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trainer.py
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trainer.py
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# -*- encoding: utf-8 -*-
'''
@File : trainer.py
@Contact : [email protected]
@License : (C)Copyright 2017-2020, HeXin
@Modify Time @Author @Version @Desciption
------------ ------- -------- -----------
2019/11/6 19:23 xin 1.0 None
'''
import torch
import torch.nn as nn
from torch import optim
from tensorboardX import SummaryWriter
from tqdm import tqdm
import numpy as np
import logging
from evaluate import eval_func, re_rank
from evaluate import euclidean_dist
from utils import AvgerageMeter, calculate_acc
import os.path as osp
import os
from common.sync_bn import convert_model
from common.optimizers import LRScheduler,WarmupMultiStepLR
from torch.optim import SGD
from utils.model import make_optimizer,make_optimizer_partial
try:
from apex import amp
from apex.parallel import DistributedDataParallel as DDP
import apex
except:
pass
class BaseTrainer(object):
def __init__(self, cfg, model, train_dl, val_dl,
loss_func, num_query, num_gpus):
self.cfg = cfg
self.model = model
self.train_dl = train_dl
self.val_dl = val_dl
self.loss_func = loss_func
self.num_query = num_query
self.loss_avg = AvgerageMeter()
self.acc_avg = AvgerageMeter()
self.train_epoch = 1
self.batch_cnt = 0
self.logger = logging.getLogger('reid_baseline.train')
self.log_period = cfg.SOLVER.LOG_PERIOD
self.checkpoint_period = cfg.SOLVER.CHECKPOINT_PERIOD
self.eval_period = cfg.SOLVER.EVAL_PERIOD
self.output_dir = cfg.OUTPUT_DIR
self.device = cfg.MODEL.DEVICE
self.epochs = cfg.SOLVER.MAX_EPOCHS
if cfg.SOLVER.TENSORBOARD.USE:
summary_dir = os.path.join(cfg.OUTPUT_DIR,'summaries/')
os.makedirs(summary_dir,exist_ok=True)
self.summary_writer = SummaryWriter(log_dir=summary_dir)
self.current_iteration = 0
self.model.cuda()
self.logger.info(self.model)
if num_gpus > 1:
self.optim = make_optimizer(self.model,opt=self.cfg.SOLVER.OPTIMIZER_NAME,lr=cfg.SOLVER.BASE_LR,weight_decay=self.cfg.SOLVER.WEIGHT_DECAY,momentum=0.9)
self.scheduler = WarmupMultiStepLR(self.optim, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,cfg.SOLVER.WARMUP_EPOCH, cfg.SOLVER.WARMUP_METHOD)
self.logger.info(self.optim)
self.mix_precision = (cfg.MODEL.OPT_LEVEL != "O0")
if self.mix_precision:
self.model, self.optim = amp.initialize(self.model, self.optim, opt_level=cfg.MODEL.OPT_LEVEL)
self.logger.info('Using apex for mix_precision with opt_level {}'.format(cfg.MODEL.OPT_LEVEL))
self.model = nn.DataParallel(self.model)
if cfg.SOLVER.SYNCBN:
if self.mix_precision:
self.model = apex.parallel.convert_syncbn_model(self.model)
self.logger.info('More than one gpu used, convert model to use SyncBN.')
self.logger.info('Using apex SyncBN implementation')
else:
self.model = convert_model(self.model)
self.model.cuda()
self.logger.info('More than one gpu used, convert model to use SyncBN.')
