Revert "Modified compute_output_shape function to handle broadcasting behavior in layers.Rescaling (#21351)"

This reverts commit e233825.

Author: fchollet

keras/src/layers/preprocessing/rescaling.py

@@ -1,5 +1,3 @@
-import numpy as np
-
 from keras.src import backend
 from keras.src.api_export import keras_export
 from keras.src.layers.preprocessing.tf_data_layer import TFDataLayer
@@ -29,16 +27,8 @@ class Rescaling(TFDataLayer):
     (independently of which backend you're using).
 
     Args:
-        scale: Float, int, list, tuple or np.ndarray.
-            The scale to apply to the inputs.
-            If scalar, the same scale will be applied to
-            all features or channels of input. If a list, tuple or
-            1D array, the scaling is applied per channel.
-        offset: Float, int, list/tuple or numpy ndarray.
-            The offset to apply to the inputs.
-            If scalar, the same scale will be applied to
-            all features or channels of input. If a list, tuple or
-            1D array, the scaling is applied per channel.
+        scale: Float, the scale to apply to the inputs.
+        offset: Float, the offset to apply to the inputs.
         **kwargs: Base layer keyword arguments, such as `name` and `dtype`.
     """
 
@@ -56,66 +46,13 @@ def call(self, inputs):
         if (
             len(scale_shape) > 0
             and backend.image_data_format() == "channels_first"
-            and len(inputs.shape) > 2
         ):
             scale = self.backend.numpy.reshape(
                 scale, scale_shape + (1,) * (3 - len(scale_shape))
             )
         return self.backend.cast(inputs, dtype) * scale + offset
 
     def compute_output_shape(self, input_shape):
-        input_shape = tuple(input_shape)
-
-        if backend.image_data_format() == "channels_last":
-            channels_axis = -1
-        else:
-            channels_axis = 1
-
-        input_channels = input_shape[channels_axis]
-
-        if input_channels is None:
-            return input_shape
-
-        scale_len = None
-        offset_len = None
-
-        if isinstance(self.scale, (list, tuple)):
-            scale_len = len(self.scale)
-        elif isinstance(self.scale, np.ndarray) and self.scale.ndim == 1:
-            scale_len = self.scale.size
-        elif isinstance(self.scale, (int, float)):
-            scale_len = 1
-
-        if isinstance(self.offset, (list, tuple)):
-            offset_len = len(self.offset)
-        elif isinstance(self.offset, np.ndarray) and self.offset.ndim == 1:
-            offset_len = self.offset.size
-        elif isinstance(self.offset, (int, float)):
-            offset_len = 1
-
-        if scale_len == 1 and offset_len == 1:
-            return input_shape
-
-        broadcast_len = None
-        if scale_len is not None and scale_len != input_channels:
-            broadcast_len = scale_len
-        if offset_len is not None and offset_len != input_channels:
-            if broadcast_len is not None and offset_len != broadcast_len:
-                raise ValueError(
-                    "Inconsistent `scale` and `offset` lengths "
-                    f"for broadcasting."
-                    f" Received: `scale` = {self.scale},"
-                    f"`offset` = {self.offset}. "
-                    f"Ensure both `scale` and `offset` are either scalar "
-                    f"or list, tuples, arrays of the same length."
-                )
-            broadcast_len = offset_len
-
-        if broadcast_len:
-            output_shape = list(input_shape)
-            output_shape[channels_axis] = broadcast_len
-            return tuple(output_shape)
-
         return input_shape
 
     def get_config(self):

keras/src/layers/preprocessing/rescaling_test.py

@@ -117,20 +117,3 @@ def test_numpy_args(self):
             expected_num_losses=0,
             supports_masking=True,
         )
-
-    @pytest.mark.requires_trainable_backend
-    def test_rescaling_broadcast_output_shape(self):
-        self.run_layer_test(
-            layers.Rescaling,
-            init_kwargs={
-                "scale": [1.0, 1.0],
-                "offset": [0.0, 0.0],
-            },
-            input_shape=(2, 1),
-            expected_output_shape=(2, 2),
-            expected_num_trainable_weights=0,
-            expected_num_non_trainable_weights=0,
-            expected_num_seed_generators=0,
-            expected_num_losses=0,
-            supports_masking=True,
-        )