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PaddleDetection
未验证 提交 01d905e3 编写于 作者: G Guanghua Yu 提交者： GitHub
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add blazeface & update rcnn modelzoo (#2303) 

* add blazeface & update rcnn modelzoo

* remove condition num_priors
上级 1d1c5826
隐藏空白更改
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Showing with 1256 addition and 36 deletion
dygraph/configs/datasets/wider_face.yml 0 → 100644
浏览文件 @ 01d905e3
metric: WiderFace
num_classes: 1
TrainDataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face
anno_path: wider_face_split/wider_face_train_bbx_gt.txt
image_dir: WIDER_train/images
data_fields: ['image', 'gt_bbox', 'gt_class']
EvalDataset:
!WIDERFaceDataSet
dataset_dir: dataset/wider_face
anno_path: wider_face_split/wider_face_val_bbx_gt.txt
image_dir: WIDER_val/images
data_fields: ['image']
TestDataset:
!ImageFolder
use_default_label: true
dygraph/configs/dcn/README.md
浏览文件 @ 01d905e3
......@@ -7,10 +7,10 @@
| ResNet50-vd-FPN | Faster | c3-c5 | 1 | 2x | - | 43.7 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/faster_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 45.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/faster_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Faster | c3-c5 | 1 | 1x | - | 46.5 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) |[配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/faster_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Mask | c3-c5 | 1 | 1x | - | - | - | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/mask_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Mask | c3-c5 | 1 | 2x | - | - | - | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet50-FPN | Mask | c3-c5 | 1 | 1x | - | 42.7 | 38.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/mask_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNet50-vd-FPN | Mask | c3-c5 | 1 | 2x | - | 44.6 | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r50_vd_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/mask_rcnn_dcn_r50_vd_fpn_2x_coco.yml) |
| ResNet101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 45.6 | 40.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/mask_rcnn_dcn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | - | - | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | c3-c5 | 1 | 1x | - | 47.3 | 42.0 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/mask_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNet50-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 42.1 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_r50_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/cascade_rcnn_dcn_r50_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Cascade Faster | c3-c5 | 1 | 1x | - | 48.8 | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/dcn/cascade_rcnn_dcn_x101_vd_64x4d_fpn_1x_coco.yml) |
......
dygraph/configs/face_detection/README.md 0 → 100644
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# 人脸检测模型
## 简介
`face_detection`中提供高效、高速的人脸检测解决方案，包括最先进的模型和经典模型。
![](../../docs/images/12_Group_Group_12_Group_Group_12_935.jpg)
## 模型库
#### WIDER-FACE数据集上的mAP
| 网络结构 | 输入尺寸 | 图片个数/GPU | 学习率策略 | Easy/Medium/Hard Set | 预测时延（SD855）| 模型大小(MB) | 下载 | 配置文件 |
|:------------:|:--------:|:----:|:-------:|:-------:|:---------:|:----------:|:---------:|:--------:|
| BlazeFace | 640 | 8 | 1000e | 0.889 / 0.859 / 0.740 | - | 0.472 |[下载链接](https://paddledet.bj.bcebos.com/models/blazeface_1000e.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/face_detection/blazeface_1000e.yml) |
**注意:**
- 我们使用多尺度评估策略得到`Easy/Medium/Hard Set`里的mAP。具体细节请参考[在WIDER-FACE数据集上评估](#在WIDER-FACE数据集上评估)。
## 快速开始
### 数据准备
我们使用[WIDER-FACE数据集](http://shuoyang1213.me/WIDERFACE/)进行训练和模型测试，官方网站提供了详细的数据介绍。
- WIDER-Face数据源:
使用如下目录结构加载`wider_face`类型的数据集：
```
dataset/wider_face/
├── wider_face_split
│ ├── wider_face_train_bbx_gt.txt
│ ├── wider_face_val_bbx_gt.txt
├── WIDER_train
│ ├── images
│ │ ├── 0--Parade
│ │ │ ├── 0_Parade_marchingband_1_100.jpg
│ │ │ ├── 0_Parade_marchingband_1_381.jpg
│ │ │ │ ...
│ │ ├── 10--People_Marching
│ │ │ ...
├── WIDER_val
│ ├── images
│ │ ├── 0--Parade
│ │ │ ├── 0_Parade_marchingband_1_1004.jpg
│ │ │ ├── 0_Parade_marchingband_1_1045.jpg
│ │ │ │ ...
│ │ ├── 10--People_Marching
│ │ │ ...
```
- 手动下载数据集：
要下载WIDER-FACE数据集，请运行以下命令：
```
cd dataset/wider_face && ./download_wider_face.sh
```
### 训练与评估
训练流程与评估流程方法与其他算法一致，请参考[GETTING_STARTED_cn.md](../../docs/tutorials/GETTING_STARTED_cn.md)。
**注意:**
- 人脸检测模型目前不支持边训练边评估。
#### 在WIDER-FACE数据集上评估
评估并生成结果文件：
```shell
python -u tools/eval.py -c configs/face_detection/blazeface_1000e.yml \
-o weights=output/blazeface_1000e/model_final \
multi_scale=True
```
设置`multi_scale=True`进行多尺度评估，评估完成后，将在`output/pred`中生成txt格式的测试结果。
- 下载官方评估脚本来评估AP指标：
```
wget http://mmlab.ie.cuhk.edu.hk/projects/WIDERFace/support/eval_script/eval_tools.zip
unzip eval_tools.zip && rm -f eval_tools.zip
```
- 在`eval_tools/wider_eval.m`中修改保存结果路径和绘制曲线的名称：
```
# Modify the folder name where the result is stored.
pred_dir = './pred';
# Modify the name of the curve to be drawn
legend_name = 'Fluid-BlazeFace';
```
- `wider_eval.m` 是评估模块的主要执行程序。运行命令如下：
```
matlab -nodesktop -nosplash -nojvm -r "run wider_eval.m;quit;"
```
## Citations
```
@article{bazarevsky2019blazeface,
title={BlazeFace: Sub-millisecond Neural Face Detection on Mobile GPUs},
author={Valentin Bazarevsky and Yury Kartynnik and Andrey Vakunov and Karthik Raveendran and Matthias Grundmann},
year={2019},
eprint={1907.05047},
archivePrefix={arXiv},
```
dygraph/configs/face_detection/_base_/blazeface.yml 0 → 100644
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architecture: SSD
SSD:
backbone: BlazeNet
ssd_head: FaceHead
post_process: BBoxPostProcess
BlazeNet:
blaze_filters: [[24, 24], [24, 24], [24, 48, 2], [48, 48], [48, 48]]
double_blaze_filters: [[48, 24, 96, 2], [96, 24, 96], [96, 24, 96],
[96, 24, 96, 2], [96, 24, 96], [96, 24, 96]]
FaceHead:
in_channels: [96, 96]
anchor_generator: AnchorGeneratorSSD
loss: SSDLoss
SSDLoss:
overlap_threshold: 0.35
neg_overlap: 0.35
AnchorGeneratorSSD:
steps: [8., 16.]
