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[Feature] Add nanodet-plus and support grad clip. (#362)
* [Feature] Add nanodet-plus and support grad clip. * fix forward * add cfg * refactor * unit test
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# nanodet-plus-m-1.5x_320 | ||
# COCO mAP(0.5:0.95) = 0.299 | ||
# AP_50 = 0.454 | ||
# AP_75 = 0.312 | ||
# AP_small = 0.102 | ||
# AP_m = 0.309 | ||
# AP_l = 0.493 | ||
save_dir: workspace/nanodet-plus-m-1.5x_320 | ||
model: | ||
weight_averager: | ||
name: ExpMovingAverager | ||
decay: 0.9998 | ||
arch: | ||
name: NanoDetPlus | ||
detach_epoch: 10 | ||
backbone: | ||
name: ShuffleNetV2 | ||
model_size: 1.5x | ||
out_stages: [2,3,4] | ||
activation: LeakyReLU | ||
fpn: | ||
name: GhostPAN | ||
in_channels: [176, 352, 704] | ||
out_channels: 128 | ||
kernel_size: 5 | ||
num_extra_level: 1 | ||
use_depthwise: True | ||
activation: LeakyReLU | ||
head: | ||
name: NanoDetPlusHead | ||
num_classes: 80 | ||
input_channel: 128 | ||
feat_channels: 128 | ||
stacked_convs: 2 | ||
kernel_size: 5 | ||
strides: [8, 16, 32, 64] | ||
activation: LeakyReLU | ||
reg_max: 7 | ||
norm_cfg: | ||
type: BN | ||
loss: | ||
loss_qfl: | ||
name: QualityFocalLoss | ||
use_sigmoid: True | ||
beta: 2.0 | ||
loss_weight: 1.0 | ||
loss_dfl: | ||
name: DistributionFocalLoss | ||
loss_weight: 0.25 | ||
loss_bbox: | ||
name: GIoULoss | ||
loss_weight: 2.0 | ||
# Auxiliary head, only use in training time. | ||
aux_head: | ||
name: SimpleConvHead | ||
num_classes: 80 | ||
input_channel: 256 | ||
feat_channels: 256 | ||
stacked_convs: 4 | ||
strides: [8, 16, 32, 64] | ||
activation: LeakyReLU | ||
reg_max: 7 | ||
data: | ||
train: | ||
name: CocoDataset | ||
img_path: coco/train2017 | ||
ann_path: coco/annotations/instances_train2017.json | ||
input_size: [320,320] #[w,h] | ||
keep_ratio: False | ||
pipeline: | ||
perspective: 0.0 | ||
scale: [0.6, 1.4] | ||
stretch: [[0.8, 1.2], [0.8, 1.2]] | ||
rotation: 0 | ||
shear: 0 | ||
translate: 0.2 | ||
flip: 0.5 | ||
brightness: 0.2 | ||
contrast: [0.6, 1.4] | ||
saturation: [0.5, 1.2] | ||
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] | ||
val: | ||
name: CocoDataset | ||
img_path: coco/val2017 | ||
ann_path: coco/annotations/instances_val2017.json | ||
input_size: [320,320] #[w,h] | ||
keep_ratio: False | ||
pipeline: | ||
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] | ||
device: | ||
gpu_ids: [0] | ||
workers_per_gpu: 10 | ||
batchsize_per_gpu: 96 | ||
schedule: | ||
# resume: | ||
# load_model: | ||
optimizer: | ||
name: AdamW | ||
lr: 0.001 | ||
weight_decay: 0.05 | ||
warmup: | ||
name: linear | ||
steps: 500 | ||
ratio: 0.0001 | ||
total_epochs: 300 | ||
lr_schedule: | ||
name: CosineAnnealingLR | ||
T_max: 300 | ||
eta_min: 0.00005 | ||
val_intervals: 10 | ||
grad_clip: 35 | ||
evaluator: | ||
name: CocoDetectionEvaluator | ||
save_key: mAP | ||
