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d439dc1 | 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 | # Migrate Data Transform to OpenMMLab 2.0
## Introduction
According to the data transform interface convention of TorchVision, all data transform classes need to
implement the `__call__` method. And in the convention of OpenMMLab 1.0, we require the input and output of
the `__call__` method should be a dictionary.
In OpenMMLab 2.0, to make the data transform classes more extensible, we use `transform` method instead of
`__call__` method to implement data transformation, and all data transform classes should inherit the
[`mmcv.transforms.BaseTransfrom`](mmcv.transforms.BaseTransfrom) class. And you can still use these data
transform classes by calling.
A tutorial to implement a data transform class can be found in the [Data Transform](../advanced_tutorials/data_element.md).
In addition, we move some common data transform classes from every repositories to MMCV, and in this document,
we will compare the functionalities, usages and implementations between the original data transform classes (in [MMClassification v0.23.2](https://github.com/open-mmlab/mmclassification/tree/v0.23.2), [MMDetection v2.25.1](https://github.com/open-mmlab/mmdetection/tree/v2.25.1)) and the new data transform classes (in [MMCV v2.0.0rc1](https://github.com/open-mmlab/mmcv/tree/2.x))
## Functionality Differences
<table class="colwidths-auto docutils align-default">
<thead>
<tr>
<th></th>
<th>MMClassification (original)</th>
<th>MMDetection (original)</th>
<th>MMCV (new)</th>
</tr>
</thead>
<tbody>
<tr>
<td><code>LoadImageFromFile</code></td>
<td>Join the 'img_prefix' and 'img_info.filename' field to find the path of images and loading.</td>
<td>Join the 'img_prefix' and 'img_info.filename' field to find the path of images and loading. Support
specifying the order of channels.</td>
<td>Load images from 'img_path'. Support ignoring failed loading and specifying decode backend.</td>
</tr>
<tr>
<td><code>LoadAnnotations</code></td>
<td>Not available.</td>
<td>Load bbox, label, mask (include polygon masks), semantic segmentation. Support converting bbox coordinate system.</td>
<td>Load bbox, label, mask (not include polygon masks), semantic segmentation.</td>
</tr>
<tr>
<td><code>Pad</code></td>
<td>Pad all images in the "img_fields" field.</td>
<td>Pad all images in the "img_fields" field. Support padding to integer multiple size.</td>
<td>Pad the image in the "img" field. Support padding to integer multiple size.</td>
</tr>
<tr>
<td><code>CenterCrop</code></td>
<td>Crop all images in the "img_fields" field. Support cropping as EfficientNet style.</td>
<td>Not available.</td>
<td>Crop the image in the "img" field, the bbox in the "gt_bboxes" field, the semantic segmentation in the "gt_seg_map" field, the keypoints in the "gt_keypoints" field. Support padding the margin of the cropped image.</td>
</tr>
<tr>
<td><code>Normalize</code></td>
<td>Normalize the image.</td>
<td>No differences.</td>
<td>No differences, but we recommend to use <a href="../tutorials/model.html#datapreprocessor">data preprocessor</a> to normalize the image.</td>
</tr>
<tr>
<td><code>Resize</code></td>
<td>Resize all images in the "img_fields" field. Support resizing proportionally according to the specified edge.</td>
<td>Use <code>Resize</code> with <code>ratio_range=None</code>, the <code>img_scale</code> have a single scale, and <code>multiscale_mode="value"</code>.</td>
<td>Resize the image in the "img" field, the bbox in the "gt_bboxes" field, the semantic segmentation in the "gt_seg_map" field, the keypoints in the "gt_keypoints" field. Support specifying the ratio of new scale to original scale and support resizing proportionally.</td>
</tr>
<tr>
<td><code>RandomResize</code></td>
<td>Not available</td>
<td>Use <code>Resize</code> with <code>ratio_range=None</code>, <code>img_scale</code> have two scales and <code>multiscale_mode="range"</code>, or <code>ratio_range</code> is not None.
<pre>Resize(
img_sacle=[(640, 480), (960, 720)],
mode="range",
)</pre>
</td>
<td>Have the same resize function as <code>Resize</code>. Support sampling the scale from a scale range or scale ratio range.
<pre>RandomResize(scale=[(640, 480), (960, 720)])</pre>
</td>
</tr>
<tr>
<td><code>RandomChoiceResize</code></td>
<td>Not available</td>
<td>Use <code>Resize</code> with <code>ratio_range=None</code>, <code>img_scale</code> have multiple scales, and <code>multiscale_mode="value"</code>.
<pre>Resize(
img_sacle=[(640, 480), (960, 720)],
mode="value",
)</pre>
</td>
<td>Have the same resize function as <code>Resize</code>. Support randomly choosing the scale from multiple scales or multiple scale ratios.
<pre>RandomChoiceResize(scales=[(640, 480), (960, 720)])</pre>
</td>
</tr>
<tr>
<td><code>RandomGrayscale</code></td>
<td>Randomly grayscale all images in the "img_fields" field. Support keeping channels after grayscale.</td>
<td>Not available</td>
<td>Randomly grayscale the image in the "img" field. Support specifying the weight of each channel, and support keeping channels after grayscale.</td>
</tr>
<tr>
<td><code>RandomFlip</code></td>
<td>Randomly flip all images in the "img_fields" field. Support flipping horizontally and vertically.</td>
<td>Randomly flip all values in the "img_fields", "bbox_fields", "mask_fields" and "seg_fields". Support flipping horizontally, vertically and diagonally, and support specifying the probability of every kind of flipping.</td>
<td>Randomly flip the values in the "img", "gt_bboxes", "gt_seg_map", "gt_keypoints" field. Support flipping horizontally, vertically and diagonally, and support specifying the probability of every kind of flipping.</td>
</tr>
<tr>
<td><code>MultiScaleFlipAug</code></td>
<td>Not available</td>
<td>Used for test-time-augmentation.</td>
<td>Use <code><a href="https://mmcv.readthedocs.io/en/2.x/api/generated/mmcv.transforms.TestTimeAug.html">TestTimeAug</a></code></td>
</tr>
<tr>
<td><code>ToTensor</code></td>
<td>Convert the values in the specified fields to <code>torch.Tensor</code>.</td>
<td>No differences</td>
<td>No differences</td>
</tr>
<tr>
<td><code>ImageToTensor</code></td>
<td>Convert the values in the specified fields to <code>torch.Tensor</code> and transpose the channels to CHW.</td>
<td>No differences.</td>
<td>No differences.</td>
</tr>
</tbody>
</table>
## Implementation Differences
Take `RandomFlip` as example, the new version [RandomFlip](<>) in MMCV inherits `BaseTransfrom`, and move the
functionality implementation from `__call__` to `transform` method. In addition, the randomness related code
is placed in some extra methods and these methods need to be wrapped by `cache_randomness` decorator.
- MMDetection (original version)
```python
class RandomFlip:
def __call__(self, results):
"""Randomly flip images."""
...
# Randomly choose the flip direction
cur_dir = np.random.choice(direction_list, p=flip_ratio_list)
...
return results
```
- MMCV (new version)
```python
class RandomFlip(BaseTransfrom):
def transform(self, results):
"""Randomly flip images"""
...
cur_dir = self._random_direction()
...
return results
@cache_randomness
def _random_direction(self):
"""Randomly choose the flip direction"""
...
return np.random.choice(direction_list, p=flip_ratio_list)
```
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