aspect_ratios: [[1.], [1.]]
min_sizes: [[16.,24.], [32., 48., 64., 80., 96., 128.]]
max_sizes: [[], []]
offset: 0.5
flip: False
min_max_aspect_ratios_order: false
BBoxPostProcess:
decode:
name: SSDBox
nms:
name: MultiClassNMS
keep_top_k: 750
score_threshold: 0.01
nms_threshold: 0.3
nms_top_k: 5000
nms_eta: 1.0
dygraph/configs/face_detection/_base_/face_reader.yml 0 → 100644
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worker_num: 2
TrainReader:
inputs_def:
num_max_boxes: 90
sample_transforms:
- Decode: {}
- RandomDistort: {brightness: [0.5, 1.125, 0.875], random_apply: False}
- RandomExpand: {fill_value: [123.675, 116.28, 103.53]}
- RandomFlip: {}
- CropWithDataAchorSampling: {
anchor_sampler: [[1, 10, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 0.2, 0.0]],
batch_sampler: [
[1, 50, 1.0, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
[1, 50, 0.3, 1.0, 1.0, 1.0, 0.0, 0.0, 1.0, 0.0],
],
target_size: 640}
- Resize: {target_size: [640, 640], keep_ratio: False, interp: 1}
- NormalizeBox: {}
- PadBox: {num_max_boxes: 90}
batch_transforms:
- NormalizeImage: {mean: [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false}
- Permute: {}
batch_size: 8
shuffle: true
drop_last: true
EvalReader:
sample_transforms:
- Decode: {}
- NormalizeImage: {mean: [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false}
- Permute: {}
batch_size: 1
drop_empty: false
TestReader:
sample_transforms:
- Decode: {}
- NormalizeImage: {mean: [123, 117, 104], std: [127.502231, 127.502231, 127.502231], is_scale: false}
- Permute: {}
batch_size: 1
dygraph/configs/face_detection/_base_/optimizer_1000e.yml 0 → 100644
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epoch: 1000
LearningRate:
base_lr: 0.001
schedulers:
- !PiecewiseDecay
gamma: 0.1
milestones:
- 333
- 800
- !LinearWarmup
start_factor: 0.3333333333333333
steps: 500
OptimizerBuilder:
optimizer:
momentum: 0.0
type: RMSProp
regularizer:
factor: 0.0005
type: L2
dygraph/configs/face_detection/blazeface_1000e.yml 0 → 100644
浏览文件 @ 01d905e3
_BASE_: [
'../datasets/wider_face.yml',
'../runtime.yml',
'_base_/optimizer_1000e.yml',
'_base_/blazeface.yml',
'_base_/face_reader.yml',
]
weights: output/blazeface_1000e/model_final
multi_scale_eval: True
dygraph/configs/gn/README.md
浏览文件 @ 01d905e3
......@@ -6,6 +6,9 @@
| :------------- | :------------- | :-----------: | :------: | :--------: |:-----: | :-----: | :----: | :----: |
| ResNet50-FPN | Faster | 1 | 2x | - | 41.9 | - | [下载链接](https://paddledet.bj.bcebos.com/models/faster_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/gn/faster_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Mask | 1 | 2x | - | 42.3 | 38.4 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/gn/mask_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Cascade Faster | 1 | 2x | - | - | - | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/gn/cascade_rcnn_r50_fpn_gn_2x_coco.yml) |
| ResNet50-FPN | Cacade Mask | 1 | 2x | - | 45.0 | 39.3 | [下载链接](https://paddledet.bj.bcebos.com/models/cascade_mask_rcnn_r50_fpn_gn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x_coco.yml) |
**注意：** Faster R-CNN baseline仅使用 `2fc` head，而此处使用[`4conv1fc` head](https://arxiv.org/abs/1803.08494)（4层conv之间使用GN），并且FPN也使用GN，而对于Mask R-CNN是在mask head的4层conv之间也使用GN。
......
dygraph/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x.yml → dygraph/configs/gn/cascade_mask_rcnn_r50_fpn_gn_2x_coco.yml
浏览文件 @ 01d905e3
......@@ -5,7 +5,7 @@ _BASE_: [
'../cascade_rcnn/_base_/cascade_mask_rcnn_r50_fpn.yml',
'../cascade_rcnn/_base_/cascade_mask_fpn_reader.yml',
]
weights: output/cascade_mask_rcnn_r50_fpn_gn_2x/model_final
weights: output/cascade_mask_rcnn_r50_fpn_gn_2x_coco/model_final
CascadeRCNN:
backbone: ResNet
......
dygraph/configs/gn/cascade_rcnn_r50_fpn_gn_2x.yml → dygraph/configs/gn/cascade_rcnn_r50_fpn_gn_2x_coco.yml
浏览文件 @ 01d905e3
......@@ -5,7 +5,7 @@ _BASE_: [
'../cascade_rcnn/_base_/cascade_rcnn_r50_fpn.yml',
'../cascade_rcnn/_base_/cascade_fpn_reader.yml',
]
weights: output/cascade_rcnn_r50_fpn_gn_2x/model_final
weights: output/cascade_rcnn_r50_fpn_gn_2x_coco/model_final
FPN:
out_channel: 256
......
dygraph/configs/mask_rcnn/README.md
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......@@ -13,7 +13,7 @@
| ResNet101-FPN | Mask | 1 | 1x | ---- | 40.6 | 36.6 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r101_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/mask_rcnn/mask_rcnn_r101_fpn_1x_coco.yml) |
| ResNet101-vd-FPN | Mask | 1 | 1x | ---- | 42.4 | 38.1 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_r101_vd_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/mask_rcnn/mask_rcnn_r101_vd_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | 1 | 1x | ---- | 44.0 | 39.5 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_x101_vd_64x4d_fpn_1x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_1x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | 1 | 2x | ---- | - | - | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.yml) |
| ResNeXt101-vd-FPN | Mask | 1 | 2x | ---- | 44.6 | 39.8 | [下载链接](https://paddledet.bj.bcebos.com/models/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/dygraph/configs/mask_rcnn/mask_rcnn_x101_vd_64x4d_fpn_2x_coco.yml) |
**注意：** Mask R-CNN模型精度依赖Paddle develop分支修改，精度复现须使用[每日版本](https://www.paddlepaddle.org.cn/documentation/docs/zh/install/Tables.html#whl-dev)或2.0.1版本(将于2021.03发布)，使用Paddle 2.0.0版本会有少量精度损失。
......
