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configs/mobile/README.md
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......@@ -7,19 +7,22 @@
PaddleDetection目前提供一系列针对移动应用进行优化的模型，主要支持以下结构:
| 骨干网络 | 结构 | 输入大小 | 图片/gpu <sup>1</sup> | 学习率策略 | Box AP | 下载 | PaddleLite模型下载 |
| 骨干网络 | 结构 | 输入大小 | 图片/gpu <sup>[1](#gpu)</sup> | 学习率策略 | Box AP | 下载 | PaddleLite模型下载 |
| :----------------------- | :------------------------ | :---: | :--------------------: | :------------ | :----: | :--- | :----------------- |
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small.tar) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large.tar) |
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.2 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.pdparams) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small.tar) |
| MobileNetV3 Small | SSDLite Quant <sup>[2](#quant)</sup> | 320 | 64 | 400K (cosine) | 15.4 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small_quant.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small_quant.tar) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 23.3 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.pdparams) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large.tar) |
| MobileNetV3 Large | SSDLite Quant <sup>[2](#quant)</sup> | 320 | 64 | 400K (cosine) | 22.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large_quant.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large_quant.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_320.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_320.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_640.tar) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_640.tar) |
| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.pdparams) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3.tar) |
| MobileNetV3 Large | YOLOv3 Prune <sup>2</sup> | 320 | 8 | - | 24.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/yolov3_mobilenet_v3_prune75875_FPGM_distillby_r34.pdparams) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3_prune86_FPGM_320.tar) |
| MobileNetV3 Large | YOLOv3 Prune <sup>[3](#prune)</sup> | 320 | 8 | - | 24.6 | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/yolov3_mobilenet_v3_prune75875_FPGM_distillby_r34.pdparams) | [链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3_prune86_FPGM_320.tar) |
**注意**:
- <a name="gpu">[1]</a> 模型统一使用8卡训练.
- <a name="prune">[2]</a> 参考下面关于YOLO剪裁的说明
- <a name="gpu">[1]</a> 模型统一使用8卡训练。
- <a name="quant">[2]</a> 参考下面关于[SSDLite量化的说明](#SSDLite量化说明)。
- <a name="prune">[3]</a> 参考下面关于[YOLO剪裁的说明](#YOLOv3剪裁说明)。
## 评测结果
......@@ -37,7 +40,9 @@ PaddleDetection目前提供一系列针对移动应用进行优化的模型，
| | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
|------------------|---------|---------|---------|---------|-----------|-----------|
| SSDLite Large | 289.071 | 134.408 | 91.933 | 48.2206 | 144.914 | 55.1186 |
| SSDLite Large Quant | | | | | | |
| SSDLite Small | 122.932 | 57.1914 | 41.003 | 22.0694 | 61.5468 | 25.2106 |
| SSDLite Small Quant | | | | | | |
| YOLOv3 baseline | 1082.5 | 435.77 | 317.189 | 155.948 | 536.987 | 178.999 |
| YOLOv3 prune | 253.98 | 131.279 | 89.4124 | 48.2856 | 122.732 | 55.8626 |
| Cascade RCNN 320 | 286.526 | 125.635 | 87.404 | 46.184 | 149.179 | 52.9994 |
......@@ -48,16 +53,28 @@ PaddleDetection目前提供一系列针对移动应用进行优化的模型，
| | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
|------------------|---------|---------|---------|---------|-----------|-----------|
| SSDLite Large | 107.535 | 51.1382 | 34.6392 | 20.4978 | 50.5598 | 24.5318 |
| SSDLite Large Quant | | | | | | |
| SSDLite Small | 51.5704 | 24.5156 | 18.5486 | 11.4218 | 24.9946 | 16.7158 |
| SSDLite Small Quant | | | | | | |
| YOLOv3 baseline | 413.486 | 184.248 | 133.624 | 75.7354 | 202.263 | 126.435 |
| YOLOv3 prune | 98.5472 | 53.6228 | 34.4306 | 21.3112 | 44.0722 | 31.201 |
| Cascade RCNN 320 | 131.515 | 59.6026 | 39.4338 | 23.5802 | 58.5046 | 36.9486 |
| Cascade RCNN 640 | 473.083 | 224.543 | 156.205 | 100.686 | 231.108 | 138.391 |
## SSDLite量化说明
在SSDLite模型中我们采用完整量化训练的方式对模型进行训练，在8卡GPU下共训练40万轮，训练中将`res_conv1`与`se_block`固定不训练，执行指令为：
```shell
python slim/quantization/train.py --not_quant_pattern res_conv1 se_block \
-c configs/ssd/ssdlite_mobilenet_v3_large.yml \
--eval
```
更多量化教程请参考[模型量化压缩教程](../../docs/advanced_tutorials/slim/quantization/QUANTIZATION.md)
## YOLOv3剪裁说明
首先对YOLO检测头进行剪裁，然后再使用 YOLOv3-ResNet34 作为teacher网络对剪裁后的模型进行蒸馏, teacher网络在COCO的mAP为31.4 (输入大小320\*320).
