Python torch.sigmoid函数代码示例

    def forward_flow(self, z, xenc):

        B = z.shape[0]
        C = z.shape[1]
        f = self.flows
        logdet = 0.
        for i in range(self.n_flows):
            z = z[:,f[str(i)]['perm']]
            z1 = z[:,:C//2]
            z2 = z[:,C//2:]

            sig2 = torch.sigmoid(f[str(i)]['f1_sig'](torch.cat([z2,xenc],1)))
            mu2 = f[str(i)]['f1_mu'](torch.cat([z2,xenc],1))

            z1 = z1*sig2 + mu2

            mu1 = f[str(i)]['f2_mu'](torch.cat([z1,xenc],1))
            sig1 = torch.sigmoid(f[str(i)]['f2_sig'](torch.cat([z1,xenc],1)))

            z2 = z2*sig1 + mu1
            z = torch.cat([z1,z2],1)

            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 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_, hx):
        """
        Args:
            input_: A (batch, input_size) tensor containing input
                features.
            hx: A tuple (h_0, c_0), which contains the initial hidden
                and cell state, where the size of both states is
                (batch, hidden_size).
            time: The current timestep value, which is used to
                get appropriate running statistics.

        Returns:
            h_1, c_1: Tensors containing the next hidden and cell state.
        """

        h_0, c_0 = hx
        batch_size = h_0.size(0)
        bias_batch = (self.bias.unsqueeze(0)
                      .expand(batch_size, *self.bias.size()))
        wh = torch.mm(h_0, self.weight_hh)
        wh = torch.mm(h_0, self.weight_hh)
        wi = torch.mm(input_, self.weight_ih)
        bn_wh = self.bn_hh(wh)
        bn_wi = self.bn_ih(wi)
        f, i, o, g = torch.split(bn_wh + bn_wi + bias_batch,
                                 split_size=self.hidden_size, dim=1)
        c_1 = torch.sigmoid(f)*c_0 + torch.sigmoid(i)*torch.tanh(g)
        h_1 = torch.sigmoid(o) * torch.tanh(self.bn_c(c_1))
        return h_1, c_1

    def forward(self, inputs, mask=None, layer_cache=None, step=None):
        """
        Args:
            inputs (FloatTensor): ``(batch_size, input_len, model_dim)``

        Returns:
            (FloatTensor, FloatTensor):

            * gating_outputs ``(batch_size, input_len, model_dim)``
            * average_outputs average attention
                ``(batch_size, input_len, model_dim)``
        """

        batch_size = inputs.size(0)
        inputs_len = inputs.size(1)

        device = inputs.device
        average_outputs = self.cumulative_average(
          inputs, self.cumulative_average_mask(batch_size,
                                               inputs_len).to(device).float()
          if layer_cache is None else step, layer_cache=layer_cache)
        average_outputs = self.average_layer(average_outputs)
        gating_outputs = self.gating_layer(torch.cat((inputs,
                                                      average_outputs), -1))
        input_gate, forget_gate = torch.chunk(gating_outputs, 2, dim=2)
        gating_outputs = torch.sigmoid(input_gate) * inputs + \
            torch.sigmoid(forget_gate) * average_outputs

        return gating_outputs, average_outputs

    def forward(self, x, r):
        """
        Computes an output and data structure instructions using a
        single linear layer.

        :type x: Variable
        :param x: The input to this Controller

        :type r: Variable
        :param r: The previous item read from the neural data structure

