diff --git a/mmdet/datasets/transforms/__init__.py b/mmdet/datasets/transforms/__init__.py
index c8c40f3660c..b5ab3758382 100644
--- a/mmdet/datasets/transforms/__init__.py
+++ b/mmdet/datasets/transforms/__init__.py
@@ -18,8 +18,8 @@
MinIoURandomCrop, MixUp, Mosaic, Pad,
PhotoMetricDistortion, RandomAffine,
RandomCenterCropPad, RandomCrop, RandomErasing,
- RandomFlip, RandomShift, Resize, SegRescale,
- YOLOXHSVRandomAug)
+ RandomFlip, RandomShift, Resize, ResizeShortestEdge,
+ SegRescale, YOLOXHSVRandomAug)
from .wrappers import MultiBranch, ProposalBroadcaster, RandomOrder
__all__ = [
@@ -37,5 +37,5 @@
'LoadEmptyAnnotations', 'RandomOrder', 'CachedMosaic', 'CachedMixUp',
'FixShapeResize', 'ProposalBroadcaster', 'InferencerLoader',
'LoadTrackAnnotations', 'BaseFrameSample', 'UniformRefFrameSample',
- 'PackTrackInputs', 'PackReIDInputs', 'FixScaleResize'
+ 'PackTrackInputs', 'PackReIDInputs', 'FixScaleResize', 'ResizeShortestEdge'
]
diff --git a/mmdet/datasets/transforms/transforms.py b/mmdet/datasets/transforms/transforms.py
index f128bd82674..9768d71eaed 100644
--- a/mmdet/datasets/transforms/transforms.py
+++ b/mmdet/datasets/transforms/transforms.py
@@ -60,8 +60,7 @@ def _fixed_scale_size(
def rescale_size(old_size: tuple,
scale: Union[float, int, tuple],
- return_scale: bool = False,
- short_side_mode: bool = False) -> tuple:
+ return_scale: bool = False) -> tuple:
"""Calculate the new size to be rescaled to.
Args:
@@ -84,12 +83,8 @@ def rescale_size(old_size: tuple,
elif isinstance(scale, tuple):
max_long_edge = max(scale)
max_short_edge = min(scale)
- if short_side_mode:
- short, long = (w, h) if w <= h else (h, w)
- scale_factor = max_short_edge / short
- else:
- scale_factor = min(max_long_edge / max(h, w),
- max_short_edge / min(h, w))
+ scale_factor = min(max_long_edge / max(h, w),
+ max_short_edge / min(h, w))
else:
raise TypeError(
f'Scale must be a number or tuple of int, but got {type(scale)}')
@@ -107,8 +102,7 @@ def imrescale(
scale: Union[float, Tuple[int, int]],
return_scale: bool = False,
interpolation: str = 'bilinear',
- backend: Optional[str] = None,
- short_side_mode: bool = False,
+ backend: Optional[str] = None
) -> Union[np.ndarray, Tuple[np.ndarray, float]]:
"""Resize image while keeping the aspect ratio.
@@ -127,10 +121,7 @@ def imrescale(
ndarray: The rescaled image.
"""
h, w = img.shape[:2]
- new_size, scale_factor = rescale_size((w, h),
- scale,
- return_scale=True,
- short_side_mode=short_side_mode)
+ new_size, scale_factor = rescale_size((w, h), scale, return_scale=True)
rescaled_img = imresize(
img, new_size, interpolation=interpolation, backend=backend)
if return_scale:
@@ -259,17 +250,6 @@ class FixScaleResize(Resize):
"""Compared to Resize, FixScaleResize fixes the scaling issue when
`keep_ratio=true`."""
- def __init__(
- self,
- *args,
- short_side_mode: bool = False,
- **kwargs,
- ) -> None:
- super().__init__(*args, **kwargs)
- self.short_side_mode = short_side_mode
- if short_side_mode is True:
- assert self.scale and self.keep_ratio is True
-
def _resize_img(self, results):
"""Resize images with ``results['scale']``."""
if results.get('img', None) is not None:
@@ -279,8 +259,7 @@ def _resize_img(self, results):
results['scale'],
interpolation=self.interpolation,
return_scale=True,
- backend=self.backend,
- short_side_mode=self.short_side_mode)
+ backend=self.backend)
new_h, new_w = img.shape[:2]
h, w = results['img'].shape[:2]
w_scale = new_w / w
@@ -298,6 +277,81 @@ def _resize_img(self, results):
results['keep_ratio'] = self.keep_ratio
+@TRANSFORMS.register_module()
+class ResizeShortestEdge(BaseTransform):
+ """Resize the image and mask while keeping the aspect ratio unchanged.
