def eval_bss(model, loss_multi_func, mix_speech_len, speech_fre):
model.training = False
print '#' * 40
eval_data_gen = prepare_data('once', 'valid')
SDR_SUM = np.array([])
while True:
print '\n\n'
eval_data = eval_data_gen.next()
if eval_data == False:
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
top_k_num = eval_data['top_k'] #对于这个batch的top-k
print 'top-k this batch:', top_k_num
mix_speech_orignial = Variable(torch.from_numpy(
eval_data['mix_feas'])).cuda()
mix_speech = torch.transpose(mix_speech_orignial, 1, 3)
mix_speech = torch.transpose(mix_speech, 2, 3)
# (2L, 301L, 257L, 2L) >>> (2L, 2L, 301L, 257L)
print 'mix_speech_shape:', mix_speech.size()
images_query = Variable(
torch.from_numpy(
convert2numpy(eval_data['multi_video_fea_list'],
top_k_num))).cuda() #大小bs,topk,75,3,299,299
y_map = convert2numpy(eval_data['multi_spk_fea_list'],
top_k_num) #最终的map
print 'final map shape:', y_map.shape
predict_multi_masks = model(mix_speech, images_query)
print 'predict results shape:', predict_multi_masks.size(
) #(2L, topk, 301L, 257L, 2L)
mix_speech_multi=mix_speech_orignial.view(config.BATCH_SIZE,1,speech_fre,mix_speech_len,2) \
.expand(config.BATCH_SIZE,top_k_num,speech_fre,mix_speech_len,2)
# (2L, 301L, 257L, 2L) >> (2L, topk,301L, 257L, 2L)
predict_multi_masks_real = predict_multi_masks[:, :, :, :, 0]
predict_multi_masks_fake = predict_multi_masks[:, :, :, :, 1]
mix_speech_real = mix_speech_multi[:, :, :, :, 0]
mix_speech_fake = mix_speech_multi[:, :, :, :, 1]
y_map_real = Variable(torch.from_numpy(y_map[:, :, :, :, 0])).cuda()
y_map_fake = Variable(torch.from_numpy(y_map[:, :, :, :, 1])).cuda()
predict_real = predict_multi_masks_real * mix_speech_real - predict_multi_masks_fake * mix_speech_fake
predict_fake = predict_multi_masks_real * mix_speech_fake + predict_multi_masks_fake * mix_speech_real
print 'predict real/fake size:', predict_real.size()
loss_real = loss_multi_func(predict_real, y_map_real) #/top_k_num
loss_fake = loss_multi_func(predict_fake, y_map_fake) #/top_k_num
loss_all = loss_real + loss_fake
print 'loss:', loss_real.data[0], loss_fake.data[0]
bss_eval_cRM(predict_real, predict_fake, y_map, eval_data)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output23234/', 2))
print 'SDR_aver_now:', SDR_SUM.mean()
SDR_aver = SDR_SUM.mean()
print 'SDR_SUM (len:{}) for epoch eval : {}'.format(
SDR_SUM.shape, SDR_aver)
print '#' * 40
def eval(epoch):
# config.batch_size=1
model.eval()
# print '\n\n测试的时候请设置config里的batch_size为1！！！please set the batch_size as 1'
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'test'
test_or_valid = 'valid'
# test_or_valid = 'train'
print(('Test or valid:', test_or_valid))
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX, config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
# for iii in range(2000):
while True:
print(('-' * 30))
eval_data = next(eval_data_gen)
if eval_data == False:
print(('SDR_aver_eval_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_eval_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
# raw_tgt = [sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']]
raw_tgt= eval_data['batch_order']
feas_tgt = models.rank_feas(raw_tgt, eval_data['multi_spk_fea_list']) # 这里是目标的图谱
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.from_numpy(np.array([[0,1,2,102] for __ in range(config.batch_size)], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(torch.LongTensor(config.batch_size).zero_() + mix_speech_len).unsqueeze(0)
tgt_len = Variable(torch.LongTensor([len(one_spk) for one_spk in eval_data['multi_spk_fea_list']])).unsqueeze(0)
# tgt_len = Variable(torch.LongTensor(config.batch_size).zero_()+len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
if config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 3
feas_tgt_square = feas_tgt * feas_tgt
feas_tgt_sum_square = torch.sum(feas_tgt_square, dim=0, keepdim=True).expand(tmp_size)
WFM_mask = feas_tgt_square / (feas_tgt_sum_square + 1e-15)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
# try:
if 1 and len(opt.gpus) > 1:
samples, predicted_masks = model.module.pit_sample(src, src_len, dict_spk2idx, tgt,
beam_size=config.beam_size)
else:
samples, predicted_masks = model.pit_sample(src, src_len, dict_spk2idx, tgt,
beam_size=config.beam_size)
samples=samples.max(2)[1].data.cpu().numpy()
# except:
# continue
# '''
# expand the raw mixed-features to topk_max channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
# if samples[0][-1] != dict_spk2idx['<EOS>']:
# print '*'*40+'\nThe model is far from good. End the evaluation.\n'+'*'*40
# break
topk_max = len(samples[0]) - 1
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2])
if 1 and config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
if test_or_valid != 'test':
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(x_input_map_multi, predicted_masks, feas_tgt, )
else:
ss_loss = model.separation_loss(x_input_map_multi, predicted_masks, feas_tgt)
print(('loss for ss,this batch:', ss_loss.cpu().item()))
lera.log({
'ss_loss_' + test_or_valid: ss_loss.cpu().item(),
})
del ss_loss
# '''''
if 1 and batch_idx <= (500 / config.batch_size): # only the former batches counts the SDR
predicted_maps = predicted_masks * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval2(config, predicted_maps, eval_data['multi_spk_fea_list'], raw_tgt, eval_data,
dst='batch_output_test')
del predicted_maps, predicted_masks, x_input_map_multi
try:
sdr_aver_batch, sdri_aver_batch= bss_test.cal('batch_output_test/')
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
except(AssertionError):
print('Errors in calculating the SDR')
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SDRi_aver_now:', SDRi_SUM.mean()))
lera.log({'SDR sample'+test_or_valid: SDR_SUM.mean()})
lera.log({'SDRi sample'+test_or_valid: SDRi_SUM.mean()})
writer.add_scalars('scalar/loss',{'SDR_sample_'+test_or_valid:sdr_aver_batch},updates)
# raw_input('Press any key to continue......')
elif batch_idx == (200 / config.batch_size) + 1 and SDR_SUM.mean() > best_SDR: # only record the best SDR once.
print(('Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean())))
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
# '''
candidate += [convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>']) for s in samples]
# source += raw_src
reference += raw_tgt
print(('samples:', samples))
print(('can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:], reference[-1 * config.batch_size:])))
# alignments += [align for align in alignment]
batch_idx += 1
result = utils.eval_metrics(reference, candidate, dict_spk2idx, log_path)
print(('hamming_loss: %.8f | micro_f1: %.4f |recall: %.4f | precision: %.4f'
% (result['hamming_loss'], result['micro_f1'], result['micro_recall'], result['micro_precision'], )))
score = {}
result = utils.eval_metrics(reference, candidate, dict_spk2idx, log_path)
logging_csv([e, updates, result['hamming_loss'], \
result['micro_f1'], result['micro_precision'], result['micro_recall'],SDR_SUM.mean()])
print(('hamming_loss: %.8f | micro_f1: %.4f'
% (result['hamming_loss'], result['micro_f1'])))
score['hamming_loss'] = result['hamming_loss']
score['micro_f1'] = result['micro_f1']
1/0
return score
def eval_bss(mix_hidden_layer_3d, adjust_layer, mix_speech_classifier,
mix_speech_multiEmbedding, att_speech_layer, loss_multi_func,
dict_spk2idx, dict_idx2spk, num_labels, mix_speech_len,
speech_fre):
for i in [
mix_speech_multiEmbedding, adjust_layer, mix_speech_classifier,
mix_hidden_layer_3d, att_speech_layer
]:
i.training = False
print '#' * 40
eval_data_gen = prepare_data('once', 'valid')
SDR_SUM = np.array([])
while True:
print '\n\n'
eval_data = eval_data_gen.next()
if eval_data == False:
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden, mix_tmp_hidden = mix_hidden_layer_3d(
Variable(torch.from_numpy(eval_data['mix_feas'])).cuda())
# mix_tmp_hidden:[bs*T*hidden_units]
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
mix_speech_output = mix_speech_classifier(
Variable(torch.from_numpy(eval_data['mix_feas'])).cuda())
#从数据里得到ground truth的说话人名字和vector
# y_spk_list=[one.keys() for one in eval_data['multi_spk_fea_list']]
y_spk_list = eval_data['multi_spk_fea_list']
y_spk_gtruth, y_map_gtruth = multi_label_vector(
y_spk_list, dict_spk2idx)
# 如果训练阶段使用Ground truth的分离结果作为判别
if config.Ground_truth:
mix_speech_output = Variable(torch.from_numpy(y_map_gtruth)).cuda()
if test_all_outputchannel: #把输入的mask改成全１，可以用来测试输出所有的channel
mix_speech_output = Variable(
torch.ones(
config.BATCH_SIZE,
num_labels,
))
y_map_gtruth = np.ones([config.BATCH_SIZE, num_labels])
top_k_mask_mixspeech = top_k_mask(mix_speech_output,
alpha=0.5,
top_k=num_labels) #torch.Float型的
top_k_mask_idx = [
np.where(line == 1)[0] for line in top_k_mask_mixspeech.numpy()
]
mix_speech_multiEmbs = mix_speech_multiEmbedding(
top_k_mask_mixspeech,
top_k_mask_idx) # bs*num_labels（最多混合人个数）×Embedding的大小
if config.is_SelfTune:
mix_adjust = adjust_layer(mix_tmp_hidden, mix_speech_multiEmbs)
mix_speech_multiEmbs = mix_adjust + mix_speech_multiEmbs
assert len(top_k_mask_idx[0]) == len(top_k_mask_idx[-1])
top_k_num = len(top_k_mask_idx[0])
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d = mix_speech_hidden.view(config.BATCH_SIZE, 1,
mix_speech_len,
speech_fre,
config.EMBEDDING_SIZE)
mix_speech_hidden_5d = mix_speech_hidden_5d.expand(
config.BATCH_SIZE, top_k_num, mix_speech_len, speech_fre,
config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last = mix_speech_hidden_5d.view(
-1, mix_speech_len, speech_fre, config.EMBEDDING_SIZE)
if not config.is_ComlexMask:
mix_speech_multiEmbs = mix_speech_multiEmbs.view(
-1, config.EMBEDDING_SIZE)
else:
mix_speech_multiEmbs = mix_speech_multiEmbs.view(
-1, 2 * config.EMBEDDING_SIZE)
att_multi_speech = att_speech_layer(mix_speech_hidden_5d_last,
mix_speech_multiEmbs)
print att_multi_speech.size()
if not config.is_ComlexMask:
att_multi_speech = att_multi_speech.view(
config.BATCH_SIZE, top_k_num, mix_speech_len,
speech_fre) # bs,num_labels,len,fre这个东西
else:
att_multi_speech = att_multi_speech.view(
config.BATCH_SIZE, top_k_num, mix_speech_len, speech_fre,
2) # bs,num_labels,len,fre,2这个东西
att_multi_speech = -1 / cRM_C * torch.log(
(cRM_k - att_multi_speech) / (cRM_k + att_multi_speech))
multi_mask = att_multi_speech
if not config.is_ComlexMask:
x_input_map = Variable(torch.from_numpy(
eval_data['mix_feas'])).cuda()
# print x_input_map.size()
x_input_map_multi = x_input_map.view(
config.BATCH_SIZE, 1, mix_speech_len,
speech_fre).expand(config.BATCH_SIZE, top_k_num,
mix_speech_len, speech_fre)
# predict_multi_map=multi_mask*x_input_map_multi
predict_multi_map = multi_mask * x_input_map_multi
y_multi_map = np.zeros(
[config.BATCH_SIZE, top_k_num, mix_speech_len, speech_fre],
dtype=np.float32)
batch_spk_multi_dict = eval_data['multi_spk_fea_list']
for idx, sample in enumerate(batch_spk_multi_dict):
y_idx = sorted([dict_spk2idx[spk] for spk in sample.keys()])
assert y_idx == list(top_k_mask_idx[idx])
for jdx, oo in enumerate(y_idx):
y_multi_map[idx, jdx] = sample[dict_idx2spk[oo]]
y_multi_map = Variable(torch.from_numpy(y_multi_map)).cuda()
loss_multi_speech = loss_multi_func(predict_multi_map, y_multi_map)
#各通道和为１的loss部分,应该可以更多的带来差异
y_sum_map = Variable(
torch.ones(config.BATCH_SIZE, mix_speech_len,
speech_fre)).cuda()
predict_sum_map = torch.sum(multi_mask, 1)
loss_multi_sum_speech = loss_multi_func(predict_sum_map, y_sum_map)
# loss_multi_speech=loss_multi_speech #todo:以后可以研究下这个和为１的效果对比一下，暂时直接MSE效果已经很不错了。
print 'loss 1, losssum : ', loss_multi_speech.data.cpu().numpy(
), loss_multi_sum_speech.data.cpu().numpy()
loss_multi_speech = loss_multi_speech + 0.5 * loss_multi_sum_speech
print 'training multi-abs norm this batch:', torch.abs(
y_multi_map - predict_multi_map).norm().data.cpu().numpy()
print 'loss:', loss_multi_speech.data.cpu().numpy()
bss_eval(predict_multi_map, y_multi_map, top_k_mask_idx,
dict_idx2spk, eval_data)
else:
x_input_map = Variable(torch.from_numpy(
eval_data['mix_mag'])).cuda() # bs,len,fre,2
x_input_map_multi = x_input_map.view(
config.BATCH_SIZE, 1, mix_speech_len, speech_fre,
2).expand(config.BATCH_SIZE, top_k_num, mix_speech_len,
speech_fre, 2)
multi_mask_real = multi_mask[:, :, :, :, 0]
multi_mask_fake = multi_mask[:, :, :, :, 1]
x_input_map_real = x_input_map_multi[:, :, :, :, 0]
x_input_map_fake = x_input_map_multi[:, :, :, :, 1]
predict_map_real = multi_mask_real * x_input_map_real - multi_mask_fake * x_input_map_fake
predict_map_fake = multi_mask_real * x_input_map_fake + multi_mask_fake * x_input_map_real
y_multi_map = np.zeros(
[config.BATCH_SIZE, top_k_num, mix_speech_len, speech_fre, 2],
dtype=np.float32)
batch_spk_multi_dict = eval_data['multi_spk_fea_list']
for idx, sample in enumerate(batch_spk_multi_dict):
y_idx = sorted([dict_spk2idx[spk] for spk in sample.keys()])
assert y_idx == list(top_k_mask_idx[idx])
for jdx, oo in enumerate(y_idx):
y_multi_map[idx, jdx] = sample[dict_idx2spk[oo]]
y_multi_map = Variable(torch.from_numpy(y_multi_map)).cuda()
y_multi_map_real = y_multi_map[:, :, :, :, 0]
y_multi_map_fake = y_multi_map[:, :, :, :, 1]
loss_multi_speech_real = loss_multi_func(predict_map_real,
y_multi_map_real)
loss_multi_speech_fake = loss_multi_func(predict_map_fake,
y_multi_map_fake)
loss_multi_speech = loss_multi_speech_fake + loss_multi_speech_real
print 'loss_real:', loss_multi_speech_real.data.cpu().numpy()
print 'loss_fake:', loss_multi_speech_fake.data.cpu().numpy()
print 'loss:', loss_multi_speech.data.cpu().numpy()
bss_eval_cRM(predict_map_real, predict_map_fake, y_multi_map,
top_k_mask_idx, dict_idx2spk, eval_data)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output/', 2))
print 'SDR_aver_now:', SDR_SUM.mean()
SDR_aver = SDR_SUM.mean()
print 'SDR_SUM (len:{}) for epoch eval : {}'.format(
SDR_SUM.shape, SDR_aver)
print '#' * 40
def eval(epoch):
# config.batch_size=1
model.eval()
# print '\n\n测试的时候请设置config里的batch_size为1！！！please set the batch_size as 1'
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'test'
# test_or_valid = 'valid'
print(('Test or valid:', test_or_valid))
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
# for iii in range(2000):
while True:
print(('-' * 30))
eval_data = next(eval_data_gen)
if eval_data == False:
print(('SDR_aver_eval_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_eval_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
# raw_tgt = [sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']]
raw_tgt = eval_data['batch_order']
feas_tgt = models.rank_feas(
raw_tgt, eval_data['multi_spk_fea_list']) # 这里是目标的图谱
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
# tgt = Variable(torch.from_numpy(np.array([[0,1,2,102] for __ in range(config.batch_size)], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
# tgt = Variable(torch.from_numpy(np.array([[0,1,2,3,102] for __ in range(config.batch_size)], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
tgt = Variable(
torch.from_numpy(
np.array([
list(range(top_k + 1)) + [102]
for __ in range(config.batch_size)
],
dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
padded_mixture, mixture_lengths, padded_source = eval_data['tas_zip']
padded_mixture = torch.from_numpy(padded_mixture).float()
mixture_lengths = torch.from_numpy(mixture_lengths)
padded_source = torch.from_numpy(padded_source).float()
padded_mixture = padded_mixture.cuda().transpose(0, 1)
mixture_lengths = mixture_lengths.cuda()
padded_source = padded_source.cuda()
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in eval_data['multi_spk_fea_list']
])).unsqueeze(0)
# tgt_len = Variable(torch.LongTensor(config.batch_size).zero_()+len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
if config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 3
feas_tgt_square = feas_tgt * feas_tgt
feas_tgt_sum_square = torch.sum(feas_tgt_square,
dim=0,
keepdim=True).expand(tmp_size)
WFM_mask = feas_tgt_square / (feas_tgt_sum_square + 1e-15)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
if 1 and len(opt.gpus) > 1:
outputs, pred, targets, multi_mask, dec_enc_attn_list = model(
src,
src_len,
tgt,
tgt_len,
dict_spk2idx,
None,
mix_wav=padded_mixture
) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
else:
outputs, pred, targets, multi_mask, dec_enc_attn_list = model(
src,
src_len,
tgt,
tgt_len,
dict_spk2idx,
None,
mix_wav=padded_mixture
) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
samples = list(
pred.view(config.batch_size, top_k + 1,
-1).max(2)[1].data.cpu().numpy())
'''
if 1 and len(opt.gpus) > 1:
samples, predicted_masks = model.module.beam_sample(src, src_len, dict_spk2idx, tgt, config.beam_size,None,padded_mixture)
else:
samples, predicted_masks = model.beam_sample(src, src_len, dict_spk2idx, tgt, config.beam_size, None, padded_mixture)
multi_mask = predicted_masks
samples=[samples]
# except:
# continue
# '''
# expand the raw mixed-features to topk_max channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
# if samples[0][-1] != dict_spk2idx['<EOS>']:
# print '*'*40+'\nThe model is far from good. End the evaluation.\n'+'*'*40
# break
topk_max = top_k
x_input_map_multi = torch.unsqueeze(src,
1).expand(siz[0], topk_max, siz[1],
siz[2])
multi_mask = multi_mask.transpose(0, 1)
if test_or_valid != 'test':
if config.use_tas:
if 1 and len(opt.gpus) > 1:
ss_loss, pmt_list, max_snr_idx, *__ = model.module.separation_tas_loss(
padded_mixture, multi_mask, padded_source,
mixture_lengths)
else:
ss_loss, pmt_list, max_snr_idx, *__ = model.separation_tas_loss(
padded_mixture, multi_mask, padded_source,
mixture_lengths)
print(('loss for ss,this batch:', ss_loss.cpu().item()))
lera.log({
'ss_loss_' + test_or_valid: ss_loss.cpu().item(),
})
del ss_loss
# '''''
if 1 and batch_idx <= (500 / config.batch_size
): # only the former batches counts the SDR
utils.bss_eval_tas(config,
multi_mask,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst=log_path + 'batch_output/')
del multi_mask, x_input_map_multi
try:
sdr_aver_batch, sdri_aver_batch = bss_test.cal(log_path +
'batch_output/')
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
except (AssertionError):
print('Errors in calculating the SDR')
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SDRi_aver_now:', SDRi_SUM.mean()))
lera.log({'SDR sample' + test_or_valid: SDR_SUM.mean()})
lera.log({'SDRi sample' + test_or_valid: SDRi_SUM.mean()})
writer.add_scalars('scalar/loss',
{'SDR_sample_' + test_or_valid: sdr_aver_batch},
updates)
# raw_input('Press any key to continue......')
elif batch_idx == (200 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: # only record the best SDR once.
print(('Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean())))
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
'''
import matplotlib.pyplot as plt
ax = plt.gca()
ax.invert_yaxis()
raw_src=models.rank_feas(raw_tgt,eval_data['multi_spk_fea_list'])
att_idx=0
att =dec_enc_attn_list.data.cpu().numpy()[:,att_idx] # head,topk,T
for spk in range(3):
xx=att[:,spk]
plt.matshow(xx.reshape(8,1,-1).repeat(50,1).reshape(-1,751), cmap=plt.cm.hot, vmin=0,vmax=0.05)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'spk_{}.png'.format(spk))
plt.matshow(xx.sum(0).reshape(1, 1, -1).repeat(50, 1).reshape(-1, 751), cmap=plt.cm.hot, vmin=0, vmax=0.05)
plt.colorbar()
plt.savefig(log_path + 'batch_output/' + 'spk_sum_{}.png'.format(spk))
for head in range(8):
xx=att[head]
plt.matshow(xx.reshape(3,1,-1).repeat(100,1).reshape(-1,751), cmap=plt.cm.hot, vmin=0,vmax=0.05)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'head_{}.png'.format(head))
plt.matshow(raw_src[att_idx*2+0].transpose(0,1), cmap=plt.cm.hot, vmin=0,vmax=2)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'source0.png')
plt.matshow(raw_src[att_idx*2+1].transpose(0,1), cmap=plt.cm.hot, vmin=0,vmax=2)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'source1.png')
# '''
candidate += [
convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>'])
for s in samples
]
# source += raw_src
reference += raw_tgt
print(('samples:', samples))
print(('can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:],
reference[-1 * config.batch_size:])))
# alignments += [align for align in alignment]
batch_idx += 1
result = utils.eval_metrics(reference, candidate, dict_spk2idx,
log_path)
print((
'hamming_loss: %.8f | micro_f1: %.4f |recall: %.4f | precision: %.4f'
% (
result['hamming_loss'],
result['micro_f1'],
result['micro_recall'],
result['micro_precision'],
)))
score = {}
result = utils.eval_metrics(reference, candidate, dict_spk2idx, log_path)
logging_csv([e, updates, result['hamming_loss'], \
result['micro_f1'], result['micro_precision'], result['micro_recall'],SDR_SUM.mean()])
print(('hamming_loss: %.8f | micro_f1: %.4f' %
(result['hamming_loss'], result['micro_f1'])))
score['hamming_loss'] = result['hamming_loss']
score['micro_f1'] = result['micro_f1']
1 / 0
return score
def main():
print('go to model')
print '*' * 80
spk_global_gen=prepare_data(mode='global',train_or_test='train') #写一个假的数据生成，可以用来写模型先
global_para=spk_global_gen.next()
print global_para
spk_all_list,dict_spk2idx,dict_idx2spk,mix_speech_len,speech_fre,total_frames,spk_num_total=global_para
del spk_global_gen
num_labels=len(spk_all_list)
#此处顺序是 mix_speechs.shape,mix_feas.shape,aim_fea.shape,aim_spkid.shape,query.shape
#一个例子：(5, 17040) (5, 134, 129) (5, 134, 129) (5,) (5, 32, 400, 300, 3)
# datasize=prepare_datasize(data_generator)
# mix_speech_len,speech_fre,total_frames,spk_num_total,video_size=datasize
print 'Begin to build the maim model for Multi_Modal Cocktail Problem.'
