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controller.py
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controller.py
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import matplotlib.pyplot as plt
import torch
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
from torch.autograd import Variable
import torch.nn.functional as F
import torchvision.transforms as transforms
import argparse
import numpy as np
import time
import datetime
import os
import logging
from models.resnet import *
from models.resnet_att_v4 import *
from models.resnet_avg import *
from models.mvcnn import *
from models.mvcnn_att import *
import util
from logger import Logger
from custom_dataset import MultiViewDataSet
np.set_printoptions(suppress=True,precision=2)
MVCNN = 'mvcnn'
RESNET = 'resnet'
RESNET_ATT = 'resnet_att'
MVCNN_ATT='mvcnn_att'
MODELS = [RESNET,RESNET_ATT,MVCNN,MVCNN_ATT]
START=str(datetime.datetime.now())
parser = argparse.ArgumentParser(description='MVCNN-PyTorch')
parser.add_argument('data', metavar='DIR', help='path to dataset')
parser.add_argument('--depth', choices=[18, 34, 50, 101, 152], type=int, metavar='N', default=18, help='resnet depth (default: resnet18)')
parser.add_argument('--model', '-m', metavar='MODEL', default=RESNET, choices=MODELS,
help='pretrained model: ' + ' | '.join(MODELS) + ' (default: {})'.format(RESNET))
parser.add_argument('--epochs', default=100, type=int, metavar='N', help='number of total epochs to run (default: 100)')
parser.add_argument('-b', '--batch-size', default=8, type=int,
metavar='N', help='mini-batch size (default: 4)')
parser.add_argument('--lr', '--learning-rate', default=0.0001, type=float,
metavar='LR', help='initial learning rate (default: 0.0001)')
parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
help='momentum (default: 0.9)')
parser.add_argument('--lr-decay-freq', default=30, type=float,
metavar='W', help='learning rate decay (default: 30)')
parser.add_argument('--lr-decay', default=0.1, type=float,
metavar='W', help='learning rate decay (default: 0.1)')
parser.add_argument('--print-freq', '-p', default=10, type=int,
metavar='N', help='print frequency (default: 10)')
parser.add_argument('-r', '--resume', default='', type=str, metavar='PATH',
help='path to latest checkpoint (default: none)')
parser.add_argument('--pretrained', dest='pretrained', action='store_true', help='use pre-trained model')
args = parser.parse_args()
print('Loading data')
input_size=224
train_transforms = transforms.Compose([
transforms.RandomResizedCrop(input_size),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize([.25,.25,.25], [3.98, 3.98, 3.98])
])
val_transforms = transforms.Compose([
transforms.Resize(input_size),
transforms.CenterCrop(input_size),
transforms.ToTensor(),
transforms.Normalize([.25,.25,.25], [3.98, 3.98, 3.98])
])
device = torch.device("cuda:0" if torch.cuda.is_available() else "cpu")
# Load dataset
dset_train = MultiViewDataSet(args.data, 'train', transform=train_transforms)
n0,n1,n2=dset_train.counts['0'],dset_train.counts['1'],dset_train.counts['2']
train_sampler = torch.utils.data.sampler.WeightedRandomSampler([float(n0+n1+n2)/n0]*n0 +[float(n0+n1+n2)/n1]*n1 + [float(n0+n1+n2)/n2]*n2 , (n0+n1+n2))
train_loader = DataLoader(dset_train, sampler=train_sampler,batch_size=args.batch_size, num_workers=28,pin_memory=True)
eval_train_loader=DataLoader(dset_train,batch_size=32, num_workers=28,pin_memory=True)
print('train size',len(train_loader))
dset_val = MultiViewDataSet(args.data, 'val', transform=val_transforms)
val_sampler=torch.utils.data.sampler.WeightedRandomSampler( [1]*128,128,replacement=False)
#val_loader = DataLoader(dset_val, shuffle=True, batch_size=args.batch_size, num_workers=28,pin_memory=True)
eval_val_loader=DataLoader(dset_val,batch_size=32,num_workers=28,pin_memory=True)
#print('val size',len(val_loader))
classes = dset_train.classes
if args.model == RESNET:
if args.depth == 18:
model = resnet18(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 34:
model = resnet34(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 50:
model = resnet50(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 101:
model = resnet101(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 152:
model = resnet152(pretrained=args.pretrained, num_classes=len(classes))
else:
raise Exception('Specify number of layers for resnet in command line. --resnet N')
print('Using ' + args.model + str(args.depth))
elif args.model == RESNET_ATT:
if args.depth == 18:
model = resnet18_att(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 34:
model = resnet34_att(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 50:
model = resnet50_att(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 101:
model = resnet101_att(pretrained=args.pretrained, num_classes=len(classes))
elif args.depth == 152:
model = resnet152_att(pretrained=args.pretrained, num_classes=len(classes))
else:
raise Exception('Specify number of layers for resnet in command line. --resnet N')
print('Using ' + args.model + str(args.depth))
elif args.model=='mvcnn':
model = mvcnn(pretrained=args.pretrained,num_classes=len(classes))
print('Using ' + args.model)
else:
model = mvcnn_att(pretrained=args.pretrained,num_classes=len(classes))
print('Using ' + args.model)
model.to(device)
cudnn.benchmark = True
# print('Running on ' + str(device))
logger = Logger('logs/'+START)
# Loss and Optimizer
lr = args.lr
n_epochs = args.epochs
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
best_acc = 0.0
best_loss = 0.0
start_epoch = 0
# Helper functions
def load_checkpoint():
global best_acc, start_epoch
# Load checkpoint.
print('\n==> Loading checkpoint..')
