Integrate WASI-NN into WAMR (#1521)

Initial integration of WASI-NN based on #1225:
- Implement the library core/iwasm/libraries/wasi-nn
- Support TensorFlow, CPU, F32 at the first stage
- Add cmake variable `-DWAMR_BUILD_WASI_NN`
- Add test case based on Docker image and update document

Refer to #1573

core/iwasm/libraries/wasi-nn/test/models/average.py

@@ -0,0 +1,16 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
+    tf.keras.layers.AveragePooling2D(
+        pool_size=(5, 5), strides=None, padding="valid", data_format=None)
+
+])
+
+# Export model to tflite
+
+save_model(model, "average.tflite")

core/iwasm/libraries/wasi-nn/test/models/max.py

@@ -0,0 +1,17 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
+    tf.keras.layers.MaxPooling2D(
+        pool_size=(5, 5), strides=None, padding="valid", data_format=None)
+
+])
+
+# Export model to tflite
+
+save_model(model, "max.tflite")

core/iwasm/libraries/wasi-nn/test/models/mult_dimension.py

@@ -0,0 +1,15 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[3, 3, 1]),
+    tf.keras.layers.Conv2D(1, (1, 1), kernel_initializer=tf.keras.initializers.Constant(
+        value=1), bias_initializer='zeros'
+    )
+])
+# Export model to tflite
+
+save_model(model, "mult_dim.tflite")

core/iwasm/libraries/wasi-nn/test/models/mult_outputs.py

@@ -0,0 +1,33 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+import numpy as np
+from keras.layers import AveragePooling2D, Conv2D
+
+from tensorflow.keras import Input, Model
+
+from utils import save_model
+
+
+inputs = Input(shape=(4, 4, 1))
+
+output1 = Conv2D(1, (4, 1), kernel_initializer=tf.keras.initializers.Constant(
+    value=1), bias_initializer='zeros'
+)(inputs)
+output2 = AveragePooling2D(pool_size=(
+    4, 1), strides=None, padding="valid", data_format=None)(inputs)
+
+model = Model(inputs=inputs, outputs=[output1, output2])
+
+inp = np.arange(16).reshape((1, 4, 4, 1))
+
+print(inp)
+
+res = model.predict(inp)
+
+print(res)
+print(res[0].shape)
+print(res[1].shape)
+
+save_model(model, "mult_out.tflite")

core/iwasm/libraries/wasi-nn/test/models/sum.py

@@ -0,0 +1,17 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+
+from utils import save_model
+
+model = tf.keras.Sequential([
+    tf.keras.layers.InputLayer(input_shape=[5, 5, 1]),
+    tf.keras.layers.Conv2D(1, (5, 5), kernel_initializer=tf.keras.initializers.Constant(
+        value=1), bias_initializer='zeros'
+    )
+])
+
+# Export model to tflite
+
+save_model(model, "sum.tflite")

core/iwasm/libraries/wasi-nn/test/models/utils.py

@@ -0,0 +1,13 @@
+# Copyright (C) 2019 Intel Corporation.  All rights reserved.
+# SPDX-License-Identifier: Apache-2.0 WITH LLVM-exception
+
+import tensorflow as tf
+import pathlib
+
+
+def save_model(model, filename):
+    converter = tf.lite.TFLiteConverter.from_keras_model(model)
+    tflite_model = converter.convert()
+    tflite_models_dir = pathlib.Path("./")
+    tflite_model_file = tflite_models_dir/filename
+    tflite_model_file.write_bytes(tflite_model)