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import torch
import torch.nn as nn
import os
import pickle
import numpy as np
from torch.nn.utils import weight_norm
from .utils.build_vocab import Vocab
class Chomp1d(nn.Module):
def __init__(self, chomp_size):
super(Chomp1d, self).__init__()
self.chomp_size = chomp_size
def forward(self, x):
return x[:, :, :-self.chomp_size].contiguous()
class TemporalBlock(nn.Module):
def __init__(self, n_inputs, n_outputs, kernel_size, stride, dilation, padding, dropout=0.2):
super(TemporalBlock, self).__init__()
self.conv1 = weight_norm(nn.Conv1d(n_inputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
self.chomp1 = Chomp1d(padding)
self.relu1 = nn.ReLU()
self.dropout1 = nn.Dropout(dropout)
self.conv2 = weight_norm(nn.Conv1d(n_outputs, n_outputs, kernel_size,
stride=stride, padding=padding, dilation=dilation))
self.chomp2 = Chomp1d(padding)
self.relu2 = nn.ReLU()
self.dropout2 = nn.Dropout(dropout)
self.net = nn.Sequential(self.conv1, self.chomp1, self.relu1, self.dropout1,
self.conv2, self.chomp2, self.relu2, self.dropout2)
self.downsample = nn.Conv1d(n_inputs, n_outputs, 1) if n_inputs != n_outputs else None
self.relu = nn.ReLU()
self.init_weights()
def init_weights(self):
self.conv1.weight.data.normal_(0, 0.01)
self.conv2.weight.data.normal_(0, 0.01)
if self.downsample is not None:
self.downsample.weight.data.normal_(0, 0.01)
def forward(self, x):
out = self.net(x)
res = x if self.downsample is None else self.downsample(x)
return self.relu(out + res)
class TemporalConvNet(nn.Module):
def __init__(self, num_inputs, num_channels, kernel_size=2, dropout=0.2):
super(TemporalConvNet, self).__init__()
layers = []
num_levels = len(num_channels)
for i in range(num_levels):
dilation_size = 2 ** i
in_channels = num_inputs if i == 0 else num_channels[i-1]
out_channels = num_channels[i]
layers += [TemporalBlock(in_channels, out_channels, kernel_size, stride=1, dilation=dilation_size,
padding=(kernel_size-1) * dilation_size, dropout=dropout)]
self.network = nn.Sequential(*layers)
def forward(self, x):
return self.network(x)
class TextEncoderTCN(nn.Module):
""" based on https://github.com/locuslab/TCN/blob/master/TCN/word_cnn/model.py """
def __init__(self, args, n_words, embed_size=300, pre_trained_embedding=None,
kernel_size=2, dropout=0.3, emb_dropout=0.1, word_cache=False):
super(TextEncoderTCN, self).__init__()
if word_cache:
self.embedding = None
else:
if pre_trained_embedding is not None: # use pre-trained embedding (fasttext)
#print(pre_trained_embedding.shape)
assert pre_trained_embedding.shape[0] == n_words
assert pre_trained_embedding.shape[1] == embed_size
self.embedding = nn.Embedding.from_pretrained(torch.FloatTensor(pre_trained_embedding),
freeze=args.freeze_wordembed)
else:
self.embedding = nn.Embedding(n_words, embed_size)
num_channels = [args.hidden_size] * args.n_layer
self.tcn = TemporalConvNet(embed_size, num_channels, kernel_size, dropout=dropout)
self.decoder = nn.Linear(num_channels[-1], args.word_f)
self.drop = nn.Dropout(emb_dropout)
self.emb_dropout = emb_dropout
self.init_weights()
def init_weights(self):
