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author | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-05-05 12:08:03 +0000 |
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committer | Daniel Baumann <daniel.baumann@progress-linux.org> | 2024-05-05 12:08:18 +0000 |
commit | 5da14042f70711ea5cf66e034699730335462f66 (patch) | |
tree | 0f6354ccac934ed87a2d555f45be4c831cf92f4a /src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models | |
parent | Releasing debian version 1.44.3-2. (diff) | |
download | netdata-5da14042f70711ea5cf66e034699730335462f66.tar.xz netdata-5da14042f70711ea5cf66e034699730335462f66.zip |
Merging upstream version 1.45.3+dfsg.
Signed-off-by: Daniel Baumann <daniel.baumann@progress-linux.org>
Diffstat (limited to 'src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models')
6 files changed, 111 insertions, 0 deletions
diff --git a/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/average.py b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/average.py new file mode 100755 index 000000000..a21fe7520 --- /dev/null +++ b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/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") diff --git a/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/max.py b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/max.py new file mode 100755 index 000000000..a3ec45677 --- /dev/null +++ b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/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") diff --git a/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/mult_dimension.py b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/mult_dimension.py new file mode 100644 index 000000000..f521a93af --- /dev/null +++ b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/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") diff --git a/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/mult_outputs.py b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/mult_outputs.py new file mode 100755 index 000000000..98a50129c --- /dev/null +++ b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/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") diff --git a/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/sum.py b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/sum.py new file mode 100755 index 000000000..503125b34 --- /dev/null +++ b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/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") diff --git a/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/utils.py b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/core/iwasm/libraries/wasi-nn/test/models/utils.py new file mode 100644 index 000000000..8335f05da --- /dev/null +++ b/src/fluent-bit/lib/wasm-micro-runtime-WAMR-1.2.2/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) |