Upgrading torchvision 0.9 to 0.10
Unknown0.9.1 → 0.10.1
Fetching 2 releases from GitHub…0 / 2
UnknownUpgrade verdict
DepDelta is still reading the notes for this range, so breaking changes can't be ruled out yet.
In plain terms
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- This page updates as the notes come in.
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DepDelta is still reading the release notes in this range. This updates as they come in.
Moves torch 1.8.1 → 1.9.1. Your build may pick other versions.
API changes
Generated by DepDelta from published Python source. Not mixed with prose notes.
- added:
export Kitti
- added:
variable CSV
- changed:
function make_dataset(directory: str, class_to_idx: Dict[str, int], extensions: Optional[Tuple[str, ...]]=…, is_valid_file: Optional[Callable[[str], bool]]=…) -> List[Tuple[str, int]] → function make_dataset(directory: str, class_to_idx: Optional[Dict[str, int]]=…, extensions: Optional[Tuple[str, ...]]=…, is_valid_file: Optional[Callable[[str], bool]]=…) -> List[Tuple[str, int]] - added:
function find_classes(directory: str) -> Tuple[List[str], Dict[str, int]] - added:
method DatasetFolder.find_classes(directory: str) -> Tuple[List[str], Dict[str, int]]
- added:
class Kitti(VisionDataset) - added:
constructor Kitti(root: str, train: bool=…, transform: Optional[Callable]=…, target_transform: Optional[Callable]=…, transforms: Optional[Callable]=…, download: bool=…) - added:
field Kitti.data_url - added:
field Kitti.image_dir_name - added:
field Kitti.labels_dir_name - added:
field Kitti.resources - added:
method Kitti.download() -> None
- removed:
function open_maybe_compressed_file(path: Union[str, IO]) -> Union[IO, gzip.GzipFile] - changed:
function read_sn3_pascalvincent_tensor(path: Union[str, IO], strict: bool=…) -> torch.Tensor → function read_sn3_pascalvincent_tensor(path: str, strict: bool=…) -> torch.Tensor - added:
property EMNIST.images_file -> str - added:
property EMNIST.labels_file -> str - added:
property QMNIST.images_file -> str - added:
property QMNIST.labels_file -> str
- added:
method PhotoTour.cache() -> None
- changed:
function extract_archive(from_path: str, to_path: Optional[str]=…, remove_finished: bool=…) -> None → function extract_archive(from_path: str, to_path: Optional[str]=…, remove_finished: bool=…) -> str
- changed:
method VOCDetection.parse_voc_xml(node: ET.Element) -> Dict[str, Any] → method VOCDetection.parse_voc_xml(node: ET_Element) -> Dict[str, Any]
- added:
export ImageReadMode
- changed:
function decode_jpeg(input: torch.Tensor, mode: ImageReadMode=…) -> torch.Tensor → function decode_jpeg(input: torch.Tensor, mode: ImageReadMode=…, device: str=…) -> torch.Tensor
- removed:
method AnchorGenerator.cached_grid_anchors(grid_sizes: List[List[int]], strides: List[List[Tensor]]) -> List[Tensor] - added:
class DefaultBoxGenerator(nn.Module) - added:
constructor DefaultBoxGenerator(aspect_ratios: List[List[int]], min_ratio: float=…, max_ratio: float=…, scales: Optional[List[float]]=…, steps: Optional[List[int]]=…, clip: bool=…) - added:
method DefaultBoxGenerator.forward(image_list: ImageList, feature_maps: List[Tensor]) -> List[Tensor] - added:
method DefaultBoxGenerator.num_anchors_per_location()
- added:
class SSD(nn.Module) - added:
constructor SSD(backbone: nn.Module, anchor_generator: DefaultBoxGenerator, size: Tuple[int, int], num_classes: int, image_mean: Optional[List[float]]=…, image_std: Optional[List[float]]=…, head: Optional[nn.Module]=…, score_thresh: float=…, nms_thresh: float=…, detections_per_img: int=…, iou_thresh: float=…, topk_candidates: int=…, positive_fraction: float=…) - added:
function ssd300_vgg16(pretrained: bool=…, progress: bool=…, num_classes: int=…, pretrained_backbone: bool=…, trainable_backbone_layers: Optional[int]=…, **kwargs: Any) - added:
method SSD.compute_loss(targets: List[Dict[str, Tensor]], head_outputs: Dict[str, Tensor], anchors: List[Tensor], matched_idxs: List[Tensor]) -> Dict[str, Tensor] - added:
method SSD.eager_outputs(losses: Dict[str, Tensor], detections: List[Dict[str, Tensor]]) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]] - added:
method SSD.forward(images: List[Tensor], targets: Optional[List[Dict[str, Tensor]]]=…) -> Tuple[Dict[str, Tensor], List[Dict[str, Tensor]]] - added:
method SSD.postprocess_detections(head_outputs: Dict[str, Tensor], image_anchors: List[Tensor], image_shapes: List[Tuple[int, int]]) -> List[Dict[str, Tensor]]
- added:
function ssdlite320_mobilenet_v3_large(pretrained: bool=…, progress: bool=…, num_classes: int=…, pretrained_backbone: bool=…, trainable_backbone_layers: Optional[int]=…, norm_layer: Optional[Callable[..., nn.Module]]=…, **kwargs: Any)
- changed:
constructor GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std) → constructor GeneralizedRCNNTransform(min_size, max_size, image_mean, image_std, size_divisible=…, fixed_size=…) - changed:
method GeneralizedRCNNTransform.resize(image, target) → method GeneralizedRCNNTransform.resize(image: Tensor, target: Optional[Dict[str, Tensor]]=…) -> Tuple[Tensor, Optional[Dict[str, Tensor]]]
- changed:
constructor MobileNetV3(inverted_residual_setting: List[InvertedResidualConfig], last_channel: int, num_classes: int=…, block: Optional[Callable[..., nn.Module]]=…, norm_layer: Optional[Callable[..., nn.Module]]=…) -> None → constructor MobileNetV3(inverted_residual_setting: List[InvertedResidualConfig], last_channel: int, num_classes: int=…, block: Optional[Callable[..., nn.Module]]=…, norm_layer: Optional[Callable[..., nn.Module]]=…, **kwargs: Any) -> None
- changed:
function resize(img: Tensor, size: List[int], interpolation: InterpolationMode=…) -> Tensor → function resize(img: Tensor, size: List[int], interpolation: InterpolationMode=…, max_size: Optional[int]=…, antialias: Optional[bool]=…) -> Tensor
- changed:
function resize(img, size, interpolation=…) → function resize(img, size, interpolation=…, max_size=…)
- changed:
function resize(img: Tensor, size: List[int], interpolation: str=…) -> Tensor → function resize(img: Tensor, size: List[int], interpolation: str=…, max_size: Optional[int]=…, antialias: Optional[bool]=…) -> Tensor
- changed:
constructor Resize(size, interpolation=…) → constructor Resize(size, interpolation=…, max_size=…, antialias=…)
- added:
function draw_segmentation_masks(image: torch.Tensor, masks: torch.Tensor, alpha: float=…, colors: Optional[List[Union[str, Tuple[int, int, int]]]]=…) -> torch.Tensor