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 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):
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
print('loading checkpoint...\n', opt.restore)
checkpoints = torch.load(opt.restore, map_location={'cuda:2': 'cuda:0'})
# cuda
use_cuda = torch.cuda.is_available() and len(opt.gpus) > 0
use_cuda = True
if use_cuda:
torch.cuda.set_device(opt.gpus[0])
torch.cuda.manual_seed(opt.seed)
print(use_cuda)
# load the global statistic of the data
print('loading data...\n')
start_time = time.time()
spk_global_gen = prepare_data(mode='global',
train_or_test='train') # 数据中的一些统计参数的读取
global_para = spk_global_gen.next()
print(global_para)
spk_all_list = global_para['all_spk'] # 所有说话人的列表
dict_spk2idx = global_para['dict_spk_to_idx']
dict_idx2spk = global_para['dict_idx_to_spk']
speech_fre = global_para['num_fre'] # 语音频率总数
total_frames = global_para['num_frames'] # 语音长度
spk_num_total = global_para['total_spk_num'] # 总计说话人数目
batch_total = global_para['total_batch_num'] # 一个epoch里多少个batch
config.speech_fre = speech_fre
mix_speech_len = total_frames
config.mix_speech_len = total_frames
num_labels = len(spk_all_list)
print('loading checkpoint...\n', opt.restore)
checkpoints = torch.load(opt.restore)
# cuda
use_cuda = torch.cuda.is_available() and len(opt.gpus) > 0
use_cuda = True
if use_cuda:
torch.cuda.set_device(opt.gpus[0])
torch.cuda.manual_seed(opt.seed)
print(use_cuda)
# data
print('loading data...\n')
start_time = time.time()
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 = global_para['all_spk']
dict_spk2idx = global_para['dict_spk_to_idx']
dict_idx2spk = global_para['dict_idx_to_spk']
speech_fre = global_para['num_fre'] # 语音频率
total_frames = global_para['num_frames'] # 语音长度
spk_num_total = global_para['total_spk_num']
batch_total = global_para['total_batch_num']
config.speech_fre = speech_fre
mix_speech_len = total_frames
# mix_speech_len=626
# mix_speech_len=1251
config.mix_speech_len = total_frames
