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train_index.py
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#oding=utf-8
import os
import datetime
import pandas as pd
from dataset.dataset_index import collate_fn1, collate_fn2, dataset
import torch
import torch.nn as nn
import torch.utils.data as torchdata
from torchvision import datasets, models, transforms
from torchvision.models import resnet50
import torch.optim as optim
from torch.optim import lr_scheduler
from utils.train_util_index import train, trainlog
from utils.warmup_scheduler import WarmupMultiStepLR
from utils.auto_resume import AutoResumer
from torch.nn import CrossEntropyLoss
import logging
from models.resnet_index import resnet_swap_2loss_add as Extractor
from models.classifier import Classifier
from PIL import Image
import argparse
import numpy as np
import random
torch.backends.cudnn.benchmark = True
parser = argparse.ArgumentParser()
parser.add_argument('--data', '-d', default='CUB', type=str, choices=['CUB', 'STCAR', 'AIR'])
parser.add_argument('--stage', '-s', default=3, type=int , choices=[1, 2, 3])
parser.add_argument('--num_positive', '-n', default=3, type=int)
parser.add_argument('--desc', default='_')
args = parser.parse_args()
stage_to_size = {3: '7x', 2: '14x', 1: '28x'}
num_positive = args.num_positive
cfg = {}
time = datetime.datetime.now()
# set dataset, include{CUB_200_2011: CUB, Stanford car: STCAR, JDfood: FOOD}
print("USE DATASET <<< {} >>>".format(args.data))
sssize = stage_to_size[args.stage]
print("CALCULATE FEATURES OF <<< {} >>>".format(sssize))
cfg['dataset'] = args.data
stage = args.stage
# prepare dataset
if cfg['dataset'] == 'CUB':
rawdata_root = './datasets/CUB/all'
train_pd = pd.read_csv("./datasets/CUB/train.txt",sep=" ",header=None, names=['ImageName', 'label'])
test_pd = pd.read_csv("./datasets/CUB/test.txt",sep=" ",header=None, names=['ImageName', 'label'])
cfg['numcls'] = 200
numimage = 6033
if cfg['dataset'] == 'STCAR':
rawdata_root = './datasets/STCAR/all'
train_pd = pd.read_csv("./datasets/STCAR/train.txt",sep=" ",header=None, names=['ImageName', 'label'])
test_pd = pd.read_csv("./datasets/STCAR/test.txt",sep=" ",header=None, names=['ImageName', 'label'])
cfg['numcls'] = 196
numimage = 8144
if cfg['dataset'] == 'AIR':
rawdata_root = './datasets/AIR/all'
train_pd = pd.read_csv("./datasets/AIR/train.txt",sep=" ",header=None, names=['ImageName', 'label'])
test_pd = pd.read_csv("./datasets/AIR/test.txt",sep=" ",header=None, names=['ImageName', 'label'])
cfg['numcls'] = 100
numimage = 6667
if cfg['dataset'] == 'DOG':
rawdata_root = './datasets/st_dog/all'
train_pd = pd.read_csv("./datasets/st_dog/train.txt",sep=" ",header=None, names=['ImageName', 'label'])
test_pd = pd.read_csv("./datasets/st_dog/test.txt",sep=" ",header=None, names=['ImageName', 'label'])
cfg['numcls'] = 120
numimage = 12000
if cfg['dataset'] == 'FLW':
rawdata_root = './datasets/flower/all'
train_pd = pd.read_csv("./datasets/flower/train.txt",sep=" ",header=None, names=['ImageName', 'label'])
test_pd = pd.read_csv("./datasets/flower/test.txt",sep=" ",header=None, names=['ImageName', 'label'])
cfg['numcls'] = 102
numimage = 2040
if cfg['dataset'] == 'BUT':
rawdata_root = './datasets/butterfly_200/all'
train_pd = pd.read_csv("./datasets/butterfly_200/train.txt",sep=" ",header=None, names=['ImageName', 'label'])
test_pd = pd.read_csv("./datasets/butterfly_200/test.txt",sep=" ",header=None, names=['ImageName', 'label'])
cfg['numcls'] = 200
