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train.py
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import os, sys, numpy as np
import matplotlib.pyplot as plt
import argparse
from tqdm import tqdm
from sklearn.metrics import average_precision_score
import torch
import torch.nn as nn
from torch.autograd import Variable
import torchvision
import torchvision.transforms as transforms
#import torchnet as tnt
import multiprocessing
CORES = 2
from loader import DataLoader
from models.alexnet import Network
from trainer import train, test
from models.disnet import disnet
from Utils.TrainingUtils import adjust_learning_rate
def compute_mAP(labels,outputs):
y_true = labels.cpu().numpy()
y_pred = outputs.cpu().numpy()
AP = []
for i in range(y_true.shape[0]):
AP.append(average_precision_score(y_true[i],y_pred[i]))
return np.mean(AP)
def main(args):
if args.gpu is not None:
print('Using GPU %d'%args.gpu)
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]=str(args.gpu)
else:
print('CPU mode')
normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
std= [0.229, 0.224, 0.225])
train_transform = transforms.Compose([
transforms.RandomResizedCrop(227),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
val_transform = transforms.Compose([
transforms.Resize(256),
transforms.CenterCrop(227),
#transforms.RandomResizedCrop(227),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
normalize,
])
# DataLoader initialize
train_data = DataLoader(args.pascal_path,'trainval',transform=train_transform)
t_trainloader = torch.utils.data.DataLoader(dataset=train_data,
batch_size=args.batch,
shuffle=True,
num_workers=CORES,
pin_memory=True)
print('[DATA] Target Train loader done!')
val_data = DataLoader(args.pascal_path,'test',transform=val_transform,random_crops=args.crops)
t_testloader = torch.utils.data.DataLoader(dataset=val_data,
batch_size=args.batch,
shuffle=False,
num_workers=CORES,
pin_memory=True)
print('[DATA] Target Test loader done!')
if not args.test :
s_trainset = torchvision.datasets.ImageFolder(args.imgnet_path, transform=transforms.Compose([
transforms.RandomHorizontalFlip(),
transforms.RandomResizedCrop(227),
transforms.ToTensor(), normalize]
))
s_trainloader = torch.utils.data.DataLoader(dataset= s_trainset,
batch_size=5*args.batch,
shuffle=False,
num_workers=CORES,
pin_memory=True)
print('[DATA] Source Train loader done!')
N = len(train_data.names)
iter_per_epoch = N/args.batch
model = Network(num_classes = 21)
g_model = Network(num_classes = 21)
d_model = disnet()
if args.gpu is not None:
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
print('[MODEL] CUDA DEVICE : {}'.format(device))
model.to(device)
g_model.to(device)
d_model.to(device)
optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, model.parameters()),
lr=args.lr,momentum=0.9,weight_decay = 0.0001)
g_optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, g_model.parameters()),
lr=args.lr,momentum=0.9,weight_decay = 0.0001)
d_optimizer = torch.optim.SGD(filter(lambda p: p.requires_grad, d_model.parameters()),
lr=args.lr,momentum=0.9,weight_decay = 0.0001)
if args.model is not None:
checkpoint = torch.load(args.model)
model.load(checkpoint['model'],True)
g_model.load(checkpoint['g_model'],True)
d_model.load_state_dict(checkpoint['d_model'])
optimizer.load_state_dict(checkpoint['optimizer'])
g_optimizer.load_state_dict(checkpoint['g_optimizer'])
d_optimizer.load_state_dict(checkpoint['d_optimizer'])
############## TRAINING ###############
print('Start training: lr %f, batch size %d'%(args.lr,args.batch))
print('Checkpoint: '+args.checkpoint)
# Train the Model
steps = args.iter_start
best_mAP = 0.0
best_path = './{}/model-{}_pretrained-{}_lr-0pt001_lmd_s-{}_acc-{}.pth'.format(args.checkpoint,'alexnet','False',args.lmd_s,'{}')
if args.test:
args.epochs = 1
for epoch in range(int(iter_per_epoch*args.iter_start),args.epochs):
if not args.test:
adjust_learning_rate(optimizer, epoch, init_lr=args.lr, step=100, decay=0.1)
adjust_learning_rate(g_optimizer, epoch, init_lr=args.lr/2, step=100, decay=0.1)
adjust_learning_rate(d_optimizer, epoch, init_lr=args.lr/1.5, step=100, decay=0.1)
done = train(epoch, model, g_model, d_model, optimizer, g_optimizer,
d_optimizer, t_trainloader, s_trainloader, args.lmd_s, device)
best_mAP = test(epoch, model, g_model, d_model, optimizer, g_optimizer,
d_optimizer, t_testloader,best_mAP,best_path, device)
if __name__ == "__main__":
parser = argparse.ArgumentParser(description='Train network on Pascal VOC 2007')
parser.add_argument('--pascal_path', type=str, help='Path to Pascal VOC 2007 folder')
parser.add_argument('--imgnet_path', type=str, help='Path to ImageNet folder')
parser.add_argument('--model', default=None, type=str, help='Pretrained model')
parser.add_argument('--test', default=None, type=int, help='test model')
parser.add_argument('--gpu', default=0, type=int, help='gpu id')
parser.add_argument('--epochs', default=350, type=int, help='gpu id')
parser.add_argument('--iter_start', default=0, type=int, help='Starting iteration count')
parser.add_argument('--lmd_s', default=0.001, type=float, help='source lambda')
parser.add_argument('--batch', default=20, type=int, help='batch size')
parser.add_argument('--checkpoint', default='checkpoints/', type=str, help='checkpoint folder')
parser.add_argument('--lr', default=0.001, type=float, help='learning rate for SGD optimizer')
parser.add_argument('--crops', default=10, type=int, help='number of random crops during testing')
args = parser.parse_args()
if args.test and args.model == None:
sys.exit('You must provide model when trying to test.')
main(args)