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trainer.py
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import re
import os
import os.path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
import glob
from time import gmtime, strftime
from torch.optim import Adam, SGD
from torch.optim.lr_scheduler import StepLR, MultiStepLR
import numpy as np
from tensorboardX import SummaryWriter
import random
import tqdm
from modules.fsl_query import make_fsl
from dataloader import make_dataloader
from utils import mean_confidence_interval, AverageMeter, set_seed
class trainer(object):
def __init__(self, cfg, checkpoint_dir):
self.seed = cfg.seed
set_seed(self.seed) # should set seed for training from scratch with Conv4 backbone
self.n_way = cfg.n_way # 5
self.k_shot = cfg.k_shot # 5
self.train_query_per_class = cfg.train.query_per_class_per_episode # 10
self.val_query_per_class = cfg.test.query_per_class_per_episode # 15
self.train_episode_per_epoch = cfg.train.episode_per_epoch
self.prefix = osp.basename(checkpoint_dir)
self.writer_dir = self._prepare_summary_snapshots(self.prefix, cfg)
self.writer = SummaryWriter(self.writer_dir)
self.device = torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
self.checkpoint_dir = checkpoint_dir
self.epochs = cfg.train.epochs
self.fsl = make_fsl(cfg).to(self.device)
self.lr = cfg.train.learning_rate
self.lr_decay = cfg.train.lr_decay
self.lr_decay_epoch = cfg.train.lr_decay_epoch
if cfg.train.optim == "Adam":
self.optim = Adam(self.fsl.parameters(),lr=cfg.train.learning_rate, betas=cfg.train.adam_betas)
elif cfg.train.optim == "SGD":
self.optim = SGD(
self.fsl.parameters(),
lr=cfg.train.learning_rate,
momentum=cfg.train.sgd_mom,
weight_decay=cfg.train.sgd_weight_decay,
nesterov=True
)
else:
raise NotImplementedError
pths = [osp.basename(f) for f in glob.glob(osp.join(checkpoint_dir, "*.pth")) if "best" not in f]
if pths:
pths_epoch = [''.join(filter(str.isdigit, f[:f.find('_')])) for f in pths]
pths = [p for p, e in zip(pths, pths_epoch) if e]
pths_epoch = [int(e) for e in pths_epoch if e]
self.train_start_epoch = max(pths_epoch)
c = osp.join(checkpoint_dir, pths[pths_epoch.index(self.train_start_epoch)])
state_dict = torch.load(c)
self.fsl.load_state_dict(state_dict["fsl"], strict=False)
print("[*] Continue training from checkpoints: {}".format(c))
lr_scheduler_last_epoch = self.train_start_epoch
if "optimizer" in state_dict and state_dict["optimizer"] is not None:
self.optim.load_state_dict(state_dict["optimizer"])
else:
self.train_start_epoch = 0
lr_scheduler_last_epoch = -1
if cfg.train.lr_decay_milestones:
self.lr_scheduler = MultiStepLR(self.optim, milestones=cfg.train.lr_decay_milestones,gamma=self.lr_decay)
else:
self.lr_scheduler = StepLR(self.optim, step_size=self.lr_decay_epoch, gamma=self.lr_decay)
self.snapshot_name = lambda prefix: \
osp.join(self.checkpoint_dir, "e{}_{}way_{}shot.pth".format(prefix, self.n_way, self.k_shot))
self.snapshot_record = lambda prefix: \
osp.join(self.checkpoint_dir, "e{}_{}way_{}shot.txt".format(prefix, self.n_way, self.k_shot))
self.cfg = cfg
def _prepare_summary_snapshots(self, prefix, cfg):
summary_prefix = osp.join(cfg.train.summary_snapshot_base, prefix)
summary_dir = osp.join(summary_prefix, strftime("%Y-%m-%d-%H:%M", gmtime()))
for d_ in [summary_prefix, summary_dir]:
if not osp.exists(d_):
os.mkdir(d_)
return summary_dir
def fix_bn(self):
