Update List
2020/1/1 : Code Link:
本次開源,是幫助小白,有什麼問題,歡迎留言,有什麼建議,也歡迎留言
本次比賽,大多數同學都是使用mmdetection,使用mmdetection第一步就是應該瞭解config文件.
由於大部分代碼來源mmdet,所以暫時只上傳config。
cascade_rcnn_r50_fpn_1x.py
# fp16 settings
fp16 = dict(loss_scale=512.)
# model settings
model = dict(
type='CascadeRCNN',
num_stages=3,
pretrained='torchvision://resnet50',
backbone=dict(
type='ResNet',
depth=50,
num_stages=4,
out_indices=(0, 1, 2, 3),
frozen_stages=1,
style='pytorch',
#dcn=dict( #在最後三個block加入可變形卷積
# modulated=False, deformable_groups=1, fallback_on_stride=False),
# stage_with_dcn=(False, True, True, True)
),
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=[0.2, 0.5, 1.0, 2.0, 5.0], # 添加了0.2,5
anchor_strides=[4, 8, 16, 32, 64],
target_means=[.0, .0, .0, .0],
target_stds=[1.0, 1.0, 1.0, 1.0],
loss_cls=dict(
type='FocalLoss', use_sigmoid=True, loss_weight=1.0), # 修改了loss,爲了調控難易樣本與正負樣本比例
loss_bbox=dict(type='SmoothL1Loss', beta=1.0 / 9.0, loss_weight=1.0)),
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=11,
target_means=[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='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=11,
target_means=[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='SharedFCBBoxHead',
num_fcs=2,
in_channels=256,
fc_out_channels=1024,
roi_feat_size=7,
num_classes=11,
target_means=[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,
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_across_levels=False,
nms_pre=2000,
nms_post=2000,
max_num=2000,
nms_thr=0.7,
min_bbox_size=0),
rcnn=[
dict(
assigner=dict(
type='MaxIoUAssigner',
pos_iou_thr=0.4, # 更換
neg_iou_thr=0.4,
min_pos_iou=0.4,
ignore_iof_thr=-1),
sampler=dict(
type='OHEMSampler',
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=0.5,
neg_iou_thr=0.5,
min_pos_iou=0.5,
ignore_iof_thr=-1),
sampler=dict(
type='OHEMSampler', # 解決難易樣本,也解決了正負樣本比例問題。
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,
ignore_iof_thr=-1),
sampler=dict(
type='OHEMSampler',
num=512,
pos_fraction=0.25,
neg_pos_ub=-1,
add_gt_as_proposals=True),
pos_weight=-1,
debug=False)
],
stage_loss_weights=[1, 0.5, 0.25])
test_cfg = dict(
rpn=dict(
nms_across_levels=False,
nms_pre=1000,
nms_post=1000,
max_num=1000,
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=20)) # 這裏可以換爲sof_tnms
# dataset settings
dataset_type = 'CocoDataset'
data_root = '../../data/chongqing1_round1_train1_20191223/'
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=(492,658), keep_ratio=True), #這裏可以更換多尺度[(),()]
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=(492,658),
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, # 有的同學不知道batchsize在哪修改,其實就是修改這裏,每個gpu同時處理的images數目。
workers_per_gpu=2,
train=dict(
type=dataset_type,
ann_file=data_root + 'fixed_annotations.json', # 更換自己的json文件
img_prefix=data_root + 'images/', # images目錄
pipeline=train_pipeline),
val=dict(
type=dataset_type,
ann_file=data_root + 'fixed_annotations.json',
img_prefix=data_root + 'images/',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
ann_file=data_root + 'fixed_annotations.json',
