型压缩](#4)
|
||||
- [4.1 SKL-UGI 知识蒸馏](#4.1)
|
||||
- [4.1.1 教师模型训练](#4.1.1)
|
||||
- [4.1.2 蒸馏训练](#4.1.2)
|
||||
- [5. 超参搜索](#5)
|
||||
- [6. 模型推理部署](#6)
|
||||
- [6.1 推理模型准备](#6.1)
|
||||
- [6.1.1 基于训练得到的权重导出 inference 模型](#6.1.1)
|
||||
- [6.1.2 直接下载 inference 模型](#6.1.2)
|
||||
- [6.2 基于 Python 预测引擎推理](#6.2)
|
||||
- [6.2.1 预测单张图像](#6.2.1)
|
||||
- [6.2.2 基于文件夹的批量预测](#6.2.2)
|
||||
- [6.3 基于 C++ 预测引擎推理](#6.3)
|
||||
- [6.4 服务化部署](#6.4)
|
||||
- [6.5 端侧部署](#6.5)
|
||||
- [6.6 Paddle2ONNX 模型转换与预测](#6.6)
|
||||
|
||||
|
||||
<a name="1"></a>
|
||||
|
||||
## 1. 模型和应用场景介绍
|
||||
|
||||
该案例提供了用户使用 PaddleClas 的超轻量图像分类方案（PULC，Practical Ultra Lightweight Classification）快速构建轻量级、高精度、可落地的车辆属性识别模型。该模型可以广泛应用于车辆识别、道路监控等场景。
|
||||
|
||||
下表列出了不同车辆属性识别模型的相关指标，前两行展现了使用 Res2Net200_vd_26w_4s 和 MobileNetV3_large_x1_0 作为 backbone 训练得到的模型的相关指标，第三行至第六行依次展现了替换 backbone 为 PPLCNet_x1_0、使用 SSLD 预训练模型、使用 SSLD 预训练模型 + EDA 策略、使用 SSLD 预训练模型 + EDA 策略 + SKL-UGI 知识蒸馏策略训练得到的模型的相关指标。
|
||||
|
||||
|
||||
| 模型 | ma（%） | 延时（ms） | 存储（M） | 策略 |
|
||||
|-------|-----------|----------|---------------|---------------|
|
||||
| Res2Net200_vd_26w_4s | 91.36 | 66.58 | 293 | 使用ImageNet预训练模型 |
|
||||
| ResNet50 | 89.98 | 12.74 | 92 | 使用ImageNet预训练模型 |
|
||||
| MobileNetV3_large_x1_0 | 89.77 | 5.59 | 23 | 使用ImageNet预训练模型 |
|
||||
| PPLCNet_x1_0 | 89.57 | 2.56 | 8.2 | 使用ImageNet预训练模型 |
|
||||
| PPLCNet_x1_0 | 90.07 | 2.56 | 8.2 | 使用SSLD预训练模型 |
|
||||
| PPLCNet_x1_0 | 90.59 | 2.56 | 8.2 | 使用SSLD预训练模型+EDA策略|
|
||||
| <b>PPLCNet_x1_0<b> | <b>90.81<b> | <b>2.56<b> | <b>8.2<b> | 使用SSLD预训练模型+EDA策略+SKL-UGI知识蒸馏策略|
|
||||
|
||||
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_large_x1_0 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，精度低0.2%，但是速度提升 2 倍左右。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%，进一步地，当融合EDA策略后，精度可以再提升 0.52%，最后，在使用 SKL-UGI 知识蒸馏后，精度可以继续提升 0.23%。此时，PPLCNet_x1_0 的精度与 Res2Net200_vd_26w_4s 仅相差0.55%，但是速度快26倍。关于 PULC 的训练方法和推理部署方法将在下面详细介绍。
|
||||
|
||||
**备注：**
|
||||
|
||||
* 关于PPLCNet的介绍可以参考[PPLCNet介绍](../models/PP-LCNet.md)，相关论文可以查阅[PPLCNet paper](https://arxiv.org/abs/2109.15099)。
|
||||
|
||||
|
||||
<a name="2"></a>
|
||||
|
||||
## 2. 模型快速体验
|
||||
|
||||
```
|
||||
（pip方式，待补充）
|
||||
```
|
||||
|
||||
<a name="3"></a>
|
||||
|
||||
## 3. 模型训练、评估和预测
|
||||
|
||||
<a name="3.1"></a>
|
||||
|
||||
### 3.1 环境配置
|
||||
|
||||
* 安装：请先参考 [Paddle 安装教程](../installation/install_paddle.md) 以及 [PaddleClas 安装教程](../installation/install_paddleclas.md) 配置 PaddleClas 运行环境。
|
||||
|
||||
<a name="3.2"></a>
|
||||
|
||||
### 3.2 数据准备
|
||||
|
||||
<a name="3.2.1"></a>
|
||||
|
||||
#### 3.2.1 数据集来源
|
||||
|
||||
本案例中所使用的数据为[VeRi 数据集](https://www.v7labs.com/open-datasets/veri-dataset)。
|
||||
|
||||
<a name="3.2.2"></a>
|
||||
|
||||
#### 3.2.2 数据集获取
|
||||
|
||||
部分数据可视化如下所示。
|
||||
|
||||
<div align="center">
|
||||
<img src="../../images/PULC/docs/vehicle_attr_data_demo.png" width = "500" />
|
||||
</div>
|
||||
|
||||
首先从[VeRi数据集官网](https://www.v7labs.com/open-datasets/veri-dataset)中申请并下载数据，放在PaddleClas的`dataset`目录下，数据集目录名为`VeRi`，使用下面的命令进入该文件夹。
|
||||
|
||||
```shell
|
||||
cd PaddleClas/dataset/VeRi/
|
||||
```
|
||||
|
||||
然后使用下面的代码转换label（可以在python终端中执行下面的命令，也可以将其写入一个文件，然后使用`python3 convert.py`的方式运行该文件）。
|
||||
|
||||
|
||||
```python
|
||||
import os
|
||||
from xml.dom.minidom import parse
|
||||
|
||||
vehicleids = []
|
||||
|
||||
def convert_annotation(input_fp, output_fp):
|
||||
in_file = open(input_fp)
|
||||
list_file = open(output_fp, 'w')
|
||||
tree = parse(in_file)
|
||||
|
||||
root = tree.documentElement
|
||||
|
||||
for item in root.getElementsByTagName("Item"):
|
||||
label = ['0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0', '0']
|
||||
if item.hasAttribute("imageName"):
|
||||
name = item.getAttribute("imageName")
|
||||
if item.hasAttribute("vehicleID"):
|
||||
vehicleid = item.getAttribute("vehicleID")
|
||||
if vehicleid not in vehicleids :
|
||||
vehicleids.append(vehicleid)
|
||||
vid = vehicleids.index(vehicleid)
|
||||
if item.hasAttribute("colorID"):
|
||||
colorid = int (item.getAttribute("colorID"))
|
||||
label[colorid-1] = '1'
|
||||
if item.hasAttribute("typeID"):
|
||||
typeid = int (item.getAttribute("typeID"))
|
||||
label[typeid+9] = '1'
|
||||
label = ','.join(label)
|
||||
list_file.write(os.path.join('image_train', name) + "\t" + label + "\n")
|
||||
|
||||
list_file.close()
|
||||
|
||||
convert_annotation('train_label.xml', 'train_list.txt') #imagename vehiclenum colorid typeid
|
||||
convert_annotation('test_label.xml', 'test_list.txt')
|
||||
```
|
||||
|
||||
执行上述命令后，`VeRi`目录中具有以下数据：
|
||||
|
||||
```
|
||||
VeRi
|
||||
├── image_train
|
||||
│ ├── 0001_c001_00016450_0.jpg
|
||||
│ ├── 0001_c001_00016460_0.jpg
|
||||
│ ├── 0001_c001_00016470_0.jpg
|
||||
...
