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# -*- coding: utf-8 -*-
import os
import os.path as osp
import sys
import time
from collections import defaultdict
import numpy as np
import torch
from torch import nn
from PIL import Image
from tqdm import tqdm
from utils import calc_wer
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
from utils import *
class Trainer(object):
def __init__(self,
model=None,
criterion=None,
optimizer=None,
scheduler=None,
config={},
device=torch.device("cpu"),
logger=logger,
train_dataloader=None,
val_dataloader=None,
initial_steps=0,
initial_epochs=0):
self.steps = initial_steps
self.epochs = initial_epochs
self.model = model
self.criterion = criterion
self.optimizer = optimizer
self.scheduler = scheduler
self.train_dataloader = train_dataloader
self.val_dataloader = val_dataloader
self.config = config
self.device = device
self.finish_train = False
self.logger = logger
self.fp16_run = False
def save_checkpoint(self, checkpoint_path):
"""Save checkpoint.
Args:
checkpoint_path (str): Checkpoint path to be saved.
"""
state_dict = {
"optimizer": self.optimizer.state_dict(),
"scheduler": self.scheduler.state_dict(),
"steps": self.steps,
"epochs": self.epochs,
}
state_dict["model"] = self.model.state_dict()
if not os.path.exists(os.path.dirname(checkpoint_path)):
os.makedirs(os.path.dirname(checkpoint_path))
torch.save(state_dict, checkpoint_path)
def load_checkpoint(self, checkpoint_path, load_only_params=False):
"""Load checkpoint.
Args:
checkpoint_path (str): Checkpoint path to be loaded.
load_only_params (bool): Whether to load only model parameters.
"""
state_dict = torch.load(checkpoint_path, map_location="cpu")
self._load(state_dict["model"], self.model)
if not load_only_params:
self.steps = state_dict["steps"]
self.epochs = state_dict["epochs"]
self.optimizer.load_state_dict(state_dict["optimizer"])
# overwrite schedular argument parameters
state_dict["scheduler"].update(**self.config.get("scheduler_params", {}))
self.scheduler.load_state_dict(state_dict["scheduler"])
def _load(self, states, model, force_load=True):
model_states = model.state_dict()
for key, val in states.items():
try:
if key not in model_states:
continue
if isinstance(val, nn.Parameter):
val = val.data
if val.shape != model_states[key].shape:
self.logger.info("%s does not have same shape" % key)
print(val.shape, model_states[key].shape)
if not force_load:
continue
min_shape = np.minimum(np.array(val.shape), np.array(model_states[key].shape))
slices = [slice(0, min_index) for min_index in min_shape]
model_states[key][slices].copy_(val[slices])
else:
model_states[key].copy_(val)
except:
self.logger.info("not exist :%s" % key)
print("not exist ", key)
@staticmethod
def get_gradient_norm(model):
total_norm = 0
for p in model.parameters():
param_norm = p.grad.data.norm(2)
total_norm += param_norm.item() ** 2
total_norm = np.sqrt(total_norm)
return total_norm
@staticmethod
def length_to_mask(lengths):
mask = torch.arange(lengths.max()).unsqueeze(0).expand(lengths.shape[0], -1).type_as(lengths)
mask = torch.gt(mask+1, lengths.unsqueeze(1))
return mask
def _get_lr(self):
for param_group in self.optimizer.param_groups:
lr = param_group['lr']
break
return lr
@staticmethod
def get_image(arrs):
pil_images = []
height = 0
width = 0
for arr in arrs:
uint_arr = (((arr - arr.min()) / (arr.max() - arr.min())) * 255).astype(np.uint8)
pil_image = Image.fromarray(uint_arr)
pil_images.append(pil_image)
height += uint_arr.shape[0]
width = max(width, uint_arr.shape[1])
palette = Image.new('L', (width, height))
curr_heigth = 0
for pil_image in pil_images:
palette.paste(pil_image, (0, curr_heigth))
