CODEWITHSUNDEEP
tensorflow
Syed Ahmed
Syed Ahmed

Reputation: 149

How to apply tf.map_fn on a sequence feature? Getting an error: TensorArray dtype is string but Op is trying to write dtype uint8

I am writing a sequence to sequence model that maps video to text. I have the frames of the video encoded as JPEG strings in a sequence feature of the SequenceExample proto. When building my input pipeline, I am doing the following to get an array of decoded jpegs:

encoded_video, caption = parse_sequence_example(
                    serialized_sequence_example,
                    video_feature="video/frames",
                    caption_feature="video/caption_ids")
decoded_video = tf.map_fn(lambda x: tf.image.decode_jpeg(x, channels=3), encoded_video)

However, I am getting the following error:

InvalidArgumentError (see above for traceback): TensorArray dtype is string but Op is trying to write dtype uint8.

My goal is to apply image = tf.image.convert_image_dtype(image, dtype=tf.float32) after decoding it to get the pixel values of uint8 between [0,255] to float between [0,1].

I tried to the following:

decoded_video = tf.map_fn(lambda x: tf.image.decode_jpeg(x, channels=3), encoded_video, dtype=tf.uint8)
converted_video = tf.map_fn(lambda x: tf.image.convert_image_dtype(x, dtype=tf.float32), decoded_video)

However, I still get the same error. Anybody has any idea what might be going wrong? Thanks in advance.

Upvotes: 4

Views: 1971

Answers (1)

Syed Ahmed
Syed Ahmed

Reputation: 149

Nevermind. Just had to explicitly add a dtype of tf.float32 in the following line:

converted_video = tf.map_fn(lambda x: tf.image.convert_image_dtype(x, dtype=tf.float32), decoded_video, dtype=tf.float32)

Upvotes: 5

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