Python torch.add函数代码示例

    def __call__(self, image_batch, theta_aff, theta_aff_tps, use_cuda=True):
        
        sampling_grid_aff = self.affTnf(image_batch=None,
                                        theta_batch=theta_aff.view(-1,2,3),
                                        return_sampling_grid=True,
                                        return_warped_image=False)
      
        sampling_grid_aff_tps = self.tpsTnf(image_batch=None,
                                       theta_batch=theta_aff_tps,
                                       return_sampling_grid=True,
                                       return_warped_image=False)
        
        if self.padding_crop_factor is not None:
            sampling_grid_aff_tps = sampling_grid_aff_tps*self.padding_crop_factor;

        # put 1e10 value in region out of bounds of sampling_grid_aff
        in_bound_mask_aff = ((sampling_grid_aff[:,:,:,0]>-1) * (sampling_grid_aff[:,:,:,0]<1) * (sampling_grid_aff[:,:,:,1]>-1) * (sampling_grid_aff[:,:,:,1]<1)).unsqueeze(3)
        in_bound_mask_aff = in_bound_mask_aff.expand_as(sampling_grid_aff)
        sampling_grid_aff = torch.mul(in_bound_mask_aff.float(),sampling_grid_aff)
        sampling_grid_aff = torch.add((in_bound_mask_aff.float()-1)*(1e10),sampling_grid_aff)       
        
        # compose transformations
        sampling_grid_aff_tps_comp = F.grid_sample(sampling_grid_aff.transpose(2,3).transpose(1,2), sampling_grid_aff_tps).transpose(1,2).transpose(2,3)
            
        # put 1e10 value in region out of bounds of sampling_grid_aff_tps_comp
        in_bound_mask_aff_tps=((sampling_grid_aff_tps[:,:,:,0]>-1) * (sampling_grid_aff_tps[:,:,:,0]<1) * (sampling_grid_aff_tps[:,:,:,1]>-1) * (sampling_grid_aff_tps[:,:,:,1]<1)).unsqueeze(3)
        in_bound_mask_aff_tps=in_bound_mask_aff_tps.expand_as(sampling_grid_aff_tps_comp)
        sampling_grid_aff_tps_comp=torch.mul(in_bound_mask_aff_tps.float(),sampling_grid_aff_tps_comp)
        sampling_grid_aff_tps_comp = torch.add((in_bound_mask_aff_tps.float()-1)*(1e10),sampling_grid_aff_tps_comp)       

        # sample transformed image
        warped_image_batch = F.grid_sample(image_batch, sampling_grid_aff_tps_comp)
        
        return warped_image_batch

 def forward(self, img, att_size=14):
     x0 = self.conv(img)
     x = self.pool_mil(x0)
     x = x.squeeze(2).squeeze(2)
     x = self.l1(x)
     x1 = torch.add(torch.mul(x.view(x.size(0), 1000, -1), -1), 1)
     cumprod = torch.cumprod(x1, 2)
     out = torch.max(x, torch.add(torch.mul(cumprod[:, :, -1], -1), 1))
     return out

 def forward(self, x):
     x0 = self.conv.forward(x.float())
     x = self.pool_mil(x0)
     x = x.squeeze(2).squeeze(2)
     x1 = torch.add(torch.mul(x0.view(x.size(0), 1000, -1), -1), 1)
     cumprod = torch.cumprod(x1, 2)
     out = torch.max(x, torch.add(torch.mul(cumprod[:, :, -1], -1), 1))
     #out = F.softmax(out)
     return out

    def match(self, passage_encoders, question_encoders, wq_matrix, wp_matrix, fw = True):
        
        '''
        passage_encoders (pn_steps, batch, hidden_size)
        question_encoders (qn_steps, batch, hidden_size)
        wq_matrix (qn_steps, batch, hidden_size)
        wp_matrix (pn_steps, batch, hidden_size)
        '''
        if fw:
            match_lstm = self.fw_match_lstm
            start = 0
            end = passage_encoders.size(0)
            stride = 1
        else:
            match_lstm = self.bw_match_lstm
            start = passage_encoders.size(0) - 1
            end = -1
            stride = -1
        
        hx = Variable(torch.zeros(passage_encoders.size(1), self.hidden_size)).cuda()
        cx = Variable(torch.zeros(passage_encoders.size(1), self.hidden_size)).cuda()
        
        match_encoders = [0 for i in range(passage_encoders.size(0))]
        
        for i in range(start, end, stride):
            
            wphp = wp_matrix[i]
            wrhr = self.whr_net(hx)

