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eval_searched_arch.py
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eval_searched_arch.py
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from __future__ import print_function
import os
import argparse
import socket
import time
import sys
from pathlib import Path
lib_dir = (Path(__file__).parent / 'eval_lib').resolve()
if str(lib_dir) not in sys.path: sys.path.insert(0, str(lib_dir))
import torch
import torch.optim as optim
import torch.nn as nn
import torch.backends.cudnn as cudnn
from torch.utils.data import DataLoader
import random
import numpy as np
import logging
from rfs_models import model_pool
from rfs_models.util import create_model, count_params
from rfs_dataset.mini_imagenet import ImageNet, MetaImageNet
from rfs_dataset.tiered_imagenet import TieredImageNet, MetaTieredImageNet
from rfs_dataset.cifar import CIFAR100, MetaCIFAR100
from rfs_dataset.transform_cfg import transforms_options, transforms_list
from rfs_util import adjust_learning_rate, accuracy, AverageMeter
from flop_benchmark import get_model_infos
from eval.meta_eval import meta_test
from eval.cls_eval import validate
from ptflops import get_model_complexity_info
import torch_optimizer
import fewshot_test
def parse_option(argv):
parser = argparse.ArgumentParser('argument for training')
parser.add_argument('--eval_freq', type=int, default=10, help='meta-eval frequency')
parser.add_argument('--print_freq', type=int, default=100, help='print frequency')
parser.add_argument('--tb_freq', type=int, default=500, help='tb frequency')
parser.add_argument('--save_freq', type=int, default=5, help='save frequency')
parser.add_argument('--batch_size', type=int, default=64, help='batch_size')
parser.add_argument('--num_workers', type=int, default=8, help='num of workers to use')
parser.add_argument('--epochs', type=int, default=100, help='number of training epochs')
parser.add_argument('--resume', default='', type=str, help='Checkpoint path for resume training.')
parser.add_argument('--start_epoch', type=int, default=1, help='Start epoch')
# optimization
parser.add_argument('--learning_rate', type=float, default=0.05, help='learning rate')
parser.add_argument('--lr_decay_epochs', type=str, default='60,80', help='where to decay lr, can be a list')
parser.add_argument('--lr_decay_rate', type=float, default=0.1, help='decay rate for learning rate')
parser.add_argument('--weight_decay', type=float, default=5e-4, help='weight decay')
parser.add_argument('--momentum', type=float, default=0.9, help='momentum')
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
parser.add_argument('--radam', action='store_true', help='use Radam optimizer')
# dataset
parser.add_argument('--model', type=str, default='resnet12', choices=model_pool)
parser.add_argument('--dataset', type=str, default='miniImageNet', choices=['miniImageNet', 'tieredImageNet',
'CIFAR-FS', 'FC100'])
parser.add_argument('--transform', type=str, default='A', choices=transforms_list)
parser.add_argument('--use_trainval', action='store_true', help='use trainval set')
# learning rate scheduler
parser.add_argument('--scheduler', type=str, default='default', help='using cosine annealing', choices=['default', 'cosine', 'reducelronplateau','steplr'])
# specify folder
parser.add_argument('--model_path', type=str, default='', help='path to save model')
parser.add_argument('--tb_path', type=str, default='', help='path to tensorboard')
parser.add_argument('--data_root', type=str, default='', help='path to data root')
# meta setting
parser.add_argument('--n_test_runs', type=int, default=1000, metavar='N',
help='Number of test runs')
parser.add_argument('--n_ways', type=int, default=5, metavar='N',
help='Number of classes for doing each classification run')
parser.add_argument('--n_shots', type=int, default=5, metavar='N',
help='Number of shots in test')
parser.add_argument('--n_queries', type=int, default=15, metavar='N',
help='Number of query in test')
parser.add_argument('--n_aug_support_samples', default=5, type=int,
help='The number of augmented samples for each meta test sample')
parser.add_argument('--test_batch_size', type=int, default=1, metavar='test_batch_size',
