Python torch.Tensor方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def update(self, gt, pred):
    """
    gt, pred are tensors of size (..., 1, H, W) in the range [0, 1].
    """
    C, H, W = gt.size()[-3:]
    if isinstance(gt, torch.Tensor):
      gt = Variable(gt)
    if isinstance(pred, torch.Tensor):
      pred = Variable(pred)

    mse_score = self.mse_loss(pred, gt)
    eps = 1e-4
    pred.data[pred.data < eps] = eps
    pred.data[pred.data > 1 - eps] = 1 -eps
    bce_score = self.bce_loss(pred, gt)
    bce_score = bce_score.item() * C * H * W
    mse_score = mse_score.item() * C * H * W
    self.bce_results.append(bce_score)
    self.mse_results.append(mse_score) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def pose_inv_full(pose):
  '''
  param pose: N x 6
  Inverse the 2x3 transformer matrix.
  '''
  N, _ = pose.size()
  b = pose.view(N, 2, 3)[:, :, 2:]
  # A^{-1}
  # Calculate determinant
  determinant = (pose[:, 0] * pose[:, 4] - pose[:, 1] * pose[:, 3] + 1e-8).view(N, 1)
  indices = Variable(torch.LongTensor([4, 1, 3, 0]).cuda())
  scale = Variable(torch.Tensor([1, -1, -1, 1]).cuda())
  A_inv = torch.index_select(pose, 1, indices) * scale / determinant
  A_inv = A_inv.view(N, 2, 2)
  # b' = - A^{-1} b
  b_inv = - A_inv.matmul(b).view(N, 2, 1)
  transformer_inv = torch.cat([A_inv, b_inv], dim=2)
  return transformer_inv 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def centers_to_bboxes(self, point_list):
        """Get bboxes according to center points.

        Only used in :class:`MaxIoUAssigner`.
        """
        bbox_list = []
        for i_img, point in enumerate(point_list):
            bbox = []
            for i_lvl in range(len(self.point_strides)):
                scale = self.point_base_scale * self.point_strides[i_lvl] * 0.5
                bbox_shift = torch.Tensor([-scale, -scale, scale,
                                           scale]).view(1, 4).type_as(point[0])
                bbox_center = torch.cat(
                    [point[i_lvl][:, :2], point[i_lvl][:, :2]], dim=1)
                bbox.append(bbox_center + bbox_shift)
            bbox_list.append(bbox)
        return bbox_list 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def roi_rescale(self, rois, scale_factor):
        """Scale RoI coordinates by scale factor.

        Args:
            rois (torch.Tensor): RoI (Region of Interest), shape (n, 5)
            scale_factor (float): Scale factor that RoI will be multiplied by.

        Returns:
            torch.Tensor: Scaled RoI.
        """

        cx = (rois[:, 1] + rois[:, 3]) * 0.5
        cy = (rois[:, 2] + rois[:, 4]) * 0.5
        w = rois[:, 3] - rois[:, 1]
        h = rois[:, 4] - rois[:, 2]
        new_w = w * scale_factor
        new_h = h * scale_factor
        x1 = cx - new_w * 0.5
        x2 = cx + new_w * 0.5
        y1 = cy - new_h * 0.5
        y2 = cy + new_h * 0.5
        new_rois = torch.stack((rois[:, 0], x1, y1, x2, y2), dim=-1)
        return new_rois 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def smooth_l1_loss(pred, target, beta=1.0):
    """Smooth L1 loss.

    Args:
        pred (torch.Tensor): The prediction.
        target (torch.Tensor): The learning target of the prediction.
        beta (float, optional): The threshold in the piecewise function.
            Defaults to 1.0.

    Returns:
        torch.Tensor: Calculated loss
    """
    assert beta > 0
    assert pred.size() == target.size() and target.numel() > 0
    diff = torch.abs(pred - target)
    loss = torch.where(diff < beta, 0.5 * diff * diff / beta,
                       diff - 0.5 * beta)
    return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def forward(self,
                pred,
                target,
                weight=None,
                avg_factor=None,
                reduction_override=None):
        """Forward function.

