Base64 images with Keras and Google Cloud ML

Question

I'm predicting image classes using Keras. It works in Google Cloud ML (GCML), but for efficiency need change it to pass base64 strings instead of json array. Related Documentation

I can easily run python code to decode a base64 string into json array, but when using GCML I don't have the opportunity to run a preprocessing step (unless maybe use a Lambda layer in Keras, but I don't think that is the correct approach).

Another answer suggested adding tf.placeholder with type of tf.string, which makes sense, but how to incorporate that into the Keras model?

Here is complete code for training the model and saving the exported model for GCML...

import os
import numpy as np
import tensorflow as tf
import keras
from keras import backend as K
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.preprocessing import image
from tensorflow.python.platform import gfile

IMAGE_HEIGHT = 138
IMAGE_WIDTH = 106
NUM_CLASSES = 329

def preprocess(filename):
    # decode the image file starting from the filename
    # end up with pixel values that are in the -1, 1 range
    image_contents = tf.read_file(filename)
    image = tf.image.decode_png(image_contents, channels=1)
    image = tf.image.convert_image_dtype(image, dtype=tf.float32) # 0-1
    image = tf.expand_dims(image, 0) # resize_bilinear needs batches
    image = tf.image.resize_bilinear(image, [IMAGE_HEIGHT, IMAGE_WIDTH], align_corners=False)
    image = tf.subtract(image, 0.5)
    image = tf.multiply(image, 2.0) # -1 to 1
    image = tf.squeeze(image,[0])
    return image



filelist = gfile.ListDirectory("images")
sess = tf.Session()
with sess.as_default():
    x = np.array([np.array(     preprocess(os.path.join("images", filename)).eval()      ) for filename in filelist])

input_shape = (IMAGE_HEIGHT, IMAGE_WIDTH, 1)   # 1, because preprocessing made grayscale

# in our case the labels come from part of the filename
y = np.array([int(filename[filename.index('_')+1:-4]) for filename in filelist])
# convert class labels to numbers
y = keras.utils.to_categorical(y, NUM_CLASSES)

########## TODO: something here? ##########
image = K.placeholder(shape=(), dtype=tf.string)
decoded = tf.image.decode_jpeg(image, channels=3)
# scores = build_model(decoded)


model = Sequential()

# model.add(decoded)

model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(64, activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(num_classes, activation='softmax'))

model.compile(loss=keras.losses.categorical_crossentropy,
            optimizer=keras.optimizers.Adadelta(),
            metrics=['accuracy'])

model.fit(
    x,
    y,
    batch_size=64,
    epochs=20,
    verbose=1,
    validation_split=0.2,
    shuffle=False
    )

predict_signature = tf.saved_model.signature_def_utils.build_signature_def(
    inputs={'input_bytes':tf.saved_model.utils.build_tensor_info(model.input)},
    ########## TODO: something here? ##########
    # inputs={'input': image },    # input name must have "_bytes" suffix to use base64.
    outputs={'formId': tf.saved_model.utils.build_tensor_info(model.output)},
    method_name=tf.saved_model.signature_constants.PREDICT_METHOD_NAME
)

builder = tf.saved_model.builder.SavedModelBuilder("exported_model")

builder.add_meta_graph_and_variables(
    sess=K.get_session(),
    tags=[tf.saved_model.tag_constants.SERVING],
    signature_def_map={
        tf.saved_model.signature_constants.DEFAULT_SERVING_SIGNATURE_DEF_KEY: predict_signature
    },
    legacy_init_op=tf.group(tf.tables_initializer(), name='legacy_init_op')
)

builder.save()

This is related to my previous question.

Update:

The heart of the question is how to incorporate the placeholder that calls decode into the Keras model. In other words, after creating the placeholder that decodes the base64 string to a tensor, how to incorporate that into what Keras runs? I assume it needs to be a layer.

image = K.placeholder(shape=(), dtype=tf.string)
decoded = tf.image.decode_jpeg(image, channels=3)
model = Sequential()

# Something like this, but this fails because it is a tensor, not a Keras layer.  Possibly this is where a Lambda layer comes in?
model.add(decoded)
model.add(Conv2D(32, kernel_size=(2, 2), activation='relu', input_shape=input_shape))
...

Update 2:

Trying to use a lambda layer to accomplish this...

import keras
from keras.models import Sequential
from keras.layers import Lambda
from keras import backend as K
import tensorflow as tf

image = K.placeholder(shape=(), dtype=tf.string)
model = Sequential()
model.add(Lambda(lambda image: tf.image.decode_jpeg(image, channels=3), input_shape=() ))

Gives the error: TypeError: Input 'contents' of 'DecodeJpeg' Op has type float32 that does not match expected type of string.

Answer 1

first of all I use tf.keras but this should not be a big problem. So here is an example of how you can read a base64 decoded jpeg:

def preprocess_and_decode(img_str, new_shape=[299,299]):
    img = tf.io.decode_base64(img_str)
    img = tf.image.decode_jpeg(img, channels=3)
    img = tf.image.resize_images(img, new_shape, method=tf.image.ResizeMethod.BILINEAR, align_corners=False)
    # if you need to squeeze your input range to [0,1] or [-1,1] do it here
    return img
InputLayer = Input(shape = (1,),dtype="string")
OutputLayer = Lambda(lambda img : tf.map_fn(lambda im : preprocess_and_decode(im[0]), img, dtype="float32"))(InputLayer)
base64_model = tf.keras.Model(InputLayer,OutputLayer)   

The code above creates a model that takes a jpeg of any size, resizes it to 299x299 and returns as 299x299x3 tensor. This model can be exported directly to saved_model and used for Cloud ML Engine serving. It is a little bit stupid, since the only thing it does is the convertion of base64 to tensor.

If you need to redirect the output of this model to the input of an existing trained and compiled model (e.g inception_v3) you have to do the following:

base64_input = base64_model.input
final_output = inception_v3(base64_model.output)
new_model = tf.keras.Model(base64_input,final_output)

This new_model can be saved. It takes base64 jpeg and returns classes identified by the inception_v3 part.