curr_heigth += pil_image.size[1]
return palette
def run(self, batch):
self.optimizer.zero_grad()
batch = [b.to(self.device) for b in batch]
text_input, text_input_length, mel_input, mel_input_length = batch
mel_input_length = mel_input_length // (2 ** self.model.n_down)
future_mask = self.model.get_future_mask(
mel_input.size(2)//(2**self.model.n_down), unmask_future_steps=0).to(self.device)
mel_mask = self.model.length_to_mask(mel_input_length)
text_mask = self.model.length_to_mask(text_input_length)
ppgs, s2s_pred, s2s_attn = self.model(
mel_input, src_key_padding_mask=mel_mask, text_input=text_input)
loss_ctc = self.criterion['ctc'](ppgs.log_softmax(dim=2).transpose(0, 1),
text_input, mel_input_length, text_input_length)
loss_s2s = 0
for _s2s_pred, _text_input, _text_length in zip(s2s_pred, text_input, text_input_length):
loss_s2s += self.criterion['ce'](_s2s_pred[:_text_length], _text_input[:_text_length])
loss_s2s /= text_input.size(0)
loss = loss_ctc + loss_s2s
loss.backward()
torch.nn.utils.clip_grad_value_(self.model.parameters(), 5)
self.optimizer.step()
self.scheduler.step()
return {'loss': loss.item(),
'ctc': loss_ctc.item(),
's2s': loss_s2s.item()}
def _train_epoch(self):
train_losses = defaultdict(list)
self.model.train()
for train_steps_per_epoch, batch in enumerate(tqdm(self.train_dataloader, desc="[train]"), 1):
losses = self.run(batch)
for key, value in losses.items():
train_losses["train/%s" % key].append(value)
train_losses = {key: np.mean(value) for key, value in train_losses.items()}
train_losses['train/learning_rate'] = self._get_lr()
return train_losses
@torch.no_grad()
def _eval_epoch(self):
self.model.eval()
eval_losses = defaultdict(list)
eval_images = defaultdict(list)
for eval_steps_per_epoch, batch in enumerate(tqdm(self.val_dataloader, desc="[eval]"), 1):
batch = [b.to(self.device) for b in batch]
text_input, text_input_length, mel_input, mel_input_length = batch
mel_input_length = mel_input_length // (2 ** self.model.n_down)
future_mask = self.model.get_future_mask(
mel_input.size(2)//(2**self.model.n_down), unmask_future_steps=0).to(self.device)
mel_mask = self.model.length_to_mask(mel_input_length)
text_mask = self.model.length_to_mask(text_input_length)
ppgs, s2s_pred, s2s_attn = self.model(
mel_input, src_key_padding_mask=mel_mask, text_input=text_input)
loss_ctc = self.criterion['ctc'](ppgs.log_softmax(dim=2).transpose(0, 1),
text_input, mel_input_length, text_input_length)
loss_s2s = 0
for _s2s_pred, _text_input, _text_length in zip(s2s_pred, text_input, text_input_length):
loss_s2s += self.criterion['ce'](_s2s_pred[:_text_length], _text_input[:_text_length])
loss_s2s /= text_input.size(0)
loss = loss_ctc + loss_s2s
eval_losses["eval/ctc"].append(loss_ctc.item())
eval_losses["eval/s2s"].append(loss_s2s.item())
eval_losses["eval/loss"].append(loss.item())
_, amax_ppgs = torch.max(ppgs, dim=2)
wers = [calc_wer(target[:text_length],
pred[:mel_length],
ignore_indexes=list(range(5))) \
for target, pred, text_length, mel_length in zip(
text_input.cpu(), amax_ppgs.cpu(), text_input_length.cpu(), mel_input_length.cpu())]
eval_losses["eval/wer"].extend(wers)
_, amax_s2s = torch.max(s2s_pred, dim=2)
acc = [torch.eq(target[:length], pred[:length]).float().mean().item() \
for target, pred, length in zip(text_input.cpu(), amax_s2s.cpu(), text_input_length.cpu())]
eval_losses["eval/acc"].extend(acc)
if eval_steps_per_epoch <= 2:
eval_images["eval/image"].append(
self.get_image([s2s_attn[0].cpu().numpy()]))
eval_losses = {key: np.mean(value) for key, value in eval_losses.items()}
eval_losses.update(eval_images)
return eval_losses |