dygraph/dataset/wider_face/download_wider_face.sh 0 → 100755
浏览文件 @ 01d905e3
# All rights `PaddleDetection` reserved
# References:
# @inproceedings{yang2016wider,
# Author = {Yang, Shuo and Luo, Ping and Loy, Chen Change and Tang, Xiaoou},
# Booktitle = {IEEE Conference on Computer Vision and Pattern Recognition (CVPR)},
# Title = {WIDER FACE: A Face Detection Benchmark},
# Year = {2016}}
DIR="$( cd "$(dirname "$0")" ; pwd -P )"
cd "$DIR"
# Download the data.
echo "Downloading..."
wget https://dataset.bj.bcebos.com/wider_face/WIDER_train.zip
wget https://dataset.bj.bcebos.com/wider_face/WIDER_val.zip
wget https://dataset.bj.bcebos.com/wider_face/wider_face_split.zip
# Extract the data.
echo "Extracting..."
unzip -q WIDER_train.zip
unzip -q WIDER_val.zip
unzip -q wider_face_split.zip
dygraph/ppdet/data/source/widerface.py
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......@@ -16,7 +16,7 @@ import os
import numpy as np
from ppdet.core.workspace import register, serializable
from .dataset import DataSet
from .dataset import DetDataset
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
......@@ -24,7 +24,7 @@ logger = setup_logger(__name__)
@register
@serializable
class WIDERFaceDataSet(DataSet):
class WIDERFaceDataSet(DetDataset):
"""
Load WiderFace records with 'anno_path'
......@@ -39,20 +39,23 @@ class WIDERFaceDataSet(DataSet):
dataset_dir=None,
image_dir=None,
anno_path=None,
data_fields=['image'],
sample_num=-1,
with_lmk=False):
super(WIDERFaceDataSet, self).__init__(
dataset_dir=dataset_dir,
image_dir=image_dir,
anno_path=anno_path,
data_fields=data_fields,
sample_num=sample_num,
dataset_dir=dataset_dir)
with_lmk=with_lmk)
self.anno_path = anno_path
self.sample_num = sample_num
self.roidbs = None
self.cname2cid = None
self.with_lmk = with_lmk
def load_roidb_and_cname2cid(self, ):
def parse_dataset(self):
anno_path = os.path.join(self.dataset_dir, self.anno_path)
image_dir = os.path.join(self.dataset_dir, self.image_dir)
......@@ -67,7 +70,7 @@ class WIDERFaceDataSet(DataSet):
im_fname = item[0]
im_id = np.array([ct])
gt_bbox = np.zeros((len(item) - 1, 4), dtype=np.float32)
gt_class = np.ones((len(item) - 1, 1), dtype=np.int32)
gt_class = np.zeros((len(item) - 1, 1), dtype=np.int32)
gt_lmk_labels = np.zeros((len(item) - 1, 10), dtype=np.float32)
lmk_ignore_flag = np.zeros((len(item) - 1, 1), dtype=np.int32)
for index_box in range(len(item)):
......@@ -82,9 +85,14 @@ class WIDERFaceDataSet(DataSet):
widerface_rec = {
'im_file': im_fname,
'im_id': im_id,
} if 'image' in self.data_fields else {}
gt_rec = {
'gt_bbox': gt_bbox,
'gt_class': gt_class,
}
for k, v in gt_rec.items():
if k in self.data_fields:
widerface_rec[k] = v
if self.with_lmk:
widerface_rec['gt_keypoint'] = gt_lmk_labels
widerface_rec['keypoint_ignore'] = lmk_ignore_flag
......@@ -105,18 +113,24 @@ class WIDERFaceDataSet(DataSet):
file_dict = {}
num_class = 0
exts = ['jpg', 'jpeg', 'png', 'bmp']
exts += [ext.upper() for ext in exts]
for i in range(len(lines_input_txt)):
line_txt = lines_input_txt[i].strip('\n\t\r')
if '.jpg' in line_txt:
if i != 0:
num_class += 1
file_dict[num_class] = []
file_dict[num_class].append(line_txt)
if '.jpg' not in line_txt:
split_str = line_txt.split(' ')
if len(split_str) == 1:
img_file_name = os.path.split(split_str[0])[1]
split_txt = img_file_name.split('.')
if len(split_txt) < 2:
continue
elif split_txt[-1] in exts:
if i != 0:
num_class += 1
file_dict[num_class] = [line_txt]
else:
if len(line_txt) <= 6:
continue
result_boxs = []
split_str = line_txt.split(' ')
xmin = float(split_str[0])
ymin = float(split_str[1])
w = float(split_str[2])
......
dygraph/ppdet/data/transform/operators.py
浏览文件 @ 01d905e3
......@@ -994,7 +994,7 @@ class CropWithDataAchorSampling(BaseOperator):
[max sample, max trial, min scale, max scale,
min aspect ratio, max aspect ratio,
min overlap, max overlap, min coverage, max coverage]
target_size (bool): target image size.
target_size (int): target image size.
das_anchor_scales (list[float]): a list of anchor scales in data
anchor smapling.
min_size (float): minimum size of sampled bbox.
......@@ -1026,6 +1026,10 @@ class CropWithDataAchorSampling(BaseOperator):
gt_bbox = sample['gt_bbox']
gt_class = sample['gt_class']
image_height, image_width = im.shape[:2]
gt_bbox[:, 0] /= image_width
gt_bbox[:, 1] /= image_height
gt_bbox[:, 2] /= image_width
gt_bbox[:, 3] /= image_height
gt_score = None
if 'gt_score' in sample:
gt_score = sample['gt_score']
......@@ -1073,10 +1077,16 @@ class CropWithDataAchorSampling(BaseOperator):
continue
im = crop_image_sampling(im, sample_bbox, image_width,
image_height, self.target_size)
height, width = im.shape[:2]
crop_bbox[:, 0] *= width
crop_bbox[:, 1] *= height
crop_bbox[:, 2] *= width
crop_bbox[:, 3] *= height
sample['image'] = im
sample['gt_bbox'] = crop_bbox
sample['gt_class'] = crop_class
sample['gt_score'] = crop_score
if 'gt_score' in sample:
sample['gt_score'] = crop_score
if 'gt_keypoint' in sample.keys():
sample['gt_keypoint'] = gt_keypoints[0]
sample['keypoint_ignore'] = gt_keypoints[1]
......@@ -1124,10 +1134,16 @@ class CropWithDataAchorSampling(BaseOperator):
ymin = int(sample_bbox[1] * image_height)
ymax = int(sample_bbox[3] * image_height)
im = im[ymin:ymax, xmin:xmax]
height, width = im.shape[:2]
crop_bbox[:, 0] *= width
crop_bbox[:, 1] *= height
crop_bbox[:, 2] *= width
crop_bbox[:, 3] *= height
sample['image'] = im
sample['gt_bbox'] = crop_bbox
sample['gt_class'] = crop_class
sample['gt_score'] = crop_score
if 'gt_score' in sample:
sample['gt_score'] = crop_score
if 'gt_keypoint' in sample.keys():
sample['gt_keypoint'] = gt_keypoints[0]
sample['keypoint_ignore'] = gt_keypoints[1]
......
