I was wondering if anyone had insight into the potential
source of errors related to .png and .gif file types returned by the ‘Using tf.data for finer control’ portion of the ‘Load and preprocess images’ tutorial?
The tutorial ran smoothly when I used the original image data, but I ran into errors when I used my own data.
Thanks!
What is your error with .png and .gif?
‘animated gifs can only be decoded by tf.io.decode_gif or tf.io.decode_image’
'PNG warning: iCCP: known incorrect sRBG profile
‘PNG warning: cHRM: inconsistent chromaticities’
The tutorial works without errors using my own data if I remove all .png and all .gif files and just use the .jpg files.
What is your TF version?
is the error inside the function
decode_img
?
if so you might need to use other functions internally, instead of decode_jpeg , use decode_image that takes care of the proper image type
print(tf.version) returns 2.6.2
I’m not quite sure (yet). I will look into decode_image. In the meantime, here is the full error message:
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/tmp/ipykernel_34/1576256020.py in <module>
6 train_ds,
7 validation_data=val_ds,
----> 8 epochs=3
9 )
/opt/conda/lib/python3.7/site-packages/keras/engine/training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)
1182 _r=1):
1183 callbacks.on_train_batch_begin(step)
-> 1184 tmp_logs = self.train_function(iterator)
1185 if data_handler.should_sync:
1186 context.async_wait()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in __call__(self, *args, **kwds)
883
884 with OptionalXlaContext(self._jit_compile):
--> 885 result = self._call(*args, **kwds)
886
887 new_tracing_count = self.experimental_get_tracing_count()
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/def_function.py in _call(self, *args, **kwds)
915 # In this case we have created variables on the first call, so we run the
916 # defunned version which is guaranteed to never create variables.
--> 917 return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
918 elif self._stateful_fn is not None:
919 # Release the lock early so that multiple threads can perform the call
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in __call__(self, *args, **kwargs)
3038 filtered_flat_args) = self._maybe_define_function(args, kwargs)
3039 return graph_function._call_flat(
-> 3040 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access
3041
3042 @property
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in _call_flat(self, args, captured_inputs, cancellation_manager)
1962 # No tape is watching; skip to running the function.
1963 return self._build_call_outputs(self._inference_function.call(
-> 1964 ctx, args, cancellation_manager=cancellation_manager))
1965 forward_backward = self._select_forward_and_backward_functions(
1966 args,
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/function.py in call(self, ctx, args, cancellation_manager)
594 inputs=args,
595 attrs=attrs,
--> 596 ctx=ctx)
597 else:
598 outputs = execute.execute_with_cancellation(
/opt/conda/lib/python3.7/site-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:
InvalidArgumentError: Got 53 frames, but animated gifs can only be decoded by tf.io.decode_gif or tf.io.decode_image
[[{{node DecodeJpeg}}]]
[[IteratorGetNext]] [Op:__inference_train_function_1903]
Function call stack:
train_function
I think my suggestion will help you given the text in the exception:
animated gifs can only be decoded by tf.io.decode_gif or tf.io.decode_image
decode_image is called internally from:
But in this case we are entering in:
I don’t know if with the current API we could enter instead in this branch:
def decode_img(img):
img = tf.io.decode_image(img, expand_animations = False, channels=3)
return tf.image.resize(img, [img_height, img_width])
The above seems to prevent error messages. I appreciate your help!