"""
与autoencoder.py的区别在于，autoencoder.py是(B,1,80,T) ->(B,C,80/8,T/8),现在vae要变成(B,80,T) -> (B,80/downsample_c,T/downsample_t)
"""
import os
import torch
import torch.nn as nn
import pytorch_lightning as pl
import torch.nn.functional as F
from contextlib import contextmanager
from packaging import version
import numpy as np
from ldm.modules.distributions.distributions import DiagonalGaussianDistribution
from torch.optim.lr_scheduler import LambdaLR
from ldm.util import instantiate_from_config
class AutoencoderKL(pl.LightningModule):
def __init__(self,
embed_dim,
ddconfig,
lossconfig,
ckpt_path=None,
ignore_keys=[],
image_key="image",
monitor=None,
):
super().__init__()
self.image_key = image_key
self.encoder = Encoder1D(**ddconfig)
self.decoder = Decoder1D(**ddconfig)
self.loss = instantiate_from_config(lossconfig)
assert ddconfig["double_z"]
self.quant_conv = torch.nn.Conv1d(2*ddconfig["z_channels"], 2*embed_dim, 1)
self.post_quant_conv = torch.nn.Conv1d(embed_dim, ddconfig["z_channels"], 1)
self.embed_dim = embed_dim
if monitor is not None:
self.monitor = monitor
if ckpt_path is not None:
self.init_from_ckpt(ckpt_path, ignore_keys=ignore_keys)
def init_from_ckpt(self, path, ignore_keys=list()):
sd = torch.load(path, map_location="cpu")["state_dict"]
keys = list(sd.keys())
for k in keys:
for ik in ignore_keys:
if k.startswith(ik):
print("Deleting key {} from state_dict.".format(k))
del sd[k]
self.load_state_dict(sd, strict=False)
print(f"AutoencoderKL Restored from {path} Done")
def encode(self, x):
h = self.encoder(x)
moments = self.quant_conv(h)
posterior = DiagonalGaussianDistribution(moments)
return posterior
def decode(self, z):
z = self.post_quant_conv(z)
dec = self.decoder(z)
return dec
def forward(self, input, sample_posterior=True):
posterior = self.encode(input)
if sample_posterior:
z = posterior.sample()
else:
z = posterior.mode()
dec = self.decode(z)
return dec, posterior
def get_input(self, batch, k):
x = batch[k]
assert len(x.shape) == 3
x = x.to(memory_format=torch.contiguous_format).float()
return x
def training_step(self, batch, batch_idx, optimizer_idx):
inputs = self.get_input(batch, self.image_key)
# print(inputs.shape)
reconstructions, posterior = self(inputs)
if optimizer_idx == 0:
# train encoder+decoder+logvar
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("aeloss", aeloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_ae, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return aeloss
if optimizer_idx == 1:
# train the discriminator
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, optimizer_idx, self.global_step,
last_layer=self.get_last_layer(), split="train")
self.log("discloss", discloss, prog_bar=True, logger=True, on_step=True, on_epoch=True)
self.log_dict(log_dict_disc, prog_bar=False, logger=True, on_step=True, on_epoch=False)
return discloss
def validation_step(self, batch, batch_idx):
inputs = self.get_input(batch, self.image_key)
reconstructions, posterior = self(inputs)
aeloss, log_dict_ae = self.loss(inputs, reconstructions, posterior, 0, self.global_step,
last_layer=self.get_last_layer(), split="val")
discloss, log_dict_disc = self.loss(inputs, reconstructions, posterior, 1, self.global_step,
last_layer=self.get_last_layer(), split="val")
self.log("val/rec_loss", log_dict_ae["val/rec_loss"])
self.log_dict(log_dict_ae)
self.log_dict(log_dict_disc)
return self.log_dict
def test_step(self, batch, batch_idx):
