Python torch.log函数代码示例

def sample_from_discretized_mix_logistic_1d(l, nr_mix):
    # Pytorch ordering
    l = l.permute(0, 2, 3, 1)
    ls = [int(y) for y in l.size()]
    xs = ls[:-1] + [1] #[3]

    # unpack parameters
    logit_probs = l[:, :, :, :nr_mix]
    l = l[:, :, :, nr_mix:].contiguous().view(xs + [nr_mix * 2]) # for mean, scale

    # sample mixture indicator from softmax
    temp = torch.FloatTensor(logit_probs.size())
    if l.is_cuda : temp = temp.cuda()
    temp.uniform_(1e-5, 1. - 1e-5)
    temp = logit_probs.data - torch.log(- torch.log(temp))
    _, argmax = temp.max(dim=3)
   
    one_hot = to_one_hot(argmax, nr_mix)
    sel = one_hot.view(xs[:-1] + [1, nr_mix])
    # select logistic parameters
    means = torch.sum(l[:, :, :, :, :nr_mix] * sel, dim=4) 
    log_scales = torch.clamp(torch.sum(
        l[:, :, :, :, nr_mix:2 * nr_mix] * sel, dim=4), min=-7.)
    u = torch.FloatTensor(means.size())
    if l.is_cuda : u = u.cuda()
    u.uniform_(1e-5, 1. - 1e-5)
    u = Variable(u)
    x = means + torch.exp(log_scales) * (torch.log(u) - torch.log(1. - u))
    x0 = torch.clamp(torch.clamp(x[:, :, :, 0], min=-1.), max=1.)
    out = x0.unsqueeze(1)
    return out

    def forward(self, feat, right, wrong, batch_wrong, fake=None, fake_diff_mask=None):

        num_wrong = wrong.size(1)
        batch_size = feat.size(0)

        feat = feat.view(-1, self.ninp, 1)
        right_dis = torch.bmm(right.view(-1, 1, self.ninp), feat)
        wrong_dis = torch.bmm(wrong, feat)
        batch_wrong_dis = torch.bmm(batch_wrong, feat)

        wrong_score = torch.sum(torch.exp(wrong_dis - right_dis.expand_as(wrong_dis)),1) \
                + torch.sum(torch.exp(batch_wrong_dis - right_dis.expand_as(batch_wrong_dis)),1)

        loss_dis = torch.sum(torch.log(wrong_score + 1))
        loss_norm = right.norm() + feat.norm() + wrong.norm() + batch_wrong.norm()

        if fake:
            fake_dis = torch.bmm(fake.view(-1, 1, self.ninp), feat)
            fake_score = torch.masked_select(torch.exp(fake_dis - right_dis), fake_diff_mask)

            margin_score = F.relu(torch.log(fake_score + 1) - self.margin)
            loss_fake = torch.sum(margin_score)
            loss_dis += loss_fake
            loss_norm += fake.norm()

        loss = (loss_dis + 0.1 * loss_norm) / batch_size
        if fake:
            return loss, loss_fake.data[0] / batch_size
        else:
            return loss

def relax_grad2(x, logits, b, surrogate, mixtureweights):
    B = logits.shape[0]
    C = logits.shape[1]

    cat = Categorical(logits=logits)
    # u = torch.rand(B,C).clamp(1e-10, 1.-1e-10).cuda()
    u = myclamp(torch.rand(B,C).cuda())
    gumbels = -torch.log(-torch.log(u))
    z = logits + gumbels
    # b = torch.argmax(z, dim=1) #.view(B,1)
    logq = cat.log_prob(b).view(B,1)

    surr_input = torch.cat([z, x, logits.detach()], dim=1)
    cz = surrogate.net(surr_input)

    z_tilde = sample_relax_given_b(logits, b)
    surr_input = torch.cat([z_tilde, x, logits.detach()], dim=1)
    cz_tilde = surrogate.net(surr_input)

    logpx_given_z = logprob_undercomponent(x, component=b)
    logpz = torch.log(mixtureweights[b]).view(B,1)
    logpxz = logpx_given_z + logpz #[B,1]

    f = logpxz - logq 
    net_loss = - torch.mean( (f.detach() - cz_tilde.detach()) * logq - logq +  cz - cz_tilde )

    grad = torch.autograd.grad([net_loss], [logits], create_graph=True, retain_graph=True)[0] #[B,C]
    pb = torch.exp(logq)

    return grad, pb

    def pixelcnn_generate(self, z1, z2):
        # Sampling from PixelCNN
        x_zeros = torch.zeros(
            (z1.size(0), self.args.input_size[0], self.args.input_size[1], self.args.input_size[2]))
        if self.args.cuda:
            x_zeros = x_zeros.cuda()