        :rtype: tuple
        :return: A tuple of the form (y, (v, u, d)), interpreted as
            follows:
                - output y
                - pop a strength u from the data structure
                - push v with strength d to the data structure
        """
        self._hidden = self._rnn(torch.cat([x, r], 1), self._hidden)
        nn_output = self._linear(self._hidden)

        output = nn_output[:, self._n_args + self._read_size:].contiguous()

        read_params = torch.sigmoid(nn_output[:, :self._n_args + self._read_size])
        v = read_params[:, self._n_args:].contiguous()
        instructions = tuple(read_params[:, j].contiguous()
                             for j in xrange(self._n_args))

        self._log(x, torch.sigmoid(output), v, *instructions)

        return output, ((v,) + instructions)

def predict(model, batch, flipped_batch, use_gpu):
    image_ids, inputs = batch['image_id'], batch['input']
    if use_gpu:
        inputs = inputs.cuda()
    outputs, _, _ = model(inputs)
    probs = torch.sigmoid(outputs)

    if flipped_batch is not None:
        flipped_image_ids, flipped_inputs = flipped_batch['image_id'], flipped_batch['input']
        # assert image_ids == flipped_image_ids
        if use_gpu:
            flipped_inputs = flipped_inputs.cuda()
        flipped_outputs, _, _ = model(flipped_inputs)
        flipped_probs = torch.sigmoid(flipped_outputs)

        probs += torch.flip(flipped_probs, (3,))  # flip back and add
        probs *= 0.5

    probs = probs.squeeze(1).cpu().numpy()
    if args.resize:
        probs = np.swapaxes(probs, 0, 2)
        probs = cv2.resize(probs, (orig_img_size, orig_img_size), interpolation=cv2.INTER_LINEAR)
        probs = np.swapaxes(probs, 0, 2)
    else:
        probs = probs[:, y0:y1, x0:x1]
    return probs

    def _make_images_board(self, model):
        model.eval()
        num_imgs = 64
        fuseTrans = self.cfg.fuseTrans

        batch = next(iter(self.data_loaders[1]))
        input_images, renderTrans, depthGT, maskGT = utils.unpack_batch_novel(batch, self.cfg.device)

        with torch.set_grad_enabled(False):
            XYZ, maskLogit = model(input_images)
            # ------ build transformer ------
            XYZid, ML = transform.fuse3D(
                self.cfg, XYZ, maskLogit, fuseTrans) # [B,3,VHW],[B,1,VHW]
            newDepth, newMaskLogit, collision = transform.render2D(
                self.cfg, XYZid, ML, renderTrans)  # [B,N,1,H,W]

        return {'RGB': utils.make_grid( input_images[:num_imgs]),
                'depth': utils.make_grid(
                    ((1-newDepth)*(collision==1).float())[:num_imgs, 0, 0:1, :, :]),
                'depthGT': utils.make_grid(
                    1-depthGT[:num_imgs, 0, 0:1, :, :]),
                'mask': utils.make_grid(
                    torch.sigmoid(maskLogit[:num_imgs, 0:1,:, :])),
                'mask_rendered': utils.make_grid(
                    torch.sigmoid(newMaskLogit[:num_imgs, 0, 0:1, :, :])),
                'maskGT': utils.make_grid(
                    maskGT[:num_imgs, 0, 0:1, :, :]),
                }

    def custom_cross_entropy(x, y):
        sigmoid_x = torch.sigmoid(x)
        sigmoid_x2 = torch.sigmoid(x ** 2)
        neg_log_sigmoid_x = -1 * torch.log(sigmoid_x)
        neg_log_1_minus_sigmoid_x2 = -1 * torch.log(1 - sigmoid_x2)

        l1 = torch.mul(y, neg_log_sigmoid_x)
        l2 = torch.mul(1 - y, neg_log_1_minus_sigmoid_x2)

        return torch.sum(l1 + l2)

    def forward(self, x=None, warmup=1., inf_net=None): #, k=1): #, marginf_type=0):

        outputs = {}
        B = x.shape[0]

        if inf_net is None:
            # mu, logvar = self.inference_net(x)
            z, logits = self.q.sample(x) 
        else:
            # mu, logvar = inf_net.inference_net(x)   
            z, logqz = inf_net.sample(x) 

