[Feature] Add albu transform (#2710)

.circleci/test.yml

@@ -77,6 +77,7 @@ jobs:
           command: |
             python -m pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/cpu/torch<< parameters.torch >>/index.html
             python -m pip install -r requirements.txt
+            python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
       - run:
           name: Build and install
           command: |
@@ -119,6 +120,8 @@ jobs:
           command: |
             docker exec mmseg pip install mmcv-full -f https://download.openmmlab.com/mmcv/dist/${MMCV_CUDA}/torch${MMCV_TORCH}/index.html
             docker exec mmseg pip install -r requirements.txt
+            docker exec mmseg pip install typing-extensions -U
+            docker exec mmseg pip install albumentations --use-pep517 qudida albumentations
       - run:
           name: Build and install
           command: |

.github/workflows/build.yml

@@ -64,6 +64,7 @@ jobs:
       - name: Install unittest dependencies
         run: |
           pip install -r requirements.txt
+          pip install albumentations>=0.3.2 --no-binary qudida,albumentations
       - name: Build and install
         run: rm -rf .eggs && pip install -e .
       - name: Run unittests and generate coverage report
@@ -133,6 +134,7 @@ jobs:
           python -m pip install -U openmim
           mim install mmcv-full
           python -m pip install -r requirements.txt
+          python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
           python -c 'import mmcv; print(mmcv.__version__)'
       - name: Build and install
         run: |
@@ -200,6 +202,7 @@ jobs:
           python -m pip install openmim
           mim install mmcv-full
           python -m pip install -r requirements.txt
+          python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
           python -c 'import mmcv; print(mmcv.__version__)'
       - name: Build and install
         run: |
@@ -263,6 +266,7 @@ jobs:
           python -m pip install openmim
           mim install mmcv-full
           python -m pip install -r requirements.txt
+          python -m pip install albumentations>=0.3.2 --no-binary qudida,albumentations
           python -c 'import mmcv; print(mmcv.__version__)'
       - name: Build and install
         run: |
@@ -301,7 +305,9 @@ jobs:
           pip install -U openmim
           mim install mmcv-full
       - name: Install unittest dependencies
-        run: pip install -r requirements/tests.txt -r requirements/optional.txt
+        run: |
+          pip install -r requirements/tests.txt -r requirements/optional.txt
+          pip install albumentations>=0.3.2 --no-binary qudida,albumentations
       - name: Build and install
         run: pip install -e .
       - name: Run unittests

docs/en/get_started.md

@@ -63,6 +63,9 @@ Case b: If you use mmsegmentation as a dependency or third-party package, instal
 pip install mmsegmentation
 ```
 
+**Note:**
+If you would like to use albumentations, we suggest using pip install -U albumentations --no-binary qudida,albumentations. If you simply use pip install albumentations>=0.3.2, it will install opencv-python-headless simultaneously (even though you have already installed opencv-python). We recommended checking the environment after installing albumentations to ensure that opencv-python and opencv-python-headless are not installed at the same time, because it might cause unexpected issues if they both installed. Please refer to [official documentation](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies) for more details.
+
 ## Verify the installation
 
 To verify whether MMSegmentation is installed correctly, we provide some sample codes to run an inference demo.

docs/zh_cn/get_started.md

@@ -67,6 +67,9 @@ pip install -v -e .
 pip install mmsegmentation
 ```
 
+**注意：**
+如果你想使用 albumentations，我们建议使用 pip install-U albumentations --no-binary qudida,albumentations 进行安装。如果您仅使用 pip install albumentations>=0.3.2 进行安装，它将同时安装 opencv-python-headless（即使您已经安装了 opencv-python）。我们建议在安装了 albumentations 后检查环境，以确保没有同时安装 opencv-python 和 opencv-python-headless，因为如果两者都安装了，可能会导致意外问题。请参阅[官方文档](https://albumentations.ai/docs/getting_started/installation/#note-on-opencv-dependencies)了解更多详细信息。
+
 ## 验证安装
 
 为了验证 MMSegmentation 是否安装正确，我们提供了一些示例代码来执行模型推理。

mmseg/datasets/pipelines/__init__.py

@@ -4,7 +4,7 @@ from .formatting import (Collect, ImageToTensor, ToDataContainer, ToTensor,
                          Transpose, to_tensor)
 from .loading import LoadAnnotations, LoadImageFromFile
 from .test_time_aug import MultiScaleFlipAug
-from .transforms import (CLAHE, AdjustGamma, Normalize, Pad,
+from .transforms import (CLAHE, AdjustGamma, Albu, Normalize, Pad,
                          PhotoMetricDistortion, RandomCrop, RandomCutOut,
                          RandomFlip, RandomMosaic, RandomRotate, Rerange,
                          Resize, RGB2Gray, SegRescale)
@@ -15,5 +15,5 @@ __all__ = [
     'MultiScaleFlipAug', 'Resize', 'RandomFlip', 'Pad', 'RandomCrop',
     'Normalize', 'SegRescale', 'PhotoMetricDistortion', 'RandomRotate',
     'AdjustGamma', 'CLAHE', 'Rerange', 'RGB2Gray', 'RandomCutOut',
-    'RandomMosaic'
+    'RandomMosaic', 'Albu'
 ]

mmseg/datasets/pipelines/transforms.py

@@ -1,6 +1,8 @@
 # Copyright (c) OpenMMLab. All rights reserved.
 import copy
+import inspect
 
