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# model settings
model = dict(
type="CascadeRCNN",
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=True,
style="pytorch",
init_cfg=dict(type="Pretrained", checkpoint="torchvision://resnet50"),
),
neck=dict(
type="FPN",
in_channels=[256, 512, 1024, 2048],
out_channels=256,
num_outs=5,
),
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, 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="CascadeRoIHead",
num_stages=3,
stage_loss_weights=[1, 0.5, 0.25],
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=10,
bbox_coder=dict(
type="DeltaXYWHBBoxCoder",
target_means=[0.0, 0.0, 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="SmoothL1Loss", beta=1.0, loss_weight=1.0),
),
dict(
type="Shared2FCBBoxHead",
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=10,
bbox_coder=dict(
type="DeltaXYWHBBoxCoder",
target_means=[0.0, 0.0, 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=False, loss_weight=1.0
),
loss_bbox=dict(type="SmoothL1Loss", beta=1.0, loss_weight=1.0),
),
dict(
type="Shared2FCBBoxHead",
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=10,
bbox_coder=dict(
type="DeltaXYWHBBoxCoder",
target_means=[0.0, 0.0, 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=False, loss_weight=1.0
),
loss_bbox=dict(type="SmoothL1Loss", beta=1.0, 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=0,
pos_weight=-1,
debug=False,
),
rpn_proposal=dict(
nms_pre=2000,
max_per_img=2000,
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,
),
dict(
assigner=dict(
type="MaxIoUAssigner",
pos_iou_thr=0.6,
neg_iou_thr=0.6,
min_pos_iou=0.6,
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,
),
dict(
assigner=dict(
type="MaxIoUAssigner",
pos_iou_thr=0.7,
neg_iou_thr=0.7,
min_pos_iou=0.7,
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=0.05,
nms=dict(type="nms", iou_threshold=0.5),
max_per_img=100,
),
),
)
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