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train_classifier.py
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train_classifier.py
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import torch
import torch.nn as nn
import numpy as np
import copy
from library import inputs
from Utils.checkpoints import save_context, Logger
from Utils import flags
from Utils import config
import Torture
from library import evaluation
from library import loss_cla as loss_classifier
from library.mean_teacher import optim_weight_swa
FLAGS = flags.FLAGS
KEY_ARGUMENTS = config.load_config(FLAGS.config_file)
text_logger, MODELS_FOLDER, SUMMARIES_FOLDER = save_context(__file__, KEY_ARGUMENTS)
torch.manual_seed(1234)
torch.backends.cudnn.deterministic = True
torch.backends.cudnn.benchmark = True
np.random.seed(1235)
torch.cuda.manual_seed(1236)
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
FLAGS.device = device
itr = inputs.get_data_iter(subset=FLAGS.n_labels)
itr_u = inputs.get_data_iter()
netC, optim_c = inputs.get_classifier_optimizer()
netC = netC.to(device)
netC = nn.DataParallel(netC)
netC_T, _ = inputs.get_classifier_optimizer()
netC_T = netC_T.to(device)
netC_T = nn.DataParallel(netC_T)
netC.train()
netC_T.train()
Torture.update_average(netC_T, netC, 0)
for p in netC_T.parameters():
p.requires_grad_(False)
if FLAGS.c_step == "ramp_swa":
netC_swa, _ = inputs.get_classifier_optimizer()
netC_swa = netC_swa.to(device)
netC_swa = nn.DataParallel(netC_swa)
netC_swa.train()
swa_optim = optim_weight_swa.WeightSWA(netC_swa)
for p in netC_swa.parameters():
p.requires_grad_(False)
Torture.update_average(netC_swa, netC, 0)
checkpoint_io = Torture.utils.checkpoint.CheckpointIO(checkpoint_dir=MODELS_FOLDER)
if FLAGS.c_step == "ramp_swa":
checkpoint_io.register_modules(netC=netC, netC_T=netC_T, netC_swa=netC_swa)
else:
checkpoint_io.register_modules(netC=netC, netC_T=netC_T)
logger = Logger(log_dir=SUMMARIES_FOLDER)
# train
print_interval = 50
test_interval = 500
max_iter = FLAGS.n_iter
loss_func = loss_classifier.c_loss_dict[FLAGS.c_loss]
step_func = loss_classifier.c_step_func[FLAGS.c_step]
logger_prefix = "Itera {}/{} ({:.0f}%)"
for i in range(max_iter):
tloss, l_loss, u_loss = loss_func(netC, netC_T, i, itr, itr_u, device)
if FLAGS.c_step == "ramp_swa":
step_func(optim_c, swa_optim, netC, netC_T, i, tloss)
else:
step_func(optim_c, netC, netC_T, i, tloss)
logger.add("training", "loss", tloss.item(), i + 1)
logger.add("training", "l_loss", l_loss.item(), i + 1)
logger.add("training", "u_loss", u_loss.item(), i + 1)
if (i + 1) % print_interval == 0:
prefix = logger_prefix.format(i + 1, max_iter, (100 * i + 1) / max_iter)
cats = ["training"]
logger.log_info(prefix, text_logger.info, cats=cats)
if (i + 1) % test_interval == 0:
netC.eval()
netC_T.eval()
if FLAGS.c_step == "ramp_swa":
netC_swa.train()
for _ in range(300):
data_u, _ = itr_u.__next__()
_ = netC_swa(data_u.to(device))
netC_swa.eval()
total_s, correct_s, loss_s = evaluation.test_classifier(netC_swa)
logger.add("testing", "loss_s", loss_s.item(), i + 1)
logger.add("testing", "accuracy_s", 100 * (correct_s / total_s), i + 1)
total_t, correct_t, loss_t = evaluation.test_classifier(netC_T)
logger.add("testing", "loss_t", loss_t.item(), i + 1)
logger.add("testing", "accuracy_t", 100 * (correct_t / total_t), i + 1)
total_t, correct_t, loss_t = evaluation.test_classifier(netC)
logger.add("testing", "loss", loss_t.item(), i + 1)
logger.add("testing", "accuracy", 100 * (correct_t / total_t), i + 1)
prefix = logger_prefix.format(i + 1, max_iter, (100 * i + 1) / max_iter)
cats = ["testing"]
logger.log_info(prefix, text_logger.info, cats=cats)
netC.train()
netC_T.train()
if (i + 1) % FLAGS.save_every == 0:
logger.save_stats("{:08d}.pkl".format(i))
file_name = "model" + str(i + 1) + ".pt"
checkpoint_io.save(file_name)