首先对YOLO检测头进行剪裁，然后再使用 YOLOv3-ResNet34 作为teacher网络对剪裁后的模型进行蒸馏, teacher网络在COCO上的mAP为31.4 (输入大小320\*320).
可以使用如下两种方式进行剪裁:
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configs/mobile/README_en.md 100755 → 100644
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......@@ -7,10 +7,12 @@ English | [简体中文](README.md)
This directory contains models optimized for mobile applications, at present the following models included:
| Backbone | Architecture | Input | Image/gpu <sup>1</sup> | Lr schd | Box AP | Download | PaddleLite Model Download |
| Backbone | Architecture | Input | Image/gpu <sup>[1](#gpu)</sup> | Lr schd | Box AP | Download | PaddleLite Model Download |
| :----------------------- | :------------------------ | :---: | :--------------------: | :------------ | :----: | :------- | :------------------------ |
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small.tar) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 22.8 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large.tar) |
| MobileNetV3 Small | SSDLite | 320 | 64 | 400K (cosine) | 16.2 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.pdparam) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small.tar) |
| MobileNetV3 Small | SSDLite Quant <sup>[2](#quant)</sup> | 320 | 64 | 400K (cosine) | 15.4 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small_quant.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_small_quant.tar) |
| MobileNetV3 Large | SSDLite | 320 | 64 | 400K (cosine) | 23.3 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.pdparam) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large.tar) |
| MobileNetV3 Large | SSDLite Quant <sup>[2](#quant)</sup> | 320 | 64 | 400K (cosine) | 22.6 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large_quant.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/ssdlite_mobilenet_v3_large_quant.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 320 | 2 | 500k (cosine) | 25.0 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_320.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_320.tar) |
| MobileNetV3 Large w/ FPN | Cascade RCNN | 640 | 2 | 500k (cosine) | 30.2 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/cascade_rcnn_mobilenetv3_fpn_640.tar) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/cascade_rcnn_mobilenetv3_fpn_640.tar) |
| MobileNetV3 Large | YOLOv3 | 320 | 8 | 500K | 27.1 | [Link](https://paddlemodels.bj.bcebos.com/object_detection/yolov3_mobilenet_v3.pdparams) | [Link](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/lite/yolov3_mobilenet_v3.tar) |
......@@ -19,7 +21,8 @@ This directory contains models optimized for mobile applications, at present the
**Notes**:
- <a name="gpu">[1]</a> All models are trained on 8 GPUs.
- <a name="prune">[2]</a> See the note section on how YOLO head is pruned
- <a name="quant">[2]</a> See the note section on [SSDLite quantization](#Notes-on-SSDLite-quant)。
- <a name="prune">[3]</a> See the note section on [how YOLO head is pruned](#Notes-on-YOLOv3-pruning).
