iicd/xautodl
1
0
Fork 0
Code Issues Pull Requests Actions Packages Projects Releases Wiki Activity

update scripts-cluster

Browse Source
This commit is contained in:
Xuanyi Dong
2019-03-31 22:49:43 +08:00
parent 280c9f3099
commit 4bac459bf9
20 changed files with 118 additions and 1248 deletions
Download Patch File Download Diff File Expand all files Collapse all files

20
exps-cnn/train_utils.py
View File

@@ -2,7 +2,7 @@ import os, sys, time
from copy import deepcopy
import torch
import torchvision.transforms as transforms
from shutil import copyfile
from utils import print_log, obtain_accuracy, AverageMeter
from utils import time_string, convert_secs2time
@@ -11,6 +11,7 @@ from utils import Cutout
from nas import NetworkCIFAR as Network
from datasets import get_datasets
def obtain_best(accuracies):
if len(accuracies) == 0: return (0, 0)
tops = [value for key, value in accuracies.items()]
@@ -18,7 +19,7 @@ def obtain_best(accuracies):
return s2b[-1]
def main_procedure(config, dataset, data_path, args, genotype, init_channels, layers, log):
def main_procedure(config, dataset, data_path, args, genotype, init_channels, layers, pure_evaluate, log):
train_data, test_data, class_num = get_datasets(dataset, data_path, config.cutout)
@@ -57,10 +58,17 @@ def main_procedure(config, dataset, data_path, args, genotype, init_channels, la
else:
raise ValueError('Can not find the schedular type : {:}'.format(config.type))
checkpoint_path = os.path.join(args.save_path, 'checkpoint-{:}-model.pth'.format(dataset))
if os.path.isfile(checkpoint_path):
checkpoint = torch.load( checkpoint_path )
checkpoint_path = os.path.join(args.save_path, 'checkpoint-{:}-model.pth'.format(dataset))
checkpoint_best = os.path.join(args.save_path, 'checkpoint-{:}-best.pth'.format(dataset))
if pure_evaluate:
print_log('-'*20 + 'Pure Evaluation' + '-'*20, log)
basemodel.load_state_dict( pure_evaluate )
with torch.no_grad():
valid_acc1, valid_acc5, valid_los = _train(test_loader, model, criterion, optimizer, 'test', -1, config, args.print_freq, log)
return (valid_acc1, valid_acc5)
elif os.path.isfile(checkpoint_path):
checkpoint = torch.load( checkpoint_path )
start_epoch = checkpoint['epoch']
basemodel.load_state_dict(checkpoint['state_dict'])
optimizer.load_state_dict(checkpoint['optimizer'])
@@ -96,12 +104,14 @@ def main_procedure(config, dataset, data_path, args, genotype, init_channels, la
'accuracies': accuracies},
checkpoint_path)
best_acc = obtain_best( accuracies )
if accuracies[epoch] == best_acc: copyfile(checkpoint_path, checkpoint_best)
print_log('----> Best Accuracy : Acc@1={:.2f}, Acc@5={:.2f}, Error@1={:.2f}, Error@5={:.2f}'.format(best_acc[0], best_acc[1], 100-best_acc[0], 100-best_acc[1]), log)
print_log('----> Save into {:}'.format(checkpoint_path), log)
# measure elapsed time
epoch_time.update(time.time() - start_time)
start_time = time.time()
return obtain_best( accuracies )
def _train(xloader, model, criterion, optimizer, mode, epoch, config, print_freq, log):