842 lines
30 KiB
Python
842 lines
30 KiB
Python
import functools
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from abc import abstractmethod
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import math
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from typing import Union, Type, Callable, Optional, List
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import numpy as np
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import torch
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import torch as th
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import torch.nn as nn
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import torch.nn.functional as F
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import torchvision # For debugging, not actually used.
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from kornia.augmentation import ColorJitter
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from torch import Tensor
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from torchvision.models import resnet50
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from torchvision.models.resnet import BasicBlock, Bottleneck, conv1x1
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from models.diffusion.fp16_util import convert_module_to_f16, convert_module_to_f32
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from models.diffusion.nn import (
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conv_nd,
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linear,
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avg_pool_nd,
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zero_module,
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normalization,
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timestep_embedding,
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)
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from trainer.networks import register_model
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from utils.util import checkpoint
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import torch_intermediary as ml
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class AttentionPool2d(nn.Module):
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"""
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Adapted from CLIP: https://github.com/openai/CLIP/blob/main/clip/model.py
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"""
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def __init__(
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self,
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spacial_dim: int,
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embed_dim: int,
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num_heads_channels: int,
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output_dim: int = None,
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):
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super().__init__()
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self.positional_embedding = nn.Parameter(
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th.randn(embed_dim, spacial_dim ** 2 + 1) / embed_dim ** 0.5
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)
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self.qkv_proj = conv_nd(1, embed_dim, 3 * embed_dim, 1)
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self.c_proj = conv_nd(1, embed_dim, output_dim or embed_dim, 1)
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self.num_heads = embed_dim // num_heads_channels
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self.attention = QKVAttention(self.num_heads)
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def forward(self, x):
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b, c, *_spatial = x.shape
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x = x.reshape(b, c, -1) # NC(HW)
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x = th.cat([x.mean(dim=-1, keepdim=True), x], dim=-1) # NC(HW+1)
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x = x + self.positional_embedding[None, :, :].to(x.dtype) # NC(HW+1)
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x = self.qkv_proj(x)
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x = self.attention(x)
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x = self.c_proj(x)
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return x[:, :, 0]
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class TimestepBlock(nn.Module):
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"""
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Any module where forward() takes timestep embeddings as a second argument.
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"""
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@abstractmethod
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def forward(self, x, emb):
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"""
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Apply the module to `x` given `emb` timestep embeddings.
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"""
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class TimestepEmbedSequential(nn.Sequential, TimestepBlock):
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"""
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A sequential module that passes timestep embeddings to the children that
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support it as an extra input.
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"""
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def forward(self, x, emb):
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for layer in self:
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if isinstance(layer, TimestepBlock):
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x = layer(x, emb)
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else:
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x = layer(x)
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return x
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class Upsample(nn.Module):
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"""
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An upsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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upsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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if use_conv:
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self.conv = conv_nd(dims, self.channels, self.out_channels, 3, padding=1)
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def forward(self, x):
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assert x.shape[1] == self.channels
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if self.dims == 3:
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x = F.interpolate(
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x, (x.shape[2], x.shape[3] * 2, x.shape[4] * 2), mode="nearest"
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)
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else:
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x = F.interpolate(x, scale_factor=2, mode="nearest")
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if self.use_conv:
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x = self.conv(x)
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return x
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class Downsample(nn.Module):
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"""
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A downsampling layer with an optional convolution.
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:param channels: channels in the inputs and outputs.
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:param use_conv: a bool determining if a convolution is applied.
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:param dims: determines if the signal is 1D, 2D, or 3D. If 3D, then
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downsampling occurs in the inner-two dimensions.
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"""
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def __init__(self, channels, use_conv, dims=2, out_channels=None):
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super().__init__()
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self.channels = channels
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.dims = dims
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stride = 2 if dims != 3 else (1, 2, 2)
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if use_conv:
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self.op = conv_nd(
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dims, self.channels, self.out_channels, 3, stride=stride, padding=1
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)
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else:
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assert self.channels == self.out_channels
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self.op = avg_pool_nd(dims, kernel_size=stride, stride=stride)
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def forward(self, x):
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assert x.shape[1] == self.channels
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return self.op(x)
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class ResBlock(TimestepBlock):
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"""
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A residual block that can optionally change the number of channels.
