Support resize data augmentation according to original image size (#291)

* Support resize data augmentation according to original image size (img_scale=None and retio_range is tuple)

* fix docstring

* fix bug

* add unittest

* img_scale=None in TTA

* fix bug

* add unittest

* fix typos

* fix bug

mmseg/datasets/pipelines/test_time_aug.py

@@ -41,7 +41,7 @@ class MultiScaleFlipAug(object):
 
     Args:
         transforms (list[dict]): Transforms to apply in each augmentation.
-        img_scale (tuple | list[tuple]): Images scales for resizing.
+        img_scale (None | tuple | list[tuple]): Images scales for resizing.
         img_ratios (float | list[float]): Image ratios for resizing
         flip (bool): Whether apply flip augmentation. Default: False.
         flip_direction (str | list[str]): Flip augmentation directions,
@@ -58,20 +58,27 @@ def __init__(self,
                  flip_direction='horizontal'):
         self.transforms = Compose(transforms)
         if img_ratios is not None:
-            # mode 1: given a scale and a range of image ratio
             img_ratios = img_ratios if isinstance(img_ratios,
                                                   list) else [img_ratios]
             assert mmcv.is_list_of(img_ratios, float)
-            assert isinstance(img_scale, tuple) and len(img_scale) == 2
+        if img_scale is None:
+            # mode 1: given img_scale=None and a range of image ratio
+            self.img_scale = None
+            assert mmcv.is_list_of(img_ratios, float)
+        elif isinstance(img_scale, tuple) and mmcv.is_list_of(
+                img_ratios, float):
+            assert len(img_scale) == 2
+            # mode 2: given a scale and a range of image ratio
             self.img_scale = [(int(img_scale[0] * ratio),
                                int(img_scale[1] * ratio))
                               for ratio in img_ratios]
         else:
-            # mode 2: given multiple scales
+            # mode 3: given multiple scales
             self.img_scale = img_scale if isinstance(img_scale,
                                                      list) else [img_scale]
-        assert mmcv.is_list_of(self.img_scale, tuple)
+        assert mmcv.is_list_of(self.img_scale, tuple) or self.img_scale is None
         self.flip = flip
+        self.img_ratios = img_ratios
         self.flip_direction = flip_direction if isinstance(
             flip_direction, list) else [flip_direction]
         assert mmcv.is_list_of(self.flip_direction, str)
@@ -95,8 +102,14 @@ def __call__(self, results):
         """
 
         aug_data = []
+        if self.img_scale is None and mmcv.is_list_of(self.img_ratios, float):
+            h, w = results['img'].shape[:2]
+            img_scale = [(int(h * ratio), int(w * ratio))
+                         for ratio in self.img_ratios]
+        else:
+            img_scale = self.img_scale
         flip_aug = [False, True] if self.flip else [False]
-        for scale in self.img_scale:
+        for scale in img_scale:
             for flip in flip_aug:
                 for direction in self.flip_direction:
                     _results = results.copy()

mmseg/datasets/pipelines/transforms.py

@@ -14,17 +14,21 @@ class Resize(object):
     contains the key "scale", then the scale in the input dict is used,
     otherwise the specified scale in the init method is used.
 
-    ``img_scale`` can either be a tuple (single-scale) or a list of tuple
-    (multi-scale). There are 3 multiscale modes:
+    ``img_scale`` can be Nong, a tuple (single-scale) or a list of tuple
+    (multi-scale). There are 4 multiscale modes:
 
-    - ``ratio_range is not None``: randomly sample a ratio from the ratio range
-    and multiply it with the image scale.
+    - ``ratio_range is not None``:
+    1. When img_scale is None, img_scale is the shape of image in results
+        (img_scale = results['img'].shape[:2]) and the image is resized based
+        on the original size. (mode 1)
+    2. When img_scale is a tuple (single-scale), randomly sample a ratio from
+        the ratio range and multiply it with the image scale. (mode 2)
 
     - ``ratio_range is None and multiscale_mode == "range"``: randomly sample a
-    scale from the a range.
+    scale from the a range. (mode 3)
 
     - ``ratio_range is None and multiscale_mode == "value"``: randomly sample a
-    scale from multiple scales.
+    scale from multiple scales. (mode 4)
 
     Args:
         img_scale (tuple or list[tuple]): Images scales for resizing.
@@ -49,10 +53,11 @@ def __init__(self,
             assert mmcv.is_list_of(self.img_scale, tuple)
 
         if ratio_range is not None:
-            # mode 1: given a scale and a range of image ratio
-            assert len(self.img_scale) == 1
+            # mode 1: given img_scale=None and a range of image ratio
+            # mode 2: given a scale and a range of image ratio
+            assert self.img_scale is None or len(self.img_scale) == 1
         else:
-            # mode 2: given multiple scales or a range of scales
+            # mode 3 and 4: given multiple scales or a range of scales
             assert multiscale_mode in ['value', 'range']
 
         self.multiscale_mode = multiscale_mode
@@ -150,8 +155,12 @@ def _random_scale(self, results):
         """
 
         if self.ratio_range is not None:
-            scale, scale_idx = self.random_sample_ratio(
-                self.img_scale[0], self.ratio_range)
+            if self.img_scale is None:
+                scale, scale_idx = self.random_sample_ratio(
+                    results['img'].shape[:2], self.ratio_range)
+            else:
+                scale, scale_idx = self.random_sample_ratio(
+                    self.img_scale[0], self.ratio_range)
         elif len(self.img_scale) == 1:
             scale, scale_idx = self.img_scale[0], 0
         elif self.multiscale_mode == 'range':

tests/test_data/test_transform.py

@@ -38,6 +38,7 @@ def test_resize():
     resize_module = build_from_cfg(transform, PIPELINES)
 
     results = dict()
+    # (288, 512, 3)
     img = mmcv.imread(
         osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
     results['img'] = img
@@ -92,6 +93,15 @@ def test_resize():
     resized_results = resize_module(results.copy())
     assert max(resized_results['img_shape'][:2]) <= 1333 * 1.1
 
