Move more symbolic functions to examples

examples/HED/hed.py

@@ -13,13 +13,38 @@ import sys
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-import tensorpack.tfutils.symbolic_functions as symbf
 from tensorpack.dataflow import dataset
 from tensorpack.utils.gpu import get_nr_gpu
 from tensorpack.tfutils import optimizer
 from tensorpack.tfutils.summary import *
 
 
+def class_balanced_sigmoid_cross_entropy(logits, label, name='cross_entropy_loss'):
+    """
+    The class-balanced cross entropy loss,
+    as in `Holistically-Nested Edge Detection
+    <http://arxiv.org/abs/1504.06375>`_.
+
+    Args:
+        logits: of shape (b, ...).
+        label: of the same shape. the ground truth in {0,1}.
+    Returns:
+        class-balanced cross entropy loss.
+    """
+    with tf.name_scope('class_balanced_sigmoid_cross_entropy'):
+        y = tf.cast(label, tf.float32)
+
+        count_neg = tf.reduce_sum(1. - y)
+        count_pos = tf.reduce_sum(y)
+        beta = count_neg / (count_neg + count_pos)
+
+        pos_weight = beta / (1 - beta)
+        cost = tf.nn.weighted_cross_entropy_with_logits(logits=logits, targets=y, pos_weight=pos_weight)
+        cost = tf.reduce_mean(cost * (1 - beta))
+        zero = tf.equal(count_pos, 0.0)
+    return tf.where(zero, 0.0, cost, name=name)
+
+
 class Model(ModelDesc):
     def _get_inputs(self):
         return [InputDesc(tf.float32, [None, None, None, 3], 'image'),
@@ -76,7 +101,7 @@ class Model(ModelDesc):
         costs = []
         for idx, b in enumerate([b1, b2, b3, b4, b5, final_map]):
             output = tf.nn.sigmoid(b, name='output{}'.format(idx + 1))
-            xentropy = symbf.class_balanced_sigmoid_cross_entropy(
+            xentropy = class_balanced_sigmoid_cross_entropy(
                 b, edgemap,
                 name='xentropy{}'.format(idx + 1))
             costs.append(xentropy)

examples/Saliency/saliency-maps.py

@@ -4,6 +4,8 @@
 import cv2
 import sys
 import os
+
+from contextlib import contextmanager
 import numpy as np
 import tensorflow as tf
 import tensorflow.contrib.slim as slim
@@ -15,6 +17,28 @@ import tensorpack.utils.viz as viz
 IMAGE_SIZE = 224
 
 
+@contextmanager
+def guided_relu():
+    """
+    Returns:
+        A context where the gradient of :meth:`tf.nn.relu` is replaced by
+        guided back-propagation, as described in the paper:
+        `Striving for Simplicity: The All Convolutional Net
+        <https://arxiv.org/abs/1412.6806>`_
+    """
+    from tensorflow.python.ops import gen_nn_ops   # noqa
+
+    @tf.RegisterGradient("GuidedReLU")
+    def GuidedReluGrad(op, grad):
+        return tf.where(0. < grad,
+                        gen_nn_ops._relu_grad(grad, op.outputs[0]),
+                        tf.zeros(grad.get_shape()))
+
+    g = tf.get_default_graph()
+    with g.gradient_override_map({'Relu': 'GuidedReLU'}):
+        yield
+
+
 class Model(tp.ModelDesc):
     def _get_inputs(self):
         return [tp.InputDesc(tf.float32, (IMAGE_SIZE, IMAGE_SIZE, 3), 'image')]
@@ -22,7 +46,7 @@ class Model(tp.ModelDesc):
     def _build_graph(self, inputs):
         orig_image = inputs[0]
         mean = tf.get_variable('resnet_v1_50/mean_rgb', shape=[3])
-        with tp.symbolic_functions.guided_relu():
+        with guided_relu():
             with slim.arg_scope(resnet_v1.resnet_arg_scope(is_training=False)):
                 image = tf.expand_dims(orig_image - mean, 0)
                 logits, _ = resnet_v1.resnet_v1_50(image, 1000)

examples/mnist-visualizations.py

@@ -15,7 +15,6 @@ os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
 from tensorpack.dataflow import dataset
 import tensorflow as tf
-import tensorpack.tfutils.symbolic_functions as symbf
 
 IMAGE_SIZE = 28
 
@@ -106,8 +105,7 @@ class Model(ModelDesc):
         cost = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=label)
         cost = tf.reduce_mean(cost, name='cross_entropy_loss')
 
-        wrong = symbf.prediction_incorrect(logits, label, name='incorrect')
-        accuracy = symbf.accuracy(logits, label)
+        accuracy = tf.reduce_mean(tf.to_float(tf.nn.in_top_k(logits, label, 1)), name='accuracy')
 
         wd_cost = tf.multiply(1e-5,
                               regularize_cost('fc.*/W', tf.nn.l2_loss),
@@ -144,7 +142,7 @@ def get_config():
         callbacks=[
             ModelSaver(),
             InferenceRunner(
-                dataset_test, [ScalarStats('cross_entropy_loss'), ClassificationError('incorrect')]),
+                dataset_test, ScalarStats(['cross_entropy_loss', 'accuracy'])),
         ],
         steps_per_epoch=dataset_train.size(),
         max_epoch=100,

examples/svhn-digit-convnet.py

@@ -9,7 +9,6 @@ import os
 
 os.environ['TENSORPACK_TRAIN_API'] = 'v2'   # will become default soon
 from tensorpack import *
-from tensorpack.tfutils.symbolic_functions import prediction_incorrect
 from tensorpack.dataflow import dataset
 from tensorpack.tfutils.summary import *
 import tensorflow as tf

tensorpack/tfutils/symbolic_functions.py

@@ -17,6 +17,7 @@ def prediction_incorrect(logits, label, topk=1, name='incorrect_vector'):
                    tf.float32, name=name)
 
 
+@deprecated("Please implement it by yourself.", "2018-02-28")
 def accuracy(logits, label, topk=1, name='accuracy'):
     """
     Args:
@@ -46,6 +47,7 @@ def batch_flatten(x):
     return tf.reshape(x, tf.stack([tf.shape(x)[0], -1]))
 
 
+@deprecated("Please implement it by yourself.", "2018-02-28")
 def class_balanced_cross_entropy(pred, label, name='cross_entropy_loss'):
     """
     The class-balanced cross entropy loss,
@@ -73,6 +75,7 @@ def class_balanced_cross_entropy(pred, label, name='cross_entropy_loss'):
     return cost
 
 
+@deprecated("Please implement it by yourself.", "2018-02-28")
 def class_balanced_sigmoid_cross_entropy(logits, label, name='cross_entropy_loss'):
     """
     This function accepts logits rather than predictions, and is more numerically stable than
@@ -203,6 +206,7 @@ def psnr(prediction, ground_truth, maxp=None, name='psnr'):
 
 
 @contextmanager
+@deprecated("Please implement it by yourself.", "2018-02-28")
 def guided_relu():
     """
     Returns: