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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,47 @@ | ||
| """ | ||
| The following code compares the speed of tensorflow against onnxruntime | ||
| with a model downloaded from Tensorflow Hub. | ||
| """ | ||
| import time | ||
| import numpy | ||
| from tqdm import tqdm | ||
| import tensorflow_hub as hub | ||
| import onnxruntime as ort | ||
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| def generate_random_images(shape=(100, 100), n=10): | ||
| imgs = [] | ||
| for i in range(n): | ||
| sh = (1,) + shape + (3,) | ||
| img = numpy.clip(numpy.abs(numpy.random.randn(*sh)), 0, 1) * 255 | ||
| img = img.astype(numpy.float32) | ||
| imgs.append(img) | ||
| return imgs | ||
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| def measure_time(fct, imgs): | ||
| results = [] | ||
| times = [] | ||
| for img in tqdm(imgs): | ||
| begin = time.perf_counter() | ||
| result = fct(img) | ||
| end = time.perf_counter() | ||
| results.append(result) | ||
| times.append(end - begin) | ||
| return results, times | ||
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| imgs = generate_random_images() | ||
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| # Download model from https://tfhub.dev/captain-pool/esrgan-tf2/1 | ||
| # python -m tf2onnx.convert --saved-model esrgan --output "esrgan-tf2.onnx" --opset 12 | ||
| ort = ort.InferenceSession('esrgan-tf2.onnx') | ||
| fct_ort = lambda img: ort.run(None, {'input_0:0': img}) | ||
| results_ort, duration_ort = measure_time(fct_ort, imgs) | ||
| print(len(imgs), duration_ort) | ||
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| model = hub.load("https://tfhub.dev/captain-pool/esrgan-tf2/1") | ||
| results_tf, duration_tf = measure_time(model, imgs) | ||
| print(len(imgs), duration_tf) | ||
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| print("ratio ORT / TF", sum(duration_ort) / sum(duration_tf)) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,75 @@ | ||
| """ | ||
| This example retrieves a model from tensorflowhub. | ||
| It is converted into ONNX. Predictions are compared to | ||
| the predictions from tensorflow to check there is no | ||
| discrepencies. Inferencing time is also compared between | ||
| *onnxruntime*, *tensorflow* and *tensorflow.lite*. | ||
| """ | ||
| from onnxruntime import InferenceSession | ||
| import os | ||
| import sys | ||
| import subprocess | ||
| import timeit | ||
| import numpy as np | ||
| import tensorflow as tf | ||
| from tensorflow import keras | ||
| from tensorflow.keras import Input | ||
| try: | ||
| import tensorflow_hub as tfhub | ||
| except ImportError: | ||
| # no tensorflow_hub | ||
| print("tensorflow_hub not installed.") | ||
| sys.exit(0) | ||
|
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| ######################################## | ||
| # Downloads the model. | ||
| hub_layer = tfhub.KerasLayer( | ||
| "https://tfhub.dev/google/efficientnet/b0/classification/1") | ||
| model = keras.Sequential() | ||
| model.add(Input(shape=(224, 224, 3), dtype=tf.float32)) | ||
| model.add(hub_layer) | ||
| print(model.summary()) | ||
|
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| ######################################## | ||
| # Saves the model. | ||
| if not os.path.exists("efficientnetb0clas"): | ||
| os.mkdir("efficientnetb0clas") | ||
| tf.keras.models.save_model(model, "efficientnetb0clas") | ||
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| input_names = [n.name for n in model.inputs] | ||
| output_names = [n.name for n in model.outputs] | ||
| print('inputs:', input_names) | ||
| print('outputs:', output_names) | ||
|
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| ######################################## | ||
| # Testing the model. | ||
| input = np.random.randn(2, 224, 224, 3).astype(np.float32) | ||
| expected = model.predict(input) | ||
| print(expected) | ||
|
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| ######################################## | ||
| # Run the command line. | ||
| proc = subprocess.run( | ||
| 'python -m tf2onnx.convert --saved-model efficientnetb0clas ' | ||
| '--output efficientnetb0clas.onnx --opset 12'.split(), | ||
| capture_output=True) | ||
| print(proc.returncode) | ||
| print(proc.stdout.decode('ascii')) | ||
| print(proc.stderr.decode('ascii')) | ||
|
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| ######################################## | ||
| # Runs onnxruntime. | ||
| session = InferenceSession("efficientnetb0clas.onnx") | ||
| got = session.run(None, {'input_1:0': input}) | ||
| print(got[0]) | ||
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| ######################################## | ||
| # Measures the differences. | ||
| print(np.abs(got[0] - expected).max()) | ||
|
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| ######################################## | ||
| # Measures processing time. | ||
| print('tf:', timeit.timeit('model.predict(input)', | ||
| number=10, globals=globals())) | ||
| print('ort:', timeit.timeit("session.run(None, {'input_1:0': input})", | ||
| number=10, globals=globals())) |
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,70 @@ | ||
| """ | ||
| This example builds a simple model without training. | ||
| It is converted into ONNX. Predictions are compared to | ||
| the predictions from tensorflow to check there is no | ||
| discrepencies. Inferencing time is also compared between | ||
| *onnxruntime*, *tensorflow* and *tensorflow.lite*. | ||
| """ | ||
| from onnxruntime import InferenceSession | ||
| import os | ||
| import subprocess | ||
| import timeit | ||
| import numpy as np | ||
| import tensorflow as tf | ||
| from tensorflow import keras | ||
| from tensorflow.keras import layers, Input | ||
|
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| ######################################## | ||
| # Creates the model. | ||
| model = keras.Sequential() | ||
| model.add(Input((4, 4))) | ||
| model.add(layers.SimpleRNN(8)) | ||
| model.add(layers.Dense(2)) | ||
| print(model.summary()) | ||
| input_names = [n.name for n in model.inputs] | ||
| output_names = [n.name for n in model.outputs] | ||
| print('inputs:', input_names) | ||
| print('outputs:', output_names) | ||
|
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| ######################################## | ||
| # Training | ||
| # .... | ||
| # Skipped. | ||
|
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| ######################################## | ||
| # Testing the model. | ||
| input = np.random.randn(2, 4, 4).astype(np.float32) | ||
| expected = model.predict(input) | ||
| print(expected) | ||
|
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| ######################################## | ||
| # Saves the model. | ||
| if not os.path.exists("simple_rnn"): | ||
| os.mkdir("simple_rnn") | ||
| tf.keras.models.save_model(model, "simple_rnn") | ||
|
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| ######################################## | ||
| # Run the command line. | ||
| proc = subprocess.run('python -m tf2onnx.convert --saved-model simple_rnn ' | ||
| '--output simple_rnn.onnx --opset 12'.split(), | ||
| capture_output=True) | ||
| print(proc.returncode) | ||
| print(proc.stdout.decode('ascii')) | ||
| print(proc.stderr.decode('ascii')) | ||
|
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| ######################################## | ||
| # Runs onnxruntime. | ||
| session = InferenceSession("simple_rnn.onnx") | ||
| got = session.run(None, {'input_1:0': input}) | ||
| print(got[0]) | ||
|
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| ######################################## | ||
| # Measures the differences. | ||
| print(np.abs(got[0] - expected).max()) | ||
|
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| ######################################## | ||
| # Measures processing time. | ||
| print('tf:', timeit.timeit('model.predict(input)', | ||
| number=100, globals=globals())) | ||
| print('ort:', timeit.timeit("session.run(None, {'input_1:0': input})", | ||
| number=100, globals=globals())) |
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