mmyolo.datasets.transforms.mix_img_transforms 源代码
# Copyright (c) OpenMMLab. All rights reserved.
import collections
import copy
from abc import ABCMeta, abstractmethod
from typing import Optional, Sequence, Tuple, Union
import mmcv
import numpy as np
from mmcv.transforms import BaseTransform
from mmdet.structures.bbox import autocast_box_type
from mmengine.dataset import BaseDataset
from mmengine.dataset.base_dataset import Compose
from numpy import random
from mmyolo.registry import TRANSFORMS
class BaseMixImageTransform(BaseTransform, metaclass=ABCMeta):
"""A Base Transform of multiple images mixed.
Suitable for training on multiple images mixed data augmentation like
mosaic and mixup.
Cached mosaic transform will random select images from the cache
and combine them into one output image if use_cached is True.
Args:
pre_transform(Sequence[str]): Sequence of transform object or
config dict to be composed. Defaults to None.
prob(float): The transformation probability. Defaults to 1.0.
use_cached (bool): Whether to use cache. Defaults to False.
max_cached_images (int): The maximum length of the cache. The larger
the cache, the stronger the randomness of this transform. As a
rule of thumb, providing 10 caches for each image suffices for
randomness. Defaults to 40.
random_pop (bool): Whether to randomly pop a result from the cache
when the cache is full. If set to False, use FIFO popping method.
Defaults to True.
max_refetch (int): The maximum number of retry iterations for getting
valid results from the pipeline. If the number of iterations is
greater than `max_refetch`, but results is still None, then the
iteration is terminated and raise the error. Defaults to 15.
"""
def __init__(self,
pre_transform: Optional[Sequence[str]] = None,
prob: float = 1.0,
use_cached: bool = False,
max_cached_images: int = 40,
random_pop: bool = True,
max_refetch: int = 15):
self.max_refetch = max_refetch
self.prob = prob
self.use_cached = use_cached
self.max_cached_images = max_cached_images
self.random_pop = random_pop
self.results_cache = []
if pre_transform is None:
self.pre_transform = None
else:
self.pre_transform = Compose(pre_transform)
@abstractmethod
def get_indexes(self, dataset: Union[BaseDataset,
list]) -> Union[list, int]:
"""Call function to collect indexes.
Args:
dataset (:obj:`Dataset` or list): The dataset or cached list.
Returns:
list or int: indexes.
"""
pass
@abstractmethod
def mix_img_transform(self, results: dict) -> dict:
"""Mixed image data transformation.
Args:
results (dict): Result dict.
Returns:
results (dict): Updated result dict.
"""
pass
@autocast_box_type()
def transform(self, results: dict) -> dict:
"""Data augmentation function.
The transform steps are as follows:
1. Randomly generate index list of other images.
2. Before Mosaic or MixUp need to go through the necessary
pre_transform, such as MixUp' pre_transform pipeline
include: 'LoadImageFromFile','LoadAnnotations',
'Mosaic' and 'RandomAffine'.
3. Use mix_img_transform function to implement specific
mix operations.
Args:
results (dict): Result dict.
Returns:
results (dict): Updated result dict.
"""
if random.uniform(0, 1) > self.prob:
return results
if self.use_cached:
# Be careful: deep copying can be very time-consuming
# if results includes dataset.
dataset = results.pop('dataset', None)
self.results_cache.append(copy.deepcopy(results))
if len(self.results_cache) > self.max_cached_images:
if self.random_pop:
index = random.randint(0, len(self.results_cache) - 1)
else:
index = 0
self.results_cache.pop(index)
if len(self.results_cache) <= 4:
return results
else:
assert 'dataset' in results
# Be careful: deep copying can be very time-consuming
# if results includes dataset.
dataset = results.pop('dataset', None)
for _ in range(self.max_refetch):
# get index of one or three other images
if self.use_cached:
indexes = self.get_indexes(self.results_cache)
else:
indexes = self.get_indexes(dataset)
if not isinstance(indexes, collections.abc.Sequence):
indexes = [indexes]
if self.use_cached:
mix_results = [
copy.deepcopy(self.results_cache[i]) for i in indexes
]
else:
# get images information will be used for Mosaic or MixUp
mix_results = [
copy.deepcopy(dataset.get_data_info(index))
for index in indexes
]
if self.pre_transform is not None:
for i, data in enumerate(mix_results):
# pre_transform may also require dataset
data.update({'dataset': dataset})
# before Mosaic or MixUp need to go through
# the necessary pre_transform
_results = self.pre_transform(data)
_results.pop('dataset')
mix_results[i] = _results
if None not in mix_results:
results['mix_results'] = mix_results
break
print('Repeated calculation')
else:
raise RuntimeError(
'The loading pipeline of the original dataset'
' always return None. Please check the correctness '
'of the dataset and its pipeline.')
# Mosaic or MixUp
results = self.mix_img_transform(results)
if 'mix_results' in results:
results.pop('mix_results')
results['dataset'] = dataset
return results
[文档]@TRANSFORMS.register_module()
class Mosaic(BaseMixImageTransform):
"""Mosaic augmentation.
Given 4 images, mosaic transform combines them into
one output image. The output image is composed of the parts from each sub-
image.
