652 lines
21 KiB
Python
652 lines
21 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# Copyright (c) Alibaba, Inc. and its affiliates.
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import numpy as np
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import torch
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import torch.nn as nn
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import torch.nn.functional as F
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__all__ = ['AutoencoderKL']
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def nonlinearity(x):
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# swish
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return x * torch.sigmoid(x)
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def Normalize(in_channels, num_groups=32):
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return torch.nn.GroupNorm(
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num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
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class DiagonalGaussianDistribution(object):
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def __init__(self, parameters, deterministic=False):
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self.parameters = parameters
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self.mean, self.logvar = torch.chunk(parameters, 2, dim=1)
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self.logvar = torch.clamp(self.logvar, -30.0, 20.0)
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self.deterministic = deterministic
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self.std = torch.exp(0.5 * self.logvar)
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self.var = torch.exp(self.logvar)
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if self.deterministic:
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self.var = self.std = torch.zeros_like(
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self.mean).to(device=self.parameters.device)
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def sample(self):
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x = self.mean + self.std * torch.randn(
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self.mean.shape).to(device=self.parameters.device)
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return x
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def kl(self, other=None):
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if self.deterministic:
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return torch.Tensor([0.])
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else:
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if other is None:
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return 0.5 * torch.sum(
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torch.pow(self.mean, 2) + self.var - 1.0 - self.logvar,
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dim=[1, 2, 3])
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else:
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return 0.5 * torch.sum(
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torch.pow(self.mean - other.mean, 2) / other.var
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+ self.var / other.var - 1.0 - self.logvar + other.logvar,
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dim=[1, 2, 3])
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def nll(self, sample, dims=[1, 2, 3]):
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if self.deterministic:
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return torch.Tensor([0.])
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logtwopi = np.log(2.0 * np.pi)
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return 0.5 * torch.sum(
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logtwopi + self.logvar
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+ torch.pow(sample - self.mean, 2) / self.var,
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dim=dims)
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def mode(self):
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return self.mean
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class Downsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=2, padding=0)
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def forward(self, x):
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if self.with_conv:
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pad = (0, 1, 0, 1)
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x = torch.nn.functional.pad(x, pad, mode='constant', value=0)
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x = self.conv(x)
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else:
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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
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return x
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class ResnetBlock(nn.Module):
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def __init__(self,
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*,
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in_channels,
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out_channels=None,
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conv_shortcut=False,
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dropout,
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temb_channels=512):
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super().__init__()
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self.in_channels = in_channels
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out_channels = in_channels if out_channels is None else out_channels
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self.out_channels = out_channels
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self.use_conv_shortcut = conv_shortcut
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self.norm1 = Normalize(in_channels)
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self.conv1 = torch.nn.Conv2d(
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in_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if temb_channels > 0:
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self.temb_proj = torch.nn.Linear(temb_channels, out_channels)
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self.norm2 = Normalize(out_channels)
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self.dropout = torch.nn.Dropout(dropout)
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self.conv2 = torch.nn.Conv2d(
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out_channels, out_channels, kernel_size=3, stride=1, padding=1)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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self.conv_shortcut = torch.nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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else:
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self.nin_shortcut = torch.nn.Conv2d(
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in_channels,
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out_channels,
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kernel_size=1,
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stride=1,
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padding=0)
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def forward(self, x, temb):
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h = x
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h = self.norm1(h)
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h = nonlinearity(h)
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h = self.conv1(h)
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if temb is not None:
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h = h + self.temb_proj(nonlinearity(temb))[:, :, None, None]