dygraph/ppdet/engine/callbacks.py
浏览文件 @ 01d905e3
......@@ -17,6 +17,7 @@ from __future__ import division
from __future__ import print_function
import os
import sys
import datetime
import paddle
......@@ -169,3 +170,15 @@ class Checkpointer(Callback):
else:
save_model(self.model.model, self.model.optimizer, save_dir,
save_name, epoch_id + 1)
class WiferFaceEval(Callback):
def __init__(self, model):
super(WiferFaceEval, self).__init__(model)
def on_epoch_begin(self, status):
assert self.model.mode == 'eval', \
"WiferFaceEval can only be set during evaluation"
for metric in self.model._metrics:
metric.update(self.model.model)
sys.exit()
dygraph/ppdet/engine/trainer.py
浏览文件 @ 01d905e3
......@@ -31,10 +31,10 @@ from paddle.static import InputSpec
from ppdet.core.workspace import create
from ppdet.utils.checkpoint import load_weight, load_pretrain_weight
from ppdet.utils.visualizer import visualize_results
from ppdet.metrics import Metric, COCOMetric, VOCMetric, get_categories, get_infer_results
from ppdet.metrics import Metric, COCOMetric, VOCMetric, WiderFaceMetric, get_categories, get_infer_results
import ppdet.utils.stats as stats
from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer
from .callbacks import Callback, ComposeCallback, LogPrinter, Checkpointer, WiferFaceEval
from .export_utils import _dump_infer_config
from ppdet.utils.logger import setup_logger
......@@ -90,8 +90,6 @@ class Trainer(object):
self.start_epoch = 0
self.end_epoch = cfg.epoch
self._weights_loaded = False
# initial default callbacks
self._init_callbacks()
......@@ -105,6 +103,8 @@ class Trainer(object):
self._compose_callback = ComposeCallback(self._callbacks)
elif self.mode == 'eval':
self._callbacks = [LogPrinter(self)]
if self.cfg.metric == 'WiderFace':
self._callbacks.append(WiferFaceEval(self))
self._compose_callback = ComposeCallback(self._callbacks)
else:
self._callbacks = []
......@@ -128,6 +128,15 @@ class Trainer(object):
class_num=self.cfg.num_classes,
map_type=self.cfg.map_type)
]
elif self.cfg.metric == 'WiderFace':
multi_scale = self.cfg.multi_scale_eval if 'multi_scale_eval' in self.cfg else True
self._metrics = [
WiderFaceMetric(
image_dir=os.path.join(self.dataset.dataset_dir,
self.dataset.image_dir),
anno_file=self.dataset.get_anno(),
multi_scale=multi_scale)
]
else:
logger.warn("Metric not support for metric type {}".format(
self.cfg.metric))
......@@ -165,15 +174,10 @@ class Trainer(object):
weight_type)
logger.debug("Load {} weights {} to start training".format(
weight_type, weights))
self._weights_loaded = True
def train(self, validate=False):
assert self.mode == 'train', "Model not in 'train' mode"
# if no given weights loaded, load backbone pretrain weights as default
if not self._weights_loaded:
self.load_weights(self.cfg.pretrain_weights)
model = self.model
if self.cfg.fleet:
model = fleet.distributed_model(model)
......
dygraph/ppdet/metrics/category.py
浏览文件 @ 01d905e3
......@@ -19,6 +19,7 @@ from __future__ import print_function
import os
from ppdet.data.source.voc import pascalvoc_label
from ppdet.data.source.widerface import widerface_label
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
......@@ -75,6 +76,9 @@ def get_categories(metric_type, anno_file=None):
logger.warn("only default categories support for OID19")
return _oid19_category()
elif metric_type.lower() == 'widerface':
return _widerface_category()
else:
raise ValueError("unknown metric type {}".format(metric_type))
......@@ -274,6 +278,16 @@ def _vocall_category():
return clsid2catid, catid2name
def _widerface_category():
label_map = widerface_label()
label_map = sorted(label_map.items(), key=lambda x: x[1])
cats = [l[0] for l in label_map]
clsid2catid = {i: i for i in range(len(cats))}
catid2name = {i: name for i, name in enumerate(cats)}
return clsid2catid, catid2name
def _oid19_category():
clsid2catid = {k: k + 1 for k in range(500)}
......
dygraph/ppdet/metrics/metrics.py
浏览文件 @ 01d905e3
......@@ -25,17 +25,26 @@ import numpy as np
from .category import get_categories
from .map_utils import prune_zero_padding, DetectionMAP
from .coco_utils import get_infer_results, cocoapi_eval
from .widerface_utils import face_eval_run
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
__all__ = ['Metric', 'COCOMetric', 'VOCMetric', 'get_infer_results']
__all__ = [
'Metric', 'COCOMetric', 'VOCMetric', 'WiderFaceMetric', 'get_infer_results'
]
class Metric(paddle.metric.Metric):
def name(self):
return self.__class__.__name__
def reset(self):
pass
def accumulate(self):
pass
# paddle.metric.Metric defined :metch:`update`, :meth:`accumulate`
# :metch:`reset`, in ppdet, we also need following 2 methods:
......@@ -194,3 +203,21 @@ class VOCMetric(Metric):
def get_results(self):
self.detection_map.get_map()
class WiderFaceMetric(Metric):
def __init__(self, image_dir, anno_file, multi_scale=True):
self.image_dir = image_dir
self.anno_file = anno_file
self.multi_scale = multi_scale
self.clsid2catid, self.catid2name = get_categories('widerface')
def update(self, model):
face_eval_run(
model,
self.image_dir,
self.anno_file,
pred_dir='output/pred',
eval_mode='widerface',
multi_scale=self.multi_scale)
dygraph/ppdet/metrics/widerface_utils.py 0 → 100644
浏览文件 @ 01d905e3
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import os
import cv2
import numpy as np
from collections import OrderedDict
import paddle
from ppdet.utils.logger import setup_logger
logger = setup_logger(__name__)
__all__ = ['face_eval_run', 'lmk2out']
def face_eval_run(model,
image_dir,
gt_file,
pred_dir='output/pred',
eval_mode='widerface',
multi_scale=False):
# load ground truth files
with open(gt_file, 'r') as f:
gt_lines = f.readlines()
imid2path = []
pos_gt = 0
while pos_gt < len(gt_lines):
name_gt = gt_lines[pos_gt].strip('\n\t').split()[0]
imid2path.append(name_gt)
pos_gt += 1
n_gt = int(gt_lines[pos_gt].strip('\n\t').split()[0])
pos_gt += 1 + n_gt
logger.info('The ground truth file load {} images'.format(len(imid2path)))
dets_dist = OrderedDict()
for iter_id, im_path in enumerate(imid2path):
image_path = os.path.join(image_dir, im_path)
if eval_mode == 'fddb':
image_path += '.jpg'
assert os.path.exists(image_path)
image = cv2.imread(image_path)
image = cv2.cvtColor(image, cv2.COLOR_BGR2RGB)
if multi_scale:
shrink, max_shrink = get_shrink(image.shape[0], image.shape[1])
det0 = detect_face(model, image, shrink)
det1 = flip_test(model, image, shrink)
[det2, det3] = multi_scale_test(model, image, max_shrink)
det4 = multi_scale_test_pyramid(model, image, max_shrink)
det = np.row_stack((det0, det1, det2, det3, det4))
dets = bbox_vote(det)
else:
dets = detect_face(model, image, 1)
if eval_mode == 'widerface':
save_widerface_bboxes(image_path, dets, pred_dir)
else:
dets_dist[im_path] = dets
if iter_id % 100 == 0:
logger.info('Test iter {}'.format(iter_id))
if eval_mode == 'fddb':
save_fddb_bboxes(dets_dist, pred_dir)
logger.info("Finish evaluation.")
