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May 13, 2019 17:55
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# model settings | |
model = dict( | |
type='FasterRCNN', | |
pretrained='modelzoo://resnet101', | |
backbone=dict( | |
type='ResNet', | |
depth=101, | |
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, | |
num_outs=5), | |
rpn_head=dict( | |
type='RPNHead', | |
in_channels=256, | |
feat_channels=256, | |
anchor_scales=[4], | |
anchor_ratios=[0.5, 1.0, 2.0], | |
anchor_strides=[4, 8, 16, 32, 64], | |
target_means=[.0, .0, .0, .0], | |
target_stds=[1.0, 1.0, 1.0, 1.0], | |
use_sigmoid_cls=True), | |
bbox_roi_extractor=dict( | |
type='SingleRoIExtractor', | |
roi_layer=dict(type='RoIAlign', out_size=7, sample_num=2), | |
out_channels=256, | |
featmap_strides=[4, 8, 16, 32]), | |
bbox_head=dict( | |
type='SharedFCBBoxHead', | |
num_fcs=2, | |
in_channels=256, | |
fc_out_channels=1024, | |
roi_feat_size=7, | |
num_classes=81, | |
target_means=[0., 0., 0., 0.], | |
target_stds=[0.1, 0.1, 0.2, 0.2], | |
reg_class_agnostic=False)) | |
# 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, | |
smoothl1_beta=1 / 9.0, | |
debug=False), | |
rcnn=dict( | |
assigner=dict( | |
type='MaxIoUAssigner', | |
pos_iou_thr=0.5, | |
neg_iou_thr=0.5, | |
min_pos_iou=0.5, | |
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_across_levels=False, | |
nms_pre=2000, | |
nms_post=2000, | |
max_num=2000, | |
nms_thr=0.7, | |
min_bbox_size=0), | |
rcnn=dict( | |
score_thr=0.05, nms=dict(type='nms', iou_thr=0.5), max_per_img=100) | |
# soft-nms is also supported for rcnn testing | |
# e.g., nms=dict(type='soft_nms', iou_thr=0.5, min_score=0.05) | |
) | |
# dataset settings | |
dataset_type = 'CustomDataset' | |
data_root = '/home/ubuntu/tp/' | |
img_norm_cfg = dict( | |
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True) | |
data = dict( | |
imgs_per_gpu=8, | |
workers_per_gpu=2, | |
train=dict( | |
type=dataset_type, | |
ann_file=data_root + 'annotations/tp_train.pkl', | |
img_prefix=data_root + 'mmdet-train/', | |
img_scale=(700, 700), | |
img_norm_cfg=img_norm_cfg, | |
size_divisor=32, | |
flip_ratio=0.5, | |
with_mask=False, | |
with_crowd=False, | |
with_label=True), | |
val=dict( | |
type=dataset_type, | |
ann_file=data_root + 'annotations/tp_val.pkl', | |
img_prefix=data_root + 'mmdet-val/', | |
img_scale=(700, 700), | |
img_norm_cfg=img_norm_cfg, | |
size_divisor=32, | |
flip_ratio=0, | |
with_mask=False, | |
with_crowd=False, | |
with_label=True), | |
test=dict( | |
type=dataset_type, | |
ann_file=data_root + 'annotations/tp_val.pkl', | |
img_prefix=data_root + 'mmdet-val/', | |
img_scale=(700, 700), | |
img_norm_cfg=img_norm_cfg, | |
size_divisor=32, | |
flip_ratio=0, | |
with_mask=False, | |
with_label=False, | |
test_mode=True)) | |
# optimizer | |
optimizer = dict(type='SGD', lr=0.0025, 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 = 1 | |
dist_params = dict(backend='nccl') | |
log_level = 'INFO' | |
work_dir = './work_dirs/epoch4_tboard_bs8_imsz700_faster_rcnn_r101_fpn_1x' | |
load_from = None | |
resume_from = None | |
workflow = [('train', 1)] |
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