Python torch.floor方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def map_roi_levels(self, rois, num_levels):
        """Map rois to corresponding feature levels by scales.

        - scale < finest_scale * 2: level 0
        - finest_scale * 2 <= scale < finest_scale * 4: level 1
        - finest_scale * 4 <= scale < finest_scale * 8: level 2
        - scale >= finest_scale * 8: level 3

        Args:
            rois (Tensor): Input RoIs, shape (k, 5).
            num_levels (int): Total level number.

        Returns:
            Tensor: Level index (0-based) of each RoI, shape (k, )
        """
        scale = torch.sqrt(
            (rois[:, 3] - rois[:, 1]) * (rois[:, 4] - rois[:, 2]))
        target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6))
        target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
        return target_lvls 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def map_roi_levels(self, rois, num_levels):
        """Map rois to corresponding feature levels by scales.

        - scale < finest_scale: level 0
        - finest_scale <= scale < finest_scale * 2: level 1
        - finest_scale * 2 <= scale < finest_scale * 4: level 2
        - scale >= finest_scale * 4: level 3

        Args:
            rois (Tensor): Input RoIs, shape (k, 5).
            num_levels (int): Total level number.

        Returns:
            Tensor: Level index (0-based) of each RoI, shape (k, )
        """
        scale = torch.sqrt(
            (rois[:, 3] - rois[:, 1] + 1) * (rois[:, 4] - rois[:, 2] + 1))
        target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6))
        target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
        return target_lvls 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def map_roi_levels(self, rois, num_levels):
        """Map rrois to corresponding feature levels by scales.

        - scale < finest_scale: level 0
        - finest_scale <= scale < finest_scale * 2: level 1
        - finest_scale * 2 <= scale < finest_scale * 4: level 2
        - scale >= finest_scale * 4: level 3

        Args:
            rois (Tensor): Input RRoIs, shape (k, 6). (index, x, y, w, h, angle)
            num_levels (int): Total level number.

        Returns:
            Tensor: Level index (0-based) of each RoI, shape (k, )
        """
        scale = torch.sqrt(rois[:, 3] * rois[:, 4])
        target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6))
        target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
        return target_lvls 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def fit_positive(rows, cols, yx_min, yx_max, anchors):
    device_id = anchors.get_device() if torch.cuda.is_available() else None
    batch_size, num, _ = yx_min.size()
    num_anchors, _ = anchors.size()
    valid = torch.prod(yx_min < yx_max, -1)
    center = (yx_min + yx_max) / 2
    ij = torch.floor(center)
    i, j = torch.unbind(ij.long(), -1)
    index = i * cols + j
    anchors2 = anchors / 2
    iou_matrix = utils.iou.torch.iou_matrix((yx_min - center).view(-1, 2), (yx_max - center).view(-1, 2), -anchors2, anchors2).view(batch_size, -1, num_anchors)
    iou, index_anchor = iou_matrix.max(-1)
    _positive = []
    cells = rows * cols
    for valid, index, index_anchor in zip(torch.unbind(valid), torch.unbind(index), torch.unbind(index_anchor)):
        index, index_anchor = (t[valid] for t in (index, index_anchor))
        t = utils.ensure_device(torch.ByteTensor(cells, num_anchors).zero_(), device_id)
        t[index, index_anchor] = 1
        _positive.append(t)
    return torch.stack(_positive) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def create_dummy_data(data_dir, num_examples=1000, maxlen=20):

    def _create_dummy_data(filename):
        data = torch.rand(num_examples * maxlen)
        data = 97 + torch.floor(26 * data).int()
        with open(os.path.join(data_dir, filename), 'w') as h:
            offset = 0
            for _ in range(num_examples):
                ex_len = random.randint(1, maxlen)
                ex_str = ' '.join(map(chr, data[offset:offset+ex_len]))
                print(ex_str, file=h)
                offset += ex_len