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:param channels: the number of input channels.
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:param emb_channels: the number of timestep embedding channels.
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:param dropout: the rate of dropout.
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:param out_channels: if specified, the number of out channels.
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:param use_conv: if True and out_channels is specified, use a spatial
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convolution instead of a smaller 1x1 convolution to change the
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channels in the skip connection.
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param up: if True, use this block for upsampling.
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:param down: if True, use this block for downsampling.
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"""
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def __init__(
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self,
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channels,
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emb_channels,
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dropout,
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out_channels=None,
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use_conv=False,
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use_scale_shift_norm=False,
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dims=2,
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up=False,
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down=False,
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):
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super().__init__()
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self.channels = channels
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self.emb_channels = emb_channels
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self.dropout = dropout
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self.out_channels = out_channels or channels
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self.use_conv = use_conv
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self.use_scale_shift_norm = use_scale_shift_norm
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self.in_layers = nn.Sequential(
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normalization(channels),
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nn.SiLU(),
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conv_nd(dims, channels, self.out_channels, 3, padding=1),
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)
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self.updown = up or down
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if up:
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self.h_upd = Upsample(channels, False, dims)
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self.x_upd = Upsample(channels, False, dims)
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elif down:
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self.h_upd = Downsample(channels, False, dims)
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self.x_upd = Downsample(channels, False, dims)
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else:
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self.h_upd = self.x_upd = nn.Identity()
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self.emb_layers = nn.Sequential(
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nn.SiLU(),
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linear(
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emb_channels,
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2 * self.out_channels if use_scale_shift_norm else self.out_channels,
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),
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)
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self.out_layers = nn.Sequential(
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normalization(self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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zero_module(
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conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1)
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),
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)
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if self.out_channels == channels:
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self.skip_connection = nn.Identity()
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elif use_conv:
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self.skip_connection = conv_nd(
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dims, channels, self.out_channels, 3, padding=1
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)
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else:
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self.skip_connection = conv_nd(dims, channels, self.out_channels, 1)
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def forward(self, x, emb):
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"""
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Apply the block to a Tensor, conditioned on a timestep embedding.
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:param x: an [N x C x ...] Tensor of features.
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:param emb: an [N x emb_channels] Tensor of timestep embeddings.
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:return: an [N x C x ...] Tensor of outputs.
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"""
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return checkpoint(
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self._forward, x, emb
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)
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def _forward(self, x, emb):
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if self.updown:
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in_rest, in_conv = self.in_layers[:-1], self.in_layers[-1]
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h = in_rest(x)
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h = self.h_upd(h)
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x = self.x_upd(x)
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h = in_conv(h)
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else:
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h = self.in_layers(x)
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emb_out = self.emb_layers(emb).type(h.dtype)
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while len(emb_out.shape) < len(h.shape):
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emb_out = emb_out[..., None]
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if self.use_scale_shift_norm:
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out_norm, out_rest = self.out_layers[0], self.out_layers[1:]
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scale, shift = th.chunk(emb_out, 2, dim=1)
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h = out_norm(h) * (1 + scale) + shift
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h = out_rest(h)
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else:
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h = h + emb_out
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h = self.out_layers(h)
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return self.skip_connection(x) + h
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class AttentionBlock(nn.Module):
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"""
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An attention block that allows spatial positions to attend to each other.
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Originally ported from here, but adapted to the N-d case.
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https://github.com/hojonathanho/diffusion/blob/1e0dceb3b3495bbe19116a5e1b3596cd0706c543/diffusion_tf/models/unet.py#L66.
