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_base_ = [ | |
'../sam-vith.py', | |
'../../datasets/tao/tao_dataset_v1.py', | |
'../../default_runtime.py' | |
] | |
default_scope = 'mmdet' | |
detector = _base_.model | |
detector['init_cfg'] = dict( | |
type='Pretrained', | |
checkpoint= 'saved_models/pretrain_weights/sam_vit_h_4b8939_mmdet.pth' | |
# noqa: E501 | |
) | |
detector['type'] = 'SamMasa' | |
del _base_.model | |
model = dict( | |
type='MASA', | |
freeze_detector=True, | |
unified_backbone=True, | |
load_public_dets = True, | |
benchmark = 'tao', | |
public_det_path = 'results/public_dets/tao_val_dets/teta_50_internms/detic_tao_val_det/', | |
data_preprocessor=dict( | |
type='TrackDataPreprocessor', | |
# Image normalization parameters | |
mean=[123.675, 116.28, 103.53], | |
std=[58.395, 57.12, 57.375], | |
bgr_to_rgb=True, | |
# Image padding parameters | |
pad_mask=False, # In instance segmentation, the mask needs to be padded | |
pad_size_divisor=1024), # Padding the image to multiples of 32 | |
detector=detector, | |
masa_adapter=[ | |
dict( | |
type='SimpleFPN', | |
in_channels=[1280, 1280, 1280, 1280], | |
out_channels=256, | |
use_residual=True, | |
num_outs=5), | |
dict( | |
type='DyHead', | |
in_channels=256, | |
out_channels=256, | |
num_blocks=3) | |
], | |
rpn_head=dict( | |
type='RPNHead', | |
in_channels=256, | |
feat_channels=256, | |
anchor_generator=dict( | |
type='AnchorGenerator', | |
scales=[8], | |
ratios=[0.5, 1.0, 2.0], | |
strides=[4, 8, 16, 32, 64]), | |
bbox_coder=dict( | |
type='DeltaXYWHBBoxCoder', | |
target_means=[.0, .0, .0, .0], | |
target_stds=[1.0, 1.0, 1.0, 1.0]), | |
loss_cls=dict( | |
type='CrossEntropyLoss', use_sigmoid=True, loss_weight=1.0), | |
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0) | |
), | |
roi_head=dict( | |
type='StandardRoIHead', | |
bbox_roi_extractor=dict( | |
type='SingleRoIExtractor', | |
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | |
out_channels=256, | |
featmap_strides=[4, 8, 16, 32]), | |
bbox_head=dict( | |
type='Shared2FCBBoxHead', | |
in_channels=256, | |
fc_out_channels=1024, | |
roi_feat_size=7, | |
num_classes=1, | |
bbox_coder=dict( | |
type='DeltaXYWHBBoxCoder', | |
target_means=[0., 0., 0., 0.], | |
target_stds=[0.1, 0.1, 0.2, 0.2]), | |
reg_class_agnostic=True, | |
loss_cls=dict( | |
type='CrossEntropyLoss', use_sigmoid=False, loss_weight=1.0), | |
loss_bbox=dict(type='L1Loss', loss_weight=1.0))), | |
# model training and testing settings | |
train_cfg=dict( | |
rpn=dict( | |
assigner=dict( | |
type='MaxIoUAssigner', | |
pos_iou_thr=0.7, | |
neg_iou_thr=0.3, | |
min_pos_iou=0.3, | |
match_low_quality=True, | |
ignore_iof_thr=-1), | |
sampler=dict( | |
type='RandomSampler', | |
num=256, | |
pos_fraction=0.5, | |
neg_pos_ub=-1, | |
add_gt_as_proposals=False), | |
allowed_border=-1, | |
pos_weight=-1, | |
debug=False), | |
rpn_proposal=dict( | |
nms_pre=2000, | |
max_per_img=1000, | |
nms=dict(type='nms', iou_threshold=0.7), | |
min_bbox_size=0), | |
rcnn=dict( | |
assigner=dict( | |
type='MaxIoUAssigner', | |
pos_iou_thr=0.5, | |
neg_iou_thr=0.5, | |
min_pos_iou=0.5, | |
