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rpn_x101_64x4d_fpn_1x.py
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rpn_x101_64x4d_fpn_1x.py
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# model settings
model = dict(
type="RPN",
pretrained="open-mmlab://resnext101_64x4d",
backbone=dict(
type="ResNeXt",
depth=101,
groups=64,
base_width=4,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
norm_cfg=dict(type="BN", requires_grad=True),
style="pytorch",
),
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_scales=[8],
anchor_ratios=[0.5, 1.0, 2.0],
anchor_strides=[4, 8, 16, 32, 64],
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),
),
)
# 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,
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,
)
)
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0,
)
)
# dataset settings
dataset_type = "CocoDataset"
data_root = "data/coco/"
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="LoadImageFromFile"),
dict(type="LoadAnnotations", with_bbox=True, with_label=False),
dict(type="Resize", img_scale=(1333, 800), keep_ratio=True),
dict(type="RandomFlip", flip_ratio=0.5),
dict(type="Normalize", **img_norm_cfg),
dict(type="Pad", size_divisor=32),
dict(type="DefaultFormatBundle"),
dict(type="Collect", keys=["img", "gt_bboxes"]),
]
test_pipeline = [
dict(type="LoadImageFromFile"),
dict(
type="MultiScaleFlipAug",
img_scale=(1333, 800),
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="Collect", keys=["img"]),
],
),
]
data = dict(
imgs_per_gpu=2,
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + "annotations/instances_train2017.json",
img_prefix=data_root + "train2017/",
pipeline=train_pipeline,
),
val=dict(
type=dataset_type,
ann_file=data_root + "annotations/instances_val2017.json",
img_prefix=data_root + "val2017/",
pipeline=test_pipeline,
),
test=dict(
type=dataset_type,
ann_file=data_root + "annotations/instances_val2017.json",
img_prefix=data_root + "val2017/",
pipeline=test_pipeline,
),
)
evaluation = dict(interval=1, metric="proposal_fast")
# optimizer
optimizer = dict(type="SGD", lr=0.02, momentum=0.9, weight_decay=0.0001)
# runner configs
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2))
lr_config = dict(
policy="step", warmup="linear", warmup_iters=500, warmup_ratio=1.0 / 3, step=[8, 11]
)
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=50,
hooks=[
dict(type="TextLoggerHook"),
# dict(type='TensorboardLoggerHook')
],
)
# yapf:enable
# runtime settings
total_epochs = 12
dist_params = dict(backend="nccl")
log_level = "INFO"
work_dir = "./work_dirs/rpn_x101_64x4d_fpn_1x"
load_from = None
resume_from = None
workflow = [("train", 1)]