def save_parameters(checkpoint_dir, iteration, model, optimizer=None):
"""Checkpoint the latest trained model parameters.
Args:
checkpoint_dir (str): the directory where checkpoint is saved.
iteration (int): the latest iteration number.
model (obj): model to be checkpointed.
optimizer (obj, optional): optimizer to be checkpointed.
Defaults to None.
Returns:
None
"""
checkpoint_path = os.path.join(checkpoint_dir, "step-{}".format(iteration))
model_dict = model.state_dict()
dg.save_dygraph(model_dict, checkpoint_path)
print("[checkpoint] Saved model to {}.pdparams".format(checkpoint_path))
if optimizer:
opt_dict = optimizer.state_dict()
dg.save_dygraph(opt_dict, checkpoint_path)
print("[checkpoint] Saved optimzier state to {}.pdopt".format(
checkpoint_path))
_save_checkpoint(checkpoint_dir, iteration)
elif 'body_conv' in k:
is_bias = k.split('_')[-1]
new_k = '.'.join(['roi_body_uv_heads', k[:-2], rename_dict[is_bias]])
new_weight_dict[new_k] = v
else:
print(k)
import sys
sys.path.append('/home/aistudio/')
import numpy as np
import paddle.fluid.dygraph as dg
from densepose.config.config import get_cfg
from densepose.modeling.build import build_model
cfg = get_cfg()
densepose = build_model(cfg)
densepose.load_dict(new_weight_dict)
import paddle.fluid.dygraph as dg
dg.save_dygraph(
densepose.state_dict(),
'/home/aistudio/densepose/pretrained_models/DensePose_ResNet101_FPN_32x8d_s1x-e2e'
)
weight_decay=0.01)
g_clip = F.dygraph_grad_clip.GradClipByGlobalNorm(1.0)
for epoch in range(EPOCH):
for step, (ids_student, ids, sids,
labels) in enumerate(train_ds.start(place)):
loss, logits = teacher_model(ids, labels=labels)
loss.backward()
if step % 10 == 0:
print('[step %03d] teacher train loss %.5f lr %.3e' %
(step, loss.numpy(), opt.current_step_lr()))
opt.minimize(loss, grad_clip=g_clip)
teacher_model.clear_gradients()
if step % 100 == 0:
f1 = evaluate_teacher(teacher_model, dev_ds)
print('teacher f1: %.5f' % f1)
D.save_dygraph(teacher_model.state_dict(), './teacher_model')
else:
state_dict, _ = D.load_dygraph('./teacher_model')
teacher_model.set_dict(state_dict)
f1 = evaluate_teacher(teacher_model, dev_ds)
print('teacher f1: %.5f' % f1)
# 定义finetune student 模型所需要的超参数
SEQLEN = 256
BATCH = 100
EPOCH = 10
LR = 1e-4
def evaluate_student(model, dataset):
all_pred, all_label = [], []
def train_model(model, loader, criterion, optimizer, clipper, writer, args,
hparams):
assert fluid.framework.in_dygraph_mode(
), "this function must be run within dygraph guard"
n_trainers = dg.parallel.Env().nranks
local_rank = dg.parallel.Env().local_rank
# amount of shifting when compute losses
linear_shift = hparams.outputs_per_step
mel_shift = hparams.outputs_per_step
global_step = 0
global_epoch = 0
ismultispeaker = model.n_speakers > 1
checkpoint_dir = os.path.join(args.output, "checkpoints")
tensorboard_dir = os.path.join(args.output, "log")
ce_loss = 0
start_time = time.time()
for epoch in range(hparams.nepochs):
epoch_loss = 0.