# data=data_generator.next()
# This part is to build the 3D mix speech embedding maps.
mix_hidden_layer_3d=MIX_SPEECH(speech_fre,mix_speech_len).cuda()
mix_speech_classifier=MIX_SPEECH_classifier(speech_fre,mix_speech_len,num_labels).cuda()
mix_speech_multiEmbedding=SPEECH_EMBEDDING(num_labels,config.EMBEDDING_SIZE,spk_num_total+config.UNK_SPK_SUPP).cuda()
print mix_hidden_layer_3d
print mix_speech_classifier
# mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(data[1])).cuda())
hidden_size=(config.EMBEDDING_SIZE)
# x=torch.arange(0,24).view(2,3,4)
# y=torch.ones([2,4])
att_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
print att_speech_layer
optimizer = torch.optim.Adam([{'params':mix_hidden_layer_3d.parameters()},
{'params':mix_speech_multiEmbedding.parameters()},
{'params':mix_speech_classifier.parameters()},
# {'params':query_video_layer.lstm_layer.parameters()},
# {'params':query_video_layer.dense.parameters()},
# {'params':query_video_layer.Linear.parameters()},
{'params':att_layer.parameters()},
{'params':att_speech_layer.parameters()},
# ], lr=0.02,momentum=0.9)
], lr=0.0002)
if 1 and config.Load_param:
# query_video_layer.load_state_dict(torch.load('param_video_layer_19'))
# mix_speech_classifier.load_state_dict(torch.load('params/param_speech_123onezeroag3_WSJ0_multilabel_epoch40'))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_WSJ0_hidden3d_180'))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_WSJ0_emblayer_180'))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_WSJ0_attlayer_180'))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbag1nosum_WSJ0_hidden3d_250',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbag1nosum_WSJ0_emblayer_250',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbag1nosum_WSJ0_attlayer_250',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2or3_db_WSJ0_hidden3d_560',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2or3_db_WSJ0_emblayer_560',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix2or3_db_WSJ0_attlayer_560',map_location={'cuda:1':'cuda:0'}))
mix_speech_classifier.load_state_dict(torch.load('params/param_speech_4lstm_multilabelloss30map_epoch440'))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbag2sum_WSJ0_hidden3d_460',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbag2sum_WSJ0_emblayer_460',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbag2sum_WSJ0_attlayer_460',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbdropout_WSJ0_hidden3d_370',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbdropout_WSJ0_emblayer_370',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbdropout_WSJ0_attlayer_370',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbdropoutag_WSJ0_attlayer_220',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbdropoutag_WSJ0_hidden3d_220',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbdropoutag_WSJ0_emblayer_220',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix2or3_dbdropoutag_WSJ0_attlayer_180',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2or3_dbdropoutag_WSJ0_hidden3d_180',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2or3_dbdropoutag_WSJ0_emblayer_180',map_location={'cuda:1':'cuda:0'}))
att_speech_layer.load_state_dict(torch.load('params/param_mix2_db2dropout_WSJ0_attlayer_495',map_location={'cuda:1':'cuda:0'}))
mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2_db2dropout_WSJ0_hidden3d_495',map_location={'cuda:1':'cuda:0'}))
mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2_db2dropout_WSJ0_emblayer_495',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix2or3_db2dropout_WSJ0_attlayer_95',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2or3_db2dropout_WSJ0_hidden3d_95',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2or3_db2dropout_WSJ0_emblayer_95',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix1to3_dbdropoutag1_WSJ0_attlayer_500',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix1to3_dbdropoutag1_WSJ0_hidden3d_500',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix1to3_dbdropoutag1_WSJ0_emblayer_500',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix2_40lstm2dbdro_WSJ0_attlayer_835',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2_40lstm2dbdro_WSJ0_hidden3d_835',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2_40lstm2dbdro_WSJ0_emblayer_835',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix2_40lstmdbdropout_WSJ0_attlayer_200',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2_40lstm3dbdropout_WSJ0_hidden3d_200',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2_40lstm3dbdropout_WSJ0_emblayer_200',map_location={'cuda:1':'cuda:0'}))
'''with Noise'''
# att_speech_layer.load_state_dict(torch.load('params/param_noicemix2or3_db2dropout_WSJ0_attlayer_80',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_noicemix2or3_db2dropout_WSJ0_hidden3d_80',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_noicemix2or3_db2dropout_WSJ0_emblayer_80',map_location={'cuda:1':'cuda:0'}))
loss_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
loss_multi_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
# loss_multi_func = torch.nn.L1Loss() # the target label is NOT an one-hotted
loss_query_class=torch.nn.CrossEntropyLoss()
print '''Begin to calculate.'''
SDR_SUM_total=np.array([])
for epoch_idx in range(config.MAX_EPOCH):
if epoch_idx>0:
print 'SDR_SUM (len:{}) for epoch {} : {}'.format(SDR_SUM.shape,epoch_idx-1,SDR_SUM.mean())
SDR_SUM=np.array([])
# print 'SDR_SUM for epoch {}:{}'.format(epoch_idx - 1, SDR_SUM.mean())
dst='batch_output'
if os.path.exists(dst):
print " cleanup: " + dst + "/"
shutil.rmtree(dst)
os.makedirs(dst)
for batch_idx in range(config.EPOCH_SIZE):
print '*' * 40,epoch_idx,batch_idx,'*'*40
train_data_gen=prepare_data('once','train')
# train_data_gen=prepare_data('once','test')
train_data_gen=prepare_data('once','eval_test')
train_data=train_data_gen.next()
mix_feas=train_data['mix_feas']
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
#从数据里得到ground truth的说话人名字和vector
# y_spk_list=[one.keys() for one in train_data['multi_spk_fea_list']]
# y_spk_list= train_data['multi_spk_fea_list']
# y_spk_gtruth,y_map_gtruth=multi_label_vector(y_spk_list,dict_spk2idx)
# 如果训练阶段使用Ground truth的分离结果作为判别
if 0 and config.Ground_truth:
mix_speech_output=Variable(torch.from_numpy(y_map_gtruth)).cuda()
if test_all_outputchannel: #把输入的mask改成全１，可以用来测试输出所有的channel
mix_speech_output=Variable(torch.ones(config.BATCH_SIZE,num_labels,))
y_map_gtruth=np.ones([config.BATCH_SIZE,num_labels])
recu_spk_list=OrderedDict() #每step对应spk以及分离出来的目标语音
speech_history=[] #将每step剩余speech 频谱的历史记录下来
bss_eval_groundtrue(train_data,batch_idx)
now_feas=train_data['mix_feas']
while True:
speech_history.append(now_feas)
max_num_labels=3
top_k_mask_mixspeech,top_k_sort_index=top_k_mask(mix_speech_output,alpha=-0.3,top_k=max_num_labels) #torch.Float型的
# top_k_mask_idx=[np.where(line==1)[0] for line in top_k_mask_mixspeech.numpy()]
top_k_mask_idx=top_k_sort_index
#过滤一下，把之前见过的spk过滤掉
print 'predict spk:',top_k_mask_idx[0]
for k in top_k_mask_idx[0]:
if k not in recu_spk_list.keys():
top_k_mask_idx=[[k]]
break
print 'flitered spk:',top_k_mask_idx[0]
# 如果过滤完了之后啥也没有了，那么就结束了
if len(top_k_mask_idx[0])==0:
break
# elif top_k_mask_idx[0][0] in speech_history
mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech,top_k_mask_idx) # bs*num_labels（最多混合人个数）×Embedding的大小
assert len(top_k_mask_idx[0])==len(top_k_mask_idx[-1])
top_k_num=len(top_k_mask_idx[0])
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'dot').cuda()
att_multi_speech=att_speech_layer(mix_speech_hidden_5d_last,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask=att_multi_speech
# multi_mask=(att_multi_speech>0.5)
# multi_mask=Variable(torch.from_numpy(np.float32(multi_mask.data.cpu().numpy()))).cuda()
# top_k_mask_mixspeech_multi=top_k_mask_mixspeech.view(config.BATCH_SIZE,top_k_num,1,1).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# multi_mask=multi_mask*Variable(top_k_mask_mixspeech_multi).cuda()
x_input_map=Variable(torch.from_numpy(now_feas)).cuda()
# print x_input_map.size()
x_input_map_multi=x_input_map.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# predict_multi_map=multi_mask*x_input_map_multi
predict_multi_map=multi_mask*x_input_map_multi #该说话人预测出来的频谱
recu_spk_list[top_k_mask_idx[0][0]]=predict_multi_map
pre_spk=dict_idx2spk[top_k_mask_idx[0][0]]
num_step=len(recu_spk_list)
print 'Now output the {} th spk , closest to spk <{}> in train list.'.format(num_step,pre_spk)
# bss_eval_recu(multi_mask,x_input_map,top_k_mask_mixspeech,pre_spk,train_data,num_step-1,batch_idx)
if num_step>=2:
# bss_eval_recu(multi_mask,x_input_map,top_k_mask_mixspeech,pre_spk,train_data,num_step,batch_idx)
break
now_feas=((1-multi_mask)*x_input_map_multi).data.cpu().numpy().reshape(1,mix_speech_len,speech_fre)
mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(now_feas)).cuda())
mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(now_feas)).cuda())
cal_spk=recu_spk_list.keys()
mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech,cal_spk) # bs*num_labels（最多混合人个数）×Embedding的大小
top_k_num=len(cal_spk)
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'dot').cuda()
att_multi_speech=att_speech_layer(mix_speech_hidden_5d_last,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask=att_multi_speech
# multi_mask=(att_multi_speech>0.5)
# multi_mask=Variable(torch.from_numpy(np.float32(multi_mask.data.cpu().numpy()))).cuda()
# top_k_mask_mixspeech_multi=top_k_mask_mixspeech.view(config.BATCH_SIZE,top_k_num,1,1).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# multi_mask=multi_mask*Variable(top_k_mask_mixspeech_multi).cuda()
x_input_map=Variable(torch.from_numpy(train_data['mix_feas'])).cuda()
# print x_input_map.size()
x_input_map_multi=x_input_map.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
bss_eval_fromGenMap(multi_mask,x_input_map,top_k_mask_mixspeech,dict_idx2spk,train_data,batch_idx)
# SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output/', 2))
# print 'SDR_SUM (len:{}) for epoch {} : {}'.format(SDR_SUM.shape,epoch_idx,SDR_SUM.mean())
# 1/0
SDR_SUM_total = np.append(SDR_SUM_total, bss_test.cal('batch_output/', 2))
print 'SDR_SUM (len:{}) for epoch {} : {}'.format(SDR_SUM_total.shape,epoch_idx,SDR_SUM_total.mean())
def train(epoch):
e = epoch
model.train()
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
if config.schedule and scheduler.get_lr()[0] > 5e-5:
scheduler.step()
print("Decaying learning rate to %g" % scheduler.get_lr()[0])
lera.log({
'lr': scheduler.get_lr()[0],
})
if opt.model == 'gated':
model.current_epoch = epoch
global e, updates, total_loss, start_time, report_total, report_correct, total_loss_sgm, total_loss_ss
train_data_gen = prepare_data('once', 'train')
while True:
print '\n'
train_data = train_data_gen.next()
if train_data == False:
print('SDR_aver_epoch:', SDR_SUM.mean())
print('SDRi_aver_epoch:', SDRi_SUM.mean())
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(train_data['mix_feas']))
# raw_tgt = [spk.keys() for spk in train_data['multi_spk_fea_list']]
raw_tgt = [
sorted(spk.keys()) for spk in train_data['multi_spk_fea_list']
]
feas_tgt = models.rank_feas(
raw_tgt,
train_data['multi_spk_fea_list']) # 这里是目标的图谱,aim_size,len,fre
# 要保证底下这几个都是longTensor(长整数）
tgt_max_len = config.MAX_MIX + 2 # with bos and eos.
tgt = Variable(
torch.from_numpy(
np.array(
[[0] + [dict_spk2idx[spk] for spk in spks] +
(tgt_max_len - len(spks) - 1) * [dict_spk2idx['<EOS>']]
for spks in raw_tgt],
dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in train_data['multi_spk_fea_list']
])).unsqueeze(0)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
# aim_list 就是找到有正经说话人的地方的标号
aim_list = (tgt[1:-1].transpose(0, 1).contiguous().view(-1) !=
dict_spk2idx['<EOS>']).nonzero().squeeze()
aim_list = aim_list.data.cpu().numpy()
outputs, targets, multi_mask, gamma = model(
src, src_len, tgt, tgt_len,
dict_spk2idx) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
# print('mask size:', multi_mask.size())
writer.add_histogram('global gamma', gamma, updates)
if 1 and len(opt.gpus) > 1:
sgm_loss, num_total, num_correct = model.module.compute_loss(
outputs, targets, opt.memory)
else:
sgm_loss, num_total, num_correct = model.compute_loss(
outputs, targets, opt.memory)
print('loss for SGM,this batch:', sgm_loss.cpu().item())
writer.add_scalars('scalar/loss', {'sgm_loss': sgm_loss.cpu().item()},
updates)
src = src.transpose(0, 1)
# expand the raw mixed-features to topk_max channel.
siz = src.size()
assert len(siz) == 3
topk_max = config.MAX_MIX # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1],
siz[2]).contiguous().view(-1, siz[1], siz[2])
x_input_map_multi = x_input_map_multi[aim_list]
multi_mask = multi_mask.transpose(0, 1)
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(x_input_map_multi,
multi_mask, feas_tgt)
else:
ss_loss = model.separation_loss(x_input_map_multi, multi_mask,
feas_tgt)
print('loss for SS,this batch:', ss_loss.cpu().item())
writer.add_scalars('scalar/loss', {'ss_loss': ss_loss.cpu().item()},
updates)
loss = sgm_loss + 5 * ss_loss
loss.backward()
# print 'totallllllllllll loss:',loss
total_loss_sgm += sgm_loss.cpu().item()
total_loss_ss += ss_loss.cpu().item()
lera.log({
'sgm_loss': sgm_loss.cpu().item(),
'ss_loss': ss_loss.cpu().item(),
'loss:': loss.cpu().item(),
})
if updates > 10 and updates % config.eval_interval in [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
]:
predicted_maps = multi_mask * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval(config,
predicted_maps,
train_data['multi_spk_fea_list'],
raw_tgt,
train_data,
dst='batch_output')
del predicted_maps, multi_mask, x_input_map_multi
sdr_aver_batch, sdri_aver_batch = bss_test.cal('batch_output/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SDRi sample': sdri_aver_batch})
writer.add_scalars('scalar/loss', {
'SDR_sample': sdr_aver_batch,
'SDRi_sample': sdri_aver_batch
}, updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
print('SDR_aver_now:', SDR_SUM.mean())
print('SDRi_aver_now:', SDRi_SUM.mean())
total_loss += loss.cpu().item()
report_correct += num_correct.cpu().item()
report_total += num_total.cpu().item()
optim.step()
updates += 1
if updates % 30 == 0:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss this batch: %6.3f,sgm loss: %6.6f,ss loss: %6.6f,label acc: %6.6f\n"
% (time.time() - start_time, epoch, updates, loss / num_total,
total_loss_sgm / 30.0, total_loss_ss / 30.0,
report_correct / report_total))
lera.log({'label_acc': report_correct / report_total})
writer.add_scalars('scalar/loss',
{'label_acc': report_correct / report_total},
updates)
total_loss_sgm, total_loss_ss = 0, 0
# continue
if 0 and updates % config.eval_interval == 0 and epoch > 3: #建议至少跑几个epoch再进行测试，否则模型还没学到东西，会有很多问题。
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss: %6.5f\n" %
(time.time() - start_time, epoch, updates,
total_loss / report_total))
print('evaluating after %d updates...\r' % updates)
original_bs = config.batch_size
score = eval(epoch) # eval的时候batch_size会变成1
print 'Orignal bs:', original_bs
config.batch_size = original_bs
print 'Now bs:', config.batch_size
for metric in config.metric:
scores[metric].append(score[metric])
lera.log({
'sgm_micro_f1': score[metric],
})
if metric == 'micro_f1' and score[metric] >= max(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
if metric == 'hamming_loss' and score[metric] <= min(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
model.train()
total_loss = 0
start_time = 0
report_total = 0
report_correct = 0
if updates % config.save_interval == 1:
save_model(log_path + 'TDAAv3_{}.pt'.format(updates))
def train(epoch):
global e, updates, total_loss, start_time, report_total, report_correct, total_loss_sgm, total_loss_ss
e = epoch
model.train()
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
if updates <= config.warmup: #如果不在warm阶段就正常规划
pass
elif config.schedule and scheduler.get_lr()[0] > 5e-7:
scheduler.step()
print(("Decaying learning rate to %g" % scheduler.get_lr()[0]))
lera.log({
'lr': [group['lr'] for group in optim.optimizer.param_groups][0],
})
if opt.model == 'gated':
model.current_epoch = epoch
train_data_gen = prepare_data('once', 'train')
while True:
if updates <= config.warmup: # 如果在warm就开始warmup
tmp_lr = config.learning_rate * min(
max(updates, 1)**(-0.5),
max(updates, 1) * (config.warmup**(-1.5)))
for param_group in optim.optimizer.param_groups:
param_group['lr'] = tmp_lr
scheduler.base_lrs = list(
[group['lr'] for group in optim.optimizer.param_groups])
if updates % 100 == 0: #记录一下
print(updates)
print("Warmup learning rate to %g" % tmp_lr)
lera.log({
'lr':
[group['lr'] for group in optim.optimizer.param_groups][0],
})
train_data = next(train_data_gen)
if train_data == False:
print(('SDR_aver_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(train_data['mix_feas']))
# raw_tgt = [spk.keys() for spk in train_data['multi_spk_fea_list']]
# raw_tgt = [sorted(spk.keys()) for spk in train_data['multi_spk_fea_list']]
raw_tgt = train_data['batch_order']
feas_tgt = models.rank_feas(
raw_tgt,
train_data['multi_spk_fea_list']) # 这里是目标的图谱,aim_size,len,fre
padded_mixture, mixture_lengths, padded_source = train_data['tas_zip']
padded_mixture = torch.from_numpy(padded_mixture).float()
mixture_lengths = torch.from_numpy(mixture_lengths)
padded_source = torch.from_numpy(padded_source).float()
padded_mixture = padded_mixture.cuda().transpose(0, 1)
mixture_lengths = mixture_lengths.cuda()
padded_source = padded_source.cuda()
# 要保证底下这几个都是longTensor(长整数）
tgt_max_len = config.MAX_MIX + 2 # with bos and eos.
tgt = Variable(
torch.from_numpy(
np.array(
[[0] + [dict_spk2idx[spk] for spk in spks] +
(tgt_max_len - len(spks) - 1) * [dict_spk2idx['<EOS>']]
for spks in raw_tgt],
dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
# tgt = Variable(torch.from_numpy(np.array([[0,1,2,102] for __ in range(config.batch_size)], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in train_data['multi_spk_fea_list']
])).unsqueeze(0)
if config.WFM:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1],
siz[2]).contiguous().view(-1, siz[1], siz[2]) # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
feas_tgt_square = feas_tgt_tmp * feas_tgt_tmp
feas_tgt_sum_square = torch.sum(feas_tgt_square,
dim=1,
keepdim=True).expand(
siz[0], topk_max, siz[1],
siz[2])
WFM_mask = feas_tgt_square / (feas_tgt_sum_square + 1e-15)
feas_tgt = x_input_map_multi.view(
siz[0], -1, siz[1], siz[2]).data * WFM_mask # bs,topk,T,F
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
WFM_mask = WFM_mask.cuda()
del x_input_map_multi
elif config.PSM:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1], siz[2]).contiguous() # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
IRM = feas_tgt_tmp / (x_input_map_multi + 1e-15)
angle_tgt = models.rank_feas(
raw_tgt, train_data['multi_spk_angle_list']).view(
siz[0], -1, siz[1], siz[2])
angle_mix = Variable(
torch.from_numpy(np.array(
train_data['mix_angle']))).unsqueeze(1).expand(
siz[0], topk_max, siz[1], siz[2]).contiguous()
ang = np.cos(angle_mix - angle_tgt)
ang = np.clip(ang, 0, None)
feas_tgt = x_input_map_multi * IRM * ang # bs,topk,T,F
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
del x_input_map_multi
elif config.frame_mask:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1], siz[2]).contiguous() # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
feas_tgt_time = torch.sum(feas_tgt_tmp, 3).transpose(1,
2) #bs,T,topk
for v1 in feas_tgt_time:
for v2 in v1:
if v2[0] > v2[1]:
v2[0] = 1
v2[1] = 0
else:
v2[0] = 0
v2[1] = 1
frame_mask = feas_tgt_time.transpose(1,
2).unsqueeze(-1) #bs,topk,t,1
feas_tgt = x_input_map_multi * frame_mask
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
if config.use_center_loss:
center_loss.zero_grad()
# aim_list 就是找到有正经说话人的地方的标号
aim_list = (tgt[1:-1].transpose(0, 1).contiguous().view(-1) !=
dict_spk2idx['<EOS>']).nonzero().squeeze()
aim_list = aim_list.data.cpu().numpy()
outputs, pred, targets, multi_mask, dec_enc_attn_list = model(
src,
src_len,
tgt,
tgt_len,
dict_spk2idx,
None,
mix_wav=padded_mixture
) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
print('mask size:', multi_mask.size())
# writer.add_histogram('global gamma',gamma, updates)
src = src.transpose(0, 1)
# expand the raw mixed-features to topk_max channel.
siz = src.size()
assert len(siz) == 3
topk_max = config.MAX_MIX # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1],
siz[2]).contiguous() #.view(-1, siz[1], siz[2])
# x_input_map_multi = x_input_map_multi[aim_list]
multi_mask = multi_mask.transpose(0, 1)
# if config.WFM:
# feas_tgt = x_input_map_multi.data * WFM_mask
if config.use_tas:
if 1 and len(opt.gpus) > 1:
ss_loss, pmt_list, max_snr_idx, *__ = model.module.separation_tas_loss(
padded_mixture, multi_mask, padded_source, mixture_lengths)
else:
ss_loss, pmt_list, max_snr_idx, *__ = model.separation_tas_loss(
padded_mixture, multi_mask, padded_source, mixture_lengths)
best_pmt = [
list(pmt_list[int(mm)].data.cpu().numpy())
for mm in max_snr_idx
]
else:
if 1 and len(opt.gpus) > 1: # 先ss获取Perm
ss_loss, best_pmt = model.module.separation_pit_loss(
x_input_map_multi, multi_mask, feas_tgt)
else:
ss_loss, best_pmt = model.separation_pit_loss(
x_input_map_multi, multi_mask, feas_tgt)
print('loss for SS,this batch:', ss_loss.cpu().item())
print('best perms for this batch:', best_pmt)
writer.add_scalars('scalar/loss', {'ss_loss': ss_loss.cpu().item()},
updates)
# 按照Best_perm重新排列spk的预测目标
targets = targets.transpose(0, 1) #bs,aim+1(EOS也在）
# print('targets',targets)
targets_old = targets
for idx, (tar, per) in enumerate(zip(targets, best_pmt)):
per.append(topk_max) #每个batch后面加个结尾，保持最后一个EOS不变
targets_old[idx] = tar[per]
targets = targets_old.transpose(0, 1)
# print('targets',targets)
if 1 and len(opt.gpus) > 1:
sgm_loss, num_total, num_correct = model.module.compute_loss(
outputs, targets, opt.memory)
else:
sgm_loss, num_total, num_correct = model.compute_loss(
outputs, targets, opt.memory)
print(('loss for SGM,this batch:', sgm_loss.cpu().item()))
writer.add_scalars('scalar/loss', {'sgm_loss': sgm_loss.cpu().item()},
updates)
if config.use_center_loss:
cen_alpha = 0.01
cen_loss = center_loss(outputs.view(-1, config.SPK_EMB_SIZE),
targets.view(-1))
print(('loss for SGM center loss,this batch:',
cen_loss.cpu().item()))
writer.add_scalars('scalar/loss',
{'center_loss': cen_loss.cpu().item()}, updates)
if not config.use_tas:
loss = sgm_loss + 5 * ss_loss
else:
loss = 50 * sgm_loss + ss_loss
loss.backward()
if config.use_center_loss:
for c_param in center_loss.parameters():
c_param.grad.data *= (0.01 /
(cen_alpha * scheduler.get_lr()[0]))
# print 'totallllllllllll loss:',loss
total_loss_sgm += sgm_loss.cpu().item()
total_loss_ss += ss_loss.cpu().item()
lera.log({
'sgm_loss': sgm_loss.cpu().item(),
'ss_loss': ss_loss.cpu().item(),
'loss:': loss.cpu().item(),
})
if updates > 10 and updates % config.eval_interval in [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
]:
if not config.use_tas:
predicted_maps = multi_mask * x_input_map_multi.view(
siz[0] * topk_max, siz[1], siz[2])
# predicted_maps=Variable(feas_tgt) # 这个是groundTruth
utils.bss_eval(config,
predicted_maps,
train_data['multi_spk_fea_list'],
raw_tgt,
train_data,
dst='batch_output1')
del predicted_maps, multi_mask, x_input_map_multi
sdr_aver_batch, sdri_aver_batch = bss_test.cal(
'batch_output1/')
else:
utils.bss_eval_tas(config,
multi_mask,
train_data['multi_spk_fea_list'],
raw_tgt,
train_data,
dst='batch_output1')
del x_input_map_multi
sdr_aver_batch, sdri_aver_batch = bss_test.cal(
'batch_output1/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SDRi sample': sdri_aver_batch})
writer.add_scalars('scalar/loss', {
'SDR_sample': sdr_aver_batch,
'SDRi_sample': sdri_aver_batch
}, updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SDRi_aver_now:', SDRi_SUM.mean()))
total_loss += loss.cpu().item()
report_correct += num_correct.cpu().item()
report_total += num_total.cpu().item()
optim.step()
updates += 1
if updates % 30 == 0:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss this batch: %6.3f,sgm loss: %6.6f,ss loss: %6.6f,label acc: %6.6f\n"
% (time.time() - start_time, epoch, updates, loss / num_total,
total_loss_sgm / 30.0, total_loss_ss / 30.0,
report_correct / report_total))
lera.log({'label_acc': report_correct / report_total})
writer.add_scalars('scalar/loss',
{'label_acc': report_correct / report_total},
updates)
total_loss_sgm, total_loss_ss = 0, 0
# continue
if 0 and updates % config.eval_interval == 0 and epoch > 3: #建议至少跑几个epoch再进行测试，否则模型还没学到东西，会有很多问题。
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss: %6.5f\n" %
(time.time() - start_time, epoch, updates,
total_loss / report_total))
print(('evaluating after %d updates...\r' % updates))
original_bs = config.batch_size
score = eval(epoch) # eval的时候batch_size会变成1
# print 'Orignal bs:',original_bs
config.batch_size = original_bs
# print 'Now bs:',config.batch_size
for metric in config.metric:
scores[metric].append(score[metric])
lera.log({
'sgm_micro_f1': score[metric],
})
if metric == 'micro_f1' and score[metric] >= max(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
if metric == 'hamming_loss' and score[metric] <= min(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
model.train()
total_loss = 0
start_time = 0
report_total = 0
report_correct = 0
if 1 and updates % config.save_interval == 1:
save_model(log_path + 'TDAAv3_PIT_{}.pt'.format(updates))
def eval(epoch):
model.eval()
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'test'
print 'Test or valid:', test_or_valid
eval_data_gen = prepare_data_aim('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
# for raw_src, src, src_len, raw_tgt, tgt, tgt_len in validloader:
SDR_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
while True:
# for ___ in range(2):
print '-' * 30
eval_data = eval_data_gen.next()
if eval_data == False:
print 'SDR_aver_eval_epoch:', SDR_SUM.mean()
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
raw_tgt = [
sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']
]
feas_tgt = models.rank_feas(raw_tgt,
eval_data['multi_spk_fea_list']) #这里是目标的图谱
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.ones(
top_k + 2, config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in eval_data['multi_spk_fea_list']
])).unsqueeze(0)
# tgt_len = Variable(torch.LongTensor(config.batch_size).zero_()+len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
if config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 4
feas_tgt_sum = torch.sum(feas_tgt, dim=1, keepdim=True)
feas_tgt_sum_square = (feas_tgt_sum *
feas_tgt_sum).expand(tmp_size)
feas_tgt_square = feas_tgt * feas_tgt
WFM_mask = feas_tgt_square / feas_tgt_sum_square
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
try:
if 1 and len(opt.gpus) > 1:
# samples, alignment = model.module.sample(src, src_len)
samples, alignment, hiddens, predicted_masks = model.module.beam_sample(
src,
src_len,
dict_spk2idx,
tgt,
beam_size=config.beam_size)
else:
samples, alignment, hiddens, predicted_masks = model.beam_sample(
src,
src_len,
dict_spk2idx,
tgt,
beam_size=config.beam_size)
# samples, alignment, hiddens, predicted_masks = model.beam_sample(src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
except TabError, info:
print '**************Error occurs here************:', info
continue
if config.top1:
predicted_masks = torch.cat([predicted_masks, 1 - predicted_masks],
1)
# '''
# expand the raw mixed-features to topk_max channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
topk_max = feas_tgt.size()[1]
assert samples[0][-1] == dict_spk2idx['<EOS>']
topk_max = len(samples[0]) - 1
x_input_map_multi = torch.unsqueeze(src,
1).expand(siz[0], topk_max, siz[1],
siz[2])
if config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
if test_or_valid == 'valid':
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(x_input_map_multi,
predicted_masks,
feas_tgt, Var)
else:
ss_loss = model.separation_loss(x_input_map_multi,
predicted_masks, feas_tgt)
print 'loss for ss,this batch:', ss_loss.data[0]
lera.log({
'ss_loss_' + test_or_valid: ss_loss.data[0],
})
del ss_loss, hiddens
# '''''
if batch_idx <= (500 / config.batch_size
): #only the former batches counts the SDR
predicted_maps = predicted_masks * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval2(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_outputjaa')
del predicted_maps, predicted_masks, x_input_map_multi
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_outputjaa/'))
print 'SDR_aver_now:', SDR_SUM.mean()
lera.log({'SDR sample': SDR_SUM.mean()})
# raw_input('Press any key to continue......')
elif batch_idx == (500 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: #only record the best SDR once.
