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import copy
import logging
import numpy as np
import torch
from detectron2.data import detection_utils as utils
from detectron2.data import transforms as T
from detectron2.data.transforms import TransformGen
from detectron2.structures import BoxMode
from PIL import Image
__all__ = ["SWINTSDatasetMapper"]
def build_transform_gen(cfg, is_train):
"""
Create a list of :class:`TransformGen` from config.
Returns:
list[TransformGen]
"""
if is_train:
min_size = cfg.INPUT.MIN_SIZE_TRAIN
max_size = cfg.INPUT.MAX_SIZE_TRAIN
sample_style = cfg.INPUT.MIN_SIZE_TRAIN_SAMPLING
else:
min_size = cfg.INPUT.MIN_SIZE_TEST
max_size = cfg.INPUT.MAX_SIZE_TEST
sample_style = "choice"
if sample_style == "range":
assert len(min_size) == 2, "more than 2 ({}) min_size(s) are provided for ranges".format(len(min_size))
logger = logging.getLogger(__name__)
tfm_gens = []
tfm_gens.append(T.RandomBrightness(0.5,2))
tfm_gens.append(T.RandomContrast(0.5,2))
tfm_gens.append(T.RandomSaturation(0.5,2))
tfm_gens.append(T.ResizeShortestEdge(min_size, max_size, sample_style))
if is_train:
logger.info("TransformGens used in training: " + str(tfm_gens))
return tfm_gens
@torch.no_grad()
class SWINTSDatasetMapper:
"""
A callable which takes a dataset dict in Detectron2 Dataset format,
and map it into a format used by SparseRCNN.
The callable currently does the following:
1. Read the image from "file_name"
2. Applies geometric transforms to the image and annotation
3. Find and applies suitable cropping to the image and annotation
4. Prepare image and annotation to Tensors
"""
def __init__(self, cfg, is_train=True):
if cfg.INPUT.CROP.ENABLED and is_train:
self.crop_gen = [
#T.ResizeShortestEdge([400, 500, 600], sample_style="choice"),
#T.RandomCrop(cfg.INPUT.CROP.TYPE, cfg.INPUT.CROP.SIZE),
T.RandomCropWithInstance(
cfg.INPUT.CROP.TYPE,
cfg.INPUT.CROP.SIZE,
cfg.INPUT.CROP.CROP_INSTANCE
)
]
self.rotate_gen = [
T.RandomRotation(angle=[-90,90],sample_style="range")
]
else:
self.crop_gen = None
self.tfm_gens = build_transform_gen(cfg, is_train)
logging.getLogger(__name__).info(
"Full TransformGens used in training: {}, crop: {}".format(str(self.tfm_gens), str(self.crop_gen))
)
self.img_format = cfg.INPUT.FORMAT
self.is_train = is_train
def __call__(self, dataset_dict):
"""
Args:
dataset_dict (dict): Metadata of one image, in Detectron2 Dataset format.
Returns:
dict: a format that builtin models in detectron2 accept
"""
dataset_dict = copy.deepcopy(dataset_dict) # it will be modified by code below
image = utils.read_image(dataset_dict["file_name"], format=self.img_format)
utils.check_image_size(dataset_dict, image)
boxes = np.asarray(
[
BoxMode.convert(
instance["bbox"], instance["bbox_mode"], BoxMode.XYXY_ABS
)
for instance in dataset_dict["annotations"]
]
)
augmentation = []
if self.crop_gen is None:
image, transforms = T.apply_transform_gens(self.tfm_gens, image)
else:
if np.random.rand() > 0.5:
augmentation = self.tfm_gens[:-1] + self.crop_gen + self.tfm_gens[-1:]
else:
augmentation = self.tfm_gens
if np.random.rand() > 0.5:
augmentation = augmentation[:-1] + self.rotate_gen + augmentation[-1:]
aug_input = T.StandardAugInput(image, boxes=boxes)
transforms = aug_input.apply_augmentations(augmentation)
image = aug_input.image
image_shape = image.shape[:2] # h, w
# print(image_shape)
# Pytorch's dataloader is efficient on torch.Tensor due to shared-memory,
# but not efficient on large generic data structures due to the use of pickle & mp.Queue.
# Therefore it's important to use torch.Tensor.
dataset_dict["image"] = torch.as_tensor(np.ascontiguousarray(image.transpose(2, 0, 1)))
if not self.is_train:
# USER: Modify this if you want to keep them for some reason.
dataset_dict.pop("annotations", None)
return dataset_dict
if "annotations" in dataset_dict:
# USER: Modify this if you want to keep them for some reason.
for anno in dataset_dict["annotations"]:
# anno.pop("segmentation", None)
anno.pop("keypoints", None)
# USER: Implement additional transformations if you have other types of data
annos = [
utils.transform_instance_annotations(obj, transforms, image_shape)
for obj in dataset_dict.pop("annotations")
if obj.get("iscrowd", 0) == 0
]
instances = utils.annotations_to_instances(annos, image_shape)
dataset_dict["instances"] = utils.filter_empty_instances(instances)
return dataset_dict
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