def get_coco(root, image_set, transforms, mode='instances'):
anno_file_template = "{}_{}2017.json"
PATHS = {
"train": ("train2017",
os.path.join("annotations",
anno_file_template.format(mode, "train"))),
"val": ("val2017",
os.path.join("annotations",
anno_file_template.format(mode, "val"))),
# "train": ("val2017", os.path.join("annotations", anno_file_template.format(mode, "val")))
}
t = [ConvertCocoPolysToMask()]
if transforms is not None:
t.append(transforms)
transforms = T.Compose(t)
img_folder, ann_file = PATHS[image_set]
img_folder = os.path.join(root, img_folder)
ann_file = os.path.join(root, ann_file)
dataset = CocoDetection(img_folder, ann_file, transforms=transforms)
if image_set == "train":
dataset = _coco_remove_images_without_annotations(dataset)
# dataset = torch.utils.data.Subset(dataset, [i for i in range(500)])
return dataset
def get_transform(train):
transforms = []
# convert PIL image to PyTorch Tensor
transforms.append(T.ToTensor())
if train:
# randomly flip training images during training
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
def get_transform(train):
transforms = []
# converts the image, a PIL image, into a PyTorch Tensor
transforms.append(T.ToTensor())
if train:
# during training, randomly flip the training images
# and ground-truth for data augmentation
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
def get_transform(train):
transforms = []
# if train:
# transforms = [T.RandomColorJitter(), T.RandomGrayscale()]
# transforms = [T.RandomColorJitter()]
transforms.append(T.ToTensor())
if train:
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)
def get_transform(train):
transforms = []
transforms.append(T.ToTensor())
if train:
transforms.append(T.RandomHorizontalFlip(0.5))
return T.Compose(transforms)