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Source code for mmedit.models.common.gca_module

# Copyright (c) OpenMMLab. All rights reserved.
import torch
import torch.nn as nn
from mmcv.cnn import ConvModule, constant_init, xavier_init
from torch.nn import functional as F


[docs]class GCAModule(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. Args: 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. eps (float): A small number to avoid dividing by 0 when calculating the normed image feature patch. Default: 1e-4. """ def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, rate=2, pad_args=dict(mode='reflect'), interpolation='nearest', penalty=-1e4, eps=1e-4): super().__init__() self.kernel_size = kernel_size self.stride = stride self.rate = rate self.pad_args = pad_args self.interpolation = interpolation self.penalty = penalty self.eps = eps # reduced the channels of input image feature. self.guidance_conv = nn.Conv2d(in_channels, in_channels // 2, 1) # convolution after the attention alpha feature self.out_conv = ConvModule( out_channels, out_channels, 1, norm_cfg=dict(type='BN'), act_cfg=None) self.init_weights() def init_weights(self): xavier_init(self.guidance_conv, distribution='uniform') xavier_init(self.out_conv.conv, distribution='uniform') constant_init(self.out_conv.norm, 1e-3)
[docs] def forward(self, img_feat, alpha_feat, unknown=None, softmax_scale=1.): """Forward function of GCAModule. Args: 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: Tensor: The augmented alpha feature. """ if alpha_feat.shape[2:4] != img_feat.shape[2:4]: raise ValueError( 'image feature size does not align with alpha feature size: ' f'image feature size {img_feat.shape[2:4]}, ' f'alpha feature size {alpha_feat.shape[2:4]}') if unknown is not None and unknown.shape[2:4] != img_feat.shape[2:4]: raise ValueError( 'image feature size does not align with unknown mask size: ' f'image feature size {img_feat.shape[2:4]}, ' f'unknown mask size {unknown.shape[2:4]}') # preprocess image feature img_feat = self.guidance_conv(img_feat) img_feat = F.interpolate( img_feat, scale_factor=1 / self.rate, mode=self.interpolation) # preprocess unknown mask unknown, softmax_scale = self.process_unknown_mask( unknown, img_feat, softmax_scale) img_ps, alpha_ps, unknown_ps = self.extract_feature_maps_patches( img_feat, alpha_feat, unknown) # create self correlation mask with shape: # (N, img_h*img_w, img_h, img_w) self_mask = self.get_self_correlation_mask(img_feat) # split tensors by batch dimension; tuple is returned img_groups = torch.split(img_feat, 1, dim=0) img_ps_groups = torch.split(img_ps, 1, dim=0) alpha_ps_groups = torch.split(alpha_ps, 1, dim=0) unknown_ps_groups = torch.split(unknown_ps, 1, dim=0) scale_groups = torch.split(softmax_scale, 1, dim=0) groups = (img_groups, img_ps_groups, alpha_ps_groups, unknown_ps_groups, scale_groups) out = [] # i is the virtual index of the sample in the current batch for img_i, img_ps_i, alpha_ps_i, unknown_ps_i, scale_i in zip(*groups): similarity_map = self.compute_similarity_map(img_i, img_ps_i) gca_score = self.compute_guided_attention_score( similarity_map, unknown_ps_i, scale_i, self_mask) out_i = self.propagate_alpha_feature(gca_score, alpha_ps_i) out.append(out_i) out = torch.cat(out, dim=0) out.reshape_as(alpha_feat) out = self.out_conv(out) + alpha_feat return out
[docs] def extract_feature_maps_patches(self, img_feat, alpha_feat, unknown): """Extract image feature, alpha feature unknown patches. Args: 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: tuple: 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). """ # extract image feature patches with shape: # (N, img_h*img_w, img_c, img_ks, img_ks) img_ks = self.kernel_size img_ps = self.extract_patches(img_feat, img_ks, self.stride) # extract alpha feature patches with shape: # (N, img_h*img_w, alpha_c, alpha_ks, alpha_ks) alpha_ps = self.extract_patches(alpha_feat, self.rate * 2, self.rate) # extract unknown mask patches with shape: (N, img_h*img_w, 1, 1) unknown_ps = self.extract_patches(unknown, img_ks, self.stride) unknown_ps = unknown_ps.squeeze(dim=2) # squeeze channel dimension unknown_ps = unknown_ps.mean(dim=[2, 3], keepdim=True) return img_ps, alpha_ps, unknown_ps