for step, inputs in tqdm(enumerate(loader())):
if len(inputs) == 9:
(text, input_lengths, mel, linear, text_positions,
frame_positions, done, target_lengths, speaker_ids) = inputs
else:
(text, input_lengths, mel, linear, text_positions,
frame_positions, done, target_lengths) = inputs
speaker_ids = None
model.train()
if not (args.train_seq2seq_only or args.train_postnet_only):
results = model(text, input_lengths, mel, speaker_ids,
text_positions, frame_positions)
mel_outputs, linear_outputs, alignments, done_hat = results
elif args.train_seq2seq_only:
if speaker_ids is not None:
speaker_embed = model.speaker_embedding(speaker_ids)
else:
speaker_embed = None
results = model.seq2seq(text, input_lengths, mel,
speaker_embed, text_positions,
frame_positions)
mel_outputs, alignments, done_hat, decoder_states = results
if model.r > 1:
mel_outputs = fluid.layers.transpose(
mel_outputs, [0, 3, 2, 1])
mel_outputs = fluid.layers.reshape(
mel_outputs,
[mel_outputs.shape[0], -1, 1, model.mel_dim])
mel_outputs = fluid.layers.transpose(
mel_outputs, [0, 3, 2, 1])
linear_outputs = None
else:
assert (
model.use_decoder_state_for_postnet_input is False
), "when train only the converter, you have no decoder states"
if speaker_ids is not None:
speaker_embed = model.speaker_embedding(speaker_ids)
else:
speaker_embed = None
linear_outputs = model.converter(mel, speaker_embed)
alignments = None
mel_outputs = None
done_hat = None
if not args.train_seq2seq_only:
n_priority_freq = int(hparams.priority_freq /
(hparams.sample_rate * 0.5) *
model.linear_dim)
linear_mask = fluid.layers.sequence_mask(
target_lengths, maxlen=linear.shape[-1], dtype="float32")
linear_mask = linear_mask[:, linear_shift:]
linear_predicted = linear_outputs[:, :, :, :-linear_shift]
linear_target = linear[:, :, :, linear_shift:]
lin_l1_loss = criterion.l1_loss(linear_predicted,
linear_target,
linear_mask,
priority_bin=n_priority_freq)
lin_div = criterion.binary_divergence(linear_predicted,
linear_target,
linear_mask)
lin_loss = criterion.binary_divergence_weight * lin_div \
+ (1 - criterion.binary_divergence_weight) * lin_l1_loss
if writer is not None and local_rank == 0:
writer.add_scalar("linear_loss", float(lin_loss.numpy()),
global_step)
writer.add_scalar("linear_l1_loss",
float(lin_l1_loss.numpy()), global_step)
writer.add_scalar("linear_binary_div_loss",
float(lin_div.numpy()), global_step)
if not args.train_postnet_only:
mel_lengths = target_lengths // hparams.downsample_step
mel_mask = fluid.layers.sequence_mask(mel_lengths,
maxlen=mel.shape[-1],
dtype="float32")
mel_mask = mel_mask[:, mel_shift:]
mel_predicted = mel_outputs[:, :, :, :-mel_shift]
mel_target = mel[:, :, :, mel_shift:]
mel_l1_loss = criterion.l1_loss(mel_predicted, mel_target,
mel_mask)
mel_div = criterion.binary_divergence(mel_predicted,
mel_target, mel_mask)
mel_loss = criterion.binary_divergence_weight * mel_div \
+ (1 - criterion.binary_divergence_weight) * mel_l1_loss
if writer is not None and local_rank == 0:
writer.add_scalar("mel_loss", float(mel_loss.numpy()),
global_step)
writer.add_scalar("mel_l1_loss",
float(mel_l1_loss.numpy()), global_step)
writer.add_scalar("mel_binary_div_loss",
float(mel_div.numpy()), global_step)
done_loss = criterion.done_loss(done_hat, done)
if writer is not None and local_rank == 0:
writer.add_scalar("done_loss", float(done_loss.numpy()),
global_step)
if hparams.use_guided_attention:
decoder_length = target_lengths.numpy() / (
hparams.outputs_per_step * hparams.downsample_step)
attn_loss = criterion.attention_loss(
alignments, input_lengths.numpy(), decoder_length)
if writer is not None and local_rank == 0:
writer.add_scalar("attention_loss",
float(attn_loss.numpy()),
global_step)
if not (args.train_seq2seq_only or args.train_postnet_only):
if hparams.use_guided_attention:
loss = lin_loss + mel_loss + done_loss + attn_loss
else:
loss = lin_loss + mel_loss + done_loss
elif args.train_seq2seq_only:
if hparams.use_guided_attention:
