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segtrack-frcnn_r50_fpn_12e_bdd10k.py
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segtrack-frcnn_r50_fpn_12e_bdd10k.py
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_base_ = './qdtrack-frcnn_r50_fpn_12e_bdd100k.py'
# model settings
model = dict(
type='QuasiDenseMaskRCNN',
pretrained='torchvision://resnet50',
roi_head=dict(
type='QuasiDenseSegRoIHead',
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[4, 8, 16, 32]),
mask_head=dict(
type='FCNMaskHeadPlus',
num_convs=4,
in_channels=256,
conv_out_channels=256,
num_classes=8,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
tracker=dict(type='QuasiDenseSegEmbedTracker'),
# model training and testing settings
train_cfg = dict(
rcnn=dict(mask_size=28)),
test_cfg = dict(
rcnn=dict(mask_thr_binary=0.5))
)
# dataset settings
dataset_type = 'BDDVideoDataset'
data_root = 'data/bdd/'
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='LoadMultiImagesFromFile'),
dict(type='SeqLoadAnnotations', with_bbox=True, with_ins_id=True,
with_mask=True),
dict(type='SeqResize', img_scale=(1296, 720), keep_ratio=True),
dict(type='SeqRandomFlip', share_params=True, flip_ratio=0.5),
dict(type='SeqNormalize', **img_norm_cfg),
dict(type='SeqPad', size_divisor=32),
dict(type='SeqDefaultFormatBundle'),
dict(
type='SeqCollect',
keys=['img', 'gt_bboxes', 'gt_labels', 'gt_match_indices', 'gt_masks'],
ref_prefix='ref'),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(1296, 720),
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='ImageToTensor', keys=['img']),
dict(type='VideoCollect', keys=['img'])
])
]
data = dict(
samples_per_gpu=2, # ori is 2
workers_per_gpu=2, # ori is 2
train=[
dict(
type=dataset_type,
ann_file=data_root + 'labels/seg_track_20/seg_track_train_cocoformat.json',
img_prefix=data_root + 'images/seg_track_20/train',
key_img_sampler=dict(interval=1),
ref_img_sampler=dict(num_ref_imgs=1, scope=3, method='uniform'),
pipeline=train_pipeline),
dict(
type=dataset_type,
load_as_video=False,
ann_file=data_root + 'labels/ins_seg/polygons/ins_seg_train_cocoformat.json',
img_prefix=data_root + 'images/10k/train',
pipeline=train_pipeline)
],
val=dict(
type=dataset_type,
ann_file=data_root + 'labels/seg_track_20/seg_track_val_cocoformat.json',
img_prefix=data_root + 'images/seg_track_20/val',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'labels/seg_track_20/seg_track_val_cocoformat.json',
#ann_file=data_root + 'labels/seg_track_20/seg_track_test_cocofmt.json',
img_prefix=data_root + 'images/seg_track_20/val',
#img_prefix=data_root + 'images/seg_track_20/test',
pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.01, momentum=0.9, weight_decay=0.0001) # ori lr=0.01
load_from = './ckpts/qdtrack-frcnn_r50_fpn_12e_bdd100k-13328aed.pth'
resume_from = None
evaluation = dict(metric=['bbox', 'segm', 'segtrack'], interval=12)