print 'Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean())
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
# '''
candidate += [
convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>'])
for s in samples
]
# source += raw_src
reference += raw_tgt
print 'samples:', samples
print 'can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:],
reference[-1 * config.batch_size:])
alignments += [align for align in alignment]
batch_idx += 1
def train(epoch):
global e, updates, total_loss, start_time, report_total,report_correct, total_loss_sgm, total_loss_ss
e = epoch
model.train()
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
if updates<=config.warmup: #如果不在warm阶段就正常规划
pass
elif config.schedule and scheduler.get_lr()[0]>4e-5:
scheduler.step()
print(("Decaying learning rate to %g" % scheduler.get_lr()[0],updates))
lera.log({
'lr': [group['lr'] for group in optim.optimizer.param_groups][0],
})
if opt.model == 'gated':
model.current_epoch = epoch
train_data_gen = prepare_data('once', 'train')
while True:
if updates <= config.warmup: # 如果在warm就开始warmup
tmp_lr = config.learning_rate * min(max(updates,1)** (-0.5),
max(updates,1) * (config.warmup ** (-1.5)))
for param_group in optim.optimizer.param_groups:
param_group['lr'] = tmp_lr
scheduler.base_lrs=list([group['lr'] for group in optim.optimizer.param_groups])
if updates%100==0: #记录一下
print(updates)
print("Warmup learning rate to %g" % tmp_lr)
lera.log({
'lr': [group['lr'] for group in optim.optimizer.param_groups][0],
})
train_data = next(train_data_gen)
if train_data == False:
print(('SDR_aver_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(train_data['mix_feas']))
# raw_tgt = [spk.keys() for spk in train_data['multi_spk_fea_list']]
# raw_tgt = [sorted(spk.keys()) for spk in train_data['multi_spk_fea_list']]
raw_tgt=train_data['batch_order']
feas_tgt = models.rank_feas(raw_tgt, train_data['multi_spk_fea_list']) # 这里是目标的图谱,bs*Topk,len,fre
# 要保证底下这几个都是longTensor(长整数）
tgt_max_len = config.MAX_MIX + 2 # with bos and eos.
tgt = Variable(torch.from_numpy(np.array(
[[0] + [dict_spk2idx[spk] for spk in spks] + (tgt_max_len - len(spks) - 1) * [dict_spk2idx['<EOS>']] for
spks in raw_tgt], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
# tgt = Variable(torch.from_numpy(np.array([[0,1,2,102] for __ in range(config.batch_size)], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(torch.LongTensor(config.batch_size).zero_() + mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([len(one_spk) for one_spk in train_data['multi_spk_fea_list']])).unsqueeze(0)
if config.WFM:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous().view(-1, siz[1], siz[ 2]) # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
feas_tgt_square = feas_tgt_tmp * feas_tgt_tmp
feas_tgt_sum_square = torch.sum(feas_tgt_square, dim=1, keepdim=True).expand(siz[0], topk_max, siz[1], siz[2])
WFM_mask = feas_tgt_square / (feas_tgt_sum_square + 1e-15)
feas_tgt = x_input_map_multi.view(siz[0], -1, siz[1], siz[2]).data * WFM_mask # bs,topk,T,F
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
WFM_mask = WFM_mask.cuda()
del x_input_map_multi
elif config.PSM:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous() # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
IRM=feas_tgt_tmp/(x_input_map_multi+1e-15)
angle_tgt=models.rank_feas(raw_tgt, train_data['multi_spk_angle_list']).view(siz[0],-1,siz[1],siz[2]) # bs,topk,T,F
angle_mix=Variable(torch.from_numpy(np.array(train_data['mix_angle']))).unsqueeze(1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous()
ang=np.cos(angle_mix-angle_tgt)
ang=np.clip(ang,0,None)
# feas_tgt = x_input_map_multi *np.clip(IRM.numpy()*ang,0,1) # bs,topk,T,F
# feas_tgt = x_input_map_multi *IRM*ang # bs,topk,T,F
feas_tgt = feas_tgt.view(siz[0],-1,siz[1],siz[2])*ang # bs,topk,T,F
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
del x_input_map_multi
elif config.frame_mask:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous() # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
feas_tgt_time=torch.sum(feas_tgt_tmp,3).transpose(1,2) #bs,T,topk
for v1 in feas_tgt_time:
for v2 in v1:
if v2[0]>v2[1]:
v2[0]=1
v2[1]=0
else:
v2[0]=0
v2[1]=1
frame_mask=feas_tgt_time.transpose(1,2).unsqueeze(-1) #bs,topk,t,1
feas_tgt=x_input_map_multi*frame_mask
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
if config.use_center_loss:
center_loss.zero_grad()
# aim_list 就是找到有正经说话人的地方的标号
aim_list = (tgt[1:-1].transpose(0, 1).contiguous().view(-1) != dict_spk2idx['<EOS>']).nonzero().squeeze()
aim_list = aim_list.data.cpu().numpy()
multi_mask, enc_attn_list = model(src, src_len, tgt, tgt_len,
dict_spk2idx) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
print('mask size:', multi_mask.size()) # topk,bs,T,F
# print('mask:', multi_mask[0,0,:3:3]) # topk,bs,T,F
# writer.add_histogram('global gamma',gamma, updates)
src = src.transpose(0, 1)
# expand the raw mixed-features to topk_max channel.
siz = src.size()
assert len(siz) == 3
topk_max = config.MAX_MIX # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous()#.view(-1, siz[1], siz[2])
# x_input_map_multi = x_input_map_multi[aim_list]
# x_input_map_multi = x_input_map_multi.transpose(0, 1) #topk,bs,T,F
multi_mask = multi_mask.transpose(0, 1)
# if config.WFM:
# feas_tgt = x_input_map_multi.data * WFM_mask
# 注意,bs是第二维
assert multi_mask.shape == x_input_map_multi.shape
assert multi_mask.size(0) == config.batch_size
if 1 and len(opt.gpus) > 1: #先ss获取Perm
ss_loss, best_pmt = model.module.separation_pit_loss(x_input_map_multi, multi_mask, feas_tgt)
else:
ss_loss, best_pmt = model.separation_pit_loss(x_input_map_multi, multi_mask, feas_tgt)
print('loss for SS,this batch:', ss_loss.cpu().item())
print('best perms for this batch:', best_pmt)
writer.add_scalars('scalar/loss',{'ss_loss':ss_loss.cpu().item()},updates)
loss = ss_loss
loss.backward()
total_loss_ss += ss_loss.cpu().item()
lera.log({
'ss_loss': ss_loss.cpu().item(),
})
if updates>3 and updates % config.eval_interval in [0, 1, 2, 3, 4, 5, 6, 7, 8, 9,]:
assert multi_mask.shape==x_input_map_multi.shape
assert multi_mask.size(0)==config.batch_size
predicted_maps = (multi_mask * x_input_map_multi).view(siz[0]*topk_max,siz[1],siz[2])
# predicted_maps=Variable(feas_tgt)
# utils.bss_eval(config, predicted_maps, train_data['multi_spk_fea_list'], raw_tgt, train_data, dst=log_path+'batch_output/')
utils.bss_eval2(config, predicted_maps, train_data['multi_spk_fea_list'], raw_tgt, train_data, dst=log_path+'batch_output')
del predicted_maps, multi_mask, x_input_map_multi
sdr_aver_batch, sdri_aver_batch= bss_test.cal(log_path+'batch_output/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SDRi sample': sdri_aver_batch})
writer.add_scalars('scalar/loss',{'SDR_sample':sdr_aver_batch,'SDRi_sample':sdri_aver_batch},updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SDRi_aver_now:', SDRi_SUM.mean()))
# Heatmap here
# n_layer个 (head*bs) x lq x dk
'''
import matplotlib.pyplot as plt
ax = plt.gca()
ax.invert_yaxis()
raw_src=models.rank_feas(raw_tgt, train_data['multi_spk_fea_list'])
att_idx=1
att = enc_attn_list[-1].view(config.trans_n_head,config.batch_size,mix_speech_len,mix_speech_len).data.cpu().numpy()[:,att_idx]
for head in range(config.trans_n_head):
xx=att[head]
plt.matshow(xx, cmap=plt.cm.hot, vmin=0,vmax=0.05)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'head_{}.png'.format(head))
plt.matshow(raw_src[att_idx*2+0].transpose(0,1), cmap=plt.cm.hot, vmin=0,vmax=2)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'source0.png')
plt.matshow(raw_src[att_idx*2+1].transpose(0,1), cmap=plt.cm.hot, vmin=0,vmax=2)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'source1.png')
1/0
'''
total_loss += loss.cpu().item()
optim.step()
updates += 1
if updates % 30 == 0:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss this batch: %6.3f,ss loss: %6.6f\n"
% (time.time() - start_time, epoch, updates, loss , total_loss_ss / 30.0))
total_loss_sgm, total_loss_ss = 0, 0
# continue
if 0 and updates % config.eval_interval == 0 and epoch > 3: #建议至少跑几个epoch再进行测试，否则模型还没学到东西，会有很多问题。
logging("time: %6.3f, epoch: %3d, updates: %8d, train loss: %6.5f\n"
% (time.time() - start_time, epoch, updates, total_loss/config.eval_interval))
print(('evaluating after %d updates...\r' % updates))
eval(epoch,'valid') # eval的时候batch_size会变成1
eval(epoch,'test') # eval的时候batch_size会变成1
model.train()
total_loss = 0
start_time = 0
report_total = 0
report_correct = 0
if 1 and updates % config.save_interval == 1:
save_model(log_path + 'Transformer_PIT_{}.pt'.format(updates))
def main():
print('go to model')
print '*' * 80
spk_global_gen=prepare_data(mode='global',train_or_test='train') #写一个假的数据生成，可以用来写模型先
global_para=spk_global_gen.next()
print global_para
spk_all_list,dict_spk2idx,dict_idx2spk,mix_speech_len,speech_fre,total_frames,spk_num_total,batch_total=global_para
del spk_global_gen
num_labels=len(spk_all_list)
print 'Begin to build the maim model for Multi_Modal Cocktail Problem.'
# This part is to build the 3D mix speech embedding maps.
mix_hidden_layer_3d=MIX_SPEECH(speech_fre,mix_speech_len).cuda()
mix_speech_classifier=MIX_SPEECH_classifier(speech_fre,mix_speech_len,num_labels).cuda()
mix_speech_multiEmbedding=SPEECH_EMBEDDING(num_labels,config.EMBEDDING_SIZE,spk_num_total+config.UNK_SPK_SUPP).cuda()
print mix_hidden_layer_3d
print mix_speech_classifier
print mix_speech_multiEmbedding
att_layer=ATTENTION(config.EMBEDDING_SIZE,'dot').cuda()
att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'dot').cuda()
adjust_layer=ADDJUST(2*config.HIDDEN_UNITS,config.EMBEDDING_SIZE)
dis_layer=Discriminator().cuda()
print att_speech_layer
print att_speech_layer.mode
print adjust_layer
print dis_layer
lr_data=0.0002
optimizer = torch.optim.Adam([{'params':mix_hidden_layer_3d.parameters()},
{'params':mix_speech_multiEmbedding.parameters()},
{'params':mix_speech_classifier.parameters()},
{'params':adjust_layer.parameters()},
{'params':att_speech_layer.parameters()},
{'params':dis_layer.parameters()},
], lr=lr_data)
if 1 and config.Load_param:
class_dict=torch.load('params/param_speech_2mix3lstm_best',map_location={'cuda:3':'cuda:0'})
for key in class_dict.keys():
if 'cnn' in key:
class_dict.pop(key)
mix_speech_classifier.load_state_dict(class_dict)
# 底下四个是TDAA-basic最强版本
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mixdotadjust4lstmdot_WSJ0_hidden3d_125',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mixdotadjust4lstmdot_WSJ0_emblayer_125',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mixdotadjust4lstmdot_WSJ0_attlayer_125',map_location={'cuda:1':'cuda:0'}))
# adjust_layer.load_state_dict(torch.load('params/param_mixdotadjust4lstmdot_WSJ0_adjlayer_125',map_location={'cuda:1':'cuda:0'}))
#加入dis-ss的结果
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdis_3434.13436424_hidden3d_395',map_location={'cuda:2':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdis_3434.13436424_emblayer_395',map_location={'cuda:2':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdis_3434.13436424_attlayer_395',map_location={'cuda:2':'cuda:0'}))
# adjust_layer.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdis_3434.13436424_adjlayer_395',map_location={'cuda:2':'cuda:0'}))
#加入dis-sp的结果
mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdissp_33401_hidden3d_185',map_location={'cuda:1':'cuda:0'}))
mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdissp_33401_emblayer_185',map_location={'cuda:1':'cuda:0'}))
att_speech_layer.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdissp_33401_attlayer_185',map_location={'cuda:1':'cuda:0'}))
adjust_layer.load_state_dict(torch.load('params/param_mixdotadjust4lstmdotdissp_33401_adjlayer_185',map_location={'cuda:1':'cuda:0'}))
loss_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
loss_multi_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
# loss_multi_func = torch.nn.L1Loss() # the target label is NOT an one-hotted
loss_dis_class=torch.nn.MSELoss()
lrs.send({
'title': 'TDAA classifier',
'batch_size':config.BATCH_SIZE,
'batch_total':batch_total,
'epoch_size':config.EPOCH_SIZE,
'loss func':loss_func.__str__(),
'initial lr':lr_data
})
print '''Begin to calculate.'''
for epoch_idx in range(1):
if epoch_idx%10==0:
for ee in optimizer.param_groups:
if ee['lr']>=1e-7:
ee['lr']/=2
lr_data=ee['lr']
lrs.send('lr',lr_data)
if epoch_idx>0:
print 'SDR_SUM (len:{}) for epoch {} : '.format(SDR_SUM.shape,epoch_idx-1,SDR_SUM.mean())
SDR_SUM=np.array([])
eval_data_gen=prepare_data('once','valid')
# eval_data_gen=prepare_data('once','test')
while 1 and True:
print '\n'
eval_data=eval_data_gen.next()
if eval_data==False:
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
now_data=eval_data['mix_feas']
top_k_num=3
# while True:
candidates=[]
predict_multi_map=np.zeros([config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre],dtype=np.float32)
for ____ in range(3):
print 'Recu step:',____
out_this_step,spk_this_step=model_step_output(now_data,mix_speech_classifier,mix_hidden_layer_3d,\
mix_speech_multiEmbedding,adjust_layer,att_speech_layer,\
dict_spk2idx,dict_idx2spk,num_labels,mix_speech_len,speech_fre)
out_this_step=out_this_step[0].data.cpu().numpy()
predict_multi_map[0,____]=out_this_step
now_data=now_data-out_this_step
candidates.append(spk_this_step)
y_multi_map=np.zeros([config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre],dtype=np.float32)
batch_spk_multi_dict=eval_data['multi_spk_fea_list']
if test_mode:
for iiii in range(config.BATCH_SIZE):
y_multi_map[iiii]=np.array(batch_spk_multi_dict[iiii].values())
y_multi_map= Variable(torch.from_numpy(y_multi_map)).cuda()
if 0: #这个是只利用推断出来的spk，回去做分离
print 'Recu only for spks.'
top_mask=torch.zeros(num_labels)
for jjj in candidates:
top_mask[int(jjj[0])]=1
top_mask=top_mask.view(1,num_labels)
try:
ccc=eval_bss(top_mask,eval_data,mix_hidden_layer_3d,adjust_layer, mix_speech_classifier, mix_speech_multiEmbedding, att_speech_layer,
loss_multi_func, dict_spk2idx, dict_idx2spk, num_labels, mix_speech_len, speech_fre)
SDR_SUM = np.append(SDR_SUM, ccc)
except:
pass
else:
print 'Recu for spks and maps.'
predict_multi_map=Variable(torch.from_numpy(predict_multi_map)).cuda()
try:
bss_eval(predict_multi_map,y_multi_map,2,dict_idx2spk,eval_data)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output2/', 2))
except:
pass
if SDR_SUM[-3:].mean()>8:
raw_input()
print 'SDR_aver_now:',SDR_SUM.mean()
print 'SDR_SUM (len:{}) for epoch eval : '.format(SDR_SUM.shape)
print '#'*40
def eval_bss(candidates,eval_data,mix_hidden_layer_3d,adjust_layer,mix_speech_classifier,mix_speech_multiEmbedding,att_speech_layer,
loss_multi_func,dict_spk2idx,dict_idx2spk,num_labels,mix_speech_len,speech_fre):
for i in [mix_speech_multiEmbedding,adjust_layer,mix_speech_classifier,mix_hidden_layer_3d,att_speech_layer]:
i.evaling=False
fea_now=eval_data['mix_feas']
while True:
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden,mix_tmp_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(fea_now)).cuda())
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
# mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(fea_now)).cuda())
if test_mode:
num_labels=2
alpha0=-0.5
else:
alpha0=0.5
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=alpha0,top_k=num_labels) #torch.Float型的
top_k_mask_mixspeech=candidates #torch.Float型的
top_k_mask_idx=[np.where(line==1)[0] for line in top_k_mask_mixspeech.numpy()]
print 'Predict spk list:',print_spk_name(dict_idx2spk,top_k_mask_idx)
mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech,top_k_mask_idx) # bs*num_labels（最多混合人个数）×Embedding的大小
mix_adjust=adjust_layer(mix_tmp_hidden,mix_speech_multiEmbs)
mix_speech_multiEmbs=mix_adjust+mix_speech_multiEmbs
assert len(top_k_mask_idx[0])==len(top_k_mask_idx[-1])
top_k_num=len(top_k_mask_idx[0])
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'dot').cuda()
att_multi_speech=att_speech_layer(mix_speech_hidden_5d_last,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask=att_multi_speech
# top_k_mask_mixspeech_multi=top_k_mask_mixspeech.view(config.BATCH_SIZE,top_k_num,1,1).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# multi_mask=multi_mask*Variable(top_k_mask_mixspeech_multi).cuda()
x_input_map=Variable(torch.from_numpy(fea_now)).cuda()
# print x_input_map.size()
x_input_map_multi=x_input_map.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# predict_multi_map=multi_mask*x_input_map_multi
predict_multi_map=multi_mask*x_input_map_multi
y_multi_map=np.zeros([config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre],dtype=np.float32)
batch_spk_multi_dict=eval_data['multi_spk_fea_list']
if test_mode:
for iiii in range(config.BATCH_SIZE):
y_multi_map[iiii]=np.array(batch_spk_multi_dict[iiii].values())
else:
for idx,sample in enumerate(batch_spk_multi_dict):
y_idx=sorted([dict_spk2idx[spk] for spk in sample.keys()])
if not test_mode:
assert y_idx==list(top_k_mask_idx[idx])
for jdx,oo in enumerate(y_idx):
y_multi_map[idx,jdx]=sample[dict_idx2spk[oo]]
y_multi_map= Variable(torch.from_numpy(y_multi_map)).cuda()
loss_multi_speech=loss_multi_func(predict_multi_map,y_multi_map)
#各通道和为１的loss部分,应该可以更多的带来差异
y_sum_map=Variable(torch.ones(config.BATCH_SIZE,mix_speech_len,speech_fre)).cuda()
predict_sum_map=torch.sum(multi_mask,1)
loss_multi_sum_speech=loss_multi_func(predict_sum_map,y_sum_map)
# loss_multi_speech=loss_multi_speech #todo:以后可以研究下这个和为１的效果对比一下，暂时直接MSE效果已经很不错了。
print 'loss 1 eval, losssum eval : ',loss_multi_speech.data.cpu().numpy(),loss_multi_sum_speech.data.cpu().numpy()
lrs.send('loss mask eval:',loss_multi_speech.data.cpu()[0])
lrs.send('loss sum eval:',loss_multi_sum_speech.data.cpu()[0])
loss_multi_speech=loss_multi_speech+0.5*loss_multi_sum_speech
print 'evaling multi-abs norm this eval batch:',torch.abs(y_multi_map-predict_multi_map).norm().data.cpu().numpy()
print 'loss:',loss_multi_speech.data.cpu().numpy()
bss_eval(predict_multi_map,y_multi_map,top_k_mask_idx,dict_idx2spk,eval_data)
return bss_test.cal('batch_output2/',2)
def eval(epoch, test_or_valid='valid'):
# config.batch_size=1
global updates, model
model.eval()
# print '\n\n测试的时候请设置config里的batch_size为1！！！please set the batch_size as 1'
reference, candidate, source, alignments = [], [], [], []
e = epoch
print(('Test or valid:', test_or_valid))
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
# for iii in range(2000):
while True:
print(('-' * 30))
eval_data = next(eval_data_gen)
if eval_data == False:
print(('SDR_aver_eval_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_eval_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_complex_two_channel'])
) # bs,T,F,2 both real and imag values
raw_tgt = eval_data['batch_order']
feas_tgt = models.rank_feas(
raw_tgt,
eval_data['multi_spk_wav_list']) # 这里是目标的图谱,bs*Topk,time_len
padded_mixture, mixture_lengths, padded_source = eval_data['tas_zip']
padded_mixture = torch.from_numpy(padded_mixture).float()
mixture_lengths = torch.from_numpy(mixture_lengths)
padded_source = torch.from_numpy(padded_source).float()
padded_mixture = padded_mixture.cuda().transpose(0, 1)
mixture_lengths = mixture_lengths.cuda()
padded_source = padded_source.cuda()
# 要保证底下这几个都是longTensor(长整数）
tgt = Variable(
torch.from_numpy(
np.array([[0, 1, 2, 102] for __ in range(config.batch_size)],
dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in eval_data['multi_spk_fea_list']
])).unsqueeze(0)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
if config.use_center_loss:
center_loss.zero_grad()
multi_mask_real, multi_mask_imag, enc_attn_list = model(
src, src_len, tgt, tgt_len,
dict_spk2idx) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
multi_mask_real = multi_mask_real.transpose(0, 1)
multi_mask_imag = multi_mask_imag.transpose(0, 1)
src_real = src[:, :, :, 0].transpose(0, 1) # bs,T,F
src_imag = src[:, :, :, 1].transpose(0, 1) # bs,T,F
print('mask size for real/imag:',
multi_mask_real.size()) # bs,topk,T,F, 已经压缩过了
print('mixture size for real/imag:', src_real.size()) # bs,T,F
predicted_maps0_real = multi_mask_real[:,
0] * src_real - multi_mask_imag[:,
0] * src_imag #bs,T,F
predicted_maps0_imag = multi_mask_real[:,
0] * src_imag + multi_mask_imag[:,
0] * src_real #bs,T,F
predicted_maps1_real = multi_mask_real[:,
1] * src_real - multi_mask_imag[:,
1] * src_imag #bs,T,F
predicted_maps1_imag = multi_mask_real[:,
1] * src_imag + multi_mask_imag[:,
1] * src_real #bs,T,F
stft_matrix_spk0 = torch.cat((predicted_maps0_real.unsqueeze(-1),
predicted_maps0_imag.unsqueeze(-1)),
3).transpose(1, 2) # bs,F,T,2
stft_matrix_spk1 = torch.cat((predicted_maps1_real.unsqueeze(-1),
predicted_maps1_imag.unsqueeze(-1)),
3).transpose(1, 2) # bs,F,T,2
wav_spk0 = models.istft_irfft(stft_matrix_spk0,
length=config.MAX_LEN,
hop_length=config.FRAME_SHIFT,
win_length=config.FRAME_LENGTH,
window='hann')
wav_spk1 = models.istft_irfft(stft_matrix_spk1,
length=config.MAX_LEN,
hop_length=config.FRAME_SHIFT,
win_length=config.FRAME_LENGTH,
window='hann')
predict_wav = torch.cat((wav_spk0.unsqueeze(1), wav_spk1.unsqueeze(1)),
1) # bs,topk,time_len
if 1 and len(opt.gpus) > 1:
ss_loss, pmt_list, max_snr_idx, *__ = model.module.separation_tas_loss(
padded_mixture, predict_wav, padded_source, mixture_lengths)
else:
ss_loss, pmt_list, max_snr_idx, *__ = model.separation_tas_loss(
padded_mixture, predict_wav, padded_source, mixture_lengths)
best_pmt = [
list(pmt_list[int(mm)].data.cpu().numpy()) for mm in max_snr_idx
]
print('loss for SS,this batch:', ss_loss.cpu().item())
print('best perms for this batch:', best_pmt)
writer.add_scalars('scalar/loss', {'ss_loss': ss_loss.cpu().item()},
updates)
lera.log({
'ss_loss_' + test_or_valid: ss_loss.cpu().item(),
})
writer.add_scalars('scalar/loss',
{'ss_loss_' + test_or_valid: ss_loss.cpu().item()},
updates + batch_idx)
del ss_loss
# if batch_idx>10:
# break
if False: #this part is to test the checkpoints sequencially.
