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Add option to optimize tf.math.reduce_prod to Myriad (OAK) #29
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import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
import numpy as np
from pprint import pprint
np.random.seed(0)
dummy_input = np.arange(1*16*16*2, dtype=np.int32).reshape([1,16,16,2])
pprint(dummy_input)
# Create a model
i = tf.keras.layers.Input(
shape=[
dummy_input.shape[1],
dummy_input.shape[2],
dummy_input.shape[3],
],
batch_size=dummy_input.shape[0],
dtype=tf.int32,
)
o = tf.math.reduce_prod(input_tensor=i, axis=3, keepdims=True)
model = tf.keras.models.Model(inputs=[i], outputs=[o])
model.summary()
output_path = 'saved_model'
tf.saved_model.save(model, output_path)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS
]
tflite_model = converter.convert()
open(f"{output_path}/test.tflite", "wb").write(tflite_model)
# Float32
interpreter = tf.lite.Interpreter('saved_model/test.tflite')
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.set_tensor(input_details[0]['index'], dummy_input)
interpreter.invoke()
ret = interpreter.get_tensor(output_details[0]['index'])
print('@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@@ Float32')
print(ret.shape)
pprint(ret)
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This is not a generalised solution, but a simple replacement of a part of the TransformTensorBilinear function I implemented for the face_landmark_with_attention model. |
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@KenjiAsaba That is indeed a non-generalizable modification, but I will try your suggestion since the temporary purpose is OAK support for face attention. 👍 |
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LGTM. Hmmm. How to repeat Multiply at a given axis. In other words, for axis=2, it is assumed that Multiply is repeated 16 times. This was the method I came up with last night, but I was worried that it would require a lot of multiplication. In any case, I will apply the changes to import os
os.environ['CUDA_VISIBLE_DEVICES'] = '-1'
import tensorflow as tf
import numpy as np
from pprint import pprint
np.random.seed(0)
dummy_input = np.arange(1*16*16*2, dtype=np.int64).reshape([1,16,16,2])
pprint(dummy_input)
# Create a model
i = tf.keras.layers.Input(
shape=[
dummy_input.shape[1],
dummy_input.shape[2],
dummy_input.shape[3],
],
batch_size=dummy_input.shape[0],
dtype=tf.int64,
)
# o = tf.math.reduce_prod(input_tensor=i, axis=3, keepdims=True)
o = tf.math.multiply(i[:,:,:,0:1], i[:,:,:,1:2])
model = tf.keras.models.Model(inputs=[i], outputs=[o])
model.summary()
output_path = 'saved_model'
tf.saved_model.save(model, output_path)
converter = tf.lite.TFLiteConverter.from_keras_model(model)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS
]
tflite_model = converter.convert()
open(f"{output_path}/test.tflite", "wb").write(tflite_model)
# Float32
interpreter = tf.lite.Interpreter('saved_model/test.tflite')
interpreter.allocate_tensors()
input_details = interpreter.get_input_details()
output_details = interpreter.get_output_details()
interpreter.set_tensor(input_details[0]['index'], dummy_input)
interpreter.invoke()
ret = interpreter.get_tensor(output_details[0]['index'])
print(ret.shape)
pprint(ret)
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@KenjiAsaba You are a genius. It is now also executable with the OpenCV AI Kit (OAK). python3 tflite2tensorflow.py \
--model_path face_landmark_with_attention.tflite \
--flatc_path ../flatc \
--schema_path ../schema.fbs \
--output_pb \
--optimizing_barracuda
python3 tflite2tensorflow.py \
--model_path face_landmark_with_attention.tflite \
--flatc_path ../flatc \
--schema_path ../schema.fbs \
--output_onnx \
--onnx_opset 11
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model saved_model/model_float32.onnx \
--data_type FP32 \
--output_dir saved_model/openvino/FP32 \
--model_name face_landmark_with_attention_192x192
$INTEL_OPENVINO_DIR/deployment_tools/model_optimizer/mo.py \
--input_model saved_model/model_float32.onnx \
--data_type FP16 \
--output_dir saved_model/openvino/FP16 \
--model_name face_landmark_with_attention_192x192
mkdir -p saved_model/openvino/myriad
${INTEL_OPENVINO_DIR}/deployment_tools/inference_engine/lib/intel64/myriad_compile \
-m saved_model/openvino/FP16/face_landmark_with_attention_192x192.xml \
-ip U8 \
-VPU_NUMBER_OF_SHAVES 4 \
-VPU_NUMBER_OF_CMX_SLICES 4 \
-o saved_model/openvino/myriad/face_landmark_with_attention_192x192.blob
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Released: v1.20.8 @KenjiAsaba Thank you so much for your cooperation! |
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It's my pleasure! |
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Issue Type
Feature Request
OS
Other
OS architecture
Other
Programming Language
Other
Framework
OpenVINO, Myriad Inference Engine
Download URL for tflite file
None
Convert Script
None
Description
Replace
tf.math.reduce_prodwithtf.math.multiplyhttps://www.tensorflow.org/api_docs/python/tf/math/reduce_prod
https://github.com/PINTO0309/PINTO_model_zoo/tree/main/282_face_landmark_with_attention
https://github.com/iwatake2222/play_with_tflite/tree/master/pj_tflite_face_landmark_with_attention
Relevant Log Output
Source code for simple inference testing code
No response
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