File size: 4,521 Bytes
985cc7f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 |
# Tutorial 3: Customize Data Pipelines
## Design of Data pipelines
Following typical conventions, we use `Dataset` and `DataLoader` for data loading
with multiple workers. `Dataset` returns a dict of data items corresponding
the arguments of models' forward method.
Since the data in semantic segmentation may not be the same size,
we introduce a new `DataContainer` type in MMCV to help collect and distribute
data of different size.
See [here](https://github.com/open-mmlab/mmcv/blob/master/mmcv/parallel/data_container.py) for more details.
The data preparation pipeline and the dataset is decomposed. Usually a dataset
defines how to process the annotations and a data pipeline defines all the steps to prepare a data dict.
A pipeline consists of a sequence of operations. Each operation takes a dict as input and also output a dict for the next transform.
The operations are categorized into data loading, pre-processing, formatting and test-time augmentation.
Here is an pipeline example for PSPNet.
```python
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', flip_ratio=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=(2048, 1024),
# 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']),
])
]
```
For each operation, we list the related dict fields that are added/updated/removed.
### Data loading
`LoadImageFromFile`
- add: img, img_shape, ori_shape
`LoadAnnotations`
- add: gt_semantic_seg, seg_fields
### Pre-processing
`Resize`
- add: scale, scale_idx, pad_shape, scale_factor, keep_ratio
- update: img, img_shape, *seg_fields
`RandomFlip`
- add: flip
- update: img, *seg_fields
`Pad`
- add: pad_fixed_size, pad_size_divisor
- update: img, pad_shape, *seg_fields
`RandomCrop`
- update: img, pad_shape, *seg_fields
`Normalize`
- add: img_norm_cfg
- update: img
`SegRescale`
- update: gt_semantic_seg
`PhotoMetricDistortion`
- update: img
### Formatting
`ToTensor`
- update: specified by `keys`.
`ImageToTensor`
- update: specified by `keys`.
`Transpose`
- update: specified by `keys`.
`ToDataContainer`
- update: specified by `fields`.
`DefaultFormatBundle`
- update: img, gt_semantic_seg
`Collect`
- add: img_meta (the keys of img_meta is specified by `meta_keys`)
- remove: all other keys except for those specified by `keys`
### Test time augmentation
`MultiScaleFlipAug`
## Extend and use custom pipelines
1. Write a new pipeline in any file, e.g., `my_pipeline.py`. It takes a dict as input and return a dict.
```python
from mmseg.datasets import PIPELINES
@PIPELINES.register_module()
class MyTransform:
def __call__(self, results):
results['dummy'] = True
return results
```
2. Import the new class.
```python
from .my_pipeline import MyTransform
```
3. Use it in config files.
```python
img_norm_cfg = dict(
mean=[123.675, 116.28, 103.53], std=[58.395, 57.12, 57.375], to_rgb=True)
crop_size = (512, 1024)
train_pipeline = [
dict(type='LoadImageFromFile'),
dict(type='LoadAnnotations'),
dict(type='Resize', img_scale=(2048, 1024), ratio_range=(0.5, 2.0)),
dict(type='RandomCrop', crop_size=crop_size, cat_max_ratio=0.75),
dict(type='RandomFlip', flip_ratio=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='MyTransform'),
dict(type='DefaultFormatBundle'),
dict(type='Collect', keys=['img', 'gt_semantic_seg']),
]
```
|