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direct.nn.didn package

direct.nn.didn package#

Submodules#

direct.nn.didn.didn module#

class direct.nn.didn.didn.DIDN(in_channels, out_channels, hidden_channels=128, num_dubs=6, num_convs_recon=9, skip_connection=False)[source][source]#

Bases: Module

Deep Iterative Down-up convolutional Neural network (DIDN) implementation as in [1].

References

[1]

Yu, Songhyun, et al. “Deep Iterative Down-Up CNN for Image Denoising.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095–103. IEEE Xplore, https://doi.org/10.1109/CVPRW.2019.00262.

static crop_to_shape(x, shape)[source][source]#

Crops x to specified shape.

Parameters:
x: torch.Tensor

Input tensor with shape (*, H, W).

shape: Tuple(int, int)

Crop shape corresponding to H, W.

Returns:
cropped_output: torch.Tensor

Cropped tensor.

Return type:

Tensor

forward(x, channel_dim=1)[source][source]#

Takes as input a torch.Tensor x and computes DIDN(x).

Parameters:
x: torch.Tensor

Input tensor.

channel_dim: int

Channel dimension. Default: 1.

Returns:
out: torch.Tensor

DIDN output tensor.

Return type:

Tensor

training: bool#
class direct.nn.didn.didn.DUB(in_channels, out_channels)[source][source]#

Bases: Module

Down-up block (DUB) for DIDN model as implemented in [1].

References

[1]

Yu, Songhyun, et al. “Deep Iterative Down-Up CNN for Image Denoising.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095–103. IEEE Xplore, https://doi.org/10.1109/CVPRW.2019.00262.

static crop_to_shape(x, shape)[source][source]#

Crops x to specified shape.

Parameters:
x: torch.Tensor

Input tensor with shape (*, H, W).

shape: Tuple(int, int)

Crop shape corresponding to H, W.

Returns:
cropped_output: torch.Tensor

Cropped tensor.

Return type:

Tensor

forward(x)[source][source]#
Parameters:
x: torch.Tensor

Input tensor.

Returns:
out: torch.Tensor

DUB output.

Return type:

Tensor

static pad(x)[source][source]#

Pads input to height and width dimensions if odd.

Parameters:
x: torch.Tensor

Input to pad.

Returns:
x: torch.Tensor

Padded tensor.

Return type:

Tensor

training: bool#
class direct.nn.didn.didn.ReconBlock(in_channels, num_convs)[source][source]#

Bases: Module

Reconstruction Block of DIDN model as implemented in [1].

References

[1]

Yu, Songhyun, et al. “Deep Iterative Down-Up CNN for Image Denoising.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095–103. IEEE Xplore, https://doi.org/10.1109/CVPRW.2019.00262.

forward(input_data)[source][source]#

Computes num_convs convolutions followed by PReLU activation on input_data.

Parameters:
input_data: torch.Tensor

Input tensor.

Return type:

Tensor

training: bool#
class direct.nn.didn.didn.Subpixel(in_channels, out_channels, upscale_factor, kernel_size, padding=0)[source][source]#

Bases: Module

Subpixel convolution layer for up-scaling of low resolution features at super-resolution as implemented in [1].

References

[1]

Yu, Songhyun, et al. “Deep Iterative Down-Up CNN for Image Denoising.” 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2019, pp. 2095–103. IEEE Xplore, https://doi.org/10.1109/CVPRW.2019.00262.

forward(x)[source][source]#

Computes Subpixel convolution on input torch.Tensor x.

Parameters:
x: torch.Tensor

Input tensor.

Return type:

Tensor

training: bool#

Module contents#

Contents