inputs = self.get_input(batch, self.image_key)# inputs shape:(b,mel_len,T)
reconstructions, posterior = self(inputs)# reconstructions:(b,mel_len,T)
mse_loss = torch.nn.functional.mse_loss(reconstructions,inputs)
self.log('test/mse_loss',mse_loss)
test_ckpt_path = os.path.basename(self.trainer.tested_ckpt_path)
savedir = os.path.join(self.trainer.log_dir,f'output_imgs_{test_ckpt_path}','fake_class')
if batch_idx == 0:
print(f"save_path is: {savedir}")
if not os.path.exists(savedir):
os.makedirs(savedir)
print(f"save_path is: {savedir}")
file_names = batch['f_name']
# print(f"reconstructions.shape:{reconstructions.shape}",file_names)
# reconstructions = (reconstructions + 1)/2 # to mel scale
reconstructions = reconstructions.cpu().numpy() # squuze channel dim
for b in range(reconstructions.shape[0]):
vname_num_split_index = file_names[b].rfind('_')# file_names[b]:video_name+'_'+num
v_n,num = file_names[b][:vname_num_split_index],file_names[b][vname_num_split_index+1:]
save_img_path = os.path.join(savedir, f'{v_n}.npy') # f'{v_n}_sample_{num}.npy' f'{v_n}.npy'
np.save(save_img_path,reconstructions[b])
return None
def configure_optimizers(self):
lr = self.learning_rate
opt_ae = torch.optim.Adam(list(self.encoder.parameters())+
list(self.decoder.parameters())+
list(self.quant_conv.parameters())+
list(self.post_quant_conv.parameters()),
lr=lr, betas=(0.5, 0.9))
opt_disc = torch.optim.Adam(self.loss.discriminator.parameters(),
lr=lr, betas=(0.5, 0.9))
return [opt_ae, opt_disc], []
def get_last_layer(self):
return self.decoder.conv_out.weight
@torch.no_grad()
def log_images(self, batch, only_inputs=False, **kwargs):
log = dict()
x = self.get_input(batch, self.image_key)
x = x.to(self.device)
if not only_inputs:
xrec, posterior = self(x)
log["samples"] = self.decode(torch.randn_like(posterior.sample())).unsqueeze(1) # (b,1,H,W)
log["reconstructions"] = xrec.unsqueeze(1)
log["inputs"] = x.unsqueeze(1)
return log
def Normalize(in_channels, num_groups=32):
return torch.nn.GroupNorm(num_groups=num_groups, num_channels=in_channels, eps=1e-6, affine=True)
def nonlinearity(x):
# swish
return x*torch.sigmoid(x)
class ResnetBlock1D(nn.Module):
def __init__(self, *, in_channels, out_channels=None, conv_shortcut=False,
dropout, temb_channels=512,kernel_size = 3):
super().__init__()
self.in_channels = in_channels
out_channels = in_channels if out_channels is None else out_channels
self.out_channels = out_channels
self.use_conv_shortcut = conv_shortcut
self.norm1 = Normalize(in_channels)
self.conv1 = torch.nn.Conv1d(in_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=kernel_size//2)
if temb_channels > 0:
self.temb_proj = torch.nn.Linear(temb_channels,
out_channels)
self.norm2 = Normalize(out_channels)
self.dropout = torch.nn.Dropout(dropout)
self.conv2 = torch.nn.Conv1d(out_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=kernel_size//2)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
self.conv_shortcut = torch.nn.Conv1d(in_channels,
out_channels,
kernel_size=kernel_size,
stride=1,
padding=kernel_size//2)
else:
self.nin_shortcut = torch.nn.Conv1d(in_channels,
out_channels,
kernel_size=1,
stride=1,
padding=0)
def forward(self, x, temb):
h = x
h = self.norm1(h)
h = nonlinearity(h)
h = self.conv1(h)
if temb is not None:
h = h + self.temb_proj(nonlinearity(temb))[:,:,None,None]
h = self.norm2(h)
h = nonlinearity(h)
h = self.dropout(h)
h = self.conv2(h)