help='Size of test batch)')
parser.add_argument('-t', '--trial', type=str, default='1', help='the experiment id')
parser.add_argument('--seed', type=int, default=-1, help='Random seed')
# specify architectures for DARTS space
parser.add_argument('--layers', type=int, default=5, help='number of layers')
parser.add_argument('--init_channels', type=int, default=48, help='num of init channels')
parser.add_argument('--genotype', type=str, default='', help='Cell genotype')
parser.add_argument('--aug_stemm', type=int, default=1, help='Stem multiplier during augmentation')
parser.add_argument('--aug_fsr', type=int, default=2, help='Feature scaling ratio during augmentation')
parser.add_argument('--aug_dp', type=float, default=0.2, help='Drop probability of augmentCNN')
opt = parser.parse_args(argv)
if opt.seed is None or opt.seed < 0:
opt.seed = random.randint(1, 100000)
if opt.dataset == 'CIFAR-FS' or opt.dataset == 'FC100':
opt.transform = 'D'
if opt.use_trainval:
opt.trial = opt.trial + '_trainval'
# set the path according to the environment
if not opt.model_path:
opt.model_path = './models_pretrained'
if not opt.tb_path:
opt.tb_path = './logger'
if not opt.data_root:
opt.data_root = './data/{}'.format(opt.dataset)
else:
opt.data_root = '{}/{}'.format(opt.data_root, opt.dataset)
opt.data_aug = True
iterations = opt.lr_decay_epochs.split(',')
opt.lr_decay_epochs = list([])
for it in iterations:
opt.lr_decay_epochs.append(int(it))
if not os.path.isdir(opt.tb_path):
os.makedirs(opt.tb_path)
if not os.path.isdir(opt.model_path):
os.makedirs(opt.model_path)
opt.n_gpu = torch.cuda.device_count()
return opt
def prepare_seed(rand_seed):
random.seed(rand_seed)
np.random.seed(rand_seed)
torch.manual_seed(rand_seed)
torch.cuda.manual_seed(rand_seed)
torch.cuda.manual_seed_all(rand_seed)
def main(argv):
#Control randomness during backbone training
opt = parse_option(argv)
torch.backends.cudnn.enabled = True
torch.backends.cudnn.benchmark = True
torch.backends.cudnn.deterministic = True
prepare_seed(opt.seed)
# Create directory if not exist
Path(opt.tb_path).mkdir(parents=True, exist_ok=True)
# Create and configure logger
logging.basicConfig(filename= os.path.join(opt.tb_path,"rfs_results.log"),
format='%(asctime)s %(message)s',
filemode='w')
# Creating a logger object
logger=logging.getLogger()
# Setting the threshold of logger to DEBUG
logger.setLevel(logging.DEBUG)
# Print all arguments
logger.info('All arguments: \n')
for arg in vars(opt):
logger.info('{}: {}'.format(arg, getattr(opt, arg)))
# Create dataloader
train_partition = 'trainval' if opt.use_trainval else 'train'
if opt.dataset == 'miniImageNet':
train_trans, test_trans = transforms_options[opt.transform]
train_loader = DataLoader(ImageNet(args=opt, partition=train_partition, transform=train_trans),
batch_size=opt.batch_size, shuffle=True, drop_last=True,
num_workers=opt.num_workers)
val_loader = DataLoader(ImageNet(args=opt, partition='val', transform=test_trans),
batch_size=opt.batch_size // 2, shuffle=False, drop_last=False,
num_workers=opt.num_workers // 2)
meta_testloader = DataLoader(MetaImageNet(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaImageNet(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 80
else:
n_cls = 64
elif opt.dataset == 'tieredImageNet':
train_trans, test_trans = transforms_options[opt.transform]
train_loader = DataLoader(TieredImageNet(args=opt, partition=train_partition, transform=train_trans),
batch_size=opt.batch_size, shuffle=True, drop_last=True,
num_workers=opt.num_workers)
val_loader = DataLoader(TieredImageNet(args=opt, partition='val', transform=test_trans),
batch_size=opt.batch_size // 2, shuffle=False, drop_last=False,
num_workers=opt.num_workers // 2)
meta_testloader = DataLoader(MetaTieredImageNet(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaTieredImageNet(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 448
else:
n_cls = 351
elif opt.dataset == 'CIFAR-FS' or opt.dataset == 'FC100':
train_trans, test_trans = transforms_options['D']
train_loader = DataLoader(CIFAR100(args=opt, partition=train_partition, transform=train_trans),
batch_size=opt.batch_size, shuffle=True, drop_last=True,
