Python functional.conv3d方法代码示例

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def padding3d(tensor, filter, mode=str('constant')):
    """
    Input shape (BN, C, T, H, W)
    """

    it, ih, iw = tensor.shape[2:]
    ft, fh, fw = filter.shape

    pt = max(0, (it - 1) + (ft - 1) + 1 - it)
    ph = max(0, (ih - 1) + (fh - 1) + 1 - ih)
    pw = max(0, (iw - 1) + (fw - 1) + 1 - iw)

    oddt = (pt % 2 != 0)
    oddh = (ph % 2 != 0)
    oddw = (pw % 2 != 0)

    if any([oddt, oddh, oddw]):
        pad = [0, int(oddt), 0, int(oddh), 0, int(oddw)]
        tensor = F.pad(tensor, pad, mode=mode)

    padding = (pt // 2, ph // 2, pw // 2)
    tensor = F.conv3d(tensor, filter, padding=padding)

    return tensor 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def __init__(self, spatial_dims: int, sigma, truncated: float = 4.0):
        """
        Args:
            spatial_dims: number of spatial dimensions of the input image.
                must have shape (Batch, channels, H[, W, ...]).
            sigma (float or sequence of floats): std.
            truncated: spreads how many stds.
        """
        super().__init__()
        self.spatial_dims = int(spatial_dims)
        _sigma = ensure_tuple_rep(sigma, self.spatial_dims)
        self.kernel = [
            torch.nn.Parameter(torch.as_tensor(gaussian_1d(s, truncated), dtype=torch.float), False) for s in _sigma
        ]
        self.padding = [same_padding(k.size()[0]) for k in self.kernel]
        self.conv_n = [F.conv1d, F.conv2d, F.conv3d][spatial_dims - 1]
        for idx, param in enumerate(self.kernel):
            self.register_parameter(f"kernel_{idx}", param) 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def compute_local_sums(I, J, filt, stride, padding, win):
    I2 = I * I
    J2 = J * J
    IJ = I * J

    I_sum = F.conv3d(I, filt, stride=stride, padding=padding)
    J_sum = F.conv3d(J, filt, stride=stride, padding=padding)
    I2_sum = F.conv3d(I2, filt, stride=stride, padding=padding)
    J2_sum = F.conv3d(J2, filt, stride=stride, padding=padding)
    IJ_sum = F.conv3d(IJ, filt, stride=stride, padding=padding)

    win_size = np.prod(win)
    u_I = I_sum / win_size
    u_J = J_sum / win_size

    cross = IJ_sum - u_J * I_sum - u_I * J_sum + u_I * u_J * win_size
    I_var = I2_sum - 2 * u_I * I_sum + u_I * u_I * win_size
    J_var = J2_sum - 2 * u_J * J_sum + u_J * u_J * win_size

    return I_var, J_var, cross 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def quaternion_conv(input, r_weight, i_weight, j_weight, k_weight, bias, stride,
                    padding, groups, dilatation):
    """
    Applies a quaternion convolution to the incoming data:
    """

    cat_kernels_4_r = torch.cat([r_weight, -i_weight, -j_weight, -k_weight], dim=1)
    cat_kernels_4_i = torch.cat([i_weight,  r_weight, -k_weight, j_weight], dim=1)
    cat_kernels_4_j = torch.cat([j_weight,  k_weight, r_weight, -i_weight], dim=1)
    cat_kernels_4_k = torch.cat([k_weight,  -j_weight, i_weight, r_weight], dim=1)

    cat_kernels_4_quaternion   = torch.cat([cat_kernels_4_r, cat_kernels_4_i, cat_kernels_4_j, cat_kernels_4_k], dim=0)

    if   input.dim() == 3:
        convfunc = F.conv1d
    elif input.dim() == 4:
        convfunc = F.conv2d
    elif input.dim() == 5:
        convfunc = F.conv3d
    else:
        raise Exception("The convolutional input is either 3, 4 or 5 dimensions."
                        " input.dim = " + str(input.dim()))

    return convfunc(input, cat_kernels_4_quaternion, bias, stride, padding, dilatation, groups) 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def update_params(self, VdivWZH, update_W, update_H, update_Z, W_alpha, H_alpha, Z_alpha):
        # type: (Tensor, bool, bool, bool, float, float, float) -> None
        VdivWZH = VdivWZH.view(self.channel, 1, self.N, self.K, self.M)
        if update_W or update_Z:
            new_W = F.conv3d(VdivWZH, self.H.mul(self.Z[:, None, None, None])[:, None], padding=self.pad_size) * self.W

