def main(opset=13):
url = "https://tfhub.dev/google/seefood/segmenter/mobile_food_segmenter_V1/1?tf-hub-format=compressed"
dest = "tf-mobile_food_segmenter_V1"
name = "mobile_food_segmenter_V1"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 513, 513, 3), scale=1.)
if True:
benchmark(url, dest, onnx_name, opset, imgs, tag='')
# The conversion works but tensorflow fails with
# TypeError: 'AutoTrackable' object is not callable
if True:
import tensorflow.compat.v2 as tf
import tensorflow_hub as hub
m = hub.KerasLayer(
'https://tfhub.dev/google/seefood/segmenter/mobile_food_segmenter_V1/1'
)
inputs = {
"X": tf.keras.Input(shape=[1, 513, 513, 3],
dtype="float32",
name="X"),
}
outputs = m(inputs)["default"]
# TypeError: pruned(images) missing required arguments: images
print(outputs)
model = tf.keras.Model(inputs, outputs)
if not os.path.exists(dest):
os.makedirs(dest)
# This model is a large model.
tf2onnx.convert.from_keras(model, opset=13, output_path=onnx_name)
def main(opset=13):
if False:
import tensorflow as tf
import tensorflow_text
import tensorflow_hub as hub
sentences = tf.constant(["Hi I'm some text"])
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
preprocessor = hub.KerasLayer(
"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3")
encoder_inputs = preprocessor(text_input)
embedded_inputs = {
k: v.numpy()
for k, v in preprocessor(sentences).items()
}
for k, v in embedded_inputs.items():
print(k, v.dtype, v.shape)
url = "https://tfhub.dev/tensorflow/mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT/1?tf-hub-format=compressed"
dest = "tf-mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT"
name = "mobilebert_en_uncased_L-24_H-128_B-512_A-4_F-4_OPT"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
inputs = generate_text_inputs()
benchmark(
url, dest, onnx_name, opset, inputs,
output_name="attention_scores") #, ort_name="mobile_bert_encoder_50")
def main(opset=13):
if False:
import tensorflow as tf
import tensorflow_text
import tensorflow_hub as hub
sentences = tf.constant(["Hi I'm some text"])
text_input = tf.keras.layers.Input(shape=(), dtype=tf.string)
encoder = hub.KerasLayer(
"https://tfhub.dev/tensorflow/lambert_en_uncased_L-24_H-1024_A-16/2", trainable=True)
preprocessor = hub.KerasLayer(
"https://tfhub.dev/tensorflow/bert_en_uncased_preprocess/3")
encoder_inputs = preprocessor(text_input)
embedded_inputs = {k: v.numpy() for k, v in preprocessor(sentences).items()}
for k, v in embedded_inputs.items():
print(k, v.dtype, v.shape)
url = "https://tfhub.dev/tensorflow/lambert_en_uncased_L-24_H-1024_A-16/2?tf-hub-format=compressed"
dest = "tf-lambert_en_uncased_L-24_H-1024_A-16"
name = "lambert_en_uncased_L-24_H-1024_A-16"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
inputs = [OrderedDict([
('input_word_ids', numpy.array([rnd.randint(0, 1000) for i in range(0, 128)], dtype=numpy.int32).reshape((1, -1))),
('input_mask', numpy.array([rnd.randint(0, 1) for i in range(0, 128)], dtype=numpy.int32).reshape((1, -1))),
('input_type_ids', numpy.array([i//5 for i in range(0, 128)], dtype=numpy.int32).reshape((1, -1)))
]) for i in range(0, 10)]
benchmark(url, dest, onnx_name, opset, inputs, output_name="pooled_output")
def main(opset=13):
url = "https://tfhub.dev/google/movenet/singlepose/thunder/3?tf-hub-format=compressed"
dest = "tf-thunder"
name = "thunder"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 256, 256, 3), dtype=numpy.int32)
benchmark(url, dest, onnx_name, opset, imgs, signature='serving_default')
def main(opset=13):
url = "https://tfhub.dev/google/inaturalist/inception_v3/feature_vector/5?tf-hub-format=compressed"
dest = "tf-inception_v3"
name = "inception_v3"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 299, 299, 3))
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/captain-pool/esrgan-tf2/1?tf-hub-format=compressed"
dest = "tf-esrgan-tf2"
name = "esrgan-tf2"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images()
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/google/seefood/segmenter/mobile_food_segmenter_V1/1?tf-hub-format=compressed"
dest = "tf-mobile_food_segmenter_V1"
name = "mobile_food_segmenter_V1"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 513, 513, 3), scale=1.)
