From 58f7e85b2ee06823f9e3e10ccb0dcf935c754f43 Mon Sep 17 00:00:00 2001 From: cnn Date: Thu, 3 Dec 2020 16:15:02 +0800 Subject: [PATCH] Remove CN comments in roadsign config. (#1791) * Update QUICK_STARTED_cn.md (#1757) fix typo * cherry-pick #1757 and #1473, test=document_fix --- configs/yolov3_mobilenet_v1_roadsign.yml | 143 ++--------------------- docs/images/000000014439_640x640.jpg | Bin 0 -> 280182 bytes docs/images/visualdl_roadsign.png | Bin 0 -> 222011 bytes docs/tutorials/QUICK_STARTED_cn.md | 8 +- 4 files changed, 12 insertions(+), 139 deletions(-) create mode 100644 docs/images/000000014439_640x640.jpg create mode 100644 docs/images/visualdl_roadsign.png diff --git a/configs/yolov3_mobilenet_v1_roadsign.yml b/configs/yolov3_mobilenet_v1_roadsign.yml index 595493a6d..294542f0f 100644 --- a/configs/yolov3_mobilenet_v1_roadsign.yml +++ b/configs/yolov3_mobilenet_v1_roadsign.yml @@ -1,187 +1,97 @@ -#####################################基础配置##################################### -# 检测算法使用YOLOv3,backbone使用MobileNet_v1 -# 检测模型的名称 architecture: YOLOv3 -# 根据硬件选择是否使用GPU use_gpu: true - # ### max_iters为最大迭代次数,而一个iter会运行batch_size * device_num张图片。batch_size在下面 TrainReader.batch_size设置。 -max_iters: 1200 -# log平滑参数,平滑窗口大小,会从取历史窗口中取log_iter大小的loss求平均值 -log_iter: 20 -# 模型保存文件夹 +max_iters: 3600 +log_smooth_window: 20 save_dir: output -# 每隔多少迭代保存模型 snapshot_iter: 200 -# ### mAP 评估方式,mAP评估方式可以选择COCO和VOC或WIDERFACE,其中VOC有11point和integral两种评估方法 -# VOC数据格式只能使用VOC mAP评估方法 metric: VOC map_type: integral -# ### pretrain_weights 可以是imagenet的预训练好的分类模型权重,也可以是在VOC或COCO数据集上的预训练的检测模型权重 -# 模型配置文件和权重文件可参考[模型库](https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/docs/MODEL_ZOO.md) pretrain_weights: https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v1.tar -# 模型保存文件夹,如果开启了--eval,会在这个文件夹下保存best_model -weights: output/yolov3_mobilenet_v1_roadsign_coco_template/ -# ### 根据用户数据设置类别数,注意这里不含背景类 +weights: output/yolov3_mobilenet_v1_roadsign/best_model num_classes: 4 -# finetune时忽略的参数,按照正则化匹配,匹配上的参数会被忽略掉 finetune_exclude_pretrained_params: ['yolo_output'] -# use_fine_grained_loss use_fine_grained_loss: false -# 检测模型的结构 YOLOv3: - # 默认是 MobileNetv1 backbone: MobileNet yolo_head: YOLOv3Head -# 检测模型的backbone MobileNet: norm_decay: 0. conv_group_scale: 1 with_extra_blocks: false -# 检测模型的Head YOLOv3Head: - # anchor_masks anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] - # 3x3 anchors anchors: [[10, 13], [16, 30], [33, 23], [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] - # yolo_loss yolo_loss: YOLOv3Loss - # nms 类型参数,可以设置为[MultiClassNMS, MultiClassSoftNMS, MatrixNMS], 默认使用 MultiClassNMS nms: - # background_label,背景标签(类别)的索引,如果设置为 0 ,则忽略背景标签(类别)。如果设置为 -1 ,则考虑所有类别。默认值:0 background_label: -1 - # NMS步骤后每个图像要保留的总bbox数。 -1表示在NMS步骤之后保留所有bbox。 keep_top_k: 100 - # 在NMS中用于剔除检测框IOU的阈值,默认值:0.3 。 nms_threshold: 0.45 - # 基于 score_threshold 的过滤检测后,根据置信度保留的最大检测次数。 nms_top_k: 1000 - # 是否归一化,默认值:True 。 normalized: false - # 过滤掉低置信度分数的边界框的阈值。 