|
||||
├── image_test
|
||||
│ ├── 0002_c002_00030600_0.jpg
|
||||
│ ├── 0002_c002_00030605_1.jpg
|
||||
│ ├── 0002_c002_00030615_1.jpg
|
||||
...
|
||||
...
|
||||
├── train_list.txt
|
||||
├── test_list.txt
|
||||
├── train_label.xml
|
||||
├── test_label.xml
|
||||
```
|
||||
|
||||
其中`train/`和`test/`分别为训练集和验证集。`train_list.txt`和`test_list.txt`分别为训练集和验证集的转换后用于训练的标签文件。
|
||||
|
||||
|
||||
<a name="3.3"></a>
|
||||
|
||||
### 3.3 模型训练
|
||||
|
||||
|
||||
在 `ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0.yaml` 中提供了基于该场景的训练配置，可以通过如下脚本启动训练：
|
||||
|
||||
```shell
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0.yaml
|
||||
```
|
||||
|
||||
验证集的最佳指标在 `90.07%` 左右（数据集较小，一般有0.3%左右的波动）。
|
||||
|
||||
|
||||
<a name="3.4"></a>
|
||||
|
||||
### 3.4 模型评估
|
||||
|
||||
训练好模型之后，可以通过以下命令实现对模型指标的评估。
|
||||
|
||||
```bash
|
||||
python3 tools/eval.py \
|
||||
-c ./ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0.yaml \
|
||||
-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"
|
||||
```
|
||||
|
||||
其中 `-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` 指定了当前最佳权重所在的路径，如果指定其他权重，只需替换对应的路径即可。
|
||||
|
||||
<a name="3.5"></a>
|
||||
|
||||
### 3.5 模型预测
|
||||
|
||||
模型训练完成之后，可以加载训练得到的预训练模型，进行模型预测。在模型库的 `tools/infer.py` 中提供了完整的示例，只需执行下述命令即可完成模型预测：
|
||||
|
||||
```bash
|
||||
python3 tools/infer.py \
|
||||
-c ./ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0.yaml \
|
||||
-o Global.pretrained_model=output/DistillationModel/best_model
|
||||
```
|
||||
|
||||
输出结果如下：
|
||||
|
||||
```
|
||||
[{'attr': 'Color: (yellow, prob: 0.9893478155136108), Type: (hatchback, prob: 0.9734100103378296)', 'pred': [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0], 'file_name': './deploy/images/PULC/vehicle_attr/0002_c002_00030670_0.jpg'}]
|
||||
```
|
||||
|
||||
**备注：**
|
||||
|
||||
* 这里`-o Global.pretrained_model="output/PPLCNet_x1_0/best_model"` 指定了当前最佳权重所在的路径，如果指定其他权重，只需替换对应的路径即可。
|
||||
|
||||
* 默认是对 `./deploy/images/PULC/vehicle_attr/0002_c002_00030670_0.jpg` 进行预测，此处也可以通过增加字段 `-o Infer.infer_imgs=xxx` 对其他图片预测。
|
||||
|
||||
<a name="4"></a>
|
||||
|
||||
## 4. 模型压缩
|
||||
|
||||
<a name="4.1"></a>
|
||||
|
||||
### 4.1 SKL-UGI 知识蒸馏
|
||||
|
||||
SKL-UGI 知识蒸馏是 PaddleClas 提出的一种简单有效的知识蒸馏方法，关于该方法的介绍，可以参考[SKL-UGI 知识蒸馏](@ruoyu)。
|
||||
|
||||
<a name="4.1.1"></a>
|
||||
|
||||
#### 4.1.1 教师模型训练
|
||||
|
||||
复用 `ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0.yaml` 中的超参数，训练教师模型，训练脚本如下：
|
||||
|
||||
```shell
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0.yaml \
|
||||
-o Arch.name=ResNet101_vd
|
||||
```
|
||||
|
||||
验证集的最佳指标为 `91.60%` 左右，当前教师模型最好的权重保存在 `output/ResNet101_vd/best_model.pdparams`。
|
||||
|
||||
<a name="4.1.2"></a>
|
||||
|
||||
#### 4.1.2 蒸馏训练
|
||||
|
||||
配置文件`ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0_distillation.yaml`提供了`SKL-UGI知识蒸馏策略`的配置。该配置将`ResNet101_vd`当作教师模型，`PPLCNet_x1_0`当作学生模型。训练脚本如下：
|
||||
|
||||
```shell
|
||||
export CUDA_VISIBLE_DEVICES=0,1,2,3
|
||||
python3 -m paddle.distributed.launch \
|
||||
--gpus="0,1,2,3" \
|
||||
tools/train.py \
|
||||
-c ./ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0_distillation.yaml \
|
||||
-o Arch.models.0.Teacher.pretrained=output/ResNet101_vd/best_model
|
||||
```
|
||||
|
||||
验证集的最佳指标为 `90.81%` 左右，当前模型最好的权重保存在 `output/DistillationModel/best_model_student.pdparams`。
|
||||
|
||||
|
||||
<a name="5"></a>
|
||||
|
||||
## 5. 超参搜索
|
||||
|
||||
在 [3.2 节](#3.2)和 [4.1 节](#4.1)所使用的超参数是根据 PaddleClas 提供的 `SHAS 超参数搜索策略` 搜索得到的，如果希望在自己的数据集上得到更好的结果，可以参考[SHAS 超参数搜索策略](#TODO)来获得更好的训练超参数。
|
||||
|
||||
**备注：** 此部分内容是可选内容，搜索过程需要较长的时间，您可以根据自己的硬件情况来选择执行。如果没有更换数据集，可以忽略此节内容。
|
||||
|
||||
<a name="6"></a>
|
||||
|
||||
## 6. 模型推理部署
|
||||
|
||||
<a name="6.1"></a>
|
||||
|
||||
### 6.1 推理模型准备
|
||||
|
||||
Paddle Inference 是飞桨的原生推理库， 作用于服务器端和云端，提供高性能的推理能力。相比于直接基于预训练模型进行预测，Paddle Inference可使用MKLDNN、CUDNN、TensorRT 进行预测加速，从而实现更优的推理性能。更多关于Paddle Inference推理引擎的介绍，可以参考[Paddle Inference官网教程](https://www.paddlepaddle.org.cn/documentation/docs/zh/guides/infer/inference/inference_cn.html)。
|
||||
|
||||
当使用 Paddle Inference 推理时，加载的模型类型为 inference 模型。本案例提供了两种获得 inference 模型的方法，如果希望得到和文档相同的结果，请选择[直接下载 inference 模型](#6.1.2)的方式。
|
||||
|
||||
<a name="6.1.1"></a>
|
||||
|
||||
### 6.1.1 基于训练得到的权重导出 inference 模型
|
||||
|
||||
此处，我们提供了将权重和模型转换的脚本，执行该脚本可以得到对应的 inference 模型：
|
||||
|
||||
```bash
|
||||
python3 tools/export_model.py \
|
||||
-c ./ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0.yaml \
|
||||
-o Global.pretrained_model=output/DistillationModel/best_model_student \
|
||||
-o Global.save_inference_dir=deploy/models/PPLCNet_x1_0_vehicle_attr_infer
|
||||
```
|
||||
执行完该脚本后会在 `deploy/models/` 下生成 `PPLCNet_x1_0_vehicle_attr_infer` 文件夹，`models` 文件夹下应有如下文件结构：
|
||||
|
||||
```
|
||||
├── PPLCNet_x1_0_vehicle_attr_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
```
|
||||
|
||||
**备注：** 此处的最佳权重是经过知识蒸馏后的权重路径，如果没有执行知识蒸馏的步骤，最佳模型保存在`output/PPLCNet_x1_0/best_model.pdparams`中。
|
||||
|
||||
<a name="6.1.2"></a>
|
||||
|
||||
### 6.1.2 直接下载 inference 模型
|
||||
|
||||
[6.1.1 小节](#6.1.1)提供了导出 inference 模型的方法，此处也提供了该场景可以下载的 inference 模型，可以直接下载体验。
|
||||
|
||||
```
|
||||
cd deploy/models
|
||||
# 下载 inference 模型并解压
|
||||
wget https://paddleclas.bj.bcebos.com/models/PULC/vehicle_attr_infer.tar && tar -xf vehicle_attr_infer.tar
|
||||
```
|
||||
|
||||
解压完毕后，`models` 文件夹下应有如下文件结构：
|
||||
|
||||
```
|
||||
├── vehicle_attr_infer
|
||||
│ ├── inference.pdiparams
|
||||
│ ├── inference.pdiparams.info
|
||||
│ └── inference.pdmodel
|
||||
```
|
||||
|
||||
<a name="6.2"></a>
|
||||
|
||||
### 6.2 基于 Python 预测引擎推理
|
||||
|
||||
|
||||
<a name="6.2.1"></a>
|
||||
|
||||
#### 6.2.1 预测单张图像
|
||||
|
||||
返回 `deploy` 目录：
|
||||
|
||||
```
|
||||
cd ../
|
||||
```
|
||||
|
||||
运行下面的命令，对图像 `./images/PULC/vehicle_attr/0002_c002_00030670_0.jpg` 进行车辆属性识别。
|
||||
|
||||
```shell