        if update_H:
            new_H = F.conv3d(VdivWZH.transpose(0, 1), torch.transpose(self.W * self.Z[:, None, None, None], 0, 1))[
                        0] * self.H
            new_H = normalize(self.fix_neg(new_H + H_alpha - 1), (1, 2, 3))
            self.H[:] = new_H

        if update_W:
            self.W[:] = normalize(self.fix_neg(new_W + W_alpha - 1), (0, 2, 3, 4))

        if update_Z:
            Z = normalize(self.fix_neg(new_W.sum((0, 2, 3, 4)) + Z_alpha - 1))
            self.Z[:] = Z 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def forward(self, x):
        if self.deterministic:
            assert self.training == False, "Flag deterministic is True. This should not be used in training."
            return F.conv3d(x, self.post_weight_mu, self.bias_mu, self.stride, self.padding, self.dilation, self.groups)
        batch_size = x.size()[0]
        # apply local reparametrisation trick see [1] Eq. (6)
        # to the parametrisation given in [3] Eq. (6)
        mu_activations = F.conv3d(x, self.weight_mu, self.bias_mu, self.stride,
                                  self.padding, self.dilation, self.groups)

        var_weights = self.weight_logvar.exp()
        var_activations = F.conv3d(x.pow(2), var_weights, self.bias_logvar.exp(), self.stride,
                                   self.padding, self.dilation, self.groups)
        # compute z
        # note that we reparametrise according to [2] Eq. (11) (not [1])
        z = reparametrize(self.z_mu.repeat(batch_size, 1, 1, 1, 1), self.z_logvar.repeat(batch_size, 1, 1, 1, 1),
                          sampling=self.training, cuda=self.cuda)
        z = z[:, :, None, None, None]

        return reparametrize(mu_activations * z, (var_activations * z.pow(2)).log(), sampling=self.training,
                             cuda=self.cuda) 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def quaternion_conv(input, r_weight, i_weight, j_weight, k_weight, bias, stride, 
                    padding, groups, dilatation):
    """
    Applies a quaternion convolution to the incoming data:
    """

    cat_kernels_4_r = torch.cat([r_weight, -i_weight, -j_weight, -k_weight], dim=1)
    cat_kernels_4_i = torch.cat([i_weight,  r_weight, -k_weight, j_weight], dim=1)
    cat_kernels_4_j = torch.cat([j_weight,  k_weight, r_weight, -i_weight], dim=1)
    cat_kernels_4_k = torch.cat([k_weight,  -j_weight, i_weight, r_weight], dim=1)
    cat_kernels_4_quaternion   = torch.cat([cat_kernels_4_r, cat_kernels_4_i, cat_kernels_4_j, cat_kernels_4_k], dim=0)

    if   input.dim() == 3:
        convfunc = F.conv1d
    elif input.dim() == 4:
        convfunc = F.conv2d
    elif input.dim() == 5:
        convfunc = F.conv3d
    else:
        raise Exception("The convolutional input is either 3, 4 or 5 dimensions."
                        " input.dim = " + str(input.dim()))

    return convfunc(input, cat_kernels_4_quaternion, bias, stride, padding, dilatation, groups) 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def test_conv3d(self):
        # Data and weight tensors
        tensor3d_in_conv = torch.randn(32, 3, 16, 224, 224, device='cuda', dtype=self.dtype)
        tensor3d_in_conv_grouped = torch.randn(32, 6, 16, 224, 224, device='cuda', dtype=self.dtype)
        conv3d_filter = torch.randn(16, 3, 3, 3, 3, device='cuda', dtype=self.dtype)
        conv3d_bias = torch.ones(16, device='cuda', dtype=self.dtype)
        # Vanilla conv3d
        conv3d_out_vanilla = F.conv3d(tensor3d_in_conv, conv3d_filter)
        # conv3d - stride > 1
        conv3d_out_strided = F.conv3d(tensor3d_in_conv, conv3d_filter, stride=2)
        # conv3d - dilation > 1
        conv3d_out_dilated = F.conv3d(tensor3d_in_conv, conv3d_filter, dilation=2)
        # conv3d - groups > 1
        conv3d_out_grouped = F.conv3d(tensor3d_in_conv_grouped, conv3d_filter, groups=2)
        # conv3d - padding with zeros
        conv3d_out_padding_zeros = F.conv3d(tensor3d_in_conv, conv3d_filter, padding=6) 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def forward(self, input):
        x = torch.stack([
            F.conv3d(input,
                     self.weight.view(*self.channel_shape, *kernel_size),
                     self.bias, self.stride, padding, self.dilation, self.groups)
            for kernel_size, padding in zip(self.kernel_sizes, self.paddings)], -1)
        x = self.linear(x)[..., 0]
        return x 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def __call__(self, img):
        """
        Args:
            img: torch tensor data to extract the contour, with shape: [batch_size, channels, height, width[, depth]]

        Returns:
            A torch tensor with the same shape as img, note:
                1. it's the binary classification result of whether a pixel is edge or not.
                2. in order to keep the original shape of mask image, we use padding as default.
                3. the edge detection is just approximate because it defects inherent to Laplace kernel,
                   ideally the edge should be thin enough, but now it has a thickness.