            _sum = torch.add(wphp, wrhr) # batch, hidden_size
            _sum = _sum.expand(wq_matrix.size(0), wq_matrix.size(1), self.hidden_size) # qn_steps, batch, hidden_size
            
            g = self.tanh(torch.add(wq_matrix, _sum)) # qn_steps, batch, hidden_size

            g = torch.transpose(g, 0, 1)# batch, qn_steps, hidden_size
            
            wg = self.w_net(g) # bactch, qn_steps, 1
            wg = wg.squeeze(-1) # bactch, qn_steps
            alpha = wg # bactch, qn_steps
            alpha = self.softmax(alpha).view(alpha.size(0), 1, alpha.size(1)) # batch,1, qn_steps
            
            
            attentionv = torch.bmm(alpha, question_encoders.transpose(0, 1)) # bacth, 1, hidden_size
            attentionv = attentionv.squeeze(1) # bacth, hidden_size
            
            inp = torch.cat([passage_encoders[i], attentionv], -1)
                        
            hx, cx = match_lstm(inp, (hx, cx)) # batch, hidden_size
            
            match_encoders[i] = hx.view(1, hx.size(0), -1)
            
        match_encoders = torch.cat(match_encoders)
        
        return match_encoders

    def updateOutput(self, input):
        self.output.resize_(1)
        assert input[0].dim() == 2

        if self.diff is None:
            self.diff = input[0].new()

        torch.add(input[0], -1, input[1], out=self.diff).abs_()

        self.output.resize_(input[0].size(0))
        self.output.zero_()
        self.output.add_(self.diff.pow_(self.norm).sum(1, keepdim=False))
        self.output.pow_(1. / self.norm)

        return self.output

    def test_train(self):
        self._metric.train()
        calls = [[torch.FloatTensor([0.0]), torch.LongTensor([0])],
                 [torch.FloatTensor([0.0, 0.1, 0.2, 0.3]), torch.LongTensor([0, 1, 2, 3])]]
        for i in range(len(self._states)):
            self._metric.process(self._states[i])
        self.assertEqual(2, len(self._metric_function.call_args_list))
        for i in range(len(self._metric_function.call_args_list)):
            self.assertTrue(torch.eq(self._metric_function.call_args_list[i][0][0], calls[i][0]).all)
            self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[i][0][1], -calls[i][1])), 1e-12).all)
        self._metric_function.reset_mock()
        self._metric.process_final({})

        self._metric_function.assert_called_once()
        self.assertTrue(torch.eq(self._metric_function.call_args_list[0][0][1], torch.LongTensor([0, 1, 2, 3, 4])).all)
        self.assertTrue(torch.lt(torch.abs(torch.add(self._metric_function.call_args_list[0][0][0], -torch.FloatTensor([0.0, 0.1, 0.2, 0.3, 0.4]))), 1e-12).all)

def unit_test(args):
    ''' test different (kinds of) predicate detectors '''
    print("Torch uninitialized 5x3 matrix:")
    x_t = torch.Tensor(5, 3)
    print(x_t)

    print("Torch randomly initialized 5x3 matrix X:")
    x_t = torch.rand(5, 3)
    if args.verbose:
        print(x_t)
        print("size:", x_t.size())

    print("Torch randomly initialized 5x3 matrix Y:")
    y_t = torch.rand(5, 3)
    if args.verbose:
        print(y_t)
    print("X + Y:")
    z_t = torch.add(x_t, y_t)
    print(z_t)


    print("slice (X + Y)[:, 1]:")
    print(z_t[:, 1])

    num_wrong = 0
    print("unit_test:  num_tests:", 1,
          " num_wrong:", num_wrong, " -- ", "FAIL" if num_wrong else "PASS")

    def test_remote_var_binary_methods(self):
        ''' Unit tests for methods mentioned on issue 1385
            https://github.com/OpenMined/PySyft/issues/1385'''
        hook = TorchHook(verbose=False)
        local = hook.local_worker
        remote = VirtualWorker(hook, 1)
        local.add_worker(remote)