loss = mel_loss + done_loss + attn_loss
else:
loss = mel_loss + done_loss
else:
loss = lin_loss
if writer is not None and local_rank == 0:
writer.add_scalar("loss", float(loss.numpy()), global_step)
if isinstance(optimizer._learning_rate,
fluid.optimizer.LearningRateDecay):
current_lr = optimizer._learning_rate.step().numpy()
else:
current_lr = optimizer._learning_rate
if writer is not None and local_rank == 0:
writer.add_scalar("learning_rate", current_lr, global_step)
epoch_loss += loss.numpy()[0]
if (local_rank == 0 and global_step > 0
and global_step % hparams.checkpoint_interval == 0):
save_states(global_step, writer, mel_outputs,
linear_outputs, alignments, mel, linear,
input_lengths.numpy(), checkpoint_dir)
step_path = os.path.join(
checkpoint_dir, "checkpoint_{:09d}".format(global_step))
dg.save_dygraph(model.state_dict(), step_path)
dg.save_dygraph(optimizer.state_dict(), step_path)
if (local_rank == 0 and global_step > 0
and global_step % hparams.eval_interval == 0):
eval_model(global_step, writer, model, checkpoint_dir,
ismultispeaker)
if args.use_data_parallel:
loss = model.scale_loss(loss)
loss.backward()
model.apply_collective_grads()
else:
loss.backward()
if not (args.train_seq2seq_only or args.train_postnet_only):
param_list = model.parameters()
elif args.train_seq2seq_only:
if ismultispeaker:
param_list = chain(model.speaker_embedding.parameters(),
model.seq2seq.parameters())
else:
param_list = model.seq2seq.parameters()
else:
if ismultispeaker:
param_list = chain(model.speaker_embedding.parameters(),
model.seq2seq.parameters())
else:
param_list = model.converter.parameters()
optimizer.minimize(loss,
grad_clip=clipper,
parameter_list=param_list)
if not (args.train_seq2seq_only or args.train_postnet_only):
model.clear_gradients()
elif args.train_seq2seq_only:
if ismultispeaker:
model.speaker_embedding.clear_gradients()
model.seq2seq.clear_gradients()
else:
if ismultispeaker:
model.speaker_embedding.clear_gradients()
model.converter.clear_gradients()
global_step += 1
average_loss_in_epoch = epoch_loss / (step + 1)
print("Epoch loss: {}".format(average_loss_in_epoch))
if writer is not None and local_rank == 0:
writer.add_scalar("average_loss_in_epoch", average_loss_in_epoch,
global_epoch)
ce_loss = average_loss_in_epoch
global_epoch += 1
end_time = time.time()
epoch_time = (end_time - start_time) / global_epoch
print("kpis\teach_epoch_duration_frame%s_card%s\t%s" %
(hparams.outputs_per_step, n_trainers, epoch_time))
print("kpis\ttrain_cost_frame%s_card%s\t%f" %
(hparams.outputs_per_step, n_trainers, ce_loss))
def main(args):
local_rank = dg.parallel.Env().local_rank if args.use_data_parallel else 0
nranks = dg.parallel.Env().nranks if args.use_data_parallel else 1
with open(args.config_path) as f:
cfg = yaml.load(f, Loader=yaml.Loader)
global_step = 0
place = (fluid.CUDAPlace(dg.parallel.Env().dev_id)
if args.use_data_parallel else fluid.CUDAPlace(0)
if args.use_gpu else fluid.CPUPlace())
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
path = os.path.join(args.log_dir, 'fastspeech')
writer = SummaryWriter(path) if local_rank == 0 else None
with dg.guard(place):
with fluid.unique_name.guard():
transformer_tts = TransformerTTS(cfg)
model_dict, _ = load_checkpoint(
str(args.transformer_step),
os.path.join(args.transtts_path, "transformer"))
transformer_tts.set_dict(model_dict)
transformer_tts.eval()
model = FastSpeech(cfg)
model.train()
optimizer = fluid.optimizer.AdamOptimizer(
learning_rate=dg.NoamDecay(1 / (
cfg['warm_up_step'] * (args.lr**2)), cfg['warm_up_step']),
parameter_list=model.parameters())
reader = LJSpeechLoader(
cfg, args, nranks, local_rank, shuffle=True).reader()
if args.checkpoint_path is not None:
model_dict, opti_dict = load_checkpoint(
str(args.fastspeech_step),
os.path.join(args.checkpoint_path, "fastspeech"))
model.set_dict(model_dict)
optimizer.set_dict(opti_dict)
global_step = args.fastspeech_step
print("load checkpoint!!!")
