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distributed_trainer.py
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distributed_trainer.py
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import os
import os.path as osp
import torch
import torch.nn as nn
import torch.nn.functional as F
import glob
from torch.optim import Adam, SGD
from torch.optim.lr_scheduler import StepLR
import numpy as np
import random
from modules.fsl_query import make_fsl
from dataloader import make_distributed_dataloader
from utils import mean_confidence_interval, AverageMeter, get_world_size, reduce_loss_dict, set_seed
from tqdm import tqdm
class DistributedTrainer(object):
def __init__(self, args, cfg, checkpoint_dir):
## Append for distributed training
self.distributed = args.distributed
assert self.distributed
self.base_rank = args.base_rank
self.local_rank = args.local_rank
self.rank = self.base_rank + self.local_rank
self.device = torch.device("cuda")
self.verbose = (self.rank == 0)
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.checkpoint_dir = checkpoint_dir
self.epochs = cfg.train.epochs
fsl = make_fsl(cfg)
fsl = torch.nn.SyncBatchNorm.convert_sync_batchnorm(fsl).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(fsl.parameters(),lr=cfg.train.learning_rate, betas=cfg.train.adam_betas)
elif cfg.train.optim == "SGD":
self.optim = SGD(
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 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)
fsl.load_state_dict(state_dict["fsl"])
if self.verbose:
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
self.log_episode_interval = 100
self.val_episode = cfg.test.episode
self.cfg = cfg
self.fsl = torch.nn.parallel.DistributedDataParallel(
fsl, device_ids=[self.local_rank], output_device=self.local_rank,
)
self.lr_scheduler = StepLR(self.optim, step_size=self.lr_decay_epoch, gamma=self.lr_decay)
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 = []
if self.verbose:
dataloader = tqdm(dataloader)
acc = AverageMeter()
for episode, (support_x, support_y, query_x, query_y) in enumerate(dataloader):
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)
total_rewards = np.sum(rewards)
accuracy = total_rewards / (query_y.numel())
if self.verbose:
acc.update(total_rewards / query_y.numel(), 1)
mesg = "Val: acc={:.3f}".format(
acc.avg
)
dataloader.set_description(mesg)
accuracies.append(accuracy)
test_accuracy, h = mean_confidence_interval(accuracies)
return test_accuracy, h
def save_model(self, prefix, accuracy, h, epoch, final_epoch=False):
filename = osp.join(self.checkpoint_dir, "e{}_{}way_{}shot.pth".format(prefix, self.n_way, self.k_shot))
recordname = osp.join(self.checkpoint_dir, "e{}_{}way_{}shot.txt".format(prefix, self.n_way, self.k_shot))
state = {
'episode': prefix,
'fsl': self.fsl.module.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))
torch.save(state, filename)
def train(self, dataloader, epoch):
losses = AverageMeter()
if self.verbose:
dataloader = tqdm(dataloader)
for episode, (support_x, support_y, query_x, query_y) in enumerate(dataloader):
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)
loss_sum = sum(loss.values())
self.optim.zero_grad()
loss_sum.backward()
self.optim.step()
losses_dict_reduced = reduce_loss_dict(loss)
losses_reduced = sum(losses_dict_reduced.values())
losses.update(losses_reduced.item(), len(query_x))
if self.verbose:
mesg = "epoch {}, loss={:.3f}".format(
epoch,
losses.avg
)
dataloader.set_description(mesg)
def run(self):
best_accuracy = 0.0
set_seed(1)
val_dataloader = make_distributed_dataloader(
self.cfg, phase="val",
batch_size=self.cfg.test.batch_size,
distributed_info={"num_replicas": get_world_size(), "rank": self.rank}
)
for epoch in range(self.epochs):
train_dataloader = make_distributed_dataloader(
self.cfg,
phase="train",
batch_size=self.cfg.train.batch_size,
distributed_info={"num_replicas": get_world_size(), "rank": self.rank}
)
self.train(train_dataloader, epoch + 1)
self.fsl.eval()
with torch.no_grad():
total_accuracies, total_h = self.validate(val_dataloader)
validation_results = {
"acc": torch.Tensor([total_accuracies]).to('cuda'),
"h": torch.Tensor([total_h]).to('cuda')
}
validation_results_reduced = reduce_loss_dict(validation_results)
total_accuracies = validation_results_reduced["acc"].item()
total_h = validation_results_reduced["h"].item()
if self.verbose:
mesg = "\t Testing epoch {} validation accuracy: {:.3f}, h: {:.3f}".format(epoch + 1, total_accuracies, total_h)
print(mesg)
if total_accuracies > best_accuracy:
best_accuracy = total_accuracies
self.save_model("best", total_accuracies, total_h, epoch + 1, True)
#self.save_model(epoch + 1, total_accuracies, total_h, epoch + 1, epoch ==(self.epochs - 1))
self.lr_scheduler.step()
self.fsl.train()
if self.cfg.train.fix_bn:
self.fix_bn()