def detect_face(model, image, shrink):
image_shape = [image.shape[0], image.shape[1]]
if shrink != 1:
h, w = int(image_shape[0] * shrink), int(image_shape[1] * shrink)
image = cv2.resize(image, (w, h))
image_shape = [h, w]
img = face_img_process(image)
image_shape = np.asarray([image_shape])
scale_factor = np.asarray([[shrink, shrink]])
data = {
"image": paddle.to_tensor(
img, dtype='float32'),
"im_shape": paddle.to_tensor(
image_shape, dtype='float32'),
"scale_factor": paddle.to_tensor(
scale_factor, dtype='float32')
}
model.eval()
detection = model(data)
detection = detection['bbox'].numpy()
# layout: xmin, ymin, xmax. ymax, score
if np.prod(detection.shape) == 1:
logger.info("No face detected")
return np.array([[0, 0, 0, 0, 0]])
det_conf = detection[:, 1]
det_xmin = detection[:, 2]
det_ymin = detection[:, 3]
det_xmax = detection[:, 4]
det_ymax = detection[:, 5]
det = np.column_stack((det_xmin, det_ymin, det_xmax, det_ymax, det_conf))
return det
def flip_test(model, image, shrink):
img = cv2.flip(image, 1)
det_f = detect_face(model, img, shrink)
det_t = np.zeros(det_f.shape)
img_width = image.shape[1]
det_t[:, 0] = img_width - det_f[:, 2]
det_t[:, 1] = det_f[:, 1]
det_t[:, 2] = img_width - det_f[:, 0]
det_t[:, 3] = det_f[:, 3]
det_t[:, 4] = det_f[:, 4]
return det_t
def multi_scale_test(model, image, max_shrink):
# Shrink detecting is only used to detect big faces
st = 0.5 if max_shrink >= 0.75 else 0.5 * max_shrink
det_s = detect_face(model, image, st)
index = np.where(
np.maximum(det_s[:, 2] - det_s[:, 0] + 1, det_s[:, 3] - det_s[:, 1] + 1)
> 30)[0]
det_s = det_s[index, :]
# Enlarge one times
bt = min(2, max_shrink) if max_shrink > 1 else (st + max_shrink) / 2
det_b = detect_face(model, image, bt)
# Enlarge small image x times for small faces
if max_shrink > 2:
bt *= 2
while bt < max_shrink:
det_b = np.row_stack((det_b, detect_face(model, image, bt)))
bt *= 2
det_b = np.row_stack((det_b, detect_face(model, image, max_shrink)))
# Enlarged images are only used to detect small faces.
if bt > 1:
index = np.where(
np.minimum(det_b[:, 2] - det_b[:, 0] + 1,
det_b[:, 3] - det_b[:, 1] + 1) < 100)[0]
det_b = det_b[index, :]
# Shrinked images are only used to detect big faces.
else:
index = np.where(
np.maximum(det_b[:, 2] - det_b[:, 0] + 1,
det_b[:, 3] - det_b[:, 1] + 1) > 30)[0]
det_b = det_b[index, :]
return det_s, det_b
def multi_scale_test_pyramid(model, image, max_shrink):
# Use image pyramids to detect faces
det_b = detect_face(model, image, 0.25)
index = np.where(
np.maximum(det_b[:, 2] - det_b[:, 0] + 1, det_b[:, 3] - det_b[:, 1] + 1)
> 30)[0]
det_b = det_b[index, :]
st = [0.75, 1.25, 1.5, 1.75]
for i in range(len(st)):
if st[i] <= max_shrink:
det_temp = detect_face(model, image, st[i])
# Enlarged images are only used to detect small faces.
if st[i] > 1:
index = np.where(
np.minimum(det_temp[:, 2] - det_temp[:, 0] + 1,
det_temp[:, 3] - det_temp[:, 1] + 1) < 100)[0]
det_temp = det_temp[index, :]
# Shrinked images are only used to detect big faces.
else:
index = np.where(
np.maximum(det_temp[:, 2] - det_temp[:, 0] + 1,
det_temp[:, 3] - det_temp[:, 1] + 1) > 30)[0]
det_temp = det_temp[index, :]
det_b = np.row_stack((det_b, det_temp))
return det_b
def to_chw(image):
"""
Transpose image from HWC to CHW.
Args:
image (np.array): an image with HWC layout.
"""
# HWC to CHW
if len(image.shape) == 3:
image = np.swapaxes(image, 1, 2)
image = np.swapaxes(image, 1, 0)
return image
def face_img_process(image,
mean=[104., 117., 123.],
std=[127.502231, 127.502231, 127.502231]):
img = np.array(image)
img = to_chw(img)
img = img.astype('float32')
img -= np.array(mean)[:, np.newaxis, np.newaxis].astype('float32')
img /= np.array(std)[:, np.newaxis, np.newaxis].astype('float32')
img = [img]
img = np.array(img)
return img
def get_shrink(height, width):
"""
Args:
height (int): image height.
width (int): image width.