## Benchmarks Results
......@@ -37,7 +40,9 @@ This directory contains models optimized for mobile applications, at present the
| | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
|------------------|---------|---------|---------|---------|-----------|-----------|
| SSDLite Large | 289.071 | 134.408 | 91.933 | 48.2206 | 144.914 | 55.1186 |
| SSDLite Large Quant | | | | | | |
| SSDLite Small | 122.932 | 57.1914 | 41.003 | 22.0694 | 61.5468 | 25.2106 |
| SSDLite Small Quant | | | | | | |
| YOLOv3 baseline | 1082.5 | 435.77 | 317.189 | 155.948 | 536.987 | 178.999 |
| YOLOv3 prune | 253.98 | 131.279 | 89.4124 | 48.2856 | 122.732 | 55.8626 |
| Cascade RCNN 320 | 286.526 | 125.635 | 87.404 | 46.184 | 149.179 | 52.9994 |
......@@ -48,13 +53,27 @@ This directory contains models optimized for mobile applications, at present the
| | SD625 | SD835 | SD845 | SD855 | Kirin 970 | Kirin 980 |
|------------------|---------|---------|---------|---------|-----------|-----------|
| SSDLite Large | 107.535 | 51.1382 | 34.6392 | 20.4978 | 50.5598 | 24.5318 |
| SSDLite Large Quant | | | | | | |
| SSDLite Small | 51.5704 | 24.5156 | 18.5486 | 11.4218 | 24.9946 | 16.7158 |
| SSDLite Small Quant | | | | | | |
| YOLOv3 baseline | 413.486 | 184.248 | 133.624 | 75.7354 | 202.263 | 126.435 |
| YOLOv3 prune | 98.5472 | 53.6228 | 34.4306 | 21.3112 | 44.0722 | 31.201 |
| Cascade RCNN 320 | 131.515 | 59.6026 | 39.4338 | 23.5802 | 58.5046 | 36.9486 |
| Cascade RCNN 640 | 473.083 | 224.543 | 156.205 | 100.686 | 231.108 | 138.391 |
## Notes on SSDLite quantization
We use a complete quantitative training method to train the SSDLite model. It is trained for a total of 400,000 rounds with the 8-card GPU. We freeze `res_conv1` and `se_block`. The command used is listed bellow:
```shell
python slim/quantization/train.py --not_quant_pattern res_conv1 se_block \
-c configs/ssd/ssdlite_mobilenet_v3_large.yml \
--eval
```
For more quantization tutorials, please refer to [Model Quantization Compression Tutorial](../../docs/advanced_tutorials/slim/quantization/QUANTIZATION.md)
## Notes on YOLOv3 pruning
We pruned the YOLO-head and distill the pruned model with YOLOv3-ResNet34 as the teacher, which has a higher mAP on COCO (31.4 with 320\*320 input).
......
configs/ssd/ssdlite_mobilenet_v3_large.yml
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......@@ -26,8 +26,10 @@ MobileNetV3:
scale: 1.0
model_name: large
extra_block_filters: [[256, 512], [128, 256], [128, 256], [64, 128]]
conv_decay: 0.00004
feature_maps: [5, 7, 8, 9, 10, 11]
lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
conv_decay: 0.00004
multiplier: 0.5
SSDLiteMultiBoxHead:
aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
......
configs/ssd/ssdlite_mobilenet_v3_large_fpn.yml 0 → 100644
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architecture: SSD
use_gpu: true
max_iters: 400000
snapshot_iter: 20000
log_smooth_window: 20
log_iter: 20
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_large_x1_0_ssld_pretrained.tar
save_dir: output
weights: output/ssdlite_mobilenet_v3_large_fpn/model_final
# 80(label_class) + 1(background)
num_classes: 81
SSD:
backbone: MobileNetV3
fpn: FPN
multi_box_head: SSDLiteMultiBoxHead
output_decoder:
background_label: 0
keep_top_k: 200
nms_eta: 1.0
nms_threshold: 0.45
nms_top_k: 400
score_threshold: 0.01
FPN:
num_chan: 256
max_level: 7
norm_type: bn
norm_decay: 0.00004
reverse_out: true
MobileNetV3:
scale: 1.0
model_name: large
extra_block_filters: [[256, 512], [128, 256], [128, 256], [64, 128]]
feature_maps: [5, 7, 8, 9, 10, 11]
lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
conv_decay: 0.00004
SSDLiteMultiBoxHead:
aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
base_size: 320
steps: [16, 32, 64, 107, 160, 320]
flip: true
clip: true
max_ratio: 95
min_ratio: 20
offset: 0.5
conv_decay: 0.00004
LearningRate:
base_lr: 0.4
schedulers:
- !CosineDecay
max_iters: 400000
- !LinearWarmup
start_factor: 0.33333
steps: 2000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
TrainReader:
inputs_def:
image_shape: [3, 320, 320]
fields: ['image', 'gt_bbox', 'gt_class']
dataset:
!COCODataSet
dataset_dir: dataset/coco
anno_path: annotations/instances_train2017.json
image_dir: train2017
sample_transforms:
- !DecodeImage
to_rgb: true
- !RandomDistort
brightness_lower: 0.875
brightness_upper: 1.125
is_order: true
- !RandomExpand
fill_value: [123.675, 116.28, 103.53]
