1. Install
! ! ! ! Do not install mmrotate
1. Version requirements
mmcv 2.0.0rc2
mmdet 3.0.0rc5
mmengine 0.5.0
do not need to install mmcv-full
- Download the mmrotate 1.x source code (do not download the default master, because the new network is only in the 1.x version)
2. Make a dataset
Because what is needed is the annotation txt file of the dataset
DOTA format in dota format:
184 2875 193 2923 146 2932 137 2885 plane 0
66 2095 75 2142 21 2154 11 2107 plane 0
...
Each row represents an object and is recorded as a 10-dimensional array A.
A[0:8]: The format of the polygon(x1, y1, x2, y2, x3, y3, x4, y4).A[8]: categoryA[9]: difficulty
My data is a json file labeled with labelme, so first convert json to txt
import json
import os
file_path='hailuo'
json_names=os.listdir(file_path)
for name in json_names:
if name.endswith('json'):
json_path=os.path.join(file_path,name)
path2 = name.split('.')[0]+'.txt'
# path2 = '1.txt'
file2 = open(path2, 'w+')
with open(json_path) as f:
data = json.load(f)
for box in data["shapes"]:
_one=box['points']
res = [str(x) for j in _one for x in j]
if len(res)==10:
continue
rest =' '.join(res)
rest=rest+' box 0\n'
file2.write(rest)
Put all the txt files in the annfiles folder and all the pictures in the images folder
3. Modify the configuration file
- G:\research\mmrotate-1.x\configs\rotated_rtmdet_base_\dota_rr.py
# dataset settings
dataset_type = 'DOTADataset'
data_root = '../data/' # 路径
file_client_args = dict(backend='disk')
train_pipeline = [
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(
type='mmdet.RandomFlip',
prob=0.75,
direction=['horizontal', 'vertical', 'diagonal']),
dict(
type='RandomRotate',
prob=0.5,
angle_range=180,
rect_obj_labels=[9, 11]),
dict(type='mmdet.PackDetInputs')
]
val_pipeline = [
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
# avoid bboxes being resized
dict(type='mmdet.LoadAnnotations', with_bbox=True, box_type='qbox'),
dict(type='ConvertBoxType', box_type_mapping=dict(gt_bboxes='rbox')),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape'
)) # 删除了'scale_factor'
]
test_pipeline = [
dict(type='mmdet.LoadImageFromFile', file_client_args=file_client_args),
dict(
type='mmdet.PackDetInputs',
meta_keys=('img_id', 'img_path', 'ori_shape', 'img_shape')) # 删除了'scale_factor'
]
train_dataloader = dict(
batch_size=2,
num_workers=8,
persistent_workers=True,
sampler=dict(type='DefaultSampler', shuffle=True),
batch_sampler=None,
pin_memory=False,
dataset=dict(
type=dataset_type,
data_root=data_root,
ann_file='trainval/annfiles/', # txt路径
data_prefix=dict(img_path='trainval/images/'), # 图片路径
img_shape=(512, 640),
filter_cfg=dict(filter_empty_gt=True),
pipeline=train_pipeline))
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='trainval/annfiles/', # txt路径
data_prefix=dict(img_path='trainval/images/'), # 图片路径
img_shape=(512, 640),
test_mode=True,
pipeline=val_pipeline))
test_dataloader = val_dataloader
val_evaluator = dict(type='DOTAMetric', metric='mAP')
test_evaluator = val_evaluator
- G:\research\mmrotate-1.x\mmrotate\datasets\dota.py
# Copyright (c) OpenMMLab. All rights reserved.
import glob
import os.path as osp
from typing import List, Tuple
from mmengine.dataset import BaseDataset
from mmrotate.registry import DATASETS
@DATASETS.register_module()
class DOTADataset(BaseDataset):
"""DOTA-v1.0 dataset for detection.
Note: ``ann_file`` in DOTADataset is different from the BaseDataset.
In BaseDataset, it is the path of an annotation file. In DOTADataset,
it is the path of a folder containing XML files.
Args:
img_shape (tuple[int]): The shape of images. Due to the huge size
of the remote sensing image, we will cut it into slices with
the same shape. Defaults to (1024, 1024).
diff_thr (int): The difficulty threshold of ground truth. Bboxes
with difficulty higher than it will be ignored. The range of this
value should be non-negative integer. Defaults to 100.