.. code:: text
mosaic transform
center_x
+------------------------------+
| pad | |
| +-----------+ pad |
| | | |
| | image1 +-----------+
| | | |
| | | image2 |
center_y |----+-+-----------+-----------+
| | cropped | |
|pad | image3 | image4 |
| | | |
+----|-------------+-----------+
| |
+-------------+
The mosaic transform steps are as follows:
1. Choose the mosaic center as the intersections of 4 images
2. Get the left top image according to the index, and randomly
sample another 3 images from the custom dataset.
3. Sub image will be cropped if image is larger than mosaic patch
Required Keys:
- img
- gt_bboxes (BaseBoxes[torch.float32]) (optional)
- gt_bboxes_labels (np.int64) (optional)
- gt_ignore_flags (bool) (optional)
- mix_results (List[dict])
Modified Keys:
- img
- img_shape
- gt_bboxes (optional)
- gt_bboxes_labels (optional)
- gt_ignore_flags (optional)
Args:
img_scale (Sequence[int]): Image size after mosaic pipeline of single
image. The shape order should be (width, height).
Defaults to (640, 640).
center_ratio_range (Sequence[float]): Center ratio range of mosaic
output. Defaults to (0.5, 1.5).
bbox_clip_border (bool, optional): Whether to clip the objects outside
the border of the image. In some dataset like MOT17, the gt bboxes
are allowed to cross the border of images. Therefore, we don't
need to clip the gt bboxes in these cases. Defaults to True.
pad_val (int): Pad value. Defaults to 114.
pre_transform(Sequence[dict]): Sequence of transform object or
config dict to be composed.
prob (float): Probability of applying this transformation.
Defaults to 1.0.
use_cached (bool): Whether to use cache. Defaults to False.
max_cached_images (int): The maximum length of the cache. The larger
the cache, the stronger the randomness of this transform. As a
rule of thumb, providing 10 caches for each image suffices for
randomness. Defaults to 40.
random_pop (bool): Whether to randomly pop a result from the cache
when the cache is full. If set to False, use FIFO popping method.
Defaults to True.
max_refetch (int): The maximum number of retry iterations for getting
valid results from the pipeline. If the number of iterations is
greater than `max_refetch`, but results is still None, then the
iteration is terminated and raise the error. Defaults to 15.
"""
def __init__(self,
img_scale: Tuple[int, int] = (640, 640),
center_ratio_range: Tuple[float, float] = (0.5, 1.5),
bbox_clip_border: bool = True,
pad_val: float = 114.0,
pre_transform: Sequence[dict] = None,
prob: float = 1.0,
use_cached: bool = False,
max_cached_images: int = 40,
random_pop: bool = True,
max_refetch: int = 15):
assert isinstance(img_scale, tuple)
assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. ' \
f'got {prob}.'
if use_cached:
assert max_cached_images >= 4, 'The length of cache must >= 4, ' \
f'but got {max_cached_images}.'
super().__init__(
pre_transform=pre_transform,
prob=prob,
use_cached=use_cached,
max_cached_images=max_cached_images,
random_pop=random_pop,
max_refetch=max_refetch)
self.img_scale = img_scale
self.center_ratio_range = center_ratio_range
self.bbox_clip_border = bbox_clip_border
self.pad_val = pad_val
[文档] def get_indexes(self, dataset: Union[BaseDataset, list]) -> list:
"""Call function to collect indexes.
Args:
dataset (:obj:`Dataset` or list): The dataset or cached list.
Returns:
list: indexes.
"""
indexes = [random.randint(0, len(dataset)) for _ in range(3)]
return indexes
[文档] def mix_img_transform(self, results: dict) -> dict:
"""Mixed image data transformation.
Args:
results (dict): Result dict.
Returns:
results (dict): Updated result dict.