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"""
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def __init__(
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self,
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channels,
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num_heads=1,
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num_head_channels=-1,
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use_new_attention_order=False,
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):
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super().__init__()
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self.channels = channels
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if num_head_channels == -1:
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self.num_heads = num_heads
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else:
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assert (
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channels % num_head_channels == 0
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), f"q,k,v channels {channels} is not divisible by num_head_channels {num_head_channels}"
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self.num_heads = channels // num_head_channels
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self.norm = normalization(channels)
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self.qkv = conv_nd(1, channels, channels * 3, 1)
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if use_new_attention_order:
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# split qkv before split heads
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self.attention = QKVAttention(self.num_heads)
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else:
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# split heads before split qkv
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self.attention = QKVAttentionLegacy(self.num_heads)
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self.proj_out = zero_module(conv_nd(1, channels, channels, 1))
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def forward(self, x):
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return checkpoint(self._forward, x)
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def _forward(self, x):
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b, c, *spatial = x.shape
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x = x.reshape(b, c, -1)
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qkv = self.qkv(self.norm(x))
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h = self.attention(qkv)
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h = self.proj_out(h)
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return (x + h).reshape(b, c, *spatial)
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def count_flops_attn(model, _x, y):
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"""
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A counter for the `thop` package to count the operations in an
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attention operation.
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Meant to be used like:
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macs, params = thop.profile(
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model,
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inputs=(inputs, timestamps),
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custom_ops={QKVAttention: QKVAttention.count_flops},
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)
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"""
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b, c, *spatial = y[0].shape
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num_spatial = int(np.prod(spatial))
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# We perform two matmuls with the same number of ops.
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# The first computes the weight matrix, the second computes
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# the combination of the value vectors.
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matmul_ops = 2 * b * (num_spatial ** 2) * c
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model.total_ops += th.DoubleTensor([matmul_ops])
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class QKVAttentionLegacy(nn.Module):
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"""
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A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
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"""
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def __init__(self, n_heads):
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super().__init__()
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self.n_heads = n_heads
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def forward(self, qkv):
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"""
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Apply QKV attention.
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:param qkv: an [N x (H * 3 * C) x T] tensor of Qs, Ks, and Vs.
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:return: an [N x (H * C) x T] tensor after attention.
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"""
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bs, width, length = qkv.shape
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assert width % (3 * self.n_heads) == 0
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ch = width // (3 * self.n_heads)
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q, k, v = qkv.reshape(bs * self.n_heads, ch * 3, length).split(ch, dim=1)
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scale = 1 / math.sqrt(math.sqrt(ch))
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weight = th.einsum(
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"bct,bcs->bts", q * scale, k * scale
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) # More stable with f16 than dividing afterwards
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weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
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a = th.einsum("bts,bcs->bct", weight, v)
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return a.reshape(bs, -1, length)
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@staticmethod
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def count_flops(model, _x, y):
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return count_flops_attn(model, _x, y)
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class QKVAttention(nn.Module):
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"""
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A module which performs QKV attention and splits in a different order.
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"""
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def __init__(self, n_heads):
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super().__init__()
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self.n_heads = n_heads
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def forward(self, qkv):
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"""
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Apply QKV attention.
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:param qkv: an [N x (3 * H * C) x T] tensor of Qs, Ks, and Vs.
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:return: an [N x (H * C) x T] tensor after attention.
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"""
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bs, width, length = qkv.shape
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assert width % (3 * self.n_heads) == 0
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ch = width // (3 * self.n_heads)
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q, k, v = qkv.chunk(3, dim=1)
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scale = 1 / math.sqrt(math.sqrt(ch))
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weight = th.einsum(
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"bct,bcs->bts",
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(q * scale).view(bs * self.n_heads, ch, length),
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(k * scale).view(bs * self.n_heads, ch, length),
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) # More stable with f16 than dividing afterwards
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weight = th.softmax(weight.float(), dim=-1).type(weight.dtype)
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a = th.einsum("bts,bcs->bct", weight, v.reshape(bs * self.n_heads, ch, length))
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return a.reshape(bs, -1, length)
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@staticmethod
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def count_flops(model, _x, y):
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return count_flops_attn(model, _x, y)
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class UNetModel(nn.Module):
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"""
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The full UNet model with attention and timestep embedding.
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:param in_channels: channels in the input Tensor.
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:param model_channels: base channel count for the model.
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:param out_channels: channels in the output Tensor.
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:param num_res_blocks: number of residual blocks per downsample.