self.decoder.bias.data.fill_(0)
self.decoder.weight.data.normal_(0, 0.01)
def forward(self, input):
#print(input.shape)
if self.embedding is None:
emb = self.drop(input)
else:
emb = self.drop(self.embedding(input))
y = self.tcn(emb.transpose(1, 2)).transpose(1, 2)
y = self.decoder(y)
return y.contiguous(), 0
class BasicBlock(nn.Module):
""" based on timm: https://github.com/rwightman/pytorch-image-models """
def __init__(self, inplanes, planes, ker_size, stride=1, downsample=None, cardinality=1, base_width=64,
reduce_first=1, dilation=1, first_dilation=None, act_layer=nn.LeakyReLU, norm_layer=nn.BatchNorm1d, attn_layer=None, aa_layer=None, drop_block=None, drop_path=None):
super(BasicBlock, self).__init__()
self.conv1 = nn.Conv1d(
inplanes, planes, kernel_size=ker_size, stride=stride, padding=first_dilation,
dilation=dilation, bias=True)
self.bn1 = norm_layer(planes)
self.act1 = act_layer(inplace=True)
self.conv2 = nn.Conv1d(
planes, planes, kernel_size=ker_size, padding=ker_size//2, dilation=dilation, bias=True)
self.bn2 = norm_layer(planes)
self.act2 = act_layer(inplace=True)
if downsample is not None:
self.downsample = nn.Sequential(
nn.Conv1d(inplanes, planes, stride=stride, kernel_size=ker_size, padding=first_dilation, dilation=dilation, bias=True),
norm_layer(planes),
)
else: self.downsample=None
self.stride = stride
self.dilation = dilation
self.drop_block = drop_block
self.drop_path = drop_path
def zero_init_last_bn(self):
nn.init.zeros_(self.bn2.weight)
def forward(self, x):
shortcut = x
x = self.conv1(x)
x = self.bn1(x)
x = self.act1(x)
x = self.conv2(x)
x = self.bn2(x)
if self.downsample is not None:
shortcut = self.downsample(shortcut)
x += shortcut
x = self.act2(x)
return x
class WavEncoder(nn.Module):
def __init__(self, out_dim):
super().__init__()
self.out_dim = out_dim
self.feat_extractor = nn.Sequential(
BasicBlock(1, 32, 15, 5, first_dilation=1600, downsample=True),
BasicBlock(32, 32, 15, 6, first_dilation=0, downsample=True),
BasicBlock(32, 32, 15, 1, first_dilation=7, ),
BasicBlock(32, 64, 15, 6, first_dilation=0, downsample=True),
BasicBlock(64, 64, 15, 1, first_dilation=7),
BasicBlock(64, 128, 15, 6, first_dilation=0,downsample=True),
)
def forward(self, wav_data):
wav_data = wav_data.unsqueeze(1)
out = self.feat_extractor(wav_data)
return out.transpose(1, 2)
class PoseGenerator(nn.Module):
"""
End2End model
audio, text and speaker ID encoder are customized based on Yoon et al. SIGGRAPH ASIA 2020
"""
def __init__(self, args):
super().__init__()
self.args = args
self.pre_length = args.pre_frames
self.gen_length = args.pose_length - args.pre_frames
self.pose_dims = args.pose_dims
self.facial_f = args.facial_f
self.speaker_f = args.speaker_f
self.audio_f = args.audio_f
self.word_f = args.word_f
self.emotion_f = args.emotion_f
self.facial_dims = args.facial_dims
self.args.speaker_dims = args.speaker_dims
self.emotion_dims = args.emotion_dims
self.in_size = self.audio_f + self.pose_dims + self.facial_f + self.word_f + 1
self.audio_encoder = WavEncoder(self.audio_f)
self.hidden_size = args.hidden_size
self.n_layer = args.n_layer
if self.facial_f is not 0:
