Python torch.ones_like方法代码示例

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def initialize(self, parameters: Tuple[Parameter, ...], *args):
        stacked = stacker(parameters, lambda u: u.t_values)

        self._mean = torch.zeros(stacked.concated.shape[1:], device=stacked.concated.device)
        self._log_std = torch.ones_like(self._mean)

        for p, msk in zip(parameters, stacked.mask):
            try:
                self._mean[msk] = p.bijection.inv(p.distr.mean)
            except NotImplementedError:
                pass

        self._mean.requires_grad_(True)
        self._log_std.requires_grad_(True)

        return self 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def test_Stacker(self):
        # ===== Define a mix of parameters ====== #
        zerod = Parameter(Normal(0., 1.)).sample_((1000,))
        oned_luring = Parameter(Normal(torch.tensor([0.]), torch.tensor([1.]))).sample_(zerod.shape)
        oned = Parameter(MultivariateNormal(torch.zeros(2), torch.eye(2))).sample_(zerod.shape)

        mu = torch.zeros((3, 3))
        norm = Independent(Normal(mu, torch.ones_like(mu)), 2)
        twod = Parameter(norm).sample_(zerod.shape)

        # ===== Stack ===== #
        params = (zerod, oned, oned_luring, twod)
        stacked = stacker(params, lambda u: u.t_values, dim=1)

        # ===== Verify it's recreated correctly ====== #
        for p, m, ps in zip(params, stacked.mask, stacked.prev_shape):
            v = stacked.concated[..., m]

            if len(p.c_shape) != 0:
                v = v.reshape(*v.shape[:-1], *ps)

            assert (p.t_values == v).all() 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def test_MultiDimensional(self):
        mu = torch.zeros(2)
        scale = torch.ones_like(mu)

        shape = 1000, 100

        mvn = Independent(Normal(mu, scale), 1)
        mvn = AffineProcess((f, g), (1., 1.), mvn, mvn)

        # ===== Initialize ===== #
        x = mvn.i_sample(shape)

        # ===== Propagate ===== #
        num = 100
        samps = [x]
        for t in range(num):
            samps.append(mvn.propagate(samps[-1]))

        samps = torch.stack(samps)
        self.assertEqual(samps.size(), torch.Size([num + 1, *shape, *mu.shape]))

        # ===== Sample path ===== #
        path = mvn.sample_path(num + 1, shape)
        self.assertEqual(samps.shape, path.shape) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def tforward(self, disp0, im, std=None):
    self.pattern = self.pattern.to(disp0.device)
    self.uv0 = self.uv0.to(disp0.device)

    uv0 = self.uv0.expand(disp0.shape[0], *self.uv0.shape[1:])
    uv1 = torch.empty_like(uv0)
    uv1[...,0] = uv0[...,0] - disp0.contiguous().view(disp0.shape[0],-1)
    uv1[...,1] = uv0[...,1]

    uv1[..., 0] = 2 * (uv1[..., 0] / (self.im_width-1) - 0.5)
    uv1[..., 1] = 2 * (uv1[..., 1] / (self.im_height-1) - 0.5)
    uv1 = uv1.view(-1, self.im_height, self.im_width, 2).clone()
    pattern = self.pattern.expand(disp0.shape[0], *self.pattern.shape[1:])
    pattern_proj = torch.nn.functional.grid_sample(pattern, uv1, padding_mode='border')
    mask = torch.ones_like(im)
    if std is not None:
      mask = mask*std

    diff = torchext.photometric_loss(pattern_proj.contiguous(), im.contiguous(), 9, self.loss_type, self.loss_eps)
    val = (mask*diff).sum() / mask.sum()
    return val, pattern_proj 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def test_sample_and_log_prob_with_context(self):
        num_samples = 10
        context_size = 20
        input_shape = [2, 3, 4]
        context_shape = [2, 3, 4]

        dist = discrete.ConditionalIndependentBernoulli(input_shape)
        context = torch.randn(context_size, *context_shape)
        samples, log_prob = dist.sample_and_log_prob(num_samples, context=context)

        self.assertIsInstance(samples, torch.Tensor)
        self.assertIsInstance(log_prob, torch.Tensor)

        self.assertEqual(samples.shape, torch.Size([context_size, num_samples] + input_shape))
        self.assertEqual(log_prob.shape, torch.Size([context_size, num_samples]))

        self.assertFalse(torch.isnan(log_prob).any())
        self.assertFalse(torch.isinf(log_prob).any())
        self.assert_tensor_less_equal(log_prob, 0.0)