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h = self.norm2(h)
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h = nonlinearity(h)
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h = self.dropout(h)
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h = self.conv2(h)
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if self.in_channels != self.out_channels:
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if self.use_conv_shortcut:
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x = self.conv_shortcut(x)
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else:
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x = self.nin_shortcut(x)
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return x + h
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class AttnBlock(nn.Module):
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = q.reshape(b, c, h * w)
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q = q.permute(0, 2, 1) # b,hw,c
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k = k.reshape(b, c, h * w) # b,c,hw
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w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w_ = w_ * (int(c)**(-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b, c, h * w)
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w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
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h_ = torch.bmm(
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v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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h_ = h_.reshape(b, c, h, w)
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h_ = self.proj_out(h_)
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return x + h_
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class AttnBlock(nn.Module): # noqa
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def __init__(self, in_channels):
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super().__init__()
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self.in_channels = in_channels
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self.norm = Normalize(in_channels)
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self.q = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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self.k = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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self.v = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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self.proj_out = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=1, stride=1, padding=0)
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def forward(self, x):
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h_ = x
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h_ = self.norm(h_)
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q = self.q(h_)
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k = self.k(h_)
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v = self.v(h_)
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# compute attention
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b, c, h, w = q.shape
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q = q.reshape(b, c, h * w)
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q = q.permute(0, 2, 1) # b,hw,c
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k = k.reshape(b, c, h * w) # b,c,hw
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w_ = torch.bmm(q, k) # b,hw,hw w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
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w_ = w_ * (int(c)**(-0.5))
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w_ = torch.nn.functional.softmax(w_, dim=2)
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# attend to values
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v = v.reshape(b, c, h * w)
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w_ = w_.permute(0, 2, 1) # b,hw,hw (first hw of k, second of q)
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h_ = torch.bmm(
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v, w_) # b, c,hw (hw of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
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h_ = h_.reshape(b, c, h, w)
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h_ = self.proj_out(h_)
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return x + h_
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class Upsample(nn.Module):
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=1, padding=1)
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def forward(self, x):
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x = torch.nn.functional.interpolate(
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x, scale_factor=2.0, mode='nearest')
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if self.with_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module): # noqa
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def __init__(self, in_channels, with_conv):
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super().__init__()
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self.with_conv = with_conv
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if self.with_conv:
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# no asymmetric padding in torch conv, must do it ourselves
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self.conv = torch.nn.Conv2d(
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in_channels, in_channels, kernel_size=3, stride=2, padding=0)
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def forward(self, x):
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if self.with_conv:
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pad = (0, 1, 0, 1)
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x = torch.nn.functional.pad(x, pad, mode='constant', value=0)
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x = self.conv(x)
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else:
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x = torch.nn.functional.avg_pool2d(x, kernel_size=2, stride=2)
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return x
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class Encoder(nn.Module):
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def __init__(self,
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*,
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ch,
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out_ch,
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ch_mult=(1, 2, 4, 8),
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num_res_blocks,
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attn_resolutions,
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dropout=0.0,
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resamp_with_conv=True,
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in_channels,
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resolution,
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z_channels,
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double_z=True,
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use_linear_attn=False,
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attn_type='vanilla',
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**ignore_kwargs):
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super().__init__()
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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# downsampling
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self.conv_in = torch.nn.Conv2d(
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in_channels, self.ch, kernel_size=3, stride=1, padding=1)
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curr_res = resolution