if self.in_channels != self.out_channels:
if self.use_conv_shortcut:
x = self.conv_shortcut(x)
else:
x = self.nin_shortcut(x)
return x+h
class AttnBlock1D(nn.Module):
def __init__(self, in_channels):
super().__init__()
self.in_channels = in_channels
self.norm = Normalize(in_channels)
self.q = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1)
self.k = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1)
self.v = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1)
self.proj_out = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=1)
def forward(self, x):
h_ = x
h_ = self.norm(h_)
q = self.q(h_)
k = self.k(h_)
v = self.v(h_)
# compute attention
b,t,c = q.shape
q = q.permute(0,2,1) # b,t,c
w_ = torch.bmm(q,k) # b,t,t w[b,i,j]=sum_c q[b,i,c]k[b,c,j]
# if still 2d attn (q:b,hw,c ,k:b,c,hw -> w_:b,hw,hw)
w_ = w_ * (int(t)**(-0.5))
w_ = torch.nn.functional.softmax(w_, dim=2)
# attend to values
w_ = w_.permute(0,2,1) # b,t,t (first t of k, second of q)
h_ = torch.bmm(v,w_) # b,c,t (t of q) h_[b,c,j] = sum_i v[b,c,i] w_[b,i,j]
h_ = self.proj_out(h_)
return x+h_
class Upsample1D(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
self.conv = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=3,
stride=1,
padding=1)
def forward(self, x):
x = torch.nn.functional.interpolate(x, scale_factor=2.0, mode="nearest") # support 3D tensor(B,C,T)
if self.with_conv:
x = self.conv(x)
return x
class Downsample1D(nn.Module):
def __init__(self, in_channels, with_conv):
super().__init__()
self.with_conv = with_conv
if self.with_conv:
# no asymmetric padding in torch conv, must do it ourselves
self.conv = torch.nn.Conv1d(in_channels,
in_channels,
kernel_size=3,
stride=2,
padding=0)
def forward(self, x):
if self.with_conv:
pad = (0,1)
x = torch.nn.functional.pad(x, pad, mode="constant", value=0)
x = self.conv(x)
else:
x = torch.nn.functional.avg_pool1d(x, kernel_size=2, stride=2)
return x
class Encoder1D(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_layers = [],down_layers = [], dropout=0.0, resamp_with_conv=True, in_channels,
z_channels, double_z=True,kernel_size=3, **ignore_kwargs):
""" out_ch is only used in decoder,not used here
"""
super().__init__()
self.ch = ch
self.temb_ch = 0
self.num_layers = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.in_channels = in_channels
print(f"downsample rates is {2**len(down_layers)}")
self.down_layers = down_layers
self.attn_layers = attn_layers
self.conv_in = torch.nn.Conv1d(in_channels,
self.ch,
kernel_size=kernel_size,
stride=1,
padding=kernel_size//2)
in_ch_mult = (1,)+tuple(ch_mult)
self.in_ch_mult = in_ch_mult
# downsampling
self.down = nn.ModuleList()
for i_level in range(self.num_layers):
block = nn.ModuleList()
attn = nn.ModuleList()
block_in = ch*in_ch_mult[i_level]
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks):
block.append(ResnetBlock1D(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout,
kernel_size=kernel_size))
block_in = block_out
if i_level in attn_layers:
# print(f"add attn in layer:{i_level}")
attn.append(AttnBlock1D(block_in))
down = nn.Module()
down.block = block
down.attn = attn
if i_level in down_layers:
down.downsample = Downsample1D(block_in, resamp_with_conv)
self.down.append(down)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock1D(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
kernel_size=kernel_size)
self.mid.attn_1 = AttnBlock1D(block_in)
