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46 lines
1.3 KiB
Python
46 lines
1.3 KiB
Python
import torch
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from PIL import Image
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from .registry import DATASETS
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from .base import BaseDataset
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def rotate(img):
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"""Rotate input image with 0, 90, 180, and 270 degrees.
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Args:
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img (Tensor): input image of shape (C, H, W).
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Returns:
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list[Tensor]: A list of four rotated images.
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"""
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return [
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img,
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torch.flip(img.transpose(1, 2), [1]),
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torch.flip(img, [1, 2]),
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torch.flip(img, [1]).transpose(1, 2)
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]
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@DATASETS.register_module
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class RotationPredDataset(BaseDataset):
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"""Dataset for rotation prediction.
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"""
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def __init__(self, data_source, pipeline):
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super(RotationPredDataset, self).__init__(data_source, pipeline)
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def __getitem__(self, idx):
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img = self.data_source.get_sample(idx)
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assert isinstance(img, Image.Image), \
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'The output from the data source must be an Image, got: {}. \
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Please ensure that the list file does not contain labels.'.format(
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type(img))
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img = self.pipeline(img)
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img = torch.stack(rotate(img), dim=0)
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rotation_labels = torch.LongTensor([0, 1, 2, 3])
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return dict(img=img, rot_label=rotation_labels)
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def evaluate(self, scores, keyword, logger=None):
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raise NotImplemented
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