        for i in range(self.args.input_size[1]):
            for j in range(self.args.input_size[2]):
                samples_mean, samples_logvar = self.p_x(Variable(x_zeros, volatile=True), z1, z2)
                samples_mean = samples_mean.view(samples_mean.size(0), self.args.input_size[0], self.args.input_size[1],
                                                 self.args.input_size[2])

                if self.args.input_type == 'binary':
                    probs = samples_mean[:, :, i, j].data
                    x_zeros[:, :, i, j] = torch.bernoulli(probs).float()
                    samples_gen = samples_mean

                elif self.args.input_type == 'gray' or self.args.input_type == 'continuous':
                    binsize = 1. / 256.
                    samples_logvar = samples_logvar.view(samples_mean.size(0), self.args.input_size[0],
                                                         self.args.input_size[1], self.args.input_size[2])
                    means = samples_mean[:, :, i, j].data
                    logvar = samples_logvar[:, :, i, j].data
                    # sample from logistic distribution
                    u = torch.rand(means.size()).cuda()
                    y = torch.log(u) - torch.log(1. - u)
                    sample = means + torch.exp(logvar) * y
                    x_zeros[:, :, i, j] = torch.floor(sample / binsize) * binsize
                    samples_gen = samples_mean

        return samples_gen

 def _mu_law(self, x):
     m = self._variable(torch.FloatTensor(1))
     m[:] = self.n_categories + 1
     s = torch.sign(x)
     x = torch.abs(x)
     x = s * (torch.log(1 + (self.n_categories * x)) / torch.log(m))
     return x

    def norm_flow(self, params, z, v, logposterior):

        h = F.tanh(params[0][0](z))
        mew_ = params[0][1](h)
        sig_ = F.sigmoid(params[0][2](h)+5.) #[PB,Z]


        z_reshaped = z.view(self.P, self.B, self.z_size)

        gradients = torch.autograd.grad(outputs=logposterior(z_reshaped), inputs=z_reshaped,
                          grad_outputs=self.grad_outputs,
                          create_graph=True, retain_graph=True, only_inputs=True)[0]
        gradients = gradients.detach()

        gradients = gradients.view(-1,self.z_size)


        v = v*sig_ + mew_*gradients

        logdet = torch.sum(torch.log(sig_), 1)


        h = F.tanh(params[1][0](v))
        mew_ = params[1][1](h)
        sig_ = F.sigmoid(params[1][2](h)+5.) #[PB,Z]

        z = z*sig_ + mew_*v

        logdet2 = torch.sum(torch.log(sig_), 1)

        #[PB]
        logdet = logdet + logdet2
        
        #[PB,Z], [PB]
        return z, v, logdet

    def compute_loss(self, outputs, masks, labels):
        """
        Our implementation of weighted BCE loss.
        """
        labels = labels.view(-1)
        masks = masks.view(-1)
        outputs = outputs.view(-1)

        # Generate the weights
        ones = torch.sum(labels)
        total = labels.nelement()
        weights = torch.FloatTensor(outputs.size()).type_as(outputs.data)
        weights[labels.long() == 1] = 1.0 - ones / total
        weights[labels.long() == 0] = ones / total
        weights = weights.view(weights.size(0), 1).expand(weights.size(0), 2)

        # Generate the log outputs
        outputs = outputs.clamp(min=1e-8)
        log_outputs = torch.log(outputs)
        neg_outputs = 1.0 - outputs
        neg_outputs = neg_outputs.clamp(min=1e-8)
        neg_log_outputs = torch.log(neg_outputs)
        all_outputs = torch.cat((log_outputs.view(-1, 1), neg_log_outputs.view(-1, 1)), 1)

        all_values = all_outputs.mul(torch.autograd.Variable(weights))
        all_labels = torch.autograd.Variable(torch.cat((labels.view(-1, 1), (1.0 - labels).view(-1, 1)), 1))
        all_masks = torch.autograd.Variable(torch.cat((masks.view(-1, 1), masks.view(-1, 1)), 1))
        loss = -torch.sum(all_values.mul(all_labels).mul(all_masks)) / outputs.size(0)
        return loss

    def sample_relax(logits, surrogate):
        cat = Categorical(logits=logits)
        u = torch.rand(B,C).clamp(1e-10, 1.-1e-10).cuda()
        gumbels = -torch.log(-torch.log(u))
        z = logits + gumbels
        b = torch.argmax(z, dim=1) #.view(B,1)
        logprob = cat.log_prob(b).view(B,1)


