1048 lines
36 KiB
Python
1048 lines
36 KiB
Python
# These codes are copied from modelscope revision c58451baead80d83281f063d12fb377fad415257
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# This code is borrowed and modified from Guided Diffusion Model,
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# made publicly available under MIT license at
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# https://github.com/IDEA-CCNL/Fengshenbang-LM/tree/main/fengshen/examples/disco_project
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import math
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from abc import abstractmethod
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import numpy as np
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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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from transformers import PretrainedConfig, PreTrainedModel
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class GroupNorm(nn.GroupNorm):
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def forward(self, x):
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return super(GroupNorm, self).forward(x.float()).type(x.dtype)
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def timestep_embedding(timesteps, dim, max_period=10000):
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"""
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Create sinusoidal timestep embeddings.
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:param timesteps: a 1-D Tensor of N indices, one per batch element.
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These may be fractional.
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:param dim: the dimension of the output.
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:param max_period: controls the minimum frequency of the embeddings.
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:return: an [N x dim] Tensor of positional embeddings.
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"""
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half = dim // 2
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freqs = th.exp(-math.log(max_period)
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* th.arange(start=0, end=half, dtype=th.float32)
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/ half).to(device=timesteps.device)
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args = timesteps[:, None].float() * freqs[None]
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embedding = th.cat([th.cos(args), th.sin(args)], dim=-1)
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if dim % 2:
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embedding = th.cat(
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[embedding, th.zeros_like(embedding[:, :1])], dim=-1)
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return embedding
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def convert_module_to_f16(ll):
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"""
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Convert primitive modules to float16.
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"""
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if isinstance(ll, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
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ll.weight.data = ll.weight.data.half()
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if ll.bias is not None:
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ll.bias.data = ll.bias.data.half()
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def convert_module_to_f32(ll):
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"""
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Convert primitive modules to float32, undoing convert_module_to_f16().
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"""
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if isinstance(ll, (nn.Conv1d, nn.Conv2d, nn.Conv3d)):
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ll.weight.data = ll.weight.data.float()
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if ll.bias is not None:
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ll.bias.data = ll.bias.data.float()
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def conv_nd(dims, *args, **kwargs):
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"""
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Create a 1D, 2D, or 3D convolution module.
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"""
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if dims == 1:
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return nn.Conv1d(*args, **kwargs)
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elif dims == 2:
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return nn.Conv2d(*args, **kwargs)
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elif dims == 3:
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return nn.Conv3d(*args, **kwargs)
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raise ValueError(f'unsupported dimensions: {dims}')
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def checkpoint(func, inputs, params, flag):
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"""
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Evaluate a function without caching intermediate activations, allowing for
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reduced memory at the expense of extra compute in the backward pass.
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:param func: the function to evaluate.
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:param inputs: the argument sequence to pass to `func`.
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:param params: a sequence of parameters `func` depends on but does not
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explicitly take as arguments.
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:param flag: if False, disable gradient checkpointing.
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"""
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if flag:
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args = tuple(inputs) + tuple(params)
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return CheckpointFunction.apply(func, len(inputs), *args)
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else:
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return func(*inputs)
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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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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(
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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),
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mode='nearest')
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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,
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self.channels,
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self.out_channels,
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3,
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stride=stride,
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padding=1)
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else:
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assert self.channels == self.out_channels
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self.op = nn.AvgPool2d(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 use_checkpoint: if True, use gradient checkpointing on this module.
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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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use_checkpoint=False,
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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_checkpoint = use_checkpoint
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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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GroupNorm(32, 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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nn.Linear(
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emb_channels,
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2 * self.out_channels
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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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GroupNorm(32, self.out_channels),
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nn.SiLU(),
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nn.Dropout(p=dropout),
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conv_nd(dims, self.out_channels, self.out_channels, 3, padding=1),
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)
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nn.init.zeros_(self.out_layers[-1].weight)
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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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else:
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self.skip_connection = conv_nd(dims, channels, self.out_channels,
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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(self._forward, (x, emb), self.parameters(),
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self.use_checkpoint)
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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_checkpoint=False,
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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.use_checkpoint = use_checkpoint
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self.norm = GroupNorm(32, 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 = conv_nd(1, channels, channels, 1)
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nn.init.zeros_(self.proj_out.weight)
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def forward(self, x):
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return checkpoint(self._forward, (x, ), self.parameters(),
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self.use_checkpoint)
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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(
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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,
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k * scale) # 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,
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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
|
|
class-conditional with `num_classes` classes.
