Update app and add example model.

Former-commit-id: 085f19cad30345560d5f3c64021c448deafbd470

Makefile

@@ -4,7 +4,7 @@ model-server: style
 	docker run --gpus all -p 8500:8500 --mount type=bind,source=${CURRENT_DIR}/style,target=/models/style -e MODEL_NAME=style -t tensorflow/serving:latest-gpu
 
 model-server-cpu: style
-	docker run -p 8500:8500 --mount type=bind,source=${CURRENT_DIR}/style,target=/models/style -e MODEL_NAME=style -t tensorflow/serving
+	docker run -p 8500:8500 --mount type=bind,source=${CURRENT_DIR}/style,target=/models/style -e MODEL_NAME=style -t tensorflow/serving:2.4.0
 
 app:
 	exec streamlit run streamlit_app.py

client.py

@@ -1,27 +1,34 @@
 import cv2
 import grpc
 import tensorflow as tf
+import tensorflow_hub as hub
 import numpy as np
 from tensorflow_serving.apis import predict_pb2, prediction_service_pb2_grpc
 
+hub_module = hub.load('https://tfhub.dev/google/magenta/arbitrary-image-stylization-v1-256/2')
+
 def style_transfer_serving(stub, content, style, resize=None):
-    content = np.array(content, dtype=np.float32)
-    style = np.array(style, dtype=np.float32)
+    content = np.array(content, dtype=np.float32) / 255.
+    style = np.array(style, dtype=np.float32) / 255.
 
     if resize:
         content = cv2.resize(content, (512, 512))
         style = cv2.resize(style, (512, 512))
 
+
     image_proto = tf.make_tensor_proto(content[np.newaxis, ...] / 255.)
     style_proto = tf.make_tensor_proto(style[np.newaxis, ...] / 255.)
 
-    request = predict_pb2.PredictRequest()
-    request.model_spec.name = 'style'
-    request.model_spec.signature_name = 'serving_default'
-    request.inputs['placeholder'].CopyFrom(image_proto)
-    request.inputs['placeholder_1'].CopyFrom(style_proto)
-    resp = stub.Predict(request)
-    stylized_image = tf.make_ndarray(resp.outputs['output_0'])[0]
+    stylized_image = hub_module(tf.constant(content[np.newaxis, ...]), tf.constant(style[np.newaxis, ...]))
+    # request = predict_pb2.PredictRequest()
+    # request.model_spec.name = 'style'
+    # request.inputs['placeholder'].CopyFrom(image_proto)
+    # request.inputs['placeholder_1'].CopyFrom(style_proto)
+    # resp = stub.Predict(request)
+    # stylized_image = tf.make_ndarray(resp.outputs['output_0'])[0]
+    stylized_image = stylized_image[0] * 255
+    stylized_image = np.array(stylized_image, dtype=np.uint8)
+    stylized_image = stylized_image
     return stylized_image
 
 if __name__ == "__main__":
@@ -29,14 +36,17 @@ if __name__ == "__main__":
         ('grpc.max_send_message_length', 200 * 1024 * 1024),
         ('grpc.max_receive_message_length', 200 * 1024 * 1024)
     ]
-    channel = grpc.insecure_channel('localhost:8500', options=options)
-    stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
-    request = predict_pb2.PredictRequest()
+    # channel = grpc.insecure_channel('localhost:8500', options=options)
+    # stub = prediction_service_pb2_grpc.PredictionServiceStub(channel)
 
-    file = tf.io.read_file('/home/albert/Downloads/pebbles.jpg')
+    file = tf.io.read_file('/home/albert/github/neural-style/assets/template_styles/pebbles.jpg')
     style = tf.io.decode_image(file)
 
-    file = tf.io.read_file('/home/albert/Downloads/sam_and_nyx/sam_faces/sam_kitchen.jpg')
+    file = tf.io.read_file('/home/albert/Downloads/sam_and_nyx/sam_stairs.jpeg')
     content = tf.io.decode_image(file)
 
-    style_transfer_serving(stub, content, style)
+    stub = None
+    result = style_transfer_serving(stub, content, style)
+    import matplotlib.pyplot as plt
+    plt.imshow(result[0])
+    plt.show()

setup.py

@@ -6,11 +6,11 @@ setup(
     packages=[],
     install_requires=[
         'opencv-python==4.4.0.46',
-        'tensorflow==2.3.0',
+        'tensorflow==2.4.1',
         'streamlit==0.70.0',
-        'numpy==1.19.4',
-        'grpcio==1.33.2',
-        'tensorflow-serving-api==2.3.0',
+        'numpy==1.19.5',
+        'grpcio==1.32.0',
+        'tensorflow-serving-api==2.4.1',
 
     ],
     url='',

streamlit_app.py

@@ -21,12 +21,11 @@ def main():
     style_file = st.sidebar.file_uploader('Upload Style', type=['jpg', 'jpeg', 'png'])
     style_options = st.sidebar.selectbox(label='Example Styles', options=os.listdir('assets/template_styles'))
     col1.subheader('Content Image')
-    col2.subheader('Style Image')
+    col2.subheader('Style Transfer')
+    st.sidebar.subheader('Style Image')
     show_image = col1.empty()
     show_style = col2.empty()
 
-    st.subheader('Style Transfer')
-    show_transfer = st.empty()
 
     style = None
     content = None
@@ -37,18 +36,18 @@ def main():
 
     if style_file:
         style = style_file.getvalue()
-        show_style.image(style, use_column_width=True)
+        st.sidebar.image(style, use_column_width=True)
     elif style_options is not None:
         with open(os.path.join('assets/template_styles', style_options), 'rb') as f:
             style = f.read()
-        show_style.image(style, use_column_width=True)
+        st.sidebar.image(style, use_column_width=True)
 
     if content is not None and style is not None:
         content_image = tf.io.decode_image(content)
         style_image = tf.image.resize(tf.io.decode_image(style), (256, 256))
         with st.spinner('Generating style transfer...'):
             style_transfer = style_transfer_serving(stub, content_image, style_image)
-            show_transfer.image(style_transfer, use_column_width=True)
+            show_style.image(style_transfer, use_column_width=True)
 
 
 if __name__ == "__main__":

style/1/variables/variables.data-00000-of-00001.REMOVED.git-id

@@ -0,0 +1 @@
+32c1044a28bd76fc7aec2d67c9bfb365b351422e