venv/lib/python3.11/site-packages/torchmetrics/detection/helpers.py

# Copyright The PyTorch Lightning team.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
from collections.abc import Sequence
from typing import Literal, Union

from torch import Tensor


def _input_validator(
    preds: Sequence[dict[str, Tensor]],
    targets: Sequence[dict[str, Tensor]],
    iou_type: Union[Literal["bbox", "segm"], tuple[Literal["bbox", "segm"], ...]] = "bbox",
    ignore_score: bool = False,
) -> None:
    """Ensure the correct input format of `preds` and `targets`."""
    if isinstance(iou_type, str):
        iou_type = (iou_type,)

    name_map = {"bbox": "boxes", "segm": "masks"}
    if any(tp not in name_map for tp in iou_type):
        raise Exception(f"IOU type {iou_type} is not supported")
    item_val_name = [name_map[tp] for tp in iou_type]

    if not isinstance(preds, Sequence):
        raise ValueError(f"Expected argument `preds` to be of type Sequence, but got {preds}")
    if not isinstance(targets, Sequence):
        raise ValueError(f"Expected argument `target` to be of type Sequence, but got {targets}")
    if len(preds) != len(targets):
        raise ValueError(
            f"Expected argument `preds` and `target` to have the same length, but got {len(preds)} and {len(targets)}"
        )

    for k in [*item_val_name, "labels"] + (["scores"] if not ignore_score else []):
        if any(k not in p for p in preds):
            raise ValueError(f"Expected all dicts in `preds` to contain the `{k}` key")

    for k in [*item_val_name, "labels"]:
        if any(k not in p for p in targets):
            raise ValueError(f"Expected all dicts in `target` to contain the `{k}` key")

    for ivn in item_val_name:
        if not all(isinstance(pred[ivn], Tensor) for pred in preds):
            raise ValueError(f"Expected all {ivn} in `preds` to be of type Tensor")
    if not ignore_score and not all(isinstance(pred["scores"], Tensor) for pred in preds):
        raise ValueError("Expected all scores in `preds` to be of type Tensor")
    if not all(isinstance(pred["labels"], Tensor) for pred in preds):
        raise ValueError("Expected all labels in `preds` to be of type Tensor")
    for ivn in item_val_name:
        if not all(isinstance(target[ivn], Tensor) for target in targets):
            raise ValueError(f"Expected all {ivn} in `target` to be of type Tensor")
    if not all(isinstance(target["labels"], Tensor) for target in targets):
        raise ValueError("Expected all labels in `target` to be of type Tensor")

    for i, item in enumerate(targets):
        for ivn in item_val_name:
            if item[ivn].size(0) != item["labels"].size(0):
                raise ValueError(
                    f"Input '{ivn}' and labels of sample {i} in targets have a"
                    f" different length (expected {item[ivn].size(0)} labels, got {item['labels'].size(0)})"
                )
    if ignore_score:
        return
    for i, item in enumerate(preds):
        for ivn in item_val_name:
            if not (item[ivn].size(0) == item["labels"].size(0) == item["scores"].size(0)):
                raise ValueError(
                    f"Input '{ivn}', labels and scores of sample {i} in predictions have a"
                    f" different length (expected {item[ivn].size(0)} labels and scores,"
                    f" got {item['labels'].size(0)} labels and {item['scores'].size(0)})"
                )


def _fix_empty_tensors(boxes: Tensor) -> Tensor:
    """Empty tensors can cause problems in DDP mode, this methods corrects them."""
    if boxes.numel() == 0 and boxes.ndim == 1:
        return boxes.unsqueeze(0)
    return boxes


def _validate_iou_type_arg(
    iou_type: Union[Literal["bbox", "segm"], tuple[Literal["bbox", "segm"], ...]] = "bbox",
) -> tuple[Literal["bbox", "segm"], ...]:
    """Validate that iou type argument is correct."""
    allowed_iou_types = ("segm", "bbox")
    if isinstance(iou_type, str):
        iou_type = (iou_type,)
    if any(tp not in allowed_iou_types for tp in iou_type):
        raise ValueError(
            f"Expected argument `iou_type` to be one of {allowed_iou_types} or a tuple of, but got {iou_type}"
        )
    return iou_type