"""
# avoid out of memory
max_shrink_v1 = (0x7fffffff / 577.0 / (height * width))**0.5
max_shrink_v2 = ((678 * 1024 * 2.0 * 2.0) / (height * width))**0.5
def get_round(x, loc):
str_x = str(x)
if '.' in str_x:
str_before, str_after = str_x.split('.')
len_after = len(str_after)
if len_after >= 3:
str_final = str_before + '.' + str_after[0:loc]
return float(str_final)
else:
return x
max_shrink = get_round(min(max_shrink_v1, max_shrink_v2), 2) - 0.3
if max_shrink >= 1.5 and max_shrink < 2:
max_shrink = max_shrink - 0.1
elif max_shrink >= 2 and max_shrink < 3:
max_shrink = max_shrink - 0.2
elif max_shrink >= 3 and max_shrink < 4:
max_shrink = max_shrink - 0.3
elif max_shrink >= 4 and max_shrink < 5:
max_shrink = max_shrink - 0.4
elif max_shrink >= 5:
max_shrink = max_shrink - 0.5
elif max_shrink <= 0.1:
max_shrink = 0.1
shrink = max_shrink if max_shrink < 1 else 1
return shrink, max_shrink
def bbox_vote(det):
order = det[:, 4].ravel().argsort()[::-1]
det = det[order, :]
if det.shape[0] == 0:
dets = np.array([[10, 10, 20, 20, 0.002]])
det = np.empty(shape=[0, 5])
while det.shape[0] > 0:
# IOU
area = (det[:, 2] - det[:, 0] + 1) * (det[:, 3] - det[:, 1] + 1)
xx1 = np.maximum(det[0, 0], det[:, 0])
yy1 = np.maximum(det[0, 1], det[:, 1])
xx2 = np.minimum(det[0, 2], det[:, 2])
yy2 = np.minimum(det[0, 3], det[:, 3])
w = np.maximum(0.0, xx2 - xx1 + 1)
h = np.maximum(0.0, yy2 - yy1 + 1)
inter = w * h
o = inter / (area[0] + area[:] - inter)
# nms
merge_index = np.where(o >= 0.3)[0]
det_accu = det[merge_index, :]
det = np.delete(det, merge_index, 0)
if merge_index.shape[0] <= 1:
if det.shape[0] == 0:
try:
dets = np.row_stack((dets, det_accu))
except:
dets = det_accu
continue
det_accu[:, 0:4] = det_accu[:, 0:4] * np.tile(det_accu[:, -1:], (1, 4))
max_score = np.max(det_accu[:, 4])
det_accu_sum = np.zeros((1, 5))
det_accu_sum[:, 0:4] = np.sum(det_accu[:, 0:4],
axis=0) / np.sum(det_accu[:, -1:])
det_accu_sum[:, 4] = max_score
try:
dets = np.row_stack((dets, det_accu_sum))
except:
dets = det_accu_sum
dets = dets[0:750, :]
keep_index = np.where(dets[:, 4] >= 0.01)[0]
dets = dets[keep_index, :]
return dets
def save_widerface_bboxes(image_path, bboxes_scores, output_dir):
image_name = image_path.split('/')[-1]
image_class = image_path.split('/')[-2]
odir = os.path.join(output_dir, image_class)
if not os.path.exists(odir):
os.makedirs(odir)
ofname = os.path.join(odir, '%s.txt' % (image_name[:-4]))
f = open(ofname, 'w')
f.write('{:s}\n'.format(image_class + '/' + image_name))
f.write('{:d}\n'.format(bboxes_scores.shape[0]))
for box_score in bboxes_scores:
xmin, ymin, xmax, ymax, score = box_score
f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'.format(xmin, ymin, (
xmax - xmin + 1), (ymax - ymin + 1), score))
f.close()
logger.info("The predicted result is saved as {}".format(ofname))
def save_fddb_bboxes(bboxes_scores,
output_dir,
output_fname='pred_fddb_res.txt'):
if not os.path.exists(output_dir):
os.makedirs(output_dir)
predict_file = os.path.join(output_dir, output_fname)
f = open(predict_file, 'w')
for image_path, dets in bboxes_scores.iteritems():
f.write('{:s}\n'.format(image_path))
f.write('{:d}\n'.format(dets.shape[0]))
for box_score in dets:
xmin, ymin, xmax, ymax, score = box_score
width, height = xmax - xmin, ymax - ymin
f.write('{:.1f} {:.1f} {:.1f} {:.1f} {:.3f}\n'
.format(xmin, ymin, width, height, score))
logger.info("The predicted result is saved as {}".format(predict_file))
return predict_file
def lmk2out(results, is_bbox_normalized=False):
"""
Args:
results: request a dict, should include: `landmark`, `im_id`,
if is_bbox_normalized=True, also need `im_shape`.
is_bbox_normalized: whether or not landmark is normalized.
"""
xywh_res = []
for t in results:
bboxes = t['bbox'][0]
lengths = t['bbox'][1][0]
im_ids = np.array(t['im_id'][0]).flatten()
if bboxes.shape == (1, 1) or bboxes is None:
continue
face_index = t['face_index'][0]
prior_box = t['prior_boxes'][0]
predict_lmk = t['landmark'][0]
prior = np.reshape(prior_box, (-1, 4))
predictlmk = np.reshape(predict_lmk, (-1, 10))
k = 0
for a in range(len(lengths)):
num = lengths[a]
im_id = int(im_ids[a])
for i in range(num):
score = bboxes[k][1]
theindex = face_index[i][0]
me_prior = prior[theindex, :]
lmk_pred = predictlmk[theindex, :]
prior_w = me_prior[2] - me_prior[0]
prior_h = me_prior[3] - me_prior[1]
prior_w_center = (me_prior[2] + me_prior[0]) / 2
prior_h_center = (me_prior[3] + me_prior[1]) / 2
lmk_decode = np.zeros((10))
for j in [0, 2, 4, 6, 8]:
lmk_decode[j] = lmk_pred[j] * 0.1 * prior_w + prior_w_center
for j in [1, 3, 5, 7, 9]:
lmk_decode[j] = lmk_pred[j] * 0.1 * prior_h + prior_h_center
im_shape = t['im_shape'][0][a].tolist()
image_h, image_w = int(im_shape[0]), int(im_shape[1])
if is_bbox_normalized:
lmk_decode = lmk_decode * np.array([
image_w, image_h, image_w, image_h, image_w, image_h,
image_w, image_h, image_w, image_h
])
lmk_res = {
'image_id': im_id,
'landmark': lmk_decode,
'score': score,
}
xywh_res.append(lmk_res)
k += 1
return xywh_res
dygraph/ppdet/modeling/architectures/ssd.py
浏览文件 @ 01d905e3
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
......