self.facial_encoder = nn.Sequential(
BasicBlock(self.facial_dims, self.facial_f//2, 7, 1, first_dilation=3, downsample=True),
BasicBlock(self.facial_f//2, self.facial_f//2, 3, 1, first_dilation=1, downsample=True),
BasicBlock(self.facial_f//2, self.facial_f//2, 3, 1, first_dilation=1, ),
BasicBlock(self.facial_f//2, self.facial_f, 3, 1, first_dilation=1, downsample=True),
)
else:
self.facial_encoder = None
self.text_encoder = None
if self.word_f is not 0:
if args.word_cache:
self.text_encoder = TextEncoderTCN(args, args.word_index_num, args.word_dims, pre_trained_embedding=None,
dropout=args.dropout_prob, word_cache=True)
else:
with open(f"{args.data_path}weights/vocab.pkl", 'rb') as f:
self.lang_model = pickle.load(f)
pre_trained_embedding = self.lang_model.word_embedding_weights
self.text_encoder = TextEncoderTCN(args, args.word_index_num, args.word_dims, pre_trained_embedding=pre_trained_embedding,
dropout=args.dropout_prob)
self.speaker_embedding = None
if self.speaker_f is not 0:
self.in_size += self.speaker_f
self.speaker_embedding = nn.Sequential(
nn.Embedding(self.args.speaker_dims, self.speaker_f),
nn.Linear(self.speaker_f, self.speaker_f),
nn.LeakyReLU(True)
)
self.emotion_embedding = None
if self.emotion_f is not 0:
self.in_size += self.emotion_f
self.emotion_embedding = nn.Sequential(
nn.Embedding(self.emotion_dims, self.emotion_f),
nn.Linear(self.emotion_f, self.emotion_f)
)
# self.emotion_embedding_tail = nn.Sequential(
# nn.Conv1d(self.emotion_f, 8, 9, 1, 4),
# nn.BatchNorm1d(8),
# nn.LeakyReLU(0.3, inplace=True),
# nn.Conv1d(8, 16, 9, 1, 4),
# nn.BatchNorm1d(16),
# nn.LeakyReLU(0.3, inplace=True),
# nn.Conv1d(16, 16, 9, 1, 4),
# nn.BatchNorm1d(16),
# nn.LeakyReLU(0.3, inplace=True),
# nn.Conv1d(16, self.emotion_f, 9, 1, 4),
# nn.BatchNorm1d(self.emotion_f),
# nn.LeakyReLU(0.3, inplace=True),
# )
self.LSTM = nn.LSTM(self.in_size+3, hidden_size=self.hidden_size, num_layers=args.n_layer, batch_first=True,
bidirectional=True, dropout=args.dropout_prob)
self.out = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size//2),
nn.LeakyReLU(True),
nn.Linear(self.hidden_size//2, 330-180)
)
self.LSTM_hands = nn.LSTM(self.in_size+150+3, hidden_size=self.hidden_size, num_layers=args.n_layer, batch_first=True,
bidirectional=True, dropout=args.dropout_prob)
self.out_hands = nn.Sequential(
nn.Linear(self.hidden_size, self.hidden_size//2),
nn.LeakyReLU(True),
nn.Linear(self.hidden_size//2, 180+3)
)
self.do_flatten_parameters = False
if torch.cuda.device_count() > 1:
self.do_flatten_parameters = True
def forward(self, pre_seq, in_audio=None, in_facial=None, in_text=None, in_id=None, in_emo=None, is_test=False):
if self.do_flatten_parameters:
self.LSTM.flatten_parameters()
text_feat_seq = audio_feat_seq = None
if in_audio is not None:
audio_feat_seq = self.audio_encoder(in_audio)
if in_text is not None:
text_feat_seq, _ = self.text_encoder(in_text)
assert(audio_feat_seq.shape[1] == text_feat_seq.shape[1])
if self.facial_f is not 0:
face_feat_seq = self.facial_encoder(in_facial.permute([0, 2, 1]))
face_feat_seq = face_feat_seq.permute([0, 2, 1])
speaker_feat_seq = None