"""
assert 'mix_results' in results
mosaic_bboxes = []
mosaic_bboxes_labels = []
mosaic_ignore_flags = []
mosaic_masks = []
mosaic_kps = []
with_mask = True if 'gt_masks' in results else False
with_kps = True if 'gt_keypoints' in results else False
# self.img_scale is wh format
img_scale_w, img_scale_h = self.img_scale
if len(results['img'].shape) == 3:
mosaic_img = np.full(
(int(img_scale_h * 2), int(img_scale_w * 2), 3),
self.pad_val,
dtype=results['img'].dtype)
else:
mosaic_img = np.full((int(img_scale_h * 2), int(img_scale_w * 2)),
self.pad_val,
dtype=results['img'].dtype)
# mosaic center x, y
center_x = int(random.uniform(*self.center_ratio_range) * img_scale_w)
center_y = int(random.uniform(*self.center_ratio_range) * img_scale_h)
center_position = (center_x, center_y)
loc_strs = ('top_left', 'top_right', 'bottom_left', 'bottom_right')
for i, loc in enumerate(loc_strs):
if loc == 'top_left':
results_patch = results
else:
results_patch = results['mix_results'][i - 1]
img_i = results_patch['img']
h_i, w_i = img_i.shape[:2]
# keep_ratio resize
scale_ratio_i = min(img_scale_h / h_i, img_scale_w / w_i)
img_i = mmcv.imresize(
img_i, (int(w_i * scale_ratio_i), int(h_i * scale_ratio_i)))
# compute the combine parameters
paste_coord, crop_coord = self._mosaic_combine(
loc, center_position, img_i.shape[:2][::-1])
x1_p, y1_p, x2_p, y2_p = paste_coord
x1_c, y1_c, x2_c, y2_c = crop_coord
# crop and paste image
mosaic_img[y1_p:y2_p, x1_p:x2_p] = img_i[y1_c:y2_c, x1_c:x2_c]
# adjust coordinate
gt_bboxes_i = results_patch['gt_bboxes']
gt_bboxes_labels_i = results_patch['gt_bboxes_labels']
gt_ignore_flags_i = results_patch['gt_ignore_flags']
padw = x1_p - x1_c
padh = y1_p - y1_c
gt_bboxes_i.rescale_([scale_ratio_i, scale_ratio_i])
gt_bboxes_i.translate_([padw, padh])
mosaic_bboxes.append(gt_bboxes_i)
mosaic_bboxes_labels.append(gt_bboxes_labels_i)
mosaic_ignore_flags.append(gt_ignore_flags_i)
if with_mask and results_patch.get('gt_masks', None) is not None:
gt_masks_i = results_patch['gt_masks']
gt_masks_i = gt_masks_i.resize(img_i.shape[:2])
gt_masks_i = gt_masks_i.translate(
out_shape=(int(self.img_scale[0] * 2),
int(self.img_scale[1] * 2)),
offset=padw,
direction='horizontal')
gt_masks_i = gt_masks_i.translate(
out_shape=(int(self.img_scale[0] * 2),
int(self.img_scale[1] * 2)),
offset=padh,
direction='vertical')
mosaic_masks.append(gt_masks_i)
if with_kps and results_patch.get('gt_keypoints',
None) is not None:
gt_kps_i = results_patch['gt_keypoints']
gt_kps_i.rescale_([scale_ratio_i, scale_ratio_i])
gt_kps_i.translate_([padw, padh])
mosaic_kps.append(gt_kps_i)
mosaic_bboxes = mosaic_bboxes[0].cat(mosaic_bboxes, 0)
mosaic_bboxes_labels = np.concatenate(mosaic_bboxes_labels, 0)
mosaic_ignore_flags = np.concatenate(mosaic_ignore_flags, 0)
if self.bbox_clip_border:
mosaic_bboxes.clip_([2 * img_scale_h, 2 * img_scale_w])
if with_mask:
mosaic_masks = mosaic_masks[0].cat(mosaic_masks)
results['gt_masks'] = mosaic_masks
if with_kps:
mosaic_kps = mosaic_kps[0].cat(mosaic_kps, 0)
mosaic_kps.clip_([2 * img_scale_h, 2 * img_scale_w])
results['gt_keypoints'] = mosaic_kps
else:
# remove outside bboxes
inside_inds = mosaic_bboxes.is_inside(
[2 * img_scale_h, 2 * img_scale_w]).numpy()
mosaic_bboxes = mosaic_bboxes[inside_inds]
mosaic_bboxes_labels = mosaic_bboxes_labels[inside_inds]
mosaic_ignore_flags = mosaic_ignore_flags[inside_inds]
if with_mask:
mosaic_masks = mosaic_masks[0].cat(mosaic_masks)[inside_inds]
results['gt_masks'] = mosaic_masks
if with_kps:
mosaic_kps = mosaic_kps[0].cat(mosaic_kps, 0)
mosaic_kps = mosaic_kps[inside_inds]
results['gt_keypoints'] = mosaic_kps
results['img'] = mosaic_img
results['img_shape'] = mosaic_img.shape
results['gt_bboxes'] = mosaic_bboxes
results['gt_bboxes_labels'] = mosaic_bboxes_labels
results['gt_ignore_flags'] = mosaic_ignore_flags
return results
def _mosaic_combine(
self, loc: str, center_position_xy: Sequence[float],
img_shape_wh: Sequence[int]) -> Tuple[Tuple[int], Tuple[int]]:
"""Calculate global coordinate of mosaic image and local coordinate of
cropped sub-image.
Args:
loc (str): Index for the sub-image, loc in ('top_left',
'top_right', 'bottom_left', 'bottom_right').
center_position_xy (Sequence[float]): Mixing center for 4 images,
(x, y).
img_shape_wh (Sequence[int]): Width and height of sub-image
Returns:
tuple[tuple[float]]: Corresponding coordinate of pasting and
cropping
- paste_coord (tuple): paste corner coordinate in mosaic image.
- crop_coord (tuple): crop corner coordinate in mosaic image.
