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train_distillation.py
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train_distillation.py
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# coding=utf-8
from __future__ import absolute_import, print_function
import argparse
import os
import sys
import torch.utils.data
from torch.backends import cudnn
from torch.autograd import Variable
import models
import losses
from utils import RandomIdentitySampler, mkdir_if_missing, logging, display
import DataSet
import pdb
import numpy as np
import torch.nn.functional as F
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
cudnn.benchmark = True
def main(args):
os.environ['CUDA_VISIBLE_DEVICES'] = args.gpu
dir = '%s_%s_dis_%s_%s_%s_%0.2f_%s' % (args.data, args.loss, args.net,args.TNet, args.Ttype, args.lamda,args.lr)
log_dir = os.path.join('checkpoints', dir)
mkdir_if_missing(log_dir)
sys.stdout = logging.Logger(os.path.join(log_dir, 'log.txt'))
display(args)
# Teacher Netowrk
if args.r is None:
Network_T = args.TNet
model_T = models.create(Network_T, Embed_dim=args.dim)
model_dict_T = model_T.state_dict()
if args.data == 'cub':
model_T = torch.load('checkpoints/cub_Tmodel.pkl')
elif args.data == 'car':
model_T = torch.load('checkpoints/car_Tmodel.pkl')
elif args.data == 'product':
model_T = torch.load('checkpoints/product_Tmodel.pkl')
else:
model_T = torch.load(args.r)
model_T = model_T.cuda()
model_T.eval()
# Student network
if args.r is None:
model = models.create(args.net, Embed_dim=args.dim)
model_dict = model.state_dict()
if args.net == 'bn':
pretrained_dict = torch.load('pretrained_models/bn_inception-239d2248.pth')
elif args.net == 'resnet101':
pretrained_dict = torch.load('pretrained_models/resnet101-5d3b4d8f.pth')
elif args.net == 'resnet50':
pretrained_dict = torch.load('pretrained_models/resnet50-19c8e357.pth')
elif args.net == 'resnet34':
pretrained_dict = torch.load('pretrained_models/resnet34-333f7ec4.pth')
elif args.net == 'resnet18':
pretrained_dict = torch.load('pretrained_models/resnet18-5c106cde.pth')
elif args.net == 'inception':
pretrained_dict = torch.load('pretrained_models/inception_v3_google-1a9a5a14.pth')
else:
print (' Oops! That was no valid models. ')
pretrained_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict}
model_dict.update(pretrained_dict)
model.load_state_dict(model_dict)
else:
model = torch.load(args.r)
if args.continue_train:
model=torch.load(log_dir+'/%d_model.pkl' % (args.start))
model = model.cuda()
torch.save(model, os.path.join(log_dir, 'model.pkl'))
print('initial model is save at %s' % log_dir)
new_param_ids = set(map(id, model.Embed.parameters()))
new_params = [p for p in model.parameters() if
id(p) in new_param_ids]
base_params = [p for p in model.parameters() if
id(p) not in new_param_ids]
param_groups = [
{'params': base_params, 'lr_mult': 0.1},
{'params': new_params, 'lr_mult': 1.0}]
optimizer = torch.optim.Adam(param_groups, lr=args.lr,
weight_decay=args.weight_decay)
if args.loss == 'knnsoftmax':
criterion = losses.create(args.loss, alpha=args.alpha, k=args.k).cuda()
else:
criterion = losses.create(args.loss).cuda()
data = DataSet.create(args.data, root=None, test=False)
train_loader = torch.utils.data.DataLoader(
data.train, batch_size=args.BatchSize,
sampler=RandomIdentitySampler(data.train, num_instances=args.num_instances),
drop_last=True, num_workers=args.nThreads)
loss_log=[]
for i in range(3):
loss_log.append([])
loss_dis = []
for i in range(3):
loss_dis.append([])
for epoch in range(args.start, args.epochs):
running_loss = 0.0
for i, data in enumerate(train_loader, 0):
inputs, labels = data
inputs = Variable(inputs.cuda())
labels = Variable(labels).cuda()
optimizer.zero_grad()
embed_feat = model(inputs)
embed_feat_T = model_T(inputs)
loss_net, inter_, dist_ap, dist_an, dis_pos, dis_neg,dis = criterion(embed_feat, labels)
loss_net_T, inter_T, dist_ap_T, dist_an_T,dis_pos_T, dis_neg_T,dis_T = criterion(embed_feat_T, labels)
lamda=args.lamda
if args.Ttype == 'relative':