- !RandomCrop
allow_no_crop: false
- !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 320
use_cv2: false
- !RandomFlipImage
is_normalized: false
- !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
batch_size: 64
shuffle: true
drop_last: true
# Number of working threads/processes. To speed up, can be set to 16 or 32 etc.
worker_num: 8
# Size of shared memory used in result queue. After increasing `worker_num`, need expand `memsize`.
memsize: 8G
# Buffer size for multi threads/processes.one instance in buffer is one batch data.
# To speed up, can be set to 64 or 128 etc.
bufsize: 32
use_process: true
EvalReader:
inputs_def:
image_shape: [3, 320, 320]
fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id']
dataset:
!COCODataSet
dataset_dir: dataset/coco
anno_path: annotations/instances_val2017.json
image_dir: val2017
sample_transforms:
- !DecodeImage
to_rgb: true
- !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 320
use_cv2: false
- !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
batch_size: 8
worker_num: 8
bufsize: 32
use_process: false
TestReader:
inputs_def:
image_shape: [3,320,320]
fields: ['image', 'im_id', 'im_shape']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
- !ResizeImage
interp: 1
max_size: 0
target_size: 320
use_cv2: false
- !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
batch_size: 1
configs/ssd/ssdlite_mobilenet_v3_small.yml
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......@@ -5,7 +5,7 @@ snapshot_iter: 20000
log_smooth_window: 20
log_iter: 20
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_pretrained.tar
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar
save_dir: output
weights: output/ssd_mobilenet_v3_small/model_final
# 80(label_class) + 1(background)
......@@ -26,8 +26,10 @@ MobileNetV3:
scale: 1.0
model_name: small
extra_block_filters: [[256, 512], [128, 256], [128, 256], [64, 128]]
conv_decay: 0.00004
feature_maps: [5, 7, 8, 9, 10, 11]
lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
conv_decay: 0.00004
multiplier: 0.5
SSDLiteMultiBoxHead:
aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
......
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architecture: SSD
use_gpu: true
max_iters: 400000
snapshot_iter: 20000
log_smooth_window: 20
log_iter: 20
metric: COCO
pretrain_weights: https://paddle-imagenet-models-name.bj.bcebos.com/MobileNetV3_small_x1_0_ssld_pretrained.tar
save_dir: output
weights: output/ssdlite_mobilenet_v3_small_fpn/model_final
# 80(label_class) + 1(background)
num_classes: 81
SSD:
backbone: MobileNetV3
fpn: FPN
multi_box_head: SSDLiteMultiBoxHead
output_decoder:
background_label: 0
keep_top_k: 200
nms_eta: 1.0
nms_threshold: 0.45
nms_top_k: 400
score_threshold: 0.01
FPN:
num_chan: 256
max_level: 7
norm_type: bn
norm_decay: 0.00004
reverse_out: true
MobileNetV3:
scale: 1.0
model_name: small
extra_block_filters: [[256, 512], [128, 256], [128, 256], [64, 128]]
feature_maps: [5, 7, 8, 9, 10, 11]
lr_mult_list: [0.25, 0.25, 0.5, 0.5, 0.75]
conv_decay: 0.00004
SSDLiteMultiBoxHead:
aspect_ratios: [[2.], [2., 3.], [2., 3.], [2., 3.], [2., 3.], [2., 3.]]