|
|
:param use_checkpoint: use gradient checkpointing to reduce memory usage.
|
|
:param num_heads: the number of attention heads in each attention layer.
|
|
:param num_head_channels: if specified, ignore num_heads and instead use
|
|
a fixed channel width per attention head.
|
|
:param num_heads_upsample: works with num_heads to set a different number
|
|
of heads for upsampling. Deprecated.
|
|
:param use_scale_shift_norm: use a FiLM-like conditioning mechanism.
|
|
:param resblock_updown: use residual blocks for up/downsampling.
|
|
:param use_new_attention_order: use a different attention pattern for potentially
|
|
increased efficiency.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
image_size,
|
|
in_channels,
|
|
model_channels,
|
|
out_channels,
|
|
num_res_blocks,
|
|
attention_resolutions,
|
|
dropout=0,
|
|
channel_mult=(1, 2, 4, 8),
|
|
conv_resample=True,
|
|
dims=2,
|
|
num_classes=None,
|
|
use_checkpoint=False,
|
|
use_fp16=False,
|
|
num_heads=1,
|
|
num_head_channels=-1,
|
|
num_heads_upsample=-1,
|
|
use_scale_shift_norm=False,
|
|
resblock_updown=False,
|
|
use_new_attention_order=False,
|
|
):
|
|
super().__init__()
|
|
|
|
if num_heads_upsample == -1:
|
|
num_heads_upsample = num_heads
|
|
|
|
self.image_size = image_size
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
self.out_channels = out_channels
|
|
self.num_res_blocks = num_res_blocks
|
|
self.attention_resolutions = attention_resolutions
|
|
self.dropout = dropout
|
|
self.channel_mult = channel_mult
|
|
self.conv_resample = conv_resample
|
|
self.num_classes = num_classes
|
|
self.use_checkpoint = use_checkpoint
|
|
self.dtype = th.float16 if use_fp16 else th.float32
|
|
self.num_heads = num_heads
|
|
self.num_head_channels = num_head_channels
|
|
self.num_heads_upsample = num_heads_upsample
|
|
|
|
time_embed_dim = model_channels * 4
|
|
self.time_embed = nn.Sequential(
|
|
nn.Linear(model_channels, time_embed_dim),
|
|
nn.SiLU(),
|
|
nn.Linear(time_embed_dim, time_embed_dim),
|
|
)
|
|
|
|
if self.num_classes is not None:
|
|
self.label_emb = nn.Embedding(num_classes, time_embed_dim)
|
|
|
|
ch = input_ch = int(channel_mult[0] * model_channels)
|
|
self.input_blocks = nn.ModuleList([
|
|
TimestepEmbedSequential(
|
|
conv_nd(dims, in_channels, ch, 3, padding=1))
|
|
])
|
|
self._feature_size = ch
|
|
input_block_chans = [ch]
|
|
ds = 1
|
|
for level, mult in enumerate(channel_mult):
|
|
for _ in range(num_res_blocks):
|
|
layers = [
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
out_channels=int(mult * model_channels),
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
)
|
|
]
|
|
ch = int(mult * model_channels)
|
|
if ds in attention_resolutions:
|
|
layers.append(
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
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_checkpoint=use_checkpoint,
|
|
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.middle_block = TimestepEmbedSequential(
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
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_checkpoint=use_checkpoint,
|
|
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=int(model_channels * mult),
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
)
|
|
]
|
|
ch = int(model_channels * mult)
|
|
if ds in attention_resolutions:
|
|
layers.append(
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
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_checkpoint=use_checkpoint,
|
|
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(
|
|
GroupNorm(32, ch),
|
|
nn.SiLU(),
|
|
conv_nd(dims, input_ch, out_channels, 3, padding=1),
|
|
)
|
|
|
|
nn.init.zeros_(self.out[-1].weight)
|
|
|
|
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, 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)
|
|
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, *args, **kwargs):
|
|
super().__init__(image_size, in_channels * 2, *args, **kwargs)
|
|
|
|
def forward(self, x, timesteps, low_res=None, **kwargs):
|
|
_, _, new_height, new_width = x.shape
|
|
upsampled = F.interpolate(
|
|
low_res, (new_height, new_width), mode='bilinear')
|
|
x = th.cat([x, upsampled], dim=1)
|
|
return super().forward(x, timesteps, **kwargs)
|
|
|
|
|
|
class EncoderUNetModel(nn.Module):
|
|
"""
|
|
The half UNet model with attention and timestep embedding.
