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two-stage-det-unihead-resnet50-coco-1x.yaml
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two-stage-det-unihead-resnet50-coco-1x.yaml
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num_classes: &num_classes 81
runtime:
task_names: det
flip: &flip
type: flip
kwargs:
flip_p: 0.5
resize: &train_resize
type: keep_ar_resize
kwargs:
scales: [800]
max_size: 1333
separate_wh: true
test_resize: &test_resize
type: keep_ar_resize
kwargs:
scales: [800]
max_size: 1333
separate_wh: true
to_tensor: &to_tensor
type: to_tensor
normalize: &normalize
type: normalize
kwargs:
mean: [0.485, 0.456, 0.406] # ImageNet pretrained statics
std: [0.229, 0.224, 0.225]
dataset: # Required.
train:
dataset:
type: coco
kwargs:
source: train # dataset id
meta_file: data/coco/annotations/instances_train2017.json
image_reader:
type: fs_opencv
kwargs:
image_dir: data/coco/train2017
color_mode: RGB
transformer: [*flip, *train_resize, *to_tensor, *normalize]
test:
dataset:
type: coco
kwargs:
source: val
meta_file: data/coco/annotations/instances_val2017.json
image_reader:
type: fs_opencv
kwargs:
image_dir: data/coco/val2017
color_mode: RGB
transformer: [*test_resize, *to_tensor, *normalize]
evaluator:
type: COCO # choices = {'COCO', 'VOC', 'MR'}
kwargs:
gt_file: data/coco/annotations/instances_val2017.json
iou_types: [bbox]
batch_sampler:
type: aspect_ratio_group
kwargs:
sampler:
type: dist
kwargs: {}
batch_size: 4
aspect_grouping: [1]
dataloader:
type: base
kwargs:
num_workers: 4
alignment: 32
trainer: # Required.
max_epoch: 13 # total epochs for the training
test_freq: 1
save_freq: 13
optimizer: # optimizer = SGD(params,lr=0.001,momentum=0.9,weight_decay=0.0001)
type: AdamW
kwargs:
lr: 6.25e-06
betas: [0.9, 0.999]
weight_decay: 0.05
lr_scheduler: # lr_scheduler = MultStepLR(optimizer, milestones=[9,14],gamma=0.1)
warmup_iter: 500 # set to be 0 to disable warmup. When warmup, target_lr = init_lr * total_batch_size
type: MultiStepLR
kwargs:
milestones: [9, 12] # epochs to decay lr
gamma: 0.1 # decay rate
saver: # Required.
save_dir: checkpoints # dir to save checkpoints
pretrain_model: pretrain/imagenet/resnet50-19c8e357.pth
results_dir: results_dir # dir to save detection results. i.e., bboxes, masks, keypoints
auto_resume: true # find last checkpoint from save_dir and resume from it automatically
# this option has the highest priority (auto_resume > opts > resume_model > pretrain_model)
hooks:
- type: auto_checkpoint
- type: train_val_logger
kwargs:
freq: 50 # 打印 log 频率
skip_first_k: 5 # 忽略前 5 轮的耗时
logdir: ./default/log # tennsorboard 日志路径
summary_writer: tensorboard
net:
- name: backbone # backbone = resnet50(frozen_layers, out_layers, out_strides)
type: resnet50
kwargs:
frozen_layers: [0, 1] # layer0...1 is fixed
out_layers: [1, 2, 3, 4] # layer1...4, commonly named Conv2...5
out_strides: [4, 8, 16, 32] # tell the strides of output features
normalize:
type: freeze_bn
initializer:
method: msra
- name: neck
prev: backbone
type: FPN
kwargs:
outplanes: 256
start_level: 2
num_level: 5 # if num_level>len(backbone.out_layers), additional conv with be stacked.
