ValueError: Dimension 2 in both shapes must be equal, but are 512 and 511. Shapes are [?,384,512] and [?,384,511]

Question

I'm trying to build a Unet convolutional neural network for image segmentation, but as I try to compile the model with the input data, I get an error message of shape incompatibility.

print(x_data.shape)
print(x_test.shape)
print(y_data.shape)
print(y_test.shape)

>>
(4, 767, 1022, 3)
(4, 767, 1022, 3)
(4, 767, 1022, 3)
(4, 767, 1022, 3)

>>>>
model = sm.Unet('resnet34', classes=1, activation='sigmoid')

model.compile(
    'Adam',
    loss=sm.losses.bce_jaccard_loss,
    metrics=[sm.metrics.iou_score],
)

>>>>
model.fit(
   x=x_data,
   y=y_data,
   batch_size=16,
   epochs=100,
   validation_data=(x_test, y_test),
)

>>
Epoch 1/100
---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-27-6cf659e4ef4f> in <module>()
      4    batch_size=16,
      5    epochs=100,
----> 6    validation_data=(x_test, y_test),
      7 )

10 frames
/usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, **kwargs)
    971           except Exception as e:  # pylint:disable=broad-except
    972             if hasattr(e, "ag_error_metadata"):
--> 973               raise e.ag_error_metadata.to_exception(e)
    974             else:
    975               raise

ValueError: in user code:

    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:806 train_function  *
        return step_function(self, iterator)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:796 step_function  **
        outputs = model.distribute_strategy.run(run_step, args=(data,))
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:1211 run
        return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2585 call_for_each_replica
        return self._call_for_each_replica(fn, args, kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/distribute/distribute_lib.py:2945 _call_for_each_replica
        return fn(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:789 run_step  **
        outputs = model.train_step(data)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/training.py:747 train_step
        y_pred = self(x, training=True)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:386 call
        inputs, training=training, mask=mask)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/functional.py:508 _run_internal_graph
        outputs = node.layer(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/engine/base_layer.py:985 __call__
        outputs = call_fn(inputs, *args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py:183 call
        return self._merge_function(inputs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/layers/merge.py:522 _merge_function
        return K.concatenate(inputs, axis=self.axis)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/backend.py:2881 concatenate
        return array_ops.concat([to_dense(x) for x in tensors], axis)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/util/dispatch.py:201 wrapper
        return target(*args, **kwargs)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/array_ops.py:1654 concat
        return gen_array_ops.concat_v2(values=values, axis=axis, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/ops/gen_array_ops.py:1222 concat_v2
        "ConcatV2", values=values, axis=axis, name=name)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/op_def_library.py:744 _apply_op_helper
        attrs=attr_protos, op_def=op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/func_graph.py:593 _create_op_internal
        compute_device)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:3485 _create_op_internal
        op_def=op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1975 __init__
        control_input_ops, op_def)
    /usr/local/lib/python3.6/dist-packages/tensorflow/python/framework/ops.py:1815 _create_c_op
        raise ValueError(str(e))

    ValueError: Dimension 2 in both shapes must be equal, but are 512 and 511. Shapes are [?,384,512] and [?,384,511]. for '{{node functional_3/decoder_stage3_concat/concat}} = ConcatV2[N=2, T=DT_FLOAT, Tidx=DT_INT32](functional_3/decoder_stage3_upsampling/resize/ResizeNearestNeighbor, functional_3/relu0/Relu, functional_3/decoder_stage3_concat/concat/axis)' with input shapes: [?,384,512,64], [?,384,511,64], [] and with computed input tensors: input[2] = <3>.

What exatcly is the issue when I already checked all input shapes match? What was overlooked and how to resolve it?

I already tried

import keras
keras.backend.set_image_data_format('channels_first')

as shown here https://github.com/titu1994/Image-Super-Resolution/issues/27 , but the issue continued.

Using Google Colab.

Answer 1

A bit late to the party, but your issue comes from the fact that the input width and height are not divisible by 32; ensure that you use values which are divisible with 32 for UNet and your problem will be solved.

You do not need to change the Colab Environment or to set channel order to channel_first .