+
+ Modified from https://github.com/facebookresearch/detectron2/blob/main/detectron2/data/transforms/augmentation_impl.py#L130 # noqa:E501
+
+ This transform attempts to scale the shorter edge to the given
+ `scale`, as long as the longer edge does not exceed `max_size`.
+ If `max_size` is reached, then downscale so that the longer
+ edge does not exceed `max_size`.
+
+ Required Keys:
+ - img
+ - gt_seg_map (optional)
+ Modified Keys:
+ - img
+ - img_shape
+ - gt_seg_map (optional))
+ Added Keys:
+ - scale
+ - scale_factor
+ - keep_ratio
+
+ Args:
+ scale (Union[int, Tuple[int, int]]): The target short edge length.
+ If it's tuple, will select the min value as the short edge length.
+ max_size (int): The maximum allowed longest edge length.
+ """
+
+ def __init__(self,
+ scale: Union[int, Tuple[int, int]],
+ max_size: Optional[int] = None,
+ resize_type: str = 'Resize',
+ **resize_kwargs) -> None:
+ super().__init__()
+ self.scale = scale
+ self.max_size = max_size
+
+ self.resize_cfg = dict(type=resize_type, **resize_kwargs)
+ self.resize = TRANSFORMS.build({'scale': 0, **self.resize_cfg})
+
+ def _get_output_shape(self, img, short_edge_length) -> Tuple[int, int]:
+ """Compute the target image shape with the given `short_edge_length`.
+
+ Args:
+ img (np.ndarray): The input image.
+ short_edge_length (Union[int, Tuple[int, int]]): The target short
+ edge length. If it's tuple, will select the min value as the
+ short edge length.
+ """
+ h, w = img.shape[:2]
+ if isinstance(short_edge_length, int):
+ size = short_edge_length * 1.0
+ elif isinstance(short_edge_length, tuple):
+ size = min(short_edge_length) * 1.0
+ scale = size / min(h, w)
+ if h < w:
+ new_h, new_w = size, scale * w
+ else:
+ new_h, new_w = scale * h, size
+
+ if self.max_size and max(new_h, new_w) > self.max_size:
+ scale = self.max_size * 1.0 / max(new_h, new_w)
+ new_h *= scale
+ new_w *= scale
+
+ new_h = int(new_h + 0.5)
+ new_w = int(new_w + 0.5)
+ return (new_w, new_h)
+
+ def transform(self, results: dict) -> dict:
+ self.resize.scale = self._get_output_shape(results['img'], self.scale)
+ return self.resize(results)
+
+
@TRANSFORMS.register_module()
class FixShapeResize(Resize):
"""Resize images & bbox & seg to the specified size.
diff --git a/projects/XDecoder/README.md b/projects/XDecoder/README.md
index 473424ac28b..9b235b55798 100644
--- a/projects/XDecoder/README.md
+++ b/projects/XDecoder/README.md
@@ -8,7 +8,9 @@
We present X-Decoder, a generalized decoding model that can predict pixel-level segmentation and language tokens seamlessly. X-Decodert takes as input two types of queries: (i) generic non-semantic queries and (ii) semantic queries induced from text inputs, to decode different pixel-level and token-level outputs in the same semantic space. With such a novel design, X-Decoder is the first work that provides a unified way to support all types of image segmentation and a variety of vision-language (VL) tasks. Further, our design enables seamless interactions across tasks at different granularities and brings mutual benefits by learning a common and rich pixel-level visual-semantic understanding space, without any pseudo-labeling. After pretraining on a mixed set of a limited amount of segmentation data and millions of image-text pairs, X-Decoder exhibits strong transferability to a wide range of downstream tasks in both zero-shot and finetuning settings. Notably, it achieves (1) state-of-the-art results on open-vocabulary segmentation and referring segmentation on eight datasets; (2) better or competitive finetuned performance to other generalist and specialist models on segmentation and VL tasks; and (3) flexibility for efficient finetuning and novel task composition (e.g., referring captioning and image editing).
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