def main(opset=13):
print("[main]")
url = "https://tfhub.dev/google/imagenet/mobilenet_v3_small_075_224/feature_vector/5?tf-hub-format=compressed"
dest = "tf-mobilenet-v3-small-075-224"
name = "mobilenet-v3-small-075-224"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
print("[benchmark]")
benchmark(url, dest, onnx_name, opset, imgs)
print("[end]")
def main(opset=13):
url = "https://tfhub.dev/google/yamnet/1?tf-hub-format=compressed"
dest = "tf-yamnet-tf"
name = "yamnet"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
tfl = os.path.join(dest, 'model.tflite')
imgs = generate_random_images(shape=(16000, ), dtype=numpy.float32, scale=0.)
# benchmark(url, dest, onnx_name, opset, imgs, convert_tflite=tfl)
onnx_name = os.path.join(dest, "%s-tfl-%d.onnx" % (name, opset))
benchmark_tflite(tfl, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/google/humpback_whale/1?tf-hub-format=compressed"
dest = "tf-humpback-whale"
name = "humpback-whale"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
print("[download data]")
FILENAME = 'gs://bioacoustics-www1/sounds/Cross_02_060203_071428.d20_7.wav'
pkl_name = os.path.join(dest, "data.pkl")
if not os.path.exists(pkl_name):
with open(pkl_name, "wb") as f:
waveform, sample_rate = tf.audio.decode_wav(
tf.io.read_file(FILENAME))
waveform = tf.expand_dims(waveform, 0) # makes a batch of size 1
context_step_samples = tf.cast(sample_rate, tf.int64)
data = dict(waveform=waveform,
context_step_samples=context_step_samples)
pickle.dump(data, f)
else:
with open(pkl_name, "rb") as f:
data = pickle.load(f)
waveform = data["waveform"]
context_step_samples = data["context_step_samples"]
print("[data] done. context_step_samples=", context_step_samples.numpy())
def benchmark_custom(local_name):
model = hub.load(local_name)
score_fn = model.signatures['score']
scores = score_fn(waveform=waveform,
context_step_samples=context_step_samples)
imgs_tf = [
dict(waveform=waveform, context_step_samples=context_step_samples)
]
results_tf, duration_tf = measure_time(
lambda inputs: score_fn(**inputs), imgs_tf)
return scores, results_tf, duration_tf
imgs = generate_random_images(shape=(1, 750000, 1), scale=1., n=2)
inputs = [
dict(waveform=waveform.numpy(),
context_step_samples=numpy.array(context_step_samples.numpy(),
dtype=numpy.int64))
]
benchmark(url,
dest,
onnx_name,
opset,
inputs,
optimize=False,
signature='score',
custom_tf=benchmark_custom)
def main(opset=13):
url = "https://tfhub.dev/google/humpback_whale/1?tf-hub-format=compressed"
dest = "tf-humpback-whale"
name = "humpback-whale"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 1024, 1))
inputs = [
dict(waveform=img,
context_step_samples=numpy.array(512, dtype=numpy.int64))
for img in imgs
]
benchmark(url, dest, onnx_name, opset, inputs, optimize=False)
def main(opset=13):
print('[begin]')
url = "https://tfhub.dev/deepmind/enformer/1?tf-hub-format=compressed"
dest = "tf-enformer"
name = "enformer"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
model = None
if not os.path.exists(onnx_name):
if model is None:
model = hub.load("https://tfhub.dev/deepmind/enformer/1").model
tf2onnx.convert.from_function(
model.predict_on_batch,
[tf.TensorSpec([None, 393216, 4], tf.float32)],
opset=13,
output_path=onnx_name)
# benchmark(url, dest, onnx_name, opset, imgs)
print("[generate dummy images]")
imgs = generate_random_images(shape=(1, 393216, 4), scale=0.)
ort = InferenceSession(onnx_name)
fct_ort = lambda img: ort.run(None, {'args_0': img})[0]
if model is None:
model = hub.load("https://tfhub.dev/deepmind/enformer/1").model
fct_tf = lambda img: model.predict_on_batch(img)
print('[benchmark tf]')
imgs_tf = [convert_to_tensor(img) for img in imgs]
results_tf, duration_tf = measure_time(fct_tf, imgs)
print("TF", len(imgs), duration_tf)
print('[benchmark ort]')
results_ort, duration_ort = measure_time(fct_ort, imgs)
print("ORT", len(imgs), duration_ort)
mean_ort = sum(duration_ort) / len(duration_ort)
mean_tf = sum(duration_tf) / len(duration_tf)
print("ratio ORT=%r / TF=%r = %r" %
(mean_ort, mean_tf, mean_ort / mean_tf))
# discrepencies
assert_almost_equal(results_tf[0]['human'], results_ort[0], decimal=4)
print('[end]')
def main(opset=13):
url = "https://tfhub.dev/google/lite-model/yamnet/classification/tflite/1?lite-format=tflite"
dest = "tf-yamnet-tflite"
name = "yamnet"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(15600, ),
dtype=numpy.float32,
scale=0.)
benchmark_tflite(url,
dest,
onnx_name,
opset,
imgs,
names=[
('stft/rfft3', 'FFT_stft/rfft4_reshape__190:0'),
('magnitude_spectrogram',
'ComplexAbsmagnitude_spectrogram__206:0'),
('log_mel_spectrogram', 'log_mel_spectrogram'),
])
# SPDX-License-Identifier: Apache-2.0
import os
import numpy
import onnxruntime as ort
import tensorflow as tf
import tensorflow_hub as hub
import tf2onnx
from _tools import generate_random_images, check_discrepencies
imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
model = tf.keras.Sequential([
hub.KerasLayer(
"https://tfhub.dev/google/imagenet/resnet_v1_101/feature_vector/5",
trainable=False)
])
model.build([None, 224, 224, 3])
expected_output = model(imgs[0])
dest = "tf-resnet_v1_101"
if not os.path.exists(dest):
os.makedirs(dest)
dest_name = os.path.join(dest, "resnet_v1_101-13-keras.onnx")
if not os.path.exists(dest_name):
tf2onnx.convert.from_keras(model, opset=13, output_path=dest_name)
sess = ort.InferenceSession(dest_name)
print('inputs', [_.name for _ in sess.get_inputs()])
ort_output = sess.run(None, {"keras_layer_input": imgs[0]})