"""
assert loc in ('top_left', 'top_right', 'bottom_left', 'bottom_right')
if loc == 'top_left':
# index0 to top left part of image
x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
max(center_position_xy[1] - img_shape_wh[1], 0), \
center_position_xy[0], \
center_position_xy[1]
crop_coord = img_shape_wh[0] - (x2 - x1), img_shape_wh[1] - (
y2 - y1), img_shape_wh[0], img_shape_wh[1]
elif loc == 'top_right':
# index1 to top right part of image
x1, y1, x2, y2 = center_position_xy[0], \
max(center_position_xy[1] - img_shape_wh[1], 0), \
min(center_position_xy[0] + img_shape_wh[0],
self.img_scale[0] * 2), \
center_position_xy[1]
crop_coord = 0, img_shape_wh[1] - (y2 - y1), min(
img_shape_wh[0], x2 - x1), img_shape_wh[1]
elif loc == 'bottom_left':
# index2 to bottom left part of image
x1, y1, x2, y2 = max(center_position_xy[0] - img_shape_wh[0], 0), \
center_position_xy[1], \
center_position_xy[0], \
min(self.img_scale[1] * 2, center_position_xy[1] +
img_shape_wh[1])
crop_coord = img_shape_wh[0] - (x2 - x1), 0, img_shape_wh[0], min(
y2 - y1, img_shape_wh[1])
else:
# index3 to bottom right part of image
x1, y1, x2, y2 = center_position_xy[0], \
center_position_xy[1], \
min(center_position_xy[0] + img_shape_wh[0],
self.img_scale[0] * 2), \
min(self.img_scale[1] * 2, center_position_xy[1] +
img_shape_wh[1])
crop_coord = 0, 0, min(img_shape_wh[0],
x2 - x1), min(y2 - y1, img_shape_wh[1])
paste_coord = x1, y1, x2, y2
return paste_coord, crop_coord
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(img_scale={self.img_scale}, '
repr_str += f'center_ratio_range={self.center_ratio_range}, '
repr_str += f'pad_val={self.pad_val}, '
repr_str += f'prob={self.prob})'
return repr_str
[文档]@TRANSFORMS.register_module()
class Mosaic9(BaseMixImageTransform):
"""Mosaic9 augmentation.
Given 9 images, mosaic transform combines them into
one output image. The output image is composed of the parts from each sub-
image.
.. code:: text
+-------------------------------+------------+
| pad | pad | |
| +----------+ | |
| | +---------------+ top_right |
| | | top | image2 |
| | top_left | image1 | |
| | image8 o--------+------+--------+---+
| | | | | |
+----+----------+ | right |pad|
| | center | image3 | |
| left | image0 +---------------+---|
| image7 | | | |
+---+-----------+---+--------+ | |
| | cropped | | bottom_right |pad|
| |bottom_left| | image4 | |
| | image6 | bottom | | |
+---|-----------+ image5 +---------------+---|
| pad | | pad |
+-----------+------------+-------------------+
The mosaic transform steps are as follows:
1. Get the center image according to the index, and randomly
sample another 8 images from the custom dataset.
2. Randomly offset the image after Mosaic
Required Keys:
- img
- gt_bboxes (BaseBoxes[torch.float32]) (optional)
- gt_bboxes_labels (np.int64) (optional)
- gt_ignore_flags (bool) (optional)
- mix_results (List[dict])
Modified Keys:
- img
- img_shape
- gt_bboxes (optional)
- gt_bboxes_labels (optional)
- gt_ignore_flags (optional)
Args:
img_scale (Sequence[int]): Image size after mosaic pipeline of single
image. The shape order should be (width, height).
Defaults to (640, 640).
bbox_clip_border (bool, optional): Whether to clip the objects outside
the border of the image. In some dataset like MOT17, the gt bboxes
are allowed to cross the border of images. Therefore, we don't
need to clip the gt bboxes in these cases. Defaults to True.
pad_val (int): Pad value. Defaults to 114.
pre_transform(Sequence[dict]): Sequence of transform object or
config dict to be composed.
prob (float): Probability of applying this transformation.
Defaults to 1.0.
use_cached (bool): Whether to use cache. Defaults to False.
max_cached_images (int): The maximum length of the cache. The larger
the cache, the stronger the randomness of this transform. As a
rule of thumb, providing 5 caches for each image suffices for
randomness. Defaults to 50.
random_pop (bool): Whether to randomly pop a result from the cache
when the cache is full. If set to False, use FIFO popping method.
Defaults to True.
max_refetch (int): The maximum number of retry iterations for getting
valid results from the pipeline. If the number of iterations is
greater than `max_refetch`, but results is still None, then the
iteration is terminated and raise the error. Defaults to 15.
"""
def __init__(self,
img_scale: Tuple[int, int] = (640, 640),
bbox_clip_border: bool = True,
pad_val: Union[float, int] = 114.0,
pre_transform: Sequence[dict] = None,
prob: float = 1.0,
use_cached: bool = False,
max_cached_images: int = 50,
random_pop: bool = True,
max_refetch: int = 15):
assert isinstance(img_scale, tuple)
assert 0 <= prob <= 1.0, 'The probability should be in range [0,1]. ' \
f'got {prob}.'
if use_cached:
assert max_cached_images >= 9, 'The length of cache must >= 9, ' \
f'but got {max_cached_images}.'
super().__init__(
pre_transform=pre_transform,
prob=prob,
use_cached=use_cached,
max_cached_images=max_cached_images,
random_pop=random_pop,
max_refetch=max_refetch)
self.img_scale = img_scale
self.bbox_clip_border = bbox_clip_border
self.pad_val = pad_val
# intermediate variables
self._current_img_shape = [0, 0]
self._center_img_shape = [0, 0]
self._previous_img_shape = [0, 0]
[文档] def get_indexes(self, dataset: Union[BaseDataset, list]) -> list:
"""Call function to collect indexes.
