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import math
import sys
import os
import argparse
import torch
import torch.distributed as dist
from torch import optim, nn
from torch.nn.parallel import DistributedDataParallel as DDP
from torch.utils.data import DataLoader
from tqdm import tqdm
from model.CPICANN import CPICANN
from model.dataset import mixDataset_cls_dynamic
from util.logger import Logger
def run_one_epoch(model, dataloader, criterion, optimizer, epoch, mode):
if mode == 'Train':
model.train()
criterion.train()
desc = 'Training... '
else:
model.eval()
criterion.eval()
desc = 'Evaluating... '
epoch_loss, cls_acc = 0, 0
if args.progress_bar:
pbar = tqdm(total=len(dataloader.dataset), desc=desc, unit='data')
iters = len(dataloader)
for i, batch in enumerate(dataloader):
data1 = batch[0].to(device)
data2 = batch[1].to(device)
ratio1 = batch[2].to(device)
ratio2 = batch[3].to(device)
label_cls = batch[4].to(device)
data = torch.einsum('ijk,i->ijk', data1, ratio1) + torch.einsum('ijk,i->ijk', data2, ratio2)
min_i = data.min(dim=2, keepdim=True)[0]
max_i = data.max(dim=2, keepdim=True)[0]
data = (data - min_i) / (max_i - min_i) * 100
if mode == 'Train':
adjust_learning_rate_withWarmup(optimizer, epoch + i / iters, args)
logits = model(data)
loss = criterion(logits, label_cls)
optimizer.zero_grad()
loss.backward()
optimizer.step()
else:
with torch.no_grad():
logits = model(data)
loss = criterion(logits, label_cls)
epoch_loss += loss.item()
if args.progress_bar:
pbar.update(len(data))
pbar.set_postfix(**{'loss': loss.item()})
return epoch_loss / iters
def print_log(epoch, loss_train, loss_val, lr):
log.printlog('---------------- Epoch {} ----------------'.format(epoch))
log.printlog('loss_train : {}'.format(round(loss_train, 6)))
log.printlog('loss_val : {}'.format(round(loss_val, 6)))
log.train_writer.add_scalar('mix_loss', loss_train, epoch)
log.val_writer.add_scalar('mix_loss', loss_val, epoch)
log.train_writer.add_scalar('lr', lr, epoch)
def save_checkpoint(state, is_best, filepath, filename):
if (state['epoch']) % 10 == 0 or state['epoch'] == 1:
os.makedirs(filepath, exist_ok=True)
torch.save(state, filepath + filename)
log.printlog('checkpoint saved!')
if is_best:
torch.save(state, '{}/model_best.pth'.format(filepath))
log.printlog('best model saved!')
def adjust_learning_rate(optimizer, epoch, schedule):
"""Decay the learning rate based on schedule"""
lr = optimizer.defaults['lr']
for milestone in schedule:
lr *= 0.1 if epoch >= milestone else 1.
for param_group in optimizer.param_groups:
param_group['lr'] = lr
def adjust_learning_rate_withWarmup(optimizer, epoch, args):
"""Decays the learning rate with half-cycle cosine after warmup"""
if epoch < args.warmup_epochs:
lr = args.lr * epoch / args.warmup_epochs
else:
lr = args.lr * 0.5 * (1. + math.cos(math.pi * (epoch - args.warmup_epochs) / (args.epochs - args.warmup_epochs)))
for param_group in optimizer.param_groups:
param_group['lr'] = lr
return lr
def main():
print('>>>> Running on {} <<<<'.format(device))
model = CPICANN(embed_dim=128, num_classes=args.num_classes)
# LOAD PRETRAINED MODEL
loaded = torch.load(args.load_path)
model.load_state_dict(loaded['model'])
model.bce_fineTune_init_weights()
model.to(device)
if rank == 0:
log.printlog(model)
trainset = mixDataset_cls_dynamic(args.data_dir_train, args.anno_struc, mode='Train')
valset = mixDataset_cls_dynamic(args.data_dir_val, args.anno_struc, mode='Eval')
if distributed:
train_sampler = torch.utils.data.distributed.DistributedSampler(trainset, shuffle=True)
val_sampler = torch.utils.data.distributed.DistributedSampler(valset, shuffle=True)
train_loader = DataLoader(trainset, batch_size=512, num_workers=16, pin_memory=True, drop_last=True, sampler=train_sampler)
val_loader = DataLoader(valset, batch_size=512, num_workers=16, pin_memory=True, drop_last=True, sampler=val_sampler)
model = DDP(model, device_ids=[device], output_device=local_rank, find_unused_parameters=False)
else:
train_loader = DataLoader(trainset, batch_size=512, num_workers=16, pin_memory=True, shuffle=True)
val_loader = DataLoader(valset, batch_size=512, num_workers=16, pin_memory=True, shuffle=True)
criterion = nn.CrossEntropyLoss()
optimizer = optim.AdamW(model.parameters(), args.lr, weight_decay=1e-4)
start_epoch = 0
for epoch in range(start_epoch + 1, args.epochs + 1):
if distributed:
train_sampler.set_epoch(epoch)
val_sampler.set_epoch(epoch)
loss_train = run_one_epoch(model, train_loader, criterion, optimizer, epoch, mode='Train')
loss_val = run_one_epoch(model, val_loader, criterion, optimizer, epoch, mode='Eval')
if rank == 0:
print_log(epoch, loss_train, loss_val, optimizer.param_groups[0]['lr'])
save_checkpoint({'epoch': epoch,
'model': model.module.state_dict() if distributed else model.state_dict(),
'optimizer': optimizer}, is_best=False,
filepath='{}/checkpoints/'.format(log.get_path()),
filename='checkpoint_{:04d}.pth'.format(epoch))
if __name__ == '__main__':
if 'RANK' in os.environ and 'WORLD_SIZE' in os.environ:
rank = int(os.environ["RANK"])
local_rank = int(os.environ["LOCAL_RANK"])
torch.cuda.set_device(rank % torch.cuda.device_count())
dist.init_process_group(backend="nccl")
device = torch.device("cuda", local_rank)
print(f"[init] == local rank: {local_rank}, global rank: {rank} ==")
distributed = True
else:
rank = 0
device = 'cuda:0'
distributed = False
parser = argparse.ArgumentParser()
parser.add_argument("-progress_bar", type=bool, default=True)
parser.add_argument('--epochs', default=200, type=int, metavar='N',
help='number of total epochs to run')
parser.add_argument('--warmup-epochs', default=20, type=int, metavar='N',
help='number of warmup epochs')
parser.add_argument('--lr', '--learning-rate', default=8e-4, type=float,
metavar='LR', help='initial (base) learning rate', dest='lr')
parser.add_argument('--load_path', default='pretrained/single-phase_checkpoint_0200.pth', type=str,
help='path to load pretrained single-phase identification model')
parser.add_argument('--data_dir_train', default='data/train', type=str)
parser.add_argument('--data_dir_val', default='data/val', type=str)
parser.add_argument('--anno_struc', default='annotation/anno_struc.csv', type=str,
help='path to annotation file for structures')
parser.add_argument('--num_classes', default=23073, type=int, metavar='N')
args = parser.parse_args()
if rank == 0:
log = Logger(val=True)
main()
print('THE END')
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