Support for ONNX exported models for inference

.gitignore

@@ -113,4 +113,8 @@ util/api_tester/proto/
 
 #flatbuffers
 wrappers/testing/fbs/
-wrappers/python/fbs/
+wrappers/python/fbs/
+
+#ONNX example
+examples/models/onnx_resnet50/cpu_codegen/Function_0_codegen.cpp
+examples/models/onnx_resnet50/resnet.onnx

docs/wrappers/python.md

@@ -81,10 +81,11 @@ PERSISTENCE=0
 These values can also be provided or overriden on the command line when building the image.
 
 # Step 3 - Build your image
-Use ```s2i build``` to create your Docker image from source code. You will need Docker installed on the machine and optionally git if your source code is in a public git repo. You can choose from two python builder images
+Use ```s2i build``` to create your Docker image from source code. You will need Docker installed on the machine and optionally git if your source code is in a public git repo. You can choose from three python builder images
 
  * Python 2 : seldonio/seldon-core-s2i-python2:0.1
  * Python 3 : seldonio/seldon-core-s2i-python3:0.1
+ * Python 3 plus ONNX support via [Intel nGraph](https://github.com/NervanaSystems/ngraph) : seldonio/seldon-core-s2i-python3-ngraph-onnx:0.1
 
 Using s2i you can build directly from a git repo or from a local source folder. See the [s2i docs](https://github.com/openshift/source-to-image/blob/master/docs/cli.md#s2i-build) for further details. The general format is:
 

examples/models/onnx_resnet50/.s2i/environment

@@ -0,0 +1,4 @@
+MODEL_NAME=ONNXResNet
+API_TYPE=REST
+SERVICE_TYPE=MODEL
+PERSISTENCE=0

examples/models/onnx_resnet50/Makefile

@@ -0,0 +1,5 @@
+
+
+clean:
+	rm -rf cpu_codegen
+	rm resnet.onnx

examples/models/onnx_resnet50/ONNXResNet.py

@@ -0,0 +1,39 @@
+from torch.autograd import Variable
+import torch.onnx
+import torchvision
+from keras.applications.imagenet_utils import decode_predictions
+from keras.applications.resnet50 import preprocess_input
+from keras.preprocessing import image
+import ngraph as ng
+import numpy as np
+from ngraph_onnx.onnx_importer.importer import import_onnx_file
+
+class ONNXResNet(object):
+
+    def __init__(self):
+        print("Loading model")
+        # Import the ONNX file
+        models = import_onnx_file('resnet.onnx')
+        # Create an nGraph runtime environment
+        runtime = ng.runtime(backend_name='CPU')
+        # Select the first model and compile it to a callable function
+        model = models[0]
+        self.resnet = runtime.computation(model['output'], *model['inputs'])
+        print("Model loaded")
+
+        #Do a test run to warm up and check all is ok
+        print("Running test on img of Zebra as warmup")
+        img = image.load_img('zebra.jpg', target_size=(224, 224))
+        img = image.img_to_array(img)
+        x = np.expand_dims(img.copy(), axis=0)
+        x = preprocess_input(x,mode='torch')
+        x = x.transpose(0,3,1,2)
+        preds = self.resnet(x)
+        print(decode_predictions(preds, top=5))
+
+    def predict(self,X,features_names):
+        print(X.shape)
+        X = preprocess_input(X,mode='torch')
+        preds = self.resnet(X)
+        print(decode_predictions(preds, top=5))
+        return preds

examples/models/onnx_resnet50/contract.json

@@ -0,0 +1,22 @@
+{
+    "features":[
+	{
+	    "name":"x",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1],
+	    "shape":[3,224,224]
+	}
+    ],
+    "targets":[
+	{
+	    "name":"class",
+	    "dtype":"FLOAT",
+	    "ftype":"continuous",
+	    "range":[0,1],
+	    "shape":[100]
+	}
+    ]
+}
+
+