match_low_quality=False, | |
ignore_iof_thr=-1), | |
sampler=dict( | |
type='RandomSampler', | |
num=512, | |
pos_fraction=0.25, | |
neg_pos_ub=-1, | |
add_gt_as_proposals=True), | |
pos_weight=-1, | |
debug=False)), | |
test_cfg=dict( | |
rpn=dict( | |
nms_pre=1000, | |
max_per_img=1000, | |
nms=dict(type='nms', iou_threshold=0.7), | |
min_bbox_size=0), | |
rcnn=dict( | |
score_thr=0.02, | |
# nms=dict(type='nms', iou_threshold=0.5), | |
nms=dict(type='nms', | |
iou_threshold=0.5, | |
class_agnostic=True, | |
split_thr=100000), | |
max_per_img=50, | |
mask_thr_binary=0.5) | |
# soft-nms is also supported for rcnn testing | |
# e.g., nms=dict(type='soft_nms', iou_threshold=0.5, min_score=0.05) | |
), | |
track_head=dict( | |
type='MasaTrackHead', | |
roi_extractor=dict( | |
type='SingleRoIExtractor', | |
roi_layer=dict(type='RoIAlign', output_size=7, sampling_ratio=0), | |
out_channels=256, | |
featmap_strides=[4, 8, 16, 32]), | |
embed_head=dict( | |
type='QuasiDenseEmbedHead', | |
num_convs=4, | |
num_fcs=1, | |
embed_channels=256, | |
norm_cfg=dict(type='GN', num_groups=32), | |
loss_track=dict(type='UnbiasedContrastLoss', loss_weight=0.25), | |
loss_track_aux=dict( | |
type='MarginL2Loss', | |
neg_pos_ub=3, | |
pos_margin=0, | |
neg_margin=0.1, | |
hard_mining=True, | |
loss_weight=1.0)), | |
# loss_bbox=dict(type='L1Loss', loss_weight=1.0), | |
train_cfg=dict( | |
assigner=dict( | |
type='MaxIoUAssigner', | |
pos_iou_thr=0.7, | |
neg_iou_thr=0.5, | |
min_pos_iou=0.5, | |
match_low_quality=False, | |
ignore_iof_thr=-1), | |
sampler=dict( | |
type='CombinedSampler', | |
num=512, | |
pos_fraction=0.8, | |
neg_pos_ub=3, | |
add_gt_as_proposals=True, | |
pos_sampler=dict(type='InstanceBalancedPosSampler'), | |
neg_sampler=dict(type='RandomSampler')))), | |
tracker=dict( | |
type='MasaTaoTracker', | |
init_score_thr=0.0001, | |
obj_score_thr=0.0001, | |
match_score_thr=0.5, | |
memo_tracklet_frames=10, | |
memo_momentum=0.8, | |
with_cats=False, | |
max_distance=-1, | |
fps=1, | |
) | |
) | |
test_pipeline = [ | |
dict( | |
type='TransformBroadcaster', | |
transforms=[ | |
dict(type='LoadImageFromFile'), | |
dict( | |
type='Resize', | |
scale=(1024, 1024), | |
keep_ratio=True), | |
dict(type='LoadTrackAnnotations') | |
]), | |
dict(type='PackTrackInputs') | |
] | |
train_dataloader = None | |
train_cfg = None | |
val_cfg = dict(type='ValLoop') | |
test_cfg = dict(type='TestLoop') | |
default_hooks = dict( | |
logger=dict(type='LoggerHook', interval=50), | |
visualization=dict(type='TrackVisualizationHook', draw=False)) | |
vis_backends = [dict(type='LocalVisBackend')] | |
visualizer = dict( | |
type='MasaTrackLocalVisualizer', vis_backends=vis_backends, name='visualizer') | |
auto_scale_lr = dict(enable=False, base_batch_size=16) | |
val_dataloader = dict( | |
dataset=dict( | |
ann_file='data/tao/annotations/tao_val_lvis_v1_classes.json', | |
pipeline=test_pipeline, | |
) | |
) | |
test_dataloader = val_dataloader | |
test_evaluator = dict( | |
ann_file='data/tao/annotations/tao_val_lvis_v1_classes.json', | |
outfile_prefix='results/masa_results/masa-sam-vith-tao-test-detic-dets', | |
) | |