for module in self.fsl.modules():
if isinstance(module, torch.nn.modules.BatchNorm2d):
module.eval()
if isinstance(module, torch.nn.modules.SyncBatchNorm):
module.eval()
def validate(self, dataloader):
accuracies = []
tqdm_gen = tqdm.tqdm(dataloader, ncols=80)
acc = AverageMeter()
loss_meter = AverageMeter()
for episode, (support_x, support_y, query_x, query_y) in enumerate(tqdm_gen):
support_x = support_x.to(self.device)
support_y = support_y.to(self.device)
query_x = query_x.to(self.device)
query_y = query_y.to(self.device)
rewards = self.fsl(support_x, support_y, query_x, query_y)
if isinstance(rewards, tuple):
rewards, losses = rewards
loss_meter.update(losses.item(), len(query_x))
total_rewards = np.sum(rewards)
accuracy = total_rewards / (query_y.numel())
acc.update(total_rewards / query_y.numel(), 1)
mesg = "Val: acc={:.4f}".format(
acc.avg
)
tqdm_gen.set_description(mesg)
accuracies.append(accuracy)
test_accuracy, h = mean_confidence_interval(accuracies)
return test_accuracy, h, loss_meter
def save_model(self, prefix, accuracy, h, epoch, final_epoch=False):
filename = self.snapshot_name(prefix)
recordname = self.snapshot_record(prefix)
state = {
'summary_dir': osp.basename(self.writer_dir),
'episode': prefix,
'fsl': self.fsl.state_dict(),
'epoch': epoch,
# "optimizer": None if not final_epoch else self.optim.state_dict()
}
with open(recordname, 'w') as f:
f.write("prefix: {}\nepoch: {}\naccuracy: {}\nh: {}\n".format(prefix, epoch, accuracy, h))
if int(re.search(r'([\d.]+)', torch.__version__).group(1).replace('.', '')) > 160:
torch.save(state, filename, _use_new_zipfile_serialization=False) # compatible with early torch versions to load
else:
torch.save(state, filename)
def train(self, dataloader, epoch):
losses = AverageMeter()
tqdm_gen = tqdm.tqdm(dataloader, ncols=80)
self.optim.zero_grad()
for episode, (support_x, support_y, query_x, query_y) in enumerate(tqdm_gen):
support_x = support_x.to(self.device)
support_y = support_y.to(self.device)
query_x = query_x.to(self.device)
query_y = query_y.to(self.device)
loss = self.fsl(support_x, support_y, query_x, query_y)
self.optim.zero_grad()
loss.backward()
self.optim.step()
losses.update(loss.item(), len(query_x))
mesg = "epoch {}, loss={:.3f}".format(
epoch,
losses.avg
)
tqdm_gen.set_description(mesg)
return losses.avg
def run(self):
print("[={}=]".format(self.prefix))
best_accuracy = 0.0
set_seed(self.seed)
val_dataloader = make_dataloader(self.cfg, phase="val", batch_size=self.cfg.test.batch_size)
for epoch in range(self.train_start_epoch, self.epochs):
train_dataloader = make_dataloader(
self.cfg, phase="train",
batch_size=self.cfg.train.batch_size
)
loss_train = self.train(train_dataloader, epoch + 1)
self.writer.add_scalar('loss_train', loss_train, epoch + 1)
self.fsl.eval()
with torch.no_grad():
val_accuracy, h, val_loss_meter = self.validate(val_dataloader)
if val_loss_meter.count > 0:
self.writer.add_scalar('loss_val', val_loss_meter.avg, epoch + 1)
if val_accuracy > best_accuracy:
best_accuracy = val_accuracy
self.save_model("best", val_accuracy, h, epoch + 1, True)
mesg = "\t Testing epoch {} validation accuracy: {:.4f}, h: {:.3f}".format(epoch + 1, val_accuracy, h)
print(mesg)
self.writer.add_scalar('acc_val', val_accuracy, epoch + 1)
self.lr_scheduler.step()
#self.save_model(epoch + 1, val_accuracy, h, epoch + 1, epoch == (self.epochs - 1))
self.fsl.train()
if self.cfg.train.fix_bn:
self.fix_bn()