Fallback to tf.contrib.layers.variance_scaling_initializer.

README.md

@@ -60,7 +60,7 @@ Dependencies:
 
 + Python 2.7 or 3
 + Python bindings for OpenCV (Optional, but required by a lot of features)
-+ TensorFlow >= 1.2.0 (Optional if you only want to use `tensorpack.dataflow` alone as a data processing library)
++ TensorFlow >= 1.3.0 (Optional if you only want to use `tensorpack.dataflow` alone as a data processing library)
 ```
 # install git, then:
 pip install -U git+https://github.com/ppwwyyxx/tensorpack.git

examples/FasterRCNN/data.py

@@ -208,7 +208,6 @@ def get_train_dataflow(add_mask=False):
         changed or skipped accordingly.
     """
 
-
     # Valid training images should have at least one fg box.
     # But this filter shall not be applied for testing.
     imgs = list(filter(lambda img: len(img['boxes']) > 0, imgs))    # log invalid training

tensorpack/libinfo.py

@@ -36,8 +36,8 @@ try:
     import tensorflow as tf  # noqa
     _version = tf.__version__.split('.')
     assert int(_version[0]) >= 1, "TF>=1.0 is required!"
-    if int(_version[1]) < 2:
-        print("TF<1.2 support will be removed after 2018-02-28!")
+    if int(_version[1]) < 3:
+        print("TF<1.3 support will be removed after 2018-03-15! Actually many examples already require TF>=1.3.")
     _HAS_TF = True
 except ImportError:
     _HAS_TF = False

tensorpack/models/conv2d.py

@@ -60,7 +60,8 @@ def Conv2D(x, out_channel, kernel_shape,
         kw_args['dilations'] = shape4d(dilation_rate, data_format=data_format)
 
     if W_init is None:
-        W_init = tf.variance_scaling_initializer(scale=2.0)
+        # W_init = tf.variance_scaling_initializer(scale=2.0)
+        W_init = tf.contrib.layers.variance_scaling_initializer(2.0)
     if b_init is None:
         b_init = tf.constant_initializer()
 
@@ -114,7 +115,8 @@ def Deconv2D(x, out_channel, kernel_shape,
     * ``b``: bias
     """
     if W_init is None:
-        W_init = tf.variance_scaling_initializer(scale=2.0)
+        # W_init = tf.variance_scaling_initializer(scale=2.0)
+        W_init = tf.contrib.layers.variance_scaling_initializer(2.0)
     if b_init is None:
         b_init = tf.constant_initializer()
 

tensorpack/models/fc.py

@@ -38,7 +38,8 @@ def FullyConnected(x, out_dim,
     x = symbf.batch_flatten(x)
 
     if W_init is None:
-        W_init = tf.variance_scaling_initializer(2.0)
+        # W_init = tf.variance_scaling_initializer(2.0)
+        W_init = tf.contrib.layers.variance_scaling_initializer(2.0)
     if b_init is None:
         b_init = tf.constant_initializer()