Deprecation warning fixes - Related to issue #479

.gitignore

@@ -10,3 +10,4 @@ dist
 docs/_build
 tensorlayer.egg-info
 tensorlayer/__pacache__
+venv/

tensorlayer/__init__.py

@@ -2,9 +2,9 @@
 from __future__ import absolute_import
 
 try:
-    install_instr = "Please make sure you install a recent enough version of TensorFlow."
     import tensorflow
 except ImportError:
+    install_instr = "Please make sure you install a recent enough version of TensorFlow."
     raise ImportError("__init__.py : Could not import TensorFlow." + install_instr)
 
 from . import activation

tensorlayer/deprecation.py

@@ -0,0 +1,42 @@
+#! /usr/bin/python
+# -*- coding: utf-8 -*-
+
+import functools
+import warnings
+
+from . import _logging as logging
+
+
+def deprecated_alias(end_support_version, **aliases):
+    def deco(f):
+        @functools.wraps(f)
+        def wrapper(*args, **kwargs):
+
+            try:
+                func_name = "{}.{}".format(args[0].__class__.__name__, f.__name__)
+            except (NameError, IndexError):
+                func_name = f.__name__
+
+            rename_kwargs(kwargs, aliases, end_support_version, func_name)
+
+            return f(*args, **kwargs)
+
+        return wrapper
+
+    return deco
+
+
+def rename_kwargs(kwargs, aliases, end_support_version, func_name):
+
+    for alias, new in aliases.items():
+
+        if alias in kwargs:
+
+            if new in kwargs:
+                raise TypeError('{}() received both {} and {}'.format(func_name, alias, new))
+
+            warnings.warn('{}() - {} is deprecated; use {}'.format(func_name, alias, new), DeprecationWarning)
+            logging.warning("DeprecationWarning: {}(): "
+                            "`{}` argument is deprecated and will be removed in version {}, "
+                            "please change for `{}.`".format(func_name, alias, end_support_version, new))
+            kwargs[new] = kwargs.pop(alias)

tensorlayer/layers/binary.py

@@ -4,6 +4,8 @@
 from .. import _logging as logging
 from .core import *
 
+from ..deprecation import deprecated_alias
+
 __all__ = [
     'BinaryDenseLayer',
     'BinaryConv2d',
@@ -124,6 +126,7 @@ class BinaryDenseLayer(Layer):
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
@@ -136,13 +139,16 @@ def __init__(
             b_init_args=None,
             name='binary_dense',
     ):
+        super(BinaryDenseLayer, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info("BinaryDenseLayer  %s: %d %s" % (name, n_units, act.__name__))
+
+        self.inputs = prev_layer.outputs
+
         if W_init_args is None:
             W_init_args = {}
         if b_init_args is None:
             b_init_args = {}
 
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
-        self.inputs = prev_layer.outputs
         if self.inputs.get_shape().ndims != 2:
             raise Exception("The input dimension must be rank 2, please reshape or flatten it")
 
@@ -151,7 +157,7 @@ def __init__(
 
         n_in = int(self.inputs.get_shape()[-1])
         self.n_units = n_units
-        logging.info("BinaryDenseLayer  %s: %d %s" % (self.name, self.n_units, act.__name__))
+
         with tf.variable_scope(name):
             W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **W_init_args)
             # W = tl.act.sign(W)    # dont update ...
@@ -228,6 +234,7 @@ class BinaryConv2d(Layer):
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
@@ -255,20 +262,20 @@ def __init__(
             # data_format=None,
             name='binary_cnn2d',
     ):
+        super(BinaryConv2d, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info("BinaryConv2d %s: n_filter:%d filter_size:%s strides:%s pad:%s act:%s" % (name, n_filter, str(filter_size), str(strides), padding,
+                                                                                               act.__name__))
+
+        self.inputs = prev_layer.outputs
+
         if W_init_args is None:
             W_init_args = {}
         if b_init_args is None:
             b_init_args = {}
-
-        if use_gemm:
-            raise Exception("TODO. The current version use tf.matmul for inferencing.")
-
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
-        self.inputs = prev_layer.outputs
         if act is None:
             act = tf.identity
-        logging.info("BinaryConv2d %s: n_filter:%d filter_size:%s strides:%s pad:%s act:%s" % (self.name, n_filter, str(filter_size), str(strides), padding,
-                                                                                               act.__name__))
+        if use_gemm:
+            raise Exception("TODO. The current version use tf.matmul for inferencing.")
 
         if len(strides) != 2:
             raise ValueError("len(strides) should be 2.")
@@ -324,6 +331,7 @@ class TernaryDenseLayer(Layer):
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
@@ -336,22 +344,24 @@ def __init__(
             b_init_args=None,
             name='ternary_dense',
     ):
+        super(TernaryDenseLayer, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info("TernaryDenseLayer  %s: %d %s" % (name, n_units, act.__name__))
+
+        self.inputs = prev_layer.outputs
+
         if W_init_args is None:
             W_init_args = {}
         if b_init_args is None:
             b_init_args = {}
 
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
-        self.inputs = prev_layer.outputs
         if self.inputs.get_shape().ndims != 2:
             raise Exception("The input dimension must be rank 2, please reshape or flatten it")
-
         if use_gemm:
             raise Exception("TODO. The current version use tf.matmul for inferencing.")
 