Args:
dataset (:obj:`Dataset` or list): The dataset or cached list.
Returns:
list: indexes.
"""
indexes = [random.randint(0, len(dataset)) for _ in range(8)]
return indexes
[文档] def mix_img_transform(self, results: dict) -> dict:
"""Mixed image data transformation.
Args:
results (dict): Result dict.
Returns:
results (dict): Updated result dict.
"""
assert 'mix_results' in results
mosaic_bboxes = []
mosaic_bboxes_labels = []
mosaic_ignore_flags = []
img_scale_w, img_scale_h = self.img_scale
if len(results['img'].shape) == 3:
mosaic_img = np.full(
(int(img_scale_h * 3), int(img_scale_w * 3), 3),
self.pad_val,
dtype=results['img'].dtype)
else:
mosaic_img = np.full((int(img_scale_h * 3), int(img_scale_w * 3)),
self.pad_val,
dtype=results['img'].dtype)
# index = 0 is mean original image
# len(results['mix_results']) = 8
loc_strs = ('center', 'top', 'top_right', 'right', 'bottom_right',
'bottom', 'bottom_left', 'left', 'top_left')
results_all = [results, *results['mix_results']]
for index, results_patch in enumerate(results_all):
img_i = results_patch['img']
# keep_ratio resize
img_i_h, img_i_w = img_i.shape[:2]
scale_ratio_i = min(img_scale_h / img_i_h, img_scale_w / img_i_w)
img_i = mmcv.imresize(
img_i,
(int(img_i_w * scale_ratio_i), int(img_i_h * scale_ratio_i)))
paste_coord = self._mosaic_combine(loc_strs[index],
img_i.shape[:2])
padw, padh = paste_coord[:2]
x1, y1, x2, y2 = (max(x, 0) for x in paste_coord)
mosaic_img[y1:y2, x1:x2] = img_i[y1 - padh:, x1 - padw:]
gt_bboxes_i = results_patch['gt_bboxes']
gt_bboxes_labels_i = results_patch['gt_bboxes_labels']
gt_ignore_flags_i = results_patch['gt_ignore_flags']
gt_bboxes_i.rescale_([scale_ratio_i, scale_ratio_i])
gt_bboxes_i.translate_([padw, padh])
mosaic_bboxes.append(gt_bboxes_i)
mosaic_bboxes_labels.append(gt_bboxes_labels_i)
mosaic_ignore_flags.append(gt_ignore_flags_i)
# Offset
offset_x = int(random.uniform(0, img_scale_w))
offset_y = int(random.uniform(0, img_scale_h))
mosaic_img = mosaic_img[offset_y:offset_y + 2 * img_scale_h,
offset_x:offset_x + 2 * img_scale_w]
mosaic_bboxes = mosaic_bboxes[0].cat(mosaic_bboxes, 0)
mosaic_bboxes.translate_([-offset_x, -offset_y])
mosaic_bboxes_labels = np.concatenate(mosaic_bboxes_labels, 0)
mosaic_ignore_flags = np.concatenate(mosaic_ignore_flags, 0)
if self.bbox_clip_border:
mosaic_bboxes.clip_([2 * img_scale_h, 2 * img_scale_w])
else:
# remove outside bboxes
inside_inds = mosaic_bboxes.is_inside(
[2 * img_scale_h, 2 * img_scale_w]).numpy()
mosaic_bboxes = mosaic_bboxes[inside_inds]
mosaic_bboxes_labels = mosaic_bboxes_labels[inside_inds]
mosaic_ignore_flags = mosaic_ignore_flags[inside_inds]
results['img'] = mosaic_img
results['img_shape'] = mosaic_img.shape
results['gt_bboxes'] = mosaic_bboxes
results['gt_bboxes_labels'] = mosaic_bboxes_labels
results['gt_ignore_flags'] = mosaic_ignore_flags
return results
def _mosaic_combine(self, loc: str,
img_shape_hw: Tuple[int, int]) -> Tuple[int, ...]:
"""Calculate global coordinate of mosaic image.
Args:
loc (str): Index for the sub-image.
img_shape_hw (Sequence[int]): Height and width of sub-image
Returns:
paste_coord (tuple): paste corner coordinate in mosaic image.
