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February 26, 2020 14:52
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mm cfg
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# fp16 settings | |
fp16 = dict(loss_scale=512.) | |
# model settings | |
model = dict( | |
type='RetinaNet', | |
pretrained='torchvision://resnet50', | |
backbone=dict( | |
type='ResNet', | |
depth=50, | |
num_stages=4, | |
out_indices=(0, 1, 2, 3), | |
frozen_stages=1, | |
style='pytorch'), | |
neck=dict( | |
type='FPN', | |
in_channels=[256, 512, 1024, 2048], | |
out_channels=256, | |
start_level=1, | |
add_extra_convs=True, | |
num_outs=5), | |
bbox_head=dict( | |
type='RetinaHead', | |
num_classes=2, | |
in_channels=256, | |
stacked_convs=4, | |
feat_channels=256, | |
octave_base_scale=4, | |
scales_per_octave=3, | |
# anchor_ratios=[1.25, 1.5, 2], | |
anchor_ratios=[0.5, 1, 2], | |
anchor_strides=[8, 16, 32, 64, 128], | |
target_means=[.0, .0, .0, .0], | |
target_stds=[1.0, 1.0, 1.0, 1.0], | |
loss_cls=dict( | |
type='FocalLoss', | |
use_sigmoid=True, | |
gamma=2.0, | |
alpha=0.25, | |
loss_weight=1.0), | |
loss_bbox=dict(type='SmoothL1Loss', beta=0.11, loss_weight=1.0))) | |
# training and testing settings | |
train_cfg = dict( | |
assigner=dict( | |
type='MaxIoUAssigner', | |
pos_iou_thr=0.5, | |
neg_iou_thr=0.4, | |
min_pos_iou=0, | |
ignore_iof_thr=-1), | |
allowed_border=-1, | |
pos_weight=-1, | |
debug=True) | |
test_cfg = dict( | |
nms_pre=1000, | |
min_bbox_size=0, | |
score_thr=0.05, | |
nms=dict(type='nms', iou_thr=0.5), | |
max_per_img=100) | |
# dataset settings | |
dataset_type = 'CustomDataset' | |
data_root = 'data/signatures/' | |
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), | |
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', 'gt_labels']), | |
] | |
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=8, | |
workers_per_gpu=8, | |
train=dict( | |
type=dataset_type, | |
ann_file=data_root + 'train_labels.pkl', | |
img_prefix=data_root + 'train/', | |
pipeline=train_pipeline), | |
val=dict( | |
type=dataset_type, | |
ann_file=data_root + 'valid_labels.pkl', | |
img_prefix=data_root + 'train/', | |
pipeline=test_pipeline), | |
test=dict( | |
type=dataset_type, | |
ann_file=data_root + 'valid_labels.pkl', | |
img_prefix=data_root + 'train/', | |
pipeline=test_pipeline), | |
) | |
evaluation = dict(interval=1, metric='bbox') | |
# optimizer | |
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001) | |
optimizer_config = dict(grad_clip=dict(max_norm=35, norm_type=2)) | |
# learning policy | |
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/retinanet_r50_fpn_fp16_1x' | |
load_from = None | |
resume_from = None | |
workflow = [('train', 1)] |
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