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vfnet_train_all.py
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vfnet_train_all.py
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_base_ = [
'/SSD/IEEE/src/mmdetection/configs/_base_/datasets/coco_detection.py',
'/SSD/IEEE/src/mmdetection/configs/_base_/schedules/schedule_1x.py', '/SSD/IEEE/src/mmdetection/configs/_base_/default_runtime.py'
]
dataset_type = 'CocoDataset'
classes_list = ('D00','D10','D20','D40')
num_classes = 4
# model settings
model = dict(
type='VFNet',
backbone=dict(
type='ResNeXt',
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=True,
style='pytorch',
dcn=dict(type='DCNv2', deform_groups=1, fallback_on_stride=False),
stage_with_dcn=(False, True, True, True),
init_cfg=dict(
type='Pretrained', checkpoint='open-mmlab://resnext101_64x4d')),
neck=dict(
type='FPN',
in_channels=[256, 512, 1024, 2048],
out_channels=256,
start_level=1,
add_extra_convs='on_output', # use P5
num_outs=5,
relu_before_extra_convs=True),
bbox_head=dict(
type='VFNetHead',
num_classes=num_classes,
in_channels=256,
stacked_convs=3,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
center_sampling=False,
dcn_on_last_conv=False,
use_atss=True,
use_vfl=True,
loss_cls=dict(
type='VarifocalLoss',
use_sigmoid=True,
alpha=0.75,
gamma=2.0,
iou_weighted=True,
loss_weight=1.0),
loss_bbox=dict(type='GIoULoss', loss_weight=1.5),
loss_bbox_refine=dict(type='GIoULoss', loss_weight=2.0)),
# training and testing settings
train_cfg=dict(
assigner=dict(type='ATSSAssigner', topk=9),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms', iou_threshold=0.6),
max_per_img=100))
# data setting
dataset_type = 'CocoDataset'
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='LoadImageFromFile'),
dict(type='LoadAnnotations', with_bbox=True),
dict(type='Resize', img_scale=(1333, 800), keep_ratio=True),
dict(type='RandomFlip', flip_ratio=0.5),
# Add advanced augmentations
dict(type='PhotoMetricDistortion'),
# dict(type='MixUp'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_bboxes', 'gt_labels']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1333, 800),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size_divisor=32),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=10,
workers_per_gpu=40,
train=dict(
type='ConcatDataset',
pipeline=train_pipeline,
datasets=[
dict(
type='CocoDataset',
classes=classes_list,
ann_file='/SSD/IEEE/src/prepare_data/train_labels/India/train/annotations/coco/train_balance.json',
img_prefix='/SSD/IEEE/data/India/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/United_States/train/annotations/coco/train_balance.json',
img_prefix='/SSD/IEEE/data/United_States/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file='/SSD/IEEE/src/prepare_data/train_labels/United_States/train/annotations/coco/test_balance.json',
img_prefix='/SSD/IEEE/data/United_States/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file='/SSD/IEEE/src/prepare_data/train_labels/China_MotorBike/train/annotations/coco/train_balance.json',
img_prefix='/SSD/IEEE/data/China_MotorBike/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/China_MotorBike/train/annotations/coco/test_balance.json',
img_prefix='/SSD/IEEE/data/China_MotorBike/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/Czech/train/annotations/coco/train_balance.json',
img_prefix='/SSD/IEEE/data/Czech/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/Czech/train/annotations/coco/test_balance.json',
img_prefix='/SSD/IEEE/data/Czech/train/images',
pipeline=train_pipeline),
# NORWAY
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/Norway/train/annotations/coco/train_balance.json',
img_prefix='/SSD/IEEE/data/Norway/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/Norway/train/annotations/coco/test_balance.json',
img_prefix='/SSD/IEEE/data/Norway/train/images',
pipeline=train_pipeline),
# JAPAN
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/Japan/train/annotations/coco/train_balance.json',
img_prefix='/SSD/IEEE/data/Japan/train/images',
pipeline=train_pipeline),
dict(
type='CocoDataset',
classes=classes_list,
ann_file= '/SSD/IEEE/src/prepare_data/train_labels/Japan/train/annotations/coco/test_balance.json',
img_prefix='/SSD/IEEE/data/Japan/train/images',
pipeline=train_pipeline),
]),
val=dict(
type=dataset_type,
classes = classes_list,
ann_file='/SSD/IEEE/src/prepare_data/train_labels/India/train/annotations/coco/test_balance.json',
img_prefix='/SSD/IEEE/data/India/train/images'),
test=dict(
type=dataset_type,
classes = classes_list,
ann_file='/SSD/IEEE/src/prepare_data/train_labels/India/train/annotations/coco/test_balance.json',
img_prefix='/SSD/IEEE/data/India/train/images'))
# optimizer
optimizer = dict(
lr=0.01, paramwise_cfg=dict(bias_lr_mult=2., bias_decay_mult=0.))
optimizer_config = dict(grad_clip=None)
# learning policy
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.1,
step=[8, 11])
# Evaluation times
evaluation = dict(interval=1, metric='bbox', classwise = True)
checkpoint_config = dict(interval=5)
runner = dict(type='EpochBasedRunner', max_epochs=20)
work_dir = './work_dirs/india_vfnet_resnet101_train_all_aug'