misc changes

examples/HED/hed.py

@@ -133,7 +133,7 @@ def get_data(name):
     if isTrain:
         shape_aug = [
             imgaug.RandomResize(xrange=(0.7,1.5), yrange=(0.7,1.5),
-                aspect_ratio_thres=0.1),
+                aspect_ratio_thres=0.15),
             imgaug.RotationAndCropValid(90),
             CropMultiple16(),
             imgaug.Flip(horiz=True),
@@ -192,8 +192,7 @@ def get_config():
             ModelSaver(),
             HumanHyperParamSetter('learning_rate'),
             InferenceRunner(dataset_val,
-                            BinaryClassificationStats('prediction',
-                                                      'edgemap'))
+                            BinaryClassificationStats('prediction', 'edgemap'))
         ]),
         model=Model(),
         step_per_epoch=step_per_epoch,

tensorpack/dataflow/imgaug/noname.py

@@ -85,6 +85,7 @@ class RandomResize(ImageAugmentor):
             cnt += 1
             if cnt > 50:
                 logger.warn("RandomResize failed to augment an image")
+                return img.shape[1], img.shape[0]
 
     def _augment(self, img, dsize):
         return cv2.resize(img, dsize, interpolation=cv2.INTER_CUBIC)

tensorpack/tfutils/symbolic_functions.py

@@ -29,15 +29,6 @@ def batch_flatten(x):
         return tf.reshape(x, [-1, np.prod(shape)])
     return tf.reshape(x, tf.pack([tf.shape(x)[0], -1]))
 
-def logSoftmax(x):
-    """
-    Batch log softmax.
-    :param x: NxC tensor.
-    :returns: NxC tensor.
-    """
-    logger.warn("symbf.logSoftmax is deprecated in favor of tf.nn.log_softmax")
-    return tf.nn.log_softmax(x)
-
 def class_balanced_binary_class_cross_entropy(pred, label, name='cross_entropy_loss'):
     """
     The class-balanced cross entropy loss for binary classification,
@@ -56,10 +47,9 @@ def class_balanced_binary_class_cross_entropy(pred, label, name='cross_entropy_l
     beta = count_neg / (count_neg + count_pos)
 
     eps = 1e-8
-    loss_pos = -beta * tf.reduce_mean(y * tf.log(tf.abs(z) + eps), 1)
-    loss_neg = (1. - beta) * tf.reduce_mean((1. - y) * tf.log(tf.abs(1. - z) + eps), 1)
-    cost = tf.sub(loss_pos, loss_neg)
-    cost = tf.reduce_mean(cost, name=name)
+    loss_pos = -beta * tf.reduce_mean(y * tf.log(z + eps))
+    loss_neg = (1. - beta) * tf.reduce_mean((1. - y) * tf.log(1. - z + eps))
+    cost = tf.sub(loss_pos, loss_neg, name=name)
     return cost
 
 def print_stat(x, message=None):