some further simplification

examples/cifar-convnet.py

 #!/usr/bin/env python
 # -*- coding: UTF-8 -*-
-# File: cifar10-convnet.py
+# File: cifar-convnet.py
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
 import numpy
 import tensorflow as tf
@@ -19,7 +19,8 @@ from tensorpack.dataflow import *
 
 """
 A small convnet model for cifar 10 or cifar100 dataset.
-90% validation accuracy after 40k step.
+
+For Cifar10: 90% validation accuracy after 40k step.
 """
 
 class Model(ModelDesc):
@@ -141,7 +142,8 @@ if __name__ == '__main__':
     parser = argparse.ArgumentParser()
     parser.add_argument('--gpu', help='comma separated list of GPU(s) to use.') # nargs='*' in multi mode
     parser.add_argument('--load', help='load model')
-    parser.add_argument('--classnum', help='specify cifar10 or cifar100, input 10 for cifar10 or 100 for cifar100')
+    parser.add_argument('--classnum', help='10 for cifar10 or 100 for cifar100',
+                        type=int, default=10)
     args = parser.parse_args()
 
     basename = os.path.basename(__file__)
@@ -153,13 +155,8 @@ if __name__ == '__main__':
     else:
         os.environ['CUDA_VISIBLE_DEVICES'] = '0'
 
-    if args.classnum:
-        cifar_classnum = int(args.classnum)
-    else:
-        cifar_classnum = 10
-
     with tf.Graph().as_default():
-        config = get_config(cifar_classnum)
+        config = get_config(args.classnum)
         if args.load:
             config.session_init = SaverRestore(args.load)
         if args.gpu:

tensorpack/dataflow/dataset/cifar.py

 #!/usr/bin/env python2
 # -*- coding: UTF-8 -*-
-# File: cifar10.py
+# File: cifar.py
 # Author: Yuxin Wu <ppwwyyxx@gmail.com>
+#         Yukun Chen <cykustc@gmail.com>
+
 import os, sys
 import pickle
 import numpy as np
@@ -19,7 +21,7 @@ __all__ = ['Cifar10', 'Cifar100']
 
 
 DATA_URL_CIFAR_10 = 'http://www.cs.toronto.edu/~kriz/cifar-10-python.tar.gz'
-DATA_URL_CIFAR_100 = 'https://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
+DATA_URL_CIFAR_100 = 'http://www.cs.toronto.edu/~kriz/cifar-100-python.tar.gz'
 
 def maybe_download_and_extract(dest_directory, cifar_classnum):
     """Download and extract the tarball from Alex's website.
@@ -52,7 +54,7 @@ def read_cifar(filenames, cifar_classnum):
         data = dic[b'data']
         if cifar_classnum == 10:
             label = dic[b'labels']
-            IMG_NUM = 10000
+            IMG_NUM = 10000 # cifar10 data are split into blocks of 10000
         elif cifar_classnum == 100:
             label = dic[b'fine_labels']
             IMG_NUM = 50000 if 'train' in fname else 10000
@@ -71,10 +73,8 @@ def get_filenames(dir, cifar_classnum):
         filenames.append(os.path.join(
             dir, 'cifar-10-batches-py', 'test_batch'))
     elif cifar_classnum == 100:
-        filenames = [os.path.join(
-                         dir, 'cifar-100-python', 'train')]
-        filenames.append(os.path.join(
-            dir, 'cifar-100-python', 'test'))
+        filenames = [os.path.join(dir, 'cifar-100-python', 'train'),
+                     os.path.join(dir, 'cifar-100-python', 'test')]
     return filenames
 
 class CifarBase(DataFlow):
@@ -92,15 +92,12 @@ class CifarBase(DataFlow):
         assert cifar_classnum == 10 or cifar_classnum == 100
         self.cifar_classnum = cifar_classnum
         if dir is None:
-            dir = os.path.join(os.path.dirname(__file__), 'cifar-10-batches-py'
-                               if cifar_classnum==10 else 'cifar100_data')
+            dir = os.path.join(os.path.dirname(__file__),
+                    'cifar{}_data'.format(cifar_classnum))
         maybe_download_and_extract(dir, self.cifar_classnum)
-        if self.cifar_classnum == 10:
-            fnames = get_filenames(dir, 10)
-        else:
-            fnames = get_filenames(dir, 100)
+        fnames = get_filenames(dir, cifar_classnum)
         if train_or_test == 'train':
-            self.fs = fnames[:5] if cifar_classnum==10 else fnames[:1]
+            self.fs = fnames[:-1]
         else:
             self.fs = [fnames[-1]]
         for f in self.fs:

tensorpack/train/trainer.py

@@ -125,7 +125,6 @@ class QueueInputTrainer(Trainer):
     def _get_model_inputs(self):
         """ Dequeue a datapoint from input_queue and return"""
         ret = self.input_queue.dequeue(name='input_deque')
-        print ret
         if isinstance(ret, tf.Tensor): # only one input
             ret = [ret]
         assert len(ret) == len(self.input_vars)