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cnn_train.py
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#!/usr/bin/env python3
import time
import math
import numpy as np
import torch
import torch.nn as nn
from torch.nn import init
import torch.nn.parallel
import torch.backends.cudnn as cudnn
import torch.optim as optim
import torch.utils.data
import torchvision.datasets as dset
import torchvision.transforms as transforms
from torch.autograd import Variable
from cnn_model import CGP2CNN
from my_data_loader import get_train_valid_loader, get_train_valid_loader_tinyimagenet
from utils import Cutout
import torchvision
from datastuff import get_test_loader, get_distortion_tests
# __init__: load dataset
# __call__: training the CNN defined by CGP list
class CNN_train():
def __init__(self, dataset_name, validation=True, verbose=True,
img_size=32, batchsize=128, data_num=500, mode="full",
config=None):
self.verbose = verbose
self.img_size = img_size
self.validation = validation
self.batchsize = batchsize
self.dataset_name = dataset_name
self.data_num = data_num
self.mode = mode
self.config = config
# load dataset
if dataset_name == 'cifar10' or dataset_name == 'tinyimagenet':
if dataset_name == 'cifar10':
self.n_class = 10
self.channel = 3
if self.validation:
# TODO: are we actually testing on the validation set here!?
self.dataloader, self.test_dataloader = get_train_valid_loader(
data_dir='./', batch_size=self.batchsize, augment=True,
random_seed=2018, num_workers=1, pin_memory=True,
data_num=self.data_num)
else:
train_dataset = dset.CIFAR10(root='./', train=True, download=True,
transform=transforms.Compose([
transforms.RandomCrop(
32, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
(0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
Cutout(16),
]))
test_dataset = dset.CIFAR10(root='./', train=False, download=True,
transform=transforms.Compose([
# transforms.Scale(self.img_size),
transforms.ToTensor(),
transforms.Normalize(
(0.49139968, 0.48215827, 0.44653124), (0.24703233, 0.24348505, 0.26158768)),
]))
self.dataloader = torch.utils.data.DataLoader(
train_dataset, batch_size=self.batchsize, shuffle=True,
num_workers=int(4), drop_last=True)
self.test_dataloader = torch.utils.data.DataLoader(
test_dataset, batch_size=self.batchsize, shuffle=True,
num_workers=int(4), drop_last=True)
elif dataset_name == 'tinyimagenet':
self.n_class = 200
self.channel = 3
if self.validation:
self.dataloader, self.test_dataloader = get_train_valid_loader_tinyimagenet(
data_dir='/home/suganuma/dataset/tiny-imagenet-200/train',
batch_size=self.batchsize, augment=True, random_seed=2018,
num_workers=4, pin_memory=False, data_num=self.data_num)
else:
if self.mode == "full":
transform_train = transforms.Compose([
transforms.RandomCrop(64, padding=4),
transforms.RandomHorizontalFlip(),
transforms.ToTensor(),
transforms.Normalize(
(0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)),
Cutout(16), ])
trainset = torchvision.datasets.ImageFolder(
root='/home/suganuma/dataset/tiny-imagenet-200/train',
transform=transform_train)
self.dataloader = torch.utils.data.DataLoader(
trainset, batch_size=self.batchsize, shuffle=True,
num_workers=8, drop_last=True)
else:
self.dataloader, _ = get_train_valid_loader_tinyimagenet(
data_dir='/home/suganuma/dataset/tiny-imagenet-200/train',
batch_size=self.batchsize, augment=True,
random_seed=2018, num_workers=4, pin_memory=False,
data_num=self.data_num)
print("train num", self.data_num)
transform_test = transforms.Compose([
transforms.ToTensor(),
transforms.Normalize((0.4914, 0.4822, 0.4465), (0.2023, 0.1994, 0.2010)), ])
testset = torchvision.datasets.ImageFolder(
root='/home/suganuma/dataset/tiny-imagenet-200/val',
transform=transform_test)
self.test_dataloader = torch.utils.data.DataLoader(
testset, batch_size=self.batchsize, shuffle=False,
num_workers=4, drop_last=True)
else:
print('\tInvalid input dataset name at CNN_train()')
exit(1)
def __call__(self, cgp, gpuID, num_epoch=30, out_model='mymodel.model'):
if self.verbose:
print('GPUID :', gpuID)
print('num_epoch :', num_epoch)
print('batch_size:', self.batchsize)
print('data_num:', self.data_num)
# model
torch.backends.cudnn.benchmark = True
model = CGP2CNN(cgp, self.channel, self.n_class, self.img_size,
arch_type=self.config['arch_type'])
# model = nn.DataParallel(model)
model = model.cuda(gpuID)
# Loss and Optimizer
criterion = nn.CrossEntropyLoss()
criterion = criterion.cuda(gpuID)
optimizer = optim.Adam(
model.parameters(), lr=0.001, betas=(0.5, 0.999))
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(
optimizer, float(num_epoch))
# Train loop
for epoch in range(1, num_epoch+1):
print('epoch', epoch)
scheduler.step()
start_time = time.time()
train_loss = 0
total = 0
correct = 0
model.train()
for _, (data, target) in enumerate(self.dataloader):
data = Variable(data, requires_grad=False).cuda(gpuID)
target = Variable(target, requires_grad=False).cuda(gpuID)
optimizer.zero_grad()
try:
logits = model(data)
except:
import traceback
traceback.print_exc()
return 0.
