Remove private Keras imports.

PiperOrigin-RevId: 565742783
Change-Id: I970b9ee988f5f520d6d12b42fcfa8249f7fadc2e

qkeras/qconvolutional.py

@@ -18,7 +18,6 @@
 from __future__ import print_function
 import warnings
 
-from keras.utils import conv_utils
 import tensorflow as tf
 from tensorflow.keras import constraints
 from tensorflow.keras import initializers
@@ -43,6 +42,59 @@
 from tensorflow_model_optimization.python.core.sparsity.keras.prunable_layer import PrunableLayer
 
 
+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.
+
+  Args:
+      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
+
+  # Get the dilated kernel size
+  filter_size = filter_size + (filter_size - 1) * (dilation - 1)
+  pad = 0
+  length = 0
+
+  # 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
+
+
 class QConv1D(Conv1D, PrunableLayer):
   """1D convolution layer (e.g. spatial convolution over images)."""
 
@@ -448,18 +500,18 @@ def call(self, inputs):
       out_pad_h, out_pad_w = self.output_padding
 
     # Infer the dynamic output shape:
-    out_height = conv_utils.deconv_output_length(height,
-                                                 kernel_h,
-                                                 padding=self.padding,
-                                                 output_padding=out_pad_h,
-                                                 stride=stride_h,
-                                                 dilation=self.dilation_rate[0])
-    out_width = conv_utils.deconv_output_length(width,
-                                                kernel_w,
-                                                padding=self.padding,
-                                                output_padding=out_pad_w,
-                                                stride=stride_w,
-                                                dilation=self.dilation_rate[1])
+    out_height = deconv_output_length(height,
+                                      kernel_h,
+                                      padding=self.padding,
+                                      output_padding=out_pad_h,
+                                      stride=stride_h,
+                                      dilation=self.dilation_rate[0])
+    out_width = deconv_output_length(width,
+                                     kernel_w,
+                                     padding=self.padding,
+                                     output_padding=out_pad_w,
+                                     stride=stride_w,
+                                     dilation=self.dilation_rate[1])
     if self.data_format == 'channels_first':
       output_shape = (batch_size, self.filters, out_height, out_width)
     else: