mmagic.models.editors.gca.gca_module

Module Contents

Classes

GCAModule

Guided Contextual Attention Module.

class mmagic.models.editors.gca.gca_module.GCAModule(in_channels, out_channels, kernel_size=3, stride=1, rate=2, pad_args=dict(mode='reflect'), interpolation='nearest', penalty=- 10000.0, eps=0.0001)

Bases: torch.nn.Module

Guided Contextual Attention Module.

From https://arxiv.org/pdf/2001.04069.pdf. Based on https://github.com/nbei/Deep-Flow-Guided-Video-Inpainting. This module use image feature map to augment the alpha feature map with guided contextual attention score.

Image feature and alpha feature are unfolded to small patches and later used as conv kernel. Thus, we refer the unfolding size as kernel size. Image feature patches have a default kernel size 3 while the kernel size of alpha feature patches could be specified by rate (see rate below). The image feature patches are used to convolve with the image feature itself to calculate the contextual attention. Then the attention feature map is convolved by alpha feature patches to obtain the attention alpha feature. At last, the attention alpha feature is added to the input alpha feature.

Parameters

• in_channels (int) – Input channels of the guided contextual attention module.

• out_channels (int) – Output channels of the guided contextual attention module.

• kernel_size (int) – Kernel size of image feature patches. Default 3.

• stride (int) – Stride when unfolding the image feature. Default 1.

• rate (int) – The downsample rate of image feature map. The corresponding kernel size and stride of alpha feature patches will be rate x 2 and rate. It could be regarded as the granularity of the gca module. Default: 2.

• pad_args (dict) – Parameters of padding when convolve image feature with image feature patches or alpha feature patches. Allowed keys are mode and value. See torch.nn.functional.pad() for more information. Default: dict(mode=’reflect’).

• interpolation (str) – Interpolation method in upsampling and downsampling.

• penalty (float) – Punishment hyperparameter to avoid a large correlation between each unknown patch and itself. Default: -1e4.

• eps (float) – A small number to avoid dividing by 0 when calculating the normed image feature patch. Default: 1e-4.

init_weights()

Init weights for the model.

forward(img_feat, alpha_feat, unknown=None, softmax_scale=1.0)

Forward function of GCAModule.

Parameters

• img_feat (Tensor) – Image feature map of shape (N, ori_c, ori_h, ori_w).

• alpha_feat (Tensor) – Alpha feature map of shape (N, alpha_c, ori_h, ori_w).

• unknown (Tensor, optional) – Unknown area map generated by trimap. If specified, this tensor should have shape (N, 1, ori_h, ori_w).

• softmax_scale (float, optional) – The softmax scale of the attention if unknown area is not provided in forward. Default: 1.

Returns

The augmented alpha feature.

Return type

Tensor

extract_feature_maps_patches(img_feat, alpha_feat, unknown)

Extract image feature, alpha feature unknown patches.

Parameters

• img_feat (Tensor) – Image feature map of shape (N, img_c, img_h, img_w).

• alpha_feat (Tensor) – Alpha feature map of shape (N, alpha_c, ori_h, ori_w).

• unknown (Tensor, optional) – Unknown area map generated by trimap of shape (N, 1, img_h, img_w).

Returns

3-tuple of

Tensor: Image feature patches of shape (N, img_h*img_w, img_c, img_ks, img_ks).

Tensor: Guided contextual attention alpha feature map. (N, img_h*img_w, alpha_c, alpha_ks, alpha_ks).

Tensor: Unknown mask of shape (N, img_h*img_w, 1, 1).

Return type

tuple

compute_similarity_map(img_feat, img_ps)

Compute similarity between image feature patches.

Parameters

• img_feat (Tensor) – Image feature map of shape (1, img_c, img_h, img_w).

• img_ps (Tensor) – Image feature patches tensor of shape (1, img_h*img_w, img_c, img_ks, img_ks).

Returns

Similarity map between image feature patches with shape (1, img_h*img_w, img_h, img_w).

Return type

Tensor

compute_guided_attention_score(similarity_map, unknown_ps, scale, self_mask)

Compute guided attention score.

Parameters

• similarity_map (Tensor) – Similarity map of image feature with shape (1, img_h*img_w, img_h, img_w).

• unknown_ps (Tensor) – Unknown area patches tensor of shape (1, img_h*img_w, 1, 1).

• scale (Tensor) – Softmax scale of known and unknown area: [unknown_scale, known_scale].

• self_mask (Tensor) – Self correlation mask of shape (1, img_h*img_w, img_h, img_w). At (1, i*i, i, i) mask value equals -1e4 for i in [1, img_h*img_w] and other area is all zero.

Returns

Similarity map between image feature patches with shape (1, img_h*img_w, img_h, img_w).

Return type

Tensor

propagate_alpha_feature(gca_score, alpha_ps)

Propagate alpha feature based on guided attention score.

Parameters

• gca_score (Tensor) – Guided attention score map of shape (1, img_h*img_w, img_h, img_w).

• alpha_ps (Tensor) – Alpha feature patches tensor of shape (1, img_h*img_w, alpha_c, alpha_ks, alpha_ks).

Returns

Propagated alpha feature map of shape (1, alpha_c, alpha_h, alpha_w).

Return type

Tensor

process_unknown_mask(unknown, img_feat, softmax_scale)

Process unknown mask.

Parameters

• unknown (Tensor, optional) – Unknown area map generated by trimap of shape (N, 1, ori_h, ori_w)

• img_feat (Tensor) – The interpolated image feature map of shape (N, img_c, img_h, img_w).

• softmax_scale (float, optional) – The softmax scale of the attention if unknown area is not provided in forward. Default: 1.

Returns

2-tuple of

Tensor: Interpolated unknown area map of shape (N, img_h*img_w, img_h, img_w).

Tensor: Softmax scale tensor of known and unknown area of shape (N, 2).

Return type

tuple

extract_patches(x, kernel_size, stride)

Extract feature patches.

The feature map will be padded automatically to make sure the number of patches is equal to (H / stride) * (W / stride).

Parameters

• x (Tensor) – Feature map of shape (N, C, H, W).

• kernel_size (int) – Size of each patches.

• stride (int) – Stride between patches.

Returns

Extracted patches of shape (N, (H / stride) * (W / stride) , C, kernel_size, kernel_size).

Return type

Tensor

pad(x, kernel_size, stride)

Pad input tensor.

Parameters

• x (Tensor) – Input tensor.

• kernel_size (int) – Kernel size of conv layer.

• stride (int) – Stride of conv layer.

Returns

Padded tensor

Return type

Tensor

get_self_correlation_mask(img_feat)

Create self correlation mask.

Parameters

img_feat (Tensor) – Input tensor.

Returns

Mask tensor.

Return type

Tensor

static l2_norm(x)

L2 normalization function.

Parameters

x (Tensor) – Input tensor.

Returns

L2 normalized output tensor.

Return type

Tensor