/
faster_rcnn_r50_fpn_1x_voc0712_partial.py
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/
faster_rcnn_r50_fpn_1x_voc0712_partial.py
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mmdet_config = "../../../../thirdparty/mmdetection/configs"
_base_ = f'{mmdet_config}/pascal_voc/faster_rcnn_r50_fpn_1x_voc0712_cocofmt.py'
CLASSES = ('aeroplane', 'bicycle', 'bird', 'boat', 'bottle', 'bus', 'car',
'cat', 'chair', 'cow', 'diningtable', 'dog', 'horse', 'motorbike',
'person', 'pottedplant', 'sheep', 'sofa', 'train', 'tvmonitor')
dataset_wrapper = 'PartialDataset'
dataset_type = 'ActiveCocoDataset'
data_root = "data/detection/voc_converted/"
# for pytorch-based
# img_norm_cfg = dict(
# mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
# for caffe-based
img_norm_cfg = dict(
mean=[103.530, 116.280, 123.675], std=[1.0, 1.0, 1.0], to_rgb=False)
model = dict(
backbone=dict(
norm_cfg=dict(requires_grad=False),
style="caffe",
init_cfg=dict(
type="Pretrained", checkpoint="open-mmlab://detectron2/resnet50_caffe"
),
)
)
train_transforms = [
dict(
type="RandResize",
img_scale=[(1000, 300), (1000, 900)],
multiscale_mode="range",
keep_ratio=True,
),
dict(type="RandFlip", flip_ratio=0.5),
dict(
type="OneOf",
transforms=[
dict(type=k)
for k in [
"Identity",
"AutoContrast",
"RandEqualize",
"RandSolarize",
"RandColor",
"RandContrast",
"RandBrightness",
"RandSharpness",
"RandPosterize",
]
],
),
]
strong_transforms = [
dict(
type="RandResize",
img_scale=[(1000, 300), (1000, 900)],
multiscale_mode="range",
keep_ratio=True,
),
dict(type="RandFlip", flip_ratio=0.5),
dict(
type="ShuffledSequential",
transforms=[
dict(
type="OneOf",
transforms=[
dict(type=k)
for k in [
"Identity",
"AutoContrast",
"RandEqualize",
"RandSolarize",
"RandColor",
"RandContrast",
"RandBrightness",
"RandSharpness",
"RandPosterize",
]
],
),
dict(
type="OneOf",
transforms=[
dict(type="RandTranslate", x=(-0.1, 0.1)),
dict(type="RandTranslate", y=(-0.1, 0.1)),
dict(type="RandRotate", angle=(-30, 30)),
[
dict(type="RandShear", x=(-30, 30)),
dict(type="RandShear", y=(-30, 30)),
],
],
),
],
),
dict(
type="RandErase",
n_iterations=(1, 5),
size=[0, 0.2],
squared=True,
),
]
weak_transforms = [
dict(
type="RandResize",
img_scale=[(1000, 300), (1000, 900)],
multiscale_mode="range",
keep_ratio=True,
),
dict(type="RandFlip", flip_ratio=0.5),
]
# pipelines for normal training
labeled_train_pipeline = [
dict(type="LoadImageFromFile"),
dict(type="LoadAnnotations", with_bbox=True),
dict(
type="Sequential",
transforms=train_transforms,
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="labeled"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
),
),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1000, 600),
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
# dict(type="ExtraAttrs", tag="test"),
dict(type='Pad', size_divisor=32),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect',
keys=['img'],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
# "tag",
)
),
])
]
# pipelines for partial training
partial_strong_pipeline = [
dict(
type="Sequential",
transforms=strong_transforms,
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="partial_strong"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
"transform_matrix",
),
),
]
partial_weak_pipeline = [
dict(
type="Sequential",
transforms=weak_transforms,
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="partial_weak"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
"transform_matrix",
),
),
]
partial_pipeline = [
dict(type="LoadImageFromFile"),
# load GTs, to debug query, otherwise uncomment the next line
dict(type="LoadAnnotations", with_bbox=True),
# generate fake labels for data format compatibility
# dict(type="PseudoSamples", with_bbox=True),
dict(
type="MultiBranch",
partial_strong=partial_strong_pipeline,
partial_weak=partial_weak_pipeline,
),
]
# pipelines for active evaluation
unlabel_strong_pipeline = [
dict(
type="Sequential",
transforms=strong_transforms,
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="unlabel_strong"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