batch_idx += 1
if batch_idx % 100 == 0:
updates = updates + 1000
opt.restore = '/data1/shijing_data/2020-02-14-04:58:17/Transformer_PIT_{}.pt'.format(
updates)
print('loading checkpoint...\n', opt.restore)
checkpoints = torch.load(opt.restore)
model.module.load_state_dict(checkpoints['model'])
break
continue
# '''''
if 1 and batch_idx <= (500 / config.batch_size):
utils.bss_eval_tas(config,
predict_wav,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst=log_path + 'batch_output')
sdr_aver_batch, snri_aver_batch = bss_test.cal(log_path +
'batch_output/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SI-SNRi sample': snri_aver_batch})
writer.add_scalars('scalar/loss', {
'SDR_sample': sdr_aver_batch,
'SDRi_sample': snri_aver_batch
}, updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, snri_aver_batch)
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SNRi_aver_now:', SDRi_SUM.mean()))
batch_idx += 1
if batch_idx > 100:
break
result = utils.eval_metrics(reference, candidate, dict_spk2idx,
log_path)
print((
'hamming_loss: %.8f | micro_f1: %.4f |recall: %.4f | precision: %.4f'
% (
result['hamming_loss'],
result['micro_f1'],
result['micro_recall'],
result['micro_precision'],
)))
def train(epoch):
global e, updates, total_loss, start_time, report_total, report_correct, total_loss_sgm, total_loss_ss
e = epoch
model.train()
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
if updates <= config.warmup: #如果不在warm阶段就正常规划
pass
elif config.schedule and scheduler.get_lr()[0] > 4e-5:
scheduler.step()
print(
("Decaying learning rate to %g" % scheduler.get_lr()[0], updates))
lera.log({
'lr': [group['lr'] for group in optim.optimizer.param_groups][0],
})
if opt.model == 'gated':
model.current_epoch = epoch
train_data_gen = prepare_data('once', 'train')
while True:
if updates <= config.warmup: # 如果在warm就开始warmup
tmp_lr = config.learning_rate * min(
max(updates, 1)**(-0.5),
max(updates, 1) * (config.warmup**(-1.5)))
for param_group in optim.optimizer.param_groups:
param_group['lr'] = tmp_lr
scheduler.base_lrs = list(
[group['lr'] for group in optim.optimizer.param_groups])
if updates % 100 == 0: #记录一下
print(updates)
print("Warmup learning rate to %g" % tmp_lr)
lera.log({
'lr':
[group['lr'] for group in optim.optimizer.param_groups][0],
})
train_data = next(train_data_gen)
if train_data == False:
print(('SDR_aver_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(train_data['mix_complex_two_channel'])
) # bs,T,F,2 both real and imag values
raw_tgt = train_data['batch_order']
feas_tgt = models.rank_feas(
raw_tgt,
train_data['multi_spk_wav_list']) # 这里是目标的图谱,bs*Topk,time_len
padded_mixture, mixture_lengths, padded_source = train_data['tas_zip']
padded_mixture = torch.from_numpy(padded_mixture).float()
mixture_lengths = torch.from_numpy(mixture_lengths)
padded_source = torch.from_numpy(padded_source).float()
padded_mixture = padded_mixture.cuda().transpose(0, 1)
mixture_lengths = mixture_lengths.cuda()
padded_source = padded_source.cuda()
# 要保证底下这几个都是longTensor(长整数）
tgt_max_len = config.MAX_MIX + 2 # with bos and eos.
tgt = Variable(
torch.from_numpy(
np.array(
[[0] + [dict_spk2idx[spk] for spk in spks] +
(tgt_max_len - len(spks) - 1) * [dict_spk2idx['<EOS>']]
for spks in raw_tgt],
dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
# tgt = Variable(torch.from_numpy(np.array([[0,1,2,102] for __ in range(config.batch_size)], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in train_data['multi_spk_fea_list']
])).unsqueeze(0)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
if config.use_center_loss:
center_loss.zero_grad()
multi_mask_real, multi_mask_imag, enc_attn_list = model(
src, src_len, tgt, tgt_len,
dict_spk2idx) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
multi_mask_real = multi_mask_real.transpose(0, 1)
multi_mask_imag = multi_mask_imag.transpose(0, 1)
src_real = src[:, :, :, 0].transpose(0, 1) # bs,T,F
src_imag = src[:, :, :, 1].transpose(0, 1) # bs,T,F
print('mask size for real/imag:',
multi_mask_real.size()) # bs,topk,T,F, 已经压缩过了
print('mixture size for real/imag:', src_real.size()) # bs,T,F
predicted_maps0_real = multi_mask_real[:,
0] * src_real - multi_mask_imag[:,
0] * src_imag #bs,T,F
predicted_maps0_imag = multi_mask_real[:,
0] * src_imag + multi_mask_imag[:,
0] * src_real #bs,T,F
predicted_maps1_real = multi_mask_real[:,
1] * src_real - multi_mask_imag[:,
1] * src_imag #bs,T,F
predicted_maps1_imag = multi_mask_real[:,
1] * src_imag + multi_mask_imag[:,
1] * src_real #bs,T,F
stft_matrix_spk0 = torch.cat((predicted_maps0_real.unsqueeze(-1),
predicted_maps0_imag.unsqueeze(-1)),
3).transpose(1, 2) # bs,F,T,2
stft_matrix_spk1 = torch.cat((predicted_maps1_real.unsqueeze(-1),
predicted_maps1_imag.unsqueeze(-1)),
3).transpose(1, 2) # bs,F,T,2
wav_spk0 = models.istft_irfft(stft_matrix_spk0,
length=config.MAX_LEN,
hop_length=config.FRAME_SHIFT,
win_length=config.FRAME_LENGTH,
window='hann')
wav_spk1 = models.istft_irfft(stft_matrix_spk1,
length=config.MAX_LEN,
hop_length=config.FRAME_SHIFT,
win_length=config.FRAME_LENGTH,
window='hann')
predict_wav = torch.cat((wav_spk0.unsqueeze(1), wav_spk1.unsqueeze(1)),
1) # bs,topk,time_len
if 1 and len(opt.gpus) > 1:
ss_loss, pmt_list, max_snr_idx, *__ = model.module.separation_tas_loss(
padded_mixture, predict_wav, padded_source, mixture_lengths)
else:
ss_loss, pmt_list, max_snr_idx, *__ = model.separation_tas_loss(
padded_mixture, predict_wav, padded_source, mixture_lengths)
best_pmt = [
list(pmt_list[int(mm)].data.cpu().numpy()) for mm in max_snr_idx
]
print('loss for SS,this batch:', ss_loss.cpu().item())
print('best perms for this batch:', best_pmt)
writer.add_scalars('scalar/loss', {'ss_loss': ss_loss.cpu().item()},
updates)
loss = ss_loss
loss.backward()
total_loss_ss += ss_loss.cpu().item()
lera.log({
'ss_loss': ss_loss.cpu().item(),
})
if epoch > 20 and updates > 5 and updates % config.eval_interval in [
0, 1, 2, 3, 4
]:
utils.bss_eval_tas(config,
predict_wav,
train_data['multi_spk_fea_list'],
raw_tgt,
train_data,
dst=log_path + 'batch_output')
sdr_aver_batch, snri_aver_batch = bss_test.cal(log_path +
'batch_output/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SI-SNRi sample': snri_aver_batch})
writer.add_scalars('scalar/loss', {
'SDR_sample': sdr_aver_batch,
'SDRi_sample': snri_aver_batch
}, updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, snri_aver_batch)
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SNRi_aver_now:', SDRi_SUM.mean()))
# Heatmap here
# n_layer个 (head*bs) x lq x dk
'''
import matplotlib.pyplot as plt
ax = plt.gca()
ax.invert_yaxis()
raw_src=models.rank_feas(raw_tgt, train_data['multi_spk_fea_list'])
att_idx=1
att = enc_attn_list[-1].view(config.trans_n_head,config.batch_size,mix_speech_len,mix_speech_len).data.cpu().numpy()[:,att_idx]
for head in range(config.trans_n_head):
xx=att[head]
plt.matshow(xx, cmap=plt.cm.hot, vmin=0,vmax=0.05)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'head_{}.png'.format(head))
plt.matshow(raw_src[att_idx*2+0].transpose(0,1), cmap=plt.cm.hot, vmin=0,vmax=2)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'source0.png')
plt.matshow(raw_src[att_idx*2+1].transpose(0,1), cmap=plt.cm.hot, vmin=0,vmax=2)
plt.colorbar()
plt.savefig(log_path+'batch_output/'+'source1.png')
1/0
'''
total_loss += loss.cpu().item()
optim.step()
updates += 1
if updates % 30 == 0:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss this batch: %6.3f,ss loss: %6.6f\n"
% (time.time() - start_time, epoch, updates, loss,
total_loss_ss / 30.0))
total_loss_sgm, total_loss_ss = 0, 0
# continue
if 1 and updates % config.save_interval == 1:
save_model(log_path + 'Transformer_PIT_2ch_{}.pt'.format(updates))
if 0 and updates > 0 and updates % config.eval_interval == 3: #建议至少跑几个epoch再进行测试，否则模型还没学到东西，会有很多问题。
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss: %6.5f\n" %
(time.time() - start_time, epoch, updates,
total_loss / config.eval_interval))
print(('evaluating after %d updates...\r' % updates))
eval(epoch, 'valid') # eval的时候batch_size会变成1
eval(epoch, 'test') # eval的时候batch_size会变成1
model.train()
total_loss = 0
start_time = 0
report_total = 0
report_correct = 0
def eval(epoch):
model.eval()
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'valid'
print 'Test or valid:', test_or_valid
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
# for raw_src, src, src_len, raw_tgt, tgt, tgt_len in validloader:
SDR_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
while True:
# for ___ in range(2):
print '-' * 30
eval_data = eval_data_gen.next()
if eval_data == False:
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
raw_tgt = [
sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']
]
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.ones(
top_k + 2, config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
feas_tgt = models.rank_feas(raw_tgt,
eval_data['multi_spk_fea_list']) #这里是目标的图谱
if config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 4
feas_tgt_sum = torch.sum(feas_tgt, dim=1, keepdim=True)
feas_tgt_sum_square = (feas_tgt_sum *
feas_tgt_sum).expand(tmp_size)
feas_tgt_square = feas_tgt * feas_tgt
WFM_mask = feas_tgt_square / feas_tgt_sum_square
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
if config.buffer_size or config.buffer_shift: # first convet to realtime batches
assert src.size()[1] == 1
left_padding = Variable(
torch.zeros(config.buffer_size,
src.size()[1],
src.size()[-1]).cuda())
src = torch.cat((left_padding, src), dim=0)
split_idx = 0
src_new = Variable(
torch.zeros(config.buffer_size + config.buffer_shift,
mix_speech_len / config.buffer_shift + 1,
src.size()[-1]).cuda())
batch_counter = 0
while True:
print 'split_idx at:', split_idx
split_len = config.buffer_size + config.buffer_shift # the len of every split
if split_idx + split_len > src.size(
)[0]: # if pass the right length
print 'Need to add right padding with len:', (
split_idx + split_len) - src.size()[0]
right_padding = Variable(
torch.zeros((split_idx + split_len) - src.size()[0],
src.size()[1],
src.size()[-1]).cuda())
src = torch.cat((src, right_padding), dim=0)
src_split = src[split_idx:(split_idx + split_len)]
src_new[:, batch_counter] = src_split
break
src_split = src[split_idx:(split_idx + split_len)]
src_new[:, batch_counter] = src_split
split_idx += config.buffer_shift
batch_counter += 1
assert batch_counter + 1 == src_new.size()[1]
src_len[0] = config.buffer_shift + config.buffer_size
src_len = src_len.expand(1, src_new.size()[1])
try:
if 1 and len(opt.gpus) > 1:
# samples, alignment = model.module.sample(src, src_len)
samples, alignment, hiddens, predicted_masks = model.module.beam_sample(
src_new,
src_len,
dict_spk2idx,
tgt,
beam_size=config.beam_size)
else:
samples, alignment, hiddens, predicted_masks = model.beam_sample(
src_new,
src_len,
dict_spk2idx,
tgt,
beam_size=config.beam_size)
# samples, alignment, hiddens, predicted_masks = model.beam_sample(src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
except Exception, info:
print '**************Error occurs here************:', info
continue
if config.top1:
predicted_masks = torch.cat([predicted_masks, 1 - predicted_masks],
1)
if config.buffer_size and config.buffer_shift: # then recover the whole maps
# masks:[7,topk,buffer_size+buffer_shift,fre]
masks_recover = Variable(
torch.zeros(1, predicted_masks.size(1), mix_speech_len,
speech_fre).cuda())
recover_idx = 0
for batch_counter in range(predicted_masks.size(0)):
if not batch_counter == predicted_masks.size(0) - 1:
masks_recover[:, :, recover_idx:recover_idx +
config.buffer_shift] = predicted_masks[
batch_counter, :,
-1 * config.buffer_shift:]
else: # the last shift
assert mix_speech_len - recover_idx == config.buffer_shift - right_padding.size(
0)
masks_recover[:, :, recover_idx:] = predicted_masks[
batch_counter, :,
-1 * config.buffer_shift:(-1 * right_padding.size(0))]
recover_idx += config.buffer_shift
predicted_masks = masks_recover
src = Variable(torch.from_numpy(eval_data['mix_feas'])).transpose(
0, 1).cuda()
# '''
# expand the raw mixed-features to topk channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
topk = feas_tgt.size()[1]
x_input_map_multi = torch.unsqueeze(src,
1).expand(siz[0], topk, siz[1],
siz[2])
if config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
'''
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(x_input_map_multi, predicted_masks, feas_tgt,Var)
else:
ss_loss = model.separation_loss(x_input_map_multi, predicted_masks, feas_tgt,None)
print 'loss for ss,this batch:',ss_loss.data[0]
lera.log({
'ss_loss_'+test_or_valid: ss_loss.data[0],
})
del ss_loss,hiddens
# ''' ''
if batch_idx <= (500 / config.batch_size
): #only the former batches counts the SDR
# x_input_map_multi=x_input_map_multi[:,:,:config.buffer_shift]
predicted_maps = predicted_masks * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_outputwaddd')
del predicted_maps, predicted_masks, x_input_map_multi
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_outputwaddd/'))
print 'SDR_aver_now:', SDR_SUM.mean()
lera.log({'SDR sample': SDR_SUM.mean()})
# raw_input('Press any key to continue......')
elif batch_idx == (500 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: #only record the best SDR once.
print 'Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean())
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
# '''
candidate += [
convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>'])
for s in samples
]
# source += raw_src
reference += raw_tgt
print 'samples:', samples
print 'can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:],
reference[-1 * config.batch_size:])
alignments += [align for align in alignment]
batch_idx += 1
def eval(epoch, test_or_valid='train'):
# config.batch_size=1
global updates, model
model.eval()
# print '\n\n测试的时候请设置config里的batch_size为1！！！please set the batch_size as 1'
reference, candidate, source, alignments = [], [], [], []
e = epoch
print(('Test or valid:', test_or_valid))
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
# for iii in range(2000):
while True:
print(('-' * 30))
eval_data = next(eval_data_gen)
if eval_data == False:
print(('SDR_aver_eval_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_eval_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
# raw_tgt = [spk.keys() for spk in eval_data['multi_spk_fea_list']]
# raw_tgt = [sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']]
raw_tgt = eval_data['batch_order']
feas_tgt = models.rank_feas(
raw_tgt,
eval_data['multi_spk_wav_list']) # 这里是目标的图谱,bs*Topk,time_len
padded_mixture, mixture_lengths, padded_source = eval_data['tas_zip']
padded_mixture = torch.from_numpy(padded_mixture).float()
mixture_lengths = torch.from_numpy(mixture_lengths)
padded_source = torch.from_numpy(padded_source).float()
padded_mixture = padded_mixture.cuda().transpose(0, 1)
mixture_lengths = mixture_lengths.cuda()
padded_source = padded_source.cuda()
# 要保证底下这几个都是longTensor(长整数）
tgt_max_len = config.MAX_MIX + 2 # with bos and eos.
# tgt = Variable(torch.from_numpy(np.array(
# [[0] + [dict_spk2idx[spk] for spk in spks] + (tgt_max_len - len(spks) - 1) * [dict_spk2idx['<EOS>']] for
# spks in raw_tgt], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
tgt = Variable(
torch.from_numpy(
np.array([[0, 1, 2, 102] for __ in range(config.batch_size)],
dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in eval_data['multi_spk_fea_list']
])).unsqueeze(0)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
if config.use_center_loss:
center_loss.zero_grad()
multi_mask, enc_attn_list = model(
src, src_len, tgt, tgt_len,
dict_spk2idx) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
multi_mask = multi_mask.transpose(0, 1)
print('mask size:', multi_mask.size()) # bs,topk,T,F
predicted_maps0_spectrogram = multi_mask[:, 0] * src.transpose(
0, 1) #bs,T,F
predicted_maps1_spectrogram = multi_mask[:, 1] * src.transpose(
0, 1) #bs,T,F
if True: # Analyze the optimal assignments
predicted_spectrogram = torch.cat([
predicted_maps0_spectrogram.unsqueeze(1),
predicted_maps1_spectrogram.unsqueeze(1)
], 1)
feas_tgt_tmp = models.rank_feas(
raw_tgt,
eval_data['multi_spk_fea_list']) # 这里是目标的图谱,bs*Topk,len,fre
src = src.transpose(0, 1)
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1], siz[2]).contiguous() # bs,topk,T,F
feas_tgt_tmp = feas_tgt_tmp.view(siz[0], -1, siz[1], siz[2])
angle_tgt = models.rank_feas(
raw_tgt, eval_data['multi_spk_angle_list']).view(
siz[0], -1, siz[1], siz[2]) # bs,topk,T,F
angle_mix = Variable(
torch.from_numpy(np.array(
eval_data['mix_angle']))).unsqueeze(1).expand(
siz[0], topk_max, siz[1], siz[2]).contiguous()
ang = np.cos(angle_mix - angle_tgt)
ang = np.clip(ang, 0, None)
feas_tgt_tmp = feas_tgt_tmp.view(siz[0], -1, siz[1],
siz[2]) * ang # bs,topk,T,F
feas_tgt_tmp = feas_tgt_tmp.cuda()
del x_input_map_multi
src = src.transpose(0, 1)
MSE_func = nn.MSELoss().cuda()
best_perms_this_batch = []
for bs_idx in range(siz[0]):
best_perms_this_sample = []
for tt in range(siz[1]): # 对每一帧
tar = feas_tgt_tmp[bs_idx, :, tt] #topk,F
est = predicted_spectrogram[bs_idx, :, tt] #topk,F
best_loss_mse_this_batch = -1
for idx, per in enumerate([[0, 1], [1, 0]]):
if idx == 0:
best_loss_mse_this_batch = MSE_func(est[per], tar)
perm_this_frame = per
predicted_spectrogram[bs_idx, :, tt] = est[per]
else:
loss = MSE_func(est[per], tar)
if loss <= best_loss_mse_this_batch:
best_loss_mse_this_batch = loss
perm_this_frame = per
predicted_spectrogram[bs_idx, :, tt] = est[per]
best_perms_this_sample.append(perm_this_frame)
best_perms_this_batch.append(best_perms_this_sample)
print(
'different assignment ratio:',
np.mean(np.min(
np.array(best_perms_this_batch).sum(1) / 751, 1)))
# predicted_maps0_spectrogram = predicted_spectrogram[:,0]
# predicted_maps1_spectrogram = predicted_spectrogram[:,1]
_mix_spec = eval_data['mix_phase'] # bs,T,F,2
angle_mix = np.angle(_mix_spec)
predicted_maps0_real = predicted_maps0_spectrogram * torch.from_numpy(
np.cos(angle_mix)).cuda() # e(ix) = cosx + isin x
predicted_maps0_imag = predicted_maps0_spectrogram * torch.from_numpy(
np.sin(angle_mix)).cuda() # e(ix) = cosx + isin x
predicted_maps1_real = predicted_maps1_spectrogram * torch.from_numpy(
np.cos(angle_mix)).cuda() # e(ix) = cosx + isin x
predicted_maps1_imag = predicted_maps1_spectrogram * torch.from_numpy(
np.sin(angle_mix)).cuda() # e(ix) = cosx + isin x
stft_matrix_spk0 = torch.cat((predicted_maps0_real.unsqueeze(-1),
predicted_maps0_imag.unsqueeze(-1)),
3).transpose(1, 2) # bs,F,T,2
stft_matrix_spk1 = torch.cat((predicted_maps1_real.unsqueeze(-1),
predicted_maps1_imag.unsqueeze(-1)),
3).transpose(1, 2) # bs,F,T,2
wav_spk0 = models.istft_irfft(stft_matrix_spk0,
length=config.MAX_LEN,
hop_length=config.FRAME_SHIFT,
win_length=config.FRAME_LENGTH,
window='hann')
wav_spk1 = models.istft_irfft(stft_matrix_spk1,
length=config.MAX_LEN,
hop_length=config.FRAME_SHIFT,
win_length=config.FRAME_LENGTH,
window='hann')
predict_wav = torch.cat((wav_spk0.unsqueeze(1), wav_spk1.unsqueeze(1)),
1) # bs,topk,time_len
if 1 and len(opt.gpus) > 1:
ss_loss, pmt_list, max_snr_idx, *__ = model.module.separation_tas_loss(
padded_mixture, predict_wav, padded_source, mixture_lengths)
else:
ss_loss, pmt_list, max_snr_idx, *__ = model.separation_tas_loss(
padded_mixture, predict_wav, padded_source, mixture_lengths)
best_pmt = [
list(pmt_list[int(mm)].data.cpu().numpy()) for mm in max_snr_idx
]
print('loss for SS,this batch:', ss_loss.cpu().item())
print('best perms for this batch:', best_pmt)
writer.add_scalars('scalar/loss', {'ss_loss': ss_loss.cpu().item()},
updates)
lera.log({
'ss_loss_' + test_or_valid: ss_loss.cpu().item(),
})
writer.add_scalars('scalar/loss',
{'ss_loss_' + test_or_valid: ss_loss.cpu().item()},
updates + batch_idx)
del ss_loss
# if batch_idx>10:
# break
if False: #this part is to test the checkpoints sequencially.
batch_idx += 1
if batch_idx % 100 == 0:
updates = updates + 1000
opt.restore = '/data1/shijing_data/2020-02-14-04:58:17/Transformer_PIT_{}.pt'.format(
updates)
print('loading checkpoint...\n', opt.restore)
checkpoints = torch.load(opt.restore)
model.module.load_state_dict(checkpoints['model'])
break
continue
# '''''
if 1 and batch_idx <= (500 / config.batch_size):
utils.bss_eval_tas(config,
predict_wav,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst=log_path + 'batch_output')
sdr_aver_batch, snri_aver_batch = bss_test.cal(log_path +
'batch_output/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SI-SNRi sample': snri_aver_batch})
writer.add_scalars('scalar/loss', {
'SDR_sample': sdr_aver_batch,
'SDRi_sample': snri_aver_batch
}, updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, snri_aver_batch)
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SNRi_aver_now:', SDRi_SUM.mean()))
batch_idx += 1
if batch_idx > 100:
break
result = utils.eval_metrics(reference, candidate, dict_spk2idx,
log_path)
print((
'hamming_loss: %.8f | micro_f1: %.4f |recall: %.4f | precision: %.4f'
% (
result['hamming_loss'],
result['micro_f1'],
result['micro_recall'],
result['micro_precision'],
)))
def main():
print('go to model')
print '*' * 80
spk_global_gen = prepare_data(mode='global',
train_or_test='train') #写一个假的数据生成，可以用来写模型先
global_para = spk_global_gen.next()
print global_para
spk_all_list, dict_spk2idx, dict_idx2spk, mix_speech_len, speech_fre, total_frames, spk_num_total = global_para
del spk_global_gen
num_labels = len(spk_all_list)
# data_generator=prepare_data('once','train')
# data_generator=prepare_data_fake(train_or_test='train',num_labels=num_labels) #写一个假的数据生成，可以用来写模型先
#此处顺序是 mix_speechs.shape,mix_feas.shape,aim_fea.shape,aim_spkid.shape,query.shape
#一个例子：(5, 17040) (5, 134, 129) (5, 134, 129) (5,) (5, 32, 400, 300, 3)
# datasize=prepare_datasize(data_generator)
# mix_speech_len,speech_fre,total_frames,spk_num_total,video_size=datasize
print 'Begin to build the maim model for Multi_Modal Cocktail Problem.'