    _create_dummy_data('train.in')
    _create_dummy_data('train.out')
    _create_dummy_data('valid.in')
    _create_dummy_data('valid.out')
    _create_dummy_data('test.in')
    _create_dummy_data('test.out') 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def _init_buffers(self):
        m_min = 0. if self.f_min == 0 else 2595 * np.log10(1. + (self.f_min / 700))
        m_max = 2595 * np.log10(1. + (self.f_max / 700))

        m_pts = torch.linspace(m_min, m_max, self.n_mels + 2)
        f_pts = (700 * (10**(m_pts / 2595) - 1))

        bins = torch.floor(((self.n_fft - 1) * 2) * f_pts / self.sr).long()

        fb = torch.zeros(self.n_fft, self.n_mels)
        for m in range(1, self.n_mels + 1):
            f_m_minus = bins[m - 1].item()
            f_m = bins[m].item()
            f_m_plus = bins[m + 1].item()

            if f_m_minus != f_m:
                fb[f_m_minus:f_m, m - 1] = (torch.arange(f_m_minus, f_m) - f_m_minus) / (f_m - f_m_minus)
            if f_m != f_m_plus:
                fb[f_m:f_m_plus, m - 1] = (f_m_plus - torch.arange(f_m, f_m_plus)) / (f_m_plus - f_m)
        self.register_buffer("fb", fb) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def interpolate(feat, pt):
    # feat: c,h,w
    # pt: K,2
    # return: c,k
    h,w = feat.shape[1], feat.shape[2]
    x = pt[:,0] * (w-1)
    y = pt[:,1] * (h-1)
    x0 = torch.floor(x)
    y0 = torch.floor(y)

    val=feat[:,y0.long(),x0.long()]*(x0+1-x)*(y0+1-y)+\
            feat[:,y0.long()+1,x0.long()]*(x0+1-x)*(y-y0)+\
            feat[:,y0.long(),x0.long()+1]*(x-x0)*(y0+1-y)+\
            feat[:,y0.long()+1,x0.long()+1]*(x-x0)*(y-y0)
    
    return val 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def lidar_to_img(points, img_size):
    # pdb.set_trace()
    lidar_data = np.array(points[:, :2])
    lidar_data *= 9.9999
    lidar_data -= (0.5 * img_size, 0.5 * img_size)
    lidar_data = np.fabs(lidar_data)
    lidar_data = lidar_data.astype(np.int32)
    lidar_data = np.reshape(lidar_data, (-1, 2))
    lidar_img = np.zeros((img_size, img_size))
    lidar_img[tuple(lidar_data.T)] = 255
    return torch.tensor(lidar_img).cuda()


# def lidar_to_img(points, img_size):
#     # pdb.set_trace()
#     lidar_data = points[:, :2]
#     lidar_data *= 9.9999
#     lidar_data -= torch.tensor((0.5 * img_size, 0.5 * img_size)).cuda()
#     lidar_data = torch.abs(lidar_data)
#     lidar_data = torch.floor(lidar_data).long()
#     lidar_data = lidar_data.view(-1, 2)
#     lidar_img = torch.zeros((img_size, img_size)).cuda()
#     lidar_img[lidar_data.permute(1,0)] = 255
#     return lidar_img 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def tobin(self, target):
        indxneg = target.data[:,0,:,:] < 0
        eps = torch.zeros(target.data[:,0,:,:].size()).cuda()
        epsind = target.data[:,0,:,:] == 0
        eps[epsind] += 1e-5
        angle = torch.atan(target.data[:,1,:,:] / (target.data[:,0,:,:] + eps))
        angle[indxneg] += np.pi
        angle += np.pi / 2 # 0 to 2pi
        angle = torch.clamp(angle, 0, 2 * np.pi - 1e-3)
        radius = torch.sqrt(target.data[:,0,:,:] ** 2 + target.data[:,1,:,:] ** 2)
        radius = torch.clamp(radius, 0, self.fmax - 1e-3)
        quantized_angle = torch.floor(self.abins * angle / (2 * np.pi))
        if self.quantize_strategy == 'linear':
            quantized_radius = torch.floor(self.rbins * radius / self.fmax)
        elif self.quantize_strategy == 'quadratic':
            quantized_radius = torch.floor(self.rbins * torch.sqrt(radius / self.fmax))
        else:
            raise Exception("No such quantize strategy: {}".format(self.quantize_strategy))
        quantized_target = torch.autograd.Variable(torch.cat([torch.unsqueeze(quantized_angle, 1), torch.unsqueeze(quantized_radius, 1)], dim=1))
        return quantized_target.type(torch.cuda.LongTensor) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def drop_connect(inputs, is_training, drop_connect_rate):
    """
    Apply drop connect to random inputs in a batch.
    """
    if not is_training:
        return inputs