         n_in = int(self.inputs.get_shape()[-1])
         self.n_units = n_units
-        logging.info("TernaryDenseLayer  %s: %d %s" % (self.name, self.n_units, act.__name__))
+
         with tf.variable_scope(name):
             W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **W_init_args)
             # W = tl.act.sign(W)    # dont update ...
@@ -430,6 +440,7 @@ class TernaryConv2d(Layer):
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
@@ -457,20 +468,18 @@ def __init__(
             # data_format=None,
             name='ternary_cnn2d',
     ):
+        super(TernaryConv2d, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info("TernaryConv2d %s: n_filter:%d filter_size:%s strides:%s pad:%s act:%s" % (name, n_filter, str(filter_size), str(strides), padding,
+                                                                                                act.__name__))
+
         if W_init_args is None:
             W_init_args = {}
         if b_init_args is None:
             b_init_args = {}
-
-        if use_gemm:
-            raise Exception("TODO. The current version use tf.matmul for inferencing.")
-
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
-        self.inputs = prev_layer.outputs
         if act is None:
             act = tf.identity
-        logging.info("TernaryConv2d %s: n_filter:%d filter_size:%s strides:%s pad:%s act:%s" % (self.name, n_filter, str(filter_size), str(strides), padding,
-                                                                                                act.__name__))
+        if use_gemm:
+            raise Exception("TODO. The current version use tf.matmul for inferencing.")
 
         if len(strides) != 2:
             raise ValueError("len(strides) should be 2.")
@@ -508,7 +517,7 @@ class DorefaDenseLayer(Layer):
 
     Parameters
     ----------
-    layer : :class:`Layer`
+    prev_layer : :class:`Layer`
         Previous layer.
     bitW : int
         The bits of this layer's parameter
@@ -533,6 +542,7 @@ class DorefaDenseLayer(Layer):
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
@@ -547,22 +557,24 @@ def __init__(
             b_init_args=None,
             name='dorefa_dense',
     ):
+        super(DorefaDenseLayer, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info("DorefaDenseLayer  %s: %d %s" % (name, n_units, act.__name__))
+
+        self.inputs = prev_layer.outputs
+
         if W_init_args is None:
             W_init_args = {}
         if b_init_args is None:
             b_init_args = {}
 
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
-        self.inputs = prev_layer.outputs
         if self.inputs.get_shape().ndims != 2:
             raise Exception("The input dimension must be rank 2, please reshape or flatten it")
-
         if use_gemm:
             raise Exception("TODO. The current version use tf.matmul for inferencing.")
 
         n_in = int(self.inputs.get_shape()[-1])
         self.n_units = n_units
-        logging.info("DorefaDenseLayer  %s: %d %s" % (self.name, self.n_units, act.__name__))
+
         with tf.variable_scope(name):
             W = tf.get_variable(name='W', shape=(n_in, n_units), initializer=W_init, dtype=LayersConfig.tf_dtype, **W_init_args)
             # W = tl.act.sign(W)    # dont update ...
@@ -596,7 +608,7 @@ class DorefaConv2d(Layer):
 
     Parameters
     ----------
-    layer : :class:`Layer`
+    prev_layer : :class:`Layer`
         Previous layer.
     bitW : int
         The bits of this layer's parameter
@@ -644,6 +656,7 @@ class DorefaConv2d(Layer):
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
@@ -673,21 +686,22 @@ def __init__(
             # data_format=None,
             name='dorefa_cnn2d',
     ):
+        super(DorefaConv2d, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info("DorefaConv2d %s: n_filter:%d filter_size:%s strides:%s pad:%s act:%s" % (name, n_filter, str(filter_size), str(strides), padding,
+                                                                                               act.__name__))
+
+        self.inputs = prev_layer.outputs
+
         if W_init_args is None:
             W_init_args = {}
         if b_init_args is None:
             b_init_args = {}
+        if act is None:
+            act = tf.identity
 
         if use_gemm:
             raise Exception("TODO. The current version use tf.matmul for inferencing.")
 
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
-        self.inputs = prev_layer.outputs
-        if act is None:
-            act = tf.identity
-        logging.info("DorefaConv2d %s: n_filter:%d filter_size:%s strides:%s pad:%s act:%s" % (self.name, n_filter, str(filter_size), str(strides), padding,
-                                                                                               act.__name__))
-
         if len(strides) != 2:
             raise ValueError("len(strides) should be 2.")
         try:
@@ -720,23 +734,25 @@ class SignLayer(Layer):
 
     Parameters
     ----------
-    layer : :class:`Layer`
+    prev_layer : :class:`Layer`
         Previous layer.
     name : a str
         A unique layer name.
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
             name='sign',
     ):
+        super(SignLayer, self).__init__(prev_layer=prev_layer, name=name)
 
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
         self.inputs = prev_layer.outputs
 
         logging.info("SignLayer  %s" % (self.name))
+
         with tf.variable_scope(name):
             # self.outputs = tl.act.sign(self.inputs)
             self.outputs = quantize(self.inputs)
@@ -749,7 +765,7 @@ class ScaleLayer(Layer):
 
     Parameters
     ----------
-    layer : :class:`Layer`
+    prev_layer : :class:`Layer`
         Previous layer.
     init_scale : float
         The initial value for the scale factor.
@@ -758,17 +774,18 @@ class ScaleLayer(Layer):
 
     """
 
+    @deprecated_alias(layer='prev_layer', end_support_version=1.9)  # TODO remove this line for the 1.9 release
     def __init__(
             self,
             prev_layer,
             init_scale=0.05,
             name='scale',
     ):
+        super(ScaleLayer, self).__init__(prev_layer=prev_layer, name=name)
+        logging.info("ScaleLayer  %s: init_scale: %f" % (name, init_scale))
 
-        Layer.__init__(self, prev_layer=prev_layer, name=name)
         self.inputs = prev_layer.outputs
 
-        logging.info("ScaleLayer  %s: init_scale: %f" % (self.name, init_scale))
         with tf.variable_scope(name):
             # scale = tf.get_variable(name='scale_factor', init, trainable=True, )
             scale = tf.get_variable("scale", shape=[1], initializer=tf.constant_initializer(value=init_scale))