Python torch.sort方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def lovasz_hinge_flat(logits, labels):
    """
    Binary Lovasz hinge loss
      logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
      labels: [P] Tensor, binary ground truth labels (0 or 1)
      ignore: label to ignore
    """
    if len(labels) == 0:
        # only void pixels, the gradients should be 0
        return logits.sum() * 0.
    signs = 2. * labels.float() - 1.
    errors = (1. - logits * Variable(signs))
    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
    perm = perm.data
    gt_sorted = labels[perm]
    grad = lovasz_grad(gt_sorted)
    loss = torch.dot(F.relu(errors_sorted), Variable(grad))
    return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def forward(self, x, x_len, atten_mask):
        CoAtt = torch.bmm(x, x.transpose(1, 2))
        CoAtt = atten_mask.unsqueeze(1) * CoAtt - (1 - atten_mask).unsqueeze(1) * INF
        CoAtt = torch.softmax(CoAtt, dim=-1)
        new_x = torch.cat([torch.bmm(CoAtt, x), x], -1)

        sorted_x_len, indx = torch.sort(x_len, 0, descending=True)
        new_x = pack_padded_sequence(new_x[indx], sorted_x_len.data.tolist(), batch_first=True)

        h0 = to_cuda(torch.zeros(2, x_len.size(0), self.hidden_size // 2), self.use_cuda)
        c0 = to_cuda(torch.zeros(2, x_len.size(0), self.hidden_size // 2), self.use_cuda)
        packed_h, (packed_h_t, _) = self.model(new_x, (h0, c0))

        # restore the sorting
        _, inverse_indx = torch.sort(indx, 0)
        packed_h_t = torch.cat([packed_h_t[i] for i in range(packed_h_t.size(0))], -1)
        restore_packed_h_t = packed_h_t[inverse_indx]
        output = restore_packed_h_t
        return output 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def cxcy_to_gcxgcy(cxcy, priors_cxcy):
    """
    Encode bounding boxes (that are in center-size form) w.r.t. the corresponding prior boxes (that are in center-size form).

    For the center coordinates, find the offset with respect to the prior box, and scale by the size of the prior box.
    For the size coordinates, scale by the size of the prior box, and convert to the log-space.

    In the model, we are predicting bounding box coordinates in this encoded form.

    :param cxcy: bounding boxes in center-size coordinates, a tensor of size (n_priors, 4)
    :param priors_cxcy: prior boxes with respect to which the encoding must be performed, a tensor of size (n_priors, 4)
    :return: encoded bounding boxes, a tensor of size (n_priors, 4)
    """

    # The 10 and 5 below are referred to as 'variances' in the original Caffe repo, completely empirical
    # They are for some sort of numerical conditioning, for 'scaling the localization gradient'
    # See https://github.com/weiliu89/caffe/issues/155
    return torch.cat([(cxcy[:, :2] - priors_cxcy[:, :2]) / (priors_cxcy[:, 2:] / 10),  # g_c_x, g_c_y
                      torch.log(cxcy[:, 2:] / priors_cxcy[:, 2:]) * 5], 1)  # g_w, g_h 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def forward(self, inputs, targets):

        N, C, H, W = inputs.size()
        masks = torch.zeros(N, C, H, W).to(targets.device).scatter_(1, targets.view(N, 1, H, W), 1)

        loss = 0.

        for mask, input in zip(masks.view(N, -1), inputs.view(N, -1)):

            max_margin_errors = 1. - ((mask * 2 - 1) * input)
            errors_sorted, indices = torch.sort(max_margin_errors, descending=True)
            labels_sorted = mask[indices.data]

            inter = labels_sorted.sum() - labels_sorted.cumsum(0)
            union = labels_sorted.sum() + (1. - labels_sorted).cumsum(0)
            iou = 1. - inter / union

            p = len(labels_sorted)
            if p > 1:
                iou[1:p] = iou[1:p] - iou[0:-1]

