1 files changed, 34 insertions, 11 deletions
diff --git a/tensorflow/python/keras/utils/conv_utils.py b/tensorflow/python/keras/utils/conv_utils.py
index 8ebca1418d..f486e631e5 100644
--- a/tensorflow/python/keras/utils/conv_utils.py
+++ b/tensorflow/python/keras/utils/conv_utils.py
@@ -137,26 +137,49 @@ def conv_input_length(output_length, filter_size, padding, stride):
   return (output_length - 1) * stride - 2 * pad + filter_size
 
 
-def deconv_output_length(input_length, filter_size, padding, stride):
+def deconv_output_length(input_length, filter_size, padding,
+                         output_padding=None, stride=0, dilation=1):
   """Determines output length of a transposed convolution given input length.
 
   Arguments:
-      input_length: integer.
-      filter_size: integer.
-      padding: one of "same", "valid", "full".
-      stride: integer.
+      input_length: Integer.
+      filter_size: Integer.
+      padding: one of `"same"`, `"valid"`, `"full"`.
+      output_padding: Integer, amount of padding along the output dimension.
+          Can be set to `None` in which case the output length is inferred.
+      stride: Integer.
+      dilation: Integer.
 
   Returns:
       The output length (integer).
   """
+  assert padding in {'same', 'valid', 'full'}
   if input_length is None:
     return None
-  input_length *= stride
-  if padding == 'valid':
-    input_length += max(filter_size - stride, 0)
-  elif padding == 'full':
-    input_length -= (stride + filter_size - 2)
-  return input_length
+
+  # Get the dilated kernel size
+  filter_size = filter_size + (filter_size - 1) * (dilation - 1)
+
+  # Infer length if output padding is None, else compute the exact length
+  if output_padding is None:
+    if padding == 'valid':
+      length = input_length * stride + max(filter_size - stride, 0)
+    elif padding == 'full':
+      length = input_length * stride - (stride + filter_size - 2)
+    elif padding == 'same':
+      length = input_length * stride
+
+  else:
+    if padding == 'same':
+      pad = filter_size // 2
+    elif padding == 'valid':
+      pad = 0
+    elif padding == 'full':
+      pad = filter_size - 1
+
+    length = ((input_length - 1) * stride + filter_size - 2 * pad +
+              output_padding)
+  return length
 
 
 def normalize_data_format(value):