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# _base_ = ["../_base_/schedules/schedule_1x.py", "../_base_/default_runtime.py"]
# model settings
data_preprocessor = dict(
type="DetDataPreprocessor",
mean=[0, 0, 0],
std=[255.0, 255.0, 255.0],
bgr_to_rgb=True,
pad_size_divisor=32,
)
model = dict(
type="YOLOV3",
data_preprocessor=data_preprocessor,
backbone=dict(
type="Darknet",
depth=53,
out_indices=(3, 4, 5),
init_cfg=dict(type="Pretrained", checkpoint="open-mmlab://darknet53"),
),
neck=dict(
type="YOLOV3Neck",
num_scales=3,
in_channels=[1024, 512, 256],
out_channels=[512, 256, 128],
),
bbox_head=dict(
type="YOLOV3Head",
num_classes=1,
in_channels=[512, 256, 128],
out_channels=[1024, 512, 256],
anchor_generator=dict(
type="YOLOAnchorGenerator",
base_sizes=[
[(116, 90), (156, 198), (373, 326)],
[(30, 61), (62, 45), (59, 119)],
[(10, 13), (16, 30), (33, 23)],
],
strides=[32, 16, 8],
),
bbox_coder=dict(type="YOLOBBoxCoder"),
featmap_strides=[32, 16, 8],
loss_cls=dict(
type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0, reduction="sum"
),
loss_conf=dict(
type="CrossEntropyLoss", use_sigmoid=True, loss_weight=1.0, reduction="sum"
),
loss_xy=dict(
type="CrossEntropyLoss", use_sigmoid=True, loss_weight=2.0, reduction="sum"
),
loss_wh=dict(type="MSELoss", loss_weight=2.0, reduction="sum"),
),
# training and testing settings
train_cfg=dict(
assigner=dict(
type="GridAssigner", pos_iou_thr=0.5, neg_iou_thr=0.5, min_pos_iou=0
)
),
test_cfg=dict(
nms_pre=1000,
min_bbox_size=0,
score_thr=0.05,
conf_thr=0.005,
nms=dict(type="nms", iou_threshold=0.45),
max_per_img=100,
),
)
# dataset settings
dataset_type = "CocoDataset"
data_root = "data/coco/"
# Example to use different file client
# Method 1: simply set the data root and let the file I/O module
# automatically infer from prefix (not support LMDB and Memcache yet)
# data_root = 's3://openmmlab/datasets/detection/coco/'
# Method 2: Use `backend_args`, `file_client_args` in versions before 3.0.0rc6
# backend_args = dict(
# backend='petrel',
# path_mapping=dict({
# './data/': 's3://openmmlab/datasets/detection/',
# 'data/': 's3://openmmlab/datasets/detection/'
# }))
backend_args = None
train_pipeline = [
dict(type="LoadImageFromFile", backend_args=backend_args),
dict(type="LoadAnnotations", with_bbox=True),
dict(
type="Expand",
mean=data_preprocessor["mean"],
to_rgb=data_preprocessor["bgr_to_rgb"],
ratio_range=(1, 2),
),
dict(
type="MinIoURandomCrop",
min_ious=(0.4, 0.5, 0.6, 0.7, 0.8, 0.9),
min_crop_size=0.3,
),
dict(type="RandomResize", scale=[(320, 320), (608, 608)], keep_ratio=True),
dict(type="RandomFlip", prob=0.5),
dict(type="PhotoMetricDistortion"),
dict(type="PackDetInputs"),
]
test_pipeline = [
dict(type="LoadImageFromFile", backend_args=backend_args),
dict(type="Resize", scale=(608, 608), keep_ratio=True),
dict(type="LoadAnnotations", with_bbox=True),
dict(
type="PackDetInputs",
meta_keys=("img_id", "img_path", "ori_shape", "img_shape", "scale_factor"),
),
]
train_dataloader = dict(
batch_size=8,
num_workers=4,
persistent_workers=True,
sampler=dict(type="DefaultSampler", shuffle=True),
batch_sampler=dict(type="AspectRatioBatchSampler"),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file="annotations/instances_train2017.json",
data_prefix=dict(img="train2017/"),
filter_cfg=dict(filter_empty_gt=True, min_size=32),
pipeline=train_pipeline,
backend_args=backend_args,
),
)
val_dataloader = dict(
batch_size=1,
num_workers=2,
persistent_workers=True,
drop_last=False,
sampler=dict(type="DefaultSampler", shuffle=False),
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file="annotations/instances_val2017.json",
data_prefix=dict(img="val2017/"),
test_mode=True,
pipeline=test_pipeline,
backend_args=backend_args,
),
)
test_dataloader = val_dataloader
val_evaluator = dict(
type="CocoMetric",
ann_file=data_root + "annotations/instances_val2017.json",
metric="bbox",
backend_args=backend_args,
)
test_evaluator = val_evaluator
train_cfg = dict(max_epochs=273, val_interval=7)
# optimizer
optim_wrapper = dict(
type="OptimWrapper",
optimizer=dict(type="SGD", lr=0.001, momentum=0.9, weight_decay=0.0005),
clip_grad=dict(max_norm=35, norm_type=2),
)
# learning policy
param_scheduler = [
dict(type="LinearLR", start_factor=0.1, by_epoch=False, begin=0, end=2000),
dict(type="MultiStepLR", by_epoch=True, milestones=[218, 246], gamma=0.1),
]
default_hooks = dict(checkpoint=dict(type="CheckpointHook", interval=7))
# NOTE: `auto_scale_lr` is for automatically scaling LR,
# USER SHOULD NOT CHANGE ITS VALUES.
# base_batch_size = (8 GPUs) x (8 samples per GPU)
auto_scale_lr = dict(base_batch_size=64)
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