tqdm & humanparam

requirements.txt

 termcolor
-tqdm
+tqdm>4.6.1
 msgpack-python
 msgpack-numpy
 subprocess32; python_version < '3.0'

tensorpack/callbacks/inference.py

@@ -107,7 +107,7 @@ class InferenceRunner(Callback):
 
         sess = tf.get_default_session()
         self.ds.reset_state()
-        with tqdm(total=self.ds.size(), ascii=True) as pbar:
+        with tqdm(total=self.ds.size(), **get_tqdm_kwargs()) as pbar:
             for dp in self.ds.get_data():
                 #feed = dict(zip(self.input_vars, dp))   # TODO custom dp mapping?
                 #outputs = sess.run(self.output_tensors, feed_dict=feed)

tensorpack/callbacks/param.py

@@ -122,7 +122,7 @@ class HumanHyperParamSetter(HyperParamSetter):
     """
     Set hyperparameters by loading the value from a file each time it get called.
     """
-    def __init__(self, param, file_name):
+    def __init__(self, param, file_name='hyper.txt'):
         """
         :param file_name: a file containing the value of the variable.
             Each line in the file is a k:v pair, where k is

tensorpack/dataflow/dataset/ilsvrc.py

@@ -144,12 +144,12 @@ class ILSVRC12(RNGDataFlow):
         Produce original images or shape [h, w, 3], and label
         """
         idxs = np.arange(len(self.imglist))
-        isTrain = self.name == 'train'
+        add_label_to_fname = (self.name != 'train' and self.dir_structure != 'original')
         if self.shuffle:
             self.rng.shuffle(idxs)
         for k in idxs:
             fname, label = self.imglist[k]
-            if not isTrain and self.dir_structure != 'original':
+            if add_label_to_fname:
                 fname = os.path.join(self.full_dir, self.synset[label], fname)
             else:
                 fname = os.path.join(self.full_dir, fname)

tensorpack/dataflow/format.py

@@ -107,7 +107,6 @@ class CaffeLMDB(LMDBData):
         :param shuffle: about 3 times slower
         """
         super(CaffeLMDB, self).__init__(lmdb_dir, shuffle)
-
         self.cpb = get_caffe_pb()
 
     def get_data(self):

tensorpack/dataflow/imgaug/imgproc.py

@@ -98,7 +98,7 @@ class Gamma(ImageAugmentor):
         return self._rand_range(*self.range)
     def _augment(self, img, gamma):
         lut = ((np.arange(256, dtype='float32') / 255) ** (1. / (1. + gamma)) * 255).astype('uint8')
-        img = (img * 255.0).astype('uint8')
-        img = cv2.LUT(img, lut).astype('float32') / 255.0
+        img = img.astype('uint8')
+        img = cv2.LUT(img, lut).astype('float32')
         return img
 

tensorpack/train/base.py

@@ -126,11 +126,7 @@ class Trainer(object):
                             epoch, self.global_step + self.config.step_per_epoch)):
                         for step in tqdm.trange(
                                 self.config.step_per_epoch,
-                                leave=True, mininterval=0.5,
-                                smoothing=0.5,
-                                dynamic_ncols=True,
-                                ascii=True):
-                                #bar_format='{l_bar}{bar}|{n_fmt}/{total_fmt} [{elapsed}<{remaining},{rate_noinv_fmt}]'):
+                                **get_tqdm_kwargs(leave=True)):
                             if self.coord.should_stop():
                                 return
                             self.run_step()

tensorpack/utils/utils.py

@@ -17,7 +17,9 @@ __all__ = ['change_env', 'map_arg',
         'get_rng', 'memoized',
         'get_nr_gpu',
         'get_gpus',
-        'get_dataset_dir']
+        'get_dataset_dir',
+        'get_tqdm_kwargs'
+        ]
 
 #def expand_dim_if_necessary(var, dp):
 #    """
@@ -113,3 +115,18 @@ def get_dataset_dir(*args):
     assert os.path.isdir(d), d
     return os.path.join(d, *args)
 
+
+def get_tqdm_kwargs(**kwargs):
+    default = dict(
+            smoothing=0.5,
+            dynamic_ncols=True,
+            ascii=True,
+            bar_format='{l_bar}{bar}|{n_fmt}/{total_fmt}[{elapsed}<{remaining},{rate_noinv_fmt}]'
+            )
+    f = kwargs.get('file', sys.stderr)
+    if f.isatty():
+        default['mininterval'] = 0.5
+    else:
+        default['mininterval'] = 60
+    default.update(kwargs)
+    return default