mirror of https://github.com/coqui-ai/TTS.git
Remove variables
Eren Golge
parent
95654de860
commit
7bc27fac82
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@ -9,7 +9,7 @@ class PrenetTests(unittest.TestCase):
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def test_in_out(self):
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layer = Prenet(128, out_features=[256, 128])
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dummy_input = T.autograd.Variable(T.rand(4, 128))
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dummy_input = T.rand(4, 128)
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print(layer)
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output = layer(dummy_input)
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@ -21,7 +21,7 @@ class CBHGTests(unittest.TestCase):
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def test_in_out(self):
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layer = CBHG(128, K=6, projections=[128, 128], num_highways=2)
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dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
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dummy_input = T.rand(4, 8, 128)
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print(layer)
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output = layer(dummy_input)
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@ -34,8 +34,8 @@ class DecoderTests(unittest.TestCase):
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def test_in_out(self):
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layer = Decoder(in_features=256, memory_dim=80, r=2)
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dummy_input = T.autograd.Variable(T.rand(4, 8, 256))
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dummy_memory = T.autograd.Variable(T.rand(4, 2, 80))
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dummy_input = T.rand(4, 8, 256)
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dummy_memory = T.rand(4, 2, 80)
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output, alignment = layer(dummy_input, dummy_memory)
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@ -48,7 +48,7 @@ class EncoderTests(unittest.TestCase):
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def test_in_out(self):
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layer = Encoder(128)
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dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
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dummy_input = T.rand(4, 8, 128)
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print(layer)
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output = layer(dummy_input)
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@ -62,24 +62,22 @@ class L1LossMaskedTests(unittest.TestCase):
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def test_in_out(self):
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layer = L1LossMasked()
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dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_target = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_length = T.autograd.Variable((T.ones(4) * 8).long())
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dummy_input = T.ones(4, 8, 128).float()
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dummy_target = T.ones(4, 8, 128).float()
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dummy_length = (T.ones(4) * 8).long()
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output = layer(dummy_input, dummy_target, dummy_length)
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assert output.shape[0] == 0
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assert len(output.shape) == 1
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assert output.data[0] == 0.0
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assert output.item() == 0.0
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dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
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dummy_length = T.autograd.Variable((T.ones(4) * 8).long())
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dummy_input = T.ones(4, 8, 128).float()
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dummy_target = T.zeros(4, 8, 128).float()
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dummy_length = (T.ones(4) * 8).long()
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output = layer(dummy_input, dummy_target, dummy_length)
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assert output.data[0] == 1.0, "1.0 vs {}".format(output.data[0])
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assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
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dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
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dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
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dummy_length = T.autograd.Variable((T.arange(5, 9)).long())
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dummy_input = T.ones(4, 8, 128).float()
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dummy_target = T.zeros(4, 8, 128).float()
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dummy_length = (T.arange(5, 9)).long()
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mask = ((_sequence_mask(dummy_length).float() - 1.0)
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* 100.0).unsqueeze(2)
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output = layer(dummy_input + mask, dummy_target, dummy_length)
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assert output.data[0] == 1.0, "1.0 vs {}".format(output.data[0])
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assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
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94
train.py
94
train.py
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@ -12,7 +12,6 @@ import numpy as np
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import torch.nn as nn
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from torch import optim
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from torch.autograd import Variable
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from torch.utils.data import DataLoader
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from tensorboardX import SummaryWriter
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@ -94,41 +93,41 @@ def train(model, criterion, data_loader, optimizer, epoch):
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optimizer.zero_grad()
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# convert inputs to variables
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text_input_var = Variable(text_input)
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mel_spec_var = Variable(mel_input)
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mel_lengths_var = Variable(mel_lengths)
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linear_spec_var = Variable(linear_input, volatile=True)
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text_input.requires_grad_()
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mel_spec.requires_grad_()
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# mel_lengths.requires_grad_()
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# linear_spec.requires_grad_()
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# dispatch data to GPU
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if use_cuda:
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text_input_var = text_input_var.cuda()
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mel_spec_var = mel_spec_var.cuda()
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mel_lengths_var = mel_lengths_var.cuda()
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linear_spec_var = linear_spec_var.cuda()
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text_input = text_input.cuda()
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mel_spec = mel_spec.cuda()
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mel_lengths = mel_lengths.cuda()
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linear_spec = linear_spec.cuda()
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# create attention mask
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if c.mk > 0.0:
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N = text_input_var.shape[1]
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T = mel_spec_var.shape[1] // c.r
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N = text_input.shape[1]
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T = mel_spec.shape[1] // c.r
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M = create_attn_mask(N, T, 0.03)
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mk = mk_decay(c.mk, c.epochs, epoch)
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# forward pass
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mel_output, linear_output, alignments =\
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model.forward(text_input_var, mel_spec_var)
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model.forward(text_input, mel_spec)
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# loss computation
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mel_loss = criterion(mel_output, mel_spec_var, mel_lengths_var)
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linear_loss = 0.5 * criterion(linear_output, linear_spec_var, mel_lengths_var) \
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mel_loss = criterion(mel_output, mel_spec, mel_lengths)
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linear_loss = 0.5 * criterion(linear_output, linear_spec, mel_lengths) \
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+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
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linear_spec_var[:, :, :n_priority_freq],
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mel_lengths_var)
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linear_spec[:, :, :n_priority_freq],
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mel_lengths)
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loss = mel_loss + linear_loss
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if c.mk > 0.0:
