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add github config path for yolox-pai (#166) 

* add github config path
pull/170/head
zouxinyi0625 2022-08-26 13:50:48 +08:00 committed by GitHub
parent 6821cdc9db
commit 19e570adde
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5 changed files with 426 additions and 39 deletions
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2
.github/workflows/citest.yaml vendored
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@ -67,7 +67,7 @@ jobs:
PYTHONPATH=. python tests/run.py
# blade test env will be updated!
# blade test env will be updated in docker images!
# ut-torch181-blade:
# # The type of runner that the job will run on
# runs-on: [unittest-t4]

188
configs/detection/yolox/pai_yoloxs_8xb16_300e_coco.py 100644
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@ -0,0 +1,188 @@
_base_ = '../../base.py'
# model settings s m l x
model = dict(
type='YOLOX',
test_conf=0.01,
nms_thre=0.65,
backbone='RepVGGYOLOX',
model_type='s', # s m l x tiny nano
head=dict(
type='YOLOXHead',
model_type='s',
obj_loss_type='BCE',
reg_loss_type='giou',
num_classes=80,
decode_in_inference=
True # set to False when test speed to ignore decode and nms
))
# s m l x
img_scale = (640, 640)
random_size = (14, 26)
scale_ratio = (0.1, 2)
CLASSES = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush'
]
# dataset settings
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='MMMosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='MMRandomAffine',
scaling_ratio_range=scale_ratio,
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='MMMixUp', # s m x l; tiny nano will detele
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(
type='MMPhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(type='MMRandomFlip', flip_ratio=0.5),
dict(type='MMResize', keep_ratio=True),
dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='MMResize', img_scale=img_scale, keep_ratio=True),
dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
]
train_dataset = dict(
type='DetImagesMixDataset',
data_source=dict(
type='DetSourceCoco',
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True)
],
classes=CLASSES,
filter_empty_gt=True,
iscrowd=False),
pipeline=train_pipeline,
dynamic_scale=img_scale)
val_dataset = dict(
type='DetImagesMixDataset',
imgs_per_gpu=2,
data_source=dict(
type='DetSourceCoco',
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True)
],
classes=CLASSES,
filter_empty_gt=False,
test_mode=True,
iscrowd=True),
pipeline=test_pipeline,
dynamic_scale=None,
label_padding=False)
data = dict(
imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)
# additional hooks
interval = 10
custom_hooks = [
dict(
type='YOLOXModeSwitchHook',
no_aug_epochs=15,
skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'),
priority=48),
dict(
type='SyncRandomSizeHook',
ratio_range=random_size,
img_scale=img_scale,
interval=interval,
priority=48),
dict(
type='SyncNormHook',
num_last_epochs=15,
interval=interval,
priority=48)
]
# evaluation
eval_config = dict(
interval=10,
gpu_collect=False,
visualization_config=dict(
vis_num=10,
score_thr=0.5,
) # show by TensorboardLoggerHookV2 and WandbLoggerHookV2
)
eval_pipelines = [
dict(
mode='test',
data=data['val'],
evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
)
]
checkpoint_config = dict(interval=interval)
# optimizer
optimizer = dict(
type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4, nesterov=True)
optimizer_config = {}
# learning policy
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5, # 5 epoch
num_last_epochs=15,
min_lr_ratio=0.05)
# exponetial model average
ema = dict(decay=0.9998)
# runtime settings
total_epochs = 300
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHookV2'),
# dict(type='WandbLoggerHookV2'),
])
export = dict(export_type = 'ori', preprocess_jit = False, batch_size=1, blade_config=dict(enable_fp16=True, fp16_fallback_op_ratio=0.01), use_trt_efficientnms=False)

62
configs/detection/yolox_tiny.py → configs/detection/yolox/pai_yoloxs_asff_8xb16_300e_coco.py
View File

