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train_NL.py
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train_NL.py
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from util import utils
import parser
from net import models
import sys
import random
from tqdm import tqdm
import numpy as np
import math
from util.loss import TripletLoss
from util.cmc import Video_Cmc
import torch
import torch.nn as nn
import torch.optim as optim
from torchvision.transforms import Compose,ToTensor,Normalize,Resize
import torch.backends.cudnn as cudnn
cudnn.benchmark=True
import os
os.environ['CUDA_VISIBLE_DEVICES']='0'
torch.multiprocessing.set_sharing_strategy('file_system')
def validation(network,dataloader,args):
network.eval()
pbar = tqdm(total=len(dataloader),ncols=100,leave=True)
pbar.set_description('Inference')
gallery_features = []
gallery_labels = []
gallery_cams = []
with torch.no_grad():
for c,data in enumerate(dataloader):
seqs = data[0].cuda()
label = data[1]
cams = data[2]
B,C,H,W = seqs.shape
seqs = seqs.reshape(B//args.S,args.S,C,H,W)
feat = network(seqs)#.cpu().numpy() #[xx,128]
if args.temporal == 'max':
feat = torch.max(feat.reshape(feat.shape[0]//args.S,args.S,-1),dim=1)[0]
elif args.temporal == 'mean':
feat = torch.mean(feat.reshape(feat.shape[0]//args.S,args.S,-1),dim=1)
elif args.temporal in ['Done'] :
feat = feat
gallery_features.append(feat.cpu())
gallery_labels.append(label)
gallery_cams.append(cams)
pbar.update(1)
pbar.close()
gallery_features = torch.cat(gallery_features,dim=0).numpy()
gallery_labels = torch.cat(gallery_labels,dim=0).numpy()
gallery_cams = torch.cat(gallery_cams,dim=0).numpy()
Cmc,mAP = Video_Cmc(gallery_features,gallery_labels,gallery_cams,dataloader.dataset.query_idx,10000)
network.train()
return Cmc[0],mAP
if __name__ == '__main__':
#Parse args
args = parser.parse_args()
# set transformation (H flip is inside dataset)
train_transform = Compose([Resize((256,128)),ToTensor(),Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])])
test_transform = Compose([Resize((256,128)),ToTensor(),Normalize(mean=[0.485,0.456,0.406],std=[0.229,0.224,0.225])])
print('Start dataloader...')
train_dataloader = utils.Get_Video_train_DataLoader(args.train_txt,args.train_info, train_transform, shuffle=True,num_workers=args.num_workers,\
S=args.S,track_per_class=args.track_per_class,class_per_batch=args.class_per_batch)
num_class = train_dataloader.dataset.n_id
test_dataloader = utils.Get_Video_test_DataLoader(args.test_txt,args.test_info,args.query_info,test_transform,batch_size=args.batch_size,\
shuffle=False,num_workers=args.num_workers,S=args.S,distractor=True)
print('End dataloader...')
network = nn.DataParallel(models.CNN(args.latent_dim,model_type=args.model_type,num_class=num_class,non_layers=args.non_layers,stripes=args.stripes,temporal=args.temporal).cuda())
if args.load_ckpt is not None:
state = torch.load(args.load_ckpt)
network.load_state_dict(state,strict=False)
# log
os.system('mkdir -p %s'%(args.ckpt))
f = open(os.path.join(args.ckpt,args.log_path),'a')
f.close()
# Train loop
# 1. Criterion
criterion_triplet = TripletLoss('soft',True)
critetion_id = nn.CrossEntropyLoss().cuda()
# 2. Optimizer
if args.optimizer == 'sgd':
optimizer = optim.SGD(network.parameters(),lr = args.lr,momentum=0.9,weight_decay = 1e-4)
else:
optimizer = optim.Adam(network.parameters(),lr = args.lr,weight_decay = 5e-5)
if args.lr_step_size != 0:
scheduler = optim.lr_scheduler.StepLR(optimizer, args.lr_step_size, 0.1)
id_loss_list = []
trip_loss_list = []
track_id_loss_list = []
best_cmc = 0
for e in range(args.n_epochs):
print('epoch',e)
if (e+1)%10 == 0:
cmc,map = validation(network,test_dataloader,args)
print('CMC: %.4f, mAP : %.4f'%(cmc,map))
f = open(os.path.join(args.ckpt,args.log_path),'a')
f.write('epoch %d, rank-1 %f , mAP %f\n'%(e,cmc,map))
if args.frame_id_loss:
f.write('Frame ID loss : %r\n'%(id_loss_list))
if args.track_id_loss:
f.write('Track ID loss : %r\n'%(track_id_loss_list))
f.write('Trip Loss : %r\n'%(trip_loss_list))
id_loss_list = []
trip_loss_list = []
track_id_loss_list = []
if cmc >= best_cmc:
torch.save(network.state_dict(),os.path.join(args.ckpt,'ckpt_best.pth'))
best_cmc = cmc
f.write('best\n')
f.close()
total_id_loss = 0
total_trip_loss = 0
total_track_id_loss = 0
pbar = tqdm(total=len(train_dataloader),ncols=100,leave=True)
for i,data in enumerate(train_dataloader):
seqs = data[0]#.cuda()
labels = data[1].cuda()
B,T,C,H,W = seqs.shape
feat, output = network(seqs)
if args.temporal == 'max':
pool_feat = torch.max(feat.reshape(feat.shape[0]//args.S,args.S,-1),dim=1)[0]
pool_output = torch.max(output.reshape(output.shape[0]//args.S,args.S,-1),dim=1)[0]
elif args.temporal == 'mean':
pool_feat = torch.mean(feat.reshape(feat.shape[0]//args.S,args.S,-1),dim=1)
pool_output = torch.mean(output.reshape(output.shape[0]//args.S,args.S,-1),dim=1)
elif args.temporal in ['Done'] :
pool_feat = feat
pool_output = output
trip_loss = criterion_triplet(pool_feat,labels,dis_func='eu')
total_trip_loss += trip_loss.mean().item()
total_loss = trip_loss.mean()
# Frame level ID loss
if args.frame_id_loss == True:
expand_labels = (labels.unsqueeze(1)).repeat(1,args.S).reshape(-1)
id_loss = critetion_id(output,expand_labels)
total_id_loss += id_loss.item()
coeff = 1
total_loss += coeff*id_loss
if args.track_id_loss == True:
track_id_loss = critetion_id(pool_output,labels)
total_track_id_loss += track_id_loss.item()
coeff = 1
total_loss += coeff*track_id_loss
#####################
optimizer.zero_grad()
total_loss.backward()
optimizer.step()
pbar.update(1)
pbar.close()
if args.lr_step_size !=0:
scheduler.step()
avg_id_loss = '%.4f'%(total_id_loss/len(train_dataloader))
avg_trip_loss = '%.4f'%(total_trip_loss/len(train_dataloader))
avg_track_id_loss = '%.4f'%(total_track_id_loss/len(train_dataloader))
print('Trip : %s , ID : %s , Track_ID : %s'%(avg_trip_loss,avg_id_loss,avg_track_id_loss))
id_loss_list.append(avg_id_loss)
trip_loss_list.append(avg_trip_loss)
track_id_loss_list.append(avg_track_id_loss)