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attention_loss = criterion(alignments, M, mel_lengths_var)
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attention_loss = criterion(alignments, M, mel_lengths)
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loss += mk * attention_loss
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avg_attn_loss += attention_loss.data[0]
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progbar_display['attn_loss'] = attention_loss.data[0]
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avg_attn_loss += attention_loss.item()
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progbar_display['attn_loss'] = attention_loss.item()
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# backpass and check the grad norm
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loss.backward()
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@ -142,21 +141,21 @@ def train(model, criterion, data_loader, optimizer, epoch):
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step_time = time.time() - start_time
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epoch_time += step_time
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progbar_display['total_loss'] = loss.data[0]
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progbar_display['linear_loss'] = linear_loss.data[0]
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progbar_display['mel_loss'] = mel_loss.data[0]
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progbar_display['total_loss'] = loss.item()
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progbar_display['linear_loss'] = linear_loss.item()
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progbar_display['mel_loss'] = mel_loss.item()
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progbar_display['grad_norm'] = grad_norm
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# update
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progbar.update(num_iter+1, values=list(progbar_display.items()))
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avg_linear_loss += linear_loss.data[0]
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avg_mel_loss += mel_loss.data[0]
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avg_linear_loss += linear_loss.item()
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avg_mel_loss += mel_loss.item()
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# Plot Training Iter Stats
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tb.add_scalar('TrainIterLoss/TotalLoss', loss.data[0], current_step)
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tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.data[0],
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tb.add_scalar('TrainIterLoss/TotalLoss', loss.item(), current_step)
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tb.add_scalar('TrainIterLoss/LinearLoss', linear_loss.item(),
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current_step)
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tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.data[0], current_step)
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tb.add_scalar('TrainIterLoss/MelLoss', mel_loss.item(), current_step)
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tb.add_scalar('Params/LearningRate', optimizer.param_groups[0]['lr'],
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current_step)
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tb.add_scalar('Params/GradNorm', grad_norm, current_step)
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@ -165,12 +164,12 @@ def train(model, criterion, data_loader, optimizer, epoch):
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if current_step % c.save_step == 0:
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if c.checkpoint:
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# save model
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save_checkpoint(model, optimizer, linear_loss.data[0],
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save_checkpoint(model, optimizer, linear_loss.item(),
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OUT_PATH, current_step, epoch)
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# Diagnostic visualizations
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const_spec = linear_output[0].data.cpu().numpy()
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gt_spec = linear_spec_var[0].data.cpu().numpy()
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gt_spec = linear_spec[0].data.cpu().numpy()
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const_spec = plot_spectrogram(const_spec, data_loader.dataset.ap)
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gt_spec = plot_spectrogram(gt_spec, data_loader.dataset.ap)
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@ -221,6 +220,7 @@ def evaluate(model, criterion, data_loader, current_step):
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print(" | > Validation")
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progbar = Progbar(len(data_loader.dataset) / c.batch_size)
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n_priority_freq = int(3000 / (c.sample_rate * 0.5) * c.num_freq)
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with torch.no_grad():
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for num_iter, data in enumerate(data_loader):
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start_time = time.time()
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@ -231,47 +231,41 @@ def evaluate(model, criterion, data_loader, current_step):
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mel_input = data[3]
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mel_lengths = data[4]
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# convert inputs to variables
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text_input_var = Variable(text_input)
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mel_spec_var = Variable(mel_input)
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mel_lengths_var = Variable(mel_lengths)
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linear_spec_var = Variable(linear_input, volatile=True)
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# dispatch data to GPU
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if use_cuda:
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text_input_var = text_input_var.cuda()
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mel_spec_var = mel_spec_var.cuda()
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mel_lengths_var = mel_lengths_var.cuda()
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linear_spec_var = linear_spec_var.cuda()
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text_input = text_input.cuda()
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mel_spec = mel_spec.cuda()
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mel_lengths = mel_lengths.cuda()
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linear_spec = linear_spec.cuda()
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# forward pass
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mel_output, linear_output, alignments =\
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model.forward(text_input_var, mel_spec_var)
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model.forward(text_input, mel_spec)
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# loss computation
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mel_loss = criterion(mel_output, mel_spec_var, mel_lengths_var)
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linear_loss = 0.5 * criterion(linear_output, linear_spec_var, mel_lengths_var) \
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mel_loss = criterion(mel_output, mel_spec, mel_lengths)
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linear_loss = 0.5 * criterion(linear_output, linear_spec, mel_lengths) \
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+ 0.5 * criterion(linear_output[:, :, :n_priority_freq],
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linear_spec_var[:, :, :n_priority_freq],
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mel_lengths_var)
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linear_spec[:, :, :n_priority_freq],
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mel_lengths)
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loss = mel_loss + linear_loss
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step_time = time.time() - start_time
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epoch_time += step_time
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# update
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progbar.update(num_iter+1, values=[('total_loss', loss.data[0]),
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progbar.update(num_iter+1, values=[('total_loss', loss.item()),
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('linear_loss',
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linear_loss.data[0]),
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('mel_loss', mel_loss.data[0])])
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linear_loss.item()),
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('mel_loss', mel_loss.item())])
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avg_linear_loss += linear_loss.data[0]
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avg_mel_loss += mel_loss.data[0]
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avg_linear_loss += linear_loss.item()
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avg_mel_loss += mel_loss.item()
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# Diagnostic visualizations
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idx = np.random.randint(mel_input.shape[0])
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const_spec = linear_output[idx].data.cpu().numpy()
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gt_spec = linear_spec_var[idx].data.cpu().numpy()
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gt_spec = linear_spec[idx].data.cpu().numpy()
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align_img = alignments[idx].data.cpu().numpy()
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const_spec = plot_spectrogram(const_spec, data_loader.dataset.ap)
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