base_size: 320
steps: [16, 32, 64, 107, 160, 320]
flip: true
clip: true
max_ratio: 95
min_ratio: 20
offset: 0.5
conv_decay: 0.00004
LearningRate:
base_lr: 0.4
schedulers:
- !CosineDecay
max_iters: 400000
- !LinearWarmup
start_factor: 0.33333
steps: 2000
OptimizerBuilder:
optimizer:
momentum: 0.9
type: Momentum
regularizer:
factor: 0.0005
type: L2
TrainReader:
inputs_def:
image_shape: [3, 320, 320]
fields: ['image', 'gt_bbox', 'gt_class']
dataset:
!COCODataSet
dataset_dir: dataset/coco
anno_path: annotations/instances_train2017.json
image_dir: train2017
sample_transforms:
- !DecodeImage
to_rgb: true
- !RandomDistort
brightness_lower: 0.875
brightness_upper: 1.125
is_order: true
- !RandomExpand
fill_value: [123.675, 116.28, 103.53]
- !RandomCrop
allow_no_crop: false
- !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 320
use_cv2: false
- !RandomFlipImage
is_normalized: false
- !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
batch_size: 64
shuffle: true
drop_last: true
# Number of working threads/processes. To speed up, can be set to 16 or 32 etc.
worker_num: 8
# Size of shared memory used in result queue. After increasing `worker_num`, need expand `memsize`.
memsize: 8G
# Buffer size for multi threads/processes.one instance in buffer is one batch data.
# To speed up, can be set to 64 or 128 etc.
bufsize: 32
use_process: true
EvalReader:
inputs_def:
image_shape: [3, 320, 320]
fields: ['image', 'gt_bbox', 'gt_class', 'im_shape', 'im_id']
dataset:
!COCODataSet
dataset_dir: dataset/coco
anno_path: annotations/instances_val2017.json
image_dir: val2017
sample_transforms:
- !DecodeImage
to_rgb: true
- !NormalizeBox {}
- !ResizeImage
interp: 1
target_size: 320
use_cv2: false
- !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
batch_size: 8
worker_num: 8
bufsize: 32
use_process: false
TestReader:
inputs_def:
image_shape: [3,320,320]
fields: ['image', 'im_id', 'im_shape']
dataset:
!ImageFolder
anno_path: annotations/instances_val2017.json
sample_transforms:
- !DecodeImage
to_rgb: true
- !ResizeImage
interp: 1
max_size: 0
target_size: 320
use_cv2: false
- !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
batch_size: 1
docs/MODEL_ZOO.md
浏览文件 @ 84062367
......@@ -193,9 +193,11 @@ results of image size 608/416/320 above. Deformable conv is added on stage 5 of
| Backbone | Size | Image/gpu | Lr schd | Inf time (fps) | Box AP | Download | Configs |
| :------: | :--: | :-------: | :-----: | :------------: | :----: | :----------------------------------------------------------: | :----: |
| MobileNet_v1 | 300 | 64 | 40w | - | 23.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v1.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v1.yml) |
| MobileNet_v3 small | 320 | 64 | 40w | - | 16.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mobilenet_v3_ssdlite_small.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_small.yml) |
| MobileNet_v3 large | 320 | 64 | 40w | - | 22.8 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mobilenet_v3_ssdlite_large.tar) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_large.yml) |
| MobileNet_v1 | 300 | 64 | Cosine decay(40w) | - | 23.6 | [model](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v1.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v1.yml) |
| MobileNet_v3 small | 320 | 64 | Cosine decay(40w) | - | 16.2 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_small.yml) |
| MobileNet_v3 large | 320 | 64 | Cosine decay(40w) | - | 23.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_large.yml) |
| MobileNet_v3 large w/ FPN | 320 | 64 | Cosine decay(40w) | - | 18.9 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small_fpn.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_small_fpn.yml) |
| MobileNet_v3 large w/ FPN | 320 | 64 | Cosine decay(40w) | - | 24.3 | [model](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large_fpn.pdparams) | [config](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_large_fpn.yml) |
**Notes:** `SSDLite` is trained in 8 GPU with total batch size as 512 and uses cosine decay strategy to train.
......
