Merge branch 'master' of https://github.com/tensorlayer/tensorlayer

Author: zsdonghao

CHANGELOG.md

@@ -106,8 +106,10 @@ To release a new version, please update the changelog as followed:
 
 ### Fixed
 - Fix `tf.models.Model._construct_graph` for list of outputs, e.g. STN case (PR #1010)
-- Enable better `in_channels` exception raise. (pR #1015)
+- Enable better `in_channels` exception raise. (PR #1015)
 - Set allow_pickle=True in np.load() (#PR 1021)
+- Remove `private_method` decorator (#PR 1025)
+- Copy original model's `trainable_weights` and `nontrainable_weights` when initializing `ModelLayer` (#PR 1026)
 
 ### Removed
 
@@ -116,8 +118,8 @@ To release a new version, please update the changelog as followed:
 ### Contributors
 
 - @zsdonghao
-- @ChrisWu1997: #1010 #1015
-- @warshallrho: #1017 #1021
+- @ChrisWu1997: #1010 #1015 #1025
+- @warshallrho: #1017 #1021 #1026
 - @ArnoldLIULJ: #1023
 - @JingqingZ: #1023
 

tensorlayer/layers/convolution/deformable_conv.py

@@ -235,26 +235,22 @@ def forward(self, inputs):
             outputs = self.act(outputs)
         return outputs
 
-    @private_method
     def _to_bc_h_w(self, x, x_shape):
         """(b, h, w, c) -> (b*c, h, w)"""
         x = tf.transpose(a=x, perm=[0, 3, 1, 2])
         x = tf.reshape(x, (-1, x_shape[1], x_shape[2]))
         return x
 
-    @private_method
     def _to_b_h_w_n_c(self, x, x_shape):
         """(b*c, h, w, n) -> (b, h, w, n, c)"""
         x = tf.reshape(x, (-1, x_shape[4], x_shape[1], x_shape[2], x_shape[3]))
         x = tf.transpose(a=x, perm=[0, 2, 3, 4, 1])
         return x
 
-    @private_method
     def tf_flatten(self, a):
         """Flatten tensor"""
         return tf.reshape(a, [-1])
 
-    @private_method
     def _get_vals_by_coords(self, inputs, coords, idx, out_shape):
         indices = tf.stack(
             [idx, self.tf_flatten(coords[:, :, :, :, 0]),
@@ -264,7 +260,6 @@ def _get_vals_by_coords(self, inputs, coords, idx, out_shape):
         vals = tf.reshape(vals, out_shape)
         return vals
 
-    @private_method
     def _tf_repeat(self, a, repeats):
         """Tensorflow version of np.repeat for 1D"""
         # https://github.com/tensorflow/tensorflow/issues/8521
@@ -277,7 +272,6 @@ def _tf_repeat(self, a, repeats):
         a = self.tf_flatten(a)
         return a
 
-    @private_method
     def _tf_batch_map_coordinates(self, inputs, coords):
         """Batch version of tf_map_coordinates
 
@@ -324,7 +318,6 @@ def _tf_batch_map_coordinates(self, inputs, coords):
 
         return mapped_vals
 
-    @private_method
     def _tf_batch_map_offsets(self, inputs, offsets, grid_offset):
         """Batch map offsets into input
 

tensorlayer/layers/convolution/super_resolution.py

@@ -87,7 +87,6 @@ def forward(self, inputs):
             outputs = self.act(outputs)
         return outputs
 
-    @private_method
     def _PS(self, I, r):
         X = tf.transpose(a=I, perm=[2, 1, 0])  # (r, w, b)
         X = tf.batch_to_space(input=X, block_shape=[r], crops=[[0, 0]])  # (1, r*w, b)
@@ -188,7 +187,6 @@ def forward(self, inputs):
             outputs = self.act(outputs)
         return outputs
 
-    @private_method
     def _PS(self, X, r, n_out_channels):
 
         _err_log = "SubpixelConv2d: The number of input channels == (scale x scale) x The number of output channels"

tensorlayer/layers/core.py

@@ -512,6 +512,8 @@ def __init__(self, model, name=None):
 
         # Layer weight state
         self._all_weights = model.all_weights
+        self._trainable_weights = model.trainable_weights
+        self._nontrainable_weights = model.nontrainable_weights
 
         # Layer training state
         self.is_train = True

tensorlayer/layers/noise.py

@@ -72,7 +72,7 @@ def build(self, inputs=None):
         pass
 
     def forward(self, inputs):
-        if (self.is_train and self.is_always) is False:
+        if (self.is_train or self.is_always) is False:
             return inputs
         else:
             # noise = np.random.normal(0.0 , sigma , tf.to_int64(self.inputs).get_shape())