if self.speaker_embedding:
speaker_feat_seq = self.speaker_embedding(in_id)
emo_feat_seq = None
if self.emotion_embedding:
emo_feat_seq = self.emotion_embedding(in_emo)
emo_feat_seq = emo_feat_seq.permute([0,2,1])
emo_feat_seq = self.emotion_embedding_tail(emo_feat_seq)
emo_feat_seq = emo_feat_seq.permute([0,2,1])
if audio_feat_seq.shape[1] != pre_seq.shape[1]:
diff_length = pre_seq.shape[1] - audio_feat_seq.shape[1]
audio_feat_seq = torch.cat((audio_feat_seq, audio_feat_seq[:,-diff_length:, :].reshape(1,diff_length,-1)),1)
if self.audio_f is not 0 and self.facial_f is 0:
in_data = torch.cat((pre_seq, audio_feat_seq), dim=2)
elif self.audio_f is not 0 and self.facial_f is not 0:
in_data = torch.cat((pre_seq, audio_feat_seq, face_feat_seq), dim=2)
else: pass
if text_feat_seq is not None:
in_data = torch.cat((in_data, text_feat_seq), dim=2)
if emo_feat_seq is not None:
in_data = torch.cat((in_data, emo_feat_seq), dim=2)
if speaker_feat_seq is not None:
repeated_s = speaker_feat_seq
if len(repeated_s.shape) == 2:
repeated_s = repeated_s.reshape(1, repeated_s.shape[1], repeated_s.shape[0])
repeated_s = repeated_s.repeat(1, in_data.shape[1], 1)
in_data = torch.cat((in_data, repeated_s), dim=2)
output, _ = self.LSTM(in_data)
output = output[:, :, :self.hidden_size] + output[:, :, self.hidden_size:]
output = self.out(output.reshape(-1, output.shape[2]))
decoder_outputs = output.reshape(in_data.shape[0], in_data.shape[1], -1)
return decoder_outputs
class CaMN(PoseGenerator):
def __init__(self, args):
super().__init__(args)
self.audio_fusion_dim = self.audio_f+self.speaker_f+self.emotion_f+self.word_f
self.facial_fusion_dim = self.audio_fusion_dim + self.facial_f
self.audio_fusion = nn.Sequential(
nn.Linear(self.audio_fusion_dim, self.hidden_size//2),
nn.LeakyReLU(True),
nn.Linear(self.hidden_size//2, self.audio_f),
nn.LeakyReLU(True),
)
self.facial_fusion = nn.Sequential(
nn.Linear(self.facial_fusion_dim, self.hidden_size//2),
nn.LeakyReLU(True),
nn.Linear(self.hidden_size//2, self.facial_f),
nn.LeakyReLU(True),
)
def forward(self, pre_seq, in_audio=None, in_facial=None, in_text=None, in_id=None, in_emo=None):
if self.do_flatten_parameters:
self.LSTM.flatten_parameters()
decoder_hidden = decoder_hidden_hands = None
text_feat_seq = audio_feat_seq = speaker_feat_seq = emo_feat_seq = face_feat_seq = None
in_data = None
if self.speaker_embedding:
speaker_feat_seq = self.speaker_embedding(in_id).squeeze(2)
in_data = torch.cat((in_data, speaker_feat_seq), 2) if in_data is not None else speaker_feat_seq
if self.emotion_embedding:
emo_feat_seq = self.emotion_embedding(in_emo).squeeze(2)
in_data = torch.cat((in_data, emo_feat_seq), 2)
if in_text is not None:
text_feat_seq, _ = self.text_encoder(in_text)
in_data = torch.cat((in_data, text_feat_seq), 2) if in_data is not None else text_feat_seq
if in_audio is not None:
audio_feat_seq = self.audio_encoder(in_audio)
if in_text is not None:
if (audio_feat_seq.shape[1] != text_feat_seq.shape[1]):
min_gap = text_feat_seq.shape[1] - audio_feat_seq.shape[1]
audio_feat_seq = torch.cat((audio_feat_seq, audio_feat_seq[:,-min_gap:, :]),1)
