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dataset_type = 'CocoDataset'
data_root = '/home/safouane/Downloads/benchmark_aircraft/data/'
backend_args = None
max_epochs = 500
metainfo = {
'classes': ('airplane', ),
'palette': [
(0, 128, 255),
]
}
num_classes = 1
model = dict(
type='RetinaNet',
data_preprocessor=dict(
type='DetDataPreprocessor',
mean=[
123.675,
116.28,
103.53,
],
std=[
58.395,
57.12,
57.375,
],
bgr_to_rgb=True,
pad_size_divisor=64,
batch_augments=[
dict(type='BatchFixedSizePad', size=(
640,
640,
)),
]),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(
0,
1,
2,
3,
),
frozen_stages=1,
norm_cfg=dict(type='BN', requires_grad=True),
norm_eval=False,
style='pytorch',
init_cfg=dict(type='Pretrained', checkpoint='torchvision://resnet50')),
neck=dict(
type='FPN',
in_channels=[
256,
512,
1024,
2048,
],
out_channels=256,
start_level=1,
add_extra_convs='on_input',
num_outs=5,
relu_before_extra_convs=True,
no_norm_on_lateral=True,
norm_cfg=dict(type='BN', requires_grad=True)),
bbox_head=dict(
type='RetinaSepBNHead',
num_classes=1,
in_channels=256,
stacked_convs=4,
feat_channels=256,
anchor_generator=dict(
type='AnchorGenerator',
octave_base_scale=4,
scales_per_octave=3,
ratios=[
0.5,
1.0,
2.0,
],
strides=[
8,
16,
32,
64,
128,
]),
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[
0.0,
0.0,
0.0,
0.0,
],
target_stds=[
1.0,
1.0,
1.0,
1.0,
]),
loss_cls=dict(
type='FocalLoss',
use_sigmoid=True,
gamma=2.0,
alpha=0.25,
loss_weight=1.0),
loss_bbox=dict(type='L1Loss', loss_weight=1.0),
num_ins=5,
norm_cfg=dict(type='BN', requires_grad=True)),
train_cfg=dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.5,
neg_iou_thr=0.5,
min_pos_iou=0,
ignore_iof_thr=-1),
sampler=dict(type='PseudoSampler'),
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=0.5),
max_per_img=100))
train_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(
640,
640,
),
ratio_range=(
0.8,
1.2,
),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(
640,
640,
)),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs'),
]
test_pipeline = [
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(
640,
640,
), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
)),
]
train_dataloader = dict(
batch_size=32,
num_workers=2,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=dict(type='AspectRatioBatchSampler'),
dataset=dict(
type='CocoDataset',
metainfo=dict(classes=('airplane', ), palette=[
(
220,
20,
60,
),
]),
data_root='/home/safouane/Downloads/benchmark_aircraft/data/',
ann_file='train/__coco.json',
data_prefix=dict(img='train/'),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='RandomResize',
scale=(
640,
640,
),
ratio_range=(
0.8,
1.2,
),
keep_ratio=True),
dict(type='RandomCrop', crop_size=(
640,
640,
)),
dict(type='RandomFlip', prob=0.5),
dict(type='PackDetInputs'),
],
backend_args=None))
val_dataloader = dict(
batch_size=32,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
metainfo=dict(classes=('airplane', ), palette=[
(
220,
20,
60,
),
]),
data_root='/home/safouane/Downloads/benchmark_aircraft/data/',
ann_file='val/__coco.json',
data_prefix=dict(img='val/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(
640,
640,
), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
)),
],
backend_args=None))
test_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='CocoDataset',
metainfo=dict(classes=('airplane', ), palette=[
(
220,
20,
60,
),
]),
data_root='/home/safouane/Downloads/benchmark_aircraft/data/',
ann_file='test/__coco.json',
data_prefix=dict(img='test/'),
test_mode=True,
pipeline=[
dict(type='LoadImageFromFile', backend_args=None),
dict(type='Resize', scale=(
640,
640,
), keep_ratio=True),
dict(type='LoadAnnotations', with_bbox=True),
dict(
type='PackDetInputs',
meta_keys=(
'img_id',
'img_path',
'ori_shape',
'img_shape',
'scale_factor',
)),
],
backend_args=None))
val_evaluator = dict(
type='CocoMetric',
ann_file='/home/safouane/Downloads/benchmark_aircraft/data/val/__coco.json',
metric='bbox',
format_only=False,
backend_args=None)
test_evaluator = dict(
type='CocoMetric',
ann_file=
'/home/safouane/Downloads/benchmark_aircraft/data/test/__coco.json',
metric='bbox',
format_only=False,
backend_args=None)
train_cfg = dict(type='EpochBasedTrainLoop', max_epochs=500, val_interval=10)
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')
param_scheduler = [
dict(type='LinearLR', start_factor=0.1, by_epoch=False, begin=0, end=1000),
dict(
type='MultiStepLR',
begin=0,
end=50,
by_epoch=True,
milestones=[
30,
40,
],
gamma=0.1),
]
optim_wrapper = dict(
type='OptimWrapper',
optimizer=dict(type='SGD', lr=0.015, momentum=0.9, weight_decay=0.0001),
paramwise_cfg=dict(norm_decay_mult=0, bypass_duplicate=True))
auto_scale_lr = dict(enable=False, base_batch_size=64)
default_scope = 'mmdet'
default_hooks = dict(
timer=dict(type='IterTimerHook'),
logger=dict(type='LoggerHook', interval=50),
param_scheduler=dict(type='ParamSchedulerHook'),
checkpoint=dict(
type='CheckpointHook', interval=20, max_keep_ckpts=2,
save_best='auto'),
sampler_seed=dict(type='DistSamplerSeedHook'),
visualization=dict(type='DetVisualizationHook'))
env_cfg = dict(
cudnn_benchmark=True,
mp_cfg=dict(mp_start_method='fork', opencv_num_threads=0),
dist_cfg=dict(backend='nccl'))
vis_backends = [
dict(type='LocalVisBackend'),
]
visualizer = dict(
type='DetLocalVisualizer',
vis_backends=[
dict(type='LocalVisBackend'),
dict(type='TensorboardVisBackend'),
],
name='visualizer')
log_processor = dict(type='LogProcessor', window_size=50, by_epoch=True)
log_level = 'INFO'
load_from = '/home/safouane/Downloads/benchmark_aircraft/mmlab_configs/retinanet_r50_fpn_crop640_50e_coco-9b953d76.pth'
resume = False
norm_cfg = dict(type='BN', requires_grad=True)
launcher = 'none'
work_dir = './work_dirs/retinanet_r50_fpn_crop640-50e_coco'
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