Change the train epoch size

Author: Yuhong Li

dataset.py

@@ -8,6 +8,8 @@
 
 class listDataset(Dataset):
     def __init__(self, root, shape=None, shuffle=True, transform=None,  train=False, seen=0, batch_size=1, num_workers=4):
+        if train:
+            root = root *16
         random.shuffle(root)
 
         self.nSamples = len(root)

make_model.ipynb

@@ -3,7 +3,9 @@
   {
    "cell_type": "code",
    "execution_count": 26,
-   "metadata": {},
+   "metadata": {
+    "collapsed": true
+   },
    "outputs": [],
    "source": [
     "import torch.nn as nn\n",
@@ -83,6 +85,7 @@
    "cell_type": "code",
    "execution_count": 53,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -94,6 +97,7 @@
    "cell_type": "code",
    "execution_count": 54,
    "metadata": {
+    "collapsed": true,
     "scrolled": true
    },
    "outputs": [],
@@ -122,15 +126,6 @@
    "source": [
     "model(x).shape"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "collapsed": true
-   },
-   "outputs": [],
-   "source": []
   }
  ],
  "metadata": {

train.py

@@ -185,7 +185,7 @@ def validate(val_list, model, criterion):
 
 def adjust_learning_rate(optimizer, epoch):
     """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
-    args.lr = args.lr * (0.1 ** (epoch // 30))
+    args.lr = args.lr * (0.1 ** (epoch // 200))
     for param_group in optimizer.param_groups:
         param_group['lr'] = args.lr