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:param attention_resolutions: a collection of downsample rates at which
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attention will take place. May be a set, list, or tuple.
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For example, if this contains 4, then at 4x downsampling, attention
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will be used.
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:param dropout: the dropout probability.
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:param channel_mult: channel multiplier for each level of the UNet.
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:param conv_resample: if True, use learned convolutions for upsampling and
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downsampling.
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:param dims: determines if the signal is 1D, 2D, or 3D.
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:param num_classes: if specified (as an int), then this model will be
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class-conditional with `num_classes` classes.
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:param num_heads: the number of attention heads in each attention layer.
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:param num_heads_channels: if specified, ignore num_heads and instead use
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a fixed channel width per attention head.
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:param num_heads_upsample: works with num_heads to set a different number
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of heads for upsampling. Deprecated.
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:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
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:param resblock_updown: use residual blocks for up/downsampling.
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:param use_new_attention_order: use a different attention pattern for potentially
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increased efficiency.
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"""
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def __init__(
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self,
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image_size,
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in_channels,
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model_channels,
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out_channels,
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num_res_blocks,
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attention_resolutions,
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dropout=0,
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channel_mult=(1, 2, 4, 8),
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conv_resample=True,
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dims=2,
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num_classes=None,
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use_fp16=False,
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num_heads=1,
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num_head_channels=-1,
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num_heads_upsample=-1,
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use_scale_shift_norm=False,
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resblock_updown=False,
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use_new_attention_order=False,
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):
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super().__init__()
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if num_heads_upsample == -1:
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num_heads_upsample = num_heads
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self.image_size = image_size
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self.in_channels = in_channels
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self.model_channels = model_channels
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self.out_channels = out_channels
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self.num_res_blocks = num_res_blocks
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self.attention_resolutions = attention_resolutions
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self.dropout = dropout
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self.channel_mult = channel_mult
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self.conv_resample = conv_resample
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self.num_classes = num_classes
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self.dtype = th.float16 if use_fp16 else th.float32
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self.num_heads = num_heads
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self.num_head_channels = num_head_channels
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self.num_heads_upsample = num_heads_upsample
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time_embed_dim = model_channels * 4
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self.time_embed = nn.Sequential(
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linear(model_channels, time_embed_dim),
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nn.SiLU(),
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linear(time_embed_dim, time_embed_dim),
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)
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if self.num_classes is not None:
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# nn.Embedding
