Comments (7)
PINTO0309
commented on May 21, 2024
1
@KenjiAsaba
I see. 😄 You are smart.
That is indeed a non-generalizable modification, but I will try your suggestion since the temporary purpose is OAK support for face attention. 👍
from tflite2tensorflow.
PINTO0309
commented on May 21, 2024
1
Released: v1.20.8
https://github.com/PINTO0309/tflite2tensorflow/releases/tag/v1.20.8
@KenjiAsaba Thank you so much for your cooperation!
from tflite2tensorflow.
PINTO0309
commented on May 21, 2024
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)- input [1,16,16,2]
array([[[[ 0, 1],
[ 2, 3],
[ 4, 5],
[ 6, 7],
[ 8, 9],
[ 10, 11],
[ 12, 13],
[ 14, 15],
[ 16, 17],
[ 18, 19],
[ 20, 21],
[ 22, 23],
[ 24, 25],
[ 26, 27],
[ 28, 29],
[ 30, 31]],
:
[[480, 481],
[482, 483],
[484, 485],
[486, 487],
[488, 489],
[490, 491],
[492, 493],
[494, 495],
[496, 497],
[498, 499],
[500, 501],
[502, 503],
[504, 505],
[506, 507],
[508, 509],
[510, 511]]]], dtype=int32)
- output [1, 16, 16, 1]
array([[[[ 0],
[ 6],
[ 20],
[ 42],
[ 72],
[ 110],
[ 156],
[ 210],
[ 272],
[ 342],
[ 420],
[ 506],
[ 600],
[ 702],
[ 812],
[ 930]],
:
[[230880],
[232806],
[234740],
[236682],
[238632],
[240590],
[242556],
[244530],
[246512],
[248502],
[250500],
[252506],
[254520],
[256542],
[258572],
[260610]]]], dtype=int32)
from tflite2tensorflow.
KenjiAsaba
commented on May 21, 2024
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.
KenjiAsaba@9519eae
from tflite2tensorflow.
PINTO0309
commented on May 21, 2024
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 face_attention, regenerate ONNX and attempt to convert to OAK. 😸
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)- input [1,16,16,2]
array([[[[ 0, 1],
[ 2, 3],
[ 4, 5],
[ 6, 7],
[ 8, 9],
[ 10, 11],
[ 12, 13],
[ 14, 15],
[ 16, 17],
[ 18, 19],
[ 20, 21],
[ 22, 23],
[ 24, 25],
[ 26, 27],
[ 28, 29],
[ 30, 31]],
:
[[480, 481],
[482, 483],
[484, 485],
[486, 487],
[488, 489],
[490, 491],
[492, 493],
[494, 495],
[496, 497],
[498, 499],
[500, 501],
[502, 503],
[504, 505],
[506, 507],
[508, 509],
[510, 511]]]])
- output [1,16,16,1]
array([[[[ 0],
[ 6],
[ 20],
[ 42],
[ 72],
[ 110],
[ 156],
[ 210],
[ 272],
[ 342],
[ 420],
[ 506],
[ 600],
[ 702],
[ 812],
[ 930]],
:
[[230880],
[232806],
[234740],
[236682],
[238632],
[240590],
[242556],
[244530],
[246512],
[248502],
[250500],
[252506],
[254520],
[256542],
[258572],
[260610]]]])
from tflite2tensorflow.
PINTO0309
commented on May 21, 2024
@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.blobInference Engine:
IE version ......... 2021.4.0
Build ........... 2021.4.0-3839-cd81789d294-releases/2021/4
[Warning][VPU][Config] Deprecated option was used : VPU_MYRIAD_PLATFORM
Done
from tflite2tensorflow.
KenjiAsaba
commented on May 21, 2024
It's my pleasure!
from tflite2tensorflow.
Related Issues (20)
- Face landmark model: ERROR: conv2d_21 is not in graph HOT 1
- Error on converting of face_landmark.tflite HOT 3
- Error converting face_landmark_with_attention.tflite HOT 2
- Error converting pose_detection.tflite [ No such file or directory: './pose_detection.json' ] HOT 2
- Error converting pose_detection.tflite [ No such file or directory: './pose_detection.json' ] HOT 3
- The shape of the output layer is different from the result of the tensorflow2onnx transformation. HOT 8
- Order of input channels switched on ONNX HOT 2
- Converted mediapipe palm detection coreml model cannot be deployed on IOS app HOT 7
- Got error while converting magenta_arbitrary-image-stylization-v1-256_fp16_transfer_1.tflite to .h5 format HOT 1
- ValueError: The name 'serving_default_input_1:0:0' looks a like a Tensor name, but is not a valid one. Tensor names must be of the form "<op_name>:<output_index>". HOT 1
- CoreML conversions of face_landmark_with_attention.tflite fails HOT 3
- No json file HOT 1
- OSError: SavedModel file does not exist at: saved_model/{saved_model.pbtxt|saved_model.pb} HOT 1
- fail to convert [KNIFT] HOT 2
- Use docker but no *.json error HOT 6
- model quantified slower than not quantifed on windows x64 HOT 1
- tflite2tensorflow Docker has flatc error HOT 4
- Problem converting pose_landmark (full) HOT 1
- How to add custom operators to tflite runtime? HOT 1
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from tflite2tensorflow.