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in_ch_mult = (1, ) + tuple(ch_mult)
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self.in_ch_mult = in_ch_mult
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self.down = nn.ModuleList()
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for i_level in range(self.num_resolutions):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_in = ch * in_ch_mult[i_level]
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block_out = ch * ch_mult[i_level]
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for i_block in range(self.num_res_blocks):
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block.append(
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ResnetBlock(
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in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout))
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(AttnBlock(block_in))
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down = nn.Module()
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down.block = block
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down.attn = attn
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if i_level != self.num_resolutions - 1:
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down.downsample = Downsample(block_in, resamp_with_conv)
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curr_res = curr_res // 2
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self.down.append(down)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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# end
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self.norm_out = Normalize(block_in)
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self.conv_out = torch.nn.Conv2d(
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block_in,
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2 * z_channels if double_z else z_channels,
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kernel_size=3,
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stride=1,
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padding=1)
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def forward(self, x):
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# timestep embedding
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temb = None
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# downsampling
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hs = [self.conv_in(x)]
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for i_level in range(self.num_resolutions):
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for i_block in range(self.num_res_blocks):
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h = self.down[i_level].block[i_block](hs[-1], temb)
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if len(self.down[i_level].attn) > 0:
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h = self.down[i_level].attn[i_block](h)
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hs.append(h)
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if i_level != self.num_resolutions - 1:
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hs.append(self.down[i_level].downsample(hs[-1]))
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# middle
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h = hs[-1]
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h = self.mid.block_1(h, temb)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h, temb)
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# end
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h = self.norm_out(h)
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h = nonlinearity(h)
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h = self.conv_out(h)
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return h
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class Decoder(nn.Module):
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def __init__(self,
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*,
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ch,
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out_ch,
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ch_mult=(1, 2, 4, 8),
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num_res_blocks,
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attn_resolutions,
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dropout=0.0,
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resamp_with_conv=True,
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in_channels,
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resolution,
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z_channels,
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give_pre_end=False,
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tanh_out=False,
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use_linear_attn=False,
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attn_type='vanilla',
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**ignorekwargs):
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super().__init__()
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self.ch = ch
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self.temb_ch = 0
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self.num_resolutions = len(ch_mult)
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self.num_res_blocks = num_res_blocks
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self.resolution = resolution
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self.in_channels = in_channels
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self.give_pre_end = give_pre_end
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self.tanh_out = tanh_out
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# compute in_ch_mult, block_in and curr_res at lowest res
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block_in = ch * ch_mult[self.num_resolutions - 1]
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curr_res = resolution // 2**(self.num_resolutions - 1)
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self.z_shape = (1, z_channels, curr_res, curr_res)
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print('Working with z of shape {} = {} dimensions.'.format(
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self.z_shape, np.prod(self.z_shape)))
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# z to block_in
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self.conv_in = torch.nn.Conv2d(
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z_channels, block_in, kernel_size=3, stride=1, padding=1)
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# middle
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self.mid = nn.Module()
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self.mid.block_1 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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self.mid.attn_1 = AttnBlock(block_in)
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self.mid.block_2 = ResnetBlock(
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in_channels=block_in,
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out_channels=block_in,
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temb_channels=self.temb_ch,
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dropout=dropout)
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# upsampling
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self.up = nn.ModuleList()
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for i_level in reversed(range(self.num_resolutions)):
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block = nn.ModuleList()
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attn = nn.ModuleList()
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block_out = ch * ch_mult[i_level]