"""
assert loc in ('center', 'top', 'top_right', 'right', 'bottom_right',
'bottom', 'bottom_left', 'left', 'top_left')
img_scale_w, img_scale_h = self.img_scale
self._current_img_shape = img_shape_hw
current_img_h, current_img_w = self._current_img_shape
previous_img_h, previous_img_w = self._previous_img_shape
center_img_h, center_img_w = self._center_img_shape
if loc == 'center':
self._center_img_shape = self._current_img_shape
# xmin, ymin, xmax, ymax
paste_coord = img_scale_w, \
img_scale_h, \
img_scale_w + current_img_w, \
img_scale_h + current_img_h
elif loc == 'top':
paste_coord = img_scale_w, \
img_scale_h - current_img_h, \
img_scale_w + current_img_w, \
img_scale_h
elif loc == 'top_right':
paste_coord = img_scale_w + previous_img_w, \
img_scale_h - current_img_h, \
img_scale_w + previous_img_w + current_img_w, \
img_scale_h
elif loc == 'right':
paste_coord = img_scale_w + center_img_w, \
img_scale_h, \
img_scale_w + center_img_w + current_img_w, \
img_scale_h + current_img_h
elif loc == 'bottom_right':
paste_coord = img_scale_w + center_img_w, \
img_scale_h + previous_img_h, \
img_scale_w + center_img_w + current_img_w, \
img_scale_h + previous_img_h + current_img_h
elif loc == 'bottom':
paste_coord = img_scale_w + center_img_w - current_img_w, \
img_scale_h + center_img_h, \
img_scale_w + center_img_w, \
img_scale_h + center_img_h + current_img_h
elif loc == 'bottom_left':
paste_coord = img_scale_w + center_img_w - \
previous_img_w - current_img_w, \
img_scale_h + center_img_h, \
img_scale_w + center_img_w - previous_img_w, \
img_scale_h + center_img_h + current_img_h
elif loc == 'left':
paste_coord = img_scale_w - current_img_w, \
img_scale_h + center_img_h - current_img_h, \
img_scale_w, \
img_scale_h + center_img_h
elif loc == 'top_left':
paste_coord = img_scale_w - current_img_w, \
img_scale_h + center_img_h - \
previous_img_h - current_img_h, \
img_scale_w, \
img_scale_h + center_img_h - previous_img_h
self._previous_img_shape = self._current_img_shape
# xmin, ymin, xmax, ymax
return paste_coord
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(img_scale={self.img_scale}, '
repr_str += f'pad_val={self.pad_val}, '
repr_str += f'prob={self.prob})'
return repr_str
[文档]@TRANSFORMS.register_module()
class YOLOv5MixUp(BaseMixImageTransform):
"""MixUp data augmentation for YOLOv5.
.. code:: text
The mixup transform steps are as follows:
1. Another random image is picked by dataset.
2. Randomly obtain the fusion ratio from the beta distribution,
then fuse the target
of the original image and mixup image through this ratio.
Required Keys:
- img
- gt_bboxes (BaseBoxes[torch.float32]) (optional)
- gt_bboxes_labels (np.int64) (optional)
- gt_ignore_flags (bool) (optional)
- mix_results (List[dict])
Modified Keys:
- img
- img_shape
- gt_bboxes (optional)
- gt_bboxes_labels (optional)
- gt_ignore_flags (optional)
Args:
alpha (float): parameter of beta distribution to get mixup ratio.
Defaults to 32.
beta (float): parameter of beta distribution to get mixup ratio.
Defaults to 32.
pre_transform (Sequence[dict]): Sequence of transform object or
config dict to be composed.
prob (float): Probability of applying this transformation.
Defaults to 1.0.
use_cached (bool): Whether to use cache. Defaults to False.
max_cached_images (int): The maximum length of the cache. The larger
the cache, the stronger the randomness of this transform. As a
rule of thumb, providing 10 caches for each image suffices for
randomness. Defaults to 20.
random_pop (bool): Whether to randomly pop a result from the cache
when the cache is full. If set to False, use FIFO popping method.
Defaults to True.
max_refetch (int): The maximum number of iterations. If the number of
iterations is greater than `max_refetch`, but gt_bbox is still
empty, then the iteration is terminated. Defaults to 15.
"""
def __init__(self,
alpha: float = 32.0,
beta: float = 32.0,
pre_transform: Sequence[dict] = None,
prob: float = 1.0,
use_cached: bool = False,
max_cached_images: int = 20,
random_pop: bool = True,
max_refetch: int = 15):
if use_cached:
assert max_cached_images >= 2, 'The length of cache must >= 2, ' \
f'but got {max_cached_images}.'
super().__init__(
pre_transform=pre_transform,
prob=prob,
use_cached=use_cached,
max_cached_images=max_cached_images,
random_pop=random_pop,
max_refetch=max_refetch)
self.alpha = alpha
self.beta = beta
[文档] def get_indexes(self, dataset: Union[BaseDataset, list]) -> int:
"""Call function to collect indexes.
Args:
dataset (:obj:`Dataset` or list): The dataset or cached list.
Returns:
int: indexes.
"""
return random.randint(0, len(dataset))
[文档] def mix_img_transform(self, results: dict) -> dict:
"""YOLOv5 MixUp transform function.
Args:
results (dict): Result dict
Returns:
results (dict): Updated result dict.