"transform_matrix",
),
),
]
unlabel_weak_pipeline = [
dict(
type="Sequential",
transforms=weak_transforms,
record=True,
),
dict(type="Pad", size_divisor=32),
dict(type="Normalize", **img_norm_cfg),
dict(type="ExtraAttrs", tag="unlabel_weak"),
dict(type="DefaultFormatBundle"),
dict(
type="Collect",
keys=["img", "gt_bboxes", "gt_labels"],
meta_keys=(
"filename",
"ori_shape",
"img_shape",
"img_norm_cfg",
"pad_shape",
"scale_factor",
"tag",
"transform_matrix",
),
),
]
unlabel_pipeline_test = [
dict(type="LoadImageFromFile"),
# load GTs, to debug query, otherwise uncomment the next line
dict(type="LoadAnnotations", with_bbox=True),
# generate fake labels for data format compatibility
# dict(type="PseudoSamples", with_bbox=True),
dict(
type="MultiBranchTest",
unlabel_strong=unlabel_strong_pipeline,
unlabel_weak=unlabel_weak_pipeline,
aug_times=10,
# test=test_pipeline
),
]
data = dict(
valid_len=16551, # 47223 boxes, 0712_trainval
samples_per_gpu=3,
workers_per_gpu=3,
train=dict(
_delete_=True,
type=dataset_wrapper,
labeled=dict(
type=dataset_type,
ann_file=data_root + 'annotations/labeled.json',
img_prefix=data_root + 'VOCdevkit/',
pipeline=labeled_train_pipeline,
classes=CLASSES),
mixed=dict(
type=dataset_type,
ann_file=data_root + 'annotations/mixed.json',
img_prefix=data_root + 'VOCdevkit/',
# pipeline=unlabel_pipeline,
classes=CLASSES),
partial=dict(
type=dataset_type,
ann_file=data_root + 'annotations/partial.json',
img_prefix=data_root + 'VOCdevkit/',
pipeline=partial_pipeline,
classes=CLASSES),
unlabeled=dict(
type=dataset_type,
ann_file=data_root + 'annotations/unlabeled.json',
img_prefix=data_root + 'VOCdevkit/',
# pipeline=unlabel_pipeline,
classes=CLASSES),
full=dict(
type=dataset_type,
ann_file=data_root + 'annotations/voc0712_trainval.json',
img_prefix=data_root + 'VOCdevkit/',
pipeline=labeled_train_pipeline,
classes=CLASSES)),
val=dict(
type=dataset_type,
ann_file=data_root + 'annotations/voc07_test.json',
img_prefix=data_root + 'VOCdevkit/',
pipeline=test_pipeline,
classes=CLASSES),
test=dict(
type=dataset_type,
ann_file=data_root + 'annotations/voc07_test.json',
img_prefix=data_root + 'VOCdevkit/',
pipeline=test_pipeline,
classes=CLASSES),
pool=dict(
type=dataset_type,
ann_file=data_root + 'annotations/voc0712_trainval.json',
img_prefix=data_root + 'VOCdevkit/',
pipeline=unlabel_pipeline_test,
classes=CLASSES),
sampler=dict(
train=dict(
type="MultiBalanceSampler",
sample_ratio=[1, 1],
by_prob=True,
at_least_one=True,
epoch_length=2070,
)
),
)
partial_wrapper = dict(
type="MixedTeacher",
model="${model}",
train_cfg=dict(
use_teacher_proposal=False,
pseudo_label_initial_score_thr=0.5,
rpn_pseudo_threshold=0.9,
cls_pseudo_threshold=0.9,
reg_pseudo_threshold=0.02,
jitter_times=10,
jitter_scale=0.06,
min_pseduo_box_size=0,
unsup_weight=1.0,
partial_boxmerge_iou_thresh=0.4,
partial_weight=1.0
),
test_cfg=dict(inference_on="student"),
)
active_wrapper = dict(
type="MixedTeacher",
model="${model}",
test_cfg=dict(
inference_on="student",
unlabel_weight=dict(
loss_cls=1.0,
loss_bbox=1.0
),
aug_cfg=dict(
use_teacher_proposal=False,
pseudo_label_initial_score_thr=0.5,
rev_thr=False,
rpn_pseudo_threshold=0.5,
cls_pseudo_threshold=0.5,
reg_pseudo_threshold=0.1, # reverse
jitter_times=10,
jitter_scale=0.06,
min_pseduo_box_size=0,
unsup_weight=4.0,
),
)
)
# TOOD: mixed-wrapper and partial-wrapper
custom_hooks = [
dict(type="NumClassCheckHook"),
dict(type="WeightSummary"),
dict(type="MeanTeacher", momentum=0.999, interval=1, warm_up=0),
]
evaluation = dict(type="SubModulesDistEvalHook", interval=2500, metric='bbox', save_best='bbox_mAP', classwise=True)
runner = dict(_delete_=True, type="IterBasedRunner", max_iters=12500) # full is 25000, for 2*4 samples/iter
optimizer = dict(lr=0.01)
lr_config = dict(
policy='step',
warmup='linear',
warmup_iters=500,
warmup_ratio=0.001,
step=[8500, 11500])
checkpoint_config = dict(by_epoch=False, interval=2500, max_keep_ckpts=5)
fp16 = dict(loss_scale="dynamic")
log_config = dict(
interval=50,
hooks=[
dict(type="TextLoggerHook", by_epoch=False)
],
)