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import os
import datetime
import sys
sys.path.insert(0, os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
import torch
import torch.nn as nn
from torch.nn.parallel import DistributedDataParallel as DDP
from tqdm import tqdm
from toolkit.cmetric import MultiClassificationMetric, MultilabelClassificationMetric, simple_accuracy
from toolkit.chelper import load_model
from torch import distributed as dist
from sklearn.metrics import roc_auc_score
import numpy as np
import time
def reduce_tensor(tensor, n):
rt = tensor.clone()
dist.all_reduce(rt, op=dist.ReduceOp.SUM)
rt /= n
return rt
def gather_tensor(tensor, n):
rt = [torch.zeros_like(tensor) for _ in range(n)]
dist.all_gather(rt, tensor)
return torch.cat(rt, dim=0)
class TrainEngine(object):
def __init__(self, local_rank, world_size=0, DDP=False, SyncBatchNorm=False):
# init setting
self.local_rank = local_rank
self.world_size = world_size
self.device_ = f'cuda:{local_rank}'
# create tool
self.cls_meter_ = MultilabelClassificationMetric()
self.loss_meter_ = MultiClassificationMetric()
self.top1_meter_ = MultiClassificationMetric()
self.DDP = DDP
self.SyncBN = SyncBatchNorm
def create_env(self, cfg):
# create network
self.netloc_ = load_model(cfg.network.name, cfg.network.class_num, self.SyncBN)
print(self.netloc_)
self.netloc_.cuda()
if self.DDP:
if self.SyncBN:
self.netloc_ = torch.nn.SyncBatchNorm.convert_sync_batchnorm(self.netloc_)
self.netloc_ = DDP(self.netloc_,
device_ids=[self.local_rank],
broadcast_buffers=True,
)
# create loss function
self.criterion_ = nn.CrossEntropyLoss().cuda()
# create optimizer
self.optimizer_ = torch.optim.AdamW(self.netloc_.parameters(), lr=cfg.optimizer.lr,
betas=(cfg.optimizer.beta1, cfg.optimizer.beta2), eps=cfg.optimizer.eps,
weight_decay=cfg.optimizer.weight_decay)
# create scheduler
self.scheduler_ = torch.optim.lr_scheduler.CosineAnnealingLR(self.optimizer_, cfg.train.epoch_num,
eta_min=cfg.scheduler.min_lr)
def train_multi_class(self, train_loader, epoch_idx, ema_start):
starttime = datetime.datetime.now()
# switch to train mode
self.netloc_.train()
self.loss_meter_.reset()
self.top1_meter_.reset()
# train
train_loader = tqdm(train_loader, desc='train', ascii=True)
for imgs_idx, (imgs_tensor, imgs_label, _, _) in enumerate(train_loader):
# set cuda
imgs_tensor = imgs_tensor.cuda() # [256, 3, 224, 224]
imgs_label = imgs_label.cuda()
# clear gradients(zero the parameter gradients)
self.optimizer_.zero_grad()
# calc forward
preds = self.netloc_(imgs_tensor)
# calc acc & loss
loss = self.criterion_(preds, imgs_label)
# backpropagation
loss.backward()
# update parameters
self.optimizer_.step()
# EMA update
if ema_start:
self.ema_model.update(self.netloc_)
# accumulate loss & acc
acc1 = simple_accuracy(preds, imgs_label)
if self.DDP:
loss = reduce_tensor(loss, self.world_size)
acc1 = reduce_tensor(acc1, self.world_size)
self.loss_meter_.update(loss.data.item())
self.top1_meter_.update(acc1.item())
# eval
top1 = self.top1_meter_.mean
loss = self.loss_meter_.mean
endtime = datetime.datetime.now()
self.lr_ = self.optimizer_.param_groups[0]['lr']
if self.local_rank == 0:
print('log: epoch-%d, train_top1 is %f, train_loss is %f, lr is %f, time is %d' % (
epoch_idx, top1, loss, self.lr_, (endtime - starttime).seconds))
# return
return top1, loss, self.lr_
def val_multi_class(self, val_loader, epoch_idx):
np.set_printoptions(suppress=True)
starttime = datetime.datetime.now()
# switch to train mode
self.netloc_.eval()
self.loss_meter_.reset()
self.top1_meter_.reset()
self.all_probs = []
self.all_labels = []
# eval
with torch.no_grad():
val_loader = tqdm(val_loader, desc='valid', ascii=True)
for imgs_idx, (imgs_tensor, imgs_label, _, _) in enumerate(val_loader):
