baseline sgd

check_result.py

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+import torch
+import numpy as np
+import os
+import argparse
+parser = argparse.ArgumentParser(description='PyTorch Places365 Training')
+parser.add_argument('--fdir', default='ckpt', type=str, metavar='PATH',
+                    help='path to load checkpoint (default: ckpt)')
+parser.add_argument('--fname', default='preresnet', type=str, metavar='PATH',
+                    help='checkpoint filename (default: preresnet)')
+parser.add_argument('--nckpt',default=1, type=int, help='num of checkpoints')
+parser.add_argument('--plot',default=False, type=bool, help='num of checkpoints')
+args = parser.parse_args()
+
+fdir = args.fdir
+fname = args.fname
+nckpt = args.nckpt
+
+best_prec1s = []
+for i in range(nckpt):
+    path = os.path.join(fdir,fname+str(i)+'_latest.pth.tar')
+    checkpoint = torch.load(path)
+    print(path)
+    best_prec1 = checkpoint['best_test_prec1']
+    best_prec1_val = checkpoint['best_prec1']
+    print(best_prec1)
+    print(best_prec1_val)
+    best_prec1s.append(best_prec1)
+
+best_prec1s = np.array(best_prec1s)
+bmean = np.around(np.mean(best_prec1s), decimals=2)
+bstd = np.around(np.std(best_prec1s), decimals=2)
+print('Best precision: %.2f(%.2f)'%(bmean,bstd))
+#print('Best precision: ',bmean,'(',bstd,')')
+
+#for key, val in checkpoint.iteritems():
+#  print(key)
+
+fname_acc = os.path.join(fdir,'accuracy.png')
+fname_loss = os.path.join(fdir,'losses.png')
+acc1_tr = checkpoint['acc1_tr']
+acc1_val = checkpoint['acc1_val']
+acc1_te = checkpoint['acc1_test']
+losses_tr = checkpoint['losses_tr']
+losses_val = checkpoint['losses_val']
+losses_te = checkpoint['losses_test']
+
+if(args.plot):
+    import matplotlib.pyplot as plt
+    fig = plt.figure()
+    ax = plt.subplot(2,1,1) 
+    ax.plot(acc1_tr, label='train_acc1')
+    ax.plot(acc1_val, label='val_acc1')
+    ax.plot(acc1_te, label='test_acc1')
+    ax.legend()
+    ax.grid(linestyle='--')
+    ax = plt.subplot(2,1,2) 
+    ax.plot(losses_tr, label='train_loss')
+    ax.plot(losses_val, label='val_loss')
+    ax.plot(losses_te, label='test_loss')
+    ax.legend()
+    ax.grid(linestyle='--')
+    plt.savefig(fname_acc)
+    #plt.show()
+    plt.clf()
+
+    #plt.show()

command.sh

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+#!/bin/bash
+
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=0 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=1 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=2 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=3 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=4 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=5 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=6 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=7 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=8 &&
+python train_cifar10.py -a=wideresnet --ckpt=ckpt --gpu=0,1 --boundary=9 &&
+
+
+ls