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train_svhn.py
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train_svhn.py
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import os
import sys
import time
import glob
import numpy as np
import torch
import utils
import logging
import argparse
import torch.nn as nn
import genotypes
import torch.utils
import torchvision.datasets as dset
import torch.backends.cudnn as cudnn
from torch.autograd import Variable
#from model_svhn import Network
from model import Network
from torch.utils.data import Dataset
parser = argparse.ArgumentParser("cifar")
parser.add_argument('--rnn_cell', type=str, default='vanilla', help='type of RNN cell: vanilla, sigmoid, directional.')
parser.add_argument('--data', type=str, default='./data', help='location of the data corpus')
parser.add_argument('--batch_size', type=int, default=64, help='batch size')
parser.add_argument('--learning_rate', type=float, default=0.1, help='init learning rate')
parser.add_argument('--learning_rate_min', type=float, default=0.025, help='min learning rate')
parser.add_argument('--momentum', type=float, default=0.7, help='momentum')
parser.add_argument('--weight_decay', type=float, default=3e-5, help='weight decay')
parser.add_argument('--report_freq', type=float, default=25, help='report frequency')
parser.add_argument('--gpu', type=int, default=0, help='gpu device id')
parser.add_argument('--epochs', type=int, default=100, help='num of training epochs')
parser.add_argument('--init_channels', type=int, default=3, help='num of init channels')
parser.add_argument('--layers', type=int, default=3, help='total number of layers')
parser.add_argument('--model_path', type=str, default='saved_models', help='path to save the model')
parser.add_argument('--cutout', action='store_true', default=True, help='use cutout')
parser.add_argument('--cutout_length', type=int, default=12, help='cutout length')
parser.add_argument('--save', type=str, default='EXP', help='experiment name')
parser.add_argument('--seed', type=int, default=0, help='random seed')
parser.add_argument('--arch', type=str, default='DARTS_Vanilla', help='which architecture to use')
parser.add_argument('--grad_clip', type=float, default=1, help='gradient clipping')
parser.add_argument('--nhid', type=int, default=256,
help='number of hidden units per layer')
args = parser.parse_args()
args.save = 'eval-{}-{}'.format(args.save, time.strftime("%Y%m%d-%H%M%S"))
utils.create_exp_dir(args.save, scripts_to_save=glob.glob('*.py'))
log_format = '%(asctime)s %(message)s'
logging.basicConfig(stream=sys.stdout, level=logging.INFO,
format=log_format, datefmt='%m/%d %I:%M:%S %p')
fh = logging.FileHandler(os.path.join(args.save, 'log.txt'))
fh.setFormatter(logging.Formatter(log_format))
logging.getLogger().addHandler(fh)
CIFAR_CLASSES = 10
def print_param_size(model):
import numpy as np
model_parameters = filter(
lambda p: p.requires_grad, model.parameters()
)
params = sum([
np.prod(p.size()) for p in model_parameters
])
return params
class MyDataset(Dataset):
def __init__(self, train, extra, transform):
self.train = train
self.extra = extra
self.transform = transform
self.train_len = len(train)
self.extra_len = len(extra)
def __getitem__(self, index):
if index < self.train_len:
x = self.train[index]
else:
index = index - self.train_len
x = self.extra[index]
return self.transform(x)
def __len__(self):
return self.train_len + self.extra_len
def main():
if not torch.cuda.is_available():
logging.info('no gpu device available')
sys.exit(1)
np.random.seed(args.seed)
torch.cuda.set_device(args.gpu)
cudnn.benchmark = True
torch.manual_seed(args.seed)
cudnn.enabled=True
torch.cuda.manual_seed(args.seed)
logging.info('gpu device = %d' % args.gpu)
logging.info("args = %s", args)
genotype = eval("genotypes.%s" % args.arch)
model = Network(args.rnn_cell, args.init_channels, CIFAR_CLASSES, args.layers, args.nhid, genotype, ds="svhn")
model = model.cuda()
print("params:", print_param_size(model))
logging.info("param size = %fMB", utils.count_parameters_in_MB(model))
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(
model.parameters(),
args.learning_rate,
momentum=args.momentum,
weight_decay=args.weight_decay
)
train_transform, valid_transform = utils._data_transforms_svhn_train(args)
train_d = dset.SVHN(root=args.data, split="train", download=True, transform=train_transform)
extra_d = dset.SVHN(root=args.data, split="extra", download=True, transform=train_transform)
train_data = torch.utils.data.ConcatDataset([train_d, extra_d])
valid_data = dset.SVHN(root=args.data, split="test", download=True, transform=valid_transform)
train_queue = torch.utils.data.DataLoader(
train_data, batch_size=args.batch_size, shuffle=True, pin_memory=True, num_workers=4)
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=args.batch_size, shuffle=False, pin_memory=True, num_workers=4)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, int(args.epochs))
for epoch in range(args.epochs):
scheduler.step()
logging.info('epoch %d lr %e', epoch, scheduler.get_lr()[0])
train_acc, train_obj = train(train_queue, model, criterion, optimizer)
logging.info('train_acc %f', train_acc)
valid_acc, valid_obj = infer(valid_queue, model, criterion)
logging.info('valid_acc %f', valid_acc)
utils.save(model, os.path.join(args.save, 'weights_train.pt'))
def train(train_queue, model, criterion, optimizer):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
model.train()
for step, (input, target) in enumerate(train_queue):
input = Variable(input).cuda()
target = Variable(target).cuda(async=True)
optimizer.zero_grad()
logits = model(input)
loss = criterion(logits, target)
loss.backward()
nn.utils.clip_grad_norm(model.parameters(), args.grad_clip)
optimizer.step()
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('train %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
def infer(valid_queue, model, criterion):
objs = utils.AvgrageMeter()
top1 = utils.AvgrageMeter()
top5 = utils.AvgrageMeter()
for step, (input, target) in enumerate(valid_queue):
input = Variable(input, volatile=True).cuda()
target = Variable(target, volatile=True).cuda(async=True)
logits = model(input)
loss = criterion(logits, target)
prec1, prec5 = utils.accuracy(logits, target, topk=(1, 5))
n = input.size(0)
objs.update(loss.data[0], n)
top1.update(prec1.data[0], n)
top5.update(prec5.data[0], n)
if step % args.report_freq == 0:
logging.info('valid %03d %e %f %f', step, objs.avg, top1.avg, top5.avg)
return top1.avg, objs.avg
if __name__ == '__main__':
main()