log: | ||
interval: 50 | ||
|
||
class_names: ['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'] |
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# nanodet-plus-m-1.5x_416 | ||
# COCO mAP(0.5:0.95) = 0.341 | ||
# AP_50 = 0.506 | ||
# AP_75 = 0.357 | ||
# AP_small = 0.143 | ||
# AP_m = 0.363 | ||
# AP_l = 0.539 | ||
save_dir: workspace/nanodet-plus-m-1.5x_416 | ||
model: | ||
weight_averager: | ||
name: ExpMovingAverager | ||
decay: 0.9998 | ||
arch: | ||
name: NanoDetPlus | ||
detach_epoch: 10 | ||
backbone: | ||
name: ShuffleNetV2 | ||
model_size: 1.5x | ||
out_stages: [2,3,4] | ||
activation: LeakyReLU | ||
fpn: | ||
name: GhostPAN | ||
in_channels: [176, 352, 704] | ||
out_channels: 128 | ||
kernel_size: 5 | ||
num_extra_level: 1 | ||
use_depthwise: True | ||
activation: LeakyReLU | ||
head: | ||
name: NanoDetPlusHead | ||
num_classes: 80 | ||
input_channel: 128 | ||
feat_channels: 128 | ||
stacked_convs: 2 | ||
kernel_size: 5 | ||
strides: [8, 16, 32, 64] | ||
activation: LeakyReLU | ||
reg_max: 7 | ||
norm_cfg: | ||
type: BN | ||
loss: | ||
loss_qfl: | ||
name: QualityFocalLoss | ||
use_sigmoid: True | ||
beta: 2.0 | ||
loss_weight: 1.0 | ||
loss_dfl: | ||
name: DistributionFocalLoss | ||
loss_weight: 0.25 | ||
loss_bbox: | ||
name: GIoULoss | ||
loss_weight: 2.0 | ||
# Auxiliary head, only use in training time. | ||
aux_head: | ||
name: SimpleConvHead | ||
num_classes: 80 | ||
input_channel: 256 | ||
feat_channels: 256 | ||
stacked_convs: 4 | ||
strides: [8, 16, 32, 64] | ||
activation: LeakyReLU | ||
reg_max: 7 | ||
data: | ||
train: | ||
name: CocoDataset | ||
img_path: coco/train2017 | ||
ann_path: coco/annotations/instances_train2017.json | ||
input_size: [416,416] #[w,h] | ||
keep_ratio: False | ||
pipeline: | ||
perspective: 0.0 | ||
scale: [0.6, 1.4] | ||
stretch: [[0.8, 1.2], [0.8, 1.2]] | ||
rotation: 0 | ||
shear: 0 | ||
translate: 0.2 | ||
flip: 0.5 | ||
brightness: 0.2 | ||
contrast: [0.6, 1.4] | ||
saturation: [0.5, 1.2] | ||
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] | ||
val: | ||
name: CocoDataset | ||
img_path: coco/val2017 | ||
ann_path: coco/annotations/instances_val2017.json | ||
input_size: [416,416] #[w,h] | ||
keep_ratio: False | ||
pipeline: | ||
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] | ||
device: | ||
gpu_ids: [0] | ||
workers_per_gpu: 10 | ||
batchsize_per_gpu: 96 | ||
schedule: | ||
# resume: | ||
# load_model: | ||
optimizer: | ||
name: AdamW | ||
lr: 0.001 | ||
weight_decay: 0.05 | ||
warmup: | ||
name: linear | ||
steps: 500 | ||
ratio: 0.0001 | ||
total_epochs: 300 | ||
lr_schedule: | ||
name: CosineAnnealingLR | ||
T_max: 300 | ||
eta_min: 0.00005 | ||
val_intervals: 10 | ||
grad_clip: 35 | ||
evaluator: | ||
name: CocoDetectionEvaluator | ||
save_key: mAP | ||
log: | ||
interval: 50 | ||
|
||
class_names: ['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'] |
