import math
from dataclasses import dataclass
from numbers import Number
from typing import NamedTuple, Tuple, Union
import numpy as np
import torch as th
from torch import nn
import torch.nn.functional as F
from .DiffAE_support_choices import *
from .DiffAE_support_config_base import BaseConfig
from .DiffAE_model_blocks import *
from .DiffAE_model_nn import (conv_nd, linear, normalization, timestep_embedding,
torch_checkpoint, zero_module)
@dataclass
class BeatGANsUNetConfig(BaseConfig):
image_size: int = 64
in_channels: int = 3
# base channels, will be multiplied
model_channels: int = 64
# output of the unet
# suggest: 3
# you only need 6 if you also model the variance of the noise prediction (usually we use an analytical variance hence 3)
out_channels: int = 3
# how many repeating resblocks per resolution
# the decoding side would have "one more" resblock
# default: 2
num_res_blocks: int = 2
# you can also set the number of resblocks specifically for the input blocks
# default: None = above
num_input_res_blocks: int = None
# number of time embed channels and style channels
embed_channels: int = 512
# at what resolutions you want to do self-attention of the feature maps
# attentions generally improve performance
# default: [16]
# beatgans: [32, 16, 8]
attention_resolutions: Tuple[int] = (16, )
# number of time embed channels
time_embed_channels: int = None
# dropout applies to the resblocks (on feature maps)
dropout: float = 0.1
channel_mult: Tuple[int] = (1, 2, 4, 8)
input_channel_mult: Tuple[int] = None
conv_resample: bool = True
group_norm_limit: int = 32
# always 2 = 2d conv
dims: int = 2
# don't use this, legacy from BeatGANs
num_classes: int = None
use_checkpoint: bool = False
# number of attention heads
num_heads: int = 1
# or specify the number of channels per attention head
num_head_channels: int = -1
# what's this?
num_heads_upsample: int = -1
# use resblock for upscale/downscale blocks (expensive)
# default: True (BeatGANs)
resblock_updown: bool = True
# never tried
use_new_attention_order: bool = False
resnet_two_cond: bool = False
resnet_cond_channels: int = None
# init the decoding conv layers with zero weights, this speeds up training
# default: True (BeattGANs)
resnet_use_zero_module: bool = True
# gradient checkpoint the attention operation
attn_checkpoint: bool = False
def make_model(self):
return BeatGANsUNetModel(self)
class BeatGANsUNetModel(nn.Module):
def __init__(self, conf: BeatGANsUNetConfig):
super().__init__()
self.conf = conf
if conf.num_heads_upsample == -1:
self.num_heads_upsample = conf.num_heads
self.dtype = th.float32
self.time_emb_channels = conf.time_embed_channels or conf.model_channels
self.time_embed = nn.Sequential(
linear(self.time_emb_channels, conf.embed_channels),
nn.SiLU(),
linear(conf.embed_channels, conf.embed_channels),
)
if conf.num_classes is not None:
self.label_emb = nn.Embedding(conf.num_classes,
conf.embed_channels)
ch = input_ch = int(conf.channel_mult[0] * conf.model_channels)
self.input_blocks = nn.ModuleList([
TimestepEmbedSequential(
conv_nd(conf.dims, conf.in_channels, ch, 3, padding=1))
])
kwargs = dict(
use_condition=True,
two_cond=conf.resnet_two_cond,
use_zero_module=conf.resnet_use_zero_module,
# style channels for the resnet block
cond_emb_channels=conf.resnet_cond_channels,
)
self._feature_size = ch
# input_block_chans = [ch]
input_block_chans = [[] for _ in range(len(conf.channel_mult))]
input_block_chans[0].append(ch)
# number of blocks at each resolution
self.input_num_blocks = [0 for _ in range(len(conf.channel_mult))]
self.input_num_blocks[0] = 1
self.output_num_blocks = [0 for _ in range(len(conf.channel_mult))]
ds = 1
resolution = conf.image_size
for level, mult in enumerate(conf.input_channel_mult
or conf.channel_mult):
for _ in range(conf.num_input_res_blocks or conf.num_res_blocks):
layers = [
ResBlockConfig(
ch,
conf.embed_channels,
conf.dropout,
out_channels=int(mult * conf.model_channels),
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_checkpoint=conf.use_checkpoint,
**kwargs,
).make_model()
]
ch = int(mult * conf.model_channels)
if resolution in conf.attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=conf.use_checkpoint
or conf.attn_checkpoint,
num_heads=conf.num_heads,
num_head_channels=conf.num_head_channels,
group_norm_limit=conf.group_norm_limit,
use_new_attention_order=conf.
use_new_attention_order,
))
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
# input_block_chans.append(ch)
input_block_chans[level].append(ch)
self.input_num_blocks[level] += 1
# print(input_block_chans)
if level != len(conf.channel_mult) - 1:
resolution //= 2
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlockConfig(
ch,
conf.embed_channels,
conf.dropout,
out_channels=out_ch,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_checkpoint=conf.use_checkpoint,
down=True,
**kwargs,
).make_model() if conf.