|
||||
# 使用下面的命令使用 GPU 进行预测
|
||||
python3.7 python/predict_cls.py -c configs/PULC/vehicle_attr/inference_vehicle_attr.yaml -o Global.use_gpu=True
|
||||
# 使用下面的命令使用 CPU 进行预测
|
||||
python3.7 python/predict_cls.py -c configs/PULC/vehicle_attr/inference_vehicle_attr.yaml -o Global.use_gpu=False
|
||||
```
|
||||
|
||||
输出结果如下。
|
||||
|
||||
```
|
||||
0002_c002_00030670_0.jpg: attributes: Color: (yellow, prob: 0.9893478155136108), Type: (hatchback, prob: 0.97340989112854),
|
||||
predict output: [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
||||
```
|
||||
|
||||
<a name="6.2.2"></a>
|
||||
|
||||
#### 6.2.2 基于文件夹的批量预测
|
||||
|
||||
如果希望预测文件夹内的图像，可以直接修改配置文件中的 `Global.infer_imgs` 字段，也可以通过下面的 `-o` 参数修改对应的配置。
|
||||
|
||||
```shell
|
||||
# 使用下面的命令使用 GPU 进行预测，如果希望使用 CPU 预测，可以在命令后面添加 -o Global.use_gpu=False
|
||||
python3.7 python/predict_cls.py -c configs/PULC/vehicle_attr/inference_vehicle_attr.yaml -o Global.infer_imgs="./images/PULC/vehicle_attr/"
|
||||
```
|
||||
|
||||
终端中会输出该文件夹内所有图像的属性识别结果，如下所示。
|
||||
|
||||
```
|
||||
0002_c002_00030670_0.jpg: attributes: Color: (yellow, prob: 0.9893478155136108), Type: (hatchback, prob: 0.97340989112854),
|
||||
predict output: [1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0]
|
||||
0014_c012_00040750_0.jpg: attributes: Color: (red, prob: 0.9998721480369568), Type: (sedan, prob: 0.999976634979248),
|
||||
predict output: [0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0]
|
||||
```
|
||||
|
||||
<a name="6.3"></a>
|
||||
|
||||
### 6.3 基于 C++ 预测引擎推理
|
||||
|
||||
PaddleClas 提供了基于 C++ 预测引擎推理的示例，您可以参考[服务器端 C++ 预测](../inference_deployment/cpp_deploy.md)来完成相应的推理部署。如果您使用的是 Windows 平台，可以参考[基于 Visual Studio 2019 Community CMake 编译指南](../inference_deployment/cpp_deploy_on_windows.md)完成相应的预测库编译和模型预测工作。
|
||||
|
||||
<a name="6.4"></a>
|
||||
|
||||
### 6.4 服务化部署
|
||||
|
||||
Paddle Serving 提供高性能、灵活易用的工业级在线推理服务。Paddle Serving 支持 RESTful、gRPC、bRPC 等多种协议，提供多种异构硬件和多种操作系统环境下推理解决方案。更多关于Paddle Serving 的介绍，可以参考[Paddle Serving 代码仓库](https://github.com/PaddlePaddle/Serving)。
|
||||
|
||||
PaddleClas 提供了基于 Paddle Serving 来完成模型服务化部署的示例，您可以参考[模型服务化部署](../inference_deployment/paddle_serving_deploy.md)来完成相应的部署工作。
|
||||
|
||||
<a name="6.5"></a>
|
||||
|
||||
### 6.5 端侧部署
|
||||
|
||||
Paddle Lite 是一个高性能、轻量级、灵活性强且易于扩展的深度学习推理框架，定位于支持包括移动端、嵌入式以及服务器端在内的多硬件平台。更多关于 Paddle Lite 的介绍，可以参考[Paddle Lite 代码仓库](https://github.com/PaddlePaddle/Paddle-Lite)。
|
||||
|
||||
PaddleClas 提供了基于 Paddle Lite 来完成模型端侧部署的示例，您可以参考[端侧部署](../inference_deployment/paddle_lite_deploy.md)来完成相应的部署工作。
|
||||
|
||||
<a name="6.6"></a>
|
||||
|
||||
### 6.6 Paddle2ONNX 模型转换与预测
|
||||
|
||||
Paddle2ONNX 支持将 PaddlePaddle 模型格式转化到 ONNX 模型格式。通过 ONNX 可以完成将 Paddle 模型到多种推理引擎的部署，包括TensorRT/OpenVINO/MNN/TNN/NCNN，以及其它对 ONNX 开源格式进行支持的推理引擎或硬件。更多关于 Paddle2ONNX 的介绍，可以参考[Paddle2ONNX 代码仓库](https://github.com/PaddlePaddle/Paddle2ONNX)。
|
||||
|
||||
PaddleClas 提供了基于 Paddle2ONNX 来完成 inference 模型转换 ONNX 模型并作推理预测的示例，您可以参考[Paddle2ONNX 模型转换与预测](@shuilong)来完成相应的部署工作。
|
||||
|
|
@ -154,7 +154,8 @@ class MobileNetV3(TheseusLayer):
|
|||
class_expand=LAST_CONV,
|
||||
dropout_prob=0.2,
|
||||
return_patterns=None,
|
||||
return_stages=None):
|
||||
return_stages=None,
|
||||
**kwargs):
|
||||
super().__init__()
|
||||
|
||||
self.cfg = config
|
||||
|
|
|
|||
|
|
@ -94,13 +94,16 @@ class ConvBNLayer(TheseusLayer):
|
|||
stride=stride,
|
||||
padding=(filter_size - 1) // 2,
|
||||
groups=num_groups,
|
||||
weight_attr=ParamAttr(initializer=KaimingNormal(), learning_rate=lr_mult),
|
||||
weight_attr=ParamAttr(
|
||||
initializer=KaimingNormal(), learning_rate=lr_mult),
|
||||
bias_attr=False)
|
||||
|
||||
self.bn = BatchNorm2D(
|
||||
num_filters,
|
||||
weight_attr=ParamAttr(regularizer=L2Decay(0.0), learning_rate=lr_mult),
|
||||
bias_attr=ParamAttr(regularizer=L2Decay(0.0), learning_rate=lr_mult))
|
||||
weight_attr=ParamAttr(
|
||||
regularizer=L2Decay(0.0), learning_rate=lr_mult),
|
||||
bias_attr=ParamAttr(
|
||||
regularizer=L2Decay(0.0), learning_rate=lr_mult))
|
||||
self.hardswish = nn.Hardswish()
|
||||
|
||||
def forward(self, x):
|
||||
|
|
@ -128,8 +131,7 @@ class DepthwiseSeparable(TheseusLayer):
|
|||
num_groups=num_channels,
|
||||
lr_mult=lr_mult)
|
||||
if use_se:
|
||||
self.se = SEModule(num_channels,
|
||||
lr_mult=lr_mult)
|
||||
self.se = SEModule(num_channels, lr_mult=lr_mult)
|
||||
self.pw_conv = ConvBNLayer(
|
||||
num_channels=num_channels,
|
||||
filter_size=1,
|
||||
|
|
@ -189,7 +191,8 @@ class PPLCNet(TheseusLayer):
|
|||
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0, 1.0],
|
||||
use_last_conv=True,
|
||||
return_patterns=None,
|
||||
return_stages=None):
|
||||
return_stages=None,
|
||||
**kwargs):
|
||||
super().__init__()
|
||||
self.scale = scale
|
||||
self.class_expand = class_expand
|
||||
|
|
@ -271,7 +274,8 @@ class PPLCNet(TheseusLayer):
|
|||
self.avg_pool = AdaptiveAvgPool2D(1)
|
||||
if self.use_last_conv:
|
||||
self.last_conv = Conv2D(
|
||||
in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] * scale),
|
||||
in_channels=make_divisible(NET_CONFIG["blocks6"][-1][2] *
|
||||
scale),
|
||||
out_channels=self.class_expand,
|
||||
kernel_size=1,
|
||||
stride=1,
|
||||
|
|
@ -282,7 +286,8 @@ class PPLCNet(TheseusLayer):
|
|||
else:
|
||||
self.last_conv = None
|
||||
self.flatten = nn.Flatten(start_axis=1, stop_axis=-1)
|
||||
self.fc = Linear(self.class_expand if self.use_last_conv else NET_CONFIG["blocks6"][-1][2], class_num)
|
||||
self.fc = Linear(self.class_expand if self.use_last_conv else
|
||||
NET_CONFIG["blocks6"][-1][2], class_num)
|
||||
|
||||
super().init_res(
|
||||
stages_pattern,
|
||||
|
|
|
|||
|
|
@ -165,7 +165,8 @@ class BottleneckBlock(nn.Layer):
|
|||
|
||||
|
||||
class Res2Net_vd(nn.Layer):
|
||||
def __init__(self, layers=50, scales=4, width=26, class_num=1000):
|
||||