num_workers=opt.num_workers)
val_loader = DataLoader(CIFAR100(args=opt, partition='train', transform=test_trans),
batch_size=opt.batch_size // 2, shuffle=False, drop_last=False,
num_workers=opt.num_workers // 2)
meta_testloader = DataLoader(MetaCIFAR100(args=opt, partition='test',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
meta_valloader = DataLoader(MetaCIFAR100(args=opt, partition='val',
train_transform=train_trans,
test_transform=test_trans),
batch_size=opt.test_batch_size, shuffle=False, drop_last=False,
num_workers=opt.num_workers)
if opt.use_trainval:
n_cls = 80
else:
if opt.dataset == 'CIFAR-FS':
n_cls = 64
elif opt.dataset == 'FC100':
n_cls = 60
else:
raise NotImplementedError('dataset not supported: {}'.format(opt.dataset))
else:
raise NotImplementedError(opt.dataset)
# Get input channel/size for creating augmentcnn model
support_xs, _, _, _ = next(iter(meta_testloader))
batch_size, _, channel, height, width = support_xs.size()
if opt.model == 'augmentcnn':
assert height == width
opt.n_input_channels = channel
opt.input_size = height
# Create model
model = create_model(opt.model, n_cls, opt.dataset, args=opt )
# Calculate model size & number of flops
logger.info('Number of parameters: {}'.format(count_params(model)))
if opt.adam:
optimizer = torch.optim.Adam(model.parameters(),
lr=opt.learning_rate,
weight_decay=opt.weight_decay)
elif opt.radam:
optimizer = torch_optimizer.RAdam(model.parameters(),
lr = opt.learning_rate,
weight_decay=opt.weight_decay)
else:
optimizer = optim.SGD(model.parameters(),
lr=opt.learning_rate,
momentum=opt.momentum,
weight_decay=opt.weight_decay)
criterion = nn.CrossEntropyLoss()
if torch.cuda.is_available():
if opt.n_gpu > 1:
model = nn.DataParallel(model)
model = model.cuda()
criterion = criterion.cuda()
cudnn.benchmark = True
logger.info('{:<30} {}'.format('Shape of input: ', (channel, height, width)))
macs, params = get_model_complexity_info(model, (channel, height, width), as_strings=True,
print_per_layer_stat=False, verbose=False)
logger.info('Results by PTFLOPS:')
logger.info('{:<30} {:<8}'.format('Computational complexity: ', macs))
logger.info('{:<30} {:<8}'.format('Number of parameters: ', params))
flop, param = get_model_infos(model, (1, channel, height, width))
logger.info('Results by TE-NAS:')
logger.info('FLOP = {:.2f} M, Params = {:.2f} MB'.format(flop, param))
# set learning rate scheduler
if opt.scheduler == 'cosine':
eta_min = opt.learning_rate * (opt.lr_decay_rate ** 3)
scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, opt.epochs, eta_min, -1)
elif opt.scheduler == 'reducelronplateau':
scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, 'min')
elif opt.scheduler == 'steplr':
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1, gamma=0.97)
# Resume from checkpoint
if opt.resume:
assert os.path.isfile(opt.resume), 'Unable to find checkpoint file.'
print('Loading checkpoint : {}'.format(opt.resume))
checkpoint = torch.load(opt.resume)
assert opt.genotype == checkpoint['genotype'], 'Genotype mismatch, saved genotype: {}'.format(checkpoint['genotype'])
opt.start_epoch = checkpoint['epoch']
model.load_state_dict(checkpoint['model'])
optimizer.load_state_dict(checkpoint['opt'])
if opt.scheduler != 'default':
scheduler.load_state_dict(checkpoint['scheduler'])
logger.info('Load checkpoint from {}, start from epoch {}'.format(opt.resume, checkpoint['epoch']))
best_acc = 0
best_epoch = 0
# routine: supervised pre-training
for epoch in range(opt.start_epoch, opt.epochs + 1):
# Gradually change drop_path_prob as DARTS did
if opt.aug_dp != 0.0:
assert opt.model == 'augmentcnn', 'aug_dp is only used for AugmentCNN'
model.drop_path_prob(opt.aug_dp * epoch / opt.epochs)
if opt.scheduler == 'default':
adjust_learning_rate(epoch, opt, optimizer)
logger.info("==> training...")
time1 = time.time()
train_acc, train_loss = train(epoch, train_loader, model, criterion, optimizer, opt)
time2 = time.time()
logger.info('epoch {}, total time {:.2f}'.format(epoch, time2 - time1))
logger.info('train_acc at epoch {}: {}'.format(epoch, train_acc))
logger.info('train_loss at epoch {}: {}'.format(epoch, train_loss))
# regular saving
logger.info('==> Saving...')