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for i_block in range(self.num_res_blocks + 1):
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block.append(
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ResnetBlock(
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in_channels=block_in,
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out_channels=block_out,
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temb_channels=self.temb_ch,
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dropout=dropout))
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block_in = block_out
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if curr_res in attn_resolutions:
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attn.append(AttnBlock(block_in))
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up = nn.Module()
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up.block = block
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up.attn = attn
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if i_level != 0:
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up.upsample = Upsample(block_in, resamp_with_conv)
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curr_res = curr_res * 2
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self.up.insert(0, up) # prepend to get consistent order
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# end
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self.norm_out = Normalize(block_in)
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self.conv_out = torch.nn.Conv2d(
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block_in, out_ch, kernel_size=3, stride=1, padding=1)
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def forward(self, z):
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self.last_z_shape = z.shape
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# timestep embedding
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temb = None
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# z to block_in
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h = self.conv_in(z)
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# middle
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h = self.mid.block_1(h, temb)
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h = self.mid.attn_1(h)
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h = self.mid.block_2(h, temb)
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# upsampling
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for i_level in reversed(range(self.num_resolutions)):
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for i_block in range(self.num_res_blocks + 1):
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h = self.up[i_level].block[i_block](h, temb)
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if len(self.up[i_level].attn) > 0:
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h = self.up[i_level].attn[i_block](h)
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if i_level != 0:
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h = self.up[i_level].upsample(h)
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# end
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if self.give_pre_end:
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return h
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h = self.norm_out(h)
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|
h = nonlinearity(h)
|
|
h = self.conv_out(h)
|
|
if self.tanh_out:
|
|
h = torch.tanh(h)
|
|
return h
|
|
|
|
|
|
class AutoencoderKL(nn.Module):
|
|
|
|
def __init__(self,
|
|
ddconfig,
|
|
embed_dim,
|
|
ckpt_path=None,
|
|
ignore_keys=[],
|
|
image_key='image',
|
|
colorize_nlabels=None,
|
|
monitor=None,
|
|
ema_decay=None,
|
|
learn_logvar=False):
|
|
super().__init__()
|
|
self.learn_logvar = learn_logvar
|
|
self.image_key = image_key
|
|
self.encoder = Encoder(**ddconfig)
|
|
self.decoder = Decoder(**ddconfig)
|
|
assert ddconfig['double_z']
|
|
self.quant_conv = torch.nn.Conv2d(2 * ddconfig['z_channels'],
|
|
2 * embed_dim, 1)
|
|
self.post_quant_conv = torch.nn.Conv2d(embed_dim,
|
|
ddconfig['z_channels'], 1)
|
|
self.embed_dim = embed_dim
|
|
if colorize_nlabels is not None:
|
|
assert type(colorize_nlabels) == int
|
|
self.register_buffer('colorize',
|
|
torch.randn(3, colorize_nlabels, 1, 1))
|
|
if monitor is not None:
|
|
self.monitor = monitor
|
|
self.use_ema = ema_decay is not None
|
|
if ckpt_path is not None:
|
|
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
|
|
|
|
def init_from_ckpt(self, path, ignore_keys=list()):
|
|
sd = torch.load(path, map_location='cpu')['state_dict']
|
|
keys = list(sd.keys())
|
|
for key in keys:
|
|
print(key, sd[key].shape)
|
|
import collections
|
|
sd_new = collections.OrderedDict()
|
|
for k in keys:
|
|
if k.find('first_stage_model') >= 0:
|
|
k_new = k.split('first_stage_model.')[-1]
|
|
sd_new[k_new] = sd[k]
|
|
self.load_state_dict(sd_new, strict=True)
|
|
print(f'Restored from {path}')
|
|
|
|
def init_from_ckpt2(self, path, ignore_keys=list()):
|
|
sd = torch.load(path, map_location='cpu')['state_dict']
|
|
keys = list(sd.keys())
|
|
|
|
first_stage_model
|
|
for k in keys:
|
|
for ik in ignore_keys:
|
|
if k.startswith(ik):
|
|
print('Deleting key {} from state_dict.'.format(k))
|
|
del sd[k]
|
|
self.load_state_dict(sd, strict=False)
|
|
print(f'Restored from {path}')
|
|
|
|
def on_train_batch_end(self, *args, **kwargs):
|
|
if self.use_ema:
|
|
self.model_ema(self)
|
|
|
|
def encode(self, x):
|
|
h = self.encoder(x)
|
|
moments = self.quant_conv(h)
|
|
posterior = DiagonalGaussianDistribution(moments)
|
|
return posterior
|
|
|
|
def decode(self, z):
|
|
z = self.post_quant_conv(z)
|
|
dec = self.decoder(z)
|
|
return dec
|
|
|
|
def forward(self, input, sample_posterior=True):
|
|
posterior = self.encode(input)
|
|
if sample_posterior:
|
|
z = posterior.sample()
|
|
else:
|
|
z = posterior.mode()
|
|
dec = self.decode(z)
|
|
return dec, posterior
|
|
|
|
def get_input(self, batch, k):
|
|
x = batch[k]
|
|
if len(x.shape) == 3:
|
|
x = x[..., None]
|
|
x = x.permute(0, 3, 1,
|
|
2).to(memory_format=torch.contiguous_format).float()
|
|
return x
|
|
|
|
def get_last_layer(self):
|
|
return self.decoder.conv_out.weight
|
|
|
|
@torch.no_grad()
|
|
def log_images(self, batch, only_inputs=False, log_ema=False, **kwargs):
|
|
log = dict()
|
|
x = self.get_input(batch, self.image_key)
|
|
x = x.to(self.device)
|
|
if not only_inputs:
|
|
xrec, posterior = self(x)
|
|
if x.shape[1] > 3:
|
|
# colorize with random projection
|
|
assert xrec.shape[1] > 3
|
|
x = self.to_rgb(x)
|
|
xrec = self.to_rgb(xrec)
|
|
log['samples'] = self.decode(torch.randn_like(posterior.sample()))
|
|
log['reconstructions'] = xrec
|
|
if log_ema or self.use_ema:
|
|
with self.ema_scope():
|
|
xrec_ema, posterior_ema = self(x)
|
|
if x.shape[1] > 3:
|
|
# colorize with random projection
|
|
assert xrec_ema.shape[1] > 3
|
|
xrec_ema = self.to_rgb(xrec_ema)
|
|
log['samples_ema'] = self.decode(
|
|
torch.randn_like(posterior_ema.sample()))
|
|
log['reconstructions_ema'] = xrec_ema
|
|
log['inputs'] = x
|
|
return log
|
|
|
|
def to_rgb(self, x):
|
|
assert self.image_key == 'segmentation'
|
|
if not hasattr(self, 'colorize'):
|
|
self.register_buffer('colorize',
|
|
torch.randn(3, x.shape[1], 1, 1).to(x))
|
|
x = F.conv2d(x, weight=self.colorize)
|
|
x = 2. * (x - x.min()) / (x.max() - x.min()) - 1.
|
|
return x
|
|
|
|
|
|
class IdentityFirstStage(torch.nn.Module):
|
|
|
|
def __init__(self, *args, vq_interface=False, **kwargs):
|
|
self.vq_interface = vq_interface
|
|
super().__init__()
|
|
|
|
def encode(self, x, *args, **kwargs):
|
|
return x
|
|
|
|
def decode(self, x, *args, **kwargs):
|
|
return x
|
|
|
|
def quantize(self, x, *args, **kwargs):
|
|
if self.vq_interface:
|
|
return x, None, [None, None, None]
|
|
return x
|
|
|
|
def forward(self, x, *args, **kwargs):
|
|
return x
|