self.mid.block_2 = ResnetBlock1D(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout,
kernel_size=kernel_size)
# end
self.norm_out = Normalize(block_in)# GroupNorm
self.conv_out = torch.nn.Conv1d(block_in,
2*z_channels if double_z else z_channels,
kernel_size=kernel_size,
stride=1,
padding=kernel_size//2)
def forward(self, x):
# timestep embedding
temb = None
# downsampling
hs = [self.conv_in(x)]
for i_level in range(self.num_layers):
for i_block in range(self.num_res_blocks):
h = self.down[i_level].block[i_block](hs[-1], temb)
if len(self.down[i_level].attn) > 0:
h = self.down[i_level].attn[i_block](h)
hs.append(h)
if i_level in self.down_layers:
hs.append(self.down[i_level].downsample(hs[-1]))
# middle
h = hs[-1]
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# end
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
return h
class Decoder1D(nn.Module):
def __init__(self, *, ch, out_ch, ch_mult=(1,2,4,8), num_res_blocks,
attn_layers = [],down_layers = [], dropout=0.0,kernel_size=3, resamp_with_conv=True, in_channels,
z_channels, give_pre_end=False, tanh_out=False, **ignorekwargs):
super().__init__()
self.ch = ch
self.temb_ch = 0
self.num_layers = len(ch_mult)
self.num_res_blocks = num_res_blocks
self.in_channels = in_channels
self.give_pre_end = give_pre_end
self.tanh_out = tanh_out
self.down_layers = [i+1 for i in down_layers] # each downlayer add one
print(f"upsample rates is {2**len(down_layers)}")
# compute in_ch_mult, block_in and curr_res at lowest res
in_ch_mult = (1,)+tuple(ch_mult)
block_in = ch*ch_mult[self.num_layers-1]
# z to block_in
self.conv_in = torch.nn.Conv1d(z_channels,
block_in,
kernel_size=kernel_size,
stride=1,
padding=kernel_size//2)
# middle
self.mid = nn.Module()
self.mid.block_1 = ResnetBlock1D(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
self.mid.attn_1 = AttnBlock1D(block_in)
self.mid.block_2 = ResnetBlock1D(in_channels=block_in,
out_channels=block_in,
temb_channels=self.temb_ch,
dropout=dropout)
# upsampling
self.up = nn.ModuleList()
for i_level in reversed(range(self.num_layers)):
block = nn.ModuleList()
attn = nn.ModuleList()
block_out = ch*ch_mult[i_level]
for i_block in range(self.num_res_blocks+1):
block.append(ResnetBlock1D(in_channels=block_in,
out_channels=block_out,
temb_channels=self.temb_ch,
dropout=dropout))
block_in = block_out
if i_level in attn_layers:
# print(f"add attn in layer:{i_level}")
attn.append(AttnBlock1D(block_in))
up = nn.Module()
up.block = block
up.attn = attn
if i_level in self.down_layers:
up.upsample = Upsample1D(block_in, resamp_with_conv)
self.up.insert(0, up) # prepend to get consistent order
# end
self.norm_out = Normalize(block_in)
self.conv_out = torch.nn.Conv1d(block_in,
out_ch,
kernel_size=kernel_size,
stride=1,
padding=kernel_size//2)
def forward(self, z):
#assert z.shape[1:] == self.z_shape[1:]
self.last_z_shape = z.shape
# timestep embedding
temb = None
# z to block_in
h = self.conv_in(z)
# middle
h = self.mid.block_1(h, temb)
h = self.mid.attn_1(h)
h = self.mid.block_2(h, temb)
# upsampling
for i_level in reversed(range(self.num_layers)):
for i_block in range(self.num_res_blocks+1):
h = self.up[i_level].block[i_block](h, temb)
if len(self.up[i_level].attn) > 0:
h = self.up[i_level].attn[i_block](h)
if i_level in self.down_layers:
h = self.up[i_level].upsample(h)
# end
if self.give_pre_end:
return h
h = self.norm_out(h)
h = nonlinearity(h)
h = self.conv_out(h)
if self.tanh_out:
h = torch.tanh(h)
return h