state = {
'epoch': epoch + 1,
'model': model.state_dict() if opt.n_gpu <= 1 else model.module.state_dict(),
'opt': optimizer.state_dict(),
'scheduler': None if opt.scheduler == 'default' else scheduler.state_dict(),
'genotype': opt.genotype,
}
save_file = os.path.join(opt.model_path, 'ckpt_last.pth')
torch.save(state, save_file)
# Save checkpoint for each epoch after 2/3 number of epochs of training
if epoch >= int(1.0*opt.epochs*2/3):
save_file = os.path.join(opt.model_path, 'ckpt_epoch{}.pth'.format(epoch))
torch.save(state, save_file)
if opt.scheduler == 'reducelronplateau':
scheduler.step(test_acc)
elif opt.scheduler in ['cosine','steplr']:
scheduler.step()
# save the last model
state = {
'epoch': epoch + 1,
'model': model.state_dict() if opt.n_gpu <= 1 else model.module.state_dict(),
'opt': optimizer.state_dict(),
'scheduler': None if opt.scheduler == 'default' else scheduler.state_dict(),
'genotype': opt.genotype,
}
save_file = os.path.join(opt.model_path, 'ckpt_final.pth'.format(opt.model))
torch.save(state, save_file)
logger.info('Best Acc is at epoch {} with accuracy: {} '.format(best_epoch, best_acc))
logger.info('Start to few-shot test:')
logger.info('1 shot result:')
cmd_1shot = ['--model', opt.model,
'--dataset', opt.dataset, '--data_root', os.path.dirname(opt.data_root),
'--init_channels', str(opt.init_channels), '--layers', str(opt.layers),
'--aug_stemm', str(opt.aug_stemm), '--aug_fsr', str(opt.aug_fsr),
'--genotype', opt.genotype,
# '--model_path', os.path.join(opt.model_path, 'ckpt_best.pth'),
'--model_path', os.path.join(opt.model_path, 'ckpt_final.pth'),
'--n_shots', '1']
fewshot_test.main(cmd_1shot)
logger.info('5 shots result:')
cmd_5shot = ['--model', opt.model,
'--dataset', opt.dataset, '--data_root', os.path.dirname(opt.data_root),
'--init_channels', str(opt.init_channels), '--layers', str(opt.layers),
'--aug_stemm', str(opt.aug_stemm), '--aug_fsr', str(opt.aug_fsr),
'--genotype', opt.genotype,
# '--model_path', os.path.join(opt.model_path, 'ckpt_best.pth'),
'--model_path', os.path.join(opt.model_path, 'ckpt_final.pth'),
'--n_shots', '5']
fewshot_test.main(cmd_5shot)
def train(epoch, train_loader, model, criterion, optimizer, opt):
logger = logging.getLogger(__name__)
"""One epoch training"""
model.train()
batch_time = AverageMeter()
data_time = AverageMeter()
losses = AverageMeter()
top1 = AverageMeter()
top5 = AverageMeter()
end = time.time()
for idx, (input, target, _) in enumerate(train_loader):
data_time.update(time.time() - end)
input = input.float()
if torch.cuda.is_available():
input = input.cuda()
target = target.cuda()
# ===================forward=====================
output = model(input)
loss = criterion(output, target)
acc1, acc5 = accuracy(output, target, topk=(1, 5))
losses.update(loss.item(), input.size(0))
top1.update(acc1[0], input.size(0))
top5.update(acc5[0], input.size(0))
# ===================backward=====================
optimizer.zero_grad()
loss.backward()
optimizer.step()
# ===================meters=====================
batch_time.update(time.time() - end)
end = time.time()
# tensorboard logger
pass
# print info
if idx % opt.print_freq == 0:
logger.info('Epoch: [{0}][{1}/{2}]\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
'Data {data_time.val:.3f} ({data_time.avg:.3f})\t'
'Loss {loss.val:.4f} ({loss.avg:.4f})\t'
'Acc@1 {top1.val:.3f} ({top1.avg:.3f})\t'
'Acc@5 {top5.val:.3f} ({top5.avg:.3f})'.format(
epoch, idx, len(train_loader), batch_time=batch_time,
data_time=data_time, loss=losses, top1=top1, top5=top5))
sys.stdout.flush()
logger.info(' * Acc@1 {top1.avg:.3f} Acc@5 {top5.avg:.3f}'
.format(top1=top1, top5=top5))
return top1.avg, losses.avg
if __name__ == '__main__':
main(sys.argv[1:])