+import cv2
 import mmcv
 import numpy as np
 from mmcv.utils import deprecated_api_warning, is_tuple_of
@@ -8,6 +10,13 @@ from numpy import random
 
 from ..builder import PIPELINES
 
+try:
+    import albumentations
+    from albumentations import Compose
+except ImportError:
+    albumentations = None
+    Compose = None
+
 
 @PIPELINES.register_module()
 class ResizeToMultiple(object):
@@ -1333,3 +1342,146 @@ class RandomMosaic(object):
         repr_str += f'pad_val={self.pad_val}, '
         repr_str += f'seg_pad_val={self.pad_val})'
         return repr_str
+
+
+@PIPELINES.register_module()
+class Albu:
+    """Albumentation augmentation. Adds custom transformations from
+    Albumentations library. Please, visit
+    `https://albumentations.readthedocs.io` to get more information. An example
+    of ``transforms`` is as followed:
+
+    .. code-block::
+        [
+            dict(
+                type='ShiftScaleRotate',
+                shift_limit=0.0625,
+                scale_limit=0.0,
+                rotate_limit=0,
+                interpolation=1,
+                p=0.5),
+            dict(
+                type='RandomBrightnessContrast',
+                brightness_limit=[0.1, 0.3],
+                contrast_limit=[0.1, 0.3],
+                p=0.2),
+            dict(type='ChannelShuffle', p=0.1),
+            dict(
+                type='OneOf',
+                transforms=[
+                    dict(type='Blur', blur_limit=3, p=1.0),
+                    dict(type='MedianBlur', blur_limit=3, p=1.0)
+                ],
+                p=0.1),
+        ]
+    Args:
+        transforms (list[dict]): A list of albu transformations
+        keymap (dict): Contains {'input key':'albumentation-style key'}
+        update_pad_shape (bool): Whether to update padding shape according to \
+            the output shape of the last transform
+    """
+
+    def __init__(self, transforms, keymap=None, update_pad_shape=False):
+        if Compose is None:
+            raise ImportError(
+                'albumentations is not installed, '
+                'we suggest install albumentation by '
+                '"pip install albumentations>=0.3.2 --no-binary qudida,albumentations"'  # noqa
+            )
+
+        # Args will be modified later, copying it will be safer
+        transforms = copy.deepcopy(transforms)
+
+        self.transforms = transforms
+        self.filter_lost_elements = False
+        self.update_pad_shape = update_pad_shape
+
+        self.aug = Compose([self.albu_builder(t) for t in self.transforms])
+
+        if not keymap:
+            self.keymap_to_albu = {
+                'img': 'image',
+                'gt_masks': 'masks',
+            }
+        else:
+            self.keymap_to_albu = copy.deepcopy(keymap)
+        self.keymap_back = {v: k for k, v in self.keymap_to_albu.items()}
+
+    def albu_builder(self, cfg):
+        """Import a module from albumentations.
+
+        It inherits some of :func:`build_from_cfg` logic.
+        Args:
+            cfg (dict): Config dict. It should at least contain the key "type".
+        Returns:
+            obj: The constructed object.
+        """
+
+        assert isinstance(cfg, dict) and 'type' in cfg
+        args = cfg.copy()
+
+        obj_type = args.pop('type')
+        if mmcv.is_str(obj_type):
+            if albumentations is None:
+                raise ImportError(
+                    'albumentations is not installed, '
+                    'we suggest install albumentation by '
+                    '"pip install albumentations>=0.3.2 --no-binary qudida,albumentations"'  # noqa
+                )
+            obj_cls = getattr(albumentations, obj_type)
+        elif inspect.isclass(obj_type):
+            obj_cls = obj_type
+        else:
+            raise TypeError(
+                f'type must be a str or valid type, but got {type(obj_type)}')
+
+        if 'transforms' in args:
+            args['transforms'] = [
+                self.albu_builder(transform)
+                for transform in args['transforms']
+            ]
+
+        return obj_cls(**args)
+
+    @staticmethod
+    def mapper(d, keymap):
+        """Dictionary mapper.
+
+        Renames keys according to keymap provided.
+        Args:
+            d (dict): old dict
+            keymap (dict): {'old_key':'new_key'}
+        Returns:
+            dict: new dict.
+        """
+
+        updated_dict = {}
+        for k, _ in zip(d.keys(), d.values()):
+            new_k = keymap.get(k, k)
+            updated_dict[new_k] = d[k]
+        return updated_dict
+
+    def __call__(self, results):
+        # dict to albumentations format
+        results = self.mapper(results, self.keymap_to_albu)
+
+        # Convert to RGB since Albumentations works with RGB images
+        results['image'] = cv2.cvtColor(results['image'], cv2.COLOR_BGR2RGB)
+
+        results = self.aug(**results)
+
+        # Convert back to BGR
+        results['image'] = cv2.cvtColor(results['image'], cv2.COLOR_RGB2BGR)
+
+        # back to the original format
+        results = self.mapper(results, self.keymap_back)
+
+        # update final shape
+        if self.update_pad_shape:
+            results['pad_shape'] = results['img'].shape
+
+        return results
+
+    def __repr__(self):
+        repr_str = self.__class__.__name__ + f'(transforms={self.transforms})'
+        return repr_str

