Fixing 'Incompatible Shapes' error

I am trying to train this model on my own data. I have been able to get it to work before with my own data, but I wanted to have my images semantically segmented beforehand, so I used a different model to do so, and I think in doing so I must have changed my environment enough to start getting this error because I highly doubt it’s an issue with the new images I’m using. They are the same size as the previous. I have run the requirements.txt file and still am getting this issue. I have posted the error below. Any help on what the problem might be and how to fix it would be greatly appreciated. Thank you in advance!

Starting training… Train for 200 steps, validate for 1169 steps Epoch 1/100 2020-12-06 20:35:50.965473: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Invalid argument: Incompatible shapes: [3] vs. [256,512,4] [[{{node Equal_29}}]] [[IteratorGetNext]] 2020-12-06 20:35:50.987892: W tensorflow/core/common_runtime/base_collective_executor.cc:217] BaseCollectiveExecutor::StartAbort Invalid argument: Incompatible shapes: [3] vs. [256,512,4] [[{{node Equal_29}}]] [[IteratorGetNext]] [[metrics/mean_io_u_with_one_hot_labels/StatefulPartitionedCall/confusion_matrix/assert_non_negative/assert_less_equal/Assert/AssertGuard/else/_6/Assert/data_1/_20]] 1/200 […] - ETA: 35:21WARNING:tensorflow:Can save best model only with val_mean_io_u_with_one_hot_labels available, skipping. WARNING:tensorflow:Early stopping conditioned on metric val_mean_io_u_with_one_hot_labels which is not available. Available metrics are: Traceback (most recent call last): File “./train.py”, line 185, in <module> callbacks=callbacks) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training.py”, line 819, in fit use_multiprocessing=use_multiprocessing) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py”, line 342, in fit total_epochs=epochs) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2.py”, line 128, in run_one_epoch batch_outs = execution_function(iterator) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/keras/engine/training_v2_utils.py”, line 98, in execution_function distributed_function(input_fn)) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py”, line 568, in call result = self._call(*args, **kwds) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/eager/def_function.py”, line 632, in _call return self._stateless_fn(*args, **kwds) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”, line 2363, in call return graph_function._filtered_call(args, kwargs) # pylint: disable=protected-access File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”, line 1611, in _filtered_call self.captured_inputs) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”, line 1692, in _call_flat ctx, args, cancellation_manager=cancellation_manager)) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/eager/function.py”, line 545, in call ctx=ctx) File “/home/techlab_grizzly/Desktop/Cam2BEV/env37/lib/python3.7/site-packages/tensorflow_core/python/eager/execute.py”, line 67, in quick_execute six.raise_from(core._status_to_exception(e.code, message), None) File “<string>”, line 3, in raise_from tensorflow.python.framework.errors_impl.InvalidArgumentError: Incompatible shapes: [3] vs. [256,512,4] [[{{node Equal_29}}]] [[IteratorGetNext]] [Op:__inference_distributed_function_17137] Function call stack: distributed_function

[boycejam]

I accidentally closed this issue so just commenting again to make sure you can see the last comment:

https://github.com/boycejam/Our_BEV_Files

Above is the link to a repo I made containing the files you mentioned. Let me know if there is anything else you need

[lreiher]

Problem is that your input image has a fourth alpha channel, s.t. the resized image has shape (256, 512, 4). This causes the crash during one-hot-encoding.

I will push a fix tomorrow, s.t. an image will always be loaded as RGB instead of RGBA, even if present. In the meantime, you can fix it yourself by replacing utils.py#L77 with

img = tf.image.decode_png(img, channels=3)

Some more notes on your files:

• The standard implementation expects semantically segmented input and output images, which are then one-hot-en/decoded as part of the pipeline. Your images are a blend of the real-world-image and the semantic segmentation. One-hot-en/decoding will not work properly this way.
• Your input image color-codes vehicles in a purple-ish way, but the standard 0,0,142 (RGB) blue is listed in the convert_10.xml. You need to check the colors you specify there.
• Your label image has shape (640, 480), while your input image has shape (480, 640). Keep in mind that both will be center-cropped/resized to (256, 512).
• It’s important that you provide a good estimate of the homography matrix. Just saying as I couldn’t have a look at your homography file.