            loss += torch.dot(nn.functional.relu(errors_sorted), iou)

        return loss / N 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def lovasz_hinge_flat(self, logits, labels):
        """
        Binary Lovasz hinge loss
          logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
          labels: [P] Tensor, binary ground truth labels (0 or 1)
          ignore: label to ignore
        """
        if len(labels) == 0:
            # only void pixels, the gradients should be 0
            return logits.sum() * 0.
        signs = 2. * labels.float() - 1.
        errors = (1. - logits * Variable(signs))
        errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
        perm = perm.data
        gt_sorted = labels[perm]
        grad = lovasz_grad(gt_sorted)
        loss = torch.dot(F.relu(errors_sorted), Variable(grad))
        return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def __init__(self, dim=-1, k=None):
        """sparsemax: normalizing sparse transform (a la softmax).

        Solves the projection:

            min_p ||x - p||_2   s.t.    p >= 0, sum(p) == 1.

        Parameters
        ----------
        dim : int
            The dimension along which to apply sparsemax.

        k : int or None
            number of largest elements to partial-sort over. For optimal
            performance, should be slightly bigger than the expected number of
            nonzeros in the solution. If the solution is more than k-sparse,
            this function is recursively called with a 2*k schedule.
            If `None`, full sorting is performed from the beginning.
        """
        self.dim = dim
        self.k = k
        super(Sparsemax, self).__init__() 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def __calculate_eps__(self, matrix, num_nodes, avg_degree):
        r"""Calculates threshold necessary to achieve a given average degree.

        Args:
            matrix (Tensor): Adjacency matrix or edge weights.
            num_nodes (int): Number of nodes.
            avg_degree (int): Target average degree.

        :rtype: (:class:`float`)
        """
        sorted_edges = torch.sort(matrix.flatten(), descending=True).values
        if avg_degree * num_nodes > len(sorted_edges):
            return -np.inf

        left = sorted_edges[avg_degree * num_nodes - 1]
        right = sorted_edges[avg_degree * num_nodes]
        return (left + right) / 2.0 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def lovasz_hinge_flat(logits, labels):
    """
    Binary Lovasz hinge loss
      logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
      labels: [P] Tensor, binary ground truth labels (0 or 1)
      ignore: label to ignore
    """
    if len(labels) == 0:
        # only void pixels, the gradients should be 0
        return logits.sum() * 0.
    signs = 2. * labels.float() - 1.
    errors = (1. - logits * signs)

    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
    perm = perm.data
    gt_sorted = labels[perm]
    grad = lovasz_grad(gt_sorted)
    loss = torch.dot(F.elu(errors_sorted), grad)
    return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def lovasz_softmax_flat(probas, labels, only_present=False):
    """
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
    """
    C = probas.size(1)
    losses = []
    for c in range(C):
        fg = (labels == c).float()  # foreground for class c
        if only_present and fg.sum() == 0:
            continue

        errors = (fg - probas[:, c]).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        losses.append(torch.dot(errors_sorted, lovasz_grad(fg_sorted)))
    return mean(losses) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def lovasz_softmax_flat(probas, labels, only_present=False):
    """
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
    """
    C = probas.size(1)
    losses = []
    for c in range(C):
        fg = (labels == c).float() # foreground for class c
        if only_present and fg.sum() == 0:
            continue

