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import numpy as np
import random
import torch
import torch.nn as nn
import torch.nn.functional as F
from modules.audio2motion.cnn_models import LambdaLayer
class Discriminator1DFactory(nn.Module):
def __init__(self, time_length, kernel_size=3, in_dim=1, hidden_size=128, norm_type='bn'):
super(Discriminator1DFactory, self).__init__()
padding = kernel_size // 2
def discriminator_block(in_filters, out_filters, first=False):
"""
Input: (B, c, T)
Output:(B, c, T//2)
"""
conv = nn.Conv1d(in_filters, out_filters, kernel_size, 2, padding)
block = [
conv, # padding = kernel//2
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25)
]
if norm_type == 'bn' and not first:
block.append(nn.BatchNorm1d(out_filters, 0.8))
if norm_type == 'in' and not first:
block.append(nn.InstanceNorm1d(out_filters, affine=True))
block = nn.Sequential(*block)
return block
if time_length >= 8:
self.model = nn.ModuleList([
discriminator_block(in_dim, hidden_size, first=True),
discriminator_block(hidden_size, hidden_size),
discriminator_block(hidden_size, hidden_size),
])
ds_size = time_length // (2 ** 3)
elif time_length == 3:
self.model = nn.ModuleList([
nn.Sequential(*[
nn.Conv1d(in_dim, hidden_size, 3, 1, 0),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.Conv1d(hidden_size, hidden_size, 1, 1, 0),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.BatchNorm1d(hidden_size, 0.8),
nn.Conv1d(hidden_size, hidden_size, 1, 1, 0),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.BatchNorm1d(hidden_size, 0.8)
])
])
ds_size = 1
elif time_length == 1:
self.model = nn.ModuleList([
nn.Sequential(*[
nn.Linear(in_dim, hidden_size),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
nn.Linear(hidden_size, hidden_size),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25),
])
])
ds_size = 1
self.adv_layer = nn.Linear(hidden_size * ds_size, 1)
def forward(self, x):
"""
:param x: [B, C, T]
:return: validity: [B, 1], h: List of hiddens
"""
h = []
if x.shape[-1] == 1:
x = x.squeeze(-1)
for l in self.model:
x = l(x)
h.append(x)
if x.ndim == 2:
b, ct = x.shape
use_sigmoid = True
else:
b, c, t = x.shape
ct = c * t
use_sigmoid = False
x = x.view(b, ct)
validity = self.adv_layer(x) # [B, 1]
if use_sigmoid:
validity = torch.sigmoid(validity)
return validity, h
class CosineDiscriminator1DFactory(nn.Module):
def __init__(self, time_length, kernel_size=3, in_dim=1, hidden_size=128, norm_type='bn'):
super().__init__()
padding = kernel_size // 2
def discriminator_block(in_filters, out_filters, first=False):
"""
Input: (B, c, T)
Output:(B, c, T//2)
"""
conv = nn.Conv1d(in_filters, out_filters, kernel_size, 2, padding)
block = [
conv, # padding = kernel//2
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout2d(0.25)
]
if norm_type == 'bn' and not first:
block.append(nn.BatchNorm1d(out_filters, 0.8))
if norm_type == 'in' and not first:
block.append(nn.InstanceNorm1d(out_filters, affine=True))
block = nn.Sequential(*block)
return block
self.model1 = nn.ModuleList([
discriminator_block(in_dim, hidden_size, first=True),
discriminator_block(hidden_size, hidden_size),
discriminator_block(hidden_size, hidden_size),
])
self.model2 = nn.ModuleList([
discriminator_block(in_dim, hidden_size, first=True),
discriminator_block(hidden_size, hidden_size),
discriminator_block(hidden_size, hidden_size),
])
self.relu = nn.ReLU()
def forward(self, x1, x2):
"""
:param x1: [B, C, T]
:param x2: [B, C, T]
:return: validity: [B, 1], h: List of hiddens
"""
h1, h2 = [], []
for l in self.model1:
x1 = l(x1)
h1.append(x1)
for l in self.model2:
x2 = l(x2)
h2.append(x1)
b,c,t = x1.shape
x1 = x1.view(b, c*t)
x2 = x2.view(b, c*t)
x1 = self.relu(x1)
x2 = self.relu(x2)
# x1 = F.normalize(x1, p=2, dim=1)
# x2 = F.normalize(x2, p=2, dim=1)
validity = F.cosine_similarity(x1, x2)
return validity, [h1,h2]
class MultiWindowDiscriminator(nn.Module):
def __init__(self, time_lengths, cond_dim=80, in_dim=64, kernel_size=3, hidden_size=128, disc_type='standard', norm_type='bn', reduction='sum'):
super(MultiWindowDiscriminator, self).__init__()
self.win_lengths = time_lengths
self.reduction = reduction
self.disc_type = disc_type
if cond_dim > 0:
self.use_cond = True
self.cond_proj_layers = nn.ModuleList()
self.in_proj_layers = nn.ModuleList()
else:
self.use_cond = False
self.conv_layers = nn.ModuleList()
for time_length in time_lengths:
conv_layer = [
Discriminator1DFactory(
time_length, kernel_size, in_dim=64, hidden_size=hidden_size,
norm_type=norm_type) if self.disc_type == 'standard'
else CosineDiscriminator1DFactory(time_length, kernel_size, in_dim=64,
hidden_size=hidden_size,norm_type=norm_type)
]
self.conv_layers += conv_layer
if self.use_cond:
self.cond_proj_layers.append(nn.Linear(cond_dim, 64))
self.in_proj_layers.append(nn.Linear(in_dim, 64))
def clip(self, x, cond, x_len, win_length, start_frames=None):
'''Ramdom clip x to win_length.