        self.assertFalse(torch.isnan(samples).any())
        self.assertFalse(torch.isinf(samples).any())
        binary = (samples == 1.0) | (samples == 0.0)
        self.assertEqual(binary, torch.ones_like(binary)) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def weighted_cross_entropy_loss(preds, edges):
    """ Calculate sum of weighted cross entropy loss. """
    # Reference:
    #   hed/src/caffe/layers/sigmoid_cross_entropy_loss_layer.cpp
    #   https://github.com/s9xie/hed/issues/7
    mask = (edges > 0.5).float()
    b, c, h, w = mask.shape
    num_pos = torch.sum(mask, dim=[1, 2, 3], keepdim=True).float()  # Shape: [b,].
    num_neg = c * h * w - num_pos                     # Shape: [b,].
    weight = torch.zeros_like(mask)
    #weight[edges > 0.5]  = num_neg / (num_pos + num_neg)
    #weight[edges <= 0.5] = num_pos / (num_pos + num_neg)
    weight.masked_scatter_(edges > 0.5,
        torch.ones_like(edges) * num_neg / (num_pos + num_neg))
    weight.masked_scatter_(edges <= 0.5,
        torch.ones_like(edges) * num_pos / (num_pos + num_neg))
    # Calculate loss.
    # preds=torch.sigmoid(preds)
    losses = F.binary_cross_entropy_with_logits(
        preds.float(), edges.float(), weight=weight, reduction='none')
    loss = torch.sum(losses) / b
    return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def test_parallel_transport0_preserves_inner_products(a, k):
    man = lorentz.Lorentz(k=k)
    a = man.projx(a)

    v_0 = torch.rand_like(a) + 1e-5
    u_0 = torch.rand_like(a) + 1e-5

    zero = torch.ones_like(a)
    d = zero.size(1) - 1
    zero = torch.cat(
        (zero.narrow(1, 0, 1) * torch.sqrt(k), zero.narrow(1, 1, d) * 0.0), dim=1
    )

    v_0 = man.proju(zero, v_0)  # project on tangent plane
    u_0 = man.proju(zero, u_0)  # project on tangent plane

    v_a = man.transp0(a, v_0)
    u_a = man.transp0(a, u_0)

    vu_0 = man.inner(v_0, u_0, keepdim=True)
    vu_a = man.inner(v_a, u_a, keepdim=True)
    np.testing.assert_allclose(vu_a, vu_0, atol=1e-5, rtol=1e-5) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def test_parallel_transport0_back(a, b, k):
    man = lorentz.Lorentz(k=k)
    a = man.projx(a)
    b = man.projx(b)

    v_0 = torch.rand_like(a) + 1e-5
    v_0 = man.proju(a, v_0)  # project on tangent plane

    zero = torch.ones_like(a)
    d = zero.size(1) - 1
    zero = torch.cat(
        (zero.narrow(1, 0, 1) * torch.sqrt(k), zero.narrow(1, 1, d) * 0.0), dim=1
    )

    v_t = man.transp0back(a, v_0)
    v_t = man.transp0(b, v_t)

    v_s = man.transp(a, zero, v_0)
    v_s = man.transp(zero, b, v_s)

    np.testing.assert_allclose(v_t, v_s, atol=1e-5, rtol=1e-5) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def test_zero_point_ops(a, k):
    man = lorentz.Lorentz(k=k)
    a = man.projx(a)

    zero = torch.ones_like(a)
    d = zero.size(1) - 1
    zero = torch.cat(
        (zero.narrow(1, 0, 1) * torch.sqrt(k), zero.narrow(1, 1, d) * 0.0), dim=1
    )
    inner_z = man.inner0(a)
    inner = man.inner(None, a, zero)
    np.testing.assert_allclose(inner, inner_z, atol=1e-5, rtol=1e-5)

    lmap_z = man.logmap0back(a)
    lmap = man.logmap(a, zero)

    np.testing.assert_allclose(lmap, lmap_z, atol=1e-5, rtol=1e-5) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def __getitem__(self, idx):
        '''