"""
METAINFO = {
'classes':
('box',),
# palette is a list of color tuples, which is used for visualization.
'palette': [(165, 42, 42),]
} # class 修改
def __init__(self,
img_shape: Tuple[int, int] = (1024, 1024),
diff_thr: int = 100,
**kwargs) -> None:
self.img_shape = img_shape
self.diff_thr = diff_thr
super().__init__(**kwargs)
def load_data_list(self) -> List[dict]:
"""Load annotations from an annotation file named as ``self.ann_file``
Returns:
List[dict]: A list of annotation.
""" # noqa: E501
cls_map = {
c: i
for i, c in enumerate(self.metainfo['classes'])
} # in mmdet v2.0 label is 0-based
data_list = []
if self.ann_file == '':
img_files = glob.glob(
osp.join(self.data_prefix['img_path'], '*.png'))
for img_path in img_files:
data_info = {
}
data_info['img_path'] = img_path
img_name = osp.split(img_path)[1]
data_info['file_name'] = img_name
img_id = img_name[:-4]
data_info['img_id'] = img_id
data_info['height'] = self.img_shape[0]
data_info['width'] = self.img_shape[1]
instance = dict(bbox=[], bbox_label=[], ignore_flag=0)
data_info['instances'] = [instance]
data_list.append(data_info)
return data_list
else:
txt_files = glob.glob(osp.join(self.ann_file, '*.txt'))
if len(txt_files) == 0:
raise ValueError('There is no txt file in '
f'{
self.ann_file}')
for txt_file in txt_files:
data_info = {
}
img_id = osp.split(txt_file)[1][:-4]
data_info['img_id'] = img_id
img_name = img_id + '.png'
data_info['file_name'] = img_name
data_info['img_path'] = osp.join(self.data_prefix['img_path'],
img_name)
data_info['height'] = self.img_shape[0]
data_info['width'] = self.img_shape[1]
instances = []
with open(txt_file) as f:
s = f.readlines()
for si in s:
instance = {
}
bbox_info = si.split()
instance['bbox'] = [float(i) for i in bbox_info[:8]]
cls_name = bbox_info[8]
if len(cls_name)>3:
print(11)
continue
instance['bbox_label'] = cls_map[cls_name]
difficulty = int(bbox_info[9])
if difficulty > self.diff_thr:
instance['ignore_flag'] = 1
else:
instance['ignore_flag'] = 0
instances.append(instance)
data_info['instances'] = instances
data_list.append(data_info)
return data_list
def filter_data(self) -> List[dict]:
"""Filter annotations according to filter_cfg.
Returns:
List[dict]: Filtered results.
"""
if self.test_mode:
return self.data_list
filter_empty_gt = self.filter_cfg.get('filter_empty_gt', False) \
if self.filter_cfg is not None else False
valid_data_infos = []
for i, data_info in enumerate(self.data_list):
if filter_empty_gt and len(data_info['instances']) == 0:
continue
valid_data_infos.append(data_info)
return valid_data_infos
def get_cat_ids(self, idx: int) -> List[int]:
"""Get DOTA category ids by index.
Args:
idx (int): Index of data.
Returns:
List[int]: All categories in the image of specified index.
"""
instances = self.get_data_info(idx)['instances']
return [instance['bbox_label'] for instance in instances]
@DATASETS.register_module()
class DOTAv15Dataset(DOTADataset):
"""DOTA-v1.5 dataset for detection.
Note: ``ann_file`` in DOTAv15Dataset is different from the BaseDataset.
In BaseDataset, it is the path of an annotation file. In DOTAv15Dataset,
it is the path of a folder containing XML files.
"""
METAINFO = {
'classes':
('plane', 'baseball-diamond', 'bridge', 'ground-track-field',
'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout',
'harbor', 'swimming-pool', 'helicopter', 'container-crane'),
# palette is a list of color tuples, which is used for visualization.
'palette': [(165, 42, 42), (189, 183, 107), (0, 255, 0), (255, 0, 0),
(138, 43, 226), (255, 128, 0), (255, 0, 255),
(0, 255, 255), (255, 193, 193), (0, 51, 153),
(255, 250, 205), (0, 139, 139), (255, 255, 0),
(147, 116, 116), (0, 0, 255), (220, 20, 60)]
}
@DATASETS.register_module()
class DOTAv2Dataset(DOTADataset):
"""DOTA-v2.0 dataset for detection.