numimage = 10270
print('Dataset:',cfg['dataset'])
print('train images:', train_pd.shape)
print('test images:', test_pd.shape)
print('num classes:', cfg['numcls'])
print("********************************************************")
print('Set transform')
data_transforms = {
'swap': transforms.Compose([
transforms.Resize((512,512)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((448,448)),
transforms.RandomHorizontalFlip(),
]),
'swap2': None,
'unswap': transforms.Compose([
transforms.Resize((512,512)),
transforms.RandomRotation(degrees=15),
transforms.RandomCrop((448,448)),
transforms.RandomHorizontalFlip(),
]),
'totensor': transforms.Compose([
transforms.Resize((448,448)),
transforms.ToTensor(),
transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225]),
]),
'None': transforms.Compose([
transforms.Resize((512,512)),
transforms.CenterCrop((448,448)),
]),
}
data_set = {}
data_set['train'] = dataset(cfg,imgroot=rawdata_root,anno_pd=train_pd, stage=stage, num_positive=num_positive,
unswap=data_transforms["unswap"],swap=data_transforms["None"],swap2 = data_transforms["swap2"],totensor=data_transforms["totensor"],train=True
)
data_set['val'] = dataset(cfg,imgroot=rawdata_root,anno_pd=test_pd, stage=stage, num_positive=num_positive,
unswap=data_transforms["None"],swap=data_transforms["None"],swap2 = data_transforms["swap2"],totensor=data_transforms["totensor"],train=False
)
dataloader = {}
dataloader['train']=torch.utils.data.DataLoader(data_set['train'], batch_size=16,
shuffle=True, num_workers=16, collate_fn=collate_fn1)
dataloader['val']=torch.utils.data.DataLoader(data_set['val'], batch_size=16,
shuffle=False, num_workers=16, collate_fn=collate_fn2)
print('done')
print('**********************************************')
print('Set cache dir')
filename = args.desc + '_' + str(time.month) + str(time.day) + str(time.hour) + '_' + cfg['dataset'] + '_' + sssize + '_num_' + str(num_positive)
save_dir = './net_model/' + filename
if not os.path.exists(save_dir):
os.makedirs(save_dir)
logfile = save_dir + '/' + filename +'.log'
trainlog(logfile)
print('done')
print('*********************************************')
print('choose model and train set')
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
model = [Extractor(stage=stage), Classifier(2048, cfg['numcls'])]
print('swap + 2 loss')
model = [e.cuda() for e in model]
if torch.cuda.device_count() > 1:
print("Let's use", torch.cuda.device_count(), "GPUs!")
model = [nn.DataParallel(e) for e in model]
base_lr = 0.001
resume = None
start_epoch = 0
if resume is not None:
logging.info('resuming finetune from %s'%resume)
state_dicts = torch.load(resume)
[m.load_state_dict(d) for m, d in zip(model, state_dicts)]
start_epoch = None
params = []
for idx, m in enumerate(model):
for key, value in m.named_parameters():
if not value.requires_grad:
continue
lr = base_lr
momentum = 0.9
if isinstance(m.module, Classifier) or 'lrx' in key:
print('[learning rate] {} is set to x10'.format(key))
lr = base_lr * 10
params += [{"params": [value], "lr": lr, "momentum": momentum}]
optimizer = optim.SGD(params)
criterion = CrossEntropyLoss()
scheduler = WarmupMultiStepLR(optimizer,
warmup_epoch = 2,
milestones = [60, 120, 180, 240, 300])
resumer = AutoResumer(scheduler, save_dir)
train(cfg,
model,
epoch_num=360,
start_epoch=start_epoch,
optimizer=optimizer,
criterion=criterion,
scheduler=scheduler,
resumer=resumer,
data_set=data_set,
data_loader=dataloader,
num_positive=num_positive,
save_dir=save_dir)