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| import cv2 | |
| import math | |
| import numbers | |
| import numpy as np | |
| import torch | |
| import torch.nn.functional as F | |
| import torchvision | |
| from torch import nn, einsum | |
| from einops import rearrange, repeat | |
| from ... import share | |
| from lib.utils.iimage import IImage | |
| # params | |
| painta_res = [16, 32] | |
| painta_on = True | |
| token_idx = [1,2] | |
| # GaussianSmoothing is taken from https://github.com/yuval-alaluf/Attend-and-Excite/blob/main/utils/gaussian_smoothing.py | |
| class GaussianSmoothing(nn.Module): | |
| """ | |
| Apply gaussian smoothing on a | |
| 1d, 2d or 3d tensor. Filtering is performed seperately for each channel | |
| in the input using a depthwise convolution. | |
| Arguments: | |
| channels (int, sequence): Number of channels of the input tensors. Output will | |
| have this number of channels as well. | |
| kernel_size (int, sequence): Size of the gaussian kernel. | |
| sigma (float, sequence): Standard deviation of the gaussian kernel. | |
| dim (int, optional): The number of dimensions of the data. | |
| Default value is 2 (spatial). | |
| """ | |
| def __init__(self, channels, kernel_size, sigma, dim=2): | |
| super(GaussianSmoothing, self).__init__() | |
| if isinstance(kernel_size, numbers.Number): | |
| kernel_size = [kernel_size] * dim | |
| if isinstance(sigma, numbers.Number): | |
| sigma = [sigma] * dim | |
| # The gaussian kernel is the product of the | |
| # gaussian function of each dimension. | |
| kernel = 1 | |
| meshgrids = torch.meshgrid( | |
| [ | |
| torch.arange(size, dtype=torch.float32) | |
| for size in kernel_size | |
| ] | |
| ) | |
| for size, std, mgrid in zip(kernel_size, sigma, meshgrids): | |
| mean = (size - 1) / 2 | |
| kernel *= 1 / (std * math.sqrt(2 * math.pi)) * \ | |
| torch.exp(-((mgrid - mean) / (2 * std)) ** 2) | |
| # Make sure sum of values in gaussian kernel equals 1. | |
| kernel = kernel / torch.sum(kernel) | |
| # Reshape to depthwise convolutional weight | |
| kernel = kernel.view(1, 1, *kernel.size()) | |
| kernel = kernel.repeat(channels, *[1] * (kernel.dim() - 1)) | |
| self.register_buffer('weight', kernel) | |
| self.groups = channels | |
| if dim == 1: | |
| self.conv = F.conv1d | |
| elif dim == 2: | |
| self.conv = F.conv2d | |
| elif dim == 3: | |
| self.conv = F.conv3d | |
| else: | |
| raise RuntimeError( | |
| 'Only 1, 2 and 3 dimensions are supported. Received {}.'.format(dim) | |
| ) | |
| def forward(self, input): | |
| """ | |
| Apply gaussian filter to input. | |
| Arguments: | |
| input (torch.Tensor): Input to apply gaussian filter on. | |
| Returns: | |
| filtered (torch.Tensor): Filtered output. | |
| """ | |
| return self.conv(input, weight=self.weight.to(input.dtype), groups=self.groups, padding='same') | |
| def forward(self, x, context=None, mask=None): | |
| is_cross = context is not None | |
| att_type = "self" if context is None else "cross" | |
| h = self.heads | |
| q = self.to_q(x) | |
| context = x if context is None else context | |
| k = self.to_k(context) | |
| v = self.to_v(context) | |
| q, k, v = map(lambda t: rearrange(t, 'b n (h d) -> (b h) n d', h=h), (q, k, v)) | |
| sim = einsum('b i d, b j d -> b i j', q, k) * self.scale | |
| sim_before = sim | |
| del q, k | |
| if mask is not None: | |
| mask = rearrange(mask, 'b ... -> b (...)') | |
| max_neg_value = -torch.finfo(sim.dtype).max | |
| mask = repeat(mask, 'b j -> (b h) () j', h=h) | |
| sim.masked_fill_(~mask, max_neg_value) | |
| if hasattr(share, '_crossattn_similarity_res8') and x.shape[1] == share.input_shape.res8 and att_type == 'cross': | |
| share._crossattn_similarity_res8.append(torch.stack(share.reshape(sim).chunk(2))) # Chunk into 2 parts to differentiate the unconditional and conditional parts | |
| if hasattr(share, '_crossattn_similarity_res16') and x.shape[1] == share.input_shape.res16 and att_type == 'cross': | |
| share._crossattn_similarity_res16.append(torch.stack(share.reshape(sim).chunk(2))) # Chunk into 2 parts to differentiate the unconditional and conditional parts | |
| if hasattr(share, '_crossattn_similarity_res32') and x.shape[1] == share.input_shape.res32 and att_type == 'cross': | |
| share._crossattn_similarity_res32.append(torch.stack(share.reshape(sim).chunk(2))) # Chunk into 2 parts to differentiate the unconditional and conditional parts | |
| if hasattr(share, '_crossattn_similarity_res64') and x.shape[1] == share.input_shape.res64 and att_type == 'cross': | |
| share._crossattn_similarity_res64.append(torch.stack(share.reshape(sim).chunk(2))) # Chunk into 2 parts to differentiate the unconditional and conditional parts | |
| sim = sim.softmax(dim=-1) | |
| out = einsum('b i j, b j d -> b i d', sim, v) | |
| out = rearrange(out, '(b h) n d -> b n (h d)', h=h) | |
| if is_cross: | |
| return self.to_out(out) | |
| return self.to_out(out), v, sim_before | |
| def painta_rescale(y, self_v, self_sim, cross_sim, self_h, to_out): | |
| mask = share.painta_mask.get_res(self_v) | |
| shape = share.painta_mask.get_shape(self_v) | |
| res = share.painta_mask.get_res_val(self_v) | |
| mask = (mask > 0.5).to(y.dtype) | |
| m = mask.to(self_v.device) | |
| m = rearrange(m, 'b c h w -> b (h w) c').contiguous() | |
| m = torch.matmul(m, m.permute(0, 2, 1)) + (1-m) | |
| cross_sim = cross_sim[:, token_idx].sum(dim=1) | |
| cross_sim = cross_sim.reshape(shape) | |
| gaussian_smoothing = GaussianSmoothing(channels=1, kernel_size=3, sigma=0.5, dim=2).cuda() | |
| cross_sim = gaussian_smoothing(cross_sim.unsqueeze(0))[0] # optional smoothing | |
| cross_sim = cross_sim.reshape(-1) | |
| cross_sim = ((cross_sim - torch.median(cross_sim.ravel())) / torch.max(cross_sim.ravel())).clip(0, 1) | |
| if painta_on and res in painta_res: | |
| c = (1 - m) * cross_sim.reshape(1, 1, -1) + m | |
| self_sim = self_sim * c | |
| self_sim = self_sim.softmax(dim=-1) | |
| out = einsum('b i j, b j d -> b i d', self_sim, self_v) | |
| out = rearrange(out, '(b h) n d -> b n (h d)', h=self_h) | |
| out = to_out(out) | |
| else: | |
| out = y | |
| return out | |