        x = Var(torch.FloatTensor([1, 2, 3, 4])).send(remote)
        y = Var(torch.FloatTensor([[1, 2, 3, 4]])).send(remote)
        z = torch.matmul(x, y.t())
        assert (torch.equal(z.get(), Var(torch.FloatTensor([30]))))
        z = torch.add(x, y)
        assert (torch.equal(z.get(), Var(torch.FloatTensor([[2, 4, 6, 8]]))))
        x = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
        y = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
        z = torch.cross(x, y, dim=1)
        assert (torch.equal(z.get(), Var(torch.FloatTensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]]))))
        x = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
        y = Var(torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])).send(remote)
        z = torch.dist(x, y)
        assert (torch.equal(z.get(), Var(torch.FloatTensor([0.]))))
        x = Var(torch.FloatTensor([1, 2, 3])).send(remote)
        y = Var(torch.FloatTensor([1, 2, 3])).send(remote)
        z = torch.dot(x, y)
        print(torch.equal(z.get(), Var(torch.FloatTensor([14]))))
        z = torch.eq(x, y)
        assert (torch.equal(z.get(), Var(torch.ByteTensor([1, 1, 1]))))
        z = torch.ge(x, y)
        assert (torch.equal(z.get(), Var(torch.ByteTensor([1, 1, 1]))))

 def test_local_var_binary_methods(self):
     ''' Unit tests for methods mentioned on issue 1385
         https://github.com/OpenMined/PySyft/issues/1385'''
     x = torch.FloatTensor([1, 2, 3, 4])
     y = torch.FloatTensor([[1, 2, 3, 4]])
     z = torch.matmul(x, y.t())
     assert (torch.equal(z, torch.FloatTensor([30])))
     z = torch.add(x, y)
     assert (torch.equal(z, torch.FloatTensor([[2, 4, 6, 8]])))
     x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
     y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
     z = torch.cross(x, y, dim=1)
     assert (torch.equal(z, torch.FloatTensor([[0, 0, 0], [0, 0, 0], [0, 0, 0]])))
     x = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
     y = torch.FloatTensor([[1, 2, 3], [3, 4, 5], [5, 6, 7]])
     z = torch.dist(x, y)
     t = torch.FloatTensor([z])
     assert (torch.equal(t, torch.FloatTensor([0.])))
     x = torch.FloatTensor([1, 2, 3])
     y = torch.FloatTensor([1, 2, 3])
     z = torch.dot(x, y)
     t = torch.FloatTensor([z])
     assert torch.equal(t, torch.FloatTensor([14]))
     z = torch.eq(x, y)
     assert (torch.equal(z, torch.ByteTensor([1, 1, 1])))
     z = torch.ge(x, y)
     assert (torch.equal(z, torch.ByteTensor([1, 1, 1])))

    def forward(self, x):
        x = self.embed(x)
        x = self.dropout(x)
        # x = x.view(len(x), x.size(1), -1)
        # x = embed.view(len(x), embed.size(1), -1)
        bilstm_out, self.hidden = self.bilstm(x, self.hidden)

        bilstm_out = torch.transpose(bilstm_out, 0, 1)
        bilstm_out = torch.transpose(bilstm_out, 1, 2)
        # bilstm_out = F.max_pool1d(bilstm_out, bilstm_out.size(2)).squeeze(2)
        bilstm_out = F.max_pool1d(bilstm_out, bilstm_out.size(2))
        bilstm_out = bilstm_out.squeeze(2)

        hidden2lable = self.hidden2label1(F.tanh(bilstm_out))

        gate_layer = F.sigmoid(self.gate_layer(bilstm_out))
        # calculate highway layer values
        gate_hidden_layer = torch.mul(hidden2lable, gate_layer)
        # if write like follow ,can run,but not equal the HighWay NetWorks formula
        # gate_input = torch.mul((1 - gate_layer), hidden2lable)
        gate_input = torch.mul((1 - gate_layer), bilstm_out)
        highway_output = torch.add(gate_hidden_layer, gate_input)

        logit = self.logit_layer(highway_output)

        return logit

	def fade_in_layer(self,x,alpha):
		for l in self.layers:
			x = l(x)
		x_new = self.next_block(x)
		x = self.toRGB(x)
		x_new = self.new_toRGB(x_new)
		return torch.add(x.mul(1.0-alpha),x_new.mul(alpha))

    def forward(self, context_ids, doc_ids, target_noise_ids):
        """Sparse computation of scores (unnormalized log probabilities)
        that should be passed to the negative sampling loss.

