def _collate_and_decode(
self,
data: Tuple[np.ndarray, int],
*,
decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
) -> Dict[str, Any]:
image_array, category_idx = data
image: Union[Image, io.BytesIO]
if decoder is raw:
image = Image(image_array)
else:
image_buffer = image_buffer_from_array(image_array.transpose((1, 2, 0)))
image = decoder(image_buffer) if decoder else image_buffer # type: ignore[assignment]
label = Label(category_idx, category=self.categories[category_idx])
return dict(image=image, label=label)
def _collate_and_decode_sample(
self,
data: Tuple[np.ndarray, np.ndarray],
*,
decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
) -> Dict[str, Any]:
image_array, label_array = data
if decoder is raw:
image = Image(image_array.transpose((2, 0, 1)))
else:
image_buffer = image_buffer_from_array(image_array)
image = decoder(
image_buffer
) if decoder else image_buffer # type: ignore[assignment]
return dict(
image=image,
label=Label(int(label_array) % 10),
)
def _collate_and_decode_sample(
self,
data: Dict[str, Any],
*,
decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
) -> Dict[str, Any]:
raw_image = torch.tensor([int(idx) for idx in data["pixels"].split()], dtype=torch.uint8).reshape(48, 48)
label_id = data.get("emotion")
label_idx = int(label_id) if label_id is not None else None
image: Union[Image, io.BytesIO]
if decoder is raw:
image = Image(raw_image)
else:
image_buffer = image_buffer_from_array(raw_image.numpy())
image = decoder(image_buffer) if decoder else image_buffer # type: ignore[assignment]
return dict(
image=image,
label=Label(label_idx, category=self.info.categories[label_idx]) if label_idx is not None else None,
)
def _collate_and_decode(
self,
data: Tuple[torch.Tensor, torch.Tensor],
*,
config: DatasetConfig,
decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
) -> Dict[str, Any]:
image, label = data
if decoder is raw:
image = Image(image)
else:
image_buffer = image_buffer_from_array(image.numpy())
image = decoder(
image_buffer
) if decoder else image_buffer # type: ignore[assignment]
label = Label(label,
dtype=torch.int64,
category=self.info.categories[int(label)])
return dict(image=image, label=label)
def _collate_and_decode_sample(
self,
data: Tuple[str, ...],
*,
decoder: Optional[Callable[[io.IOBase], torch.Tensor]],
) -> Dict[str, Any]:
image_data = torch.tensor([float(pixel) for pixel in data[:256]],
dtype=torch.uint8).reshape(16, 16)
label_data = [int(label) for label in data[256:] if label]
if decoder is raw:
image = image_data.unsqueeze(0)
else:
image_buffer = image_buffer_from_array(image_data.numpy())
image = decoder(
image_buffer
) if decoder else image_buffer # type: ignore[assignment]
label = next((idx for idx, one_hot_label in enumerate(label_data)
if one_hot_label))
category = self.info.categories[label]
return dict(image=image, label=label, category=category)