# set cuda
imgs_tensor = imgs_tensor.cuda()
imgs_label = imgs_label.cuda()
# calc forward
preds = self.netloc_(imgs_tensor)
# calc acc & loss
loss = self.criterion_(preds, imgs_label)
# accumulate loss & acc
acc1 = simple_accuracy(preds, imgs_label)
outputs_scores = nn.functional.softmax(preds, dim=1)
outputs_scores = torch.cat((outputs_scores, imgs_label.unsqueeze(-1)), dim=-1)
if self.DDP:
loss = reduce_tensor(loss, self.world_size)
acc1 = reduce_tensor(acc1, self.world_size)
outputs_scores = gather_tensor(outputs_scores, self.world_size)
outputs_scores, label = outputs_scores[:, -2], outputs_scores[:, -1]
self.all_probs += [float(i) for i in outputs_scores]
self.all_labels += [ float(i) for i in label]
self.loss_meter_.update(loss.item())
self.top1_meter_.update(acc1.item())
# eval
top1 = self.top1_meter_.mean
loss = self.loss_meter_.mean
auc = roc_auc_score(self.all_labels, self.all_probs)
endtime = datetime.datetime.now()
if self.local_rank == 0:
print('log: epoch-%d, val_top1 is %f, val_loss is %f, auc is %f, time is %d' % (
epoch_idx, top1, loss, auc, (endtime - starttime).seconds))
# update lr
self.scheduler_.step()
# return
return top1, loss, auc
def val_ema(self, val_loader, epoch_idx):
np.set_printoptions(suppress=True)
starttime = datetime.datetime.now()
# switch to train mode
self.ema_model.module.eval()
self.loss_meter_.reset()
self.top1_meter_.reset()
self.all_probs = []
self.all_labels = []
# eval
with torch.no_grad():
val_loader = tqdm(val_loader, desc='valid', ascii=True)
for imgs_idx, (imgs_tensor, imgs_label, _, _) in enumerate(val_loader):
# set cuda
imgs_tensor = imgs_tensor.cuda()
imgs_label = imgs_label.cuda()
# calc forward
preds = self.ema_model.module(imgs_tensor)
# calc acc & loss
loss = self.criterion_(preds, imgs_label)
# accumulate loss & acc
acc1 = simple_accuracy(preds, imgs_label)
outputs_scores = nn.functional.softmax(preds, dim=1)
outputs_scores = torch.cat((outputs_scores, imgs_label.unsqueeze(-1)), dim=-1)
if self.DDP:
loss = reduce_tensor(loss, self.world_size)
acc1 = reduce_tensor(acc1, self.world_size)
outputs_scores = gather_tensor(outputs_scores, self.world_size)
outputs_scores, label = outputs_scores[:, -2], outputs_scores[:, -1]
self.all_probs += [float(i) for i in outputs_scores]
self.all_labels += [ float(i) for i in label]
self.loss_meter_.update(loss.item())
self.top1_meter_.update(acc1.item())
# eval
top1 = self.top1_meter_.mean
loss = self.loss_meter_.mean
auc = roc_auc_score(self.all_labels, self.all_probs)
endtime = datetime.datetime.now()
if self.local_rank == 0:
print('log: epoch-%d, ema_val_top1 is %f, ema_val_loss is %f, ema_auc is %f, time is %d' % (
epoch_idx, top1, loss, auc, (endtime - starttime).seconds))
# return
return top1, loss, auc
def save_checkpoint(self, file_root, epoch_idx, train_map, val_map, ema_start):
file_name = os.path.join(file_root,
time.strftime('%Y%m%d-%H-%M', time.localtime()) + '-' + str(epoch_idx) + '.pth')
if self.DDP:
stact_dict = self.netloc_.module.state_dict()
else:
stact_dict = self.netloc_.state_dict()
torch.save(
{
'epoch_idx': epoch_idx,
'state_dict': stact_dict,
'train_map': train_map,
'val_map': val_map,
'lr': self.lr_,
'optimizer': self.optimizer_.state_dict(),
'scheduler': self.scheduler_.state_dict()
}, file_name)
if ema_start:
ema_file_name = os.path.join(file_root,
time.strftime('%Y%m%d-%H-%M', time.localtime()) + '-EMA-' + str(epoch_idx) + '.pth')
ema_stact_dict = self.ema_model.module.module.state_dict()
torch.save(
{
'epoch_idx': epoch_idx,
'state_dict': ema_stact_dict,
'train_map': train_map,
'val_map': val_map,
'lr': self.lr_,
'optimizer': self.optimizer_.state_dict(),
'scheduler': self.scheduler_.state_dict()
}, ema_file_name)
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