resblock_updown else Downsample(ch,
conf.conv_resample,
dims=conf.dims,
out_channels=out_ch)))
ch = out_ch
# input_block_chans.append(ch)
input_block_chans[level + 1].append(ch)
self.input_num_blocks[level + 1] += 1
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlockConfig(
ch,
conf.embed_channels,
conf.dropout,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_checkpoint=conf.use_checkpoint,
**kwargs,
).make_model(),
AttentionBlock(
ch,
use_checkpoint=conf.use_checkpoint or conf.attn_checkpoint,
num_heads=conf.num_heads,
num_head_channels=conf.num_head_channels,
group_norm_limit=conf.group_norm_limit,
use_new_attention_order=conf.use_new_attention_order,
),
ResBlockConfig(
ch,
conf.embed_channels,
conf.dropout,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_checkpoint=conf.use_checkpoint,
**kwargs,
).make_model(),
)
self._feature_size += ch
self.output_blocks = nn.ModuleList([])
for level, mult in list(enumerate(conf.channel_mult))[::-1]:
for i in range(conf.num_res_blocks + 1):
# print(input_block_chans)
# ich = input_block_chans.pop()
try:
ich = input_block_chans[level].pop()
except IndexError:
# this happens only when num_res_block > num_enc_res_block
# we will not have enough lateral (skip) connecions for all decoder blocks
ich = 0
# print('pop:', ich)
layers = [
ResBlockConfig(
# only direct channels when gated
channels=ch + ich,
emb_channels=conf.embed_channels,
dropout=conf.dropout,
out_channels=int(conf.model_channels * mult),
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_checkpoint=conf.use_checkpoint,
# lateral channels are described here when gated
has_lateral=True if ich > 0 else False,
lateral_channels=None,
**kwargs,
).make_model()
]
ch = int(conf.model_channels * mult)
if resolution in conf.attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=conf.use_checkpoint
or conf.attn_checkpoint,
num_heads=self.num_heads_upsample,
num_head_channels=conf.num_head_channels,
group_norm_limit=conf.group_norm_limit,
use_new_attention_order=conf.
use_new_attention_order,
))
if level and i == conf.num_res_blocks:
resolution *= 2
out_ch = ch
layers.append(
ResBlockConfig(
ch,
conf.embed_channels,
conf.dropout,
out_channels=out_ch,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_checkpoint=conf.use_checkpoint,
up=True,
**kwargs,
).make_model() if (
conf.resblock_updown
) else Upsample(ch,
conf.conv_resample,
dims=conf.dims,
out_channels=out_ch))
ds //= 2
self.output_blocks.append(TimestepEmbedSequential(*layers))
self.output_num_blocks[level] += 1
self._feature_size += ch
# print(input_block_chans)
# print('inputs:', self.input_num_blocks)
# print('outputs:', self.output_num_blocks)
if conf.resnet_use_zero_module:
self.out = nn.Sequential(
normalization(ch, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
nn.SiLU(),
zero_module(
conv_nd(conf.dims,
input_ch,
conf.out_channels,
3,
padding=1)),
)
else:
self.out = nn.Sequential(
normalization(ch, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
nn.SiLU(),
conv_nd(conf.dims, input_ch, conf.out_channels, 3, padding=1),
)
def forward(self, x, t, y=None, **kwargs):
"""
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.conf.num_classes is not None
), "must specify y if and only if the model is class-conditional"
# hs = []
hs = [[] for _ in range(len(self.conf.channel_mult))]
emb = self.time_embed(timestep_embedding(t, self.time_emb_channels))
if self.conf.num_classes is not None:
raise NotImplementedError()
# assert y.shape == (x.shape[0], )
# emb = emb + self.label_emb(y)
# new code supports input_num_blocks != output_num_blocks
h = x.type(self.dtype)
k = 0
for i in range(len(self.input_num_blocks)):
for j in range(self.input_num_blocks[i]):
h = self.input_blocks[k](h, emb=emb)
# print(i, j, h.shape)
hs[i].append(h)
k += 1
assert k == len(self.input_blocks)
h = self.middle_block(h, emb=emb)
k = 0
for i in range(len(self.output_num_blocks)):
for j in range(self.output_num_blocks[i]):
# take the lateral connection from the same layer (in reserve)
# until there is no more, use None
try:
lateral = hs[-i - 1].pop()
# print(i, j, lateral.shape)
except IndexError:
lateral = None
# print(i, j, lateral)
h = self.output_blocks[k](h, emb=emb, lateral=lateral)
k += 1
h = h.type(x.dtype)
pred = self.out(h)
return Return(pred=pred)
class Return(NamedTuple):
pred: th.Tensor
@dataclass
class BeatGANsEncoderConfig(BaseConfig):
image_size: int
in_channels: int
model_channels: int
out_hid_channels: int
out_channels: int
num_res_blocks: int
attention_resolutions: Tuple[int]
dropout: float = 0
channel_mult: Tuple[int] = (1, 2, 4, 8)
use_time_condition: bool = True
conv_resample: bool = True
group_norm_limit: int = 32
dims: int = 2
use_checkpoint: bool = False
num_heads: int = 1
num_head_channels: int = -1
resblock_updown: bool = False
use_new_attention_order: bool = False
pool: str = 'adaptivenonzero'
def make_model(self):
return BeatGANsEncoderModel(self)
class BeatGANsEncoderModel(nn.Module):
"""
The half UNet model with attention and timestep embedding.
