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chore: remove repetitive words (kornia#2902)
* chore: remove repetitive words * [pre-commit.ci] auto fixes from pre-commit.com hooks for more information, see https://pre-commit.ci --------- Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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docs/source/models/segment_anything.rst

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@@ -173,7 +173,7 @@ Read more about the :code:`SegmentationResults` on :ref:`the official docs<ancho
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Load from config
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^^^^^^^^^^^^^^^^
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You can build a SAM model by specifying the encoder parameters on the the :code:`SamConfig`, or from the model type. The
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You can build a SAM model by specifying the encoder parameters on the :code:`SamConfig`, or from the model type. The
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:code:`from_config` method will first try to build the model based on the model type, otherwise will try from the specified
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parameters. If a checkpoint URL or path for a file is seted, the method will automatically load it.
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kornia/feature/laf.py

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@@ -68,8 +68,8 @@ def get_laf_orientation(LAF: Tensor) -> Tensor:
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def rotate_laf(LAF: Tensor, angles_degrees: Tensor) -> Tensor:
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"""Apply additional rotation to the the LAFs. Compared to `set_laf_orientation`, the resulting rotation is
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original LAF orientation plus angles_degrees.
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"""Apply additional rotation to the LAFs. Compared to `set_laf_orientation`, the resulting rotation is original
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LAF orientation plus angles_degrees.
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Args:
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LAF: :math:`(B, N, 2, 3)`

kornia/geometry/calibration/distort.py

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@@ -12,7 +12,7 @@ def tilt_projection(taux: torch.Tensor, tauy: torch.Tensor, return_inverse: bool
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Args:
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taux: Rotation angle in radians around the :math:`x`-axis with shape :math:`(*, 1)`.
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tauy: Rotation angle in radians around the :math:`y`-axis with shape :math:`(*, 1)`.
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return_inverse: False to obtain the the tilt projection matrix. True for the inverse matrix.
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return_inverse: False to obtain the tilt projection matrix. True for the inverse matrix.
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Returns:
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torch.Tensor: Inverse tilt projection matrix with shape :math:`(*, 3, 3)`.

kornia/geometry/ransac.py

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@@ -160,15 +160,15 @@ def validate_inputs(self, kp1: Tensor, kp2: Tensor, weights: Optional[Tensor] =
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KORNIA_CHECK_SHAPE(kp2, ["N", "2"])
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if not (kp1.shape[0] == kp2.shape[0]) or (kp1.shape[0] < self.minimal_sample_size):
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raise ValueError(
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"kp1 and kp2 should be equal shape at at least"
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"kp1 and kp2 should be equal shape at least"
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f" [{self.minimal_sample_size}, 2], got {kp1.shape}, {kp2.shape}"
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)
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if self.model_type == "homography_from_linesegments":
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KORNIA_CHECK_SHAPE(kp1, ["N", "2", "2"])
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KORNIA_CHECK_SHAPE(kp2, ["N", "2", "2"])
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if not (kp1.shape[0] == kp2.shape[0]) or (kp1.shape[0] < self.minimal_sample_size):
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raise ValueError(
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"kp1 and kp2 should be equal shape at at least"
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"kp1 and kp2 should be equal shape at least"
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f" [{self.minimal_sample_size}, 2, 2], got {kp1.shape},"
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f" {kp2.shape}"
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)

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