        Args:
            pred (torch.Tensor): The prediction.
            target (torch.Tensor): The learning target of the prediction.
            weight (torch.Tensor, optional): The weight of loss for each
                prediction. Defaults to None.
            avg_factor (int, optional): Average factor that is used to average
                the loss. Defaults to None.
            reduction_override (str, optional): The reduction method used to
                override the original reduction method of the loss.
                Defaults to None.
        """
        assert reduction_override in (None, 'none', 'mean', 'sum')
        reduction = (
            reduction_override if reduction_override else self.reduction)
        loss_bbox = self.loss_weight * l1_loss(
            pred, target, weight, reduction=reduction, avg_factor=avg_factor)
        return loss_bbox 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def iou_loss(pred, target, eps=1e-6):
    """IoU loss.

    Computing the IoU loss between a set of predicted bboxes and target bboxes.
    The loss is calculated as negative log of IoU.

    Args:
        pred (torch.Tensor): Predicted bboxes of format (x1, y1, x2, y2),
            shape (n, 4).
        target (torch.Tensor): Corresponding gt bboxes, shape (n, 4).
        eps (float): Eps to avoid log(0).

    Return:
        torch.Tensor: Loss tensor.
    """
    ious = bbox_overlaps(pred, target, is_aligned=True).clamp(min=eps)
    loss = -ious.log()
    return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def rel_roi_point_to_rel_img_point(rois,
                                   rel_roi_points,
                                   img_shape,
                                   spatial_scale=1.):
    """Convert roi based relative point coordinates to image based absolute
    point coordinates.

    Args:
        rois (Tensor): RoIs or BBoxes, shape (N, 4) or (N, 5)
        rel_roi_points (Tensor): Point coordinates inside RoI, relative to
            RoI, location, range (0, 1), shape (N, P, 2)
        img_shape (tuple): (height, width) of image or feature map.
        spatial_scale (float): Scale points by this factor. Default: 1.

    Returns:
        Tensor: Image based relative point coordinates for sampling,
            shape (N, P, 2)
    """

    abs_img_point = rel_roi_point_to_abs_img_point(rois, rel_roi_points)
    rel_img_point = abs_img_point_to_rel_img_point(abs_img_point, img_shape,
                                                   spatial_scale)

    return rel_img_point 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def point_sample(input, points, align_corners=False, **kwargs):
    """A wrapper around :function:`grid_sample` to support 3D point_coords
    tensors Unlike :function:`torch.nn.functional.grid_sample` it assumes
    point_coords to lie inside [0, 1] x [0, 1] square.

    Args:
        input (Tensor): Feature map, shape (N, C, H, W).
        points (Tensor): Image based absolute point coordinates (normalized),
            range [0, 1] x [0, 1], shape (N, P, 2) or (N, Hgrid, Wgrid, 2).
        align_corners (bool): Whether align_corners. Default: False

    Returns:
        Tensor: Features of `point` on `input`, shape (N, C, P) or
            (N, C, Hgrid, Wgrid).
    """

    add_dim = False
    if points.dim() == 3:
        add_dim = True
        points = points.unsqueeze(2)
    output = F.grid_sample(
        input, denormalize(points), align_corners=align_corners, **kwargs)
    if add_dim:
        output = output.squeeze(3)
    return output 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def to_tensor(data):
    """Convert objects of various python types to :obj:`torch.Tensor`.

    Supported types are: :class:`numpy.ndarray`, :class:`torch.Tensor`,
    :class:`Sequence`, :class:`int` and :class:`float`.

    Args:
        data (torch.Tensor | numpy.ndarray | Sequence | int | float): Data to
            be converted.
    """

    if isinstance(data, torch.Tensor):
        return data
    elif isinstance(data, np.ndarray):
        return torch.from_numpy(data)
    elif isinstance(data, Sequence) and not mmcv.is_str(data):
        return torch.tensor(data)
    elif isinstance(data, int):
        return torch.LongTensor([data])
    elif isinstance(data, float):
        return torch.FloatTensor([data])
    else:
        raise TypeError(f'type {type(data)} cannot be converted to tensor.') 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def __call__(self, results):
        """Call function to convert image in results to :obj:`torch.Tensor` and
        transpose the channel order.