Args:
x (tensor) : (B, T, C).
cond (tensor) : (B, T, H).
x_len (tensor) : (B,).
win_length (int): target clip length
Returns:
(tensor) : (B, c_in, win_length, n_bins).
'''
clip_from_same_frame = start_frames is None
T_start = 0
# T_end = x_len.max() - win_length
T_end = x_len.min() - win_length
if T_end < 0:
return None, None, start_frames
T_end = T_end.item()
if start_frames is None:
start_frame = np.random.randint(low=T_start, high=T_end + 1)
start_frames = [start_frame] * x.size(0)
else:
start_frame = start_frames[0]
if clip_from_same_frame:
x_batch = x[:, start_frame: start_frame + win_length, :]
c_batch = cond[:, start_frame: start_frame + win_length, :] if cond is not None else None
else:
x_lst = []
c_lst = []
for i, start_frame in enumerate(start_frames):
x_lst.append(x[i, start_frame: start_frame + win_length, :])
if cond is not None:
c_lst.append(cond[i, start_frame: start_frame + win_length, :])
x_batch = torch.stack(x_lst, dim=0)
if cond is None:
c_batch = None
else:
c_batch = torch.stack(c_lst, dim=0)
return x_batch, c_batch, start_frames
def forward(self, x, x_len, cond=None, start_frames_wins=None):
'''
Args:
x (tensor): input mel, (B, T, C).
x_length (tensor): len of per mel. (B,).
Returns:
tensor : (B).
'''
validity = []
if start_frames_wins is None:
start_frames_wins = [None] * len(self.conv_layers)
h = []
for i, start_frames in zip(range(len(self.conv_layers)), start_frames_wins):
x_clip, c_clip, start_frames = self.clip(
x, cond, x_len, self.win_lengths[i], start_frames) # (B, win_length, C)
start_frames_wins[i] = start_frames
if x_clip is None:
continue
if self.disc_type == 'standard':
if self.use_cond:
x_clip = self.in_proj_layers[i](x_clip) # (B, T, C)
c_clip = self.cond_proj_layers[i](c_clip)
x_clip = x_clip + c_clip
validity_pred, h_ = self.conv_layers[i](x_clip.transpose(1,2))
elif self.disc_type == 'cosine':
assert self.use_cond is True
x_clip = self.in_proj_layers[i](x_clip) # (B, T, C)
c_clip = self.cond_proj_layers[i](c_clip)
validity_pred, h_ = self.conv_layers[i](x_clip.transpose(1,2), c_clip.transpose(1,2))
else:
raise NotImplementedError
h += h_
validity.append(validity_pred)
if len(validity) != len(self.conv_layers):
return None, start_frames_wins, h
if self.reduction == 'sum':
validity = sum(validity) # [B]
elif self.reduction == 'stack':
validity = torch.stack(validity, -1) # [B, W_L]
return validity, start_frames_wins, h
class Discriminator(nn.Module):
def __init__(self, x_dim=80, y_dim=64, disc_type='standard',
uncond_disc=False, kernel_size=3, hidden_size=128, norm_type='bn', reduction='sum', time_lengths=(8,16,32)):
"""_summary_
Args:
time_lengths (list, optional): the list of window size. Defaults to [32, 64, 128].
x_dim (int, optional): the dim of audio features. Defaults to 80, corresponding to mel-spec.
y_dim (int, optional): the dim of facial coeff. Defaults to 64, correspond to exp; other options can be 7(pose) or 71(exp+pose).
kernel (tuple, optional): _description_. Defaults to (3, 3).
c_in (int, optional): _description_. Defaults to 1.
hidden_size (int, optional): _description_. Defaults to 128.
norm_type (str, optional): _description_. Defaults to 'bn'.
reduction (str, optional): _description_. Defaults to 'sum'.
uncond_disc (bool, optional): _description_. Defaults to False.
"""
super(Discriminator, self).__init__()
self.time_lengths = time_lengths
self.x_dim, self.y_dim = x_dim, y_dim
self.disc_type = disc_type
self.reduction = reduction
self.uncond_disc = uncond_disc
if uncond_disc:
self.x_dim = 0
cond_dim = 0
else:
cond_dim = 64
self.mel_encoder = nn.Sequential(*[
nn.Conv1d(self.x_dim, 64, 3, 1, 1, bias=False),
nn.BatchNorm1d(64),
nn.GELU(),
nn.Conv1d(64, cond_dim, 3, 1, 1, bias=False)
])
self.disc = MultiWindowDiscriminator(
time_lengths=self.time_lengths,
in_dim=self.y_dim,
cond_dim=cond_dim,
kernel_size=kernel_size,
hidden_size=hidden_size, norm_type=norm_type,
reduction=reduction,
disc_type=disc_type
)
self.downsampler = LambdaLayer(lambda x: F.interpolate(x.transpose(1,2), scale_factor=0.5, mode='nearest').transpose(1,2))
@property
def device(self):
return self.disc.parameters().__next__().device
def forward(self,x, batch, start_frames_wins=None):
"""
:param x: [B, T, C]
:param cond: [B, T, cond_size]
:return:
"""
x = x.to(self.device)
if not self.uncond_disc:
mel = self.downsampler(batch['mel'].to(self.device))
mel_feat = self.mel_encoder(mel.transpose(1,2)).transpose(1,2)
else:
mel_feat = None
x_len = x.sum(-1).ne(0).int().sum([1])
disc_confidence, start_frames_wins, h = self.disc(x, x_len, mel_feat, start_frames_wins=start_frames_wins)
return disc_confidence
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