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run_cnn_test_cifar100.py
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run_cnn_test_cifar100.py
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import numpy as np
import torch
import torch.nn as nn
import torch.optim as optim
import torch.nn.functional as F
import torch.backends.cudnn as cudnn
import torchvision
import torchvision.transforms as transforms
import os
import argparse
from models import *
from utils import progress_bar
from torch.autograd import Variable
from torch.optim.lr_scheduler import MultiStepLR
from torch.optim.lr_scheduler import ReduceLROnPlateau
from torch.optim.lr_scheduler import CosineAnnealingLR
import json
from copy import deepcopy
parser = argparse.ArgumentParser(description='PyTorch CIFAR100 Training')
parser.add_argument('--lr', default=0.1, type=float, help='learning rate')
parser.add_argument('--resume', '-r', action='store_true', help='resume from checkpoint')
parser.add_argument('--method', '-m', help='optimization method')
parser.add_argument('--net', '-n', help='network archtecture')
parser.add_argument('--partial', default=1/8, type=float, help='partially adaptive parameter p in Padam')
parser.add_argument('--wd', default=5e-4, type=float, help='weight decay')
parser.add_argument('--Nepoch', default=200, type=int, help='number of epoch')
parser.add_argument('--beta1', default=0.9, type=float, help='beta1')
parser.add_argument('--beta2', default=0.999, type=float, help='beta2')
args = parser.parse_args()
use_cuda = torch.cuda.is_available()
best_acc = 0 # best test accuracy
start_epoch = 0 # start from epoch 0 or last checkpoint epoch
train_errs = []
test_errs = []
train_losses = []
test_losses = []
print('==> Preparing data..')
transform_train = transforms.Compose([
transforms.RandomCrop(32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276]),
])
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize(mean=[0.507, 0.487, 0.441], std=[0.267, 0.256, 0.276]),
])
trainset = torchvision.datasets.CIFAR100(root='./data/CIFAR100', train=True, download=True, transform=transform_train)
trainloader = torch.utils.data.DataLoader(trainset, batch_size=128, shuffle=True, num_workers=2)
testset = torchvision.datasets.CIFAR100(root='./data/CIFAR100', train=False, download=True, transform=transform_test)
testloader = torch.utils.data.DataLoader(testset, batch_size=100, shuffle=False, num_workers=2)
if args.resume:
# Load checkpoint.
print('==> Resuming from checkpoint..')
assert os.path.isdir('checkpoint'), 'Error: no checkpoint directory found!'
checkpoint = torch.load('./checkpoint/cnn_cifar100_'+args.method)
model = checkpoint['model']
start_epoch = checkpoint['epoch']
train_losses = checkpoint['train_losses']
test_losses = checkpoint['test_losses']
train_errs = checkpoint['train_errs']
test_errs = checkpoint['test_errs']
else:
print('==> Building model..')
if args.net == 'vggnet':
from models import vgg
model = vgg.VGG('VGG16', num_classes = 100)
elif args.net == 'resnet':
from models import resnet
model = resnet.ResNet18(num_classes = 100)
elif args.net == 'wideresnet':
from models import wideresnet
model = wideresnet.WResNet_cifar10(num_classes = 100, depth=16, multiplier=4)
else:
print ('Network undefined!')
if use_cuda:
model.cuda()
model = torch.nn.DataParallel(model, device_ids=range(torch.cuda.device_count()))
cudnn.benchmark = True
criterion = nn.CrossEntropyLoss()
betas = (args.beta1, args.beta2)
if args.method == 'sgdm':
optimizer = optim.SGD(model.parameters(), lr=args.lr, momentum= 0.9, weight_decay = args.wd)
elif args.method == 'adam':
import Adam
optimizer = Adam.Adam(model.parameters(), lr=args.lr, weight_decay = args.wd, betas = betas)
elif args.method == 'adamw':
import AdamW
optimizer = AdamW.AdamW(model.parameters(), lr=args.lr, weight_decay = args.wd, betas = betas)
elif args.method == 'amsgrad':
import Adam
optimizer = Adam.Adam(model.parameters(), lr=args.lr, amsgrad = True, weight_decay = args.wd, betas = betas)
elif args.method == 'padam':
import Padam
optimizer = Padam.Padam(model.parameters(), lr=args.lr, partial = args.partial, weight_decay = args.wd, betas = betas)
else:
print ('Optimizer undefined!')
scheduler = MultiStepLR(optimizer, milestones=[100,150], gamma=0.1)
for epoch in range(start_epoch+1, args.Nepoch+1):
scheduler.step()
print ('\nEpoch: %d' % epoch, ' Learning rate:', scheduler.get_lr())
model.train() # Training
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
def closure():
outputs = model(inputs)
loss = criterion(outputs, targets)
return loss
optimizer.zero_grad()
outputs = model(inputs)
loss = criterion(outputs, targets)
loss.backward()
optimizer.step(closure)
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum().item()
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss/(batch_idx+1), 100.0/total*float(correct), correct, total))
# Compute training error
train_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(trainloader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
outputs = model(inputs)
loss = criterion(outputs, targets)
train_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum().item()
progress_bar(batch_idx, len(trainloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (train_loss/(batch_idx+1), 100.0/total*(correct), correct, total))
train_errs.append(1 - correct/total)
train_losses.append(train_loss/(batch_idx+1))
model.eval() # Testing
test_loss = 0
correct = 0
total = 0
for batch_idx, (inputs, targets) in enumerate(testloader):
if use_cuda:
inputs, targets = inputs.cuda(), targets.cuda()
outputs = model(inputs)
loss = criterion(outputs, targets)
test_loss += loss.item()
_, predicted = torch.max(outputs.data, 1)
total += targets.size(0)
correct += predicted.eq(targets.data).cpu().sum().item()
progress_bar(batch_idx, len(testloader), 'Loss: %.3f | Acc: %.3f%% (%d/%d)'
% (test_loss/(batch_idx+1), 100.0/total*(correct), correct, total))
test_errs.append(1 - correct/total)
test_losses.append(test_loss/(batch_idx+1))
# Save checkpoint
acc = 100.0/total*(correct)
if acc > best_acc:
print('Saving..')
if not os.path.isdir('checkpoint'):
os.mkdir('checkpoint')
state = {
'model': model,
'epoch': epoch,
'train_errs':train_errs,
'test_errs':test_errs,
'train_losses':train_losses,
'test_losses':test_losses
}
torch.save(state, './checkpoint/cnn_cifar100_' + args.method)
best_acc = acc