score_threshold: 0.01 YOLOv3Loss: - # 这里的batch_size与训练中的batch_size(即TrainReader.batch_size)不同. - # 仅且当use_fine_grained_loss=true时,计算Loss时使用,且必须要与TrainReader.batch_size设置成一样 - batch_size: 8 - # 忽略样本的阈值 ignore_thresh ignore_thresh: 0.7 - # 是否使用label_smooth label_smooth: true LearningRate: - # ### 学习率设置 参考 https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/docs/FAQ.md#faq%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98 - # base_lr base_lr: 0.0001 - # 学习率调整策略 - # 具体实现参考[API](fluid.layers.piecewise_decay) schedulers: - # 学习率调整策略 - !PiecewiseDecay gamma: 0.1 milestones: - # ### 参考 https://github.com/PaddlePaddle/PaddleDetection/blob/release/0.4/docs/FAQ.md#faq%E5%B8%B8%E8%A7%81%E9%97%AE%E9%A2%98 - # ### 8/12 11/12 - - 800 - - 1100 - # 在训练开始时,调低学习率为base_lr * start_factor,然后逐步增长到base_lr,这个过程叫学习率热身,按照以下公式更新学习率 - # linear_step = end_lr - start_lr - # lr = start_lr + linear_step * (global_step / warmup_steps) - # 具体实现参考[API](fluid.layers.linear_lr_warmup) + - 2400 + - 3300 - !LinearWarmup start_factor: 0.3333333333333333 steps: 100 OptimizerBuilder: - # 默认使用SGD+Momentum进行训练 - # 具体实现参考[API](fluid.optimizer) optimizer: momentum: 0.9 type: Momentum - # 默认使用SGD+Momentum进行训练 - # 具体实现参考[API](fluid.optimizer) regularizer: factor: 0.0005 type: L2 -#####################################数据配置##################################### - -# 模型训练集设置参考 -# 训练、验证、测试使用的数据配置主要区别在数据路径、模型输入、数据增强参数设置 -# 如果使用 yolov3_reader.yml,下面的参数设置优先级高,会覆盖yolov3_reader.yml中的参数设置。 # _READER_: 'yolov3_reader.yml' - TrainReader: - # 训练过程中模型的输入设置 - # 包括图片,图片长宽高等基本信息,图片id,标记的目标框,类别等信息 inputs_def: fields: ['image', 'gt_bbox', 'gt_class', 'gt_score'] - # num_max_boxes,每个样本的groud truth的最多保留个数,若不够用0填充。 num_max_boxes: 50 - # 训练数据集路径 dataset: - # 指定数据集格式 !VOCDataSet - #dataset/xxx/ - #├── annotations - #│ ├── xxx1.xml - #│ ├── xxx2.xml - #│ ├── xxx3.xml - #│ | ... - #├── images - #│ ├── xxx1.png - #│ ├── xxx2.png - #│ ├── xxx3.png - #│ | ... - #├── label_list.txt (用户自定义必须提供,且文件名称必须是label_list.txt。当使用VOC数据且use_default_label=true时,可不提供 ) - #├── train.txt (训练数据集文件列表, ./images/xxx1.png ./Annotations/xxx1.xml) - #└── valid.txt (测试数据集文件列表) - # 图片文件夹相对路径,路径是相对于dataset_dir,图像路径= dataset_dir + image_dir + image_name dataset_dir: dataset/roadsign_voc - # 标记文件名 anno_path: train.txt - # 是否包含背景类,若with_background=true,num_classes需要+1 - # YOLO 系列with_background必须是false,FasterRCNN系列是true ### with_background: false + use_default_label: false sample_transforms: - # 读取Image图像为numpy数组 - # 可以选择将图片从BGR转到RGB,可以选择对一个batch中的图片做mixup增强 - !DecodeImage to_rgb: True with_mixup: True - # MixupImage - !MixupImage alpha: 1.5 beta: 1.5 - # ColorDistort - !ColorDistort {} - # RandomExpand - !RandomExpand fill_value: [123.675, 116.28, 103.53] - # 随机扩充比例,默认值是4.0 ratio: 1.5 - !RandomCrop {} - !RandomFlipImage is_normalized: false - # 归一化坐标 - !NormalizeBox {} - # 如果 bboxes 数量小于 num_max_boxes,填充值为0的 box - !PadBox num_max_boxes: 50 - # 坐标格式转化,从XYXY转成XYWH格式 - !BboxXYXY2XYWH {} - # 以下是对一个batch中的所有图片同时做的数据处理 batch_transforms: - # 多尺度训练时,从list中随机选择一个尺寸,对一个batch数据同时同时resize - !RandomShape sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] random_inter: True - # NormalizeImage - !NormalizeImage mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] @@ -199,104 +109,67 @@ TrainReader: [30, 61], [62, 45], [59, 119], [116, 90], [156, 198], [373, 326]] downsample_ratios: [32, 16, 8] - # 1个GPU的batch size,默认为1。