+    # test img_scale=None and ratio_range is tuple.
+    # img shape: (288, 512, 3)
+    transform = dict(
+        type='Resize', img_scale=None, ratio_range=(0.5, 2.0), keep_ratio=True)
+    resize_module = build_from_cfg(transform, PIPELINES)
+    resized_results = resize_module(results.copy())
+    assert int(288 * 0.5) <= resized_results['img_shape'][0] <= 288 * 2.0
+    assert int(512 * 0.5) <= resized_results['img_shape'][1] <= 512 * 2.0
+
 
 def test_flip():
     # test assertion for invalid prob

tests/test_data/test_tta.py

@@ -0,0 +1,150 @@
+import os.path as osp
+
+import mmcv
+import pytest
+from mmcv.utils import build_from_cfg
+
+from mmseg.datasets.builder import PIPELINES
+
+
+def test_multi_scale_flip_aug():
+    # test assertion if img_scale=None, img_ratios=1 (not float).
+    with pytest.raises(AssertionError):
+        tta_transform = dict(
+            type='MultiScaleFlipAug',
+            img_scale=None,
+            img_ratios=1,
+            transforms=[dict(type='Resize', keep_ratio=False)],
+        )
+        build_from_cfg(tta_transform, PIPELINES)
+
+    # test assertion if img_scale=None, img_ratios=None.
+    with pytest.raises(AssertionError):
+        tta_transform = dict(
+            type='MultiScaleFlipAug',
+            img_scale=None,
+            img_ratios=None,
+            transforms=[dict(type='Resize', keep_ratio=False)],
+        )
+        build_from_cfg(tta_transform, PIPELINES)
+
+    # test assertion if img_scale=(512, 512), img_ratios=1 (not float).
+    with pytest.raises(AssertionError):
+        tta_transform = dict(
+            type='MultiScaleFlipAug',
+            img_scale=(512, 512),
+            img_ratios=1,
+            transforms=[dict(type='Resize', keep_ratio=False)],
+        )
+        build_from_cfg(tta_transform, PIPELINES)
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 512),
+        img_ratios=[0.5, 1.0, 2.0],
+        flip=False,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+
+    results = dict()
+    # (288, 512, 3)
+    img = mmcv.imread(
+        osp.join(osp.dirname(__file__), '../data/color.jpg'), 'color')
+    results['img'] = img
+    results['img_shape'] = img.shape
+    results['ori_shape'] = img.shape
+    # Set initial values for default meta_keys
+    results['pad_shape'] = img.shape
+    results['scale_factor'] = 1.0
+
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(256, 256), (512, 512), (1024, 1024)]
+    assert tta_results['flip'] == [False, False, False]
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 512),
+        img_ratios=[0.5, 1.0, 2.0],
+        flip=True,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(256, 256), (256, 256), (512, 512),
+                                    (512, 512), (1024, 1024), (1024, 1024)]
+    assert tta_results['flip'] == [False, True, False, True, False, True]
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 512),
+        img_ratios=1.0,
+        flip=False,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(512, 512)]
+    assert tta_results['flip'] == [False]
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=(512, 512),
+        img_ratios=1.0,
+        flip=True,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(512, 512), (512, 512)]
+    assert tta_results['flip'] == [False, True]
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=None,
+        img_ratios=[0.5, 1.0, 2.0],
+        flip=False,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(144, 256), (288, 512), (576, 1024)]
+    assert tta_results['flip'] == [False, False, False]
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=None,
+        img_ratios=[0.5, 1.0, 2.0],
+        flip=True,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(144, 256), (144, 256), (288, 512),
+                                    (288, 512), (576, 1024), (576, 1024)]
+    assert tta_results['flip'] == [False, True, False, True, False, True]
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=[(256, 256), (512, 512), (1024, 1024)],
+        img_ratios=None,
+        flip=False,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(256, 256), (512, 512), (1024, 1024)]
+    assert tta_results['flip'] == [False, False, False]
+
+    tta_transform = dict(
+        type='MultiScaleFlipAug',
+        img_scale=[(256, 256), (512, 512), (1024, 1024)],
+        img_ratios=None,
+        flip=True,
+        transforms=[dict(type='Resize', keep_ratio=False)],
+    )
+    tta_module = build_from_cfg(tta_transform, PIPELINES)
+    tta_results = tta_module(results.copy())
+    assert tta_results['scale'] == [(256, 256), (256, 256), (512, 512),
+                                    (512, 512), (1024, 1024), (1024, 1024)]
+    assert tta_results['flip'] == [False, True, False, True, False, True]