|
|
|
|
For usage, see UNet.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
image_size,
|
|
in_channels,
|
|
model_channels,
|
|
out_channels,
|
|
num_res_blocks,
|
|
attention_resolutions,
|
|
dropout=0,
|
|
channel_mult=(1, 2, 4, 8),
|
|
conv_resample=True,
|
|
dims=2,
|
|
use_checkpoint=False,
|
|
use_fp16=False,
|
|
num_heads=1,
|
|
num_head_channels=-1,
|
|
num_heads_upsample=-1,
|
|
use_scale_shift_norm=False,
|
|
resblock_updown=False,
|
|
use_new_attention_order=False,
|
|
pool='adaptive',
|
|
):
|
|
super().__init__()
|
|
|
|
if num_heads_upsample == -1:
|
|
num_heads_upsample = num_heads
|
|
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
self.out_channels = out_channels
|
|
self.num_res_blocks = num_res_blocks
|
|
self.attention_resolutions = attention_resolutions
|
|
self.dropout = dropout
|
|
self.channel_mult = channel_mult
|
|
self.conv_resample = conv_resample
|
|
self.use_checkpoint = use_checkpoint
|
|
self.dtype = th.float16 if use_fp16 else th.float32
|
|
self.num_heads = num_heads
|
|
self.num_head_channels = num_head_channels
|
|
self.num_heads_upsample = num_heads_upsample
|
|
|
|
time_embed_dim = model_channels * 4
|
|
self.time_embed = nn.Sequential(
|
|
nn.Linear(model_channels, time_embed_dim),
|
|
nn.SiLU(),
|
|
nn.Linear(time_embed_dim, time_embed_dim),
|
|
)
|
|
|
|
ch = int(channel_mult[0] * model_channels)
|
|
self.input_blocks = nn.ModuleList([
|
|
TimestepEmbedSequential(
|
|
conv_nd(dims, in_channels, ch, 3, padding=1))
|
|
])
|
|
self._feature_size = ch
|
|
input_block_chans = [ch]
|
|
ds = 1
|
|
for level, mult in enumerate(channel_mult):
|
|
for _ in range(num_res_blocks):
|
|
layers = [
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
out_channels=int(mult * model_channels),
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
)
|
|
]
|
|
ch = int(mult * model_channels)
|
|
if ds in attention_resolutions:
|
|
layers.append(
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
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_checkpoint=use_checkpoint,
|
|
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.middle_block = TimestepEmbedSequential(
|
|
ResBlock(
|
|
ch,
|
|
time_embed_dim,
|
|
dropout,
|
|
dims=dims,
|
|
use_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
AttentionBlock(
|
|
ch,
|
|
use_checkpoint=use_checkpoint,
|
|
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_checkpoint=use_checkpoint,
|
|
use_scale_shift_norm=use_scale_shift_norm,
|
|
),
|
|
)
|
|
self._feature_size += ch
|
|
self.pool = pool
|
|
if pool == 'adaptive':
|
|
self.out = nn.Sequential(
|
|
GroupNorm(32, ch),
|
|
nn.SiLU(),
|
|
nn.AdaptiveAvgPool2d((1, 1)),
|
|
conv_nd(dims, ch, out_channels, 1),
|
|
nn.Flatten(),
|
|
)
|
|
nn.init.zeros_(self.out[-1].weight)
|
|
elif pool == 'attention':
|
|
assert num_head_channels != -1
|
|
self.out = nn.Sequential(
|
|
GroupNorm(32, ch),
|
|
nn.SiLU(),
|
|
AttentionPool2d((image_size // ds), ch, num_head_channels,
|
|
out_channels),
|
|
)
|
|
elif pool == 'spatial':
|
|
self.out = nn.Sequential(
|
|
nn.Linear(self._feature_size, 2048),
|
|
nn.ReLU(),
|
|
nn.Linear(2048, self.out_channels),
|
|
)
|
|
elif pool == 'spatial_v2':
|
|
self.out = nn.Sequential(
|
|
nn.Linear(self._feature_size, 2048),
|
|
GroupNorm(32, 2048),
|
|
nn.SiLU(),
|
|
nn.Linear(2048, self.out_channels),
|
|
)
|
|
else:
|
|
raise NotImplementedError(f'Unexpected {pool} pooling')
|
|
|
|
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)
|
|
|
|
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)
|
|
|
|
def forward(self, x, timesteps):
|
|
"""
|
|
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.