assert os.path.isfile(args.resume), 'Error: no checkpoint file found!'
checkpoint = torch.load(args.resume)
best_acc = checkpoint['best_acc']
start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
def train():
train_size = len(train_loader)
s=time.time()
for i, (inputs, targets) in enumerate(train_loader):
# Convert from list of 3D to 4D
inputs = np.stack(inputs, axis=1)
inputs = torch.from_numpy(inputs).cuda(device)
# inputs=torch.stack(inputs,1).cuda(device)
targets = targets.cuda(device)
#inputs, targets = Variable(inputs), Variable(targets)
# compute output
outputs = model(inputs)
#loss=cross entropy + attention entropy
# loss = criterion(outputs, targets)
loss = criterion(outputs, targets) #- 1e-3*torch.mean(model.attention * torch.log(model.attention + 1e-5))
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if (i + 1) % args.print_freq == 0:
t=time.time() - s
print("Iter [%d/%d] Loss: %.6f time taken: %.3f samples per sec: %.3f time per iteration: %.3f" % (i + 1, train_size, loss.item(), t ,args.batch_size*args.print_freq/t,t/args.print_freq))
#print('attention weights: {}'.format(model.attention[0,:].data.cpu().numpy()))
plt.imshow(model.attention[0,:,:].data.cpu().numpy(),aspect=10.0/512,vmax=1,vmin=0)
plt.pause(0.0001)
s=time.time()
# Validation and Testing
def eval(data_loader, is_test=False):
if is_test:
load_checkpoint()
# Eval
total = 0.0
correct = 0.0
total_loss = 0.0
n = 0
size = len(data_loader)
s=time.time()
with torch.no_grad():
for i, (inputs, targets) in enumerate(data_loader):
#with torch.no_grad():
# Convert from list of 3D to 4D
inputs = np.stack(inputs, axis=1)
inputs = torch.from_numpy(inputs).cuda(device)
targets = targets.cuda(device)
# compute output
outputs = model(inputs)
loss = criterion(outputs, targets)
total_loss += loss
n += 1
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += (predicted.cpu() == targets.cpu()).sum()
if (i+1)%args.print_freq==0:
t=time.time()-s
print('eval loop %d/%d time %.2f'%(i+1,size,t))
#print('attention weights: {}'.format(model.attention[0,:].data.cpu().numpy()))
plt.imshow(model.attention[0,:,:].data.cpu().numpy(),aspect=10.0/512,vmax=1,vmin=0)
plt.pause(0.0001)
s=time.time()
avg_test_acc = 100.0 * float(correct) / total
avg_loss = total_loss / n
return avg_test_acc, avg_loss
# Training / Eval loop
if args.resume:
load_checkpoint()
for epoch in range(start_epoch, n_epochs):
print('\n-----------------------------------')
print('Epoch: [%d/%d]' % (epoch+1, n_epochs))
start = time.time()
model.train()
train()
print('Time taken: %.2f sec.' % (time.time() - start))
model.eval()
print('eval mode')
# avg_train_acc, avg_loss_train = eval(eval_train_loader)
# print('\nEvaluation:')
# print('\tTrain Acc: %.2f - Loss: %.4f' % (avg_train_acc.item(), avg_loss_train.item()))
avg_val_acc, avg_loss_val = eval(eval_val_loader)
print('\nEvaluation:')
print('\tVal Acc: %.2f - Loss: %.4f' % (avg_val_acc, avg_loss_val))
# print('\tVal Acc: %.2f - Loss: %.4f' % (avg_val_acc.item(), avg_loss_val.item()))
print('\tCurrent best val acc: %.2f' % best_acc)
# Log epoch to tensorboard
# See log using: tensorboard --logdir='logs' --port=6006
#util.logEpoch(logger, model, epoch + 1, avg_loss_val, avg_val_acc,avg_loss_train,avg_train_acc)
util.logEpoch(logger, model, epoch + 1, avg_loss_val, avg_val_acc)
# Save model
if avg_val_acc > best_acc:
print('\tSaving checkpoint - Acc: %.2f' % avg_val_acc)
best_acc = avg_val_acc
best_loss = avg_loss_val
util.save_checkpoint({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'acc': avg_val_acc,
'best_acc': best_acc,
'optimizer': optimizer.state_dict(),
}, args.model, START,str(args.depth))
# Decaying Learning Rate
if (epoch + 1) % args.lr_decay_freq == 0:
lr *= args.lr_decay
optimizer = torch.optim.Adam(model.parameters(), lr=lr)
print('Learning rate:', lr)