def __init__(self, layers=50, scales=4, width=26, class_num=1000,
|
||||
**kwargs):
|
||||
super(Res2Net_vd, self).__init__()
|
||||
|
||||
self.layers = layers
|
||||
|
|
|
|||
|
|
@ -0,0 +1,132 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 10
|
||||
print_batch_step: 10
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 224, 224]
|
||||
save_inference_dir: ./inference
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: MobileNetV3_large_x1_0
|
||||
class_num: 232
|
||||
pretrained: True
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
epsilon: 0.1
|
||||
Eval:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.00002
|
||||
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_train.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_test.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: Topk
|
||||
topk: 5
|
||||
class_id_map_file: dataset/traffic_sign/label_name_id.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
Eval:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
|
||||
|
|
@ -0,0 +1,148 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
start_eval_epoch: 0
|
||||
epochs: 10
|
||||
print_batch_step: 10
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 224, 224]
|
||||
save_inference_dir: ./inference
|
||||
# training model under @to_static
|
||||
to_static: False
|
||||
use_dali: False
|
||||
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x1_0
|
||||
class_num: 232
|
||||
pretrained: True
|
||||
use_ssld: True
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
Eval:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.02
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.00004
|
||||
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/tt100k_clas_v2/label_list_train.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- TimmAutoAugment:
|
||||
prob: 0.5
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.0
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_test.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
infer_imgs: deploy/images/PULC/traffic_sign/99603_17806.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: Topk
|
||||
topk: 5
|
||||
class_id_map_file: dataset/traffic_sign/label_name_id.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
Eval:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
|
|
@ -0,0 +1,172 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 10
|
||||
print_batch_step: 10
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 224, 224]
|
||||
save_inference_dir: ./inference
|
||||
# training model under @to_static
|
||||
to_static: False
|
||||
use_dali: False
|
||||
|
||||
# mixed precision training
|
||||
AMP:
|
||||
scale_loss: 128.0
|
||||
use_dynamic_loss_scaling: True
|
||||
# O1: mixed fp16
|
||||
level: O1
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: "DistillationModel"
|
||||
class_num: &class_num 232
|
||||
# if not null, its lengths should be same as models
|
||||
pretrained_list:
|
||||
# if not null, its lengths should be same as models
|
||||
freeze_params_list:
|
||||
- True
|
||||
- False
|
||||
models:
|
||||
- Teacher:
|
||||
name: ResNet101_vd
|
||||
class_num: *class_num
|
||||
pretrained: False
|
||||
- Student:
|
||||
name: PPLCNet_x1_0
|
||||
class_num: *class_num
|
||||
pretrained: True
|
||||
use_ssld: True
|
||||
|
||||
infer_model_name: "Student"
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- DistillationDMLLoss:
|
||||
weight: 1.0
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
Eval:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.00004
|
||||
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_train_for_distillation.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- TimmAutoAugment:
|
||||
prob: 0.0
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.0
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_test.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: Topk
|
||||
topk: 5
|
||||
class_id_map_file: dataset/traffic_sign/label_name_id.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- DistillationTopkAcc:
|
||||
model_key: "Student"
|
||||
topk: [1, 5]
|
||||
Eval:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
|
|
@ -0,0 +1,149 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
start_eval_epoch: 0
|
||||
epochs: 10
|
||||
print_batch_step: 10
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 224, 224]
|
||||
save_inference_dir: ./inference
|
||||
# training model under @to_static
|
||||
to_static: False
|
||||
use_dali: False
|
||||
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: PPLCNet_x1_0
|
||||
class_num: 232
|
||||
pretrained: True
|
||||
# use_ssld: True
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
Eval:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.00004
|
||||
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_train.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
- TimmAutoAugment:
|
||||
prob: 0.0
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.0
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_test.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
# infer_imgs: dataset/traffic_sign_demo/
|
||||
infer_imgs: dataset/tt100k_clas_v2/test/
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: Topk
|
||||
topk: 5
|
||||
class_id_map_file: dataset/traffic_sign/label_name_id.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
Eval:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
|
|
@ -0,0 +1,170 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: ./output/
|
||||
device: gpu
|
||||
save_interval: 1
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
start_eval_epoch: 0
|
||||
epochs: 10
|
||||
print_batch_step: 10
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 224, 224]
|
||||
save_inference_dir: ./inference
|
||||
# training model under @to_static
|
||||
to_static: False
|
||||
use_dali: False
|
