fix typos and memory optimisation

fix typos and memory optimisation

Author: vyokky

tensorlayer/layers.py

@@ -1805,7 +1805,7 @@ def tf_batch_map_offsets(inputs, offsets, grid_offset):
     ---------
     inputs : tf.Tensor. shape = (b, h, w, c)
     offsets: tf.Tensor. shape = (b, h, w, 2*n)
-    grid_offset: Offset grids
+    grid_offset: Offset grids shape = (h, w, n, 2)
 
     Returns
     -------
@@ -1814,25 +1814,26 @@ def tf_batch_map_offsets(inputs, offsets, grid_offset):
 
     input_shape = inputs.get_shape()
     batch_size = tf.shape(inputs)[0]
-    kernel_n = int(int(offsets.get_shape()[3]) / 2)
+    kernel_n = int(int(offsets.get_shape()[3])/2)
     input_h = input_shape[1]
     input_w = input_shape[2]
     channel = input_shape[3]
-    batch_channel = batch_size * input_shape[3]
 
     # inputs (b, h, w, c) --> (b*c, h, w)
     inputs = _to_bc_h_w(inputs, input_shape)
 
     # offsets (b, h, w, 2*n) --> (b, h, w, n, 2)
     offsets = tf.reshape(offsets, (batch_size, input_h, input_w, kernel_n, 2))
     # offsets (b, h, w, n, 2) --> (b*c, h, w, n, 2)
-    offsets = tf.tile(offsets, [channel, 1, 1, 1, 1])
+    # offsets = tf.tile(offsets, [channel, 1, 1, 1, 1])
 
     coords = tf.expand_dims(grid_offset, 0)  # grid_offset --> (1, h, w, n, 2)
-    coords = tf.tile(coords, [batch_channel, 1, 1, 1, 1]) + offsets  # grid_offset --> (b*c, h, w, n, 2)
+    coords = tf.tile(coords, [batch_size, 1, 1, 1, 1]) + offsets  # grid_offset --> (b, h, w, n, 2)
+
     # clip out of bound
     coords = tf.stack([tf.clip_by_value(coords[:, :, :, :, 0], 0.0, tf.cast(input_h - 1, 'float32')),
                        tf.clip_by_value(coords[:, :, :, :, 1], 0.0, tf.cast(input_w - 1, 'float32'))], axis=-1)
+    coords = tf.tile(coords, [channel, 1, 1, 1, 1])
 
     mapped_vals = tf_batch_map_coordinates(inputs, coords)
     # (b*c, h, w, n) --> (b, h, w, n, c)
@@ -4835,23 +4836,23 @@ class ConvLSTMLayer(Layer):
         The `Layer` class feeding into this layer.
     cell_shape : tuple, the shape of each cell width*height
     filter_size : tuple, the size of filter width*height
-    cell_fn : a TensorFlow's core Convolutional RNN cell as follow.
+    cell_fn : a Convolutional RNN cell as follow.
     feature_map : a int
         The number of feature map in the layer.
     initializer : initializer
         The initializer for initializing the parameters.
     n_steps : a int
         The sequence length.
-    initial_state : None or RNN State
+    initial_state : None or ConvLSTM State
         If None, initial_state is zero_state.
     return_last : boolen
         - If True, return the last output, "Sequence input and single output"
         - If False, return all outputs, "Synced sequence input and output"
-        - In other word, if you want to apply one or more RNN(s) on this layer, set to False.
+        - In other word, if you want to apply one or more ConvLSTM(s) on this layer, set to False.
     return_seq_2d : boolen
         - When return_last = False
-        - If True, return 2D Tensor [n_example, n_hidden], for stacking DenseLayer after it.
-        - If False, return 3D Tensor [n_example/n_steps, n_steps, n_hidden], for stacking multiple RNN after it.
+        - If True, return 4D Tensor [n_example, h, w, c], for stacking DenseLayer after it.
+        - If False, return 5D Tensor [n_example/n_steps, h, w, c], for stacking multiple ConvLSTM after it.
     name : a string or None
         An optional name to attach to this layer.
 
@@ -4860,17 +4861,17 @@ class ConvLSTMLayer(Layer):
     outputs : a tensor
         The output of this RNN.
         return_last = False, outputs = all cell_output, which is the hidden state.
-            cell_output.get_shape() = (?, n_hidden)
+            cell_output.get_shape() = (?, h, w, c])
 
     final_state : a tensor or StateTuple
         When state_is_tuple = False,
-        it is the final hidden and cell states, states.get_shape() = [?, 2 * n_hidden].\n
-        When state_is_tuple = True, it stores two elements: (c, h), in that order.
+        it is the final hidden and cell states,
+        When state_is_tuple = True,
         You can get the final state after each iteration during training, then
         feed it to the initial state of next iteration.
 
     initial_state : a tensor or StateTuple
-        It is the initial state of this RNN layer, you can use it to initialize
+        It is the initial state of this ConvLSTM layer, you can use it to initialize
         your state at the begining of each epoch or iteration according to your
         training procedure.
 
@@ -4902,7 +4903,7 @@ def __init__(
         # self.inputs.get_shape().with_rank(2)
         # self.inputs.get_shape().with_rank(3)
 
-        # Input dimension should be rank 5 [batch_size, n_steps(max), n_features]
+        # Input dimension should be rank 5 [batch_size, n_steps(max), h, w, c]
         try:
             self.inputs.get_shape().with_rank(5)
         except:
@@ -4920,16 +4921,7 @@ def __init__(
             print("     non specified batch_size, uses a tensor instead.")
         self.batch_size = batch_size
 
-        # Simplified version of tensorflow.models.rnn.rnn.py's rnn().
-        # This builds an unrolled LSTM for tutorial purposes only.
-        # In general, use the rnn() or state_saving_rnn() from rnn.py.
-        #
-        # The alternative version of the code below is:
-        #
-        # from tensorflow.models.rnn import rnn
-        # inputs = [tf.squeeze(input_, [1])
-        #           for input_ in tf.split(1, num_steps, inputs)]
-        # outputs, state = rnn.rnn(cell, inputs, initial_state=self._initial_state)
+        
         outputs = []
         self.cell = cell = cell_fn(shape=cell_shape, filter_size=filter_size, num_features=feature_map)
         if initial_state is None:
@@ -4954,11 +4946,11 @@ def __init__(
         else:
             if return_seq_2d:
                 # PTB tutorial: stack dense layer after that, or compute the cost from the output
-                # 2D Tensor [n_example, n_hidden]
+                # 4D Tensor [n_example, h, w, c]
                 self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, cell_shape[0] * cell_shape[1] * feature_map])
             else:
                 # <akara>: stack more RNN layer after that
-                # 5D Tensor [n_example/n_steps, n_steps, n_hidden]
+                # 5D Tensor [n_example/n_steps, n_steps, h, w, c]
                 self.outputs = tf.reshape(tf.concat(outputs, 1), [-1, n_steps, cell_shape[0],
                                                                   cell_shape[1], feature_map])