@ -1,22 +1,27 @@
# model settings
# models s m l x
_base_ = '../../base.py'
# model settings s m l x
model = dict(
type='YOLOX',
num_classes=80,
model_type='tiny', # s m l x tiny nano
test_conf=0.01,
nms_thre=0.65)
nms_thre=0.65,
backbone='RepVGGYOLOX',
model_type='s', # s m l x tiny nano
use_att='ASFF',
head=dict(
type='YOLOXHead',
model_type='s',
obj_loss_type='BCE',
reg_loss_type='giou',
num_classes=80,
decode_in_inference=
False # set to False when test speed to ignore decode and nms
))
# s m l x
# img_scale = (640, 640)
# random_size = (14, 26)
# scale_ratio = (0.1, 2)
# tiny nano ; without mixup
img_scale = (416, 416)
random_size = (10, 20)
scale_ratio = (0.5, 1.5)
img_scale = (640, 640)
random_size = (14, 26)
scale_ratio = (0.1, 2)
CLASSES = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
@ -36,6 +41,7 @@ CLASSES = [
# dataset settings
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
@ -45,6 +51,11 @@ train_pipeline = [
type='MMRandomAffine',
scaling_ratio_range=scale_ratio,
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='MMMixUp', # s m x l; tiny nano will detele
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(
type='MMPhotoMetricDistortion',
brightness_delta=32,
@ -125,7 +136,14 @@ custom_hooks = [
]
# evaluation
eval_config = dict(interval=10, gpu_collect=False)
eval_config = dict(
interval=10,
gpu_collect=False,
visualization_config=dict(
vis_num=10,
score_thr=0.5,
) # show by TensorboardLoggerHookV2 and WandbLoggerHookV2
)
eval_pipelines = [
dict(
mode='test',
@ -137,9 +155,8 @@ eval_pipelines = [
checkpoint_config = dict(interval=interval)
# optimizer
# basic_lr_per_img = 0.01 / 64.0
optimizer = dict(
type='SGD', lr=0.01, momentum=0.9, weight_decay=5e-4, nesterov=True)
type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4, nesterov=True)
optimizer_config = {}
# learning policy
@ -164,15 +181,8 @@ log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHook')
dict(type='TensorboardLoggerHookV2'),
# dict(type='WandbLoggerHookV2'),
])
# yapf:enable
# runtime settings
dist_params = dict(backend='nccl')
cudnn_benchmark = True
log_level = 'INFO'
load_from = None
resume_from = None
workflow = [('train', 1)]
export = dict(use_jit=False)
export = dict(export_type = 'ori', preprocess_jit = False, batch_size=1, blade_config=dict(enable_fp16=True, fp16_fallback_op_ratio=0.01), use_trt_efficientnms=False)

189
configs/detection/yolox/pai_yoloxs_asff_tood3_8xb16_300e_coco.py 100644
View File