||||
|
||||
# mixed precision training
|
||||
AMP:
|
||||
scale_loss: 128.0
|
||||
use_dynamic_loss_scaling: True
|
||||
# O1: mixed fp16
|
||||
level: O1
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: SwinTransformer_tiny_patch4_window7_224
|
||||
class_num: 232
|
||||
pretrained: True
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
epsilon: 0.1
|
||||
Eval:
|
||||
- CELoss:
|
||||
weight: 1.0
|
||||
|
||||
Optimizer:
|
||||
name: AdamW
|
||||
beta1: 0.9
|
||||
beta2: 0.999
|
||||
epsilon: 1e-8
|
||||
weight_decay: 0.05
|
||||
no_weight_decay_name: absolute_pos_embed relative_position_bias_table .bias norm
|
||||
one_dim_param_no_weight_decay: True
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 2e-4
|
||||
eta_min: 2e-6
|
||||
warmup_epoch: 5
|
||||
warmup_start_lr: 2e-7
|
||||
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/traffic_sign/label_list_train.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- RandCropImage:
|
||||
size: 224
|
||||
interpolation: bicubic
|
||||
backend: pil
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- TimmAutoAugment:
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.25
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
batch_transform_ops:
|
||||
- OpSampler:
|
||||
MixupOperator:
|
||||
alpha: 0.8
|
||||
prob: 0.5
|
||||
CutmixOperator:
|
||||
alpha: 1.0
|
||||
prob: 0.5
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Eval:
|
||||
dataset:
|
||||
name: ImageNetDataset
|
||||
image_root: ./dataset/
|
||||
cls_label_path: ./dataset/tt100k_clas_v2/label_list_test.txt
|
||||
delimiter: "\t"
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
infer_imgs: docs/images/inference_deployment/whl_demo.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
resize_short: 256
|
||||
- CropImage:
|
||||
size: 224
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: Topk
|
||||
topk: 5
|
||||
class_id_map_file: dataset/traffic_sign/label_name_id.txt
|
||||
|
||||
Metric:
|
||||
Train:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
Eval:
|
||||
- TopkAcc:
|
||||
topk: [1, 5]
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,40 @@
|
|||
base_config_file: ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0_search.yaml
|
||||
distill_config_file: ppcls/configs/PULC/traffic_sign/PPLCNet_x1_0_distillation.yaml
|
||||
|
||||
gpus: 0,1,2,3
|
||||
output_dir: output/search_traffic_sign
|
||||
search_times: 1
|
||||
search_dict:
|
||||
- search_key: lrs
|
||||
replace_config:
|
||||
- Optimizer.lr.learning_rate
|
||||
search_values: [0.0075, 0.01, 0.0125]
|
||||
- search_key: resolutions
|
||||
replace_config:
|
||||
- DataLoader.Train.dataset.transform_ops.1.RandCropImage.size
|
||||
- DataLoader.Train.dataset.transform_ops.2.TimmAutoAugment.img_size
|
||||
search_values: [176, 192, 224]
|
||||
- search_key: ra_probs
|
||||
replace_config:
|
||||
- DataLoader.Train.dataset.transform_ops.2.TimmAutoAugment.prob
|
||||
search_values: [0.0, 0.1, 0.5]
|
||||
- search_key: re_probs
|
||||
replace_config:
|
||||
- DataLoader.Train.dataset.transform_ops.4.RandomErasing.EPSILON
|
||||
search_values: [0.0, 0.1, 0.5]
|
||||
- search_key: lr_mult_list
|
||||
replace_config:
|
||||
- Arch.lr_mult_list
|
||||
search_values:
|
||||
- [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
|
||||
- [0.0, 0.4, 0.4, 0.8, 0.8, 1.0]
|
||||
- [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
teacher:
|
||||
rm_keys:
|
||||
- Arch.lr_mult_list
|
||||
search_values:
|
||||
- ResNet101_vd
|
||||
- ResNet50_vd
|
||||
final_replace:
|
||||
Arch.lr_mult_list: Arch.models.1.Student.lr_mult_list
|
||||
|
||||
|
|
@ -0,0 +1,115 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: "./output/"
|
||||
device: "gpu"
|
||||
save_interval: 5
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 30
|
||||
print_batch_step: 20
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 192, 256]
|
||||
save_inference_dir: "./inference"
|
||||
use_multilabel: True
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: "MobileNetV3_large_x1_0"
|
||||
pretrained: True
|
||||
class_num: 19
|
||||
infer_add_softmax: False
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
Eval:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.0005
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/train_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- Padv2:
|
||||
size: [276, 212]
|
||||
pad_mode: 1
|
||||
fill_value: 0
|
||||
- RandomCropImage:
|
||||
size: [256, 192]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: True
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
Eval:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/test_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
|
||||
Metric:
|
||||
Eval:
|
||||
- ATTRMetric:
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,149 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: "./output/"
|
||||
device: "gpu"
|
||||
save_interval: 5
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 30
|
||||
print_batch_step: 20
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 192, 256]
|
||||
save_inference_dir: "./inference"
|
||||
use_multilabel: True
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: "PPLCNet_x1_0"
|
||||
pretrained: True
|
||||
class_num: 19
|
||||
use_ssld: True
|
||||
lr_mult_list: [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
infer_add_softmax: False
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
Eval:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.0125
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.0005
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/train_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- TimmAutoAugment:
|
||||
prob: 0.0
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: [256, 192]
|
||||
- Padv2:
|
||||
size: [276, 212]
|
||||
pad_mode: 1
|
||||
fill_value: 0
