def test(self):
self.mfcc_encoder.eval()
self.lip_feature_encoder.eval()
self.input_label = Variable(self.input_label, volatile=True)
self.audios_dis = Variable(self.audio_pred_data, volatile=True)
self.video_dis = Variable(self.video_pred_data, volatile=True)
# compute the sequence ID imbeddings
self.audio_embeddings_dis = self.mfcc_encoder.forward(
self.audios_dis).view(-1, 256 * self.opt.pred_length)
self.lip_embeddings_dis = self.lip_feature_encoder.forward(
self.video_dis).view(-1, 256 * self.opt.pred_length)
# loss between audio and lip embedding
self.lip_embedding_norm = embedding_utils.l2_norm(
self.lip_embeddings_dis)
self.audio_embedding_norm = embedding_utils.l2_norm(
self.audio_embeddings_dis)
self.lip_embeddings_buffer = Variable(self.lip_embedding_norm.data)
self.EmbeddingL2 = self.L2Contrastive.forward(
self.audio_embedding_norm, self.lip_embeddings_buffer)
# generate fake images
# classification
self.audio_pred = self.model_fusion.forward(self.audio_embeddings_dis)
self.audio_acc = self.compute_acc(self.audio_pred)
self.image_pred = self.model_fusion.forward(self.lip_embeddings_dis)
self.image_acc = self.compute_acc(self.image_pred)
self.output = (self.audio_pred + self.image_pred)
self.final_acc = self.compute_acc(self.output)
def forward(self):
self.input_label = Variable(self.input_label)
self.real_A = Variable(self.input_A) # 1张 图片
B_start = random.randint(0, self.opt.pred_length -
self.opt.sequence_length) # 随机整数 [0, 12-6]
self.audios_dis = Variable(self.audio_pred_data)
self.video_dis = Variable(self.video_pred_data)
# real_videos are the frames used for training generation,
self.real_videos = Variable(
self.video_pred_data[:, B_start:B_start +
self.opt.sequence_length, :, :, :].contiguous(
)) # 抽取6张图片
self.audios = Variable(
self.audio_pred_data[:, B_start:B_start +
self.opt.sequence_length, :, :, :].contiguous(
)) #抽取6段音频
self.video_send_to_disfc = Variable(self.input_video_dis) # 视频-> 判别器
self.mask = Variable(
self.Tensor(
self.opt.batchSize,
(self.opt.sequence_length) * self.opt.image_channel_size,
self.opt.image_size,
self.opt.image_size).fill_(0)) # （16,6*3,256,256） 全0填充
self.mask[:, :, 170:234, 64:192] = 1 # 图片 [170:234, 64:192] 填充为 1
self.mask_ones = Variable(
self.Tensor(self.opt.batchSize, self.opt.image_channel_size,
self.opt.image_size,
self.opt.image_size).fill_(1)) # （16,3,256,256） 全 1 填充
self.mask_ones[:, :, 170:234, 64:192] = 0 # 图片 [170:234, 64:192] 填充为 0
self.mfcc_encoder.train()
self.lip_feature_encoder.train()
# compute the ID embeddings
self.real_A_id_embedding = self.ID_encoder.forward(self.real_A)
# compute the sequence ID imbeddings
if self.opt.disfc_length == 12:
self.sequence_id_embedding = self.ID_encoder.forward(
self.video_dis)
else:
self.sequence_id_embedding = self.ID_encoder.forward(
self.video_send_to_disfc)
self.sequence_id_embedding = self.sequence_id_embedding[4].view(
-1, self.opt.disfc_length * 64, 64, 64)
# extract the lip feature
# self.audio_embedding = self.mfcc_encoder.forward(self.audio_A)
self.audio_embeddings_dis = self.mfcc_encoder.forward(
self.audios_dis) # 提取 音频特征 (-1, 12, 256)
self.lip_embeddings_dis = self.lip_feature_encoder.forward(
self.video_dis) # 提取 语音的视频特征 (-1, 12, 256)
self.audio_embeddings = self.audio_embeddings_dis[:, B_start:B_start +
self.opt.
sequence_length].contiguous(
)
self.lip_embeddings = self.lip_embeddings_dis[:, B_start:B_start +
self.opt.