if args.use_data_parallel:
strategy = dg.parallel.prepare_context()
model = fluid.dygraph.parallel.DataParallel(model, strategy)
for epoch in range(args.epochs):
pbar = tqdm(reader)
for i, data in enumerate(pbar):
pbar.set_description('Processing at epoch %d' % epoch)
(character, mel, mel_input, pos_text, pos_mel, text_length,
mel_lens, enc_slf_mask, enc_query_mask, dec_slf_mask,
enc_dec_mask, dec_query_slf_mask, dec_query_mask) = data
_, _, attn_probs, _, _, _ = transformer_tts(
character,
mel_input,
pos_text,
pos_mel,
dec_slf_mask=dec_slf_mask,
enc_slf_mask=enc_slf_mask,
enc_query_mask=enc_query_mask,
enc_dec_mask=enc_dec_mask,
dec_query_slf_mask=dec_query_slf_mask,
dec_query_mask=dec_query_mask)
alignment, max_attn = get_alignment(attn_probs, mel_lens,
cfg['transformer_head'])
alignment = dg.to_variable(alignment).astype(np.float32)
if local_rank == 0 and global_step % 5 == 1:
x = np.uint8(
cm.viridis(max_attn[8, :mel_lens.numpy()[8]]) * 255)
writer.add_image(
'Attention_%d_0' % global_step,
x,
0,
dataformats="HWC")
global_step += 1
#Forward
result = model(
character,
pos_text,
mel_pos=pos_mel,
length_target=alignment,
enc_non_pad_mask=enc_query_mask,
enc_slf_attn_mask=enc_slf_mask,
dec_non_pad_mask=dec_query_slf_mask,
dec_slf_attn_mask=dec_slf_mask)
mel_output, mel_output_postnet, duration_predictor_output, _, _ = result
mel_loss = layers.mse_loss(mel_output, mel)
mel_postnet_loss = layers.mse_loss(mel_output_postnet, mel)
duration_loss = layers.mean(
layers.abs(
layers.elementwise_sub(duration_predictor_output,
alignment)))
total_loss = mel_loss + mel_postnet_loss + duration_loss
if local_rank == 0:
writer.add_scalar('mel_loss',
mel_loss.numpy(), global_step)
writer.add_scalar('post_mel_loss',
mel_postnet_loss.numpy(), global_step)
writer.add_scalar('duration_loss',
duration_loss.numpy(), global_step)
writer.add_scalar('learning_rate',
optimizer._learning_rate.step().numpy(),
global_step)
if args.use_data_parallel:
total_loss = model.scale_loss(total_loss)
total_loss.backward()
model.apply_collective_grads()
else:
total_loss.backward()
optimizer.minimize(
total_loss,
grad_clip=fluid.dygraph_grad_clip.GradClipByGlobalNorm(cfg[
'grad_clip_thresh']))
model.clear_gradients()
# save checkpoint
if local_rank == 0 and global_step % args.save_step == 0:
if not os.path.exists(args.save_path):
os.mkdir(args.save_path)
save_path = os.path.join(args.save_path,
'fastspeech/%d' % global_step)
dg.save_dygraph(model.state_dict(), save_path)
dg.save_dygraph(optimizer.state_dict(), save_path)
if local_rank == 0:
writer.close()
def main(args):
local_rank = dg.parallel.Env().local_rank if args.use_data_parallel else 0
nranks = dg.parallel.Env().nranks if args.use_data_parallel else 1
with open(args.config_path) as f:
cfg = yaml.load(f, Loader=yaml.Loader)
global_step = 0
place = (fluid.CUDAPlace(dg.parallel.Env().dev_id)
if args.use_data_parallel else
fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace())