# data=data_generator.next()
# This part is to build the 3D mix speech embedding maps.
mix_hidden_layer_3d = MIX_SPEECH(speech_fre, mix_speech_len).cuda()
mix_speech_classifier = MIX_SPEECH_classifier(speech_fre, mix_speech_len,
num_labels).cuda()
mix_speech_multiEmbedding = SPEECH_EMBEDDING(
num_labels, config.EMBEDDING_SIZE,
spk_num_total + config.UNK_SPK_SUPP).cuda()
print mix_hidden_layer_3d
print mix_speech_classifier
# mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(data[1])).cuda())
# mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(data[1])).cuda())
# 技巧：alpha0的时候，就是选出top_k，top_k很大的时候，就是选出来大于alpha的
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=config.MAX_MIX)
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=3)
# print top_k_mask_mixspeech
# mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech) # bs*num_labels（最多混合人个数）×Embedding的大小
# mix_speech_multiEmbs=mix_speech_multiEmbedding(Variable(torch.from_numpy(top_k_mask_mixspeech),requires_grad=False).cuda()) # bs*num_labels（最多混合人个数）×Embedding的大小
# 需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
# 把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
# mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
# mix_speech_hidden_5d=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
# att_multi_speech=att_speech_layer(mix_speech_hidden_5d,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
# att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,-1)
# print att_multi_speech.size()
# This part is to conduct the video inputs.
# query_video_layer=VIDEO_QUERY(total_frames,config.VideoSize,spk_num_total).cuda()
query_video_layer = None
# print query_video_layer
# query_video_output,xx=query_video_layer(Variable(torch.from_numpy(data[4])))
# This part is to conduct the memory.
# hidden_size=(config.HIDDEN_UNITS)
hidden_size = (config.EMBEDDING_SIZE)
memory = MEMORY(spk_num_total + config.UNK_SPK_SUPP, hidden_size)
memory.register_spklist(spk_all_list) #把spk_list注册进空的memory里面去
# Memory function test.
print 'memory all spkid:', memory.get_all_spkid()
# print memory.get_image_num('Unknown_id')
# print memory.get_video_vector('Unknown_id')
# print memory.add_video('Unknown_id',Variable(torch.ones(300)))
# This part is to test the ATTENTION methond from query(~) to mix_speech
# x=torch.arange(0,24).view(2,3,4)
# y=torch.ones([2,4])
att_layer = ATTENTION(config.EMBEDDING_SIZE, 'align').cuda()
att_speech_layer = ATTENTION(config.EMBEDDING_SIZE, 'align').cuda()
# att=ATTENTION(4,'align')
# mask=att(x,y)#bs*max_len
# del data_generator
# del data
optimizer = torch.optim.Adam(
[
{
'params': mix_hidden_layer_3d.parameters()
},
{
'params': mix_speech_multiEmbedding.parameters()
},
{
'params': mix_speech_classifier.parameters()
},
# {'params':query_video_layer.lstm_layer.parameters()},
# {'params':query_video_layer.dense.parameters()},
# {'params':query_video_layer.Linear.parameters()},
{
'params': att_layer.parameters()
},
{
'params': att_speech_layer.parameters()
},
# ], lr=0.02,momentum=0.9)
],
lr=0.0002)
if 0 and config.Load_param:
# query_video_layer.load_state_dict(torch.load('param_video_layer_19'))
mix_speech_classifier.load_state_dict(
torch.load('params/param_speech_multilabel_epoch249'))
mix_hidden_layer_3d.load_state_dict(
torch.load('params/param_mix_speech_hidden3d_220'))
mix_speech_multiEmbedding.load_state_dict(
torch.load('params/param_mix_speech_emblayer_220'))
att_speech_layer.load_state_dict(
torch.load('params/param_mix_speech_attlayer_220'))
loss_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
loss_multi_func = torch.nn.MSELoss(
) # the target label is NOT an one-hotted
# loss_multi_func = torch.nn.L1Loss() # the target label is NOT an one-hotted
loss_query_class = torch.nn.CrossEntropyLoss()
print '''Begin to calculate.'''
for epoch_idx in range(config.MAX_EPOCH):
if epoch_idx > 0:
print 'SDR_SUM (len:{}) for epoch {} : '.format(
SDR_SUM.shape, epoch_idx - 1, SDR_SUM.mean())
SDR_SUM = np.array([])
# print_memory_state(memory.memory)
print 'SDR_SUM for epoch {}:{}'.format(epoch_idx - 1, SDR_SUM.mean())
for batch_idx in range(config.EPOCH_SIZE):
print '*' * 40, epoch_idx, batch_idx, '*' * 40
train_data_gen = prepare_data('once', 'train')
# train_data_gen=prepare_data('once','test')
train_data = train_data_gen.next()
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden = mix_hidden_layer_3d(
Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
mix_speech_output = mix_speech_classifier(
Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
#从数据里得到ground truth的说话人名字和vector
y_spk_list = [
one.keys() for one in train_data['multi_spk_fea_list']
]
y_spk_gtruth, y_map_gtruth = multi_label_vector(
y_spk_list, dict_spk2idx)
# 如果训练阶段使用Ground truth的分离结果作为判别
if config.Ground_truth:
mix_speech_output = Variable(
torch.from_numpy(y_map_gtruth)).cuda()
if test_all_outputchannel: #把输入的mask改成全１，可以用来测试输出所有的channel
mix_speech_output = Variable(
torch.ones(
config.BATCH_SIZE,
num_labels,
))
y_map_gtruth = np.ones([config.BATCH_SIZE, num_labels])
top_k_mask_mixspeech = top_k_mask(mix_speech_output,
alpha=0.5,
top_k=num_labels) #torch.Float型的
mix_speech_multiEmbs = mix_speech_multiEmbedding(
top_k_mask_mixspeech) # bs*num_labels（最多混合人个数）×Embedding的大小
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d = mix_speech_hidden.view(
config.BATCH_SIZE, 1, mix_speech_len, speech_fre,
config.EMBEDDING_SIZE)
mix_speech_hidden_5d = mix_speech_hidden_5d.expand(
config.BATCH_SIZE, num_labels, mix_speech_len, speech_fre,
config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last = mix_speech_hidden_5d.view(
-1, mix_speech_len, speech_fre, config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer = ATTENTION(config.EMBEDDING_SIZE, 'dot').cuda()
att_multi_speech = att_speech_layer(
mix_speech_hidden_5d_last,
mix_speech_multiEmbs.view(-1, config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech = att_multi_speech.view(
config.BATCH_SIZE, num_labels, mix_speech_len,
speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask = att_multi_speech
top_k_mask_mixspeech_multi = top_k_mask_mixspeech.view(
config.BATCH_SIZE, num_labels, 1,
1).expand(config.BATCH_SIZE, num_labels, mix_speech_len,
speech_fre)
multi_mask = multi_mask * Variable(
top_k_mask_mixspeech_multi).cuda()
x_input_map = Variable(torch.from_numpy(
train_data['mix_feas'])).cuda()
# print x_input_map.size()
x_input_map_multi = x_input_map.view(
config.BATCH_SIZE, 1, mix_speech_len,
speech_fre).expand(config.BATCH_SIZE, num_labels,
mix_speech_len, speech_fre)
predict_multi_map = multi_mask * x_input_map_multi
if batch_idx % 100 == 0:
print multi_mask
# print predict_multi_map
y_multi_map = np.zeros(
[config.BATCH_SIZE, num_labels, mix_speech_len, speech_fre],
dtype=np.float32)
batch_spk_multi_dict = train_data['multi_spk_fea_list']
for idx, sample in enumerate(batch_spk_multi_dict):
for spk in sample.keys():
y_multi_map[idx, dict_spk2idx[spk]] = sample[spk]
y_multi_map = Variable(torch.from_numpy(y_multi_map)).cuda()
loss_multi_speech = loss_multi_func(predict_multi_map, y_multi_map)
#各通道和为１的loss部分,应该可以更多的带来差异
y_sum_map = Variable(
torch.ones(config.BATCH_SIZE, mix_speech_len,
speech_fre)).cuda()
predict_sum_map = torch.sum(predict_multi_map, 1)
loss_multi_sum_speech = loss_multi_func(predict_sum_map, y_sum_map)
loss_multi_speech = loss_multi_speech #todo:以后可以研究下这个和为１的效果对比一下，暂时直接MSE效果已经很不错了。
print 'loss 1, losssum : ', loss_multi_speech, loss_multi_sum_speech
# loss_multi_speech=loss_multi_speech+0.5*loss_multi_sum_speech
if 1 or batch_idx == config.EPOCH_SIZE - 1:
bss_eval(predict_multi_map, y_multi_map, y_map_gtruth,
dict_idx2spk, train_data)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output/', 2))
print 'training multi-abs norm this batch:', torch.abs(
y_multi_map - predict_multi_map).norm().data.cpu().numpy()
print 'loss:', loss_multi_speech.data.cpu().numpy()
optimizer.zero_grad() # clear gradients for next train
loss_multi_speech.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if 1 and epoch_idx > 20 and epoch_idx % 10 == 0 and batch_idx == config.EPOCH_SIZE - 1:
torch.save(
mix_speech_multiEmbedding.state_dict(),
'params/param_mix_{}_emblayer_{}'.format(
config.DATASET, epoch_idx))
torch.save(
mix_hidden_layer_3d.state_dict(),
'params/param_mix_{}_hidden3d_{}'.format(
config.DATASET, epoch_idx))
torch.save(
att_speech_layer.state_dict(),
'params/param_mix_{}_attlayer_{}'.format(
config.DATASET, epoch_idx))
# print 'Parameter history:'
# for pa_gen in [{'params':mix_hidden_layer_3d.parameters()},
# {'params':mix_speech_multiEmbedding.parameters()},
# {'params':mix_hidden_layer_3d.parameters()},
# {'params':att_speech_layer.parameters()},
# {'params':att_layer.parameters()},
# {'params':mix_speech_classifier.parameters()},
# ]:
# print pa_gen['params'].next().data.cpu().numpy()[0]
# continue
1 / 0
'''视频刺激 Sepration 部分'''
# try:
# query_video_output,query_video_hidden=query_video_layer(Variable(torch.from_numpy(train_data[4])).cuda())
# except RuntimeError:
# print 'RuntimeError here.'+'#'*30
# continue
query_video_output, query_video_hidden = query_video_layer(
Variable(torch.from_numpy(train_data[4])).cuda())
if config.Comm_with_Memory:
#TODO:query更新这里要再检查一遍，最好改成函数，现在有点丑陋。
aim_idx_FromVideoQuery = torch.max(query_video_output,
dim=1)[1] #返回最大的参数
aim_spk_batch = [
dict_idx2spk[int(idx.data.cpu().numpy())]
for idx in aim_idx_FromVideoQuery
]
print 'Query class result:', aim_spk_batch, 'p:', query_video_output.data.cpu(
).numpy()
for idx, aim_spk in enumerate(aim_spk_batch):
batch_vector = torch.stack(
[memory.get_video_vector(aim_spk)])
memory.add_video(aim_spk, query_video_hidden[idx])
query_video_hidden = query_video_hidden + Variable(
batch_vector)
query_video_hidden = query_video_hidden / torch.sum(
query_video_hidden * query_video_hidden, 0)
y_class = Variable(torch.from_numpy(
np.array([
dict_spk2idx[spk] for spk in train_data['aim_spkname']
])),
requires_grad=False).cuda()
print y_class
loss_video_class = loss_query_class(query_video_output,
y_class)
mask = att_layer(mix_speech_hidden,
query_video_hidden) #bs*max_len*fre
predict_map = mask * Variable(
torch.from_numpy(train_data['mix_feas'])).cuda()
y_map = Variable(torch.from_numpy(train_data['aim_fea'])).cuda()
print 'training abs norm this batch:', torch.abs(
y_map - predict_map).norm().data.cpu().numpy()
loss_all = loss_func(predict_map, y_map)
if 0 and config.Save_param:
torch.save(query_video_layer.state_dict(),
'param_video_layer_19_forS1S5')
if 0 and epoch_idx < 20:
loss = loss_video_class
if epoch_idx % 1 == 0 and batch_idx == config.EPOCH_SIZE - 1:
torch.save(query_video_layer.state_dict(),
'param_video_layer_19_forS1S5')
else:
# loss=loss_all+0.1*loss_video_class
loss = loss_all
optimizer.zero_grad() # clear gradients for next train
loss.backward(
retain_graph=True) # backpropagation, compute gradients
optimizer.step() # apply gradients
def train(epoch):
e = epoch
model.train()
SDR_SUM = np.array([])
if config.schedule:
scheduler.step()
print("Decaying learning rate to %g" % scheduler.get_lr()[0])
if config.is_dis:
scheduler_dis.step()
lera.log({
'lr': scheduler.get_lr()[0],
})
if opt.model == 'gated':
model.current_epoch = epoch
global e, updates, total_loss, start_time, report_total, total_loss_sgm, total_loss_ss
if config.MLMSE:
global Var
train_data_gen = prepare_data('once', 'train')
# for raw_src, src, src_len, raw_tgt, tgt, tgt_len in trainloader:
while True:
try:
train_data = train_data_gen.next()
if train_data == False:
print 'SDR_aver_epoch:', SDR_SUM.mean()
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(train_data['mix_feas']))
# raw_tgt = [spk.keys() for spk in train_data['multi_spk_fea_list']]
raw_tgt = [
sorted(spk.keys()) for spk in train_data['multi_spk_fea_list']
]
feas_tgt = models.rank_feas(
raw_tgt,
train_data['multi_spk_fea_list']) #这里是目标的图谱,aim_size,len,fre
# 要保证底下这几个都是longTensor(长整数）
tgt_max_len = config.MAX_MIX + 2 # with bos and eos.
tgt = Variable(
torch.from_numpy(
np.array([[0] + [dict_spk2idx[spk] for spk in spks] +
(tgt_max_len - len(spks) - 1) *
[dict_spk2idx['<EOS>']] for spks in raw_tgt],
dtype=np.int))).transpose(0,
1) #转换成数字，然后前后加开始和结束符号。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk)
for one_spk in train_data['multi_spk_fea_list']
])).unsqueeze(0)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
# optim.optimizer.zero_grad()
# aim_list 就是找到有正经说话人的地方的标号
aim_list = (tgt[1:-1].transpose(0, 1).contiguous().view(-1) !=
dict_spk2idx['<EOS>']).nonzero().squeeze()
aim_list = aim_list.data.cpu().numpy()
outputs, targets, multi_mask = model(
src, src_len, tgt, tgt_len,
dict_spk2idx) #这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
print 'mask size:', multi_mask.size()
if 1 and len(opt.gpus) > 1:
sgm_loss, num_total, num_correct = model.module.compute_loss(
outputs, targets, opt.memory)
else:
sgm_loss, num_total, num_correct = model.compute_loss(
outputs, targets, opt.memory)
print 'loss for SGM,this batch:', sgm_loss.data[0] / num_total
src = src.transpose(0, 1)
# expand the raw mixed-features to topk_max channel.
siz = src.size()
assert len(siz) == 3
topk_max = config.MAX_MIX #最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1],
siz[2]).contiguous().view(-1, siz[1], siz[2])
x_input_map_multi = x_input_map_multi[aim_list]
multi_mask = multi_mask.transpose(0, 1)
if 1 and len(opt.gpus) > 1:
if config.MLMSE:
Var = model.module.update_var(x_input_map_multi,
multi_mask, feas_tgt)
lera.log_image(u'Var weight',
Var.data.cpu().numpy().reshape(
config.speech_fre, config.speech_fre,
1).repeat(3, 2),
clip=(-1, 1))
ss_loss = model.module.separation_loss(
x_input_map_multi, multi_mask, feas_tgt, Var)
else:
ss_loss = model.module.separation_loss(
x_input_map_multi, multi_mask, feas_tgt)
else:
ss_loss = model.separation_loss(x_input_map_multi, multi_mask,
feas_tgt)
loss = sgm_loss + 5 * ss_loss
# dis_loss model
if config.is_dis:
dis_loss = models.loss.dis_loss(config, topk_max, model_dis,
x_input_map_multi, multi_mask,
feas_tgt, func_dis)
loss = loss + dis_loss
# print 'dis_para',model_dis.parameters().next()[0]
# print 'ss_para',model.parameters().next()[0]
loss.backward()
# print 'totallllllllllll loss:',loss
total_loss_sgm += sgm_loss.data[0]
total_loss_ss += ss_loss.data[0]
lera.log({
'sgm_loss': sgm_loss.data[0],
'ss_loss': ss_loss.data[0],
'loss:': loss.data[0],
})
if (updates % config.eval_interval) in [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
]:
predicted_maps = multi_mask * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval(config,
predicted_maps,
train_data['multi_spk_fea_list'],
raw_tgt,
train_data,
dst='batch_outputjaa')
del predicted_maps, multi_mask, x_input_map_multi
# raw_input('wait to continue......')
sdr_aver_batch = bss_test.cal('batch_outputjaa/')
lera.log({'SDR sample': sdr_aver_batch})
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
print 'SDR_aver_now:', SDR_SUM.mean()
total_loss += loss.data[0]
report_total += num_total
optim.step()
if config.is_dis:
optim_dis.step()
updates += 1
if updates % 30 == 0:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss this batch: %6.3f,sgm loss: %6.6f,ss loss: %6.6f\n"
% (time.time() - start_time, epoch, updates, loss /
num_total, total_loss_sgm / 30.0, total_loss_ss / 30.0))
total_loss_sgm, total_loss_ss = 0, 0
# continue
if 0 or updates % config.eval_interval == 0 and epoch > 1:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss: %6.5f\n"
% (time.time() - start_time, epoch, updates,
total_loss / report_total))
print('evaluating after %d updates...\r' % updates)
# score = eval(epoch)
for metric in config.metric:
scores[metric].append(score[metric])
lera.log({
'sgm_micro_f1': score[metric],
})
if metric == 'micro_f1' and score[metric] >= max(
scores[metric]):
save_model(log_path + 'best_' + metric +
'_checkpoint.pt')
if metric == 'hamming_loss' and score[metric] <= min(
scores[metric]):
save_model(log_path + 'best_' + metric +
'_checkpoint.pt')
model.train()
total_loss = 0
start_time = 0
report_total = 0
except RuntimeError, eeee:
print 'Erros here eeee: ', eeee
continue
except Exception, dddd:
print '\n\n\nRare errors: ', dddd
continue
def eval(epoch,test_or_valid='valid'):
# config.batch_size=1
global updates,model
model.eval()
# print '\n\n测试的时候请设置config里的batch_size为1！！！please set the batch_size as 1'
reference, candidate, source, alignments = [], [], [], []
e = epoch
print(('Test or valid:', test_or_valid))
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX, config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
# for iii in range(2000):
while True:
print(('-' * 30))
eval_data = next(eval_data_gen)
if eval_data == False:
print(('SDR_aver_eval_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_eval_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
# raw_tgt = [sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']]
raw_tgt= eval_data['batch_order']
feas_tgt = models.rank_feas(raw_tgt, eval_data['multi_spk_fea_list']) # 这里是目标的图谱
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.from_numpy(np.array([[0,1,2,102] for __ in range(config.batch_size)], dtype=np.int))).transpose(0, 1) # 转换成数字，然后前后加开始和结束符号。
src_len = Variable(torch.LongTensor(config.batch_size).zero_() + mix_speech_len).unsqueeze(0)
tgt_len = Variable(torch.LongTensor([len(one_spk) for one_spk in eval_data['multi_spk_fea_list']])).unsqueeze(0)
# tgt_len = Variable(torch.LongTensor(config.batch_size).zero_()+len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
if config.WFM:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous().view(-1, siz[1], siz[ 2]) # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
feas_tgt_square = feas_tgt_tmp * feas_tgt_tmp
feas_tgt_sum_square = torch.sum(feas_tgt_square, dim=1, keepdim=True).expand(siz[0], topk_max, siz[1], siz[2])
WFM_mask = feas_tgt_square / (feas_tgt_sum_square + 1e-15)
feas_tgt = x_input_map_multi.view(siz[0], -1, siz[1], siz[2]).data * WFM_mask # bs,topk,T,F
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
WFM_mask = WFM_mask.cuda()
del x_input_map_multi
elif config.PSM:
siz = src.size() # bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous() # bs,topk,T,F
feas_tgt_tmp = feas_tgt.view(siz[0], -1, siz[1], siz[2])
IRM=feas_tgt_tmp/(x_input_map_multi+1e-15)
angle_tgt=models.rank_feas(raw_tgt, eval_data['multi_spk_angle_list']).view(siz[0],-1,siz[1],siz[2])
angle_mix=Variable(torch.from_numpy(np.array(eval_data['mix_angle']))).unsqueeze(1).expand(siz[0], topk_max, siz[1], siz[2]).contiguous()
ang=np.cos(angle_mix-angle_tgt)
ang=np.clip(ang,0,None)
# feas_tgt = x_input_map_multi *np.clip(IRM.numpy()*ang,0,1) # bs,topk,T,F
# feas_tgt = x_input_map_multi *IRM*ang # bs,topk,T,F
feas_tgt = feas_tgt.view(siz[0],-1,siz[1],siz[2])*ang # bs,topk,T,F
feas_tgt = feas_tgt.view(-1, siz[1], siz[2]) # bs*topk,T,F
del x_input_map_multi
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
predicted_masks, enc_attn_list = model(src, src_len, tgt, tgt_len,
dict_spk2idx) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
print('predicted mask size:', predicted_masks.size(),'should be topk,bs,T,F') # topk,bs,T,F
# try:
# '''
# expand the raw mixed-features to topk_max channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
# if samples[0][-1] != dict_spk2idx['<EOS>']:
# print '*'*40+'\nThe model is far from good. End the evaluation.\n'+'*'*40
# break
topk_max = config.MAX_MIX
x_input_map_multi = torch.unsqueeze(src, 1).expand(siz[0], topk_max, siz[1], siz[2])
predicted_masks=predicted_masks.transpose(0, 1)
# if config.WFM:
# feas_tgt = x_input_map_multi.data * WFM_mask
# 注意,bs是第二维
assert predicted_masks.shape == x_input_map_multi.shape
assert predicted_masks.size(0) == config.batch_size
if 1 and len(opt.gpus) > 1:
ss_loss,best_pmt = model.module.separation_pit_loss(x_input_map_multi, predicted_masks, feas_tgt, )
else:
ss_loss,best_pmt = model.separation_pit_loss(x_input_map_multi, predicted_masks, feas_tgt)
print(('loss for ss,this batch:', ss_loss.cpu().item()))
print('best perms for this batch:', best_pmt)
lera.log({
'ss_loss_' + test_or_valid: ss_loss.cpu().item(),
})
writer.add_scalars('scalar/loss',{'ss_loss_'+test_or_valid:ss_loss.cpu().item()},updates+batch_idx)
del ss_loss
if batch_idx>10:
break
if False: #this part is to test the checkpoints sequencially.
batch_idx += 1
if batch_idx%100==0:
updates=updates+1000
opt.restore='/data1/shijing_data/2020-02-14-04:58:17/Transformer_PIT_{}.pt'.format(updates)
print('loading checkpoint...\n', opt.restore)
checkpoints = torch.load(opt.restore)
model.module.load_state_dict(checkpoints['model'])
break
continue
# '''''
if 0 and batch_idx <= (500 / config.batch_size): # only the former batches counts the SDR
predicted_maps = predicted_masks * x_input_map_multi
predicted_maps = predicted_maps.view(-1,mix_speech_len,speech_fre)
# predicted_maps=Variable(feas_tgt)
utils.bss_eval2(config, predicted_maps, eval_data['multi_spk_fea_list'], raw_tgt, eval_data,
dst='batch_output_test')
# utils.bss_eval(config, predicted_maps, eval_data['multi_spk_fea_list'], raw_tgt, eval_data,
# dst='batch_output_test')
del predicted_maps, predicted_masks, x_input_map_multi
try:
sdr_aver_batch, sdri_aver_batch= bss_test.cal('batch_output_test/')
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
except(AssertionError):
print('Errors in calculating the SDR')
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SRi_aver_now:', SDRi_SUM.mean()))
lera.log({'SDR sample'+test_or_valid: SDR_SUM.mean()})
lera.log({'SDRi sample'+test_or_valid: SDRi_SUM.mean()})
writer.add_scalars('scalar/loss',{'SDR_sample_'+test_or_valid:sdr_aver_batch},updates)
# raw_input('Press any key to continue......')
elif batch_idx == (200 / config.batch_size) + 1 and SDR_SUM.mean() > best_SDR: # only record the best SDR once.