sequence_length].contiguous(
)
# loss between audio and lip embedding 计算 音频特征与视频特征之间的距离 及 Lc
self.lip_embedding_norm = embedding_utils.l2_norm(
self.lip_embeddings_dis.view(-1, 256 *
self.opt.pred_length)) # （-1,256*12）
self.audio_embedding_norm = embedding_utils.l2_norm(
self.audio_embeddings_dis.view(
-1, 256 * self.opt.pred_length)) # （-1，256*12）
self.lip_embeddings_buffer = Variable(self.lip_embedding_norm.data)
self.EmbeddingL2 = self.L2Contrastive.forward(
self.lip_embeddings_buffer, self.audio_embedding_norm)
# generate fake images
self.sequence_generation()
# single (batch_size * 6 * 2, 3, 256, 256)
self.fakes = torch.cat(
(self.audio_gen_fakes_batch, self.image_gen_fakes_batch),
0) # cat((A,B),0) 按行拼接 A，B
self.real_one = self.real_videos.view(-1, self.opt.image_channel_size,
self.opt.image_size,
self.opt.image_size)
self.reals = torch.cat((self.real_one, self.real_one), 0)
self.audio_reals = torch.cat(
(self.audios.view(-1, 1, self.opt.mfcc_length,
self.opt.mfcc_width),
self.audios.view(-1, 1, self.opt.mfcc_length,
self.opt.mfcc_width)), 0)
# sequence (-1, 6 * 3, 256, 256)
self.fakes_sequence = self.fakes.view(
-1, self.opt.image_channel_size * (self.opt.sequence_length),
self.opt.image_size, self.opt.image_size)
self.real_one_sequence = self.real_videos.view(
-1, self.opt.image_channel_size * (self.opt.sequence_length),
self.opt.image_size, self.opt.image_size)
self.reals_sequence = self.reals.view(
-1, self.opt.image_channel_size * self.opt.sequence_length,
self.opt.image_size, self.opt.image_size)
self.audio_reals_sequence = self.audio_reals.view(
-1, self.opt.sequence_length, self.opt.mfcc_length,
self.opt.mfcc_width)
def forward(self):
self.input_label = Variable(self.input_label)
self.real_A = Variable(self.input_A)
B_start = random.randint(
0, self.opt.pred_length - self.opt.sequence_length)
self.audios_dis = Variable(self.audio_pred_data)
self.video_dis = Variable(self.video_pred_data)
# real_videos are the frames used for training generation,
self.real_videos = Variable(
self.video_pred_data[:, B_start:B_start +
self.opt.sequence_length, :, :, :].contiguous(
))
self.audios = Variable(
self.audio_pred_data[:, B_start:B_start +
self.opt.sequence_length, :, :, :].contiguous(
))
self.video_send_to_disfc = Variable(self.input_video_dis)
self.mask = Variable(
self.Tensor(self.opt.batchSize, (self.opt.sequence_length) *
self.opt.image_channel_size, self.opt.image_size,
self.opt.image_size).fill_(0))
self.mask[:, :, 170:234, 64:192] = 1
self.mask_ones = Variable(
self.Tensor(self.opt.batchSize, self.opt.image_channel_size,
self.opt.image_size, self.opt.image_size).fill_(1))
self.mask_ones[:, :, 170:234, 64:192] = 0
self.mfcc_encoder.train()
self.lip_feature_encoder.train()
# compute the ID embeddings
self.real_A_id_embedding = self.ID_encoder.forward(self.real_A)
# compute the sequence ID imbeddings
if self.opt.disfc_length == 12:
self.sequence_id_embedding = self.ID_encoder.forward(
self.video_dis)
else:
self.sequence_id_embedding = self.ID_encoder.forward(
self.video_send_to_disfc)
self.sequence_id_embedding = self.sequence_id_embedding[4].view(
-1, self.opt.disfc_length * 64, 64, 64)
# extract the lip feature
# self.audio_embedding = self.mfcc_encoder.forward(self.audio_A)
self.audio_embeddings_dis = self.mfcc_encoder.forward(self.audios_dis)
self.lip_embeddings_dis = self.lip_feature_encoder.forward(
self.video_dis)
self.audio_embeddings = self.audio_embeddings_dis[:, B_start:B_start +
self.opt.
sequence_length].contiguous(
)
self.lip_embeddings = self.lip_embeddings_dis[:, B_start:B_start +
self.opt.
sequence_length].contiguous(
)
# loss between audio and lip embedding
self.lip_embedding_norm = embedding_utils.l2_norm(
self.lip_embeddings_dis.view(-1, 256 * self.opt.pred_length))
self.audio_embedding_norm = embedding_utils.l2_norm(
self.audio_embeddings_dis.view(-1, 256 * self.opt.pred_length))
self.lip_embeddings_buffer = Variable(self.lip_embedding_norm.data)
self.EmbeddingL2 = self.L2Contrastive.forward(
self.lip_embeddings_buffer, self.audio_embedding_norm)
# generate fake images
self.sequence_generation()
# single
self.fakes = torch.cat(
(self.audio_gen_fakes_batch, self.image_gen_fakes_batch), 0)
self.real_one = self.real_videos.view(-1, self.opt.image_channel_size,
self.opt.image_size,
self.opt.image_size)
self.reals = torch.cat((self.real_one, self.real_one), 0)
self.audio_reals = torch.cat(
(self.audios.view(-1, 1, self.opt.mfcc_length,
self.opt.mfcc_width),
self.audios.view(-1, 1, self.opt.mfcc_length,
self.opt.mfcc_width)), 0)
# sequence
self.fakes_sequence = self.fakes.view(
-1, self.opt.image_channel_size * (self.opt.sequence_length),
self.opt.image_size, self.opt.image_size)
self.real_one_sequence = self.real_videos.view(
-1, self.opt.image_channel_size * (self.opt.sequence_length),
self.opt.image_size, self.opt.image_size)
self.reals_sequence = self.reals.view(
-1, self.opt.image_channel_size * self.opt.sequence_length,
self.opt.image_size, self.opt.image_size)
self.audio_reals_sequence = self.audio_reals.view(
-1, self.opt.sequence_length, self.opt.mfcc_length,
self.opt.mfcc_width)