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self.label_emb = ml.Embedding(num_classes, time_embed_dim)
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self.input_blocks = nn.ModuleList(
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[
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TimestepEmbedSequential(
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conv_nd(dims, in_channels, model_channels, 3, padding=1)
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)
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]
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)
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self._feature_size = model_channels
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input_block_chans = [model_channels]
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ch = model_channels
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ds = 1
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for level, mult in enumerate(channel_mult):
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for _ in range(num_res_blocks):
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layers = [
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ResBlock(
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ch,
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time_embed_dim,
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|
dropout,
|
|
out_channels=mult * model_channels,
|
|
dims=dims,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
)
|
|
]
|
|
ch = mult * model_channels
|
|
if ds in attention_resolutions:
|
|
layers.append(
|
|
AttentionBlock(
|
|
ch,
|
|
num_heads=num_heads,
|
|
num_head_channels=num_head_channels,
|
|
use_new_attention_order=use_new_attention_order,
|
|
)
|
|
)
|
|
self.input_blocks.append(TimestepEmbedSequential(*layers))
|
|
self._feature_size += ch
|
|
input_block_chans.append(ch)
|
|
if level != len(channel_mult) - 1:
|
|
out_ch = ch
|
|
self.input_blocks.append(
|
|
TimestepEmbedSequential(
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
out_channels=out_ch,
|
|
dims=dims,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
down=True,
|
|
)
|
|
if resblock_updown
|
|
else Downsample(
|
|
ch, conv_resample, dims=dims, out_channels=out_ch
|
|
)
|
|
)
|
|
)
|
|
ch = out_ch
|
|
input_block_chans.append(ch)
|
|
ds *= 2
|
|
self._feature_size += ch
|
|
|
|
self.latent_join_reduce = ResBlock(ch*2, time_embed_dim, dropout, out_channels=ch, dims=dims, use_scale_shift_norm=use_scale_shift_norm)
|
|
self.middle_block = TimestepEmbedSequential(
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
dims=dims,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
AttentionBlock(
|
|
ch,
|
|
num_heads=num_heads,
|
|
num_head_channels=num_head_channels,
|
|
use_new_attention_order=use_new_attention_order,
|
|
),
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
dims=dims,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
)
|
|
self._feature_size += ch
|
|
|
|
self.output_blocks = nn.ModuleList([])
|
|
for level, mult in list(enumerate(channel_mult))[::-1]:
|
|
for i in range(num_res_blocks + 1):
|
|
ich = input_block_chans.pop()
|
|
layers = [
|
|
ResBlock(
|
|
ch + ich,
|
|
time_embed_dim,
|
|
dropout,
|
|
out_channels=model_channels * mult,
|
|
dims=dims,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
)
|
|
]
|
|
ch = model_channels * mult
|
|
if ds in attention_resolutions:
|
|
layers.append(
|
|
AttentionBlock(
|
|
ch,
|
|
num_heads=num_heads_upsample,
|
|
num_head_channels=num_head_channels,
|
|
use_new_attention_order=use_new_attention_order,
|
|
)
|
|
)
|
|
if level and i == num_res_blocks:
|
|
out_ch = ch
|
|
layers.append(
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
out_channels=out_ch,
|
|
dims=dims,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
up=True,
|
|
)
|
|
if resblock_updown
|
|
else Upsample(ch, conv_resample, dims=dims, out_channels=out_ch)
|
|
)
|
|
ds //= 2
|
|
self.output_blocks.append(TimestepEmbedSequential(*layers))
|
|
self._feature_size += ch
|
|
|
|
self.out = nn.Sequential(
|
|
normalization(ch),
|
|
nn.SiLU(),
|
|
zero_module(conv_nd(dims, model_channels, out_channels, 3, padding=1)),
|
|
)
|
|
|
|
def convert_to_fp16(self):
|
|
"""
|
|
Convert the torso of the model to float16.
|
|
"""
|
|
self.input_blocks.apply(convert_module_to_f16)
|
|
self.middle_block.apply(convert_module_to_f16)
|
|
self.output_blocks.apply(convert_module_to_f16)
|
|
|
|
def convert_to_fp32(self):
|
|
"""
|
|
Convert the torso of the model to float32.
|
|
"""
|
|
self.input_blocks.apply(convert_module_to_f32)
|
|
self.middle_block.apply(convert_module_to_f32)
|
|
self.output_blocks.apply(convert_module_to_f32)
|
|
|
|
def forward(self, x, latent, timesteps, y=None):
|
|
"""
|
|
Apply the model to an input batch.
|
|
|
|
:param x: an [N x C x ...] Tensor of inputs.
|
|
:param timesteps: a 1-D batch of timesteps.
|
|
:param y: an [N] Tensor of labels, if class-conditional.
|
|
:return: an [N x C x ...] Tensor of outputs.
|
|
"""
|
|
assert (y is not None) == (
|
|
self.num_classes is not None
|
|
), "must specify y if and only if the model is class-conditional"
|
|
|
|
hs = []
|
|
emb = self.time_embed(timestep_embedding(timesteps, self.model_channels))
|
|
|
|
if self.num_classes is not None:
|
|
assert y.shape == (x.shape[0],)
|
|
emb = emb + self.label_emb(y)
|
|
|
|
h = x.type(self.dtype)
|
|
for module in self.input_blocks:
|
|
h = module(h, emb)
|
|
hs.append(h)
|
|
b, c = latent.shape
|
|
h = torch.cat([h, latent.view(b,c,1,1).repeat(1,1,h.shape[-2],h.shape[-1])], dim=1)
|
|
h = self.latent_join_reduce(h, emb)
|
|
h = self.middle_block(h, emb)
|
|
for module in self.output_blocks:
|
|
h = th.cat([h, hs.pop()], dim=1)
|
|
h = module(h, emb)
|
|
h = h.type(x.dtype)
|
|
return self.out(h)
|
|
|
|
|
|
class SuperResModel(UNetModel):
|
|
"""
|
|
A UNetModel that performs super-resolution.
