Inference with full image.

Inference with full image.

Inference with full image.

Args: windows: (num_windows*B, window_size, window_size, C) window_size (int): Window size H (int): Height of image

tensor.transpose((2, 0, 1)) */

no c dim, because each item means a prob :param inputs: [bz, d1, d2, ...], after softmax or sigmod :param targets: [bz, d1, d

t = torch.rand(size=[4, 3]) a = torch.randn(size=[4, 3]) b = F.sigmoid(a) nn.BCEWithLogitsLoss()(a, t) == BCEWithLogi

:param inputs: [bz, c, d1, d2, ...], before softmax :param targets: [bz, d1, d2, ...], value in the range [0, c), or [bz, c, d1, d2,

Args: pic (PIL Image, numpy.ndarray or torch.tensor): Image to be converted to tensor. Returns: Tensor: Conve

Args: cls_pred (Tensor): Predicted classification logits, shape [num_query, num_class]. gt_labels (Te

Args: mask_preds (Tensor): Mask prediction logits in shape (N1, H, W). gt_masks (Tensor): Ground truth in shape (N2,

Args: cls_pred (Tensor): Predicted classification logits. gt_labels (Tensor): Labels. Returns: Te

x : [n, c, h, w] ret: [n, c, p]