From f961433c285ec2f6ae71449c8e6e83832c28d698 Mon Sep 17 00:00:00 2001 From: dengkaipeng Date: Sun, 20 Sep 2020 08:40:31 +0000 Subject: [PATCH] add ppyolo mobilenetv3 --- configs/ppyolo/README.md | 17 +- configs/ppyolo/README_cn.md | 17 +- configs/ppyolo/ppyolo_mobilenet_v3_large.yml | 167 +++++++++++++++++++ configs/ppyolo/ppyolo_mobilenet_v3_small.yml | 167 +++++++++++++++++++ ppdet/modeling/anchor_heads/yolo_head.py | 5 +- 5 files changed, 358 insertions(+), 15 deletions(-) create mode 100755 configs/ppyolo/ppyolo_mobilenet_v3_large.yml create mode 100755 configs/ppyolo/ppyolo_mobilenet_v3_small.yml diff --git a/configs/ppyolo/README.md b/configs/ppyolo/README.md index c8eb763a3..9f529a1d6 100644 --- a/configs/ppyolo/README.md +++ b/configs/ppyolo/README.md @@ -50,6 +50,9 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods: | PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml) | | PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml) | | PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_2x.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_2x.yml) | +| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 512 | 29.3 | 29.5 | 357.1 | 657.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | +| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | +| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | **Notes:** @@ -63,14 +66,14 @@ PP-YOLO improved performance and speed of YOLOv3 with following methods: ### PP-YOLO for mobile -| Model | GPU number | images/GPU | backbone | input shape | Box AP50val | Box AP50test | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | download | config | -|:------------------------:|:----------:|:----------:|:----------:| :----------:| :--------------------: | :---------------------: | :------------: | :---------------------: | :------: | :-----: | -| PP-YOLO_r18vd | 4 | 32 | ResNet18vd | 416 | 47.0 | 47.7 | 401.6 | 724.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | -| PP-YOLO_r18vd | 4 | 32 | ResNet18vd | 320 | 43.7 | 44.4 | 478.5 | 791.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | +| Model | GPU number | images/GPU | Model Size | input shape | Box APval | Kirin 990(FPS) | download | config | +|:------------- -------------:|:----------:|:----------:| :--------: | :----------:| :------------------: | :------------: | :------: | :-----: | +| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 22.0 | 14.1 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) | +| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 16.8 | 21.5 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) | -- PP-YOLO_r18vd is trained on COCO train2017 datast and evaluated on val2017 & test-dev2017 dataset，Box AP50val is evaluation results of `mAP(IoU=0.5)`. -- PP-YOLO_r18vd used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../docs/FAQ.md). -- PP-YOLO_r18vd inference speeding testing environment and configuration is same as PP-YOLO above. +- PP-YOLO_MobileNetV3 is trained on COCO train2017 datast and evaluated on val2017 dataset，Box APval is evaluation results of `mAP(IoU=0.5:0.95)`. +- PP-YOLO_MobileNetV3 used 4 GPUs for training and mini-batch size as 32 on each GPU, if GPU number and mini-batch size is changed, learning rate and iteration times should be adjusted according [FAQ](../../docs/FAQ.md). +- PP-YOLO_MobileNetV3 inference speed is tested on Kirin 990 with 1 thread. ## Getting Start diff --git a/configs/ppyolo/README_cn.md b/configs/ppyolo/README_cn.md index 836316af5..2322d34fc 100644 --- a/configs/ppyolo/README_cn.md +++ b/configs/ppyolo/README_cn.md @@ -50,6 +50,9 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度： | PP-YOLO_2x | 8 | 24 | ResNet50vd | 512 | 44.4 | 45.0 | 89.9 | 188.