loss_dis[0].append(torch.mean(torch.norm(dis-dis_T,p=2)).data[0])
loss_dis[1].append(0.0)
loss_dis[2].append(0.0)
loss_distillation = 0.0*torch.mean(F.pairwise_distance(embed_feat,embed_feat_T))
loss_distillation += torch.mean(torch.norm(dis-dis_T,p=2))
loss = loss_net + lamda * loss_distillation
elif args.Ttype == 'absolute':
loss_dis[0].append(0.0)
loss_dis[1].append(0.0)
loss_dis[2].append(torch.mean(F.pairwise_distance(embed_feat,embed_feat_T)).data[0])
loss_distillation = torch.mean(F.pairwise_distance(embed_feat,embed_feat_T))
loss = loss_net + lamda * loss_distillation
else:
print('This type does not exist')
loss.backward()
optimizer.step()
running_loss += loss.data[0]
loss_log[0].append(loss.data[0])
loss_log[1].append(loss_net.data[0])
loss_log[2].append(lamda*loss_distillation.data[0])
if epoch == 0 and i == 0:
print(50*'#')
print('Train Begin -- HA-HA-HA')
print('[Epoch %05d]\t Loss_net: %.3f \t Loss_distillation: %.3f \t Accuracy: %.3f \t Pos-Dist: %.3f \t Neg-Dist: %.3f' % (epoch + 1, loss_net, lamda*loss_distillation, inter_, dist_ap, dist_an))
if epoch % args.save_step == 0:
torch.save(model, os.path.join(log_dir, '%d_model.pkl' % epoch))
#plot loss
line1,=plt.plot(loss_log[0],'r-',label="Total loss",)
line2,=plt.plot(loss_log[1],'b-',label = "KNNsoftmax loss")
line3,=plt.plot(loss_log[2],'g--',label ="Distillation loss")
plt.title('%s_%s_dis_%s_%s_%s_%0.2f' % (args.data, args.loss, args.net,args.TNet, args.Ttype, args.lamda))
plt.legend([line1,line2,line3],['Total loss','Contrastive loss', 'Distance loss'])
plt.savefig('./fig/%s_%s_dis_%s_%s_%s_%0.2f.jpg' % (args.data, args.loss, args.net,args.TNet, args.Ttype, args.lamda))
if __name__ == '__main__':
parser = argparse.ArgumentParser(description='KNN-Softmax Training')
# hype-parameters
parser.add_argument('-lr', type=float, default=1e-5, help="learning rate of new parameters")
parser.add_argument('-BatchSize', '-b', default=128, type=int, metavar='N',
help='mini-batch size (1 = pure stochastic) Default: 256')
parser.add_argument('-num_instances', default=8, type=int, metavar='n',
help=' number of samples from one class in mini-batch')
parser.add_argument('-dim', default=512, type=int, metavar='n',
help='dimension of embedding space')
parser.add_argument('-alpha', default=40, type=int, metavar='n',
help='hyper parameter in KNN Softmax')
parser.add_argument('-k', default=16, type=int, metavar='n',
help='number of neighbour points in KNN')
# network
parser.add_argument('-data', default='cub', required=True,
help='path to Data Set')
parser.add_argument('-net', default='bn')
parser.add_argument('-loss', default='branch', required=True,
help='loss for training network')
parser.add_argument('-epochs', default=600, type=int, metavar='N',
help='epochs for training process')
parser.add_argument('-save_step', default=50, type=int, metavar='N',
help='number of epochs to save model')
# Resume from checkpoint
parser.add_argument('-r', default=None,
help='the path of the pre-trained model')
parser.add_argument('-start', default=0, type=int,
help='resume epoch')
# basic parameter
parser.add_argument('-log_dir', default=None,
help='where the trained models save')
parser.add_argument('--nThreads', '-j', default=4, type=int, metavar='N',
help='number of data loading threads (default: 2)')
parser.add_argument('--momentum', type=float, default=0.9)
parser.add_argument('--weight-decay', type=float, default=2e-4)
# distillation
parser.add_argument("-lamda",type=float, default=0.0, help="The trade-off between contrastive and other losses")
parser.add_argument("-Ttype",type=str, default='D', help='relative, absolute')
parser.add_argument('-which_epoch', type=int, default=0, help='which epoch to load? set to latest to use latest cached model')
parser.add_argument('-continue_train', action='store_true', help='continue training: load the certain model')
parser.add_argument("-gpu",type=str, default='0',help='which gpu to choose')
parser.add_argument("-TNet",type=str, default='resnet101',help='which teacher model to choose')
main(parser.parse_args())