dygraph/ppdet/modeling/backbones/__init__.py
浏览文件 @ 01d905e3
# copyright (c) 2020 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.
from . import vgg
from . import resnet
from . import darknet
from . import mobilenet_v1
from . import mobilenet_v3
from . import hrnet
from . import blazenet
from .vgg import *
from .resnet import *
......@@ -11,3 +26,4 @@ from .darknet import *
from .mobilenet_v1 import *
from .mobilenet_v3 import *
from .hrnet import *
from .blazenet import *
dygraph/ppdet/modeling/backbones/blazenet.py 0 → 100644
浏览文件 @ 01d905e3
# copyright (c) 2021 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.
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import paddle
import paddle.nn as nn
import paddle.nn.functional as F
from paddle import ParamAttr
from paddle.regularizer import L2Decay
from paddle.nn.initializer import KaimingNormal
from ppdet.core.workspace import register, serializable
from numbers import Integral
from ..shape_spec import ShapeSpec
__all__ = ['BlazeNet']
class ConvBNLayer(nn.Layer):
def __init__(self,
in_channels,
out_channels,
kernel_size,
stride,
padding,
num_groups=1,
act='relu',
conv_lr=0.1,
conv_decay=0.,
norm_decay=0.,
norm_type='bn',
name=None):
super(ConvBNLayer, self).__init__()
self.act = act
self._conv = nn.Conv2D(
in_channels,
out_channels,
kernel_size=kernel_size,
stride=stride,
padding=padding,
groups=num_groups,
weight_attr=ParamAttr(
learning_rate=conv_lr,
initializer=KaimingNormal(),
name=name + "_weights"),
bias_attr=False)
param_attr = ParamAttr(name=name + "_bn_scale")
bias_attr = ParamAttr(name=name + "_bn_offset")
if norm_type == 'sync_bn':
self._batch_norm = nn.SyncBatchNorm(
out_channels, weight_attr=param_attr, bias_attr=bias_attr)
else:
self._batch_norm = nn.BatchNorm(
out_channels,
act=None,
param_attr=param_attr,
bias_attr=bias_attr,
use_global_stats=False,
moving_mean_name=name + '_bn_mean',
moving_variance_name=name + '_bn_variance')
def forward(self, x):
x = self._conv(x)
x = self._batch_norm(x)
if self.act == "relu":
x = F.relu(x)
elif self.act == "relu6":
x = F.relu6(x)
return x
class BlazeBlock(nn.Layer):
def __init__(self,
in_channels,
out_channels1,
out_channels2,
double_channels=None,
stride=1,
use_5x5kernel=True,
name=None):
super(BlazeBlock, self).__init__()
assert stride in [1, 2]
self.use_pool = not stride == 1
self.use_double_block = double_channels is not None
self.conv_dw = []
if use_5x5kernel:
self.conv_dw.append(
self.add_sublayer(
name + "1_dw",
ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels1,
kernel_size=5,
stride=stride,
padding=2,
num_groups=out_channels1,
name=name + "1_dw")))
else:
self.conv_dw.append(
self.add_sublayer(
name + "1_dw_1",
ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels1,
kernel_size=3,
stride=1,
padding=1,
num_groups=out_channels1,
name=name + "1_dw_1")))
self.conv_dw.append(
self.add_sublayer(
name + "1_dw_2",
ConvBNLayer(
in_channels=out_channels1,
out_channels=out_channels1,
kernel_size=3,
stride=stride,
padding=1,
num_groups=out_channels1,
name=name + "1_dw_2")))
act = 'relu' if self.use_double_block else None
self.conv_pw = ConvBNLayer(
in_channels=out_channels1,
out_channels=out_channels2,
kernel_size=1,
stride=1,
padding=0,
act=act,
name=name + "1_sep")
if self.use_double_block:
self.conv_dw2 = []
if use_5x5kernel:
self.conv_dw2.append(
self.add_sublayer(
name + "2_dw",
ConvBNLayer(
in_channels=out_channels2,
out_channels=out_channels2,
kernel_size=5,
stride=1,
padding=2,
num_groups=out_channels2,
name=name + "2_dw")))
else:
self.conv_dw2.append(
self.add_sublayer(
name + "2_dw_1",
ConvBNLayer(
in_channels=out_channels2,
out_channels=out_channels2,
kernel_size=3,
stride=1,
padding=1,
num_groups=out_channels2,
name=name + "1_dw_1")))
self.conv_dw2.append(
self.add_sublayer(
name + "2_dw_2",
ConvBNLayer(
in_channels=out_channels2,
out_channels=out_channels2,
kernel_size=3,
stride=1,
padding=1,
num_groups=out_channels2,
name=name + "2_dw_2")))
self.conv_pw2 = ConvBNLayer(
in_channels=out_channels2,
out_channels=double_channels,
kernel_size=1,
stride=1,
padding=0,
name=name + "2_sep")
# shortcut
if self.use_pool:
shortcut_channel = double_channels or out_channels2
self._shortcut = []
self._shortcut.append(
self.add_sublayer(
name + '_shortcut_pool',
nn.MaxPool2D(
kernel_size=stride, stride=stride, ceil_mode=True)))
self._shortcut.append(
self.add_sublayer(
name + '_shortcut_conv',
ConvBNLayer(
in_channels=in_channels,
out_channels=shortcut_channel,
kernel_size=1,
stride=1,
padding=0,
name="shortcut" + name)))
def forward(self, x):
y = x
for conv_dw_block in self.conv_dw:
y = conv_dw_block(y)
y = self.conv_pw(y)
if self.use_double_block:
for conv_dw2_block in self.conv_dw2:
y = conv_dw2_block(y)
y = self.conv_pw2(y)
if self.use_pool:
for shortcut in self._shortcut:
x = shortcut(x)
return F.relu(paddle.add(x, y))
@register
@serializable
class BlazeNet(nn.Layer):
"""
BlazeFace, see https://arxiv.org/abs/1907.05047
Args:
blaze_filters (list): number of filter for each blaze block.
double_blaze_filters (list): number of filter for each double_blaze block.
use_5x5kernel (bool): whether or not filter size is 5x5 in depth-wise conv.