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@@ -0,0 +1,131 @@ | ||
# nanodet-plus-m_320 | ||
# COCO mAP(0.5:0.95) = 0.270 | ||
# AP_50 = 0.418 | ||
# AP_75 = 0.281 | ||
# AP_small = 0.083 | ||
# AP_m = 0.278 | ||
# AP_l = 0.451 | ||
save_dir: workspace/nanodet-plus-m_320 | ||
model: | ||
weight_averager: | ||
name: ExpMovingAverager | ||
decay: 0.9998 | ||
arch: | ||
name: NanoDetPlus | ||
detach_epoch: 10 | ||
backbone: | ||
name: ShuffleNetV2 | ||
model_size: 1.0x | ||
out_stages: [2,3,4] | ||
activation: LeakyReLU | ||
fpn: | ||
name: GhostPAN | ||
in_channels: [116, 232, 464] | ||
out_channels: 96 | ||
kernel_size: 5 | ||
num_extra_level: 1 | ||
use_depthwise: True | ||
activation: LeakyReLU | ||
head: | ||
name: NanoDetPlusHead | ||
num_classes: 80 | ||
input_channel: 96 | ||
feat_channels: 96 | ||
stacked_convs: 2 | ||
kernel_size: 5 | ||
strides: [8, 16, 32, 64] | ||
activation: LeakyReLU | ||
reg_max: 7 | ||
norm_cfg: | ||
type: BN | ||
loss: | ||
loss_qfl: | ||
name: QualityFocalLoss | ||
use_sigmoid: True | ||
beta: 2.0 | ||
loss_weight: 1.0 | ||
loss_dfl: | ||
name: DistributionFocalLoss | ||
loss_weight: 0.25 | ||
loss_bbox: | ||
name: GIoULoss | ||
loss_weight: 2.0 | ||
# Auxiliary head, only use in training time. | ||
aux_head: | ||
name: SimpleConvHead | ||
num_classes: 80 | ||
input_channel: 192 | ||
feat_channels: 192 | ||
stacked_convs: 4 | ||
strides: [8, 16, 32, 64] | ||
activation: LeakyReLU | ||
reg_max: 7 | ||
data: | ||
train: | ||
name: CocoDataset | ||
img_path: coco/train2017 | ||
ann_path: coco/annotations/instances_train2017.json | ||
input_size: [320,320] #[w,h] | ||
keep_ratio: False | ||
pipeline: | ||
perspective: 0.0 | ||
scale: [0.6, 1.4] | ||
stretch: [[0.8, 1.2], [0.8, 1.2]] | ||
rotation: 0 | ||
shear: 0 | ||
translate: 0.2 | ||
flip: 0.5 | ||
brightness: 0.2 | ||
contrast: [0.6, 1.4] | ||
saturation: [0.5, 1.2] | ||
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] | ||
val: | ||
name: CocoDataset | ||
img_path: coco/val2017 | ||
ann_path: coco/annotations/instances_val2017.json | ||
input_size: [320,320] #[w,h] | ||
keep_ratio: False | ||
pipeline: | ||
normalize: [[103.53, 116.28, 123.675], [57.375, 57.12, 58.395]] | ||
device: | ||
gpu_ids: [0] | ||
workers_per_gpu: 10 | ||
batchsize_per_gpu: 96 | ||
schedule: | ||
# resume: | ||
# load_model: | ||
optimizer: | ||
name: AdamW | ||
lr: 0.001 | ||
weight_decay: 0.05 | ||
warmup: | ||
name: linear | ||
steps: 500 | ||
ratio: 0.0001 | ||
total_epochs: 300 | ||
lr_schedule: | ||
name: CosineAnnealingLR | ||
T_max: 300 | ||
eta_min: 0.00005 | ||
val_intervals: 10 | ||
grad_clip: 35 | ||
evaluator: | ||
name: CocoDetectionEvaluator | ||
save_key: mAP | ||
log: | ||
interval: 50 | ||
|
||
class_names: ['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'] |
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