I have a trained TF model that that operates on a serialized (TFRecord) input. The image data has variable shape and is converted to a 229x229x3 shape via tf.image.resize_images(...). I would like to use the gcloud ml-engine predict platform similar to this, making sure to accept any size image as input.
I get my features tensor (which is passed to the prediction graph) from the following function:
def jpeg_serving_input_fn():
"""
Serve single jpeg feature to the prediction graph
:return: Image as a tensor
"""
input_features = tf.placeholder(dtype=tf.float32, shape=[None, None, 3],
name="PREDICT_PLACEHOLDER")
features_normalized = tf.image.resize_images(input_features, [229, 229])
image = tf.reshape(features_normalized, [1, 229, 229, 3], name="RESHAPE_PREDICT")
inputs = {
'image': image
}
The tf.reshape at the end is because my prediction graph expects a tensor of shape [batch_size, 229, 229, 3]. When I run this through the engine via
gcloud ml-engine local predict \
--model-dir=trained_model/export/ \
--json-instances=img.json
I get a PredictionError:
predict_lib_beta.PredictionError: (4, "Exception during running the graph: Cannot feed value of shape (1, 1600, 2400, 3) for Tensor u'RESHAPE_PREDICT:0', which has shape '(1, 229, 229, 3)'")
It looks to me like tf.reshape is being fed the output of tf.image.resize_images which should have the correct shape. Any thoughts on what I'm doing wrong here? Thanks in advance!
It looks like the error is caused by some code that feeds the "RESHAPE_PREDICT:0" tensor (i.e. the output of the tf.reshape() op, image) rather than the "PREDICT_PLACEHOLDER:0" tensor (i.e. the input to the tf.image.resize_images() op, input_features).
Without the whole source to your trained model, it's hard to say exactly what changes are necessary, but it might be as simple as changing the definition of inputs to:
inputs = {'image': input_features}
...so that the prediction service knows to feed values to that placeholder, rather than the fixed-shape output of tf.reshape().