Repository: keras-team/keras (Python)
Task: Bug Fix (2024-04-30 00:14:46+00:00)

Issue:
Conv3D crash when the data_format is 'channels_first' and using Tensorflow backend
According to the [document](https://keras.io/api/layers/convolution_layers/convolution3d/) of Conv3D in keras website, Conv3D should accept inputs with data format 'channels_first' or 'channels_last'.
While in this [colab](https://colab.research.google.com/drive/1LO942GsMBb_lXxvodBLj4VwRRK_p8yOl?usp=sharing), I got the following results. 
![image](https://github.com/keras-team/keras/assets/20224019/99613a56-6c19-4db3-a38e-273791df05ed)




Discussion:
According to the error message, the lack of support is only on CPU -- GPU should work fine. There's no CPU kernel for channels_first Conv3D. We can't fix that on the Keras side except by doing a transpose/counter-transpose in that case, which would be very inefficient.
Got it. I'll try it on GPU.
@fchollet 
Sorry for bothering again.
Surprisingly, I found that sometimes Conv3D can get an output when data_format is 'channels_first'.
In this [colab](https://colab.research.google.com/drive/1BUYEDhCGHguSYxZ_0pZuQQM1i2CeQk5G?usp=sharing), l1 and l2 have the same parameters, except for 'groups'. However, l1 can generate an output while l2 meets an error, as shown in the following. This is very strange. I thought 'groups' would not influence the data format of inputs.
![image](https://github.com/keras-team/keras/assets/20224019/a145f62f-60e3-4de6-8985-eee7efb436ca)


Patch:
diff --git a/keras/src/backend/tensorflow/nn.py b/keras/src/backend/tensorflow/nn.py
--- a/keras/src/backend/tensorflow/nn.py
+++ b/keras/src/backend/tensorflow/nn.py
@@ -252,6 +252,12 @@ def _conv_xla():
         # If kernel's in_channel does not match input's channels,  it indicates
         # convolution is broken down into groups.
         return _conv_xla()
+    if data_format == "channels_first" and len(inputs.shape) == 5:
+        inputs = convert_to_tensor(inputs)
+        if inputs.device.split(":")[-2] == "CPU":
+            inputs = tf.transpose(inputs, perm=(0, 2, 3, 4, 1))
+            data_format = "channels_last"
+            return tf.transpose(_conv(), perm=(0, 4, 1, 2, 3))
     return _conv()
 
 


Test patch:
diff --git a/keras/src/ops/nn_test.py b/keras/src/ops/nn_test.py
--- a/keras/src/ops/nn_test.py
+++ b/keras/src/ops/nn_test.py
@@ -1445,23 +1445,29 @@ def test_conv_2d_group_2(self, strides, dilation_rate):
         )
         self.assertAllClose(outputs, expected)
 
-    @parameterized.product(strides=(1, (1, 1, 1), 2), padding=("valid", "same"))
-    def test_conv_3d(self, strides, padding):
-        if backend.config.image_data_format() == "channels_last":
+    @parameterized.product(
+        strides=(1, (1, 1, 1), 2),
+        padding=("valid", "same"),
+        data_format=("channels_first", "channels_last"),
+    )
+    def test_conv_3d(self, strides, padding, data_format):
+        if data_format == "channels_last":
             input_shape = (2, 8, 8, 8, 3)
         else:
             input_shape = (2, 3, 8, 8, 8)
         inputs_3d = np.arange(3072, dtype=float).reshape(input_shape)
         kernel = np.arange(162, dtype=float).reshape([3, 3, 3, 3, 2])
 
-        outputs = knn.conv(inputs_3d, kernel, strides, padding=padding)
+        outputs = knn.conv(
+            inputs_3d, kernel, strides, padding=padding, data_format=data_format
+        )
         expected = np_conv3d(
             inputs_3d,
             kernel,
             bias_weights=np.zeros((2,)),
             strides=strides,
             padding=padding,
-            data_format=backend.config.image_data_format(),
+            data_format=data_format,
             dilation_rate=1,
             groups=1,
         )