From f7144154de11c9e6d3394322a141a7c73b28cee1 Mon Sep 17 00:00:00 2001 From: cnn Date: Mon, 21 Sep 2020 22:52:32 +0800 Subject: [PATCH] add yolov3_mobilenet_v1_roadsign.yml, fix bug, test=document_fix (#1461) --- configs/yolov3_mobilenet_v1_roadsign.yml | 302 +++++++++++++++++++++++ docs/images/visualdl_roadsign.png | Bin 102680 -> 222011 bytes docs/tutorials/QUICK_STARTED_cn.md | 4 +- 3 files changed, 304 insertions(+), 2 deletions(-) create mode 100644 configs/yolov3_mobilenet_v1_roadsign.yml diff --git a/configs/yolov3_mobilenet_v1_roadsign.yml b/configs/yolov3_mobilenet_v1_roadsign.yml new file mode 100644 index 000000000..464e21af0 --- /dev/null +++ b/configs/yolov3_mobilenet_v1_roadsign.yml @@ -0,0 +1,302 @@ +#####################################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: 3600 +# log平滑参数,平滑窗口大小,会从取历史窗口中取log_smooth_window大小的loss求平均值 +log_smooth_window: 20 +# 模型保存文件夹 +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/ +# ### 根据用户数据设置类别数,注意这里不含背景类 +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 + - 2400 + - 3300 + # 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) + - !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 + 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] + is_scale: True + is_channel_first: false + - !Permute + to_bgr: false + channel_first: True + # Gt2YoloTarget is only used when use_fine_grained_loss set as true, + # this operator will be deleted automatically if use_fine_grained_loss + # is set as false + - !Gt2YoloTarget + anchor_masks: [[6, 7, 8], [3, 4, 5], [0, 1, 2]] + anchors: [[10, 13], [16, 30], [33, 23], + [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 + 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 + # 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: + # 预测图像输入尺寸 + image_shape: [3, 608, 608] + fields: ['image', 'im_size', 'im_id'] + # 数据集路径 + dataset: + # 预测数据 + !ImageFolder + # anno_path + anno_path: dataset/roadsign_voc/label_list.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 + sample_transforms: + - !DecodeImage + to_rgb: True + # ResizeImage + - !ResizeImage + # 注意与上面图像尺寸保持一致 + 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 + # Permute + - !Permute + 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z!AkhJpzyxmkI4g(g@M(k-mkNBza1k6N>dxwI+h|LAOC)31wyX`Nb|WhAL8%zId-o7 ztPPi6obPW0G#0OF3#&ZDO&!#4Wea$&XjmuGb#nUs8ZcyvEM-{MsN35AFQf)Gb`w|v zkT_B%_;21sFKnerVM3y#;obkYI``k7H&h0`kLaXf#P6jBwy-(aQG8N@{uk@hprky5 zU0f3j7AyYSH(@}8oir0VDf{;ucK`z5$MdN&a{nL3tT5g{pA(St{Xv!NXM;aeV=JSQ Ila6=)7kd>53IG5A diff --git a/docs/tutorials/QUICK_STARTED_cn.md b/docs/tutorials/QUICK_STARTED_cn.md index 030d0766d..7f9b42a7e 100644 --- a/docs/tutorials/QUICK_STARTED_cn.md +++ b/docs/tutorials/QUICK_STARTED_cn.md @@ -59,9 +59,9 @@ visualdl --logdir vdl_dir/scalar/ --host --port # -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中的全局变量(覆盖配置文件中的设置) -python tools/eval.py -c configs/yolov3_mobilenet_v1_roadsign.yml-o use_gpu=true +python tools/eval.py -c configs/yolov3_mobilenet_v1_roadsign.yml -o use_gpu=true ``` - +![](../images/visualdl_roadsign.png) ### 3、预测 ``` -- GitLab