@ -0,0 +1,189 @@
_base_ = '../../base.py'
# model settings s m l x
model = dict(
type='YOLOX',
test_conf=0.01,
nms_thre=0.65,
backbone='RepVGGYOLOX',
model_type='s', # s m l x tiny nano
use_att='ASFF',
head=dict(
type='TOODHead',
model_type='s',
obj_loss_type='BCE',
reg_loss_type='giou',
num_classes=80,
decode_in_inference=
True # set to False when test speed to ignore decode and nms
))
# s m l x
img_scale = (640, 640)
random_size = (14, 26)
scale_ratio = (0.1, 2)
CLASSES = [
'person', 'bicycle', 'car', 'motorcycle', 'airplane', 'bus', 'train',
'truck', 'boat', 'traffic light', 'fire hydrant', 'stop sign',
'parking meter', 'bench', 'bird', 'cat', 'dog', 'horse', 'sheep', 'cow',
'elephant', 'bear', 'zebra', 'giraffe', 'backpack', 'umbrella', 'handbag',
'tie', 'suitcase', 'frisbee', 'skis', 'snowboard', 'sports ball', 'kite',
'baseball bat', 'baseball glove', 'skateboard', 'surfboard',
'tennis racket', 'bottle', 'wine glass', 'cup', 'fork', 'knife', 'spoon',
'bowl', 'banana', 'apple', 'sandwich', 'orange', 'broccoli', 'carrot',
'hot dog', 'pizza', 'donut', 'cake', 'chair', 'couch', 'potted plant',
'bed', 'dining table', 'toilet', 'tv', 'laptop', 'mouse', 'remote',
'keyboard', 'cell phone', 'microwave', 'oven', 'toaster', 'sink',
'refrigerator', 'book', 'clock', 'vase', 'scissors', 'teddy bear',
'hair drier', 'toothbrush'
]
# dataset settings
data_root = 'data/coco/'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
train_pipeline = [
dict(type='MMMosaic', img_scale=img_scale, pad_val=114.0),
dict(
type='MMRandomAffine',
scaling_ratio_range=scale_ratio,
border=(-img_scale[0] // 2, -img_scale[1] // 2)),
dict(
type='MMMixUp', # s m x l; tiny nano will detele
img_scale=img_scale,
ratio_range=(0.8, 1.6),
pad_val=114.0),
dict(
type='MMPhotoMetricDistortion',
brightness_delta=32,
contrast_range=(0.5, 1.5),
saturation_range=(0.5, 1.5),
hue_delta=18),
dict(type='MMRandomFlip', flip_ratio=0.5),
dict(type='MMResize', keep_ratio=True),
dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels'])
]
test_pipeline = [
dict(type='MMResize', img_scale=img_scale, keep_ratio=True),
dict(type='MMPad', pad_to_square=True, pad_val=(114.0, 114.0, 114.0)),
dict(type='MMNormalize', **img_norm_cfg),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img'])
]
train_dataset = dict(
type='DetImagesMixDataset',
data_source=dict(
type='DetSourceCoco',
ann_file=data_root + 'annotations/instances_train2017.json',
img_prefix=data_root + 'train2017/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True)
],
classes=CLASSES,
filter_empty_gt=True,
iscrowd=False),
pipeline=train_pipeline,
dynamic_scale=img_scale)
val_dataset = dict(
type='DetImagesMixDataset',
imgs_per_gpu=2,
data_source=dict(
type='DetSourceCoco',
ann_file=data_root + 'annotations/instances_val2017.json',
img_prefix=data_root + 'val2017/',
pipeline=[
dict(type='LoadImageFromFile', to_float32=True),
dict(type='LoadAnnotations', with_bbox=True)
],
classes=CLASSES,
filter_empty_gt=False,
test_mode=True,
iscrowd=True),
pipeline=test_pipeline,
dynamic_scale=None,
label_padding=False)
data = dict(
imgs_per_gpu=16, workers_per_gpu=4, train=train_dataset, val=val_dataset)
# additional hooks
interval = 10
custom_hooks = [
dict(
type='YOLOXModeSwitchHook',
no_aug_epochs=15,
skip_type_keys=('MMMosaic', 'MMRandomAffine', 'MMMixUp'),
priority=48),
dict(
type='SyncRandomSizeHook',
ratio_range=random_size,
img_scale=img_scale,
interval=interval,
priority=48),
dict(
type='SyncNormHook',
num_last_epochs=15,
interval=interval,
priority=48)
]
# evaluation
eval_config = dict(
interval=10,
gpu_collect=False,
visualization_config=dict(
vis_num=10,
score_thr=0.5,
) # show by TensorboardLoggerHookV2 and WandbLoggerHookV2
)
eval_pipelines = [
dict(
mode='test',
data=data['val'],
evaluators=[dict(type='CocoDetectionEvaluator', classes=CLASSES)],
)
]
checkpoint_config = dict(interval=interval)
# optimizer
optimizer = dict(
type='SGD', lr=0.02, momentum=0.9, weight_decay=5e-4, nesterov=True)
optimizer_config = {}
# learning policy
lr_config = dict(
policy='YOLOX',
warmup='exp',
by_epoch=False,
warmup_by_epoch=True,
warmup_ratio=1,
warmup_iters=5, # 5 epoch
num_last_epochs=15,
min_lr_ratio=0.05)
# exponetial model average
ema = dict(decay=0.9998)
# runtime settings
total_epochs = 300
# yapf:disable
log_config = dict(
interval=100,
hooks=[
dict(type='TextLoggerHook'),
dict(type='TensorboardLoggerHookV2'),
# dict(type='WandbLoggerHookV2'),
])
export = dict(export_type = 'ori', preprocess_jit = False, batch_size=1, blade_config=dict(enable_fp16=True, fp16_fallback_op_ratio=0.01), use_trt_efficientnms=False)