        errors = (fg - probas[:, c]).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        losses.append(torch.dot(errors_sorted, lovasz_grad(fg_sorted)))
    return mean(losses) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def lovasz_hinge_flat(logits, labels):
    """
    Binary Lovasz hinge loss
      logits: [P] Variable, logits at each prediction (between -\infty and +\infty)
      labels: [P] Tensor, binary ground truth labels (0 or 1)
      ignore: label to ignore
    """
    if len(labels) == 0:
        # only void pixels, the gradients should be 0
        return logits.sum() * 0.
    signs = 2. * labels.float() - 1.
    errors = (1. - logits * Variable(signs))
    errors_sorted, perm = torch.sort(errors, dim=0, descending=True)
    perm = perm.data
    gt_sorted = labels[perm]
    grad = lovasz_grad(gt_sorted)
    loss = torch.dot(F.elu(errors_sorted) + 1, Variable(grad))
    return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def lovasz_softmax_flat(probas, labels, only_present=False):
    """
    Multi-class Lovasz-Softmax loss
      probas: [P, C] Variable, class probabilities at each prediction (between 0 and 1)
      labels: [P] Tensor, ground truth labels (between 0 and C - 1)
      only_present: average only on classes present in ground truth
    """
    C = probas.size(1)
    losses = []
    for c in range(C):
        fg = (labels == c).float() # foreground for class c
        if only_present and fg.sum() == 0:
            continue
        errors = (Variable(fg) - probas[:, c]).abs()
        errors_sorted, perm = torch.sort(errors, 0, descending=True)
        perm = perm.data
        fg_sorted = fg[perm]
        losses.append(torch.dot(errors_sorted, Variable(lovasz_grad(fg_sorted))))
    return mean(losses) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def perturb_o_and_get_filtered_rank(embedding, w, s, r, o, test_size, triplets_to_filter):
    """ Perturb object in the triplets
    """
    num_entities = embedding.shape[0]
    ranks = []
    for idx in range(test_size):
        if idx % 100 == 0:
            print("test triplet {} / {}".format(idx, test_size))
        target_s = s[idx]
        target_r = r[idx]
        target_o = o[idx]
        filtered_o = filter_o(triplets_to_filter, target_s, target_r, target_o, num_entities)
        target_o_idx = int((filtered_o == target_o).nonzero())
        emb_s = embedding[target_s]
        emb_r = w[target_r]
        emb_o = embedding[filtered_o]
        emb_triplet = emb_s * emb_r * emb_o
        scores = torch.sigmoid(torch.sum(emb_triplet, dim=1))
        _, indices = torch.sort(scores, descending=True)
        rank = int((indices == target_o_idx).nonzero())
        ranks.append(rank)
    return torch.LongTensor(ranks) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def perturb_s_and_get_filtered_rank(embedding, w, s, r, o, test_size, triplets_to_filter):
    """ Perturb subject in the triplets
    """
    num_entities = embedding.shape[0]
    ranks = []
    for idx in range(test_size):
        if idx % 100 == 0:
            print("test triplet {} / {}".format(idx, test_size))
        target_s = s[idx]
        target_r = r[idx]
        target_o = o[idx]
        filtered_s = filter_s(triplets_to_filter, target_s, target_r, target_o, num_entities)
        target_s_idx = int((filtered_s == target_s).nonzero())
        emb_s = embedding[filtered_s]
        emb_r = w[target_r]
        emb_o = embedding[target_o]
        emb_triplet = emb_s * emb_r * emb_o
        scores = torch.sigmoid(torch.sum(emb_triplet, dim=1))
        _, indices = torch.sort(scores, descending=True)
        rank = int((indices == target_s_idx).nonzero())
        ranks.append(rank)
    return torch.LongTensor(ranks) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import sort [as 别名]
def scores_to_ranks(scores: torch.Tensor):
    """Convert model output scores into ranks."""
    batch_size, num_rounds, num_options = scores.size()
    scores = scores.view(-1, num_options)

    # sort in descending order - largest score gets highest rank
    sorted_ranks, ranked_idx = scores.sort(1, descending=True)

    # i-th position in ranked_idx specifies which score shall take this
    # position but we want i-th position to have rank of score at that
    # position, do this conversion
    ranks = ranked_idx.clone().fill_(0)
    for i in range(ranked_idx.size(0)):
        for j in range(num_options):
            ranks[i][ranked_idx[i][j]] = j
    # convert from 0-99 ranks to 1-100 ranks
    ranks += 1
    ranks = ranks.view(batch_size, num_rounds, num_options)
    return ranks