Note: ``ann_file`` in DOTAv2Dataset is different from the BaseDataset.
In BaseDataset, it is the path of an annotation file. In DOTAv2Dataset,
it is the path of a folder containing XML files.
"""
METAINFO = {
'classes':
('plane', 'baseball-diamond', 'bridge', 'ground-track-field',
'small-vehicle', 'large-vehicle', 'ship', 'tennis-court',
'basketball-court', 'storage-tank', 'soccer-ball-field', 'roundabout',
'harbor', 'swimming-pool', 'helicopter', 'container-crane', 'airport',
'helipad'),
# palette is a list of color tuples, which is used for visualization.
'palette': [(165, 42, 42), (189, 183, 107), (0, 255, 0), (255, 0, 0),
(138, 43, 226), (255, 128, 0), (255, 0, 255),
(0, 255, 255), (255, 193, 193), (0, 51, 153),
(255, 250, 205), (0, 139, 139), (255, 255, 0),
(147, 116, 116), (0, 0, 255), (220, 20, 60), (119, 11, 32),
(0, 0, 142)]
}
- G:\research\mmrotate-1.x\configs\rotated_rtmdet\rotated_rtmdet_l-3x-dota_ms.py
num_classes=1 修改
_base_ = [
'./_base_/default_runtime.py', './_base_/schedule_3x.py',
'./_base_/dota_rr_ms.py'
]
checkpoint = 'https://download.openmmlab.com/mmdetection/v3.0/rtmdet/cspnext_rsb_pretrain/cspnext-l_8xb256-rsb-a1-600e_in1k-6a760974.pth' # noqa
angle_version = 'le90'
model = dict(
type='mmdet.RTMDet',
data_preprocessor=dict(
type='mmdet.DetDataPreprocessor',
mean=[103.53, 116.28, 123.675],
std=[57.375, 57.12, 58.395],
bgr_to_rgb=False,
boxtype2tensor=False,
batch_augments=None),
backbone=dict(
type='mmdet.CSPNeXt',
arch='P5',
expand_ratio=0.5,
deepen_factor=1,
widen_factor=1,
channel_attention=True,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU'),
init_cfg=dict(
type='Pretrained', prefix='backbone.', checkpoint=checkpoint)),
neck=dict(
type='mmdet.CSPNeXtPAFPN',
in_channels=[256, 512, 1024],
out_channels=256,
num_csp_blocks=3,
expand_ratio=0.5,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU')),
bbox_head=dict(
type='RotatedRTMDetSepBNHead',
num_classes=1,
in_channels=256,
stacked_convs=2,
feat_channels=256,
angle_version=angle_version,
anchor_generator=dict(
type='mmdet.MlvlPointGenerator', offset=0, strides=[8, 16, 32]),
bbox_coder=dict(
type='DistanceAnglePointCoder', angle_version=angle_version),
loss_cls=dict(
type='mmdet.QualityFocalLoss',
use_sigmoid=True,
beta=2.0,
loss_weight=1.0),
loss_bbox=dict(type='RotatedIoULoss', mode='linear', loss_weight=2.0),
with_objectness=False,
exp_on_reg=True,
share_conv=True,
pred_kernel_size=1,
use_hbbox_loss=False,
scale_angle=False,
loss_angle=None,
norm_cfg=dict(type='SyncBN'),
act_cfg=dict(type='SiLU')),
train_cfg=dict(
assigner=dict(
type='mmdet.DynamicSoftLabelAssigner',
iou_calculator=dict(type='RBboxOverlaps2D'),
topk=13),
allowed_border=-1,
pos_weight=-1,
debug=False),
test_cfg=dict(
nms_pre=2000,
min_bbox_size=0,
score_thr=0.05,
nms=dict(type='nms_rotated', iou_threshold=0.1),
max_per_img=2000),
)
# batch_size = (2 GPUs) x (4 samples per GPU) = 8
train_dataloader = dict(batch_size=4, num_workers=4)
4. Error reporting
- Can't find 'scale_factor'
In addition to deleting this configuration in the configuration file, you need to modify other errors about scale_factor, because it can run after modification, so I forgot where to modify, but the effect exploded
5. Effect
The one on the left is marked, and the one on the right is the test result,
which can be applied directly! ! ! ! ! ! ! ! ! ! ! ! ! !