print(('Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean())))
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
# '''
# candidate += [convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>']) for s in samples]
# source += raw_src
# reference += raw_tgt
# print(('samples:', samples))
# print(('can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:], reference[-1 * config.batch_size:])))
# alignments += [align for align in alignment]
batch_idx += 1
result = utils.eval_metrics(reference, candidate, dict_spk2idx, log_path)
print(('hamming_loss: %.8f | micro_f1: %.4f |recall: %.4f | precision: %.4f'
% (result['hamming_loss'], result['micro_f1'], result['micro_recall'], result['micro_precision'], )))
def eval_bss(mix_hidden_layer_3d,adjust_layer,mix_speech_classifier,mix_speech_multiEmbedding,att_speech_layer,
loss_multi_func,dict_spk2idx,dict_idx2spk,num_labels,mix_speech_len,speech_fre):
for i in [mix_speech_multiEmbedding,adjust_layer,mix_speech_classifier,mix_hidden_layer_3d,att_speech_layer]:
i.training=False
print '#' * 40
eval_data_gen=prepare_data('once','valid')
SDR_SUM=np.array([])
while True:
print '\n\n'
eval_data=eval_data_gen.next()
if eval_data==False:
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden,mix_tmp_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(eval_data['mix_feas'])).cuda())
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(eval_data['mix_feas'])).cuda())
if not test_mode:
y_spk_list= eval_data['multi_spk_fea_list']
y_spk_gtruth,y_map_gtruth=multi_label_vector(y_spk_list,dict_spk2idx)
# 如果训练阶段使用Ground truth的分离结果作为判别
if not test_mode and config.Ground_truth:
mix_speech_output=Variable(torch.from_numpy(y_map_gtruth)).cuda()
if test_all_outputchannel: #把输入的mask改成全１，可以用来测试输出所有的channel
mix_speech_output=Variable(torch.ones(config.BATCH_SIZE,num_labels,))
y_map_gtruth=np.ones([config.BATCH_SIZE,num_labels])
if test_mode:
num_labels=2
alpha0=-0.5
else:
alpha0=0.5
top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=alpha0,top_k=num_labels) #torch.Float型的
top_k_mask_idx=[np.where(line==1)[0] for line in top_k_mask_mixspeech.numpy()]
print 'Predict spk list:',print_spk_name(dict_idx2spk,top_k_mask_idx)
mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech,top_k_mask_idx) # bs*num_labels（最多混合人个数）×Embedding的大小
mix_adjust=adjust_layer(mix_tmp_hidden,mix_speech_multiEmbs)
mix_speech_multiEmbs=mix_adjust+mix_speech_multiEmbs
assert len(top_k_mask_idx[0])==len(top_k_mask_idx[-1])
top_k_num=len(top_k_mask_idx[0])
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'dot').cuda()
att_multi_speech=att_speech_layer(mix_speech_hidden_5d_last,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask=att_multi_speech
# top_k_mask_mixspeech_multi=top_k_mask_mixspeech.view(config.BATCH_SIZE,top_k_num,1,1).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# multi_mask=multi_mask*Variable(top_k_mask_mixspeech_multi).cuda()
x_input_map=Variable(torch.from_numpy(eval_data['mix_feas'])).cuda()
# print x_input_map.size()
x_input_map_multi=x_input_map.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# predict_multi_map=multi_mask*x_input_map_multi
predict_multi_map=multi_mask*x_input_map_multi
y_multi_map=np.zeros([config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre],dtype=np.float32)
batch_spk_multi_dict=eval_data['multi_spk_fea_list']
if test_mode:
for iiii in range(config.BATCH_SIZE):
y_multi_map[iiii]=np.array(batch_spk_multi_dict[iiii].values())
else:
for idx,sample in enumerate(batch_spk_multi_dict):
y_idx=sorted([dict_spk2idx[spk] for spk in sample.keys()])
if not test_mode:
assert y_idx==list(top_k_mask_idx[idx])
for jdx,oo in enumerate(y_idx):
y_multi_map[idx,jdx]=sample[dict_idx2spk[oo]]
y_multi_map= Variable(torch.from_numpy(y_multi_map)).cuda()
loss_multi_speech=loss_multi_func(predict_multi_map,y_multi_map)
#各通道和为１的loss部分,应该可以更多的带来差异
y_sum_map=Variable(torch.ones(config.BATCH_SIZE,mix_speech_len,speech_fre)).cuda()
predict_sum_map=torch.sum(multi_mask,1)
loss_multi_sum_speech=loss_multi_func(predict_sum_map,y_sum_map)
# loss_multi_speech=loss_multi_speech #todo:以后可以研究下这个和为１的效果对比一下，暂时直接MSE效果已经很不错了。
print 'loss 1 eval, losssum eval : ',loss_multi_speech.data.cpu().numpy(),loss_multi_sum_speech.data.cpu().numpy()
lrs.send('loss mask eval:',loss_multi_speech.data.cpu()[0])
lrs.send('loss sum eval:',loss_multi_sum_speech.data.cpu()[0])
loss_multi_speech=loss_multi_speech+0.5*loss_multi_sum_speech
print 'evaling multi-abs norm this eval batch:',torch.abs(y_multi_map-predict_multi_map).norm().data.cpu().numpy()
print 'loss:',loss_multi_speech.data.cpu().numpy()
bss_eval(predict_multi_map,y_multi_map,top_k_mask_idx,dict_idx2spk,eval_data)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output/', 2))
print 'SDR_aver_now:',SDR_SUM.mean()
SDR_aver=SDR_SUM.mean()
print 'SDR_SUM (len:{}) for epoch eval : '.format(SDR_SUM.shape)
lrs.send('SDR eval aver',SDR_aver)
print '#'*40
def train_recu(epoch):
global e, updates, total_loss, start_time, report_total, report_correct, total_loss_sgm, total_loss_ss
e = epoch
model.train()
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
if updates <= config.warmup: #如果不在warm阶段就正常规划
pass
elif config.schedule and scheduler.get_lr()[0] > 5e-7:
scheduler.step()
print(("Decaying learning rate to %g" % scheduler.get_lr()[0]))
lera.log({
'lr': [group['lr'] for group in optim.optimizer.param_groups][0],
})
if opt.model == 'gated':
model.current_epoch = epoch
train_data_gen = prepare_data('once', 'train')
while True:
if updates <= config.warmup: # 如果在warm就开始warmup
tmp_lr = config.learning_rate * min(
max(updates, 1)**(-0.5),
max(updates, 1) * (config.warmup**(-1.5)))
for param_group in optim.optimizer.param_groups:
param_group['lr'] = tmp_lr
scheduler.base_lrs = list(
[group['lr'] for group in optim.optimizer.param_groups])
if updates % 100 == 0: #记录一下
print(updates)
print("Warmup learning rate to %g" % tmp_lr)
lera.log({
'lr':
[group['lr'] for group in optim.optimizer.param_groups][0],
})
train_data = next(train_data_gen)
if train_data == False:
print(('SDR_aver_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(train_data['mix_feas']))
# raw_tgt = [spk.keys() for spk in train_data['multi_spk_fea_list']]
# raw_tgt = [sorted(spk.keys()) for spk in train_data['multi_spk_fea_list']]
raw_tgt = train_data['batch_order']
feas_tgt = models.rank_feas(
raw_tgt,
train_data['multi_spk_fea_list']) # 这里是目标的图谱,aim_size,len,fre
if 0 and config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 3
feas_tgt_square = feas_tgt * feas_tgt
feas_tgt_sum_square = torch.sum(feas_tgt_square,
dim=0,
keepdim=True).expand(tmp_size)
WFM_mask = feas_tgt_square / (feas_tgt_sum_square + 1e-15)
WFM_mask = WFM_mask.cuda()
feas_tgt = x_input_map_multi.data * WFM_mask
# 要保证底下这几个都是longTensor(长整数）
src_original = src.transpose(0, 1) #To T,bs,F
multi_mask_all = None
for len_idx in range(config.MIN_MIX + 2, 2, -1): #逐个分离
# len_idx=3
tgt_max_len = len_idx # 4,3,2 with bos and eos.
tgt = Variable(
torch.from_numpy(
np.array([[0] + [
dict_spk2idx[spk]
for spk in spks[-1 * (tgt_max_len - 2):]
] + 1 * [dict_spk2idx['<EOS>']] for spks in raw_tgt],
dtype=np.int))).transpose(
0, 1) # 转换成数字，然后前后加开始和结束符号。4,bs
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
tgt_max_len - 2
for one_spk in train_data['multi_spk_fea_list']
])).unsqueeze(0)
if use_cuda:
src = src.cuda().transpose(0, 1) # to T,bs,fre
src_original = src_original.cuda() # TO T,bs,fre
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
model.zero_grad()
outputs, targets, multi_mask, gamma = model(
src, src_len, tgt, tgt_len, dict_spk2idx,
src_original) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
print('mask size:', multi_mask.size())
# writer.add_histogram('global gamma',gamma, updates)
if 1 and len(opt.gpus) > 1:
sgm_loss, num_total, num_correct = model.module.compute_loss(
outputs, targets, opt.memory)
else:
sgm_loss, num_total, num_correct = model.compute_loss(
outputs, targets, opt.memory)
print(('loss for SGM,this batch:', sgm_loss.cpu().item()))
writer.add_scalars(
'scalar/loss',
{'sgm_loss' + str(len_idx): sgm_loss.cpu().item()}, updates)
src = src_original.transpose(0, 1) #确保分离的时候用的是原始的语音
# expand the raw mixed-features to topk_max channel.
siz = src.size() #bs,T,F
assert len(siz) == 3
# topk_max = config.MAX_MIX # 最多可能的topk个数
topk_max = len_idx - 2 # 最多可能的topk个数
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1],
siz[2]).contiguous().view(-1, siz[1], siz[2]) #bs,topk,T,F
# x_input_map_multi = x_input_map_multi[aim_list]
multi_mask = multi_mask.transpose(0, 1)
if len_idx == 4:
aim_feas = list(range(0, 2 * config.batch_size,
2)) #每个samples的第一个说话人取出来
multi_mask_all = multi_mask #bs*topk,T,F
src = src * (1 - multi_mask[aim_feas]) #调整到bs为第一维,# bs,T,F
# src=src.transpose(0,1)*(1-multi_mask[aim_feas]) #调整到bs为第一维
src = src.detach() #第二轮用第一轮预测出来的剩下的谱
elif len_idx == 3:
aim_feas = list(range(1, 2 * config.batch_size,
2)) #每个samples的第二个说话人取出来
multi_mask_all[aim_feas] = multi_mask
feas_tgt = feas_tgt[aim_feas]
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(x_input_map_multi,
multi_mask, feas_tgt)
else:
ss_loss = model.separation_loss(x_input_map_multi, multi_mask,
feas_tgt)
print(('loss for SS,this batch:', ss_loss.cpu().item()))
writer.add_scalars(
'scalar/loss',
{'ss_loss' + str(len_idx): ss_loss.cpu().item()}, updates)
loss = sgm_loss + 5 * ss_loss
loss.backward()
optim.step()
lera.log({
'sgm_loss' + str(len_idx): sgm_loss.cpu().item(),
'ss_loss' + str(len_idx): ss_loss.cpu().item(),
'loss:' + str(len_idx): loss.cpu().item(),
})
total_loss_sgm += sgm_loss.cpu().item()
total_loss_ss += ss_loss.cpu().item()
multi_mask = multi_mask_all
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], 2, siz[1], siz[2]).contiguous().view(-1, siz[1], siz[2])
if updates > 10 and updates % config.eval_interval in [
0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10
]:
predicted_maps = multi_mask * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval(config,
predicted_maps,
train_data['multi_spk_fea_list'],
raw_tgt,
train_data,
dst='batch_output')
del predicted_maps, multi_mask, x_input_map_multi
sdr_aver_batch, sdri_aver_batch = bss_test.cal('batch_output/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SDRi sample': sdri_aver_batch})
writer.add_scalars('scalar/loss', {
'SDR_sample': sdr_aver_batch,
'SDRi_sample': sdri_aver_batch
}, updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SDRi_aver_now:', SDRi_SUM.mean()))
total_loss += loss.cpu().item()
report_correct += num_correct.cpu().item()
report_total += num_total.cpu().item()
updates += 1
if updates % 30 == 0:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss this batch: %6.3f,sgm loss: %6.6f,ss loss: %6.6f,label acc: %6.6f\n"
% (time.time() - start_time, epoch, updates, loss / num_total,
total_loss_sgm / 30.0, total_loss_ss / 30.0,
report_correct / report_total))
lera.log({'label_acc': report_correct / report_total})
writer.add_scalars('scalar/loss',
{'label_acc': report_correct / report_total},
updates)
total_loss_sgm, total_loss_ss = 0, 0
# continue
if 0 and updates % config.eval_interval == 0 and epoch > 3: #建议至少跑几个epoch再进行测试，否则模型还没学到东西，会有很多问题。
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss: %6.5f\n" %
(time.time() - start_time, epoch, updates,
total_loss / report_total))
print(('evaluating after %d updates...\r' % updates))
original_bs = config.batch_size
score = eval(epoch) # eval的时候batch_size会变成1
# print 'Orignal bs:',original_bs
config.batch_size = original_bs
# print 'Now bs:',config.batch_size
for metric in config.metric:
scores[metric].append(score[metric])
lera.log({
'sgm_micro_f1': score[metric],
})
if metric == 'micro_f1' and score[metric] >= max(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
if metric == 'hamming_loss' and score[metric] <= min(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
model.train()
total_loss = 0
start_time = 0
report_total = 0
report_correct = 0
if 1 and updates % config.save_interval == 1:
save_model(log_path + 'TDAAv3_{}.pt'.format(updates))
def eval_recu(epoch):
assert config.batch_size == 1
model.eval()
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'test'
test_or_valid = 'valid'
# test_or_valid = 'train'
print(('Test or valid:', test_or_valid))
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
while True:
print(('-' * 30))
eval_data = next(eval_data_gen)
if eval_data == False:
print(('SDR_aver_eval_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_eval_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
# raw_tgt = [sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']]
raw_tgt = eval_data['batch_order']
feas_tgt = models.rank_feas(
raw_tgt, eval_data['multi_spk_fea_list']) # 这里是目标的图谱
src_original = src.transpose(0, 1) #To T,bs,F
predict_multi_mask_all = None
samples_list = []
for len_idx in range(config.MIN_MIX + 2, 2, -1): #逐个分离
tgt_max_len = len_idx # 4,3,2 with bos and eos.
topk_k = len_idx - 2
tgt = Variable(torch.ones(
len_idx, config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
tgt_max_len - 2
for one_spk in eval_data['multi_spk_fea_list']
])).unsqueeze(0)
if use_cuda:
src = src.cuda().transpose(0, 1) # to T,bs,fre
src_original = src_original.cuda() # TO T,bs,fre
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
# try:
if len(opt.gpus) > 1:
samples, alignment, hiddens, predicted_masks = model.module.beam_sample(
src, src_len, dict_spk2idx, tgt, config.beam_size,
src_original)
else:
samples, predicted_masks = model.beam_sample(
src, src_len, dict_spk2idx, tgt, config.beam_size,
src_original)
# except:
# continue
# '''
# expand the raw mixed-features to topk_max channel.
src = src_original.transpose(0, 1) #确保分离的时候用的是原始的语音
siz = src.size()
assert len(siz) == 3
topk_max = topk_k
x_input_map_multi = torch.unsqueeze(src, 1).expand(
siz[0], topk_max, siz[1], siz[2])
if 0 and config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
if len_idx == 4:
aim_feas = list(range(0, 2 * config.batch_size,
2)) #每个samples的第一个说话人取出来
predict_multi_mask_all = predicted_masks #bs*topk,T,F
src = src * (1 - predicted_masks[aim_feas]
) #调整到bs为第一维,# bs,T,F
samples_list = samples
elif len_idx == 3:
aim_feas = list(range(1, 2 * config.batch_size,
2)) #每个samples的第二个说话人取出来
predict_multi_mask_all[aim_feas] = predicted_masks
feas_tgt = feas_tgt[aim_feas]
samples_list = [samples_list[:1] + samples]
if test_or_valid != 'test':
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(
x_input_map_multi,
predicted_masks,
feas_tgt,
)
else:
ss_loss = model.separation_loss(x_input_map_multi,
predicted_masks, feas_tgt)
print(('loss for ss,this batch:', ss_loss.cpu().item()))
lera.log({
'ss_loss_' + str(len_idx) + test_or_valid:
ss_loss.cpu().item(),
})
del ss_loss
predicted_masks = predict_multi_mask_all
if batch_idx <= (500 / config.batch_size
): # only the former batches counts the SDR
predicted_maps = predicted_masks * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval2(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_output_test')
del predicted_maps, predicted_masks, x_input_map_multi
try:
sdr_aver_batch, sdri_aver_batch = bss_test.cal(
'batch_output_test/')
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
except (AssertionError):
print('Errors in calculating the SDR')
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SDRi_aver_now:', SDRi_SUM.mean()))
lera.log({'SDR sample' + test_or_valid: SDR_SUM.mean()})
lera.log({'SDRi sample' + test_or_valid: SDRi_SUM.mean()})
writer.add_scalars('scalar/loss',
{'SDR_sample_' + test_or_valid: sdr_aver_batch},
updates)
# raw_input('Press any key to continue......')
# '''
candidate += [
convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>'])
for s in samples_list
]
# source += raw_src
reference += raw_tgt
print(('samples:', samples))
print(('can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:],
reference[-1 * config.batch_size:])))
# alignments += [align for align in alignment]
batch_idx += 1
input('wait to continue......')
result = utils.eval_metrics(reference, candidate, dict_spk2idx,
log_path)
print((
'hamming_loss: %.8f | micro_f1: %.4f |recall: %.4f | precision: %.4f'
% (
result['hamming_loss'],
result['micro_f1'],
result['micro_recall'],
result['micro_precision'],
)))
score = {}
result = utils.eval_metrics(reference, candidate, dict_spk2idx, log_path)
logging_csv([e, updates, result['hamming_loss'], \
result['micro_f1'], result['micro_precision'], result['micro_recall'],SDR_SUM.mean()])
print(('hamming_loss: %.8f | micro_f1: %.4f' %
(result['hamming_loss'], result['micro_f1'])))
score['hamming_loss'] = result['hamming_loss']
score['micro_f1'] = result['micro_f1']
1 / 0
return score
def main():
print('go to model')
print '*' * 80
spk_global_gen = prepare_data(mode='global',
train_or_test='train') #写一个假的数据生成，可以用来写模型先
global_para = spk_global_gen.next()
print global_para
spk_all_list, dict_spk2idx, dict_idx2spk, mix_speech_len, speech_fre, total_frames, spk_num_total = global_para
del spk_global_gen
num_labels = len(spk_all_list)
print 'print num_labels:', num_labels
# data_generator=prepare_data('once','train')
# data_generator=prepare_data_fake(train_or_test='train',num_labels=num_labels) #写一个假的数据生成，可以用来写模型先
#此处顺序是 mix_speechs.shape,mix_feas.shape,aim_fea.shape,aim_spkid.shape,query.shape
#一个例子：(5, 17040) (5, 134, 129) (5, 134, 129) (5,) (5, 32, 400, 300, 3)
# datasize=prepare_datasize(data_generator)
# mix_speech_len,speech_fre,total_frames,spk_num_total,video_size=datasize
print 'Begin to build the maim model for Multi_Modal Cocktail Problem.'
# data=data_generator.next()
# This part is to build the 3D mix speech embedding maps.
mix_hidden_layer_3d = MIX_SPEECH(speech_fre, mix_speech_len).cuda()
mix_speech_classifier = MIX_SPEECH_classifier(speech_fre, mix_speech_len,
num_labels).cuda()
mix_speech_multiEmbedding = SPEECH_EMBEDDING(
num_labels, config.EMBEDDING_SIZE,
spk_num_total + config.UNK_SPK_SUPP).cuda()
print mix_hidden_layer_3d
print mix_speech_classifier
# mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(data[1])).cuda())
# mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(data[1])).cuda())
# 技巧：alpha0的时候，就是选出top_k，top_k很大的时候，就是选出来大于alpha的
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=config.MAX_MIX)
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=3)
# print top_k_mask_mixspeech
# mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech) # bs*num_labels（最多混合人个数）×Embedding的大小
# mix_speech_multiEmbs=mix_speech_multiEmbedding(Variable(torch.from_numpy(top_k_mask_mixspeech),requires_grad=False).cuda()) # bs*num_labels（最多混合人个数）×Embedding的大小
# 需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
# 把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
# mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
# mix_speech_hidden_5d=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
# att_multi_speech=att_speech_layer(mix_speech_hidden_5d,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
# att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,-1)
# print att_multi_speech.size()
# This part is to conduct the video inputs.
# query_video_layer=VIDEO_QUERY(total_frames,config.VideoSize,spk_num_total).cuda()
query_video_layer = None
# print query_video_layer
# query_video_output,xx=query_video_layer(Variable(torch.from_numpy(data[4])))
# This part is to conduct the memory.
# hidden_size=(config.HIDDEN_UNITS)
hidden_size = (config.EMBEDDING_SIZE)
memory = MEMORY(spk_num_total + config.UNK_SPK_SUPP, hidden_size)
memory.register_spklist(spk_all_list) #把spk_list注册进空的memory里面去
# Memory function test.
print 'memory all spkid:', memory.get_all_spkid()
# print memory.get_image_num('Unknown_id')
# print memory.get_video_vector('Unknown_id')
# print memory.add_video('Unknown_id',Variable(torch.ones(300)))
# This part is to test the ATTENTION methond from query(~) to mix_speech
# x=torch.arange(0,24).view(2,3,4)
# y=torch.ones([2,4])
att_layer = ATTENTION(config.EMBEDDING_SIZE, 'align').cuda()
att_speech_layer = ATTENTION(config.EMBEDDING_SIZE, 'align').cuda()
# att=ATTENTION(4,'align')
# mask=att(x,y)#bs*max_len
# del data_generator
# del data
optimizer = torch.optim.Adam(
[
{
'params': mix_hidden_layer_3d.parameters()
},
{
'params': mix_speech_multiEmbedding.parameters()
},
{
'params': mix_speech_classifier.parameters()
},
# {'params':query_video_layer.lstm_layer.parameters()},
# {'params':query_video_layer.dense.parameters()},
# {'params':query_video_layer.Linear.parameters()},
{
'params': att_layer.parameters()
},
{
'params': att_speech_layer.parameters()
},
# ], lr=0.02,momentum=0.9)
],
lr=0.0002)
if 1 and config.Load_param:
# query_video_layer.load_state_dict(torch.load('param_video_layer_19'))
# mix_speech_classifier.load_state_dict(torch.load('params/param_speech_multilabel_epoch249'))
mix_speech_classifier.load_state_dict(
torch.load('params/param_speech_WSJ0_multilabel_epoch249'))
mix_hidden_layer_3d.load_state_dict(
torch.load('params/param_mix101_WSJ0_hidden3d_180'))
mix_speech_multiEmbedding.load_state_dict(
torch.load('params/param_mix101_WSJ0_emblayer_180'))
att_speech_layer.load_state_dict(
torch.load('params/param_mix101_WSJ0_attlayer_180'))
loss_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
loss_multi_func = torch.nn.MSELoss(
) # the target label is NOT an one-hotted
# loss_multi_func = torch.nn.L1Loss() # the target label is NOT an one-hotted
loss_query_class = torch.nn.CrossEntropyLoss()
print '''Begin to calculate.'''
for epoch_idx in range(config.MAX_EPOCH):
if epoch_idx > 0:
print 'SDR_SUM (len:{}) for epoch {} : '.format(
SDR_SUM.shape, epoch_idx - 1, SDR_SUM.mean())
SDR_SUM = np.array([])
# print_memory_state(memory.memory)
print 'SDR_SUM for epoch {}:{}'.format(epoch_idx - 1, SDR_SUM.mean())
for batch_idx in range(config.EPOCH_SIZE):
print '*' * 40, epoch_idx, batch_idx, '*' * 40
# train_data_gen=prepare_data('once','train')
train_data_gen = prepare_data('once', 'test')
# train_data_gen=prepare_data('once','eval_test')
train_data = train_data_gen.next()
# test_data_gen=prepare_data('once','test')
# test_data=train_data_gen.next()
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden = mix_hidden_layer_3d(
Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
mix_speech_output = mix_speech_classifier(
Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
#从数据里得到ground truth的说话人名字和vector
# y_spk_list=[one.keys() for one in train_data['multi_spk_fea_list']]
# y_spk_gtruth,y_map_gtruth=multi_label_vector(y_spk_list,dict_spk2idx)
# 如果训练阶段使用Ground truth的分离结果作为判别
if 0 and config.Ground_truth:
mix_speech_output = Variable(
torch.from_numpy(y_map_gtruth)).cuda()
if test_all_outputchannel: #把输入的mask改成全１，可以用来测试输出所有的channel
mix_speech_output = Variable(
torch.ones(
config.BATCH_SIZE,
num_labels,
))
y_map_gtruth = np.ones([config.BATCH_SIZE, num_labels])
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=0.5,top_k=num_labels) #torch.Float型的
max_num_labels = 2
top_k_mask_mixspeech = top_k_mask(
mix_speech_output, alpha=-1,
top_k=max_num_labels) #torch.Float型的
mix_speech_multiEmbs = mix_speech_multiEmbedding(
top_k_mask_mixspeech) # bs*num_labels（最多混合人个数）×Embedding的大小
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d = mix_speech_hidden.view(
config.BATCH_SIZE, 1, mix_speech_len, speech_fre,
config.EMBEDDING_SIZE)
mix_speech_hidden_5d = mix_speech_hidden_5d.expand(
config.BATCH_SIZE, num_labels, mix_speech_len, speech_fre,
config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last = mix_speech_hidden_5d.view(
-1, mix_speech_len, speech_fre, config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer = ATTENTION(config.EMBEDDING_SIZE, 'dot').cuda()
att_multi_speech = att_speech_layer(
mix_speech_hidden_5d_last,
mix_speech_multiEmbs.view(-1, config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech = att_multi_speech.view(
config.BATCH_SIZE, num_labels, mix_speech_len,
speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask = att_multi_speech
top_k_mask_mixspeech_multi = top_k_mask_mixspeech.view(
config.BATCH_SIZE, num_labels, 1,
1).expand(config.BATCH_SIZE, num_labels, mix_speech_len,
speech_fre)
multi_mask = multi_mask * Variable(
top_k_mask_mixspeech_multi).cuda()
x_input_map = Variable(torch.from_numpy(
train_data['mix_feas'])).cuda()
# print x_input_map.size()
x_input_map_multi = x_input_map.view(
config.BATCH_SIZE, 1, mix_speech_len,
speech_fre).expand(config.BATCH_SIZE, num_labels,
mix_speech_len, speech_fre)
predict_multi_map = multi_mask * x_input_map_multi
if batch_idx % 100 == 0:
print multi_mask
# print predict_multi_map
bss_eval_fromGenMap(predict_multi_map, top_k_mask_mixspeech,
dict_idx2spk, train_data)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output/', 2))
continue
optimizer.zero_grad() # clear gradients for next train
loss_multi_speech.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if 1 and epoch_idx > 20 and epoch_idx % 10 == 0 and batch_idx == config.EPOCH_SIZE - 1:
torch.save(
mix_speech_multiEmbedding.state_dict(),
'params/param_mix_{}_emblayer_{}'.format(
config.DATASET, epoch_idx))
torch.save(
mix_hidden_layer_3d.state_dict(),
'params/param_mix_{}_hidden3d_{}'.format(
config.DATASET, epoch_idx))
torch.save(
att_speech_layer.state_dict(),
'params/param_mix_{}_attlayer_{}'.format(
config.DATASET, epoch_idx))
def eval(epoch):
# config.batch_size=1
model.eval()
print '\n\n测试的时候请设置config里的batch_size为1！！！please set the batch_size as 1'
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'test'
print('Test or valid:', test_or_valid)
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
while True:
print('-' * 30)
eval_data = eval_data_gen.next()
if eval_data == False:
print('SDR_aver_eval_epoch:', SDR_SUM.mean())
print('SDRi_aver_eval_epoch:', SDRi_SUM.mean())
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
raw_tgt = [
sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']
]
feas_tgt = models.rank_feas(
raw_tgt, eval_data['multi_spk_fea_list']) # 这里是目标的图谱
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.ones(
top_k + 2, config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in eval_data['multi_spk_fea_list']
])).unsqueeze(0)
# tgt_len = Variable(torch.LongTensor(config.batch_size).zero_()+len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
if config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 4
feas_tgt_sum = torch.sum(feas_tgt, dim=1, keepdim=True)
feas_tgt_sum_square = (feas_tgt_sum *
feas_tgt_sum).expand(tmp_size)
feas_tgt_square = feas_tgt * feas_tgt
WFM_mask = feas_tgt_square / feas_tgt_sum_square
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
if 1 and len(opt.gpus) > 1:
samples, alignment, hiddens, predicted_masks = model.module.beam_sample(
src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
else:
samples, alignment, hiddens, predicted_masks = model.beam_sample(
src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
# '''
# expand the raw mixed-features to topk_max channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
# if samples[0][-1] != dict_spk2idx['<EOS>']:
# print '*'*40+'\nThe model is far from good. End the evaluation.\n'+'*'*40
# break
topk_max = len(samples[0]) - 1
x_input_map_multi = torch.unsqueeze(src,
1).expand(siz[0], topk_max, siz[1],
siz[2])
if config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
if 0 and test_or_valid == 'valid':
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(
x_input_map_multi,
predicted_masks,
feas_tgt,
)
else:
ss_loss = model.separation_loss(x_input_map_multi,
predicted_masks, feas_tgt)
print('loss for ss,this batch:', ss_loss.cpu().item())
lera.log({
'ss_loss_' + test_or_valid: ss_loss.cpu().item(),
})
del ss_loss, hiddens
# '''''
if batch_idx <= (500 / config.batch_size
): # only the former batches counts the SDR
predicted_maps = predicted_masks * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval2(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_output')
del predicted_maps, predicted_masks, x_input_map_multi
try:
SDR_SUM, SDRi_SUM = np.append(SDR_SUM,
bss_test.cal('batch_output/'))
except AssertionError, wrong_info:
print 'Errors in calculating the SDR', wrong_info
print('SDR_aver_now:', SDR_SUM.mean())
print('SDRi_aver_now:', SDRi_SUM.mean())
lera.log({'SDR sample' + test_or_valid: SDR_SUM.mean()})
lera.log({'SDRi sample' + test_or_valid: SDRi_SUM.mean()})
# raw_input('Press any key to continue......')
elif batch_idx == (500 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: # only record the best SDR once.