"""
def __init__(
self,
blaze_filters=[[24, 24], [24, 24], [24, 48, 2], [48, 48], [48, 48]],
double_blaze_filters=[[48, 24, 96, 2], [96, 24, 96], [96, 24, 96],
[96, 24, 96, 2], [96, 24, 96], [96, 24, 96]],
use_5x5kernel=True):
super(BlazeNet, self).__init__()
conv1_num_filters = blaze_filters[0][0]
self.conv1 = ConvBNLayer(
in_channels=3,
out_channels=conv1_num_filters,
kernel_size=3,
stride=2,
padding=1,
name="conv1")
in_channels = conv1_num_filters
self.blaze_block = []
self._out_channels = []
for k, v in enumerate(blaze_filters):
assert len(v) in [2, 3], \
"blaze_filters {} not in [2, 3]"
if len(v) == 2:
self.blaze_block.append(
self.add_sublayer(
'blaze_{}'.format(k),
BlazeBlock(
in_channels,
v[0],
v[1],
use_5x5kernel=use_5x5kernel,
name='blaze_{}'.format(k))))
elif len(v) == 3:
self.blaze_block.append(
self.add_sublayer(
'blaze_{}'.format(k),
BlazeBlock(
in_channels,
v[0],
v[1],
stride=v[2],
use_5x5kernel=use_5x5kernel,
name='blaze_{}'.format(k))))
in_channels = v[1]
for k, v in enumerate(double_blaze_filters):
assert len(v) in [3, 4], \
"blaze_filters {} not in [3, 4]"
if len(v) == 3:
self.blaze_block.append(
self.add_sublayer(
'double_blaze_{}'.format(k),
BlazeBlock(
in_channels,
v[0],
v[1],
double_channels=v[2],
use_5x5kernel=use_5x5kernel,
name='double_blaze_{}'.format(k))))
elif len(v) == 4:
self.blaze_block.append(
self.add_sublayer(
'double_blaze_{}'.format(k),
BlazeBlock(
in_channels,
v[0],
v[1],
double_channels=v[2],
stride=v[3],
use_5x5kernel=use_5x5kernel,
name='double_blaze_{}'.format(k))))
in_channels = v[2]
self._out_channels.append(in_channels)
def forward(self, inputs):
outs = []
y = self.conv1(inputs['image'])
for block in self.blaze_block:
y = block(y)
outs.append(y)
return [outs[-4], outs[-1]]
@property
def out_shape(self):
return [
ShapeSpec(channels=c)
for c in [self._out_channels[-4], self._out_channels[-1]]
]
dygraph/ppdet/modeling/heads/__init__.py
浏览文件 @ 01d905e3
......@@ -21,6 +21,7 @@ from . import fcos_head
from . import solov2_head
from . import ttf_head
from . import cascade_head
from . import face_head
from .bbox_head import *
from .mask_head import *
......@@ -31,3 +32,4 @@ from .fcos_head import *
from .solov2_head import *
from .ttf_head import *
from .cascade_head import *
from .face_head import *
dygraph/ppdet/modeling/heads/face_head.py 0 → 100644
浏览文件 @ 01d905e3
# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
#
# 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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
from ppdet.core.workspace import register
from paddle.regularizer import L2Decay
from paddle import ParamAttr
from ..layers import AnchorGeneratorSSD
@register
class FaceHead(nn.Layer):
"""
Head block for Face detection network
Args:
num_classes (int): Number of output classes.
in_channels (int): Number of input channels.
anchor_generator(object): instance of anchor genertor method.
kernel_size (int): kernel size of Conv2D in FaceHead.
padding (int): padding of Conv2D in FaceHead.
conv_decay (float): norm_decay (float): weight decay for conv layer weights.
loss (object): loss of face detection model.
"""
__shared__ = ['num_classes']
__inject__ = ['anchor_generator', 'loss']
def __init__(self,
num_classes=80,
in_channels=(96, 96),
anchor_generator=AnchorGeneratorSSD().__dict__,
kernel_size=3,
padding=1,
conv_decay=0.,
loss='SSDLoss'):
super(FaceHead, self).__init__()
# add background class
self.num_classes = num_classes + 1
self.in_channels = in_channels
self.anchor_generator = anchor_generator
self.loss = loss
if isinstance(anchor_generator, dict):
self.anchor_generator = AnchorGeneratorSSD(**anchor_generator)
self.num_priors = self.anchor_generator.num_priors
self.box_convs = []
self.score_convs = []
for i, num_prior in enumerate(self.num_priors):
box_conv_name = "boxes{}".format(i)
box_conv = self.add_sublayer(
box_conv_name,
nn.Conv2D(
in_channels=in_channels[i],
out_channels=num_prior * 4,
kernel_size=kernel_size,
padding=padding))
self.box_convs.append(box_conv)
score_conv_name = "scores{}".format(i)
score_conv = self.add_sublayer(
score_conv_name,
nn.Conv2D(
in_channels=in_channels[i],
out_channels=num_prior * self.num_classes,
kernel_size=kernel_size,
padding=padding))
self.score_convs.append(score_conv)
@classmethod
def from_config(cls, cfg, input_shape):
return {'in_channels': [i.channels for i in input_shape], }
def forward(self, feats, image, gt_bbox=None, gt_class=None):
box_preds = []
cls_scores = []
prior_boxes = []
for feat, box_conv, score_conv in zip(feats, self.box_convs,
self.score_convs):
box_pred = box_conv(feat)
box_pred = paddle.transpose(box_pred, [0, 2, 3, 1])
box_pred = paddle.reshape(box_pred, [0, -1, 4])
box_preds.append(box_pred)
cls_score = score_conv(feat)
cls_score = paddle.transpose(cls_score, [0, 2, 3, 1])
cls_score = paddle.reshape(cls_score, [0, -1, self.num_classes])
cls_scores.append(cls_score)
prior_boxes = self.anchor_generator(feats, image)
if self.training:
return self.get_loss(box_preds, cls_scores, gt_bbox, gt_class,
prior_boxes)
else:
return (box_preds, cls_scores), prior_boxes
def get_loss(self, boxes, scores, gt_bbox, gt_class, prior_boxes):
return self.loss(boxes, scores, gt_bbox, gt_class, prior_boxes)
dygraph/ppdet/modeling/heads/ssd_head.py
浏览文件 @ 01d905e3
# Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved.
#
# 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 paddle
import paddle.nn as nn
import paddle.nn.functional as F
......
dygraph/ppdet/modeling/layers.py
浏览文件 @ 01d905e3
......@@ -271,8 +271,12 @@ class AnchorGeneratorSSD(object):
self.num_priors = []
for aspect_ratio, min_size, max_size in zip(
aspect_ratios, self.min_sizes, self.max_sizes):
self.num_priors.append((len(aspect_ratio) * 2 + 1) * len(
_to_list(min_size)) + len(_to_list(max_size)))
if isinstance(min_size, (list, tuple)):
self.num_priors.append(
len(_to_list(min_size)) + len(_to_list(max_size)))
else:
self.num_priors.append((len(aspect_ratio) * 2 + 1) * len(
_to_list(min_size)) + len(_to_list(max_size)))
def __call__(self, inputs, image):
boxes = []
......
dygraph/ppdet/utils/checkpoint.py
浏览文件 @ 01d905e3
......@@ -136,8 +136,10 @@ def load_pretrain_weight(model,
path = _strip_postfix(pretrain_weight)
if not (os.path.isdir(path) or os.path.isfile(path) or
os.path.exists(path + '.pdparams')):
raise ValueError("Model pretrain path {} does not "
"exists.".format(path))
raise ValueError("Model pretrain path `{}` does not exists. "
"If you don't want to load pretrain model, "
"please delete `pretrain_weights` field in "
"config file.".format(path))
model_dict = model.state_dict()
......
dygraph/tools/train.py
浏览文件 @ 01d905e3
......@@ -91,7 +91,7 @@ def run(FLAGS, cfg):
trainer = Trainer(cfg, mode='train')
# load weights
if not FLAGS.slim_config:
if not FLAGS.slim_config and 'pretrain_weights' in cfg and cfg.pretrain_weights:
trainer.load_weights(cfg.pretrain_weights, FLAGS.weight_type)
# training
......
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