audio_fusion_seq = self.audio_fusion(torch.cat((audio_feat_seq, emo_feat_seq, speaker_feat_seq, text_feat_seq), dim=2).reshape(-1, self.audio_fusion_dim))
audio_feat_seq = audio_fusion_seq.reshape(*audio_feat_seq.shape)
in_data = torch.cat((in_data, audio_feat_seq), 2) if in_data is not None else audio_feat_seq
if self.facial_f is not 0:
face_feat_seq = self.facial_encoder(in_facial.permute([0, 2, 1]))
face_feat_seq = face_feat_seq.permute([0, 2, 1])
if (audio_feat_seq.shape[1] != face_feat_seq.shape[1]):
min_gap_2 = face_feat_seq.shape[1] - audio_feat_seq.shape[1]
if min_gap_2 > 0:
face_feat_seq = face_feat_seq[:,:audio_feat_seq.shape[1], :]
else:
face_feat_seq = torch.cat((face_feat_seq, face_feat_seq[:,-min_gap_2:, :]),1)
face_fusion_seq = self.facial_fusion(torch.cat((face_feat_seq, audio_feat_seq, emo_feat_seq, speaker_feat_seq, text_feat_seq), dim=2).reshape(-1, self.facial_fusion_dim))
face_feat_seq = face_fusion_seq.reshape(*face_feat_seq.shape)
in_data = torch.cat((in_data, face_feat_seq), 2) if in_data is not None else face_feat_seq
in_data = torch.cat((pre_seq, in_data), dim=2)
output, _ = self.LSTM(in_data)
output = output[:, :, :self.hidden_size] + output[:, :, self.hidden_size:]
output = self.out(output.reshape(-1, output.shape[2]))
decoder_outputs = output.reshape(in_data.shape[0], in_data.shape[1], -1)
in_data = torch.cat((in_data, decoder_outputs), dim=2)
output_hands, _ = self.LSTM_hands(in_data)
output_hands = output_hands[:, :, :self.hidden_size] + output_hands[:, :, self.hidden_size:]
output_hands = self.out_hands(output_hands.reshape(-1, output_hands.shape[2]))
decoder_outputs_hands = output_hands.reshape(in_data.shape[0], in_data.shape[1], -1)
decoder_outputs_final = torch.zeros((in_data.shape[0], in_data.shape[1], 333)).to(in_data.device)
decoder_outputs_final[:, :, 0:150] = decoder_outputs[:, :, 0:150]
decoder_outputs_final[:, :, 150:333] = decoder_outputs_hands[:, :, 0:183]
return {
"rec_pose": decoder_outputs_final,
}
class ConvDiscriminator(nn.Module):
def __init__(self, args):
super().__init__()
self.input_size = args.pose_dims
self.hidden_size = 64
self.pre_conv = nn.Sequential(
nn.Conv1d(self.input_size, 16, 3),
nn.BatchNorm1d(16),
nn.LeakyReLU(True),
nn.Conv1d(16, 8, 3),
nn.BatchNorm1d(8),
nn.LeakyReLU(True),
nn.Conv1d(8, 8, 3),
)
self.LSTM = nn.LSTM(8, hidden_size=self.hidden_size, num_layers=4, bidirectional=True,
dropout=0.3, batch_first=True)
self.out = nn.Linear(self.hidden_size, 1)
self.out2 = nn.Linear(34-6, 1)
self.do_flatten_parameters = False
if torch.cuda.device_count() > 1:
self.do_flatten_parameters = True
def forward(self, poses):
if self.do_flatten_parameters:
self.LSTM.flatten_parameters()
poses = poses.transpose(1, 2)
feat = self.pre_conv(poses)
feat = feat.transpose(1, 2)
output, _ = self.LSTM(feat)
output = output[:, :, :self.hidden_size] + output[:, :, self.hidden_size:]
batch_size = poses.shape[0]
output = output.contiguous().view(-1, output.shape[2])
output = self.out(output) # apply linear to every output
output = output.view(batch_size, -1)
output = self.out2(output)
output = torch.sigmoid(output)
return output |