benchmark(url, dest, onnx_name, opset, imgs, tag='')
def main(opset=13):
url = "https://tfhub.dev/google/imagenet/resnet_v1_101/feature_vector/5?tf-hub-format=compressed"
dest = "tf-resnet_v1_101"
name = "resnet_v1_101"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/google/imagenet/resnet_v2_152/classification/5?tf-hub-format=compressed"
dest = "tf-resnet_v2_152"
name = "resnet_v2_152"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 224, 224, 3))
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/google/imagenet/mobilenet_v3_large_075_224/classification/5?tf-hub-format=compressed"
dest = "tf-mobilenet-v3-large-075-224"
name = "mobilenet-v3-large-075-224"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 224, 224, 3), scale=1.)
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/deepmind/enformer/1?tf-hub-format=compressed"
dest = "tf-enformer"
name = "enformer"
onnx_name = os.path.join(dest, "%s-%d.zip" % (name, opset))
imgs = generate_random_images(shape=(1, 224, 224, 3))
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/mediapipe/tfjs-model/blazeposedetector/1/default/1?tfjs-format=compressed"
dest = "tf-blazeposedetector"
name = "blazeposedetector"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 513, 513, 3), scale=1.)
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/tensorflow/tutorials/spam-detection/1?tf-hub-format=compressed"
dest = "tf-spam-detection"
name = "spam-detection"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images((1, 20), dtype=numpy.int32)
benchmark(url, dest, onnx_name, opset, imgs)
def main(opset=13):
url = "https://tfhub.dev/google/imagenet/nasnet_large/feature_vector/5?tf-hub-format=compressed"
dest = "tf-nasnet-large"
name = "nasnet-large"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
imgs = generate_random_images(shape=(1, 331, 331, 3))
benchmark(url, dest, onnx_name, opset, imgs)
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/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):
url = "https://tfhub.dev/tensorflow/bert_en_wwm_uncased_L-24_H-1024_A-16/4?tf-hub-format=compressed"
dest = "tf-bert-en-wwm-uncased-L-24-H-1024-A-16"
name = "bert-en-wwm-uncased-L-24-H-1024-A-16"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
inputs = [
OrderedDict([('input_word_ids',
numpy.array([rnd.randint(0, 1000) for i in range(0, 32)],
dtype=numpy.int32).reshape((1, -1))),
('input_mask',
numpy.array([rnd.randint(0, 1) for i in range(0, 32)],
dtype=numpy.int32).reshape((1, -1))),
('input_type_ids',
numpy.array([i // 5 for i in range(0, 32)],
dtype=numpy.int32).reshape((1, -1)))])
for i in range(0, 10)
]
benchmark(url, dest, onnx_name, opset, inputs, output_name="pooled_output")
def main(opset=13):
url = "https://tfhub.dev/tensorflow/talkheads_ggelu_bert_en_large/2?tf-hub-format=compressed"
dest = "tf-talkheads_ggelu_bert_en_large"
name = "talkheads_ggelu_bert_en_large"
onnx_name = os.path.join(dest, "%s-%d.onnx" % (name, opset))
inputs = [
OrderedDict([
('input_word_ids',
numpy.array([rnd.randint(0, 1000) for i in range(0, 128)],
dtype=numpy.int32).reshape((1, -1))),
('input_mask',
numpy.array([rnd.randint(0, 1) for i in range(0, 128)],
dtype=numpy.int32).reshape((1, -1))),
('input_type_ids',
numpy.array([i // 5 for i in range(0, 128)],
dtype=numpy.int32).reshape((1, -1)))
]) for i in range(0, 10)
]
benchmark(url, dest, onnx_name, opset, inputs, output_name="pooled_output")