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | | PP-YOLO_2x | 8 | 24 | ResNet50vd | 416 | 42.7 | 43.2 | 109.1 | 215.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | | PP-YOLO_2x | 8 | 24 | ResNet50vd | 320 | 39.5 | 40.1 | 132.2 | 242.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo.yml) | +| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 512 | 29.3 | 29.5 | 357.1 | 657.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | +| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 416 | 28.6 | 28.9 | 409.8 | 719.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | +| PP-YOLO_ResNet18vd | 4 | 32 | ResNet18vd | 320 | 26.2 | 26.4 | 480.7 | 763.4 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | **注意:** @@ -64,14 +67,14 @@ PP-YOLO从如下方面优化和提升YOLOv3模型的精度和速度： ### PP-YOLO 移动端模型 -| 模型 | GPU个数 | 每GPU图片个数 | 骨干网络 | 输入尺寸 | Box AP50val | Box AP50test | V100 FP32(FPS) | V100 TensorRT FP16(FPS) | 模型下载 | 配置文件 | -|:------------------------:|:-------:|:-------------:|:----------:| :-------:| :--------------------: | :---------------------: |------------: | :---------------------: | :------: | :------: | -| PP-YOLO_r18vd | 4 | 32 | ResNet18vd | 416 | 47.0 | 47.7 | 401.6 | 724.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | -| PP-YOLO_r18vd | 4 | 32 | ResNet18vd | 320 | 43.7 | 44.4 | 478.5 | 791.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_r18vd.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_r18vd.yml) | +| 模型 | GPU个数 | 每GPU图片个数 | 模型体积 | 输入尺寸 | Box APval | Kirin 990 (FPS) | 模型下载 | 配置文件 | +|:----------------------------:|:-------:|:-------------:|:----------:| :-------:| :------------------: | :-------------: |------------: | :---------------------: | :------: | :------: | +| PP-YOLO_MobileNetV3_large | 4 | 32 | 18MB | 320 | 22.0 | 14.1 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_large.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_large.yml) | +| PP-YOLO_MobileNetV3_small | 4 | 32 | 11MB | 320 | 16.8 | 21.5 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ppyolo_mobilenet_v3_small.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ppyolo/ppyolo_mobilenet_v3_small.yml) | -- PP-YOLO_r18vd 模型使用COCO数据集中train2017作为训练集，使用val2017和test-dev2017作为测试集，Box AP50val为`mAP(IoU=0.5)`评估结果。 -- PP-YOLO_r18vd 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。 -- PP-YOLO_r18vd 模型推理速度测试环境配置和测试方法与PP-YOLO模型一致。 +- PP-YOLO_MobileNetV3 模型使用COCO数据集中train2017作为训练集，使用val2017作为测试集，Box AP50val为`mAP(IoU=0.5:0.95)`评估结果。 +- PP-YOLO_MobileNetV3 模型训练过程中使用4GPU，每GPU batch size为32进行训练，如训练GPU数和batch size不使用上述配置，须参考[FAQ](../../docs/FAQ.md)调整学习率和迭代次数。 +- PP-YOLO_MobileNetV3 模型推理速度测试环境配置为麒麟990芯片单线程。 ## 使用说明 diff --git a/configs/ppyolo/ppyolo_mobilenet_v3_large.yml b/configs/ppyolo/ppyolo_mobilenet_v3_large.yml new file mode 100755 index 000000000..cb1d5cbc2 --- /dev/null +++ b/configs/ppyolo/ppyolo_mobilenet_v3_large.yml @@ -0,0 +1,167 @@ +architecture: YOLOv3 +use_gpu: true +max_iters: 250000 +log_smooth_window: 20 +log_iter: 20 +save_dir: output +snapshot_iter: 10000 +metric: COCO +pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar +weights: output/ppyolo_tiny/model_final +num_classes: 80 +use_fine_grained_loss: true +use_ema: true +ema_decay: 0.9998 + +YOLOv3: + backbone: MobileNetV3 + yolo_head: YOLOv3Head + use_fine_grained_loss: true + +MobileNetV3: + norm_type: sync_bn + norm_decay: 0. + model_name: large + scale: 1. + extra_block_filters: [] + feature_maps: [1, 2, 3, 4, 6] + + +YOLOv3Head: + anchor_masks: [[3, 4, 5], [0, 1, 2]] + anchors: [[10, 14], [23, 27], [37, 58], + [81, 82], [135, 169], [344, 319]] + norm_decay: 0. + conv_block_num: 0 + scale_x_y: 1.05 + yolo_loss: YOLOv3Loss + spp: true + nms: + background_label: -1 + keep_top_k: 100 + nms_threshold: 0.45 + nms_top_k: 1000 + normalized: false + score_threshold: 0.01 + drop_block: true + +YOLOv3Loss: + batch_size: 32 + ignore_thresh: 0.7 + scale_x_y: 1.05 + label_smooth: false + use_fine_grained_loss: true + iou_loss: IouLoss + +IouLoss: + loss_weight: 2.5 + max_height: 608 + max_width: 608 + +LearningRate: + base_lr: 0.00666 + schedulers: + - !PiecewiseDecay + gamma: 0.1 + milestones: + - 150000 + - 200000 + - !LinearWarmup + start_factor: 0. + steps: 4000 + +OptimizerBuilder: + optimizer: + momentum: 0.9 + type: Momentum + regularizer: + factor: 0.0005 + type: L2 + +_READER_: 'ppyolo_reader.yml' +TrainReader: + inputs_def: + fields: ['image', 'gt_bbox', 'gt_class', 'gt_score'] + num_max_boxes: 50 + dataset: + !COCODataSet + image_dir: train2017 + anno_path: annotations/instances_train2017.json + dataset_dir: train_data/dataset/coco + with_background: false + sample_transforms: + - !DecodeImage + to_rgb: True + with_mixup: True + - !MixupImage + alpha: 1.5 + beta: 1.5 + - !ColorDistort {} + - !RandomExpand + fill_value: [123.675, 116.28, 103.53] + - !RandomCrop {} + - !RandomFlipImage + is_normalized: false + - !NormalizeBox {} + - !PadBox + num_max_boxes: 50 + - !BboxXYXY2XYWH {} + batch_transforms: + - !RandomShape + sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] + random_inter: True + - !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: [[3, 4, 5], [0, 1, 2]] + anchors: [[10, 14], [23, 27], [37, 58], + [81, 82], [135, 169], [344, 319]] + downsample_ratios: [32, 16] + batch_size: 32 + shuffle: true + mixup_epoch: 500 + drop_last: true + worker_num: 16 + bufsize: 16 + memsize: 8G + use_process: true + +EvalReader: + inputs_def: + fields: ['image', 'im_size', 'im_id'] + num_max_boxes: 50 + dataset: + !COCODataSet + image_dir: val2017 + anno_path: annotations/instances_val2017.json + dataset_dir: dataset/coco + with_background: false + sample_transforms: + - !DecodeImage + to_rgb: True + - !ResizeImage + target_size: 320 + interp: 2 + - !NormalizeImage + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + is_scale: True + is_channel_first: false + - !PadBox + num_max_boxes: 50 + - !Permute + to_bgr: false + channel_first: True + batch_size: 8 + drop_empty: false + worker_num: 2 + bufsize: 4 diff --git a/configs/ppyolo/ppyolo_mobilenet_v3_small.yml b/configs/ppyolo/ppyolo_mobilenet_v3_small.yml new file mode 100755 index 000000000..5f8cf034e --- /dev/null +++ b/configs/ppyolo/ppyolo_mobilenet_v3_small.yml @@ -0,0 +1,167 @@ +architecture: YOLOv3 +use_gpu: true +max_iters: 250000 +log_smooth_window: 20 +log_iter: 20 +save_dir: output +snapshot_iter: 10000 +metric: COCO +pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar +weights: output/ppyolo_tiny/model_final +num_classes: 80 +use_fine_grained_loss: true +use_ema: true +ema_decay: 0.9998 + +YOLOv3: + backbone: MobileNetV3 + yolo_head: YOLOv3Head + use_fine_grained_loss: true + +MobileNetV3: + norm_type: sync_bn + norm_decay: 0. + model_name: small + scale: 1. + extra_block_filters: [] + feature_maps: [1, 2, 3, 4, 6] + + +YOLOv3Head: + anchor_masks: [[3, 4, 5], [0, 1, 2]] + anchors: [[10, 14], [23, 27], [37, 58], + [81, 82], [135, 169], [344, 319]] + norm_decay: 0. + conv_block_num: 0 + scale_x_y: 1.05 + yolo_loss: YOLOv3Loss + spp: true + nms: + background_label: -1 + keep_top_k: 100 + nms_threshold: 0.45 + nms_top_k: 1000 + normalized: false + score_threshold: 0.01 + drop_block: true + +YOLOv3Loss: + batch_size: 32 + ignore_thresh: 0.7 + scale_x_y: 1.05 + label_smooth: false + use_fine_grained_loss: true + iou_loss: IouLoss + +IouLoss: + loss_weight: 2.5 + max_height: 608 + max_width: 608 + +LearningRate: + base_lr: 0.00666 + schedulers: + - !PiecewiseDecay + gamma: 0.1 + milestones: + - 150000 + - 200000 + - !LinearWarmup + start_factor: 0. + steps: 4000 + +OptimizerBuilder: + optimizer: + momentum: 0.9 + type: Momentum + regularizer: + factor: 0.0005 + type: L2 + +_READER_: 'ppyolo_reader.yml' +TrainReader: + inputs_def: + fields: ['image', 'gt_bbox', 'gt_class', 'gt_score'] + num_max_boxes: 50 + dataset: + !COCODataSet + image_dir: train2017 + anno_path: annotations/instances_train2017.json + dataset_dir: train_data/dataset/coco + with_background: false + sample_transforms: + - !DecodeImage + to_rgb: True + with_mixup: True + - !MixupImage + alpha: 1.5 + beta: 1.5 + - !ColorDistort {} + - !RandomExpand + fill_value: [123.675, 116.28, 103.53] + - !RandomCrop {} + - !RandomFlipImage + is_normalized: false + - !NormalizeBox {} + - !PadBox + num_max_boxes: 50 + - !BboxXYXY2XYWH {} + batch_transforms: + - !RandomShape + sizes: [320, 352, 384, 416, 448, 480, 512, 544, 576, 608] + random_inter: True + - !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: [[3, 4, 5], [0, 1, 2]] + anchors: [[10, 14], [23, 27], [37, 58], + [81, 82], [135, 169], [344, 319]] + downsample_ratios: [32, 16] + batch_size: 32 + shuffle: true + mixup_epoch: 500 + drop_last: true + worker_num: 16 + bufsize: 16 + memsize: 8G + use_process: true + +EvalReader: + inputs_def: + fields: ['image', 'im_size', 'im_id'] + num_max_boxes: 50 + dataset: + !COCODataSet + image_dir: val2017 + anno_path: annotations/instances_val2017.json + dataset_dir: dataset/coco + with_background: false + sample_transforms: + - !DecodeImage + to_rgb: True + - !ResizeImage + target_size: 320 + interp: 2 + - !NormalizeImage + mean: [0.485, 0.456, 0.406] + std: [0.229, 0.224, 0.225] + is_scale: True + is_channel_first: false + - !PadBox + num_max_boxes: 50 + - !Permute + to_bgr: false + channel_first: True + batch_size: 8 + drop_empty: false + worker_num: 2 + bufsize: 4 diff --git a/ppdet/modeling/anchor_heads/yolo_head.py b/ppdet/modeling/anchor_heads/yolo_head.py index 64b3cd41c..a0c3d2bc4 100644 --- a/ppdet/modeling/anchor_heads/yolo_head.py +++ b/ppdet/modeling/anchor_heads/yolo_head.py @@ -263,7 +263,10 @@ class YOLOv3Head(object): keep_prob=self.keep_prob, is_test=is_test) - if self.drop_block and is_first: + if self.use_spp and conv_block_num == 0 and is_first: + conv = self._spp_module(conv, name="spp") + + if self.drop_block and (is_first or conv_block_num == 0): conv = DropBlock( conv, block_size=self.block_size, -- GitLab