    keep_prob = 1 - drop_connect_rate

    # compute drop connect tensor
    batch_size = inputs.shape[0]
    random_tensor = keep_prob
    random_tensor += torch.rand(
        [batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device
    )
    binary_tensor = torch.floor(random_tensor)
    outputs = (inputs / keep_prob) * binary_tensor
    return outputs 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def alpha_dropout(
        x,
        p=0.05,
        alpha=-1.7580993408473766,
        fixedPointMean=0,
        fixedPointVar=1,
        training=False,
):
    keep_prob = 1 - p
    if keep_prob == 1 or not training:
        return x
    a = np.sqrt(fixedPointVar / (keep_prob * (
        (1 - keep_prob) * pow(alpha - fixedPointMean, 2) + fixedPointVar)))
    b = fixedPointMean - a * (
        keep_prob * fixedPointMean + (1 - keep_prob) * alpha)
    keep_prob = 1 - p

    random_tensor = keep_prob + torch.rand(x.size())
    binary_tensor = Variable(torch.floor(random_tensor))
    x = x.mul(binary_tensor)
    ret = x + alpha * (1 - binary_tensor)
    ret.mul_(a).add_(b)
    return ret 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def drop_connect(inputs, drop_p, training):
    """ Drop connect. """
    if not training:
        return inputs * (1. - drop_p)
    batch_size = inputs.shape[0]
    random_tensor = torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
    binary_tensor = random_tensor > drop_p
    output = inputs * binary_tensor.float()
    # output = inputs / (1. - drop_p) * binary_tensor.float()
    return output

    # if not training: return inputs
    # batch_size = inputs.shape[0]
    # keep_prob = 1 - drop_p
    # random_tensor = keep_prob
    # random_tensor += torch.rand([batch_size, 1, 1, 1], dtype=inputs.dtype, device=inputs.device)
    # binary_tensor = torch.floor(random_tensor)
    # output = inputs / keep_prob * binary_tensor
    # return output 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def map_roi_levels(self, rois, num_levels):
        """Map rois to corresponding feature levels by scales.

        - scale < finest_scale * 2: level 0
        - finest_scale * 2 <= scale < finest_scale * 4: level 1
        - finest_scale * 4 <= scale < finest_scale * 8: level 2
        - scale >= finest_scale * 8: level 3

        Args:
            rois (Tensor): Input RoIs, shape (k, 5).
            num_levels (int): Total level number.

        Returns:
            Tensor: Level index (0-based) of each RoI, shape (k, )
        """
        scale = torch.sqrt(
            (rois[:, 3] - rois[:, 1] + 1) * (rois[:, 4] - rois[:, 2] + 1))
        target_lvls = torch.floor(torch.log2(scale / self.finest_scale + 1e-6))
        target_lvls = target_lvls.clamp(min=0, max=num_levels - 1).long()
        return target_lvls 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def _joint_actions_2_action_pair(joint_action, n_actions,use_delegate_action=True):
    if isinstance(joint_action, int):
        if use_delegate_action:
            if joint_action != 0.0:
                _action1 = (joint_action - 1.0) // n_actions
                _action2 = (joint_action - 1.0) % n_actions
            else:
                _action1 = float("nan")
                _action2 = float("nan")
        else:
            _action1 = th.floor(joint_action / n_actions)
            _action2 = (joint_action) % n_actions
        return _action1, _action2
    else:
        if use_delegate_action:
            mask = (joint_action == 0.0)
            joint_action[mask] = 1.0
            _action1 = th.floor((joint_action-1.0) / n_actions)
            _action2 = (joint_action-1.0) % n_actions
            _action1[mask] = float("nan")
            _action2[mask] = float("nan")
        else:
            _action1 = th.floor(joint_action / n_actions)
            _action2 = (joint_action) % n_actions
        return _action1, _action2 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import floor [as 别名]
def _joint_actions_2_action_pair_aa(joint_action, n_actions, avail_actions1, avail_actions2, use_delegate_action=True):
    joint_action = joint_action.clone()
    if use_delegate_action:
        mask = (joint_action == 0.0)
        joint_action[mask] = 1.0
        _action1 = th.floor((joint_action-1.0) / n_actions)
        _action2 = (joint_action-1.0) % n_actions
        _action1[mask] = float("nan")
        _action2[mask] = float("nan")
    else:
        _action1 = th.floor(joint_action / n_actions)
        _action2 = (joint_action) % n_actions

    aa_m1 = _action1 != _action1
    aa_m2 = _action2 != _action2
    _action1[aa_m1] = 0
    _action2[aa_m2] = 0
    aa1 = avail_actions1.data.gather(-1, ( _action1.long() ))
    aa2 = avail_actions2.data.gather(-1, ( _action2.long() ))
    _action1[aa1 == 0] = float("nan")
    _action2[aa2 == 0] = float("nan")
    _action1[aa_m1] = float("nan")
    _action2[aa_m2] = float("nan")
    return _action1, _action2