        Args:
            results (dict): Result dict contains the image data to convert.

        Returns:
            dict: The result dict contains the image converted
                to :obj:`torch.Tensor` and transposed to (C, H, W) order.
        """
        for key in self.keys:
            img = results[key]
            if len(img.shape) < 3:
                img = np.expand_dims(img, -1)
            results[key] = to_tensor(img.transpose(2, 0, 1))
        return results 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def scale_boxes(bboxes, scale):
    """Expand an array of boxes by a given scale.

    Args:
        bboxes (Tensor): Shape (m, 4)
        scale (float): The scale factor of bboxes

    Returns:
        (Tensor): Shape (m, 4). Scaled bboxes
    """
    assert bboxes.size(1) == 4
    w_half = (bboxes[:, 2] - bboxes[:, 0]) * .5
    h_half = (bboxes[:, 3] - bboxes[:, 1]) * .5
    x_c = (bboxes[:, 2] + bboxes[:, 0]) * .5
    y_c = (bboxes[:, 3] + bboxes[:, 1]) * .5

    w_half *= scale
    h_half *= scale

    boxes_scaled = torch.zeros_like(bboxes)
    boxes_scaled[:, 0] = x_c - w_half
    boxes_scaled[:, 2] = x_c + w_half
    boxes_scaled[:, 1] = y_c - h_half
    boxes_scaled[:, 3] = y_c + h_half
    return boxes_scaled 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def is_located_in(points, bboxes):
    """Are points located in bboxes.

    Args:
      points (Tensor): Points, shape: (m, 2).
      bboxes (Tensor): Bounding boxes, shape: (n, 4).

    Return:
      Tensor: Flags indicating if points are located in bboxes, shape: (m, n).
    """
    assert points.size(1) == 2
    assert bboxes.size(1) == 4
    return (points[:, 0].unsqueeze(1) > bboxes[:, 0].unsqueeze(0)) & \
           (points[:, 0].unsqueeze(1) < bboxes[:, 2].unsqueeze(0)) & \
           (points[:, 1].unsqueeze(1) > bboxes[:, 1].unsqueeze(0)) & \
           (points[:, 1].unsqueeze(1) < bboxes[:, 3].unsqueeze(0)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def bbox_flip(bboxes, img_shape, direction='horizontal'):
    """Flip bboxes horizontally or vertically.

    Args:
        bboxes (Tensor): Shape (..., 4*k)
        img_shape (tuple): Image shape.
        direction (str): Flip direction, options are "horizontal" and
            "vertical". Default: "horizontal"


    Returns:
        Tensor: Flipped bboxes.
    """
    assert bboxes.shape[-1] % 4 == 0
    assert direction in ['horizontal', 'vertical']
    flipped = bboxes.clone()
    if direction == 'vertical':
        flipped[..., 1::4] = img_shape[0] - bboxes[..., 3::4]
        flipped[..., 3::4] = img_shape[0] - bboxes[..., 1::4]
    else:
        flipped[:, 0::4] = img_shape[1] - bboxes[:, 2::4]
        flipped[:, 2::4] = img_shape[1] - bboxes[:, 0::4]
    return flipped 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import Tensor [as 别名]
def bbox2roi(bbox_list):
    """Convert a list of bboxes to roi format.

    Args:
        bbox_list (list[Tensor]): a list of bboxes corresponding to a batch
            of images.

    Returns:
        Tensor: shape (n, 5), [batch_ind, x1, y1, x2, y2]
    """
    rois_list = []
    for img_id, bboxes in enumerate(bbox_list):
        if bboxes.size(0) > 0:
            img_inds = bboxes.new_full((bboxes.size(0), 1), img_id)
            rois = torch.cat([img_inds, bboxes[:, :4]], dim=-1)
        else:
            rois = bboxes.new_zeros((0, 5))
        rois_list.append(rois)
    rois = torch.cat(rois_list, 0)
    return rois