|
|
|
|
Expects an extra kwarg `low_res` to condition on a low-resolution image.
|
|
"""
|
|
|
|
def __init__(self, image_size, in_channels, num_corruptions=0, *args, **kwargs):
|
|
self.num_corruptions = 0
|
|
super().__init__(image_size, in_channels * 2 + num_corruptions, *args, **kwargs)
|
|
|
|
def forward(self, x, timesteps, latent, low_res=None, corruption_factor=None, **kwargs):
|
|
b, _, new_height, new_width = x.shape
|
|
upsampled = F.interpolate(low_res, (new_height, new_width), mode="bilinear")
|
|
if corruption_factor is not None:
|
|
corruption_factor = corruption_factor.view(b, -1, 1, 1).repeat(1, 1, new_height, new_width)
|
|
else:
|
|
corruption_factor = torch.zeros((b, self.num_corruptions, new_height, new_width), dtype=torch.float, device=x.device)
|
|
upsampled = torch.cat([upsampled, corruption_factor], dim=1)
|
|
x = th.cat([x, upsampled], dim=1)
|
|
res = super().forward(x, latent, timesteps, **kwargs)
|
|
return res
|
|
|
|
|
|
class ResNetEncoder(nn.Module):
|
|
|
|
def __init__(
|
|
self,
|
|
block: Type[Union[BasicBlock, Bottleneck]] = Bottleneck,
|
|
layers: List[int] = [3, 4, 6, 3],
|
|
depth: int = 4,
|
|
output_dim: int = 512,
|
|
zero_init_residual: bool = False,
|
|
groups: int = 1,
|
|
width_per_group: int = 64,
|
|
replace_stride_with_dilation: Optional[List[bool]] = None,
|
|
norm_layer: Optional[Callable[..., nn.Module]] = None
|
|
) -> None:
|
|
super(ResNetEncoder, self).__init__()
|
|
if norm_layer is None:
|
|
norm_layer = functools.partial(nn.GroupNorm, 8)
|
|
self._norm_layer = norm_layer
|
|
|
|
self.inplanes = 64
|
|
self.dilation = 1
|
|
if replace_stride_with_dilation is None:
|
|
# each element in the tuple indicates if we should replace
|
|
# the 2x2 stride with a dilated convolution instead
|
|
replace_stride_with_dilation = [False, False, False]
|
|
if len(replace_stride_with_dilation) != 3:
|
|
raise ValueError("replace_stride_with_dilation should be None "
|
|
"or a 3-element tuple, got {}".format(replace_stride_with_dilation))
|
|
self.groups = groups
|
|
self.base_width = width_per_group
|
|
self.conv1 = nn.Conv2d(3, self.inplanes, kernel_size=7, stride=2, padding=3,
|
|
bias=False)
|
|
self.bn1 = norm_layer(self.inplanes)
|
|
self.relu = nn.ReLU(inplace=True)
|
|
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
|
|
self.depth = depth
|
|
self.layer1 = self._make_layer(block, 64, layers[0])
|
|
self.layer2 = self._make_layer(block, 128, layers[1], stride=2,
|
|
dilate=replace_stride_with_dilation[0])
|
|
f=128
|
|
if self.depth > 2:
|
|
self.layer3 = self._make_layer(block, 256, layers[2], stride=2,
|
|
dilate=replace_stride_with_dilation[1])
|
|
f=256
|
|
if self.depth > 3:
|
|
self.layer4 = self._make_layer(block, 512, layers[3], stride=2,
|
|
dilate=replace_stride_with_dilation[2])
|
|
f=512
|
|
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
|
|
self.fc = ml.Linear(f * block.expansion, output_dim)
|
|
|
|
for m in self.modules():
|
|
if isinstance(m, nn.Conv2d):
|
|
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
|
|
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
|
|
