70 lines
2.1 KiB
Markdown
70 lines
2.1 KiB
Markdown
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# Tutorial 2: Customize Data Pipelines
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- [Tutorial 2: Customize Data Pipelines](#tutorial-2-customize-data-pipelines)
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- [Overview of `Pipeline`](#overview-of-pipeline)
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- [Creating new augmentations in `Pipeline`](#creating-new-augmentations-in-pipeline)
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## Overview of `Pipeline`
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`DataSource` and `Pipeline` are two important components in `Dataset`. We have introduced `DataSource` in [add_new_dataset](./1_new_dataset.md). And the `Pipeline` is responsible for applying a series of data augmentations to images, such as random flip.
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Here is a config example of `Pipeline` for `SimCLR` training:
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```py
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train_pipeline = [
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dict(type='RandomResizedCrop', size=224),
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dict(type='RandomHorizontalFlip'),
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dict(
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type='RandomAppliedTrans',
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transforms=[
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dict(
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type='ColorJitter',
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brightness=0.8,
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contrast=0.8,
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saturation=0.8,
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hue=0.2)
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],
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p=0.8),
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dict(type='RandomGrayscale', p=0.2),
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dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5)
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]
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```
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Every augmentation in the `Pipeline` receives an image as input and outputs an augmented image.
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## Creating new augmentations in `Pipeline`
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1.Write a new transformation function in [transforms.py](../../mmselfsup/datasets/pipelines/transforms.py) and overwrite the `__call__` function, which takes a `Pillow` image as input:
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```py
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@PIPELINES.register_module()
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class MyTransform(object):
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def __call__(self, img):
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# apply transforms on img
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return img
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```
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2.Use it in config files. We reuse the config file shown above and add `MyTransform` to it.
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```py
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train_pipeline = [
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dict(type='RandomResizedCrop', size=224),
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dict(type='RandomHorizontalFlip'),
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dict(type='MyTransform'),
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dict(
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type='RandomAppliedTrans',
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transforms=[
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dict(
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type='ColorJitter',
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brightness=0.8,
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contrast=0.8,
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saturation=0.8,
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hue=0.2)
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],
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p=0.8),
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dict(type='RandomGrayscale', p=0.2),
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dict(type='GaussianBlur', sigma_min=0.1, sigma_max=2.0, p=0.5)
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]
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```
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