print('Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean()))
best_SDR = SDR_SUM.mean()
def eval(epoch):
# config.batch_size=1
model.eval()
# print '\n\n测试的时候请设置config里的batch_size为1！！！please set the batch_size as 1'
reference, candidate, source, alignments = [], [], [], []
e = epoch
# test_or_valid = 'test'
test_or_valid = 'valid'
print('Test or valid:', test_or_valid)
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
while True:
print('-' * 30)
eval_data = next(eval_data_gen)
if eval_data == False:
print('SDR_aver_eval_epoch:', SDR_SUM.mean())
print('SDRi_aver_eval_epoch:', SDRi_SUM.mean())
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
raw_tgt = [
sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']
]
feas_tgt = models.rank_feas(
raw_tgt, eval_data['multi_spk_fea_list']) # 这里是目标的图谱
padded_mixture, mixture_lengths, padded_source = eval_data['tas_zip']
padded_mixture = torch.from_numpy(padded_mixture).float()
mixture_lengths = torch.from_numpy(mixture_lengths)
padded_source = torch.from_numpy(padded_source).float()
padded_mixture = padded_mixture.cuda().transpose(0, 1)
mixture_lengths = mixture_lengths.cuda()
padded_source = padded_source.cuda()
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.ones(
top_k + 2, config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor([
len(one_spk) for one_spk in eval_data['multi_spk_fea_list']
])).unsqueeze(0)
# tgt_len = Variable(torch.LongTensor(config.batch_size).zero_()+len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
if config.WFM:
tmp_size = feas_tgt.size()
#assert len(tmp_size) == 4
feas_tgt_sum = torch.sum(feas_tgt, dim=1, keepdim=True)
feas_tgt_sum_square = (feas_tgt_sum *
feas_tgt_sum).expand(tmp_size)
feas_tgt_square = feas_tgt * feas_tgt
WFM_mask = feas_tgt_square / feas_tgt_sum_square
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
if 1 and len(opt.gpus) > 1:
samples, alignment, hiddens, predicted_masks = model.module.beam_sample(
src, src_len, dict_spk2idx, tgt, config.beam_size,
padded_mixture)
else:
samples, alignment, hiddens, predicted_masks = model.beam_sample(
src, src_len, dict_spk2idx, tgt, config.beam_size,
padded_mixture)
# '''
# expand the raw mixed-features to topk_max channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
# if samples[0][-1] != dict_spk2idx['<EOS>']:
# print '*'*40+'\nThe model is far from good. End the evaluation.\n'+'*'*40
# break
topk_max = len(samples[0]) - 1
x_input_map_multi = torch.unsqueeze(src,
1).expand(siz[0], topk_max, siz[1],
siz[2])
if config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
if not config.use_tas and test_or_valid == 'valid':
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(
x_input_map_multi,
predicted_masks,
feas_tgt,
)
else:
ss_loss = model.separation_loss(x_input_map_multi,
predicted_masks, feas_tgt)
print('loss for ss,this batch:', ss_loss.cpu().item())
# lera.log({
# 'ss_loss_' + test_or_valid: ss_loss.cpu().item(),
# })
del ss_loss, hiddens
# '''''
if batch_idx <= (100 / config.batch_size
): # only the former batches counts the SDR
if config.use_tas:
utils.bss_eval_tas(config,
predicted_masks,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_output1')
else:
predicted_maps = predicted_masks * x_input_map_multi
utils.bss_eval2(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_output1')
del predicted_maps
del predicted_masks, x_input_map_multi
try:
#SDR_SUM,SDRi_SUM = np.append(SDR_SUM, bss_test.cal('batch_output1/'))
sdr_aver_batch, sdri_aver_batch = bss_test.cal(
'batch_output1/')
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
except: # AssertionError,wrong_info:
print('Errors in calculating the SDR', wrong_info)
print('SDR_aver_now:', SDR_SUM.mean())
print('SDRi_aver_now:', SDRi_SUM.mean())
# lera.log({'SDR sample'+test_or_valid: SDR_SUM.mean()})
# lera.log({'SDRi sample'+test_or_valid: SDRi_SUM.mean()})
# raw_input('Press any key to continue......')
elif batch_idx == (500 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: # only record the best SDR once.
print('Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean()))
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
# '''
candidate += [
convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>'])
for s in samples
]
# source += raw_src
reference += raw_tgt
print('samples:', samples)
print('can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:],
reference[-1 * config.batch_size:]))
alignments += [align for align in alignment]
batch_idx += 1
score = {}
result = utils.eval_metrics(reference, candidate, dict_spk2idx, log_path)
logging_csv([e, updates, result['hamming_loss'], \
result['micro_f1'], result['micro_precision'], result['micro_recall']])
print('hamming_loss: %.8f | micro_f1: %.4f' %
(result['hamming_loss'], result['micro_f1']))
score['hamming_loss'] = result['hamming_loss']
score['micro_f1'] = result['micro_f1']
return score
def main():
print('go to model')
print '*' * 80
spk_global_gen=prepare_data(mode='global',train_or_test='train') #写一个假的数据生成，可以用来写模型先
global_para=spk_global_gen.next()
print global_para
spk_all_list,dict_spk2idx,dict_idx2spk,mix_speech_len,speech_fre,total_frames,spk_num_total=global_para
del spk_global_gen
num_labels=len(spk_all_list)
# data_generator=prepare_data('once','train')
# data_generator=prepare_data_fake(train_or_test='train',num_labels=num_labels) #写一个假的数据生成，可以用来写模型先
#此处顺序是 mix_speechs.shape,mix_feas.shape,aim_fea.shape,aim_spkid.shape,query.shape
#一个例子：(5, 17040) (5, 134, 129) (5, 134, 129) (5,) (5, 32, 400, 300, 3)
# datasize=prepare_datasize(data_generator)
# mix_speech_len,speech_fre,total_frames,spk_num_total,video_size=datasize
print 'Begin to build the maim model for Multi_Modal Cocktail Problem.'
# data=data_generator.next()
# This part is to build the 3D mix speech embedding maps.
mix_hidden_layer_3d=MIX_SPEECH(speech_fre,mix_speech_len).cuda()
mix_speech_classifier=MIX_SPEECH_classifier(speech_fre,mix_speech_len,num_labels).cuda()
mix_speech_multiEmbedding=SPEECH_EMBEDDING(num_labels,config.EMBEDDING_SIZE,spk_num_total+config.UNK_SPK_SUPP).cuda()
print mix_hidden_layer_3d
print mix_speech_classifier
# mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(data[1])).cuda())
# mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(data[1])).cuda())
# 技巧：alpha0的时候，就是选出top_k，top_k很大的时候，就是选出来大于alpha的
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=config.MAX_MIX)
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=3)
# print top_k_mask_mixspeech
# mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech) # bs*num_labels（最多混合人个数）×Embedding的大小
# mix_speech_multiEmbs=mix_speech_multiEmbedding(Variable(torch.from_numpy(top_k_mask_mixspeech),requires_grad=False).cuda()) # bs*num_labels（最多混合人个数）×Embedding的大小
# 需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
# 把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
# mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
# mix_speech_hidden_5d=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
# att_multi_speech=att_speech_layer(mix_speech_hidden_5d,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
# att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,-1)
# print att_multi_speech.size()
# This part is to conduct the video inputs.
# query_video_layer=VIDEO_QUERY(total_frames,config.VideoSize,spk_num_total).cuda()
query_video_layer=None
# print query_video_layer
# query_video_output,xx=query_video_layer(Variable(torch.from_numpy(data[4])))
# This part is to conduct the memory.
# hidden_size=(config.HIDDEN_UNITS)
hidden_size=(config.EMBEDDING_SIZE)
# x=torch.arange(0,24).view(2,3,4)
# y=torch.ones([2,4])
att_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
# att=ATTENTION(4,'align')
# mask=att(x,y)#bs*max_len
# del data_generator
# del data
optimizer = torch.optim.Adam([{'params':mix_hidden_layer_3d.parameters()},
{'params':mix_speech_multiEmbedding.parameters()},
{'params':mix_speech_classifier.parameters()},
# {'params':query_video_layer.lstm_layer.parameters()},
# {'params':query_video_layer.dense.parameters()},
# {'params':query_video_layer.Linear.parameters()},
{'params':att_layer.parameters()},
{'params':att_speech_layer.parameters()},
# ], lr=0.02,momentum=0.9)
], lr=0.0002)
if 0 and config.Load_param:
# query_video_layer.load_state_dict(torch.load('param_video_layer_19'))
# mix_speech_classifier.load_state_dict(torch.load('params/param_speech_multilabel_epoch249'))
mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_WSJ0_hidden3d_180'))
mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_WSJ0_emblayer_180'))
att_speech_layer.load_state_dict(torch.load('params/param_mix101_WSJ0_attlayer_180'))
loss_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
loss_multi_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
# loss_multi_func = torch.nn.L1Loss() # the target label is NOT an one-hotted
loss_query_class=torch.nn.CrossEntropyLoss()
print '''Begin to calculate.'''
for epoch_idx in range(config.MAX_EPOCH):
if epoch_idx%50==0:
for ee in optimizer.param_groups:
ee['lr']/=2
if epoch_idx>0:
print 'SDR_SUM (len:{}) for epoch {} : '.format(SDR_SUM.shape,epoch_idx-1,SDR_SUM.mean())
SDR_SUM=np.array([])
# print_memory_state(memory.memory)
print 'SDR_SUM for epoch {}:{}'.format(epoch_idx - 1, SDR_SUM.mean())
for batch_idx in range(config.EPOCH_SIZE):
print '*' * 40,epoch_idx,batch_idx,'*'*40
train_data_gen=prepare_data('once','train')
# train_data_gen=prepare_data('once','test')
train_data=train_data_gen.next()
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
#从数据里得到ground truth的说话人名字和vector
# y_spk_list=[one.keys() for one in train_data['multi_spk_fea_list']]
y_spk_list= train_data['multi_spk_fea_list']
y_spk_gtruth,y_map_gtruth=multi_label_vector(y_spk_list,dict_spk2idx)
# 如果训练阶段使用Ground truth的分离结果作为判别
if config.Ground_truth:
mix_speech_output=Variable(torch.from_numpy(y_map_gtruth)).cuda()
if test_all_outputchannel: #把输入的mask改成全１，可以用来测试输出所有的channel
mix_speech_output=Variable(torch.ones(config.BATCH_SIZE,num_labels,))
y_map_gtruth=np.ones([config.BATCH_SIZE,num_labels])
top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=0.5,top_k=num_labels) #torch.Float型的
top_k_mask_idx=[np.where(line==1)[0] for line in top_k_mask_mixspeech.numpy()]
mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech,top_k_mask_idx) # bs*num_labels（最多混合人个数）×Embedding的大小
assert len(top_k_mask_idx[0])==len(top_k_mask_idx[-1])
top_k_num=len(top_k_mask_idx[0])
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'dot').cuda()
att_multi_speech=att_speech_layer(mix_speech_hidden_5d_last,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask=att_multi_speech
# top_k_mask_mixspeech_multi=top_k_mask_mixspeech.view(config.BATCH_SIZE,top_k_num,1,1).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# multi_mask=multi_mask*Variable(top_k_mask_mixspeech_multi).cuda()
x_input_map=Variable(torch.from_numpy(train_data['mix_feas'])).cuda()
# print x_input_map.size()
x_input_map_multi=x_input_map.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# predict_multi_map=multi_mask*x_input_map_multi
predict_multi_map=multi_mask*x_input_map_multi
if 0 and batch_idx%100==0:
print multi_mask
# print predict_multi_map
y_multi_map=np.zeros([config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre],dtype=np.float32)
batch_spk_multi_dict=train_data['multi_spk_fea_list']
for idx,sample in enumerate(batch_spk_multi_dict):
y_idx=sorted([dict_spk2idx[spk] for spk in sample.keys()])
assert y_idx==list(top_k_mask_idx[idx])
for jdx,oo in enumerate(y_idx):
y_multi_map[idx,jdx]=sample[dict_idx2spk[oo]]
y_multi_map= Variable(torch.from_numpy(y_multi_map)).cuda()
loss_multi_speech=loss_multi_func(predict_multi_map,y_multi_map)
#各通道和为１的loss部分,应该可以更多的带来差异
y_sum_map=Variable(torch.ones(config.BATCH_SIZE,mix_speech_len,speech_fre)).cuda()
predict_sum_map=torch.sum(multi_mask,1)
loss_multi_sum_speech=loss_multi_func(predict_sum_map,y_sum_map)
# loss_multi_speech=loss_multi_speech #todo:以后可以研究下这个和为１的效果对比一下，暂时直接MSE效果已经很不错了。
print 'loss 1, losssum : ',loss_multi_speech.data.cpu().numpy(),loss_multi_sum_speech.data.cpu().numpy()
loss_multi_speech=loss_multi_speech+0.5*loss_multi_sum_speech
print 'training multi-abs norm this batch:',torch.abs(y_multi_map-predict_multi_map).norm().data.cpu().numpy()
print 'loss:',loss_multi_speech.data.cpu().numpy()
if 1 or batch_idx==config.EPOCH_SIZE-1:
bss_eval(predict_multi_map,y_multi_map,top_k_mask_idx,dict_idx2spk,train_data)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output/', 2))
optimizer.zero_grad() # clear gradients for next train
loss_multi_speech.backward() # backpropagation, compute gradients
optimizer.step() # apply gradients
if 1 and epoch_idx>80 and epoch_idx%10==0 and batch_idx==config.EPOCH_SIZE-1:
torch.save(mix_speech_multiEmbedding.state_dict(),'params/param_mix_{}_emblayer_{}'.format(config.DATASET,epoch_idx))
torch.save(mix_hidden_layer_3d.state_dict(),'params/param_mix_{}_hidden3d_{}'.format(config.DATASET,epoch_idx))
torch.save(att_speech_layer.state_dict(),'params/param_mix_{}_attlayer_{}'.format(config.DATASET,epoch_idx))
def train(epoch):
global e, updates, total_loss, start_time, report_total, report_correct, total_loss_sgm, total_loss_ss
e = epoch
model.train()
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
if updates <= config.warmup: #如果不在warm阶段就正常规划
pass
elif config.schedule and scheduler.get_lr()[0] > 5e-7:
scheduler.step()
print(("Decaying learning rate to %g" % scheduler.get_lr()[0]))
lera.log({
'lr': [group['lr'] for group in optim.optimizer.param_groups][0],
})
if opt.model == 'gated':
model.current_epoch = epoch
# train_data_gen = prepare_data('once', 'train')
train_data_gen = musdb.DB(root="~/MUSDB18/",
subsets='train',
split='train')
train_data_gen = batch_generator(
list(train_data_gen),
config.batch_size,
)
# while 1:
# mix,ref=next(train_data_gen)
# import soundfile as sf
# sf.write('mix.wav',mix[0,0],44100)
# sf.write('vocal.wav',ref[0,0,0],44100)
# sf.write('drum.wav',ref[0,1,0],44100)
# sf.write('bass.wav',ref[0,2,0],44100)
# sf.write('other.wav',ref[0,3,0],44100)
# pass
while True:
if updates <= config.warmup: # 如果在warm就开始warmup
tmp_lr = config.learning_rate * min(
max(updates, 1)**(-0.5),
max(updates, 1) * (config.warmup**(-1.5)))
for param_group in optim.optimizer.param_groups:
param_group['lr'] = tmp_lr
scheduler.base_lrs = list(
[group['lr'] for group in optim.optimizer.param_groups])
if updates % 100 == 0: #记录一下
print(updates)
print("Warmup learning rate to %g" % tmp_lr)
lera.log({
'lr':
[group['lr'] for group in optim.optimizer.param_groups][0],
})
train_data = next(train_data_gen)
if train_data == False:
print(('SDR_aver_epoch:', SDR_SUM.mean()))
print(('SDRi_aver_epoch:', SDRi_SUM.mean()))
break # 如果这个epoch的生成器没有数据了，直接进入下一个epoch
padded_mixture, mixture_lengths, padded_source = train_data
# source:bs,2channel,T target:bs,4(vocals,drums,bass,other),2channel,T
padded_mixture = torch.from_numpy(padded_mixture).float()
topk_this_batch = padded_source.shape[1]
mixture_lengths = torch.from_numpy(mixture_lengths)
padded_source = torch.from_numpy(padded_source).float()
# 要保证底下这几个都是longTensor(长整数）
if use_cuda:
padded_mixture = padded_mixture.cuda().transpose(0, 1)
mixture_lengths = mixture_lengths.cuda()
padded_source = padded_source.cuda()
# src = src.cuda().transpose(0, 1)
# tgt = tgt.cuda()
# src_len = src_len.cuda()
# tgt_len = tgt_len.cuda()
# feas_tgt = feas_tgt.cuda()
if 0 and loss < -5:
import soundfile as sf
idx_in_batch = 0
sf.write(
str(idx_in_batch) + '_mix.wav',
padded_mixture.transpose(
0, 1).data.cpu().numpy()[idx_in_batch].transpose(), 44100)
sf.write(
str(idx_in_batch) + '_ref_vocal.wav',
padded_source.data.cpu().numpy()[idx_in_batch, 0].transpose(),
44100)
sf.write(
str(idx_in_batch) + '_ref_drum.wav',
padded_source.data.cpu().numpy()[idx_in_batch, 1].transpose(),
44100)
sf.write(
str(idx_in_batch) + '_ref_bass.wav',
padded_source.data.cpu().numpy()[idx_in_batch, 2].transpose(),
44100)
sf.write(
str(idx_in_batch) + '_ref_other.wav',
padded_source.data.cpu().numpy()[idx_in_batch, 3].transpose(),
44100)
model.zero_grad()
outputs, pred, spks_ordre_list, multi_mask, y_map = model(
None,
None,
None,
None,
dict_spk2idx,
None,
mix_wav=padded_mixture,
clean_wavs=padded_source.transpose(
0, 1)) # 这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
print('mask size:', multi_mask.size())
print('y map size:', y_map.size())
# print('spk order:', spks_ordre_list) # bs,topk
# writer.add_histogram('global gamma',gamma, updates)
multi_mask = multi_mask.transpose(0, 1)
y_map = y_map.transpose(0, 1)
spks_ordre_list = spks_ordre_list.transpose(0, 1)
# expand the raw mixed-features to topk_max channel.
topk_max = topk_this_batch # 最多可能的topk个数
if config.greddy_tf and config.add_last_silence:
multi_mask, silence_channel = torch.split(multi_mask,
[topk_this_batch, 1],
dim=1)
silence_channel = silence_channel[:, 0]
assert len(padded_source.shape) == 3
# padded_source = torch.cat([padded_source,torch.zeros(padded_source.size(0),1,padded_source.size(2))],1)
if 1 and len(opt.gpus) > 1:
ss_loss_silence = model.module.silence_loss(silence_channel)
else:
ss_loss_silence = model.silence_loss(silence_channel)
print('loss for SS silence,this batch:',
ss_loss_silence.cpu().item())
writer.add_scalars(
'scalar/loss',
{'ss_loss_silence': ss_loss_silence.cpu().item()}, updates)
lera.log({'ss_loss_silence': ss_loss_silence.cpu().item()})
if torch.isnan(ss_loss_silence):
ss_loss_silence = 0
if config.use_tas:
# print('source',padded_source)
# print('est', multi_mask)
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_tas_sdr_order_loss(
padded_mixture.transpose(0, 1), multi_mask, y_map,
mixture_lengths)
else:
ss_loss = model.separation_tas_sdr_order_loss(
padded_mixture, multi_mask, y_map, mixture_lengths)
# best_pmt=[list(pmt_list[int(mm)].data.cpu().numpy()) for mm in max_snr_idx]
print('loss for SS,this batch:', ss_loss.cpu().item())
# print('best perms for this batch:', best_pmt)
print('greddy perms for this batch:',
[ii for ii in spks_ordre_list.data.cpu().numpy()])
writer.add_scalars('scalar/loss', {'ss_loss': ss_loss.cpu().item()},
updates)
loss = ss_loss
if config.add_last_silence:
loss = loss + 0.1 * ss_loss_silence
loss.backward()
# print 'totallllllllllll loss:',loss
total_loss_ss += ss_loss.cpu().item()
lera.log({
'ss_loss_' + str(topk_this_batch): ss_loss.cpu().item(),
'loss:': loss.cpu().item(),
'pre_min': multi_mask.data.cpu().numpy().min(),
'pre_max': multi_mask.data.cpu().numpy().max(),
})
if 1 or loss < -5:
import soundfile as sf
idx_in_batch = 0
y0 = multi_mask.data.cpu().numpy()[idx_in_batch, 0]
y1 = multi_mask.data.cpu().numpy()[idx_in_batch, 1]
y2 = multi_mask.data.cpu().numpy()[idx_in_batch, 2]
y3 = multi_mask.data.cpu().numpy()[idx_in_batch, 3]
# sf.write(str(idx_in_batch)+'_pre_0.wav',multi_mask.data.cpu().numpy()[idx_in_batch,0].transpose(),44100)
# sf.write(str(idx_in_batch)+'_pre_1.wav',multi_mask.data.cpu().numpy()[idx_in_batch,1].transpose(),44100)
# sf.write(str(idx_in_batch)+'_pre_2.wav',multi_mask.data.cpu().numpy()[idx_in_batch,2].transpose(),44100)
# sf.write(str(idx_in_batch)+'_pre_3.wav',multi_mask.data.cpu().numpy()[idx_in_batch,3].transpose(),44100)
print('y0 range:', y0.min(), y0.max())
print('y1 range:', y1.min(), y1.max())
print('y2 range:', y2.min(), y2.max())
print('y3 range:', y3.min(), y3.max())
# input('wait')
print('*' * 50)
if 0 and updates > 10 and updates % config.eval_interval in [
0, 1, 2, 3, 4, 5
]:
utils.bss_eval_tas(config,
multi_mask,
train_data['multi_spk_fea_list'],
raw_tgt,
train_data,
dst=log_path + '/batch_output1')
sdr_aver_batch, sdri_aver_batch = bss_test.cal(log_path +
'/batch_output1/')
lera.log({'SDR sample': sdr_aver_batch})
lera.log({'SDRi sample': sdri_aver_batch})
writer.add_scalars('scalar/loss', {
'SDR_sample': sdr_aver_batch,
'SDRi_sample': sdri_aver_batch
}, updates)
SDR_SUM = np.append(SDR_SUM, sdr_aver_batch)
SDRi_SUM = np.append(SDRi_SUM, sdri_aver_batch)
print(('SDR_aver_now:', SDR_SUM.mean()))
print(('SDRi_aver_now:', SDRi_SUM.mean()))
total_loss += loss.cpu().item()
optim.step()
updates += 1
if updates % 30 == 0:
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss this batch: %6.3f,sgm loss: %6.6f,ss loss: %6.6f,label acc: %6.6f\n"
% (time.time() - start_time, epoch, updates, 0,
total_loss_sgm / 30.0, total_loss_ss / 30.0, 0))
# lera.log({'label_acc':report_correct/report_total})
# writer.add_scalars('scalar/loss',{'label_acc':report_correct/report_total},updates)
total_loss_sgm, total_loss_ss = 0, 0
# continue
if 0 and updates % config.eval_interval == 0 and epoch > 3: #建议至少跑几个epoch再进行测试，否则模型还没学到东西，会有很多问题。
logging(
"time: %6.3f, epoch: %3d, updates: %8d, train loss: %6.5f\n" %
(time.time() - start_time, epoch, updates, 0))
print(('evaluating after %d updates...\r' % updates))
original_bs = config.batch_size
score = eval(epoch) # eval的时候batch_size会变成1
# print 'Orignal bs:',original_bs
config.batch_size = original_bs
# print 'Now bs:',config.batch_size
for metric in config.metric:
scores[metric].append(score[metric])
lera.log({
'sgm_micro_f1': score[metric],
})
if metric == 'micro_f1' and score[metric] >= max(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
if metric == 'hamming_loss' and score[metric] <= min(
scores[metric]):
save_model(log_path + 'best_' + metric + '_checkpoint.pt')
model.train()
total_loss = 0
start_time = 0
report_total = 0
report_correct = 0
if updates > 10 and updates % config.save_interval == 1:
save_model(log_path + 'TDAAv4_conditional_{}.pt'.format(updates))
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
print '#' * 30 + 'ReID part ' + '#' * 30
# '''''
elif batch_idx <= (5000 / config.batch_size
): #only the former batches counts the SDR
predicted_maps = predicted_masks * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_output23jo')
del predicted_maps, predicted_masks, x_input_map_multi
SDR, SDRi = bss_test.cal('batch_output23jo/')
# SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output23jo/'))
SDR_SUM = np.append(SDR_SUM, SDR)
SDRi_SUM = np.append(SDRi_SUM, SDRi)
print 'SDR_aver_now:', SDR_SUM.mean()
print 'SDRi_aver_now:', SDRi_SUM.mean()
lera.log({'SDR sample': SDR_SUM.mean()})
lera.log({'SDRi sample': SDRi_SUM.mean()})
elif batch_idx == (5000 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: #only record the best SDR once.
