acc.count

tensorpack/predict/dataset.py

@@ -66,7 +66,7 @@ class MultiProcessDatasetPredictor(DatasetPredictorBase):
         Run prediction in multiprocesses, on either CPU or GPU. Mix mode not supported.
         :param nr_proc: number of processes to use
         :param use_gpu: use GPU or CPU.
-            nr_proc cannot be larger than the total number of GPUs available
+            if GPU, then nr_proc cannot be larger than the total number of GPUs available
             in CUDA_VISIBLE_DEVICES or in the system.
         """
         assert config.return_input == False, "return_input not supported for MultiProcessDatasetPredictor"

tensorpack/utils/discretize.py

@@ -31,10 +31,15 @@ class Discretizer1D(Discretizer):
 
 class UniformDiscretizer1D(Discretizer1D):
     def __init__(self, minv, maxv, spacing):
+        """
+        :params minv: minimum value of the first bin
+        :params maxv: maximum value of the last bin
+        :param spacing: width of a bin
+        """
         self.minv = float(minv)
         self.maxv = float(maxv)
         self.spacing = float(spacing)
-        self.nr_bin = np.ceil((self.maxv - self.minv) / self.spacing)
+        self.nr_bin = int(np.ceil((self.maxv - self.minv) / self.spacing))
 
     def get_nr_bin(self):
         return self.nr_bin
@@ -51,6 +56,8 @@ class UniformDiscretizer1D(Discretizer1D):
                 0, self.nr_bin - 1))
 
     def get_distribution(self, v, smooth_factor=0.05, smooth_radius=2):
+        """ return a smoothed one-hot distribution of the sample v.
+        """
         b = self.get_bin(v)
         ret = np.zeros((self.nr_bin, ), dtype='float32')
         ret[b] = 1.0

tensorpack/utils/stat.py

@@ -52,6 +52,10 @@ class Accuracy(object):
             return 0
         return self.corr * 1.0 / self.tot
 
+    @property
+    def count(self):
+        return self.tot
+
 
 class BinaryStatistics(object):
     """

tensorpack/utils/utils.py

@@ -92,6 +92,6 @@ def get_dataset_dir(name):
         assert os.path.isdir(d)
     else:
         d = os.path.join(os.path.dirname(__file__), '..', 'dataflow', 'dataset')
-        logger.info("TENSORPACK_DATASET not set, using {} to keep dataset.".format(d))
+        logger.info("TENSORPACK_DATASET not set, using {} for dataset.".format(d))
     return os.path.join(d, name)