"""
assert 'mix_results' in results
retrieve_results = results['mix_results'][0]
retrieve_img = retrieve_results['img']
ori_img = results['img']
assert ori_img.shape == retrieve_img.shape
# Randomly obtain the fusion ratio from the beta distribution,
# which is around 0.5
ratio = np.random.beta(self.alpha, self.beta)
mixup_img = (ori_img * ratio + retrieve_img * (1 - ratio))
retrieve_gt_bboxes = retrieve_results['gt_bboxes']
retrieve_gt_bboxes_labels = retrieve_results['gt_bboxes_labels']
retrieve_gt_ignore_flags = retrieve_results['gt_ignore_flags']
mixup_gt_bboxes = retrieve_gt_bboxes.cat(
(results['gt_bboxes'], retrieve_gt_bboxes), dim=0)
mixup_gt_bboxes_labels = np.concatenate(
(results['gt_bboxes_labels'], retrieve_gt_bboxes_labels), axis=0)
mixup_gt_ignore_flags = np.concatenate(
(results['gt_ignore_flags'], retrieve_gt_ignore_flags), axis=0)
if 'gt_masks' in results:
assert 'gt_masks' in retrieve_results
mixup_gt_masks = results['gt_masks'].cat(
[results['gt_masks'], retrieve_results['gt_masks']])
results['gt_masks'] = mixup_gt_masks
results['img'] = mixup_img.astype(np.uint8)
results['img_shape'] = mixup_img.shape
results['gt_bboxes'] = mixup_gt_bboxes
results['gt_bboxes_labels'] = mixup_gt_bboxes_labels
results['gt_ignore_flags'] = mixup_gt_ignore_flags
return results
[文档]@TRANSFORMS.register_module()
class YOLOXMixUp(BaseMixImageTransform):
"""MixUp data augmentation for YOLOX.
.. code:: text
mixup transform
+---------------+--------------+
| mixup image | |
| +--------|--------+ |
| | | | |
+---------------+ | |
| | | |
| | image | |
| | | |
| | | |
| +-----------------+ |
| pad |
+------------------------------+
The mixup transform steps are as follows:
1. Another random image is picked by dataset and embedded in
the top left patch(after padding and resizing)
2. The target of mixup transform is the weighted average of mixup
image and origin image.
Required Keys:
- img
- gt_bboxes (BaseBoxes[torch.float32]) (optional)
- gt_bboxes_labels (np.int64) (optional)
- gt_ignore_flags (bool) (optional)
- mix_results (List[dict])
Modified Keys:
- img
- img_shape
- gt_bboxes (optional)
- gt_bboxes_labels (optional)
- gt_ignore_flags (optional)
Args:
img_scale (Sequence[int]): Image output size after mixup pipeline.
The shape order should be (width, height). Defaults to (640, 640).
ratio_range (Sequence[float]): Scale ratio of mixup image.
Defaults to (0.5, 1.5).
flip_ratio (float): Horizontal flip ratio of mixup image.
Defaults to 0.5.
pad_val (int): Pad value. Defaults to 114.
bbox_clip_border (bool, optional): Whether to clip the objects outside
the border of the image. In some dataset like MOT17, the gt bboxes
are allowed to cross the border of images. Therefore, we don't
need to clip the gt bboxes in these cases. Defaults to True.
pre_transform(Sequence[dict]): Sequence of transform object or
config dict to be composed.
prob (float): Probability of applying this transformation.
Defaults to 1.0.
use_cached (bool): Whether to use cache. Defaults to False.
max_cached_images (int): The maximum length of the cache. The larger
the cache, the stronger the randomness of this transform. As a
rule of thumb, providing 10 caches for each image suffices for
randomness. Defaults to 20.
random_pop (bool): Whether to randomly pop a result from the cache
when the cache is full. If set to False, use FIFO popping method.
Defaults to True.
max_refetch (int): The maximum number of iterations. If the number of
iterations is greater than `max_refetch`, but gt_bbox is still
empty, then the iteration is terminated. Defaults to 15.
"""
def __init__(self,
img_scale: Tuple[int, int] = (640, 640),
ratio_range: Tuple[float, float] = (0.5, 1.5),
flip_ratio: float = 0.5,
pad_val: float = 114.0,
bbox_clip_border: bool = True,
pre_transform: Sequence[dict] = None,
prob: float = 1.0,
use_cached: bool = False,
max_cached_images: int = 20,
random_pop: bool = True,
max_refetch: int = 15):
assert isinstance(img_scale, tuple)
if use_cached:
assert max_cached_images >= 2, 'The length of cache must >= 2, ' \
f'but got {max_cached_images}.'
super().__init__(
pre_transform=pre_transform,
prob=prob,
use_cached=use_cached,
max_cached_images=max_cached_images,
random_pop=random_pop,
max_refetch=max_refetch)
self.img_scale = img_scale
self.ratio_range = ratio_range
self.flip_ratio = flip_ratio
self.pad_val = pad_val
self.bbox_clip_border = bbox_clip_border
[文档] def get_indexes(self, dataset: Union[BaseDataset, list]) -> int:
"""Call function to collect indexes.
Args:
dataset (:obj:`Dataset` or list): The dataset or cached list.
Returns:
int: indexes.
"""
return random.randint(0, len(dataset))
[文档] def mix_img_transform(self, results: dict) -> dict:
"""YOLOX MixUp transform function.
Args:
results (dict): Result dict.
Returns:
results (dict): Updated result dict.
