Kingdrone b997a13e28
[Feature] Support ISPRS Potsdam Dataset. (#1097)
* add isprs potsdam dataset

* add isprs dataset configs

* fix lint error

* fix potsdam conversion bug

* fix error in potsdam class

* fix error in potsdam class

* add vaihingen dataset

* add vaihingen dataset

* add vaihingen dataset

* fix some description errors.

* fix some description errors.

* fix some description errors.

* upload models & logs of Potsdam

* remove vaihingen and add unit test

* add chinese readme

* add pseudodataset

* use mmcv and add class_names

* use f-string

* add new dataset unittest

* add docstring and remove global variables args

* fix metafile error in PSPNet

* fix pretrained value

* Add dataset info

* fix typo

Co-authored-by: MengzhangLI <mcmong@pku.edu.cn>
2022-01-18 14:15:15 +08:00

55 lines
1.7 KiB
Python

# dataset settings
dataset_type = 'PotsdamDataset'
data_root = 'data/potsdam'
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 512)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations', reduce_zero_label=True),
dict(type='Resize', img_scale=(512, 512), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', prob=0.5),
dict(type='PhotoMetricDistortion'),
dict(type='Normalize', **img_norm_cfg),
dict(type='Pad', size=crop_size, pad_val=0, seg_pad_val=255),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
test_pipeline = [
dict(type='LoadImageFromFile'),
dict(
type='MultiScaleFlipAug',
img_scale=(512, 512),
# img_ratios=[0.5, 0.75, 1.0, 1.25, 1.5, 1.75],
flip=False,
transforms=[
dict(type='Resize', keep_ratio=True),
dict(type='RandomFlip'),
dict(type='Normalize', **img_norm_cfg),
dict(type='ImageToTensor', keys=['img']),
dict(type='Collect', keys=['img']),
])
]
data = dict(
samples_per_gpu=4,
workers_per_gpu=4,
train=dict(
type=dataset_type,
data_root=data_root,
img_dir='img_dir/train',
ann_dir='ann_dir/train',
pipeline=train_pipeline),
val=dict(
type=dataset_type,
data_root=data_root,
img_dir='img_dir/val',
ann_dir='ann_dir/val',
pipeline=test_pipeline),
test=dict(
type=dataset_type,
data_root=data_root,
img_dir='img_dir/val',
ann_dir='ann_dir/val',
pipeline=test_pipeline))