        """
        channels = img.shape[1]
        if img.ndim == 4:
            kernel = torch.tensor([[-1, -1, -1], [-1, 8, -1], [-1, -1, -1]], dtype=torch.float32, device=img.device)
            kernel = kernel.repeat(channels, 1, 1, 1)
            contour_img = F.conv2d(img, kernel, bias=None, stride=1, padding=1, dilation=1, groups=channels)
        elif img.ndim == 5:
            kernel = -1 * torch.ones(3, 3, 3, dtype=torch.float32, device=img.device)
            kernel[1, 1, 1] = 26
            kernel = kernel.repeat(channels, 1, 1, 1, 1)
            contour_img = F.conv3d(img, kernel, bias=None, stride=1, padding=1, dilation=1, groups=channels)
        else:
            raise RuntimeError("the dimensions of img should be 4 or 5.")

        torch.clamp_(contour_img, min=0.0, max=1.0)
        return contour_img 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def reconstruct(self, W, Z, H):
        out = F.conv3d(H[None, ...], W.mul(Z[:, None, None, None]).flip((2, 3, 4)), padding=self.pad_size)[0]
        if self.channel == 1:
            return out[0]
        return out 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def reconstruct(self, H, W):
        out = F.conv3d(H[None, ...], W.flip((2, 3, 4)), padding=self.pad_size)[0]
        if self.channel == 1:
            return out[0]
        return out 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def get_W_positive(self, WH, beta, H_sum):
        H = self.H
        if beta == 1:
            if H_sum is None:
                H_sum = H.sum((1, 2, 3))
            denominator = H_sum[None, :, None, None, None]
        else:
            if beta != 2:
                WH = WH.pow(beta - 1)
            WH = WH.view(self.channel, 1, self.N, self.K, self.M)
            WHHt = F.conv3d(WH, H[:, None])
            denominator = WHHt

        return denominator, H_sum 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def get_H_positive(self, WH, beta, W_sum):
        W = self.W
        if beta == 1:
            if W_sum is None:
                W_sum = W.sum((0, 2, 3, 4))
            denominator = W_sum[:, None, None, None]
        else:
            if beta != 2:
                WH = WH.pow(beta - 1)
            WH = WH.view(1, self.channel, self.N, self.K, self.M)
            WtWH = F.conv3d(WH, W.transpose(0, 1))[0]
            denominator = WtWH
        return denominator, W_sum 

# 需要导入模块: from torch.nn import functional [as 别名]
# 或者: from torch.nn.functional import conv3d [as 别名]
def __init__(self, fixed_image, moving_image,fixed_mask=None, moving_mask=None, sigma=[3], kernel_type="box", size_average=True, reduce=True):
        super(LCC, self).__init__(fixed_image, moving_image, fixed_mask, moving_mask,  size_average, reduce)

        self._name = "lcc"
        self.warped_moving_image = th.empty_like(self._moving_image.image, dtype=self._dtype, device=self._device)
        self._kernel = None

        dim = len(self._moving_image.size)
        sigma = np.array(sigma)

        if sigma.size != dim:
            sigma_app = sigma[-1]
            while sigma.size != dim:
                sigma = np.append(sigma, sigma_app)

        if kernel_type == "box":
            kernel_size = sigma*2 + 1
            self._kernel = th.ones(*kernel_size.tolist(), dtype=self._dtype, device=self._device) \
                           / float(np.product(kernel_size)**2)
        elif kernel_type == "gaussian":
            self._kernel = utils.gaussian_kernel(sigma, dim, asTensor=True, dtype=self._dtype, device=self._device)

        self._kernel.unsqueeze_(0).unsqueeze_(0)

        if dim == 2:
            self._lcc_loss = self._lcc_loss_2d  # 2d lcc

            self._mean_fixed_image = F.conv2d(self._fixed_image.image, self._kernel)
            self._variance_fixed_image = F.conv2d(self._fixed_image.image.pow(2), self._kernel) \
                                         - (self._mean_fixed_image.pow(2))
        elif dim == 3:
            self._lcc_loss = self._lcc_loss_3d  # 3d lcc

            self._mean_fixed_image = F.conv3d(self._fixed_image.image, self._kernel)
            self._variance_fixed_image = F.conv3d(self._fixed_image.image.pow(2), self._kernel) \
                                         - (self._mean_fixed_image.pow(2))