docs/MODEL_ZOO_cn.md
浏览文件 @ 84062367
......@@ -185,9 +185,11 @@ Paddle提供基于ImageNet的骨架网络预训练模型。所有预训练模型
| 骨架网络 | 输入尺寸 | 每张GPU图片个数 | 学习率策略|推理时间(fps) | Box AP | 下载 | 配置文件 |
| :----------: | :--: | :-----: | :-----: |:------------: |:----: | :-------: | :----: |
| MobileNet_v1 | 300 | 64 | 40w | - | 23.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v1.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v1.yml) |
| MobileNet_v3 small | 320 | 64 | 40w | - | 16.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mobilenet_v3_ssdlite_small.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_small.yml) |
| MobileNet_v3 large | 320 | 64 | 40w | - | 22.8 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mobilenet_v3_ssdlite_large.tar) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_large.yml) |
| MobileNet_v1 | 300 | 64 | Cosine decay(40w) | - | 23.6 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/ssdlite_mobilenet_v1.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v1.yml) |
| MobileNet_v3 small | 320 | 64 | Cosine decay(40w) | - | 16.2 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_small.yml) |
| MobileNet_v3 large | 320 | 64 | Cosine decay(40w) | - | 23.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_large.yml) |
| MobileNet_v3 large w/ FPN | 320 | 64 | Cosine decay(40w) | - | 18.9 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_small_fpn.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_small_fpn.yml) |
| MobileNet_v3 large w/ FPN | 320 | 64 | Cosine decay(40w) | - | 24.3 | [下载链接](https://paddlemodels.bj.bcebos.com/object_detection/mobile_models/ssdlite_mobilenet_v3_large_fpn.pdparams) | [配置文件](https://github.com/PaddlePaddle/PaddleDetection/tree/master/configs/ssd/ssdlite_mobilenet_v3_large_fpn.yml) |
**注意事项:** SSDLite模型使用学习率余弦衰减策略在8卡GPU下总batch size为512。
......
ppdet/modeling/architectures/ssd.py
浏览文件 @ 84062367
......@@ -40,16 +40,18 @@ class SSD(object):
"""
__category__ = 'architecture'
__inject__ = ['backbone', 'multi_box_head', 'output_decoder']
__inject__ = ['backbone', 'multi_box_head', 'output_decoder', 'fpn']
__shared__ = ['num_classes']
def __init__(self,
backbone,
fpn=None,
multi_box_head='MultiBoxHead',
output_decoder=SSDOutputDecoder().__dict__,
num_classes=21):
super(SSD, self).__init__()
self.backbone = backbone
self.fpn = fpn
self.multi_box_head = multi_box_head
self.num_classes = num_classes
self.output_decoder = output_decoder
......@@ -70,6 +72,9 @@ class SSD(object):
# backbone
body_feats = self.backbone(im)
if self.fpn is not None:
body_feats, spatial_scale = self.fpn.get_output(body_feats)
if isinstance(body_feats, OrderedDict):
body_feat_names = list(body_feats.keys())
body_feats = [body_feats[name] for name in body_feat_names]
......
ppdet/modeling/backbones/fpn.py
浏览文件 @ 84062367
......@@ -41,6 +41,8 @@ class FPN(object):
spatial_scale (list): feature map scaling factor
has_extra_convs (bool): whether has extral convolutions in higher levels
norm_type (str|None): normalization type, 'bn'/'sync_bn'/'affine_channel'
norm_decay (float): weight decay for normalization layer weights.
reverse_out (bool): whether to flip the output.
"""
__shared__ = ['norm_type', 'freeze_norm']
......@@ -51,8 +53,10 @@ class FPN(object):
spatial_scale=[1. / 32., 1. / 16., 1. / 8., 1. / 4.],
has_extra_convs=False,
norm_type=None,
norm_decay=0.,
freeze_norm=False,
use_c5=True):
use_c5=True,
reverse_out=False):
self.freeze_norm = freeze_norm
self.num_chan = num_chan
self.min_level = min_level
......@@ -60,7 +64,9 @@ class FPN(object):
self.spatial_scale = spatial_scale
self.has_extra_convs = has_extra_convs
self.norm_type = norm_type
self.norm_decay = norm_decay
self.use_c5 = use_c5
self.reverse_out = reverse_out
def _add_topdown_lateral(self, body_name, body_input, upper_output):
lateral_name = 'fpn_inner_' + body_name + '_lateral'