需要注意:每个iter迭代会运行batch_size * device_num张图片 batch_size: 8 - # 是否shuffle shuffle: true - # mixup,-1表示不做Mixup数据增强。注意,这里是epoch为单位 mixup_epoch: 250 - # 注意,在某些情况下,drop_last=false时训练过程中可能会出错,建议训练时都设置为true drop_last: true - # 若选用多进程,设置使用多进程/线程的数目 - # 开启多进程后,占用内存会成倍增加,根据内存设置### worker_num: 4 - # 共享内存bufsize。注意,缓存是以batch为单位,缓存的样本数据总量为batch_size * bufsize,所以请注意不要设置太大,请根据您的硬件设置。 bufsize: 2 - # 是否使用多进程 use_process: true EvalReader: - # 评估过程中模型的输入设置 - # 包括图片,图片长宽高等基本信息,图片id,标记的目标框,类别等信息 inputs_def: fields: ['image', 'im_size', 'im_id', 'gt_bbox', 'gt_class', 'is_difficult'] - # num_max_boxes,每个样本的groud truth的最多保留个数,若不够用0填充。 num_max_boxes: 50 - # 数据集路径 dataset: !VOCDataSet - # 图片文件夹相对路径,路径是相对于dataset_dir,图像路径= dataset_dir + image_dir + image_name dataset_dir: dataset/roadsign_voc - # 评估文件列表 anno_path: valid.txt - # 是否包含背景类,若with_background=true,num_classes需要+1 - # YOLO 系列with_background必须是false,FasterRCNN系列是true ### with_background: false + use_default_label: false sample_transforms: - # 读取Image图像为numpy数组 - # 可以选择将图片从BGR转到RGB,可以选择对一个batch中的图片做mixup增强 - !DecodeImage to_rgb: True - # ResizeImage - !ResizeImage target_size: 608 interp: 2 - # NormalizeImage - !NormalizeImage mean: [0.485, 0.456, 0.406] std: [0.229, 0.224, 0.225] is_scale: True is_channel_first: false - # 如果 bboxes 数量小于 num_max_boxes,填充值为0的 box - !PadBox num_max_boxes: 50 - !Permute to_bgr: false channel_first: True - # 1个GPU的batch size,默认为1。需要注意:每个iter迭代会运行batch_size * device_num张图片 batch_size: 8 - # drop_empty drop_empty: false - # 若选用多进程,设置使用多进程/线程的数目 - # 开启多进程后,占用内存会成倍增加,根据内存设置### worker_num: 4 - # 共享内存bufsize。注意,缓存是以batch为单位,缓存的样本数据总量为batch_size * bufsize,所以请注意不要设置太大,请根据您的硬件设置。 bufsize: 2 TestReader: - # 预测过程中模型的输入设置 - # 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-如果想通过VisualDL实时观察loss变化曲线,在训练命令种添加--use_vdl=true,以及通过--vdl_log_dir设置日志保存路径。 +如果想通过VisualDL实时观察loss变化曲线,在训练命令中添加--use_vdl=true,以及通过--vdl_log_dir设置日志保存路径。 **但注意VisualDL需Python>=3.5** @@ -62,7 +62,7 @@ visualdl --logdir vdl_dir/scalar/ --host --port ``` # 评估 默认使用训练过程中保存的best_model # -c 参数表示指定使用哪个配置文件 -# -o 参数表示指定配置文件种的全局变量(覆盖配置文件种的设置),需使用单卡评估 +# -o 参数表示指定配置文件中的全局变量(覆盖配置文件中的设置),需使用单卡评估 CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/yolov3_mobilenet_v1_roadsign.yml-o use_gpu=true ``` @@ -71,7 +71,7 @@ CUDA_VISIBLE_DEVICES=0 python tools/eval.py -c configs/yolov3_mobilenet_v1_roads ### 3、预测 ``` # -c 参数表示指定使用哪个配置文件 -# -o 参数表示指定配置文件种的全局变量(覆盖配置文件种的设置) +# -o 参数表示指定配置文件中的全局变量(覆盖配置文件中的设置) # --infer_img 参数指定预测图像路径 # 预测结束后会在output文件夹中生成一张画有预测结果的同名图像 -- GitLab