nn.init.constant_(m.weight, 1)
|
|
nn.init.constant_(m.bias, 0)
|
|
|
|
# Zero-initialize the last BN in each residual branch,
|
|
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
|
|
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
|
|
if zero_init_residual:
|
|
for m in self.modules():
|
|
if isinstance(m, Bottleneck):
|
|
nn.init.constant_(m.bn3.weight, 0) # type: ignore[arg-type]
|
|
elif isinstance(m, BasicBlock):
|
|
nn.init.constant_(m.bn2.weight, 0) # type: ignore[arg-type]
|
|
|
|
def _make_layer(self, block: Type[Union[BasicBlock, Bottleneck]], planes: int, blocks: int,
|
|
stride: int = 1, dilate: bool = False) -> nn.Sequential:
|
|
norm_layer = self._norm_layer
|
|
downsample = None
|
|
previous_dilation = self.dilation
|
|
if dilate:
|
|
self.dilation *= stride
|
|
stride = 1
|
|
if stride != 1 or self.inplanes != planes * block.expansion:
|
|
downsample = nn.Sequential(
|
|
conv1x1(self.inplanes, planes * block.expansion, stride),
|
|
norm_layer(planes * block.expansion),
|
|
)
|
|
|
|
layers = []
|
|
layers.append(block(self.inplanes, planes, stride, downsample, self.groups,
|
|
self.base_width, previous_dilation, norm_layer))
|
|
self.inplanes = planes * block.expansion
|
|
for _ in range(1, blocks):
|
|
layers.append(block(self.inplanes, planes, groups=self.groups,
|
|
base_width=self.base_width, dilation=self.dilation,
|
|
norm_layer=norm_layer))
|
|
|
|
return nn.Sequential(*layers)
|
|
|
|
def _forward_impl(self, x: Tensor) -> Tensor:
|
|
# See note [TorchScript super()]
|
|
x = self.conv1(x)
|
|
x = self.bn1(x)
|
|
x = self.relu(x)
|
|
x = self.maxpool(x)
|
|
|
|
x = self.layer1(x)
|
|
x = self.layer2(x)
|
|
if self.depth > 2:
|
|
x = self.layer3(x)
|
|
if self.depth > 3:
|
|
x = self.layer4(x)
|
|
|
|
x = self.avgpool(x)
|
|
x = torch.flatten(x, 1)
|
|
x = self.fc(x)
|
|
|
|
return x
|
|
|
|
def forward(self, x: Tensor) -> Tensor:
|
|
return self._forward_impl(x)
|
|
|
|
|
|
class UnetWithBuiltInLatentEncoder(nn.Module):
|
|
def __init__(self, **kwargs):
|
|
depth_map = {
|
|
256: 4,
|
|
128: 3,
|
|
64: 2
|
|
}
|
|
super().__init__()
|
|
self.encoder = ResNetEncoder(depth=depth_map[kwargs['image_size']])
|
|
self.unet = SuperResModel(**kwargs)
|
|
|
|
def forward(self, x, timesteps, alt_hq, low_res=None, **kwargs):
|
|
latent = self.encoder(alt_hq)
|
|
return self.unet(x, timesteps, latent, low_res, **kwargs)
|
|
|
|
|
|
@register_model
|
|
def register_unet_diffusion_latent_guide(opt_net, opt):
|
|
return UnetWithBuiltInLatentEncoder(**opt_net['args'])
|
|
|
|
|
|
if __name__ == '__main__':
|
|
attention_ds = []
|
|
for res in "16,8".split(","):
|
|
attention_ds.append(128 // int(res))
|
|
srm = UnetWithBuiltInLatentEncoder(image_size=64, in_channels=3, model_channels=64, out_channels=3, num_res_blocks=1, attention_resolutions=attention_ds, num_heads=4,
|
|
num_heads_upsample=-1, use_scale_shift_norm=True)
|
|
x = torch.randn(1,3,64,64)
|
|
alt_x = torch.randn(1,3,64,64)
|
|
l = torch.randn(1,3,32,32)
|
|
ts = torch.LongTensor([555])
|
|
y = srm(x, ts, alt_x, low_res=l)
|
|
print(y.shape, y.mean(), y.std(), y.min(), y.max())
|