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
path = os.path.join(args.log_dir, 'vocoder')
writer = SummaryWriter(path) if local_rank == 0 else None
with dg.guard(place):
model = Vocoder(cfg, args.batch_size)
model.train()
optimizer = fluid.optimizer.AdamOptimizer(
learning_rate=dg.NoamDecay(
1 / (cfg['warm_up_step'] * (args.lr**2)), cfg['warm_up_step']),
parameter_list=model.parameters())
if args.checkpoint_path is not None:
model_dict, opti_dict = load_checkpoint(
str(args.vocoder_step),
os.path.join(args.checkpoint_path, "vocoder"))
model.set_dict(model_dict)
optimizer.set_dict(opti_dict)
global_step = args.vocoder_step
print("load checkpoint!!!")
if args.use_data_parallel:
strategy = dg.parallel.prepare_context()
model = fluid.dygraph.parallel.DataParallel(model, strategy)
reader = LJSpeechLoader(cfg, args, nranks, local_rank,
is_vocoder=True).reader()
for epoch in range(args.epochs):
pbar = tqdm(reader)
for i, data in enumerate(pbar):
pbar.set_description('Processing at epoch %d' % epoch)
mel, mag = data
mag = dg.to_variable(mag.numpy())
mel = dg.to_variable(mel.numpy())
global_step += 1
mag_pred = model(mel)
loss = layers.mean(
layers.abs(layers.elementwise_sub(mag_pred, mag)))
if args.use_data_parallel:
loss = model.scale_loss(loss)
loss.backward()
model.apply_collective_grads()
else:
loss.backward()
optimizer.minimize(
loss,
grad_clip=fluid.dygraph_grad_clip.GradClipByGlobalNorm(
cfg['grad_clip_thresh']))
model.clear_gradients()
if local_rank == 0:
writer.add_scalars('training_loss', {
'loss': loss.numpy(),
}, global_step)
if global_step % args.save_step == 0:
if not os.path.exists(args.save_path):
os.mkdir(args.save_path)
save_path = os.path.join(args.save_path,
'vocoder/%d' % global_step)
dg.save_dygraph(model.state_dict(), save_path)
dg.save_dygraph(optimizer.state_dict(), save_path)
if local_rank == 0:
writer.close()
def main(args):
local_rank = dg.parallel.Env().local_rank if args.use_data_parallel else 0
nranks = dg.parallel.Env().nranks if args.use_data_parallel else 1
with open(args.config_path) as f:
cfg = yaml.load(f, Loader=yaml.Loader)
global_step = 0
place = (fluid.CUDAPlace(dg.parallel.Env().dev_id)
if args.use_data_parallel else
fluid.CUDAPlace(0) if args.use_gpu else fluid.CPUPlace())
if not os.path.exists(args.log_dir):
os.mkdir(args.log_dir)
path = os.path.join(args.log_dir, 'transformer')
writer = SummaryWriter(path) if local_rank == 0 else None
with dg.guard(place):
model = TransformerTTS(cfg)
model.train()
optimizer = fluid.optimizer.AdamOptimizer(
learning_rate=dg.NoamDecay(
1 / (cfg['warm_up_step'] * (args.lr**2)), cfg['warm_up_step']),
parameter_list=model.parameters())
if args.checkpoint_path is not None:
model_dict, opti_dict = load_checkpoint(
str(args.transformer_step),
os.path.join(args.checkpoint_path, "transformer"))
model.set_dict(model_dict)
optimizer.set_dict(opti_dict)
global_step = args.transformer_step
print("load checkpoint!!!")
