Copy squeeze_batch_dims (private TF API) to keras.

PiperOrigin-RevId: 351886087
Change-Id: Id1540eb2c4338e3442207cd1cdca02c83d0c7227

tensorflow/python/keras/layers/convolutional.py

@@ -260,7 +260,7 @@ class Conv(Layer):
           def _apply_fn(o):
             return nn.bias_add(o, self.bias, data_format=self._tf_data_format)
 
-          outputs = nn_ops.squeeze_batch_dims(
+          outputs = conv_utils.squeeze_batch_dims(
               outputs, _apply_fn, inner_rank=self.rank + 1)
         else:
           outputs = nn.bias_add(

tensorflow/python/keras/utils/conv_utils.py

@@ -22,7 +22,10 @@ import itertools
 import numpy as np
 from six.moves import range  # pylint: disable=redefined-builtin
 
+from tensorflow.python.framework import ops
+from tensorflow.python.framework import tensor_shape
 from tensorflow.python.keras import backend
+from tensorflow.python.ops import array_ops
 
 
 def convert_data_format(data_format, ndim):
@@ -467,3 +470,50 @@ def conv_output_shape(input_shape, kernel_shape, strides, padding):
   output_shape = tuple(
       [0 if input_shape[d] == 0 else output_shape[d] for d in dims])
   return output_shape
+
+
+def squeeze_batch_dims(inp, op, inner_rank):
+  """Returns `unsqueeze_batch(op(squeeze_batch(inp)))`.
+
+  Where `squeeze_batch` reshapes `inp` to shape
+  `[prod(inp.shape[:-inner_rank])] + inp.shape[-inner_rank:]`
+  and `unsqueeze_batch` does the reverse reshape but on the output.
+
+  Args:
+    inp: A tensor with dims `batch_shape + inner_shape` where `inner_shape`
+      is length `inner_rank`.
+    op: A callable that takes a single input tensor and returns a single.
+      output tensor.
+    inner_rank: A python integer.
+
+  Returns:
+    `unsqueeze_batch_op(squeeze_batch(inp))`.
+  """
+  with ops.name_scope_v2('squeeze_batch_dims'):
+    shape = inp.shape
+
+    inner_shape = shape[-inner_rank:]
+    if not inner_shape.is_fully_defined():
+      inner_shape = array_ops.shape(inp)[-inner_rank:]
+
+    batch_shape = shape[:-inner_rank]
+    if not batch_shape.is_fully_defined():
+      batch_shape = array_ops.shape(inp)[:-inner_rank]
+
+    if isinstance(inner_shape, tensor_shape.TensorShape):
+      inp_reshaped = array_ops.reshape(inp, [-1] + inner_shape.as_list())
+    else:
+      inp_reshaped = array_ops.reshape(
+          inp, array_ops.concat(([-1], inner_shape), axis=-1))
+
+    out_reshaped = op(inp_reshaped)
+
+    out_inner_shape = out_reshaped.shape[-inner_rank:]
+    if not out_inner_shape.is_fully_defined():
+      out_inner_shape = array_ops.shape(out_reshaped)[-inner_rank:]
+
+    out = array_ops.reshape(
+        out_reshaped, array_ops.concat((batch_shape, out_inner_shape), axis=-1))
+
+    out.set_shape(inp.shape[:-inner_rank] + out.shape[-inner_rank:])
+    return out