The TF model and ONNX model converted from the TF model have different output shapes, when tf.keras.layers.Conv3DTranspose is used

[ktsumura]

Describe the bug The TF model and ONNX model converted from the TF model have different output shapes, when tf.keras.layers.Conv3DTranspose is used.

To Reproduce

1. Run the following code.

   
   import numpy as np
   import onnx
   import onnxruntime as ort
   import tensorflow as tf
   import tf2onnx

def transposed_conv_3d_demo(): tconv_func = tf.keras.layers.Conv3DTranspose( 256, (1, 4, 4), strides=(1, 2, 2), padding='same', output_padding=None, data_format='channels_first', dilation_rate=(1, 1, 1), activation=None, use_bias=True)

input_image = np.ndarray([1, 1, 5, 8, 8]).astype(np.float32)

# Create a keras model
inputs = {'in_image': tf.keras.Input(shape=[1, None, 8, 8], name='in_image')}
outputs = {'out_image': tconv_func(inputs['in_image'])}
keras_model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
keras_pred = keras_model.predict(input_image)
print('The 3D keras model output shape: {}'.format(str(keras_pred['out_image'].shape)))

# Create and save an ONNX model
input_signature = [tf.TensorSpec([None, 1, None, 8, 8], tf.float32, name='in_image')]
onnx_model, _ = tf2onnx.convert.from_keras(keras_model, input_signature, opset=18)
onnx.save(onnx_model, ".//onnx_model.onnx")

# ONNX runtime inference
session = ort.InferenceSession(".//onnx_model.onnx", providers=['CUDAExecutionProvider'])
onnx_pred = session.run(None, {"in_image": input_image})
print('The 3D ONNX model output shape: {}'.format(onnx_pred[0].shape))

def transposed_conv_2d_demo():

Conv2DTranspose works (with onnx 1.14.0)

tconv_func = tf.keras.layers.Conv2DTranspose(
    256,
    (4, 4),
    strides=(2, 2),
    padding='same',
    output_padding=None,
    data_format='channels_first',
    dilation_rate=(1, 1),
    activation=None,
    use_bias=True)

input_image = np.ndarray([1, 1, 8, 8]).astype(np.float32)

# Create a keras model
inputs = {'in_image': tf.keras.Input(shape=[1, 8, 8], name='in_image')}
outputs = {'out_image': tconv_func(inputs['in_image'])}
keras_model = tf.keras.models.Model(inputs=inputs, outputs=outputs)
keras_pred = keras_model.predict(input_image)
print('The 2D keras model output shape: {}'.format(str(keras_pred['out_image'].shape)))

# Create and save an ONNX model
input_signature = [tf.TensorSpec([None, 1, 8, 8], tf.float32, name='in_image')]
onnx_model, _ = tf2onnx.convert.from_keras(keras_model, input_signature, opset=18)
onnx.save(onnx_model, ".//onnx_model.onnx")

# ONNX runtime inference
session = ort.InferenceSession(".//onnx_model.onnx", providers=['CUDAExecutionProvider'])
onnx_pred = session.run(None, {"in_image": input_image})
print('The 2D ONNX model output shape: {}'.format(onnx_pred[0].shape))

if name == 'main': transposed_conv_3d_demo() transposed_conv_2d_demo()

2. The output shapes are as follows.

1/1 [==============================] - 2s 2s/step The 3D keras model output shape: (1, 256, 5, 16, 16) The 3D ONNX model output shape: (1, 256, 5, 16, 18) 2 0 2 3 - 1 2 - 0 4 1 3 : 0 3 : 2 4 . 6 3 9 0 2 8 9 [ W : o n n x r u n t i m e : , s e s s i o n s t a t e . c c : 1 1 6 2 o n n x r u n t i m e : : V e r i f y E a c h N o d e I s A s s i g n e d T o A n E p ] S o m e n o d e s w e r e n o t a s s i g n e d t o t h e p r e f e r r e d e x e c u t i o n p r o v i d e r s w h i c h m a y o r m a y n o t h a v e a n n e g a t i v e i m p a c t o n p e r f o r m a n c e . e . g . O R T e x p l i c i t l y a s s i g n s s h a p e r e l a t e d o p s t o C P U t o i m p r o v e p e r f . 2 0 2 3 - 1 2 - 0 4 1 3 : 0 3 : 2 4 . 6 3 9 4 3 1 3 [ W : o n n x r u n t i m e : , s e s s i o n s t a t e . c c : 1 1 6 4 o n n x r u n t i m e : : V e r i f y E a c h N o d e I s A s s i g n e d T o A n E p ] R e r u n n i n g w i t h v e r b o s e o u t p u t o n a n o n - m i n i m a l b u i l d w i l l s h o w n o d e a s s i g n m e n t s . 2 0 2 3 - 1 2 - 0 4 1 3 : 0 3 : 2 4 . 6 4 5 1 5 3 5 [ W : o n n x r u n t i m e : , e x e c u t i o n f r a m e . c c : 8 5 7 o n n x r u n t i m e : : E x e c u t i o n F r a m e : : V e r i f y O u t p u t S i z e s ] E x p e c t e d s h a p e f r o m m o d e l o f { - 1 , 2 5 6 , - 1 , 1 6 , 1 6 } d o e s n o t m a t c h a c t u a l s h a p e o f { 1 , 2 5 6 , 5 , 1 6 , 1 8 } f o r o u t p u t c o n v 3 d t r a n s p o s e 1/1 [==============================] - 0s 39ms/step The 2D keras model output shape: (1, 256, 16, 16) The 2D ONNX model output shape: (1, 256, 16, 16)

**ONNX model file**
N/A

**Python, ONNX, ONNX-TF, Tensorflow version**
OS: Windows 11 Pro
Python: 3.9
onnx: 1.14.0
(onnx 1.15.0 doesn't work due to https://github.com/onnx/tensorflow-onnx/issues/2262)
tf2onnx: 1.15.1
onnxruntime-gpu: 1.16.3
Tensorflow: 2.10.1

**Additional context**
I tried both providers=['CUDAExecutionProvider'] and providers=['CPUExecutionProvider'], and the results were the same.