extensions/sd-webui-controlnet/annotator/normalbae/models/submodules/submodules.py

import torch
import torch.nn as nn
import torch.nn.functional as F


########################################################################################################################


# Upsample + BatchNorm
class UpSampleBN(nn.Module):
    def __init__(self, skip_input, output_features):
        super(UpSampleBN, self).__init__()

        self._net = nn.Sequential(nn.Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
                                  nn.BatchNorm2d(output_features),
                                  nn.LeakyReLU(),
                                  nn.Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
                                  nn.BatchNorm2d(output_features),
                                  nn.LeakyReLU())

    def forward(self, x, concat_with):
        up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
        f = torch.cat([up_x, concat_with], dim=1)
        return self._net(f)


# Upsample + GroupNorm + Weight Standardization
class UpSampleGN(nn.Module):
    def __init__(self, skip_input, output_features):
        super(UpSampleGN, self).__init__()

        self._net = nn.Sequential(Conv2d(skip_input, output_features, kernel_size=3, stride=1, padding=1),
                                  nn.GroupNorm(8, output_features),
                                  nn.LeakyReLU(),
                                  Conv2d(output_features, output_features, kernel_size=3, stride=1, padding=1),
                                  nn.GroupNorm(8, output_features),
                                  nn.LeakyReLU())

    def forward(self, x, concat_with):
        up_x = F.interpolate(x, size=[concat_with.size(2), concat_with.size(3)], mode='bilinear', align_corners=True)
        f = torch.cat([up_x, concat_with], dim=1)
        return self._net(f)


# Conv2d with weight standardization
class Conv2d(nn.Conv2d):
    def __init__(self, in_channels, out_channels, kernel_size, stride=1,
                 padding=0, dilation=1, groups=1, bias=True):
        super(Conv2d, self).__init__(in_channels, out_channels, kernel_size, stride,
                 padding, dilation, groups, bias)

    def forward(self, x):
        weight = self.weight
        weight_mean = weight.mean(dim=1, keepdim=True).mean(dim=2,
                                  keepdim=True).mean(dim=3, keepdim=True)
        weight = weight - weight_mean
        std = weight.view(weight.size(0), -1).std(dim=1).view(-1, 1, 1, 1) + 1e-5
        weight = weight / std.expand_as(weight)
        return F.conv2d(x, weight, self.bias, self.stride,
                        self.padding, self.dilation, self.groups)


# normalize
def norm_normalize(norm_out):
    min_kappa = 0.01
    norm_x, norm_y, norm_z, kappa = torch.split(norm_out, 1, dim=1)
    norm = torch.sqrt(norm_x ** 2.0 + norm_y ** 2.0 + norm_z ** 2.0) + 1e-10
    kappa = F.elu(kappa) + 1.0 + min_kappa
    final_out = torch.cat([norm_x / norm, norm_y / norm, norm_z / norm, kappa], dim=1)
    return final_out


# uncertainty-guided sampling (only used during training)
@torch.no_grad()
def sample_points(init_normal, gt_norm_mask, sampling_ratio, beta):
    device = init_normal.device
    B, _, H, W = init_normal.shape
    N = int(sampling_ratio * H * W)
    beta = beta

    # uncertainty map
    uncertainty_map = -1 * init_normal[:, 3, :, :]  # B, H, W

    # gt_invalid_mask (B, H, W)
    if gt_norm_mask is not None:
        gt_invalid_mask = F.interpolate(gt_norm_mask.float(), size=[H, W], mode='nearest')
        gt_invalid_mask = gt_invalid_mask[:, 0, :, :] < 0.5
        uncertainty_map[gt_invalid_mask] = -1e4

    # (B, H*W)
    _, idx = uncertainty_map.view(B, -1).sort(1, descending=True)

    # importance sampling
    if int(beta * N) > 0:
        importance = idx[:, :int(beta * N)]    # B, beta*N

        # remaining
        remaining = idx[:, int(beta * N):]     # B, H*W - beta*N

        # coverage
        num_coverage = N - int(beta * N)

        if num_coverage <= 0:
            samples = importance
        else:
            coverage_list = []
            for i in range(B):
                idx_c = torch.randperm(remaining.size()[1])    # shuffles "H*W - beta*N"
                coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1))     # 1, N-beta*N
            coverage = torch.cat(coverage_list, dim=0)                                      # B, N-beta*N
            samples = torch.cat((importance, coverage), dim=1)                              # B, N

    else:
        # remaining
        remaining = idx[:, :]  # B, H*W

        # coverage
        num_coverage = N

        coverage_list = []
        for i in range(B):
            idx_c = torch.randperm(remaining.size()[1])  # shuffles "H*W - beta*N"
            coverage_list.append(remaining[i, :][idx_c[:num_coverage]].view(1, -1))  # 1, N-beta*N
        coverage = torch.cat(coverage_list, dim=0)  # B, N-beta*N
        samples = coverage

    # point coordinates
    rows_int = samples // W         # 0 for first row, H-1 for last row
    rows_float = rows_int / float(H-1)         # 0 to 1.0
    rows_float = (rows_float * 2.0) - 1.0       # -1.0 to 1.0

    cols_int = samples % W          # 0 for first column, W-1 for last column
    cols_float = cols_int / float(W-1)         # 0 to 1.0
    cols_float = (cols_float * 2.0) - 1.0       # -1.0 to 1.0

    point_coords = torch.zeros(B, 1, N, 2)
    point_coords[:, 0, :, 0] = cols_float             # x coord
    point_coords[:, 0, :, 1] = rows_float             # y coord
    point_coords = point_coords.to(device)
    return point_coords, rows_int, cols_int