[docs] def compute_similarity_map(self, img_feat, img_ps): """Compute similarity between image feature patches. Args: 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: Tensor: Similarity map between image feature patches with shape \ (1, img_h*img_w, img_h, img_w). """ img_ps = img_ps[0] # squeeze dim 0 # convolve the feature to get correlation (similarity) map escape_NaN = torch.FloatTensor([self.eps]).to(img_feat) img_ps_normed = img_ps / torch.max(self.l2_norm(img_ps), escape_NaN) img_feat = self.pad(img_feat, self.kernel_size, self.stride) similarity_map = F.conv2d(img_feat, img_ps_normed) return similarity_map
[docs] def compute_guided_attention_score(self, similarity_map, unknown_ps, scale, self_mask): """Compute guided attention score. Args: 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: Tensor: Similarity map between image feature patches with shape \ (1, img_h*img_w, img_h, img_w). """ # scale the correlation with predicted scale factor for known and # unknown area unknown_scale, known_scale = scale[0] out = similarity_map * ( unknown_scale * unknown_ps.gt(0.).float() + known_scale * unknown_ps.le(0.).float()) # mask itself, self-mask only applied to unknown area out = out + self_mask * unknown_ps gca_score = F.softmax(out, dim=1) return gca_score
[docs] def propagate_alpha_feature(self, gca_score, alpha_ps): """Propagate alpha feature based on guided attention score. Args: 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: Tensor: Propagated alpha feature map of shape \ (1, alpha_c, alpha_h, alpha_w). """ alpha_ps = alpha_ps[0] # squeeze dim 0 if self.rate == 1: gca_score = self.pad(gca_score, kernel_size=2, stride=1) alpha_ps = alpha_ps.permute(1, 0, 2, 3) out = F.conv2d(gca_score, alpha_ps) / 4. else: out = F.conv_transpose2d( gca_score, alpha_ps, stride=self.rate, padding=1) / 4. return out
[docs] def process_unknown_mask(self, unknown, img_feat, softmax_scale): """Process unknown mask. Args: 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: tuple: 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). """ n, _, h, w = img_feat.shape if unknown is not None: unknown = unknown.clone() unknown = F.interpolate( unknown, scale_factor=1 / self.rate, mode=self.interpolation) unknown_mean = unknown.mean(dim=[2, 3]) known_mean = 1 - unknown_mean unknown_scale = torch.clamp( torch.sqrt(unknown_mean / known_mean), 0.1, 10).to(img_feat) known_scale = torch.clamp( torch.sqrt(known_mean / unknown_mean), 0.1, 10).to(img_feat) softmax_scale = torch.cat([unknown_scale, known_scale], dim=1) else: unknown = torch.ones((n, 1, h, w)).to(img_feat) softmax_scale = torch.FloatTensor( [softmax_scale, softmax_scale]).view(1, 2).repeat(n, 1).to(img_feat) return unknown, softmax_scale
[docs] def extract_patches(self, 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)`. Args: x (Tensor): Feature map of shape (N, C, H, W). kernel_size (int): Size of each patches. stride (int): Stride between patches. Returns: Tensor: Extracted patches of shape \ (N, (H / stride) * (W / stride) , C, kernel_size, kernel_size). """ n, c, _, _ = x.shape x = self.pad(x, kernel_size, stride) x = F.unfold(x, (kernel_size, kernel_size), stride=(stride, stride)) x = x.permute(0, 2, 1) x = x.reshape(n, -1, c, kernel_size, kernel_size) return x
def pad(self, x, kernel_size, stride): left = (kernel_size - stride + 1) // 2 right = (kernel_size - stride) // 2 pad = (left, right, left, right) return F.pad(x, pad, **self.pad_args) def get_self_correlation_mask(self, img_feat): _, _, h, w = img_feat.shape # As ONNX does not support dynamic num_classes, we have to convert it # into an integer self_mask = F.one_hot( torch.arange(h * w).view(h, w), num_classes=int(h * w)) self_mask = self_mask.permute(2, 0, 1).view(1, h * w, h, w) # use large negative value to mask out self-correlation before softmax self_mask = self_mask * self.penalty return self_mask.to(img_feat) @staticmethod def l2_norm(x): x = x**2 x = x.sum(dim=[1, 2, 3], keepdim=True) return torch.sqrt(x)
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