|
||||
- RandomCropImage:
|
||||
size: [256, 192]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.5
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: True
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
Eval:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/test_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
Infer:
|
||||
infer_imgs: ./deploy/images/PULC/vehicle_attr/0002_c002_00030670_0.jpg
|
||||
batch_size: 10
|
||||
transforms:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- ToCHWImage:
|
||||
PostProcess:
|
||||
name: VehicleAttribute
|
||||
color_threshold: 0.5
|
||||
type_threshold: 0.5
|
||||
|
||||
Metric:
|
||||
Eval:
|
||||
- ATTRMetric:
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,150 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: "./output/"
|
||||
device: "gpu"
|
||||
save_interval: 5
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 30
|
||||
print_batch_step: 20
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 192, 256]
|
||||
save_inference_dir: "./inference"
|
||||
use_multilabel: True
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: "DistillationModel"
|
||||
class_num: &class_num 19
|
||||
# if not null, its lengths should be same as models
|
||||
pretrained_list:
|
||||
# if not null, its lengths should be same as models
|
||||
freeze_params_list:
|
||||
- True
|
||||
- False
|
||||
use_ssld: True
|
||||
models:
|
||||
- Teacher:
|
||||
name: ResNet101_vd
|
||||
class_num: *class_num
|
||||
- Student:
|
||||
name: PPLCNet_x1_0
|
||||
class_num: *class_num
|
||||
pretrained: True
|
||||
use_ssld: True
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- DistillationMultiLabelLoss:
|
||||
weight: 1.0
|
||||
model_names: ["Student"]
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
- DistillationDMLLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
sum_across_class_dim: False
|
||||
model_name_pairs:
|
||||
- ["Student", "Teacher"]
|
||||
|
||||
Eval:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.0005
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/train_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- TimmAutoAugment:
|
||||
prob: 0.0
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: [256, 192]
|
||||
- Padv2:
|
||||
size: [276, 212]
|
||||
pad_mode: 1
|
||||
fill_value: 0
|
||||
- RandomCropImage:
|
||||
size: [256, 192]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.0
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: True
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
Eval:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/test_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
|
||||
Metric:
|
||||
Eval:
|
||||
- ATTRMetric:
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,129 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: "./output/"
|
||||
device: "gpu"
|
||||
save_interval: 5
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 30
|
||||
print_batch_step: 20
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 192, 256]
|
||||
save_inference_dir: "./inference"
|
||||
use_multilabel: True
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: "PPLCNet_x1_0"
|
||||
pretrained: True
|
||||
use_ssld: True
|
||||
class_num: 19
|
||||
infer_add_softmax: False
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
Eval:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.0005
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/train_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- TimmAutoAugment:
|
||||
prob: 0.0
|
||||
config_str: rand-m9-mstd0.5-inc1
|
||||
interpolation: bicubic
|
||||
img_size: [256, 192]
|
||||
- Padv2:
|
||||
size: [276, 212]
|
||||
pad_mode: 1
|
||||
fill_value: 0
|
||||
- RandomCropImage:
|
||||
size: [256, 192]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
- RandomErasing:
|
||||
EPSILON: 0.0
|
||||
sl: 0.02
|
||||
sh: 1.0/3.0
|
||||
r1: 0.3
|
||||
attempt: 10
|
||||
use_log_aspect: True
|
||||
mode: pixel
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: True
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
Eval:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/test_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 128
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
|
||||
Metric:
|
||||
Eval:
|
||||
- ATTRMetric:
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,122 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: "./output/mo"
|
||||
device: "gpu"
|
||||
save_interval: 5
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 30
|
||||
print_batch_step: 20
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 192, 256]
|
||||
save_inference_dir: "./inference"
|
||||
use_multilabel: True
|
||||
|
||||
# mixed precision training
|
||||
AMP:
|
||||
scale_loss: 128.0
|
||||
use_dynamic_loss_scaling: True
|
||||
# O1: mixed fp16
|
||||
level: O1
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: "Res2Net200_vd_26w_4s"
|
||||
pretrained: True
|
||||
class_num: 19
|
||||
infer_add_softmax: False
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
Eval:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.0005
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/train_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- Padv2:
|
||||
size: [276, 212]
|
||||
pad_mode: 1
|
||||
fill_value: 0
|
||||
- RandomCropImage:
|
||||
size: [256, 192]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: True
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
Eval:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/test_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
|
||||
Metric:
|
||||
Eval:
|
||||
- ATTRMetric:
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,116 @@
|
|||
# global configs
|
||||
Global:
|
||||
checkpoints: null
|
||||
pretrained_model: null
|
||||
output_dir: "./output/"
|
||||
device: "gpu"
|
||||