|
|
:return: an [N x K] Tensor of outputs.
|
|
"""
|
|
emb = self.time_embed(
|
|
timestep_embedding(timesteps, self.model_channels))
|
|
|
|
results = []
|
|
h = x.type(self.dtype)
|
|
for module in self.input_blocks:
|
|
h = module(h, emb)
|
|
if self.pool.startswith('spatial'):
|
|
results.append(h.type(x.dtype).mean(dim=(2, 3)))
|
|
h = self.middle_block(h, emb)
|
|
if self.pool.startswith('spatial'):
|
|
results.append(h.type(x.dtype).mean(dim=(2, 3)))
|
|
h = th.cat(results, axis=-1)
|
|
return self.out(h)
|
|
else:
|
|
h = h.type(x.dtype)
|
|
return self.out(h)
|
|
|
|
|
|
class UNetConfig(PretrainedConfig):
|
|
|
|
def __init__(self,
|
|
image_size=512,
|
|
in_channels=3,
|
|
model_channels=256,
|
|
out_channels=6,
|
|
num_res_blocks=2,
|
|
attention_resolutions=[16, 32, 64],
|
|
dropout=0.0,
|
|
channel_mult=(0.5, 1, 1, 2, 2, 4, 4),
|
|
num_classes=None,
|
|
use_checkpoint=False,
|
|
use_fp16=True,
|
|
num_heads=4,
|
|
num_head_channels=64,
|
|
num_heads_upsample=-1,
|
|
use_scale_shift_norm=True,
|
|
resblock_updown=True,
|
|
use_new_attention_order=False,
|
|
**kwargs):
|
|
self.image_size = image_size
|
|
self.in_channels = in_channels
|
|
self.model_channels = model_channels
|
|
self.out_channels = out_channels
|
|
self.num_res_blocks = num_res_blocks
|
|
self.attention_resolutions = attention_resolutions
|
|
self.dropout = dropout
|
|
self.channel_mult = channel_mult
|
|
self.num_classes = num_classes
|
|
self.use_checkpoint = use_checkpoint
|
|
self.use_fp16 = use_fp16
|
|
self.num_heads = num_heads
|
|
self.num_head_channels = num_head_channels
|
|
self.num_heads_upsample = num_heads_upsample
|
|
self.use_scale_shift_norm = use_scale_shift_norm
|
|
self.resblock_updown = resblock_updown
|
|
self.use_new_attention_order = use_new_attention_order
|
|
super().__init__(**kwargs)
|
|
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class HFUNetModel(PreTrainedModel):
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config_class = UNetConfig
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def __init__(self, config):
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super().__init__(config)
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self.model = UNetModel(
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image_size=config.image_size,
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in_channels=config.in_channels,
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model_channels=config.model_channels,
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out_channels=config.out_channels,
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num_res_blocks=config.num_res_blocks,
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attention_resolutions=config.attention_resolutions,
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dropout=config.dropout,
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channel_mult=config.channel_mult,
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num_classes=config.num_classes,
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use_checkpoint=config.use_checkpoint,
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use_fp16=config.use_fp16,
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num_heads=config.num_heads,
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num_head_channels=config.num_head_channels,
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num_heads_upsample=config.num_heads_upsample,
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use_scale_shift_norm=config.use_scale_shift_norm,
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resblock_updown=config.resblock_updown,
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use_new_attention_order=config.use_new_attention_order,
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)
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def forward(self, x, timesteps, y=None):
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return self.model.forward(x, timesteps, y)
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def convert_to_fp16(self):
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"""
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Convert the torso of the model to float16.
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"""
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self.model.input_blocks.apply(convert_module_to_f16)
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self.model.middle_block.apply(convert_module_to_f16)
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self.model.output_blocks.apply(convert_module_to_f16)
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