10
docs/source/model_zoo_det.md
View File

@ -1,15 +1,15 @@
# Detection Model Zoo
## YOLOX
## YOLOX-PAI
Pretrained on COCO2017 dataset.
| Algorithm | Config | Params | Speed<sup>V100<br/><sub>fp16 b32 </sub> | mAP<sup>val<br/><sub>0.5:0.95</sub> | AP<sup>val<br/><sub>50</sub> | Download |
|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------------------------------------|-------------------------------------|------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
|-----------------------|---------------------------------------------------------------------------------------------------------------------------------------------------------|--------|-----------------------------------------|-------------------------------------|------------------------------|----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------|
| YOLOX-s | [yolox_s_8xb16_300e_coco](https://github.com/alibaba/EasyCV/tree/master/configs/detection/yolox/yolox_s_8xb16_300e_coco.py) | 9M | 0.68ms | 40.0 | 58.9 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_s_bs16_lr002/epoch_300.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_s_bs16_lr002/log.txt) |
| PAI-YOLOXs | [yoloxs_pai_8xb16_300e_coco](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/config/pai_yoloxs.py) | 16M | 0.71ms | 41.4 | 60.0 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/model/pai_yoloxs.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/log/pai_yoloxs.json) |
| PAI-YOLOXs-ASFF | [yoloxs_pai_asff_8xb16_300e_coco](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/config/pai_yoloxs_asff.py) | 21M | 0.87ms | 42.8 | 61.8 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/model/pai_yoloxs_asff.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/log/pai_yoloxs_asff.json) |
| PAI-YOLOXs-ASFF-TOOD3 | [yoloxs_pai_asff_tood3_8xb16_300e_coco](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/config/pai_yoloxs_asff_tood3.py) | 24M | 1.15ms | 43.9 | 62.1 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/model/pai_yoloxs_asff_tood3.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/log/pai_yoloxs_asff_tood3.json) |
| PAI-YOLOXs | [yoloxs_pai_8xb16_300e_coco](https://github.com/alibaba/EasyCV/tree/master/configs/detection/yolox/pai_yoloxs_8xb16_300e_coco.py) | 16M | 0.71ms | 41.4 | 60.0 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/model/pai_yoloxs.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/log/pai_yoloxs.json) |
| PAI-YOLOXs-ASFF | [yoloxs_pai_asff_8xb16_300e_coco](https://github.com/alibaba/EasyCV/tree/master/configs/detection/yolox/pai_yoloxs_asff_8xb16_300e_coco.py) | 21M | 0.87ms | 42.8 | 61.8 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/model/pai_yoloxs_asff.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/log/pai_yoloxs_asff.json) |
| PAI-YOLOXs-ASFF-TOOD3 | [yoloxs_pai_asff_tood3_8xb16_300e_coco](https://github.com/alibaba/EasyCV/tree/master/configs/detection/yolox/pai_yoloxs_asff_tood3_8xb16_300e_coco.py) | 24M | 1.15ms | 43.9 | 62.1 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/model/pai_yoloxs_asff_tood3.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox-pai/log/pai_yoloxs_asff_tood3.json) |
| YOLOX-m | [yolox_m_8xb16_300e_coco](https://github.com/alibaba/EasyCV/tree/master/configs/detection/yolox/yolox_m_8xb16_300e_coco.py) | 25M | 1.52ms | 46.3 | 64.9 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_m_bs16_lr002/epoch_300.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_m_bs16_lr002/log.txt) |
| YOLOX-l | [yolox_l_8xb8_300e_coco](https://github.com/alibaba/EasyCV/tree/master/configs/detection/yolox/yolox_m_8xb8_300e_coco.py) | 54M | 2.47ms | 48.9 | 67.5 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_l_bs8_lr001/epoch_290.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_l_bs8_lr001/log.txt) |
| YOLOX-x | [yolox_x_8xb8_300e_coco](https://github.com/alibaba/EasyCV/tree/master/configs/detection/yolox/yolox_x_8xb8_300e_coco.py) | 99M | 4.74ms | 50.9 | 69.2 | [model](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_x_bs8_lr001/epoch_290.pth) - [log](http://pai-vision-data-hz.oss-cn-zhangjiakou.aliyuncs.com/EasyCV/modelzoo/detection/yolox/yolox_x_bs8_lr001/log.txt) |