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update scripts-cluster

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Xuanyi Dong
2019-03-31 22:49:43 +08:00
parent 280c9f3099
commit 4bac459bf9
20 changed files with 118 additions and 1248 deletions
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19
exps-cnn/train_utils_imagenet.py
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@@ -3,7 +3,7 @@ from copy import deepcopy
import torch
import torch.nn as nn
import torchvision.transforms as transforms
from shutil import copyfile
from utils import print_log, obtain_accuracy, AverageMeter
from utils import time_string, convert_secs2time
@@ -37,7 +37,7 @@ class CrossEntropyLabelSmooth(nn.Module):
return loss
def main_procedure_imagenet(config, data_path, args, genotype, init_channels, layers, log):
def main_procedure_imagenet(config, data_path, args, genotype, init_channels, layers, pure_evaluate, log):
# training data and testing data
train_data, valid_data, class_num = get_datasets('imagenet-1k', data_path, -1)
@@ -48,8 +48,6 @@ def main_procedure_imagenet(config, data_path, args, genotype, init_channels, la
valid_queue = torch.utils.data.DataLoader(
valid_data, batch_size=config.batch_size, shuffle=False, pin_memory=True, num_workers=args.workers)
class_num = 1000
print_log('-------------------------------------- main-procedure', log)
print_log('config : {:}'.format(config), log)
print_log('genotype : {:}'.format(genotype), log)
@@ -84,9 +82,16 @@ def main_procedure_imagenet(config, data_path, args, genotype, init_channels, la
checkpoint_path = os.path.join(args.save_path, 'checkpoint-imagenet-model.pth')
if os.path.isfile(checkpoint_path):
checkpoint = torch.load( checkpoint_path )
checkpoint_best = os.path.join(args.save_path, 'checkpoint-imagenet-best.pth')
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(valid_queue, model, criterion, None, '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'])
@@ -122,12 +127,14 @@ def main_procedure_imagenet(config, 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):