print 'Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean())
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
# '''
candidate += [
def eval(epoch):
model.eval()
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'test'
print 'Test or valid:', test_or_valid
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
# for raw_src, src, src_len, raw_tgt, tgt, tgt_len in validloader:
SDR_SUM = np.array([])
SDRi_SUM = np.array([])
batch_idx = 0
global best_SDR, Var
while True:
# for ___ in range(2):
print '-' * 30
eval_data = eval_data_gen.next()
if eval_data == False:
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
raw_tgt = [
sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']
]
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.ones(
top_k + 2, config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
feas_tgt = models.rank_feas(raw_tgt,
eval_data['multi_spk_fea_list']) #这里是目标的图谱
if config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 4
feas_tgt_square = feas_tgt * feas_tgt
feas_tgt_square_sum = torch.sum(feas_tgt_square,
dim=1,
keepdim=True).expand(tmp_size)
WFM_mask = feas_tgt_square / (feas_tgt_square_sum + 1e-10)
if use_cuda:
src = src.cuda().transpose(0, 1)
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
try:
if 1 and len(opt.gpus) > 1:
# samples, alignment = model.module.sample(src, src_len)
samples, alignment, hiddens, predicted_masks = model.module.beam_sample(
src,
src_len,
dict_spk2idx,
tgt,
beam_size=config.beam_size)
else:
samples, alignment, hiddens, predicted_masks = model.beam_sample(
src,
src_len,
dict_spk2idx,
tgt,
beam_size=config.beam_size)
# samples, alignment, hiddens, predicted_masks = model.beam_sample(src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
except Exception, info:
print '**************Error eval occurs here************:', info
continue
if len(samples[0]) != 3:
print 'Wrong num of mixtures, passed.'
continue
if config.top1:
predicted_masks = torch.cat([predicted_masks, 1 - predicted_masks],
1)
# '''
# expand the raw mixed-features to topk channel.
src = src.transpose(0, 1)
siz = src.size()
assert len(siz) == 3
topk = feas_tgt.size()[1]
x_input_map_multi = torch.unsqueeze(src,
1).expand(siz[0], topk, siz[1],
siz[2])
if config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
if 1 and len(opt.gpus) > 1:
ss_loss = model.module.separation_loss(x_input_map_multi,
predicted_masks, feas_tgt,
None)
else:
ss_loss = model.separation_loss(x_input_map_multi, predicted_masks,
feas_tgt, None)
print 'loss for ss,this batch:', ss_loss.data[0]
lera.log({
'ss_loss_' + test_or_valid: ss_loss.data[0],
})
del ss_loss, hiddens
if 0 and config.reID:
print '#' * 30 + 'ReID part ' + '#' * 30
predict_multi_map = predicted_masks * x_input_map_multi
predict_multi_map = predict_multi_map.view(-1, mix_speech_len,
speech_fre).transpose(
0, 1)
tgt_reID = Variable(torch.ones(
3, top_k * config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len_reID = Variable(
torch.LongTensor(topk * config.batch_size).zero_() +
mix_speech_len).unsqueeze(0).cuda()
try:
if 1 and len(opt.gpus) > 1:
# samples, alignment = model.module.sample(src, src_len)
samples, alignment, hiddens, predicted_masks = model.module.beam_sample(
predict_multi_map,
src_len_reID,
dict_spk2idx,
tgt_reID,
beam_size=config.beam_size)
else:
samples, alignment, hiddens, predicted_masks = model.beam_sample(
predict_multi_map,
src_len_reID,
dict_spk2idx,
tgt_reID,
beam_size=config.beam_size)
# samples, alignment, hiddens, predicted_masks = model.beam_sample(src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
except Exception, info:
print '**************Error eval occurs here************:', info
# outputs_reID, targets_reID, multi_mask_reID = model(predict_multi_map, src_len_reID, tgt_reID, tgt_len_reID) #这里的outputs就是hidden_outputs，还没有进行最后分类的隐层，可以直接用
if batch_idx <= (500 / config.batch_size
): #only the former batches counts the SDR
# predicted_maps=predicted_masks*x_input_map_multi
predicted_maps = predicted_masks * predict_multi_map.transpose(
0, 1).unsqueeze(1)
predicted_maps = predicted_maps.transpose(0, 1)
# predicted_maps=Variable(feas_tgt)
utils.bss_eval(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_output23jo')
del predicted_maps, predicted_masks, x_input_map_multi, predict_multi_map
SDR, SDRi = bss_test.cal('batch_output23jo/')
# SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output23jo/'))
SDR_SUM = np.append(SDR_SUM, SDR)
SDRi_SUM = np.append(SDRi_SUM, SDRi)
print 'SDR_aver_now:', SDR_SUM.mean()
print 'SDRi_aver_now:', SDRi_SUM.mean()
lera.log({'SDR sample': SDR_SUM.mean()})
lera.log({'SDRi sample': SDRi_SUM.mean()})
elif batch_idx == (500 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: #only record the best SDR once.
print 'Best SDR from {}---->{}'.format(best_SDR,
SDR_SUM.mean())
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
print '#' * 30 + 'ReID part ' + '#' * 30
def main():
print('go to model')
print '*' * 80
spk_global_gen = prepare_data(mode='global',
train_or_test='train') #写一个假的数据生成，可以用来写模型先
global_para = spk_global_gen.next()
print global_para
spk_all_list, dict_spk2idx, dict_idx2spk, mix_speech_len, speech_fre, total_frames, spk_num_total = global_para
del spk_global_gen
num_labels = len(spk_all_list)
# data_generator=prepare_data('once','train')
# data_generator=prepare_data_fake(train_or_test='train',num_labels=num_labels) #写一个假的数据生成，可以用来写模型先
#此处顺序是 mix_speechs.shape,mix_feas.shape,aim_fea.shape,aim_spkid.shape,query.shape
#一个例子：(5, 17040) (5, 134, 129) (5, 134, 129) (5,) (5, 32, 400, 300, 3)
# datasize=prepare_datasize(data_generator)
# mix_speech_len,speech_fre,total_frames,spk_num_total,video_size=datasize
print 'Begin to build the maim model for Multi_Modal Cocktail Problem.'
# data=data_generator.next()
# This part is to build the 3D mix speech embedding maps.
mix_hidden_layer_3d = MIX_SPEECH(speech_fre, mix_speech_len).cuda()
mix_speech_classifier = MIX_SPEECH_classifier(speech_fre, mix_speech_len,
num_labels).cuda()
mix_speech_multiEmbedding = SPEECH_EMBEDDING(
num_labels, config.EMBEDDING_SIZE,
spk_num_total + config.UNK_SPK_SUPP).cuda()
print mix_hidden_layer_3d
print mix_speech_classifier
# mix_speech_hidden=mix_hidden_layer_3d(Variable(torch.from_numpy(data[1])).cuda())
# mix_speech_output=mix_speech_classifier(Variable(torch.from_numpy(data[1])).cuda())
# 技巧：alpha0的时候，就是选出top_k，top_k很大的时候，就是选出来大于alpha的
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=config.MAX_MIX)
# top_k_mask_mixspeech=top_k_mask(mix_speech_output,alpha=config.ALPHA,top_k=3)
# print top_k_mask_mixspeech
# mix_speech_multiEmbs=mix_speech_multiEmbedding(top_k_mask_mixspeech) # bs*num_labels（最多混合人个数）×Embedding的大小
# mix_speech_multiEmbs=mix_speech_multiEmbedding(Variable(torch.from_numpy(top_k_mask_mixspeech),requires_grad=False).cuda()) # bs*num_labels（最多混合人个数）×Embedding的大小
# 需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
# 把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
# mix_speech_hidden_5d=mix_speech_hidden.view(config.BATCH_SIZE,1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# mix_speech_hidden_5d=mix_speech_hidden_5d.expand(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,config.EMBEDDING_SIZE).contiguous()
# mix_speech_hidden_5d=mix_speech_hidden_5d.view(-1,mix_speech_len,speech_fre,config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
# att_multi_speech=att_speech_layer(mix_speech_hidden_5d,mix_speech_multiEmbs.view(-1,config.EMBEDDING_SIZE))
# print att_multi_speech.size()
# att_multi_speech=att_multi_speech.view(config.BATCH_SIZE,num_labels,mix_speech_len,speech_fre,-1)
# print att_multi_speech.size()
# This part is to conduct the video inputs.
# query_video_layer=VIDEO_QUERY(total_frames,config.VideoSize,spk_num_total).cuda()
query_video_layer = None
# print query_video_layer
# query_video_output,xx=query_video_layer(Variable(torch.from_numpy(data[4])))
# This part is to conduct the memory.
# hidden_size=(config.HIDDEN_UNITS)
hidden_size = (config.EMBEDDING_SIZE)
# x=torch.arange(0,24).view(2,3,4)
# y=torch.ones([2,4])
att_layer = ATTENTION(config.EMBEDDING_SIZE, 'align').cuda()
att_speech_layer = ATTENTION(config.EMBEDDING_SIZE, 'align').cuda()
# att=ATTENTION(4,'align')
# mask=att(x,y)#bs*max_len
# del data_generator
# del data
optimizer = torch.optim.Adam(
[
{
'params': mix_hidden_layer_3d.parameters()
},
{
'params': mix_speech_multiEmbedding.parameters()
},
{
'params': mix_speech_classifier.parameters()
},
# {'params':query_video_layer.lstm_layer.parameters()},
# {'params':query_video_layer.dense.parameters()},
# {'params':query_video_layer.Linear.parameters()},
{
'params': att_layer.parameters()
},
{
'params': att_speech_layer.parameters()
},
# ], lr=0.02,momentum=0.9)
],
lr=0.00005)
if 1 and config.Load_param:
# query_video_layer.load_state_dict(torch.load('param_video_layer_19'))
# mix_speech_classifier.load_state_dict(torch.load('params/param_speech_123onezeroag3_WSJ0_multilabel_epoch40'))
mix_speech_classifier.load_state_dict(
torch.load(
'params/param_speech_123onezeroag4_WSJ0_multilabel_epoch70'))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_WSJ0_hidden3d_180'))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_WSJ0_emblayer_180'))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_WSJ0_attlayer_180'))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbag1nosum_WSJ0_hidden3d_350',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbag1nosum_WSJ0_emblayer_350',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbag1nosum_WSJ0_attlayer_350',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2or3_db_WSJ0_hidden3d_560',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2or3_db_WSJ0_emblayer_560',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix2or3_db_WSJ0_attlayer_560',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbag2sum_WSJ0_hidden3d_460',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbag2sum_WSJ0_emblayer_460',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbag2sum_WSJ0_attlayer_460',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbdropout_WSJ0_hidden3d_370',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbdropout_WSJ0_emblayer_370',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbdropout_WSJ0_attlayer_370',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_4db_WSJ0_hidden3d_110',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_4db_WSJ0_emblayer_110',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_4db_WSJ0_attlayer_110',map_location={'cuda:1':'cuda:0'}))
# mix_speech_classifier.load_state_dict(torch.load('params/param_speech_4lstm_multilabelloss30map_epoch440'))
# att_speech_layer.load_state_dict(torch.load('params/param_mix101_dbdropoutag_WSJ0_attlayer_220',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix101_dbdropoutag_WSJ0_hidden3d_220',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix101_dbdropoutag_WSJ0_emblayer_220',map_location={'cuda:1':'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix2_db2dropout_WSJ0_attlayer_495',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix2_db2dropout_WSJ0_hidden3d_495',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix2_db2dropout_WSJ0_emblayer_495',map_location={'cuda:1':'cuda:0'}))
att_speech_layer.load_state_dict(
torch.load('params/param_mix2_db2dropout_WSJ0_attlayer_90',
map_location={'cuda:1': 'cuda:0'}))
mix_hidden_layer_3d.load_state_dict(
torch.load('params/param_mix2_db2dropout_WSJ0_hidden3d_90',
map_location={'cuda:1': 'cuda:0'}))
mix_speech_multiEmbedding.load_state_dict(
torch.load('params/param_mix2_db2dropout_WSJ0_emblayer_90',
map_location={'cuda:1': 'cuda:0'}))
# att_speech_layer.load_state_dict(torch.load('params/param_mix1to3_dbdropoutag1_WSJ0_attlayer_430',map_location={'cuda:1':'cuda:0'}))
# mix_hidden_layer_3d.load_state_dict(torch.load('params/param_mix1to3_dbdropoutag1_WSJ0_hidden3d_430',map_location={'cuda:1':'cuda:0'}))
# mix_speech_multiEmbedding.load_state_dict(torch.load('params/param_mix1to3_dbdropoutag1_WSJ0_emblayer_430',map_location={'cuda:1':'cuda:0'}))
loss_func = torch.nn.MSELoss() # the target label is NOT an one-hotted
loss_multi_func = torch.nn.MSELoss(
) # the target label is NOT an one-hotted
# loss_multi_func = torch.nn.L1Loss() # the target label is NOT an one-hotted
loss_query_class = torch.nn.CrossEntropyLoss()
print '''Begin to calculate.'''
for epoch_idx in range(config.MAX_EPOCH):
if epoch_idx > 0:
print 'SDR_SUM (len:{}) for epoch {} : {}'.format(
SDR_SUM.shape, epoch_idx - 1, SDR_SUM.mean())
SDR_SUM = np.array([])
# print_memory_state(memory.memory)
print 'SDR_SUM for epoch {}:{}'.format(epoch_idx - 1, SDR_SUM.mean())
for batch_idx in range(config.EPOCH_SIZE):
print '*' * 40, epoch_idx, batch_idx, '*' * 40
train_data_gen = prepare_data('once', 'train')
# train_data_gen=prepare_data('once','test')
# train_data_gen=prepare_data('once','eval_test')
train_data = train_data_gen.next()
'''混合语音len,fre,Emb 3D表示层'''
mix_speech_hidden = mix_hidden_layer_3d(
Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
# 暂时关掉video部分,因为s2 s3 s4 的视频数据不全暂时
'''Speech self Sepration 语音自分离部分'''
mix_speech_output = mix_speech_classifier(
Variable(torch.from_numpy(train_data['mix_feas'])).cuda())
#从数据里得到ground truth的说话人名字和vector
y_spk_list = [
one.keys() for one in train_data['multi_spk_fea_list']
]
y_spk_list = train_data['multi_spk_fea_list']
y_spk_gtruth, y_map_gtruth = multi_label_vector(
y_spk_list, dict_spk2idx)
# 如果训练阶段使用Ground truth的分离结果作为判别
if 1 and config.Ground_truth:
mix_speech_output = Variable(
torch.from_numpy(y_map_gtruth)).cuda()
if 0 and test_all_outputchannel: #把输入的mask改成全１，可以用来测试输出所有的channel
mix_speech_output = Variable(
torch.ones(
config.BATCH_SIZE,
num_labels,
))
y_map_gtruth = np.ones([config.BATCH_SIZE, num_labels])
max_num_labels = 2
top_k_mask_mixspeech, top_k_sort_index = top_k_mask(
mix_speech_output, alpha=-0.5,
top_k=max_num_labels) #torch.Float型的
top_k_mask_idx = [
np.where(line == 1)[0]
for line in top_k_mask_mixspeech.numpy()
]
mix_speech_multiEmbs = mix_speech_multiEmbedding(
top_k_mask_mixspeech,
top_k_mask_idx) # bs*num_labels（最多混合人个数）×Embedding的大小
assert len(top_k_mask_idx[0]) == len(top_k_mask_idx[-1])
top_k_num = len(top_k_mask_idx[0])
#需要计算：mix_speech_hidden[bs,len,fre,emb]和mix_mulEmbedding[bs,num_labels,EMB]的Ａttention
#把 前者扩充为bs*num_labels,XXXXXXXXX的，后者也是，然后用ＡＴＴ函数计算它们再转回来就好了
mix_speech_hidden_5d = mix_speech_hidden.view(
config.BATCH_SIZE, 1, mix_speech_len, speech_fre,
config.EMBEDDING_SIZE)
mix_speech_hidden_5d = mix_speech_hidden_5d.expand(
config.BATCH_SIZE, top_k_num, mix_speech_len, speech_fre,
config.EMBEDDING_SIZE).contiguous()
mix_speech_hidden_5d_last = mix_speech_hidden_5d.view(
-1, mix_speech_len, speech_fre, config.EMBEDDING_SIZE)
# att_speech_layer=ATTENTION(config.EMBEDDING_SIZE,'align').cuda()
att_speech_layer = ATTENTION(config.EMBEDDING_SIZE, 'dot').cuda()
att_multi_speech = att_speech_layer(
mix_speech_hidden_5d_last,
mix_speech_multiEmbs.view(-1, config.EMBEDDING_SIZE))
# print att_multi_speech.size()
att_multi_speech = att_multi_speech.view(
config.BATCH_SIZE, top_k_num, mix_speech_len,
speech_fre) # bs,num_labels,len,fre这个东西
# print att_multi_speech.size()
multi_mask = att_multi_speech
# top_k_mask_mixspeech_multi=top_k_mask_mixspeech.view(config.BATCH_SIZE,top_k_num,1,1).expand(config.BATCH_SIZE,top_k_num,mix_speech_len,speech_fre)
# multi_mask=multi_mask*Variable(top_k_mask_mixspeech_multi).cuda()
x_input_map = Variable(torch.from_numpy(
train_data['mix_feas'])).cuda()
# print x_input_map.size()
x_input_map_multi = x_input_map.view(
config.BATCH_SIZE, 1, mix_speech_len,
speech_fre).expand(config.BATCH_SIZE, top_k_num,
mix_speech_len, speech_fre)
# predict_multi_map=multi_mask*x_input_map_multi
predict_multi_map = multi_mask * x_input_map_multi
bss_eval_fromGenMap(multi_mask, x_input_map, top_k_mask_mixspeech,
dict_idx2spk, train_data, top_k_sort_index)
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output/', 2))
print 'SDR_SUM (len:{}) for epoch {} : {}'.format(
SDR_SUM.shape, epoch_idx, SDR_SUM.mean())
def eval(epoch):
model.eval()
reference, candidate, source, alignments = [], [], [], []
e = epoch
test_or_valid = 'valid'
print 'Test or valid:', test_or_valid
eval_data_gen = prepare_data('once', test_or_valid, config.MIN_MIX,
config.MAX_MIX)
# for raw_src, src, src_len, raw_tgt, tgt, tgt_len in validloader:
SDR_SUM = np.array([])
batch_idx = 0
global best_SDR
while True:
# for ___ in range(2):
print '-' * 30
eval_data = eval_data_gen.next()
if eval_data == False:
break #如果这个epoch的生成器没有数据了，直接进入下一个epoch
src = Variable(torch.from_numpy(eval_data['mix_feas']))
raw_tgt = [
sorted(spk.keys()) for spk in eval_data['multi_spk_fea_list']
]
top_k = len(raw_tgt[0])
# 要保证底下这几个都是longTensor(长整数）
# tgt = Variable(torch.from_numpy(np.array([[0]+[dict_spk2idx[spk] for spk in spks]+[dict_spk2idx['<EOS>']] for spks in raw_tgt],dtype=np.int))).transpose(0,1) #转换成数字，然后前后加开始和结束符号。
tgt = Variable(torch.ones(
top_k + 2, config.batch_size)) # 这里随便给一个tgt，为了测试阶段tgt的名字无所谓其实。
src_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
mix_speech_len).unsqueeze(0)
tgt_len = Variable(
torch.LongTensor(config.batch_size).zero_() +
len(eval_data['multi_spk_fea_list'][0])).unsqueeze(0)
feas_tgt = models.rank_feas(raw_tgt,
eval_data['multi_spk_fea_list']) #这里是目标的图谱
relitu(mix_speech_len, speech_fre, feas_tgt.numpy()[0, 0].transpose())
relitu(mix_speech_len, speech_fre, feas_tgt.numpy()[0, 1].transpose())
# 1/0
if config.WFM:
tmp_size = feas_tgt.size()
assert len(tmp_size) == 4
feas_tgt_sum = torch.sum(feas_tgt, dim=1, keepdim=True)
feas_tgt_sum_square = (feas_tgt_sum *
feas_tgt_sum).expand(tmp_size)
feas_tgt_square = feas_tgt * feas_tgt
WFM_mask = feas_tgt_square / feas_tgt_sum_square
if use_cuda:
src = src.cuda()
tgt = tgt.cuda()
src_len = src_len.cuda()
tgt_len = tgt_len.cuda()
feas_tgt = feas_tgt.cuda()
if config.WFM:
WFM_mask = WFM_mask.cuda()
if len(opt.gpus) > 1:
samples, alignment = model.module.sample(src, src_len)
else:
samples, alignment, hiddens, predicted_masks = model.beam_sample(
src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
# try:
# samples, alignment, hiddens, predicted_masks = model.beam_sample(src, src_len, dict_spk2idx, tgt, beam_size=config.beam_size)
# except Exception,info:
# print '**************Error occurs here************:', info
# continue
if config.top1:
predicted_masks = torch.cat([predicted_masks, 1 - predicted_masks],
1)
# '''
# expand the raw mixed-features to topk channel.
siz = src.size()
assert len(siz) == 3
topk = feas_tgt.size()[1]
x_input_map_multi = torch.unsqueeze(src,
1).expand(siz[0], topk, siz[1],
siz[2])
if config.WFM:
feas_tgt = x_input_map_multi.data * WFM_mask
ss_loss = model.separation_loss(x_input_map_multi, predicted_masks,
feas_tgt)
print 'loss for ss,this batch:', ss_loss.data[0]
del ss_loss, hiddens
# '''''
if batch_idx <= (3000 / config.batch_size
): #only the former batches counts the SDR
predicted_maps = predicted_masks * x_input_map_multi
# predicted_maps=Variable(feas_tgt)
utils.bss_eval(config,
predicted_maps,
eval_data['multi_spk_fea_list'],
raw_tgt,
eval_data,
dst='batch_output23jo')
del predicted_maps, predicted_masks, x_input_map_multi
SDR_SUM = np.append(SDR_SUM, bss_test.cal('batch_output23jo/'))
print 'SDR_aver_now:', SDR_SUM.mean()
# 1/0
raw_input('Press any key to continue......')
continue
elif batch_idx == (500 / config.batch_size) + 1 and SDR_SUM.mean(
) > best_SDR: #only record the best SDR once.
print 'Best SDR from {}---->{}'.format(best_SDR, SDR_SUM.mean())
best_SDR = SDR_SUM.mean()
# save_model(log_path+'checkpoint_bestSDR{}.pt'.format(best_SDR))
# '''
candidate += [
convertToLabels(dict_idx2spk, s, dict_spk2idx['<EOS>'])
for s in samples
]
# source += raw_src
reference += raw_tgt
print 'samples:', samples
print 'can:{}, \nref:{}'.format(candidate[-1 * config.batch_size:],
reference[-1 * config.batch_size:])
alignments += [align for align in alignment]
batch_idx += 1
if opt.unk:
cands = []
for s, c, align in zip(source, candidate, alignments):
cand = []
for word, idx in zip(c, align):
if word == dict.UNK_WORD and idx < len(s):
try:
cand.append(s[idx])
except:
cand.append(word)
print("%d %d\n" % (len(s), idx))
else:
cand.append(word)
cands.append(cand)
candidate = cands
score = {}
result = utils.eval_metrics(reference, candidate, dict_spk2idx, log_path)
logging_csv([e, updates, result['hamming_loss'], \
result['micro_f1'], result['micro_precision'], result['micro_recall']])
print('hamming_loss: %.8f | micro_f1: %.4f' %
(result['hamming_loss'], result['micro_f1']))
score['hamming_loss'] = result['hamming_loss']
score['micro_f1'] = result['micro_f1']
return score