        # print (z[0])
        # b = harden(z)
        # print (b[0])
        
        # logpz = torch.sum( self.prior.log_prob(b), dim=1)

        # print (logpz[0])
        # print (logpz.shape)
        # fdasf

        probs_q = torch.sigmoid(logits)
        probs_q = torch.clamp(probs_q, min=.00000001, max=.9999999)
        probs_p = torch.ones(B, self.z_size).cuda() *.5
        KL = probs_q*torch.log(probs_q/probs_p) + (1-probs_q)*torch.log((1-probs_q)/(1-probs_p))
        KL = torch.sum(KL, dim=1)

        # print (z.shape)
        # Decode Image
        x_hat = self.generator.forward(z)
        alpha = torch.sigmoid(x_hat)
        beta = Beta(alpha*self.beta_scale, (1.-alpha)*self.beta_scale)
        x_noise = torch.clamp(x + torch.FloatTensor(x.shape).uniform_(0., 1./256.).cuda(), min=1e-5, max=1-1e-5)
        logpx = beta.log_prob(x_noise) #[120,3,112,112]  # add uniform noise here

        logpx = torch.sum(logpx.view(B, -1),1) # [PB]  * self.w_logpx

        # print (logpx.shape,logpz.shape,logqz.shape)
        # fsdfda

        log_ws = logpx - KL #+ logpz - logqz

        outputs['logpx'] = torch.mean(logpx)
        outputs['x_recon'] = alpha
        # outputs['welbo'] = torch.mean(logpx + warmup*( logpz - logqz))
        outputs['welbo'] = torch.mean(logpx + warmup*(KL))
        outputs['elbo'] = torch.mean(log_ws)
        outputs['logws'] = log_ws
        outputs['z'] = z
        outputs['logpz'] = torch.zeros(1) #torch.mean(logpz)
        outputs['logqz'] = torch.mean(KL)
        # outputs['logvar'] = logvar

        return outputs

def predict_transform(
    prediction, inp_dim, anchors, num_classes, CUDA=True
):
    batch_size = prediction.size(0)
    stride = inp_dim // prediction.size(2)
    grid_size = inp_dim // stride
    bbox_attrs = 5 + num_classes
    num_anchors = len(anchors)

    prediction = prediction.view(
        batch_size, bbox_attrs * num_anchors, grid_size * grid_size)
    prediction = prediction.transpose(1, 2).contiguous()
    prediction = prediction.view(
        batch_size, grid_size * grid_size * num_anchors, bbox_attrs)

    anchors = [(a[0] / stride, a[1] / stride) for a in anchors]

    # Sigmoid the center_X, center_Y and object confidence
    prediction[:,:,0] = torch.sigmoid(prediction[:,:,0])
    prediction[:,:,1] = torch.sigmoid(prediction[:,:,1])
    prediction[:,:,4] = torch.sigmoid(prediction[:,:,4])

    # Add the centre offsets
    grid = np.arange(grid_size)
    a, b = np.meshgrid(grid, grid)

    x_offset = torch.FloatTensor(a).view(-1, 1)
    y_offset = torch.FloatTensor(b).view(-1, 1)

    if CUDA:
        x_offset = x_offset.cuda()
        y_offset = y_offset.cuda()

    x_y_offset = torch.cat((x_offset, y_offset), 1).repeat(1, num_anchors).view(-1, 2).unsqueeze(0)

    prediction[:, :, :2] += x_y_offset

    # log space transform height and the width
    anchors = torch.FloatTensor(anchors)

    if CUDA:
        anchors = anchors.cuda()

    anchors = anchors.repeat(grid_size * grid_size, 1).unsqueeze(0)

    prediction[:,:,2:4] = torch.exp(prediction[:,:,2:4]) * anchors

    prediction[:,:,5:5 + num_classes] = (
        torch.sigmoid((prediction[:, :, 5:5 + num_classes])))

    prediction[:, :, 4] *= stride

    return prediction

    def forward(self, data, last_hidden):
        hx, cx = last_hidden
        m = self.wmx(data) * self.wmh(hx)
        gates = self.wx(data) + self.wh(m)
        i, f, o, u = gates.chunk(4, 1)

