File size: 7,360 Bytes
811e03d |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 |
import logging
import numpy as np
import torch
from time import time
from torch import optim
from tqdm import tqdm
from utils import ensure_dir,set_color,get_local_time
import os
class Trainer(object):
def __init__(self, args, model):
self.args = args
self.model = model
self.logger = logging.getLogger()
self.lr = args.lr
self.learner = args.learner
self.weight_decay = args.weight_decay
self.epochs = args.epochs
self.eval_step = min(args.eval_step, self.epochs)
self.device = args.device
self.device = torch.device(self.device)
self.ckpt_dir = args.ckpt_dir
saved_model_dir = "{}".format(get_local_time())
self.ckpt_dir = os.path.join(self.ckpt_dir,saved_model_dir)
ensure_dir(self.ckpt_dir)
self.best_loss = np.inf
self.best_collision_rate = np.inf
self.best_loss_ckpt = "best_loss_model.pth"
self.best_collision_ckpt = "best_collision_model.pth"
self.optimizer = self._build_optimizer()
self.model = self.model.to(self.device)
def _build_optimizer(self):
params = self.model.parameters()
learner = self.learner
learning_rate = self.lr
weight_decay = self.weight_decay
if learner.lower() == "adam":
optimizer = optim.Adam(params, lr=learning_rate, weight_decay=weight_decay)
elif learner.lower() == "sgd":
optimizer = optim.SGD(params, lr=learning_rate, weight_decay=weight_decay)
elif learner.lower() == "adagrad":
optimizer = optim.Adagrad(
params, lr=learning_rate, weight_decay=weight_decay
)
for state in optimizer.state.values():
for k, v in state.items():
if torch.is_tensor(v):
state[k] = v.to(self.device)
elif learner.lower() == "rmsprop":
optimizer = optim.RMSprop(
params, lr=learning_rate, weight_decay=weight_decay
)
elif learner.lower() == 'adamw':
optimizer = optim.AdamW(
params, lr=learning_rate, weight_decay=weight_decay
)
else:
self.logger.warning(
"Received unrecognized optimizer, set default Adam optimizer"
)
optimizer = optim.Adam(params, lr=learning_rate)
return optimizer
def _check_nan(self, loss):
if torch.isnan(loss):
raise ValueError("Training loss is nan")
def _train_epoch(self, train_data, epoch_idx):
self.model.train()
total_loss = 0
total_recon_loss = 0
iter_data = tqdm(
train_data,
total=len(train_data),
ncols=100,
desc=set_color(f"Train {epoch_idx}","pink"),
)
for batch_idx, data in enumerate(iter_data):
data = data.to(self.device)
self.optimizer.zero_grad()
out, rq_loss, indices = self.model(data)
loss, loss_recon = self.model.compute_loss(out, rq_loss, xs=data)
self._check_nan(loss)
loss.backward()
self.optimizer.step()
total_loss += loss.item()
total_recon_loss += loss_recon.item()
return total_loss, total_recon_loss
@torch.no_grad()
def _valid_epoch(self, valid_data):
self.model.eval()
iter_data =tqdm(
valid_data,
total=len(valid_data),
ncols=100,
desc=set_color(f"Evaluate ", "pink"),
)
indices_set = set()
num_sample = 0
for batch_idx, data in enumerate(iter_data):
num_sample += len(data)
data = data.to(self.device)
indices = self.model.get_indices(data)
indices = indices.view(-1,indices.shape[-1]).cpu().numpy()
for index in indices:
code = "-".join([str(int(_)) for _ in index])
indices_set.add(code)
collision_rate = (num_sample - len(indices_set))/num_sample
return collision_rate
def _save_checkpoint(self, epoch, collision_rate=1, ckpt_file=None):
ckpt_path = os.path.join(self.ckpt_dir,ckpt_file) if ckpt_file \
else os.path.join(self.ckpt_dir, 'epoch_%d_collision_%.4f_model.pth' % (epoch, collision_rate))
state = {
"args": self.args,
"epoch": epoch,
"best_loss": self.best_loss,
"best_collision_rate": self.best_collision_rate,
"state_dict": self.model.state_dict(),
"optimizer": self.optimizer.state_dict(),
}
torch.save(state, ckpt_path, pickle_protocol=4)
self.logger.info(
set_color("Saving current", "blue") + f": {ckpt_path}"
)
def _generate_train_loss_output(self, epoch_idx, s_time, e_time, loss, recon_loss):
train_loss_output = (
set_color("epoch %d training", "green")
+ " ["
+ set_color("time", "blue")
+ ": %.2fs, "
) % (epoch_idx, e_time - s_time)
train_loss_output += set_color("train loss", "blue") + ": %.4f" % loss
train_loss_output +=", "
train_loss_output += set_color("reconstruction loss", "blue") + ": %.4f" % recon_loss
return train_loss_output + "]"
def fit(self, data):
cur_eval_step = 0
for epoch_idx in range(self.epochs):
# train
training_start_time = time()
train_loss, train_recon_loss = self._train_epoch(data, epoch_idx)
training_end_time = time()
train_loss_output = self._generate_train_loss_output(
epoch_idx, training_start_time, training_end_time, train_loss, train_recon_loss
)
self.logger.info(train_loss_output)
if train_loss < self.best_loss:
self.best_loss = train_loss
# self._save_checkpoint(epoch=epoch_idx,ckpt_file=self.best_loss_ckpt)
# eval
if (epoch_idx + 1) % self.eval_step == 0:
valid_start_time = time()
collision_rate = self._valid_epoch(data)
if collision_rate < self.best_collision_rate:
self.best_collision_rate = collision_rate
cur_eval_step = 0
self._save_checkpoint(epoch_idx, collision_rate=collision_rate,
ckpt_file=self.best_collision_ckpt)
else:
cur_eval_step += 1
valid_end_time = time()
valid_score_output = (
set_color("epoch %d evaluating", "green")
+ " ["
+ set_color("time", "blue")
+ ": %.2fs, "
+ set_color("collision_rate", "blue")
+ ": %f]"
) % (epoch_idx, valid_end_time - valid_start_time, collision_rate)
self.logger.info(valid_score_output)
if epoch_idx>1000:
self._save_checkpoint(epoch_idx, collision_rate=collision_rate)
return self.best_loss, self.best_collision_rate
|