img_prefix=data_root + 'images/',
pipeline=test_pipeline))
# optimizer
optimizer = dict(type='SGD', lr=0.001, momentum=0.9, weight_decay=0.0001) # lr = 0.00125*batch_size,不能過大,否則梯度爆炸。
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=[6, 12, 19])
checkpoint_config = dict(interval=1)
# yapf:disable
log_config = dict(
interval=64,
hooks=[
dict(type='TextLoggerHook'), # 控制檯輸出信息的風格
# dict(type='TensorboardLoggerHook') # 需要安裝tensorflow and tensorboard纔可以使用
])
# yapf:enable
# runtime settings
total_epochs = 20
dist_params = dict(backend='nccl')
log_level = 'INFO'
work_dir = '../work_dirs/cascade_rcnn_r50_fpn_1x' # 日誌目錄
load_from = '../work_dirs/cascade_rcnn_r50_fpn_1x/latest.pth' # 模型加載目錄文件
#load_from = '../work_dirs/cascade_rcnn_r50_fpn_1x/cascade_rcnn_r50_coco_pretrained_weights_classes_11.pth'
resume_from = None
workflow = [('train', 1)]
源代碼修改部分
1.有同學說沒有,segmentation字段,其實,只要註釋源代碼中那一行,就可以簡單處理。
2.對於label=0,可以在coco.py中修改過濾條件。
for i, ann in enumerate(ann_info):
if ann.get('ignore', False):
continue
x1, y1, w, h = ann['bbox']
if ann['area'] <= 0 or w < 1 or h < 1:
continue
if ann['category_id'] == 0:
continue
bbox = [round(x1,2), round(y1,2), round(x1 + w - 1,2), round(y1 + h - 1,2)]
if ann.get('iscrowd', False):
gt_bboxes_ignore.append(bbox)
else:
gt_bboxes.append(bbox)
gt_labels.append(ann['category_id'])
# gt_masks_ann.append(ann['segmentation'])
if gt_bboxes:
gt_bboxes = np.array(gt_bboxes, dtype=np.float32)
gt_labels = np.array(gt_labels, dtype=np.int64)
安裝
1.用conda創建一個新的虛擬環境
conda create -n mmdetection python=3.7
conda activate mmdetection
# 安裝必要模塊
conda install pytorch=1.1.0 torchvision=0.3.0 cudatoolkit=10.0 -c pytorch
pip install cython && pip --no-cache-dir install -r requirements.txt
# 安裝mmdetection
git clone https://github.com/open-mmlab/mmdetection.git
cd mmdetection
# 安裝
python setup.py install
# 編譯
python setup.py develop
2.demo測試安裝是否成功
#如果安裝成功,則該文件可以運行成功。
#coding=utf-8
from mmdet.apis import init_detector
from mmdet.apis import inference_detector
from mmdet.apis import show_result
# 模型配置文件
config_file = './configs/cascade_rcnn_r50_fpn_1x.py'
# 預訓練模型文件
checkpoint_file = '../../checkpoints/cascade_rcnn_r50_fpn_20e_20181123-db483a09.pth'
# 通過模型配置文件與預訓練文件構建模型
model = init_detector(config_file, checkpoint_file, device='cuda:0')
# 測試單張圖片並進行展示
img = 'demo.jpg'
result = inference_detector(model, img)
show_result(img, result, model.CLASSES)
3.訓練
python tools/train.py configs/.py --gpus 1
知識補充
Soft-NMS
Soft-NMS 改進了之前比較暴力的 NMS,當 IOU 超過某個閾值後,不再直接刪除該框,而是降低它的置信度 (得分),如果得分低到一個閾值,就會被排除;但是如果降低後仍然較高,就會被保留。
OHEM
OHEM (online hard example mining),翻譯過來就是在線難例挖掘,就是對所有的 ROI 的損失進行評估,選擇損失較大的來優化網絡,詳情移步:OHEM 論文解讀
損失選擇
針對分類的損失函數可以試試如 GHM-C Loss,針對迴歸的損失函數可以試試如 GHM-R Loss。IOU可以使用 GIou Loss,Generalized Intersection over Union: A Metric and A Loss for Bounding Box Regression
。
warmup lr
翻譯一下就是對學習率進行預熱,最開始是在 ResNet 的論文中提到的一種方法,原始是先在前幾個 epoch 或 iter 或目標達到一個水準之前以小於預設值得 lr 進行訓練,然後再恢復 lr 到初始值。後來 Facebook 提出了改良版本,詳情請移步論文: Gradual warmup[5]
公衆號 持續更新
數據領取: 公衆號關鍵字:天池酒瓶