For usage, see UNet.
"""
def __init__(self, conf: BeatGANsEncoderConfig):
super().__init__()
self.conf = conf
self.dtype = th.float32
if conf.use_time_condition:
time_embed_dim = conf.model_channels * 4
self.time_embed = nn.Sequential(
linear(conf.model_channels, time_embed_dim),
nn.SiLU(),
linear(time_embed_dim, time_embed_dim),
)
else:
time_embed_dim = None
ch = int(conf.channel_mult[0] * conf.model_channels)
self.input_blocks = nn.ModuleList([
TimestepEmbedSequential(
conv_nd(conf.dims, conf.in_channels, ch, 3, padding=1))
])
self._feature_size = ch
input_block_chans = [ch]
ds = 1
resolution = conf.image_size
for level, mult in enumerate(conf.channel_mult):
for _ in range(conf.num_res_blocks):
layers = [
ResBlockConfig(
ch,
time_embed_dim,
conf.dropout,
out_channels=int(mult * conf.model_channels),
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_condition=conf.use_time_condition,
use_checkpoint=conf.use_checkpoint,
).make_model()
]
ch = int(mult * conf.model_channels)
if resolution in conf.attention_resolutions:
layers.append(
AttentionBlock(
ch,
use_checkpoint=conf.use_checkpoint,
num_heads=conf.num_heads,
num_head_channels=conf.num_head_channels,
group_norm_limit=conf.group_norm_limit,
use_new_attention_order=conf.
use_new_attention_order,
))
self.input_blocks.append(TimestepEmbedSequential(*layers))
self._feature_size += ch
input_block_chans.append(ch)
if level != len(conf.channel_mult) - 1:
resolution //= 2
out_ch = ch
self.input_blocks.append(
TimestepEmbedSequential(
ResBlockConfig(
ch,
time_embed_dim,
conf.dropout,
out_channels=out_ch,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_condition=conf.use_time_condition,
use_checkpoint=conf.use_checkpoint,
down=True,
).make_model() if (
conf.resblock_updown
) else Downsample(ch,
conf.conv_resample,
dims=conf.dims,
out_channels=out_ch)))
ch = out_ch
input_block_chans.append(ch)
ds *= 2
self._feature_size += ch
self.middle_block = TimestepEmbedSequential(
ResBlockConfig(
ch,
time_embed_dim,
conf.dropout,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_condition=conf.use_time_condition,
use_checkpoint=conf.use_checkpoint,
).make_model(),
AttentionBlock(
ch,
use_checkpoint=conf.use_checkpoint,
num_heads=conf.num_heads,
num_head_channels=conf.num_head_channels,
group_norm_limit=conf.group_norm_limit,
use_new_attention_order=conf.use_new_attention_order,
),
ResBlockConfig(
ch,
time_embed_dim,
conf.dropout,
group_norm_limit=conf.group_norm_limit,
dims=conf.dims,
use_condition=conf.use_time_condition,
use_checkpoint=conf.use_checkpoint,
).make_model(),
)
self._feature_size += ch
if conf.pool == "adaptivenonzero":
self.out = nn.Sequential(
normalization(ch, limit=conf.group_norm_limit if "group_norm_limit" in conf.__dict__ else 32),
nn.SiLU(),
nn.AdaptiveAvgPool2d((1, 1)) if conf.dims == 2 else nn.AdaptiveAvgPool3d((1, 1, 1)),
conv_nd(conf.dims, ch, conf.out_channels, 1),
nn.Flatten(),
)
else:
raise NotImplementedError(f"Unexpected {conf.pool} pooling")
def forward(self, x, t=None, return_Nd_feature=False):
"""
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.
"""
if self.conf.use_time_condition:
emb = self.time_embed(timestep_embedding(t, self.model_channels))
else:
emb = None
results = []
h = x.type(self.dtype)
for module in self.input_blocks:
h = module(h, emb=emb)
if self.conf.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3) if self.conf.dims == 2 else (2, 3, 4)))
h = self.middle_block(h, emb=emb)
if self.conf.pool.startswith("spatial"):
results.append(h.type(x.dtype).mean(dim=(2, 3) if self.conf.dims == 2 else (2, 3, 4)))
h = th.cat(results, axis=-1)
else:
h = h.type(x.dtype)
h_Nd = h
h = self.out(h)
if return_Nd_feature:
return h, h_Nd
else:
return h
def forward_flatten(self, x):
"""
transform the last Nd feature into a flatten vector
"""
h = self.out(x)
return h
class SuperResModel(BeatGANsUNetModel):
"""
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)