......@@ -74,6 +80,7 @@ class FPN(object):
1,
initializer=initializer,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
freeze_norm=self.freeze_norm,
name=lateral_name,
norm_name=lateral_name)
......@@ -89,8 +96,14 @@ class FPN(object):
learning_rate=2.,
regularizer=L2Decay(0.)),
name=lateral_name)
topdown = fluid.layers.resize_nearest(
upper_output, scale=2., name=topdown_name)
if body_input.shape[2] == -1 and body_input.shape[3] == -1:
topdown = fluid.layers.resize_nearest(
upper_output, scale=2., name=topdown_name)
else:
topdown = fluid.layers.resize_nearest(
upper_output,
out_shape=[body_input.shape[2], body_input.shape[3]],
name=topdown_name)
return lateral + topdown
......@@ -122,6 +135,7 @@ class FPN(object):
1,
initializer=initializer,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
freeze_norm=self.freeze_norm,
name=fpn_inner_name,
norm_name=fpn_inner_name)
......@@ -158,6 +172,7 @@ class FPN(object):
3,
initializer=initializer,
norm_type=self.norm_type,
norm_decay=self.norm_decay,
freeze_norm=self.freeze_norm,
name=fpn_name,
norm_name=fpn_name)
......@@ -217,5 +232,8 @@ class FPN(object):
fpn_dict[fpn_name] = fpn_blob
fpn_name_list.insert(0, fpn_name)
spatial_scale.insert(0, spatial_scale[0] * 0.5)
if self.reverse_out:
fpn_name_list = fpn_name_list[::-1]
res_dict = OrderedDict([(k, fpn_dict[k]) for k in fpn_name_list])
return res_dict, spatial_scale
ppdet/modeling/backbones/mobilenet_v3.py
浏览文件 @ 84062367
......@@ -45,10 +45,11 @@ class MobileNetV3(object):
feature_maps (list): index of stages whose feature maps are returned.
extra_block_filters (list): number of filter for each extra block.
lr_mult_list (list): learning rate ratio of different blocks, lower learning rate ratio
is need for pretrained model got using distillation(default as
is need for pretrained model got using distillation(default as
[1.0, 1.0, 1.0, 1.0, 1.0]).
freeze_norm (bool): freeze normalization layers
feature_maps (list): feature maps used in two-stage rcnn models(default as None).
freeze_norm (bool): freeze normalization layers.
multiplier (float): The multiplier by which to reduce the convolution expansion and
number of channels.
"""
__shared__ = ['norm_type']
......@@ -62,7 +63,8 @@ class MobileNetV3(object):
norm_decay=0.0,
extra_block_filters=[[256, 512], [128, 256], [128, 256], [64, 128]],
lr_mult_list=[1.0, 1.0, 1.0, 1.0, 1.0],
freeze_norm=False, ):
freeze_norm=False,
multiplier=1.0):
if isinstance(feature_maps, Integral):
feature_maps = [feature_maps]
......@@ -122,6 +124,13 @@ class MobileNetV3(object):
else:
raise NotImplementedError
if multiplier != 1.0:
self.cfg[-3][2] = int(self.cfg[-3][2] * multiplier)
self.cfg[-2][1] = int(self.cfg[-2][1] * multiplier)
self.cfg[-2][2] = int(self.cfg[-2][2] * multiplier)
self.cfg[-1][1] = int(self.cfg[-1][1] * multiplier)
self.cfg[-1][2] = int(self.cfg[-1][2] * multiplier)
def _conv_bn_layer(self,
input,
filter_size,
......@@ -279,21 +288,25 @@ class MobileNetV3(object):
if self.block_stride in self.feature_maps:
self.end_points.append(conv0)
conv1 = self._conv_bn_layer(
input=conv0,
filter_size=filter_size,
num_filters=num_mid_filter,
stride=stride,
padding=int((filter_size - 1) // 2),
if_act=True,
act=act,
num_groups=num_mid_filter,
use_cudnn=False,
name=name + '_depthwise')
with fluid.name_scope('res_conv1'):
conv1 = self._conv_bn_layer(
input=conv0,
filter_size=filter_size,
num_filters=num_mid_filter,
stride=stride,
padding=int((filter_size - 1) // 2),
if_act=True,
act=act,
num_groups=num_mid_filter,
use_cudnn=False,
name=name + '_depthwise')
if use_se:
conv1 = self._se_block(
input=conv1, num_out_filter=num_mid_filter, name=name + '_se')
with fluid.name_scope('se_block'):
conv1 = self._se_block(
input=conv1,
num_out_filter=num_mid_filter,
name=name + '_se')
conv2 = self._conv_bn_layer(
input=conv1,
......
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