"""
assert 'mix_results' in results
assert len(
results['mix_results']) == 1, 'MixUp only support 2 images now !'
if results['mix_results'][0]['gt_bboxes'].shape[0] == 0:
# empty bbox
return results
retrieve_results = results['mix_results'][0]
retrieve_img = retrieve_results['img']
jit_factor = random.uniform(*self.ratio_range)
is_filp = random.uniform(0, 1) > self.flip_ratio
if len(retrieve_img.shape) == 3:
out_img = np.ones((self.img_scale[1], self.img_scale[0], 3),
dtype=retrieve_img.dtype) * self.pad_val
else:
out_img = np.ones(
self.img_scale[::-1], dtype=retrieve_img.dtype) * self.pad_val
# 1. keep_ratio resize
scale_ratio = min(self.img_scale[1] / retrieve_img.shape[0],
self.img_scale[0] / retrieve_img.shape[1])
retrieve_img = mmcv.imresize(
retrieve_img, (int(retrieve_img.shape[1] * scale_ratio),
int(retrieve_img.shape[0] * scale_ratio)))
# 2. paste
out_img[:retrieve_img.shape[0], :retrieve_img.shape[1]] = retrieve_img
# 3. scale jit
scale_ratio *= jit_factor
out_img = mmcv.imresize(out_img, (int(out_img.shape[1] * jit_factor),
int(out_img.shape[0] * jit_factor)))
# 4. flip
if is_filp:
out_img = out_img[:, ::-1, :]
# 5. random crop
ori_img = results['img']
origin_h, origin_w = out_img.shape[:2]
target_h, target_w = ori_img.shape[:2]
padded_img = np.ones((max(origin_h, target_h), max(
origin_w, target_w), 3)) * self.pad_val
padded_img = padded_img.astype(np.uint8)
padded_img[:origin_h, :origin_w] = out_img
x_offset, y_offset = 0, 0
if padded_img.shape[0] > target_h:
y_offset = random.randint(0, padded_img.shape[0] - target_h)
if padded_img.shape[1] > target_w:
x_offset = random.randint(0, padded_img.shape[1] - target_w)
padded_cropped_img = padded_img[y_offset:y_offset + target_h,
x_offset:x_offset + target_w]
# 6. adjust bbox
retrieve_gt_bboxes = retrieve_results['gt_bboxes']
retrieve_gt_bboxes.rescale_([scale_ratio, scale_ratio])
if self.bbox_clip_border:
retrieve_gt_bboxes.clip_([origin_h, origin_w])
if is_filp:
retrieve_gt_bboxes.flip_([origin_h, origin_w],
direction='horizontal')
# 7. filter
cp_retrieve_gt_bboxes = retrieve_gt_bboxes.clone()
cp_retrieve_gt_bboxes.translate_([-x_offset, -y_offset])
if self.bbox_clip_border:
cp_retrieve_gt_bboxes.clip_([target_h, target_w])
# 8. mix up
mixup_img = 0.5 * ori_img + 0.5 * padded_cropped_img
retrieve_gt_bboxes_labels = retrieve_results['gt_bboxes_labels']
retrieve_gt_ignore_flags = retrieve_results['gt_ignore_flags']
mixup_gt_bboxes = cp_retrieve_gt_bboxes.cat(
(results['gt_bboxes'], cp_retrieve_gt_bboxes), dim=0)
mixup_gt_bboxes_labels = np.concatenate(
(results['gt_bboxes_labels'], retrieve_gt_bboxes_labels), axis=0)
mixup_gt_ignore_flags = np.concatenate(
(results['gt_ignore_flags'], retrieve_gt_ignore_flags), axis=0)
if not self.bbox_clip_border:
# remove outside bbox
inside_inds = mixup_gt_bboxes.is_inside([target_h,
target_w]).numpy()
mixup_gt_bboxes = mixup_gt_bboxes[inside_inds]
mixup_gt_bboxes_labels = mixup_gt_bboxes_labels[inside_inds]
mixup_gt_ignore_flags = mixup_gt_ignore_flags[inside_inds]
if 'gt_keypoints' in results:
# adjust kps
retrieve_gt_keypoints = retrieve_results['gt_keypoints']
retrieve_gt_keypoints.rescale_([scale_ratio, scale_ratio])
if self.bbox_clip_border:
retrieve_gt_keypoints.clip_([origin_h, origin_w])
if is_filp:
retrieve_gt_keypoints.flip_([origin_h, origin_w],
direction='horizontal')
# filter
cp_retrieve_gt_keypoints = retrieve_gt_keypoints.clone()
cp_retrieve_gt_keypoints.translate_([-x_offset, -y_offset])
if self.bbox_clip_border:
cp_retrieve_gt_keypoints.clip_([target_h, target_w])
# mixup
mixup_gt_keypoints = cp_retrieve_gt_keypoints.cat(
(results['gt_keypoints'], cp_retrieve_gt_keypoints), dim=0)
if not self.bbox_clip_border:
# remove outside bbox
mixup_gt_keypoints = mixup_gt_keypoints[inside_inds]
results['gt_keypoints'] = mixup_gt_keypoints
results['img'] = mixup_img.astype(np.uint8)
results['img_shape'] = mixup_img.shape
results['gt_bboxes'] = mixup_gt_bboxes
results['gt_bboxes_labels'] = mixup_gt_bboxes_labels
results['gt_ignore_flags'] = mixup_gt_ignore_flags
return results
def __repr__(self) -> str:
repr_str = self.__class__.__name__
repr_str += f'(img_scale={self.img_scale}, '
repr_str += f'ratio_range={self.ratio_range}, '
repr_str += f'flip_ratio={self.flip_ratio}, '
repr_str += f'pad_val={self.pad_val}, '
repr_str += f'max_refetch={self.max_refetch}, '
repr_str += f'bbox_clip_border={self.bbox_clip_border})'
return repr_str