requirements/readthedocs.txt

@@ -1,4 +1,5 @@
 mmcv
 prettytable
+scipy
 torch
 torchvision

requirements/runtime.txt

@@ -3,3 +3,4 @@ mmcls>=0.20.1
 numpy
 packaging
 prettytable
+scipy

tests/test_data/test_transform.py

@@ -688,3 +688,63 @@ def test_mosaic():
     mosaic_module = build_from_cfg(transform, PIPELINES)
     results = mosaic_module(results)
     assert results['img'].shape[:2] == (20, 24)
+
+
+def test_albu_transform():
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../data'),
+        img_info=dict(filename='color.jpg'))
+
+    # Define simple pipeline
+    load = dict(type='LoadImageFromFile')
+    load = build_from_cfg(load, PIPELINES)
+
+    albu_transform = dict(
+        type='Albu', transforms=[dict(type='ChannelShuffle', p=1)])
+    albu_transform = build_from_cfg(albu_transform, PIPELINES)
+
+    normalize = dict(type='Normalize', mean=[0] * 3, std=[0] * 3, to_rgb=True)
+    normalize = build_from_cfg(normalize, PIPELINES)
+
+    # Execute transforms
+    results = load(results)
+    results = albu_transform(results)
+    results = normalize(results)
+
+    assert results['img'].dtype == np.float32
+
+
+def test_albu_channel_order():
+    results = dict(
+        img_prefix=osp.join(osp.dirname(__file__), '../data'),
+        img_info=dict(filename='color.jpg'))
+
+    # Define simple pipeline
+    load = dict(type='LoadImageFromFile')
+    load = build_from_cfg(load, PIPELINES)
+
+    # Transform is modifying B channel
+    albu_transform = dict(
+        type='Albu',
+        transforms=[
+            dict(
+                type='RGBShift',
+                r_shift_limit=0,
+                g_shift_limit=0,
+                b_shift_limit=200,
+                p=1)
+        ])
+    albu_transform = build_from_cfg(albu_transform, PIPELINES)
+
+    # Execute transforms
+    results_load = load(results)
+    results_albu = albu_transform(results_load)
+
+    # assert only Green and Red channel are not modified
+    np.testing.assert_array_equal(results_albu['img'][..., 1:],
+                                  results_load['img'][..., 1:])
+
+    # assert Blue channel is modified
+    with pytest.raises(AssertionError):
+        np.testing.assert_array_equal(results_albu['img'][..., 0],
+                                      results_load['img'][..., 0])

tests/test_models/test_backbones/test_beit.py

@@ -140,8 +140,7 @@ def test_beit_init():
         }
     }
     model = BEiT(img_size=(512, 512))
-    with pytest.raises(AttributeError):
+    ckpt = model.resize_rel_pos_embed(ckpt)
-        model.resize_rel_pos_embed(ckpt)
 
     # pretrained=None
     # init_cfg=123, whose type is unsupported

tests/test_models/test_backbones/test_mae.py

@@ -138,10 +138,19 @@ def test_mae_init():
         }
     }
     model = MAE(img_size=(512, 512))
-    with pytest.raises(AttributeError):
+    ckpt = model.resize_rel_pos_embed(ckpt)
-        model.resize_rel_pos_embed(ckpt)
 
     # test resize abs pos embed
+    value = torch.randn(732, 16)
+    abs_pos_embed_value = torch.rand(1, 17, 768)
+    ckpt = {
+        'state_dict': {
+            'layers.0.attn.relative_position_index': 0,
+            'layers.0.attn.relative_position_bias_table': value,
+            'pos_embed': abs_pos_embed_value
+        }
+    }
+    model = MAE(img_size=(512, 512))
     ckpt = model.resize_abs_pos_embed(ckpt['state_dict'])
 
     # pretrained=None