if args.use_data_parallel:
strategy = dg.parallel.prepare_context()
model = fluid.dygraph.parallel.DataParallel(model, strategy)
reader = LJSpeechLoader(cfg, args, nranks, local_rank,
shuffle=True).reader()
for epoch in range(args.epochs):
pbar = tqdm(reader)
for i, data in enumerate(pbar):
pbar.set_description('Processing at epoch %d' % epoch)
character, mel, mel_input, pos_text, pos_mel, text_length, _, enc_slf_mask, enc_query_mask, dec_slf_mask, enc_dec_mask, dec_query_slf_mask, dec_query_mask = data
global_step += 1
mel_pred, postnet_pred, attn_probs, stop_preds, attn_enc, attn_dec = model(
character,
mel_input,
pos_text,
pos_mel,
dec_slf_mask=dec_slf_mask,
enc_slf_mask=enc_slf_mask,
enc_query_mask=enc_query_mask,
enc_dec_mask=enc_dec_mask,
dec_query_slf_mask=dec_query_slf_mask,
dec_query_mask=dec_query_mask)
mel_loss = layers.mean(
layers.abs(layers.elementwise_sub(mel_pred, mel)))
post_mel_loss = layers.mean(
layers.abs(layers.elementwise_sub(postnet_pred, mel)))
loss = mel_loss + post_mel_loss
# Note: When used stop token loss the learning did not work.
if args.stop_token:
label = (pos_mel == 0).astype(np.float32)
stop_loss = cross_entropy(stop_preds, label)
loss = loss + stop_loss
if local_rank == 0:
writer.add_scalars(
'training_loss', {
'mel_loss': mel_loss.numpy(),
'post_mel_loss': post_mel_loss.numpy()
}, global_step)
if args.stop_token:
writer.add_scalar('stop_loss', stop_loss.numpy(),
global_step)
if args.use_data_parallel:
writer.add_scalars(
'alphas', {
'encoder_alpha':
model._layers.encoder.alpha.numpy(),
'decoder_alpha':
model._layers.decoder.alpha.numpy(),
}, global_step)
else:
writer.add_scalars(
'alphas', {
'encoder_alpha': model.encoder.alpha.numpy(),
'decoder_alpha': model.decoder.alpha.numpy(),
}, global_step)
writer.add_scalar('learning_rate',
optimizer._learning_rate.step().numpy(),
global_step)
if global_step % args.image_step == 1:
for i, prob in enumerate(attn_probs):
for j in range(4):
x = np.uint8(
cm.viridis(prob.numpy()[j * args.batch_size
// 2]) * 255)
writer.add_image('Attention_%d_0' %
global_step,
x,
i * 4 + j,
dataformats="HWC")
for i, prob in enumerate(attn_enc):
for j in range(4):
x = np.uint8(
cm.viridis(prob.numpy()[j * args.batch_size
// 2]) * 255)
writer.add_image('Attention_enc_%d_0' %
global_step,
x,
i * 4 + j,
dataformats="HWC")
for i, prob in enumerate(attn_dec):
for j in range(4):
x = np.uint8(
cm.viridis(prob.numpy()[j * args.batch_size
// 2]) * 255)
writer.add_image('Attention_dec_%d_0' %
global_step,
x,
i * 4 + j,
dataformats="HWC")
if args.use_data_parallel:
loss = model.scale_loss(loss)
loss.backward()
model.apply_collective_grads()
else:
loss.backward()
optimizer.minimize(
loss,
grad_clip=fluid.dygraph_grad_clip.GradClipByGlobalNorm(
cfg['grad_clip_thresh']))
model.clear_gradients()
# save checkpoint
if local_rank == 0 and global_step % args.save_step == 0:
if not os.path.exists(args.save_path):
os.mkdir(args.save_path)
save_path = os.path.join(args.save_path,
'transformer/%d' % global_step)
dg.save_dygraph(model.state_dict(), save_path)
dg.save_dygraph(optimizer.state_dict(), save_path)
if local_rank == 0:
writer.close()