        i = torch.sigmoid(i)
        f = torch.sigmoid(f)
        u = torch.tanh(u)
        o = torch.sigmoid(o)

        cy = f * cx + i * u
        hy = o * torch.tanh(cy)

        return hy, cy

    def test_autograd_closure(self):
        x = Variable(torch.Tensor([0.4]), requires_grad=True)
        y = Variable(torch.Tensor([0.7]), requires_grad=True)

        trace = torch._C._tracer_enter((x, y), 1)

        z = torch.sigmoid(x * (x + y))
        w = torch.abs(x * x * x + y) + Variable(torch.ones(1))

        torch._C._tracer_exit((z, w))
        torch._C._jit_pass_lint(trace)

        (z * w).backward()
        torch._C._jit_pass_dce(trace)
        torch._C._jit_pass_lint(trace)

        x_grad = x.grad.data.clone()
        x.grad.data.zero_()

        function = torch._C._jit_createAutogradClosure(trace)
        torch._C._jit_pass_lint(trace)
        z2, w2 = function()(x, y)
        (z2 * w2).backward()
        self.assertEqual(z, z2)
        self.assertEqual(w, w2)
        self.assertEqual(x.grad.data, x_grad)

    def forward(self, words):
        projected = [self.projectors[name](self.embedders[name](words)) for name in self.emb_names]

        if self.args.attnnet == 'none':
            out = sum(projected)
        else:
            projected_cat = torch.cat([p.unsqueeze(2) for p in projected], 2)
            s_len, b_size, _, emb_dim = projected_cat.size()
            attn_input = projected_cat

            if self.args.attnnet.startswith('dep_'):
                attn_input = attn_input.view(s_len, b_size * self.n_emb, -1)
                self.m_attn = self.attn_1(self.attn_0(attn_input)[0])
                self.m_attn = self.m_attn.view(s_len, b_size, self.n_emb)
            elif self.args.attnnet.startswith('no_dep_'):
                self.m_attn = self.attn_1(self.attn_0(attn_input)).squeeze(3)

            if self.args.attnnet.endswith('_gating'):
                self.m_attn = torch.sigmoid(self.m_attn)
            elif self.args.attnnet.endswith('_softmax'):
                self.m_attn = F.softmax(self.m_attn, dim=2)

            attended = projected_cat * self.m_attn.view(s_len, b_size, self.n_emb, 1).expand_as(projected_cat)
            out = attended.sum(2)

        if self.args.nonlin == 'relu':
            out = F.relu(out)
        if self.args.emb_dropout > 0.0:
            out = self.dropout(out)
        return out

 def forward(self, input, hidden_state):
     hidden,c=hidden_state#hidden and c are images with several channels
     #print 'hidden ',hidden.size()
     #print 'input ',input.size()
     combined = torch.cat((input, hidden), 1)#oncatenate in the channels
     #print 'combined',combined.size()
     A=self.conv(combined)
     (ai,af,ao,ag)=torch.split(A,self.num_features,dim=1)#it should return 4 tensors
     i=torch.sigmoid(ai)
     f=torch.sigmoid(af)
     o=torch.sigmoid(ao)
     g=torch.tanh(ag)
     
     next_c=f*c+i*g
     next_h=o*torch.tanh(next_c)
     return next_h, next_c

 def forward(self, x):
     out = F.leaky_relu(self.conv1(x), 0.05) # (?, 32, 14, 14)
     out = F.leaky_relu(self.conv2(out), 0.05) # (?, 64, 7, 7)
     out = F.leaky_relu(self.conv3(out), 0.05) # (?, 128, 3, 3)
     out = F.leaky_relu(self.conv4(out), 0.05) # (?, 256, 1, 1)
     out = out.squeeze()
     return torch.sigmoid(self.linear(out))