        # czs = []
        # for j in range(1):
        #     z = sample_relax_z(logits)
        #     surr_input = torch.cat([z, x, logits.detach()], dim=1)
        #     cz = surrogate.net(surr_input)
        #     czs.append(cz)
        # czs = torch.stack(czs)
        # cz = torch.mean(czs, dim=0)#.view(1,1)
        surr_input = torch.cat([z, x, logits.detach()], dim=1)
        cz = surrogate.net(surr_input)


        cz_tildes = []
        for j in range(1):
            z_tilde = sample_relax_given_b(logits, b)
            surr_input = torch.cat([z_tilde, x, logits.detach()], dim=1)
            cz_tilde = surrogate.net(surr_input)
            cz_tildes.append(cz_tilde)
        cz_tildes = torch.stack(cz_tildes)
        cz_tilde = torch.mean(cz_tildes, dim=0) #.view(B,1)

        return b, logprob, cz, cz_tilde

def log_Bernoulli(x, mean, average=False, dim=None):
    probs = torch.clamp( mean, min=min_epsilon, max=max_epsilon )
    log_bernoulli = x * torch.log( probs ) + (1. - x ) * torch.log( 1. - probs )
    if average:
        return torch.mean( log_bernoulli, dim )
    else:
        return torch.sum( log_bernoulli, dim )

def bbox_transform(anchor_rois, gt_rois):
    """

    :param anchor_rois <torch.Tensor>:  
    :param gt_rois <torch.Tensor>:
    :return:
    """
    anchor_widths  = anchor_rois[:, 3] - anchor_rois[:, 0]
    anchor_heights = anchor_rois[:, 4] - anchor_rois[:, 1]
    anchor_lengths = anchor_rois[:, 5] - anchor_rois[:, 2]

    anchor_ctr_x = anchor_rois[:, 0] + 0.5 * anchor_widths
    anchor_ctr_y = anchor_rois[:, 1] + 0.5 * anchor_heights
    anchor_ctr_z = anchor_rois[:, 2] + 0.5 * anchor_lengths

    gt_widths  = gt_rois[:, 3] - gt_rois[:, 0]
    gt_heights = gt_rois[:, 4] - gt_rois[:, 1]
    gt_lengths = gt_rois[:, 5] - gt_rois[:, 2]

    gt_ctr_x = gt_rois[:, 0] + 0.5 * gt_widths
    gt_ctr_y = gt_rois[:, 1] + 0.5 * gt_heights
    gt_ctr_z = gt_rois[:, 2] + 0.5 * gt_lengths

    targets_dx = (gt_ctr_x - anchor_ctr_x) / (anchor_widths + 1e-14)
    targets_dy = (gt_ctr_y - anchor_ctr_y) / (anchor_heights + 1e-14)
    targets_dz = (gt_ctr_z - anchor_ctr_z) / (anchor_lengths + 1e-14)

    targets_dw = torch.log(gt_widths  / (anchor_widths  + 1e-14) + 1e-14)
    targets_dh = torch.log(gt_heights / (anchor_heights + 1e-14) + 1e-14)
    targets_dl = torch.log(gt_lengths / (anchor_lengths + 1e-14) + 1e-14)

    targets = torch.stack([targets_dx, targets_dy, targets_dz, targets_dw, targets_dh, targets_dl], 1)

    return targets

    def reverse_flow(self, z):

        B = z.shape[0]
        C = z.shape[1]
        f = self.flows

        logdet = 0.
        reverse_ = list(range(self.n_flows))[::-1]
        for i in reverse_:
            z1 = z[:,:C//2]
            z2 = z[:,C//2:]
            sig1 = torch.sigmoid(f[str(i)]['f2_sig'](z1))
            mu1 = f[str(i)]['f2_mu'](z1)

            z2 = (z2 - mu1) / sig1

            sig2 = torch.sigmoid(f[str(i)]['f1_sig'](z2))
            mu2 = f[str(i)]['f1_mu'](z2)

            z1 = (z1 - mu2) / sig2
            
            z = torch.cat([z1,z2],1)
            z = z[:,f[str(i)]['inv_perm']]

            sig1 = sig1.view(B, -1)
            sig2 = sig2.view(B, -1)
            logdet += torch.sum(torch.log(sig1), 1)
            logdet += torch.sum(torch.log(sig2), 1)

        return z, logdet

    def forward(self, input):
        n = len(input)
        embeds = self.input_fun(input)

