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test_ovlvis_r50.py
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test_ovlvis_r50.py
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_base_ = 'mmdet::_base_/default_runtime.py'
dataset_type = 'LVISV1Dataset'
num_classes = 1203
# 'data/metadata/lvis_v1_clip_a+cname.npy' is pre-computed
# CLIP embeddings for each category
cls_layer = dict(
type='ZeroShotClassifier',
zs_weight_path='data/metadata/lvis_v1_clip_a+cname.npy',
zs_weight_dim=512,
use_bias=0.0,
norm_weight=True,
norm_temperature=50.0)
reg_layer = [
dict(type='Linear', in_features=1024, out_features=1024),
dict(type='ReLU', inplace=True),
dict(type='Linear', in_features=1024, out_features=4)
]
find_unused_parameters = True
clip_cfg = dict( # ViT-B/32
type='CLIP',
image_encoder=None,
text_encoder=dict(
type='CLIPTextEncoder',
embed_dim=512,
context_length=77,
vocab_size=49408,
transformer_width=512, # also the word embedding dim
transformer_heads=8,
transformer_layers=12,
init_cfg=dict(
type='Pretrained',
checkpoint='checkpoints/clip_vitb32.pth')
)
)
model = dict(
type='Detic',
data_preprocessor=dict(
type='MultiBranchDataPreprocessor',
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=32),
),
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(1, 2, 3),
norm_cfg=dict(type='BN', requires_grad=False),
norm_eval=True,
init_cfg=None),
neck=dict(
type='FPN',
in_channels=[512, 1024, 2048],
out_channels=256,
start_level=0,
add_extra_convs='on_output',
num_outs=5,
init_cfg=dict(type='Caffe2Xavier', layer='Conv2d'),
relu_before_extra_convs=True),
rpn_head=dict(
type='CenterNetRPNHead',
num_classes=1,
in_channels=256,
stacked_convs=4,
feat_channels=256,
strides=[8, 16, 32, 64, 128],
conv_bias=True,
norm_cfg=dict(type='GN', num_groups=32, requires_grad=True),
loss_cls=dict(
type='HeatmapFocalLoss',
alpha=0.25,
beta=4.0,
gamma=2.0,
pos_weight=0.5,
neg_weight=0.5,
loss_weight=1.0,
ignore_high_fp=0.85,
),
loss_bbox=dict(type='GIoULoss', eps=1e-6, loss_weight=1.0),
),
roi_head=dict(
type='DeticRoIHead',
clip_cfg=clip_cfg,
num_stages=3,
stage_loss_weights=[1.0, 1.0, 1.0],
bbox_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(
type='RoIAlign',
output_size=7,
sampling_ratio=0,
use_torchvision=True),
out_channels=256,
featmap_strides=[8, 16, 32],
# approximately equal to
# canonical_box_size=224, canonical_level=4 in D2
finest_scale=112),
bbox_head=[
dict(
type='OriginalDeticBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
cls_predictor_cfg=cls_layer,
reg_predictor_cfg=reg_layer,
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=True,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='OriginalDeticBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
cls_predictor_cfg=cls_layer,
reg_predictor_cfg=reg_layer,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.05, 0.05, 0.1, 0.1]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.1,
loss_weight=1.0)),
dict(
type='OriginalDeticBBoxHead',
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=num_classes,
cls_predictor_cfg=cls_layer,
reg_predictor_cfg=reg_layer,
bbox_coder=dict(
type='DeltaXYWHBBoxCoder',
target_means=[0., 0., 0., 0.],
target_stds=[0.033, 0.033, 0.067, 0.067]),
reg_class_agnostic=True,
loss_cls=dict(
type='CrossEntropyLoss', use_sigmoid=True,
loss_weight=1.0),
loss_bbox=dict(type='SmoothL1Loss', beta=0.1, loss_weight=1.0))
],
mask_roi_extractor=dict(
type='SingleRoIExtractor',
roi_layer=dict(type='RoIAlign', output_size=14, sampling_ratio=0),
out_channels=256,
featmap_strides=[8, 16, 32],
# approximately equal to
# canonical_box_size=224, canonical_level=4 in D2
finest_scale=112),
mask_head=dict(
type='FCNMaskHead',
num_convs=4,
in_channels=256,
conv_out_channels=256,
class_agnostic=True,
num_classes=num_classes,
loss_mask=dict(
type='CrossEntropyLoss', use_mask=True, loss_weight=1.0))),
test_cfg=dict(
rpn=dict(
score_thr=0.0001,
nms_pre=1000,
max_per_img=256,
nms=dict(type='nms', iou_threshold=0.9),
min_bbox_size=0),
rcnn=dict(
score_thr=0.02,
nms=dict(type='nms', iou_threshold=0.4),
max_per_img=300,
mask_thr_binary=0.5)))
val_pipeline = test_pipeline = [
dict(
type='LoadImageFromFile',
backend_args=backend_args,
imdecode_backend=backend_args),
dict(
type='Resize',
scale=(1333, 800),
keep_ratio=True,
backend=backend_args),
dict(
type='LoadAnnotations',
with_bbox=True,
with_mask=True,
poly2mask=False),
dict(
type='PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape',
'scale_factor', 'text', 'custom_entities'))
]
test_dataloader = val_dataloader = dict(
batch_size=8,
num_workers=2,
persistent_workers=True,
drop_last=False,
pin_memory=True,
sampler=dict(type='DefaultSampler', shuffle=False),
dataset=dict(
type='LVISV1Dataset',
data_root='data/lvis_v1/',
ann_file='annotations/lvis_v1_val.json',
data_prefix=dict(img=''),
pipeline=val_pipeline,
return_classes=False))
test_evaluator = val_evaluator = dict(
type='LVISMetric',
ann_file='data/lvis_v1/annotations/lvis_v1_val.json',
metric=['bbox', 'segm'])
val_cfg = dict(type='ValLoop')
test_cfg = dict(type='TestLoop')