save_interval: 5
|
||||
eval_during_train: True
|
||||
eval_interval: 1
|
||||
epochs: 30
|
||||
print_batch_step: 20
|
||||
use_visualdl: False
|
||||
# used for static mode and model export
|
||||
image_shape: [3, 192, 256]
|
||||
save_inference_dir: "./inference"
|
||||
use_multilabel: True
|
||||
|
||||
# model architecture
|
||||
Arch:
|
||||
name: "ResNet50"
|
||||
pretrained: True
|
||||
class_num: 19
|
||||
infer_add_softmax: False
|
||||
|
||||
# loss function config for traing/eval process
|
||||
Loss:
|
||||
Train:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
Eval:
|
||||
- MultiLabelLoss:
|
||||
weight: 1.0
|
||||
weight_ratio: True
|
||||
size_sum: True
|
||||
|
||||
|
||||
Optimizer:
|
||||
name: Momentum
|
||||
momentum: 0.9
|
||||
lr:
|
||||
name: Cosine
|
||||
learning_rate: 0.01
|
||||
warmup_epoch: 5
|
||||
regularizer:
|
||||
name: 'L2'
|
||||
coeff: 0.0005
|
||||
|
||||
# data loader for train and eval
|
||||
DataLoader:
|
||||
Train:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/train_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- Padv2:
|
||||
size: [276, 212]
|
||||
pad_mode: 1
|
||||
fill_value: 0
|
||||
- RandomCropImage:
|
||||
size: [256, 192]
|
||||
- RandFlipImage:
|
||||
flip_code: 1
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: True
|
||||
shuffle: True
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
Eval:
|
||||
dataset:
|
||||
name: MultiLabelDataset
|
||||
image_root: "dataset/VeRi/"
|
||||
cls_label_path: "dataset/VeRi/test_list.txt"
|
||||
label_ratio: True
|
||||
transform_ops:
|
||||
- DecodeImage:
|
||||
to_rgb: True
|
||||
channel_first: False
|
||||
- ResizeImage:
|
||||
size: [256, 192]
|
||||
- NormalizeImage:
|
||||
scale: 1.0/255.0
|
||||
mean: [0.485, 0.456, 0.406]
|
||||
std: [0.229, 0.224, 0.225]
|
||||
order: ''
|
||||
sampler:
|
||||
name: DistributedBatchSampler
|
||||
batch_size: 64
|
||||
drop_last: False
|
||||
shuffle: False
|
||||
loader:
|
||||
num_workers: 8
|
||||
use_shared_memory: True
|
||||
|
||||
|
||||
Metric:
|
||||
Eval:
|
||||
- ATTRMetric:
|
||||
|
||||
|
||||
|
|
@ -0,0 +1,34 @@
|
|||
base_config_file: ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0_search.yaml
|
||||
distill_config_file: ppcls/configs/PULC/vehicle_attr/PPLCNet_x1_0_distillation.yaml
|
||||
|
||||
gpus: 0,1,2,3
|
||||
output_dir: output/search_vehicle_attr
|
||||
search_times: 1
|
||||
search_dict:
|
||||
- search_key: lrs
|
||||
replace_config:
|
||||
- Optimizer.lr.learning_rate
|
||||
search_values: [0.0075, 0.01, 0.0125]
|
||||
- search_key: ra_probs
|
||||
replace_config:
|
||||
- DataLoader.Train.dataset.transform_ops.2.TimmAutoAugment.prob
|
||||
search_values: [0.0, 0.1, 0.5]
|
||||
- search_key: re_probs
|
||||
replace_config:
|
||||
- DataLoader.Train.dataset.transform_ops.7.RandomErasing.EPSILON
|
||||
search_values: [0.0, 0.1, 0.5]
|
||||
- search_key: lr_mult_list
|
||||
replace_config:
|
||||
- Arch.lr_mult_list
|
||||
search_values:
|
||||
- [0.0, 0.2, 0.4, 0.6, 0.8, 1.0]
|
||||
- [0.0, 0.4, 0.4, 0.8, 0.8, 1.0]
|
||||
- [1.0, 1.0, 1.0, 1.0, 1.0, 1.0]
|
||||
teacher:
|
||||
rm_keys:
|
||||
- Arch.lr_mult_list
|
||||
search_values:
|
||||
- ResNet101_vd
|
||||
- ResNet50_vd
|
||||
final_replace:
|
||||
Arch.lr_mult_list: Arch.models.1.Student.lr_mult_list
|
||||
|
|
@ -18,6 +18,7 @@ from . import topk, threshoutput
|
|||
|
||||
from .topk import Topk, MultiLabelTopk
|
||||
from .threshoutput import ThreshOutput
|
||||
from .attr_rec import VehicleAttribute
|
||||
|
||||
|
||||
def build_postprocess(config):
|
||||
|
|
|
|||
|
|
@ -0,0 +1,71 @@
|
|||
# copyright (c) 2022 PaddlePaddle Authors. All Rights Reserve.
|
||||
#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import os
|
||||
import numpy as np
|
||||
import paddle
|
||||
import paddle.nn.functional as F
|
||||
|
||||
|
||||
class VehicleAttribute(object):
|
||||
def __init__(self, color_threshold=0.5, type_threshold=0.5):
|
||||
self.color_threshold = color_threshold
|
||||
self.type_threshold = type_threshold
|
||||
self.color_list = [
|
||||
"yellow", "orange", "green", "gray", "red", "blue", "white",
|
||||
"golden", "brown", "black"
|
||||
]
|
||||
self.type_list = [
|
||||
"sedan", "suv", "van", "hatchback", "mpv", "pickup", "bus",
|
||||
"truck", "estate"
|
||||
]
|
||||
|
||||
def __call__(self, x, file_names=None):
|
||||
if isinstance(x, dict):
|
||||
x = x['logits']
|
||||
assert isinstance(x, paddle.Tensor)
|
||||
if file_names is not None:
|
||||
assert x.shape[0] == len(file_names)
|
||||
x = F.sigmoid(x).numpy()
|
||||
|
||||
# postprocess output of predictor
|
||||
batch_res = []
|
||||
for idx, res in enumerate(x):
|
||||
res = res.tolist()
|
||||
label_res = []
|
||||
color_idx = np.argmax(res[:10])
|
||||
type_idx = np.argmax(res[10:])
|
||||
print(color_idx, type_idx)
|
||||
if res[color_idx] >= self.color_threshold:
|
||||
color_info = f"Color: ({self.color_list[color_idx]}, prob: {res[color_idx]})"
|
||||
else:
|
||||
color_info = "Color unknown"
|
||||
|
||||
if res[type_idx + 10] >= self.type_threshold:
|
||||
type_info = f"Type: ({self.type_list[type_idx]}, prob: {res[type_idx + 10]})"
|
||||
else:
|
||||
type_info = "Type unknown"
|
||||
|
||||
label_res = f"{color_info}, {type_info}"
|
||||
|
||||
threshold_list = [self.color_threshold
|
||||
] * 10 + [self.type_threshold] * 9
|
||||
pred_res = (np.array(res) > np.array(threshold_list)
|
||||
).astype(np.int8).tolist()
|
||||
batch_res.append({
|
||||
"attr": label_res,
|
||||
"pred": pred_res,
|
||||
"file_name": file_names[idx]
|
||||
})
|
||||
return batch_res
|
||||
|
|
@ -460,7 +460,7 @@ class Engine(object):
|
|||
assert self.mode == "export"
|
||||
use_multilabel = self.config["Global"].get(
|
||||
"use_multilabel",
|
||||
False) and not "ATTRMetric" in self.config["Metric"]["Eval"][0]
|
||||
