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August 19, 2020 08:53
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| # https://github.com/BloodAxe/Kaggle-2020-Alaska2/blob/master/alaska2/dataset.py#L373 | |
| class TrainingValidationDataset(Dataset): | |
| def __init__( | |
| self, | |
| images: Union[List, np.ndarray], | |
| targets: Optional[Union[List, np.ndarray]], | |
| quality: Union[List, np.ndarray], | |
| bits: Optional[Union[List, np.ndarray]], | |
| transform: Union[A.Compose, A.BasicTransform], | |
| features: List[str], | |
| ): | |
| """ | |
| :param obliterate - Augmentation that destroys embedding. | |
| """ | |
| if targets is not None: | |
| if len(images) != len(targets): | |
| raise ValueError(f"Size of images and targets does not match: {len(images)} {len(targets)}") | |
| self.images = images | |
| self.targets = targets | |
| self.transform = transform | |
| self.features = features | |
| self.quality = quality | |
| self.bits = bits | |
| def __len__(self): | |
| return len(self.images) | |
| def __repr__(self): | |
| return f"TrainingValidationDataset(len={len(self)}, targets_hist={np.bincount(self.targets)}, qf={np.bincount(self.quality)}, features={self.features})" | |
| def __getitem__(self, index): | |
| image_fname = self.images[index] | |
| try: | |
| image = cv2.imread(image_fname) | |
| if image is None: | |
| raise FileNotFoundError(image_fname) | |
| except Exception as e: | |
| print("Cannot read image ", image_fname, "at index", index) | |
| print(e) | |
| qf = self.quality[index] | |
| data = {} | |
| data["image"] = image | |
| data.update(compute_features(image, image_fname, self.features)) | |
| data = self.transform(**data) | |
| sample = {INPUT_IMAGE_ID_KEY: os.path.basename(self.images[index]), INPUT_IMAGE_QF_KEY: int(qf)} | |
| if self.bits is not None: | |
| # OK | |
| sample[INPUT_TRUE_PAYLOAD_BITS] = torch.tensor(self.bits[index], dtype=torch.float32) | |
| if self.targets is not None: | |
| target = int(self.targets[index]) | |
| sample[INPUT_TRUE_MODIFICATION_TYPE] = target | |
| sample[INPUT_TRUE_MODIFICATION_FLAG] = torch.tensor([target > 0]).float() | |
| for key, value in data.items(): | |
| if key in self.features: | |
| sample[key] = tensor_from_rgb_image(value) | |
| return sample |
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