Serving and model hub files.

Makefile

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+start: style
+	docker run -p 8500:8500 --mount type=bind,source=C:/Users/doge/github/neural-style/style,target=/models/style -e MODEL_NAME=style -t tensorflow/serving

client.py

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+import grpc
+import tensorflow as tf
+import numpy as np
+from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc
+
+if __name__ == "__main__":
+    options = [('grpc.max_message_length', 100 * 1024 * 1024)]
+    channel = grpc.insecure_channel('localhost:8500', options=options)
+    stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
+    request = predict_pb2.PredictRequest()
+
+    file = tf.io.read_file('C:\\Users\\doge\\Downloads\\sam.jpg')
+    image = tf.io.decode_image(file)
+
+    request.model_spec.name = 'style'
+    request.model_spec.signature_name = 'serving_default'
+    image_proto = tf.make_tensor_proto(np.array(image, dtype=np.float32)[np.newaxis, ...])
+    request.inputs['placeholder'].CopyFrom(image_proto)
+    request.inputs['placeholder_1'].CopyFrom(image_proto)
+    resp = stub.Predict(request)
+    print(resp)
+
+
+
+

main.py

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+import os
+import tensorflow as tf
+
+os.environ['TFHUB_MODEL_LOAD_FORMAT'] = 'COMPRESSED'
+import matplotlib.pyplot as plt
+import numpy as np
+import time
+import functools
+import PIL
+
+
+def tensor_to_image(tensor):
+    tensor *= 255
+    tensor = np.array(tensor, dtype=np.uint8)
+    if np.ndim(tensor) > 3:
+        assert tensor.shape[0] == 1
+        tensor = tensor[0]
+    return PIL.Image.fromarray(tensor)
+
+
+content_path = tf.keras.utils.get_file('YellowLabradorLooking_new.jpg',
+                                       'https://storage.googleapis.com/download.tensorflow.org/example_images/YellowLabradorLooking_new.jpg')
+style_path = tf.keras.utils.get_file('kandinsky5.jpg',
+                                     'https://storage.googleapis.com/download.tensorflow.org/example_images/Vassily_Kandinsky%2C_1913_-_Composition_7.jpg')
+
+def load_img(path_to_img):
+    max_dim = 512
+    img = tf.io.read_file(path_to_img)
+    img = tf.image.decode_image(img, channels=3)
+    img = tf.image.convert_image_dtype(img ,tf.float32)
+
+    shape = tf.cast(tf.shape(img)[:-1], tf.float32)
+    long_dim = max(shape)
+    scale = max_dim / long_dim
+
+    new_shape = tf.cast(shape * scale, tf.int32)
+
+    img = tf.image.resize(img, new_shape)
+    img = img[tf.newaxis, ...]
+    return img
+
+def imshow(image, title=None):
+    if np.ndim(image) > 3:
+        image = tf.squeeze(image, axis=0)
+
+    plt.imshow(image)
+    if title:
+        plt.title(title)
+
+content_image = load_img(content_path)
+style_image = load_img(style_path)
+
+plt.subplot(1, 2, 1)
+imshow(content_image, 'Content Image')
+plt.subplot(1, 2, 2)
+imshow(style_image, 'Style Image')
+
+import tensorflow_hub as hub
+hub_model = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+stylized_image = hub_model(tf.constant(content_image), tf.constant(style_image))[0]
+tensor_to_image(stylized_image)
+print(type(hub_model))
+
+
+tf.saved_model.save(hub_model, 'style')