        # pick first node
        scores = self.scores(embeds, 0, 0)
        choice = self.choice(n, scores)
        picks = [choice]
        loss = -torch.log(scores[choice]) / n
        outputs = []

        for i, e in enumerate(embeds):
            outputs.append(self.output)
            if picks[-1] > i:
                # skip elements until next node
                continue
            lstm_out, self.hidden = self.lstm(e.view(1, 1, -1), self.hidden)
            self.output = self.output_fun(lstm_out.view(1, -1))

            if len(picks) < self.subset:
                # pick next node
                scores = self.scores(embeds, len(picks), i + 1)
                choice = self.choice(n, scores)
                picks.append(choice)
                loss -= torch.log(scores[choice]) / (n - i)
        return loss, outputs, picks

    def predictive_elbo(self, x, k, s):
        # No pW or qW

        self.B = x.size()[0] #batch size
        # self.k = k  #number of z samples aka particles P
        # self.s = s  #number of W samples

        elbo1s = []
        for i in range(s):

            Ws, logpW, logqW = self.sample_W()  #_ , [1], [1]

            mu, logvar = self.encode(x)  #[B,Z]
            z, logpz, logqz = self.sample_z(mu, logvar, k=k) #[P,B,Z], [P,B]

            x_hat = self.decode(Ws, z) #[P,B,X]
            logpx = log_bernoulli(x_hat, x)  #[P,B]

            elbo = logpx + logpz - logqz #[P,B]
            if k>1:
                max_ = torch.max(elbo, 0)[0] #[B]
                elbo = torch.log(torch.mean(torch.exp(elbo - max_), 0)) + max_ #[B]
            # elbo1 = elbo1 #+ (logpW - logqW)*.00000001 #[B], logp(x|W)p(w)/q(w)
            elbo1s.append(elbo)

        elbo1s = torch.stack(elbo1s) #[S,B]
        if s>1:
            max_ = torch.max(elbo1s, 0)[0] #[B]
            elbo1 = torch.log(torch.mean(torch.exp(elbo1s - max_), 0)) + max_ #[B]            

        elbo = torch.mean(elbo1s) #[1]
        return elbo#, logprobs2[0], logprobs2[1], logprobs2[2], logprobs2[3], logprobs2[4]

def get_probs_and_logits(ps=None, logits=None, is_multidimensional=True):
    """
    Convert probability values to logits, or vice-versa. Either ``ps`` or
    ``logits`` should be specified, but not both.

    :param ps: tensor of probabilities. Should be in the interval *[0, 1]*.
        If, ``is_multidimensional = True``, then must be normalized along
        axis -1.
    :param logits: tensor of logit values.  For the multidimensional case,
        the values, when exponentiated along the last dimension, must sum
        to 1.
    :param is_multidimensional: determines the computation of ps from logits,
        and vice-versa. For the multi-dimensional case, logit values are
        assumed to be log probabilities, whereas for the uni-dimensional case,
        it specifically refers to log odds.
    :return: tuple containing raw probabilities and logits as tensors.
    """
    assert (ps is None) != (logits is None)
    if ps is not None:
        eps = _get_clamping_buffer(ps)
        ps_clamped = ps.clamp(min=eps, max=1 - eps)
    if is_multidimensional:
        if ps is None:
            ps = softmax(logits, -1)
        else:
            logits = torch.log(ps_clamped)
    else:
        if ps is None:
            ps = F.sigmoid(logits)
        else:
            logits = torch.log(ps_clamped) - torch.log1p(-ps_clamped)
    return ps, logits

def poisson_nll_loss(input, target, log_input=True, full=False, size_average=True):
    r"""Poisson negative log likelihood loss.

    See :class:`~torch.nn.PoissonNLLLoss` for details.

    Args:
        input: expectation of underlying Poisson distribution.
        target: random sample :math:`target \sim Pois(input)`.
        log_input: if True the loss is computed as
            `exp(input) - target * input`, if False then loss is
            `input - target * log(input)`. Default: True
        full: whether to compute full loss, i. e. to add the Stirling
            approximation term. Default: False
            `target * log(target) - target + 0.5 * log(2 * pi * target)`.
        size_average: By default, the losses are averaged over observations for
            each minibatch. However, if the field sizeAverage is set to False,
            the losses are instead summed for each minibatch. Default: True
    """
    if log_input:
        loss = torch.exp(input) - target * input
    else:
        loss = input - target * torch.log(input)
    if full:
        mask = target > 1
        loss[mask] += (target * torch.log(target) - target + 0.5 * torch.log(2 * math.pi * target))[mask]
    if size_average:
        return torch.mean(loss)
    else:
        return torch.sum(loss)