loss = criterion(logits, target)
loss.backward()
optimizer.step()
train_loss += loss.item()
_, predicted = logits.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()
print('Train set : Average loss: {:.4f}'.format(train_loss))
print('Train set : Average Acc : {:.4f}'.format(correct/total))
print('time ', time.time()-start_time)
if self.validation:
if epoch == num_epoch:
t_acc = self.test(model, criterion, gpuID)
else:
if epoch % 10 == 0:
t_acc = self.test(model, criterion, gpuID)
# save the model
torch.save(model.state_dict(), f"{self.config['save_dir']}model_0.pth")
t_acc, accs = self.test_all(model, criterion, gpuID)
return t_acc, accs
def test_all(self, model, criterion, gpuID):
accs = {}
test_names = [
'brightness',
'contrast',
'defocus_blur',
'elastic_transform',
'fog',
'frost',
'gaussian_blur',
'gaussian_noise',
'glass_blur',
'impulse_noise',
'jpeg_compression',
# 'labels',
'motion_blur',
'pixelate',
'saturate',
'shot_noise',
'snow',
'spatter',
'speckle_noise',
'zoom_blur'
]
test_dists = get_distortion_tests()
dloader = self.test_dataloader
acc = self.test(model, criterion, gpuID)
accs['normal'] = acc
for (idx, test_file) in enumerate(test_dists):
self.test_dataloader = get_test_loader(test_file)
acc = self.test(model, criterion, gpuID)
accs[test_names[idx]] = acc
self.test_dataloader = dloader # reassign to the original test set
return accs['normal'], accs
# For validation/test
def test(self, model, criterion, gpuID):
total = 0
correct = 0
class_correct = list(0. for i in range(self.n_class))
class_total = list(0. for i in range(self.n_class))
acc_list = [0] * (self.n_class+1)
model.eval()
with torch.no_grad():
for _, (data, target) in enumerate(self.test_dataloader):
data = Variable(data, requires_grad=False).cuda(gpuID)
target = Variable(target, requires_grad=False).cuda(gpuID)
try:
logits = model(data)
except:
import traceback
traceback.print_exc()
return 0.
_, predicted = logits.max(1)
total += target.size(0)
correct += predicted.eq(target).sum().item()
c = (predicted == target).squeeze()
for i in range(self.batchsize):
label = target[i]
class_correct[label] += c[i].item()
class_total[label] += 1
print('Accuracy of the network on the test images: %d %% (%d / %d)' %
(100 * correct / total, correct, total))
# for i in range(self.n_class):
# acc_list[i] = 100 * class_correct[i] / class_total[i]
# print('Accuracy of %d: (%d/%d)' % (i, class_correct[i], class_total[i]))
# print('Accuracy of %d: %2d %% (%d/%d)' % (i, 100 * class_correct[i] / class_total[i], class_correct[i], class_total[i]))
# print('Test set : (%d/%d)' % (correct, total))
# print('Test set : Average Acc : {:.4f}'.format(correct/total))
return (correct/total)