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9a964a6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 | # -*- 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
import matplotlib.pyplot as plt
import logging
logger = logging.getLogger(__name__)
logger.setLevel(logging.DEBUG)
class Trainer(object):
def __init__(self,
model=None,
criterion=None,
optimizer=None,
scheduler=None,
config={},
loss_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.loss_config = loss_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
def run(self, batch):
self.optimizer.zero_grad()
batch = [b.to(self.device) for b in batch]
x, f0, sil = batch
f0_pred, sil_pred = self.model(x.transpose(-1, -2))
loss_f0 = self.loss_config['lambda_f0'] * self.criterion['l1'](f0_pred.squeeze(), f0)
loss_sil = self.criterion['ce'](sil_pred, sil)
loss = loss_f0 + loss_sil
loss.backward()
self.optimizer.step()
self.scheduler.step()
return {'loss': loss.item(),
'f0': loss_f0.item(),
'sil': loss_sil.item()}
def _train_epoch(self):
self.epochs += 1
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]
x, f0, sil = batch
f0_pred, sil_pred = self.model(x.transpose(-1, -2))
loss_f0 = self.loss_config['lambda_f0'] * self.criterion['l1'](f0_pred.squeeze(), f0)
loss_sil = self.criterion['ce'](sil_pred, sil)
loss = loss_f0 + loss_sil
eval_losses["eval/loss"].append(loss.item())
eval_losses["eval/f0"].append(loss_f0.item())
eval_losses["eval/sil"].append(loss_sil.item())
eval_losses = {key: np.mean(value) for key, value in eval_losses.items()}
eval_losses.update(eval_images)
return eval_losses
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