out_strides: [4, 8, 16, 32, 64] # strides of output features. aka., anchor strides for roi_head
downsample: pool # method to downsample, for FPN, it's pool, for RetienaNet, it's conv
upsample: nearest # method to interp, nearest or bilinear
initializer:
method: xavier
- name: roi_head
prev: neck
type: NaiveRPN
kwargs:
feat_planes: 256 # channels of intermediate conv
num_classes: 2 # number of classes including backgroudn. for rpn, it's 2; for RetinaNet, it's 81
num_anchors: 3
class_activation: sigmoid
num_level: 5
initializer:
method: normal
std: 0.01
- name: rpn_post_process
prev: roi_head
type: rpn_post
kwargs:
num_classes: 2 # number of classes including backgroudn. for rpn, it's 2; for RetinaNet, it's 81
cfg:
cls_loss:
type: sigmoid_cross_entropy
loc_loss:
type: l1_loss
anchor_generator:
type: hand_craft
kwargs:
anchor_ratios: [0.5, 1, 2] # anchor strides are provided as feature strides by feature extractor
anchor_scales: [8] # scale of anchors relative to feature map
roi_supervisor:
type: rpn
kwargs:
allowed_border: -1
matcher:
type: max_iou
kwargs:
positive_iou_thresh: 0.7
negative_iou_thresh: 0.3
ignore_iou_thresh: 0.5
allow_low_quality_match: true
low_quality_thresh: 0.3 # !this option is not supported yet, but we have future plan
sampler:
type: naive_a100 # support A100 training
kwargs:
batch_size: 256
positive_percent: 0.5
train:
roi_predictor:
type: base
kwargs:
pre_nms_score_thresh: 0.0
pre_nms_top_n: 2000
post_nms_top_n: 1000
roi_min_size: 0
nms:
type: naive
nms_iou_thresh: 0.7
merger:
type: rpn
kwargs:
top_n: 1000
test:
roi_predictor:
type: base
kwargs:
pre_nms_score_thresh: 0.0
pre_nms_top_n: 1000
post_nms_top_n: 1000
roi_min_size: 0
nms:
type: naive
nms_iou_thresh: 0.7
merger:
type: rpn
kwargs:
top_n: 1000
- name: bbox_head
prev: neck
type: UniBboxFC
kwargs:
feat_planes: 1024
num_classes: *num_classes
num_point: &num_point 16
offset_bias_val: 3.0
cls_block_num: 2
loc_block_num: 3
dropout: 0.1
initializer:
method: msra
cfg:
cls_loss:
type: softmax_cross_entropy
kwargs:
class_dim: -1 # last dim is the class dim
centerness_loss:
type: sigmoid_cross_entropy
kwargs:
loss_weight: 1.0
loc_loss_iou:
type: iou_loss
kwargs:
loss_type: giou
loss_weight: 1.0
fpn:
fpn_levels: [0,1,2,3] # indices of fpn features used for this stage. these levels are supposed to be continuous
base_scale: 56 # target level of a RoI is floor(log2((w*h)**0.5/base_scale))
roipooling:
method: 'roialignpool' # choices=['roialignpool', 'psroipool', 'roipool', 'psroimaskpool']. note that 'psroipool' is for RFCN head
pool_size: 7
sampling_ratio: 0 # number of sampling points in each bin. 0 means densely sampled
bbox_normalize: &bbox_norm # not used in UniHead
means: [0, 0, 0, 0]
stds: [0.1, 0.1, 0.2, 0.2]
share_location: &share_location False # is share location in bbox regression for all classes
bbox_supervisor:
type: uni_bbox_det
kwargs:
matcher:
type: max_iou
kwargs:
ignore_iou_thresh: 0.5 # Required if provide ignore_regions
positive_iou_thresh: 0.5
negative_iou_thresh: 0.5
allow_low_quality_match: False # positive if a anchor has highest iou with any gt
low_quality_thresh: 0.5
sampler:
type: naive_a100
kwargs:
batch_size: 512
positive_percent: 0.25
bbox_predictor:
type: uni_bbox_det
kwargs:
bbox_normalize: *bbox_norm
share_location: *share_location
num_loc_point: *num_point
nms:
type: naive
nms_iou_thresh: 0.5
bbox_score_thresh: 0.0
top_n: 100