self.logger.info('Using pytorch SyncBN implementation')
self.logger.info(self.model)
# [todo] test with mix precision
if cfg.MODEL.WEIGHT != '':
self.logger.info('Loading weight from {}'.format(cfg.MODEL.WEIGHT))
param_dict = torch.load(cfg.MODEL.WEIGHT)
start_with_module = False
for k in param_dict.keys():
if k.startswith('module.'):
start_with_module = True
break
if start_with_module:
param_dict = {k[7:] : v for k, v in param_dict.items() }
print('ignore_param:')
print([k for k, v in param_dict.items() if k not in self.state_dict() or self.state_dict()[k].size() != v.size()])
print('unload_param:')
print([k for k, v in self.state_dict().items() if k not in param_dict.keys() or param_dict[k].size() != v.size()] )
param_dict = {k: v for k, v in param_dict.items() if k in self.state_dict() and self.state_dict()[k].size() == v.size()}
for i in param_dict:
self.model.state_dict()[i].copy_(param_dict[i])
# self.model.load_state_dict(param_dict)
self.logger.info('Trainer Built')
return
else:
if cfg.SOLVER.FIX_BACKBONE:
print('==>fix backbone')
param_list = []
for k,v in self.model.named_parameters():
if 'reduction_' not in k and 'fc_id_' not in k:
v.requires_grad=False#固定参数
else:
param_list.append(v)
print(k)
self.optim = make_optimizer_partial(param_list,opt=self.cfg.SOLVER.OPTIMIZER_NAME,lr=cfg.SOLVER.BASE_LR,weight_decay=self.cfg.SOLVER.WEIGHT_DECAY,momentum=0.9)
else:
self.optim = make_optimizer(self.model,opt=self.cfg.SOLVER.OPTIMIZER_NAME,lr=cfg.SOLVER.BASE_LR,weight_decay=self.cfg.SOLVER.WEIGHT_DECAY,momentum=0.9)
self.scheduler = WarmupMultiStepLR(self.optim, cfg.SOLVER.STEPS, cfg.SOLVER.GAMMA, cfg.SOLVER.WARMUP_FACTOR,cfg.SOLVER.WARMUP_EPOCH, cfg.SOLVER.WARMUP_METHOD)
self.logger.info(self.optim)
self.mix_precision = (cfg.MODEL.OPT_LEVEL != "O0")
if self.mix_precision:
self.model, self.optim = amp.initialize(self.model, self.optim, opt_level=cfg.MODEL.OPT_LEVEL)
self.logger.info('Using apex for mix_precision with opt_level {}'.format(cfg.MODEL.OPT_LEVEL))
if cfg.MODEL.WEIGHT != '':
self.logger.info('Loading weight from {}'.format(cfg.MODEL.WEIGHT))
param_dict = torch.load(cfg.MODEL.WEIGHT)
start_with_module = False
for k in param_dict.keys():
if k.startswith('module.'):
start_with_module = True
break
if start_with_module:
param_dict = {k[7:] : v for k, v in param_dict.items() }
print('ignore_param:')
print([k for k, v in param_dict.items() if k not in self.model.state_dict() or self.model.state_dict()[k].size() != v.size()])
print('unload_param:')
print([k for k, v in self.model.state_dict().items() if k not in param_dict.keys() or param_dict[k].size() != v.size()] )
param_dict = {k: v for k, v in param_dict.items() if k in self.model.state_dict() and self.model.state_dict()[k].size() == v.size()}
for i in param_dict:
self.model.state_dict()[i].copy_(param_dict[i])
# for k,v in self.model.named_parameters():
# if 'reduction_' not in k:
# print(v.requires_grad)#理想状态下，所有值都是False
return
def handle_new_batch(self):
if self.current_iteration % self.cfg.SOLVER.TENSORBOARD.LOG_PERIOD == 0:
if self.summary_writer:
self.summary_writer.add_scalar('Train/lr',self.scheduler.get_lr()[0],self.current_iteration)
self.summary_writer.add_scalar('Train/loss',self.loss_avg.avg,self.current_iteration)
self.summary_writer.add_scalar('Train/acc',self.acc_avg.avg,self.current_iteration)
self.acc_avg.reset()
self.batch_cnt += 1
self.current_iteration += 1
if self.batch_cnt % self.cfg.SOLVER.LOG_PERIOD == 0:
self.logger.info('Epoch[{}] Iteration[{}/{}] Loss: {:.3f},'
'Acc: {:.3f}, Base Lr: {:.2e}'
.format(self.train_epoch, self.batch_cnt,
len(self.train_dl), self.loss_avg.avg,
self.acc_avg.avg, self.scheduler.get_lr()[0]))
def handle_new_epoch(self):
self.batch_cnt = 1
lr = self.scheduler.get_lr()[0]
self.logger.info('Epoch {} done'.format(self.train_epoch))
self.logger.info('-' * 20)
torch.save(self.model.state_dict(), osp.join(self.output_dir,
self.cfg.MODEL.NAME + '_epoch_last.pth'))
torch.save(self.optim.state_dict(), osp.join(self.output_dir,
self.cfg.MODEL.NAME + '_epoch_last_optim.pth'))
if self.train_epoch > self.cfg.SOLVER.START_SAVE_EPOCH and self.train_epoch % self.checkpoint_period == 0:
self.save()
if (self.train_epoch > 0 and self.train_epoch % self.eval_period == 0) or self.train_epoch == 50 :
self.evaluate()
pass
self.scheduler.step()
self.train_epoch += 1
def step(self, batch):
self.model.train()
self.optim.zero_grad()
img, target = batch
img, target = img.cuda(), target.cuda()
if self.cfg.MODEL.USE_COS:
outputs = self.model(img,target)
else:
outputs = self.model(img)
if self.cfg.MODEL.NAME in ["cosinemgn","cosinemgn2d"]:
loss,tpl,pce,gce = self.loss_func(outputs, target,in_detail=True)
if self.current_iteration % self.cfg.SOLVER.TENSORBOARD.LOG_PERIOD == 0:
if self.summary_writer:
self.summary_writer.add_scalar('Train/tpl',tpl,self.current_iteration)
self.summary_writer.add_scalar('Train/gce',gce,self.current_iteration)
self.summary_writer.add_scalar('Train/pce',pce,self.current_iteration)
else:
loss,tpl,ce = self.loss_func(outputs, target,in_detail=True)
if self.current_iteration % self.cfg.SOLVER.TENSORBOARD.LOG_PERIOD == 0:
if self.summary_writer:
self.summary_writer.add_scalar('Train/tpl',tpl,self.current_iteration)
self.summary_writer.add_scalar('Train/ce',ce,self.current_iteration)
if self.mix_precision:
with amp.scale_loss(loss, self.optim) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
self.optim.step()
# acc = (score.max(1)[1] == target).float().mean()
acc = calculate_acc(self.cfg, outputs, target)
self.loss_avg.update(loss.cpu().item())
self.acc_avg.update(acc.cpu().item())
return self.loss_avg.avg, self.acc_avg.avg
def evaluate(self):
self.model.eval()
num_query = self.num_query
feats, pids, camids = [], [], []
with torch.no_grad():
for batch in tqdm(self.val_dl, total=len(self.val_dl),
leave=False):
data, pid, camid, _ = batch
data = data.cuda()
# ff = torch.FloatTensor(data.size(0), 2048).zero_()
# for i in range(2):
# if i == 1:
# data = data.index_select(3, torch.arange(data.size(3) - 1, -1, -1).long().to('cuda'))
# outputs = self.model(data)
# f = outputs.data.cpu()
# ff = ff + f
ff = self.model(data).data.cpu()
fnorm = torch.norm(ff, p=2, dim=1, keepdim=True)
ff = ff.div(fnorm.expand_as(ff))
feats.append(ff)
pids.append(pid)
camids.append(camid)
feats = torch.cat(feats, dim=0)
pids = torch.cat(pids, dim=0)
camids = torch.cat(camids, dim=0)
if self.cfg.TEST.RANDOMPERM <=0 :
query_feat = feats[:num_query]
query_pid = pids[:num_query]
query_camid = camids[:num_query]
gallery_feat = feats[num_query:]
gallery_pid = pids[num_query:]
gallery_camid = camids[num_query:]
distmat = euclidean_dist(query_feat, gallery_feat)
cmc, mAP, _ = eval_func(distmat.numpy(), query_pid.numpy(), gallery_pid.numpy(),
query_camid.numpy(), gallery_camid.numpy(),
)
else:
cmc = 0
mAP = 0
seed = torch.random.get_rng_state()
torch.manual_seed(0)
for i in range(self.cfg.TEST.RANDOMPERM):
index = torch.randperm(feats.size()[0])
# print(index[:10])
query_feat = feats[index][:num_query]
query_pid = pids[index][:num_query]
query_camid = camids[index][:num_query]
gallery_feat = feats[index][num_query:]
gallery_pid = pids[index][num_query:]
gallery_camid = camids[index][num_query:]
distmat = euclidean_dist(query_feat, gallery_feat)
_cmc, _mAP, _ = eval_func(distmat.numpy(), query_pid.numpy(), gallery_pid.numpy(),
query_camid.numpy(), gallery_camid.numpy(),
)
cmc += _cmc/self.cfg.TEST.RANDOMPERM
mAP += _mAP/self.cfg.TEST.RANDOMPERM
torch.random.set_rng_state(seed)
self.logger.info('Validation Result:')
self.logger.info('mAP: {:.2%}'.format(mAP))
for r in self.cfg.TEST.CMC:
self.logger.info('CMC Rank-{}: {:.2%}'.format(r, cmc[r - 1]))
self.logger.info('average of mAP and rank1: {:.2%}'.format((mAP+cmc[0])/2.0))
self.logger.info('-' * 20)
if self.summary_writer:
self.summary_writer.add_scalar('Valid/rank1',cmc[0],self.train_epoch)
self.summary_writer.add_scalar('Valid/mAP',mAP,self.train_epoch)
self.summary_writer.add_scalar('Valid/rank1_mAP',(mAP+cmc[0])/2.0,self.train_epoch)
def save(self):
torch.save(self.model.state_dict(), osp.join(self.output_dir,
self.cfg.MODEL.NAME + '_epoch' + str(self.train_epoch) + '.pth'))
torch.save(self.optim.state_dict(), osp.join(self.output_dir,
self.cfg.MODEL.NAME + '_epoch' + str(
self.train_epoch) + '_optim.pth'))