        Parameters
        ----------
        context_ids: torch.Tensor of size (batch_size, num_context_words)
            Vocabulary indices of context words.

        doc_ids: torch.Tensor of size (batch_size,)
            Document indices of paragraphs.

        target_noise_ids: torch.Tensor of size (batch_size, num_noise_words + 1)
            Vocabulary indices of target and noise words. The first element in
            each row is the ground truth index (i.e. the target), other
            elements are indices of samples from the noise distribution.

        Returns
        -------
            autograd.Variable of size (batch_size, num_noise_words + 1)
        """
        # combine a paragraph vector with word vectors of
        # input (context) words
        x = torch.add(
            self._D[doc_ids, :], torch.sum(self._W[context_ids, :], dim=1))

        # sparse computation of scores (unnormalized log probabilities)
        # for negative sampling
        return torch.bmm(
            x.unsqueeze(1),
            self._O[:, target_noise_ids].permute(1, 0, 2)).squeeze()

    def forward(self, title, pg):

        r_gate = F.sigmoid(self.wrx(title) + self.wrh(pg))
        i_gate = F.sigmoid(self.wix(title) + self.wih(pg))
        n_gate = F.tanh(self.wnx(title) + torch.mul(r_gate, self.wnh(pg)))
        result =  torch.mul(i_gate, pg) + torch.mul(torch.add(-i_gate, 1), n_gate)
        return result

    def get_loss(self, image_a_pred, image_b_pred, mask_a, mask_b):
        loss = 0

        # get the nonzero indices
        mask_a_indices_flat = torch.nonzero(mask_a)
        mask_b_indices_flat = torch.nonzero(mask_b)
        if len(mask_a_indices_flat) == 0:
            return Variable(torch.cuda.LongTensor([0]), requires_grad=True)
        if len(mask_b_indices_flat) == 0:
            return Variable(torch.cuda.LongTensor([0]), requires_grad=True)

        # take 5000 random pixel samples of the object, using the mask
        num_samples = 10000

        rand_numbers_a = (torch.rand(num_samples)*len(mask_a_indices_flat)).cuda()
        rand_indices_a = Variable(torch.floor(rand_numbers_a).type(torch.cuda.LongTensor), requires_grad=False)
        randomized_mask_a_indices_flat = torch.index_select(mask_a_indices_flat, 0, rand_indices_a).squeeze(1)

        rand_numbers_b = (torch.rand(num_samples)*len(mask_b_indices_flat)).cuda()
        rand_indices_b = Variable(torch.floor(rand_numbers_b).type(torch.cuda.LongTensor), requires_grad=False)
        randomized_mask_b_indices_flat = torch.index_select(mask_b_indices_flat, 0, rand_indices_b).squeeze(1)

        # index into the image and get descriptors
        M_margin = 0.5 # margin parameter
        random_img_a_object_descriptors = torch.index_select(image_a_pred, 1, randomized_mask_a_indices_flat)
        random_img_b_object_descriptors = torch.index_select(image_b_pred, 1, randomized_mask_b_indices_flat)
        pixel_wise_loss = (random_img_a_object_descriptors - random_img_b_object_descriptors).pow(2).sum(dim=2)
        pixel_wise_loss = torch.add(pixel_wise_loss, -2*M_margin)
        zeros_vec = torch.zeros_like(pixel_wise_loss)
        loss += torch.max(zeros_vec, pixel_wise_loss).sum()

        return loss

 def forward(self, x):
     if not self.equalInOut: x   = self.relu1(self.bn1(x))
     else:                   out = self.relu1(self.bn1(x))
     out = self.relu2(self.bn2(self.conv1(out if self.equalInOut else x)))
     if self.droprate > 0:
         out = F.dropout(out, p=self.droprate, training=self.training)
     out = self.conv2(out)
     return torch.add(x if self.equalInOut else self.convShortcut(x), out)