False) and "ATTRMetric" in self.config["Metric"]["Eval"][0]
|
||||
model = ExportModel(self.config["Arch"], self.model, use_multilabel)
|
||||
if self.config["Global"]["pretrained_model"] is not None:
|
||||
load_dygraph_pretrain(model.base_model,
|
||||
|
|
|
|||
|
|
@ -24,6 +24,7 @@ from .distillationloss import DistillationDistanceLoss
|
|||
from .distillationloss import DistillationRKDLoss
|
||||
from .distillationloss import DistillationKLDivLoss
|
||||
from .distillationloss import DistillationDKDLoss
|
||||
from .distillationloss import DistillationMultiLabelLoss
|
||||
from .multilabelloss import MultiLabelLoss
|
||||
from .afdloss import AFDLoss
|
||||
|
||||
|
|
|
|||
|
|
@ -22,6 +22,7 @@ from .distanceloss import DistanceLoss
|
|||
from .rkdloss import RKdAngle, RkdDistance
|
||||
from .kldivloss import KLDivLoss
|
||||
from .dkdloss import DKDLoss
|
||||
from .multilabelloss import MultiLabelLoss
|
||||
|
||||
|
||||
class DistillationCELoss(CELoss):
|
||||
|
|
@ -89,13 +90,16 @@ class DistillationDMLLoss(DMLLoss):
|
|||
def __init__(self,
|
||||
model_name_pairs=[],
|
||||
act="softmax",
|
||||
weight_ratio=False,
|
||||
sum_across_class_dim=False,
|
||||
key=None,
|
||||
name="loss_dml"):
|
||||
super().__init__(act=act)
|
||||
super().__init__(act=act, sum_across_class_dim=sum_across_class_dim)
|
||||
assert isinstance(model_name_pairs, list)
|
||||
self.key = key
|
||||
self.model_name_pairs = model_name_pairs
|
||||
self.name = name
|
||||
self.weight_ratio = weight_ratio
|
||||
|
||||
def forward(self, predicts, batch):
|
||||
loss_dict = dict()
|
||||
|
|
@ -105,7 +109,10 @@ class DistillationDMLLoss(DMLLoss):
|
|||
if self.key is not None:
|
||||
out1 = out1[self.key]
|
||||
out2 = out2[self.key]
|
||||
loss = super().forward(out1, out2)
|
||||
if self.weight_ratio is True:
|
||||
loss = super().forward(out1, out2, batch)
|
||||
else:
|
||||
loss = super().forward(out1, out2)
|
||||
if isinstance(loss, dict):
|
||||
for key in loss:
|
||||
loss_dict["{}_{}_{}_{}".format(key, pair[0], pair[1],
|
||||
|
|
@ -122,6 +129,7 @@ class DistillationDistanceLoss(DistanceLoss):
|
|||
def __init__(self,
|
||||
mode="l2",
|
||||
model_name_pairs=[],
|
||||
act=None,
|
||||
key=None,
|
||||
name="loss_",
|
||||
**kargs):
|
||||
|
|
@ -130,6 +138,13 @@ class DistillationDistanceLoss(DistanceLoss):
|
|||
self.key = key
|
||||
self.model_name_pairs = model_name_pairs
|
||||
self.name = name + mode
|
||||
assert act in [None, "sigmoid", "softmax"]
|
||||
if act == "sigmoid":
|
||||
self.act = nn.Sigmoid()
|
||||
elif act == "softmax":
|
||||
self.act = nn.Softmax(axis=-1)
|
||||
else:
|
||||
self.act = None
|
||||
|
||||
def forward(self, predicts, batch):
|
||||
loss_dict = dict()
|
||||
|
|
@ -139,6 +154,9 @@ class DistillationDistanceLoss(DistanceLoss):
|
|||
if self.key is not None:
|
||||
out1 = out1[self.key]
|
||||
out2 = out2[self.key]
|
||||
if self.act is not None:
|
||||
out1 = self.act(out1)
|
||||
out2 = self.act(out2)
|
||||
loss = super().forward(out1, out2)
|
||||
for key in loss:
|
||||
loss_dict["{}_{}_{}".format(self.name, key, idx)] = loss[key]
|
||||
|
|
@ -235,3 +253,34 @@ class DistillationDKDLoss(DKDLoss):
|
|||
loss = super().forward(out1, out2, batch)
|
||||
loss_dict[f"{self.name}_{pair[0]}_{pair[1]}"] = loss
|
||||
return loss_dict
|
||||
|
||||
|
||||
class DistillationMultiLabelLoss(MultiLabelLoss):
|
||||
"""
|
||||
DistillationMultiLabelLoss
|
||||
"""
|
||||
|
||||
def __init__(self,
|
||||
model_names=[],
|
||||
epsilon=None,
|
||||
size_sum=False,
|
||||
weight_ratio=False,
|
||||
key=None,
|
||||
name="loss_mll"):
|
||||
super().__init__(
|
||||
epsilon=epsilon, size_sum=size_sum, weight_ratio=weight_ratio)
|
||||
assert isinstance(model_names, list)
|
||||
self.key = key
|
||||
self.model_names = model_names
|
||||
self.name = name
|
||||
|
||||
def forward(self, predicts, batch):
|
||||
loss_dict = dict()
|
||||
for name in self.model_names:
|
||||
out = predicts[name]
|
||||
if self.key is not None:
|
||||
out = out[self.key]
|
||||
loss = super().forward(out, batch)
|
||||
for key in loss:
|
||||
loss_dict["{}_{}".format(key, name)] = loss[key]
|
||||
return loss_dict
|
||||
|
|
|
|||
|
|
@ -16,13 +16,15 @@ import paddle
|
|||
import paddle.nn as nn
|
||||
import paddle.nn.functional as F
|
||||
|
||||
from ppcls.loss.multilabelloss import ratio2weight
|
||||
|
||||
|
||||
class DMLLoss(nn.Layer):
|
||||
"""
|
||||
DMLLoss
|
||||
"""
|
||||
|
||||
def __init__(self, act="softmax", eps=1e-12):
|
||||
def __init__(self, act="softmax", sum_across_class_dim=False, eps=1e-12):
|
||||
super().__init__()
|
||||
if act is not None:
|
||||
assert act in ["softmax", "sigmoid"]
|
||||
|
|
@ -33,6 +35,7 @@ class DMLLoss(nn.Layer):
|
|||
else:
|
||||
self.act = None
|
||||
self.eps = eps
|
||||
self.sum_across_class_dim = sum_across_class_dim
|
||||
|
||||
def _kldiv(self, x, target):
|
||||
class_num = x.shape[-1]
|
||||
|
|
@ -40,11 +43,20 @@ class DMLLoss(nn.Layer):
|
|||
(target + self.eps) / (x + self.eps)) * class_num
|
||||
return cost
|
||||
|
||||
def forward(self, x, target):
|
||||
def forward(self, x, target, gt_label=None):
|
||||
if self.act is not None:
|
||||
x = self.act(x)
|
||||
target = self.act(target)
|
||||
loss = self._kldiv(x, target) + self._kldiv(target, x)
|
||||
loss = loss / 2
|
||||
loss = paddle.mean(loss)
|
||||
|
||||
# for multi-label dml loss
|
||||
if gt_label is not None:
|
||||
gt_label, label_ratio = gt_label[:, 0, :], gt_label[:, 1, :]
|
||||
targets_mask = paddle.cast(gt_label > 0.5, 'float32')
|
||||
weight = ratio2weight(targets_mask, paddle.to_tensor(label_ratio))
|
||||
weight = weight * (gt_label > -1)
|
||||
loss = loss * weight
|
||||
|
||||
loss = loss.sum(1).mean() if self.sum_across_class_dim else loss.mean()
|
||||
return {"DMLLoss": loss}
|
||||
|
|
|
|||
Loading…
Reference in New Issue