        :param idx: Index of the image file
        :return: returns the image and corresponding label file.
        '''
        image_name = self.imList[idx]
        label_name = self.labelList[idx]
        image = cv2.imread(image_name)
        label = cv2.imread(label_name, 0)
        label_bool = 255 * ((label > 200).astype(np.uint8))

        if self.transform:
            [image, label] = self.transform(image, label_bool)
        if self.edge:
            np_label = 255 * label.data.numpy().astype(np.uint8)
            kernel = np.ones((self.kernel_size , self.kernel_size ), np.uint8)
            erosion = cv2.erode(np_label, kernel, iterations=1)
            dilation = cv2.dilate(np_label, kernel, iterations=1)
            boundary = dilation - erosion
            edgemap = 255 * torch.ones_like(label)
            edgemap[torch.from_numpy(boundary) > 0] = label[torch.from_numpy(boundary) > 0]
            return (image, label, edgemap)
        else:
            return (image, label) 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def forward(self, input, adj):
        h = torch.mm(input, self.W)
        N = h.size()[0]

        f_1 = torch.matmul(h, self.a1)
        f_2 = torch.matmul(h, self.a2)
        e = self.leakyrelu(f_1 + f_2.transpose(0,1))

        zero_vec = -9e15*torch.ones_like(e)
        attention = torch.where(adj > 0, e, zero_vec)
        attention = F.softmax(attention, dim=1)
        attention = F.dropout(attention, self.dropout, training=self.training)
        h_prime = torch.matmul(attention, h)

        if self.concat:
            return F.elu(h_prime)
        else:
            return h_prime 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def forward(self, x):
        """
        Forward pass through adaptation network.
        :param x: (torch.tensor) Input representation to network (task level representation z).
        :return: (list::dictionaries) Dictionary for every block in layer. Dictionary contains all the parameters
                 necessary to adapt layer in base network. Base network is aware of dict structure and can pull params
                 out during forward pass.
        """
        x = self.shared_layer(x)
        block_params = []
        for block in range(self.num_blocks):
            block_param_dict = {
                'gamma1': self.gamma1_processors[block](x).squeeze() * self.gamma1_regularizers[block] +
                          torch.ones_like(self.gamma1_regularizers[block]),
                'beta1': self.beta1_processors[block](x).squeeze() * self.beta1_regularizers[block],
                'gamma2': self.gamma2_processors[block](x).squeeze() * self.gamma2_regularizers[block] +
                          torch.ones_like(self.gamma2_regularizers[block]),
                'beta2': self.beta2_processors[block](x).squeeze() * self.beta2_regularizers[block]
            }
            block_params.append(block_param_dict)
        return block_params 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def forward(self, x):
        if (not self.training or self.keep_prob==1): #set keep_prob=1 to turn off dropblock
            return x
        if self.gamma is None:
            self.gamma = self.calculate_gamma(x)
        if x.type() == 'torch.cuda.HalfTensor': #TODO: not fully support for FP16 now 
            FP16 = True
            x = x.float()
        else:
            FP16 = False
        p = torch.ones_like(x) * (self.gamma)
        mask = 1 - torch.nn.functional.max_pool2d(torch.bernoulli(p),
                                                  self.kernel_size,
                                                  self.stride,
                                                  self.padding)

        out =  mask * x * (mask.numel()/mask.sum())

        if FP16:
            out = out.half()
        return out 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def loss_discriminator(
        self, z, batch_index, predict_true_class=True, return_details=True
    ):

        n_classes = self.gene_dataset.n_batches
        cls_logits = torch.nn.LogSoftmax(dim=1)(self.discriminator(z))

        if predict_true_class:
            cls_target = one_hot(batch_index, n_classes)
        else:
            one_hot_batch = one_hot(batch_index, n_classes)
            cls_target = torch.zeros_like(one_hot_batch)
            # place zeroes where true label is
            cls_target.masked_scatter_(
                ~one_hot_batch.bool(), torch.ones_like(one_hot_batch) / (n_classes - 1)
            )

        l_soft = cls_logits * cls_target
        loss = -l_soft.sum(dim=1).mean()

        return loss 

# 需要导入模块: import torch [as 别名]
# 或者: from torch import ones_like [as 别名]
def _load_from_state_dict(
        self, state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
    ):
        version = local_metadata.get("version", None)

        if version is None or version < 2:
            # No running_mean/var in early versions
            # This will silent the warnings
            if prefix + "running_mean" not in state_dict:
                state_dict[prefix + "running_mean"] = torch.zeros_like(self.running_mean)
            if prefix + "running_var" not in state_dict:
                state_dict[prefix + "running_var"] = torch.ones_like(self.running_var)

        if version is not None and version < 3:
            # logger = logging.getLogger(__name__)
            logging.info("FrozenBatchNorm {} is upgraded to version 3.".format(prefix.rstrip(".")))
            # In version < 3, running_var are used without +eps.
            state_dict[prefix + "running_var"] -= self.eps

        super()._load_from_state_dict(
            state_dict, prefix, local_metadata, strict, missing_keys, unexpected_keys, error_msgs
        )