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TensorFlow Lite Model Analyzer API는 모델의 구조를 나열하여 TensorFlow Lite 형식의 모델을 분석하는 데 도움을 줍니다.
Model Analyzer API
TensorFlow Lite 모델 분석기에 다음 API를 사용할 수 있습니다.
tf.lite.experimental.Analyzer.analyze(model_path=None,
model_content=None,
gpu_compatibility=False)
API 세부 정보는 https://www.tensorflow.org/api_docs/python/tf/lite/experimental/Analyzer에서 찾아보거나 Python 터미널에서 help(tf.lite.experimental.Analyzer.analyze)를 실행할 수 있습니다.
간단한 Keras 모델의 기본 사용법
다음 코드는 모델 분석기의 기본 사용법을 보여줍니다. TFLite 모델 콘텐츠에서 변환된 Keras 모델의 콘텐츠를 플랫 버퍼 객체로 형식화하여 보여줍니다.
import tensorflow as tf
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(128, 128)),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
fb_model = tf.lite.TFLiteConverter.from_keras_model(model).convert()
tf.lite.experimental.Analyzer.analyze(model_content=fb_model)
2022-12-15 01:10:24.903328: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvinfer.so.7: cannot open shared object file: No such file or directory
2022-12-15 01:10:24.903458: W tensorflow/compiler/xla/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvinfer_plugin.so.7: cannot open shared object file: No such file or directory
2022-12-15 01:10:24.903469: W tensorflow/compiler/tf2tensorrt/utils/py_utils.cc:38] TF-TRT Warning: Cannot dlopen some TensorRT libraries. If you would like to use Nvidia GPU with TensorRT, please make sure the missing libraries mentioned above are installed properly.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpt6pp2cgu/assets
=== TFLite ModelAnalyzer ===
Your TFLite model has '1' subgraph(s). In the subgraph description below,
T# represents the Tensor numbers. For example, in Subgraph#0, the RESHAPE op takes
tensor #0 and tensor #1 as input and produces tensor #4 as output.
Subgraph#0 main(T#0) -> [T#6]
Op#0 RESHAPE(T#0, T#1[-1, 16384]) -> [T#4]
Op#1 FULLY_CONNECTED(T#4, T#2, T#-1) -> [T#5]
Op#2 FULLY_CONNECTED(T#5, T#3, T#-1) -> [T#6]
Tensors of Subgraph#0
T#0(serving_default_flatten_input:0) shape_signature:[-1, 128, 128], type:FLOAT32
T#1(sequential/flatten/Const) shape:[2], type:INT32 RO 8 bytes, buffer: 2, data:[-1, 16384]
T#2(sequential/dense/MatMul1) shape:[256, 16384], type:FLOAT32 RO 16777216 bytes, buffer: 3, data:[0.0149509, 0.00967362, -0.00562828, -0.0142361, -0.00967516, ...]
T#3(sequential/dense_1/MatMul) shape:[10, 256], type:FLOAT32 RO 10240 bytes, buffer: 4, data:[0.107666, 0.099612, 0.118443, 0.0015181, -0.0139817, ...]
T#4(sequential/flatten/Reshape) shape_signature:[-1, 16384], type:FLOAT32
T#5(sequential/dense/MatMul;sequential/dense/Relu;sequential/dense/BiasAdd) shape_signature:[-1, 256], type:FLOAT32
T#6(StatefulPartitionedCall:0) shape_signature:[-1, 10], type:FLOAT32
---------------------------------------------------------------
Your TFLite model has '1' signature_def(s).
Signature#0 key: 'serving_default'
- Subgraph: Subgraph#0
- Inputs:
'flatten_input' : T#0
- Outputs:
'dense_1' : T#6
---------------------------------------------------------------
Model size: 16789040 bytes
Non-data buffer size: 1476 bytes (00.01 %)
Total data buffer size: 16787564 bytes (99.99 %)
(Zero value buffers): 0 bytes (00.00 %)
* Buffers of TFLite model are mostly used for constant tensors.
And zero value buffers are buffers filled with zeros.
Non-data buffers area are used to store operators, subgraphs and etc.
You can find more details from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs
2022-12-15 01:10:30.546751: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-12-15 01:10:30.546796: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
MobileNetV3Large Keras 모델의 기본 사용법
이 API는 MobileNetV3Large와 같은 대형 모델에서 효과가 있습니다. 출력이 크기 때문에 주로 이용하는 텍스트 편집기로 찾아볼 수 있습니다.
model = tf.keras.applications.MobileNetV3Large()
fb_model = tf.lite.TFLiteConverter.from_keras_model(model).convert()
tf.lite.experimental.Analyzer.analyze(model_content=fb_model)
WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not 224. Weights for input shape (224, 224) will be loaded as the default.
WARNING:tensorflow:`input_shape` is undefined or non-square, or `rows` is not 224. Weights for input shape (224, 224) will be loaded as the default.
Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/mobilenet_v3/weights_mobilenet_v3_large_224_1.0_float.h5
22661472/22661472 [==============================] - 0s 0us/step
WARNING:absl:Found untraced functions such as _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op, _jit_compiled_convolution_op while saving (showing 5 of 64). These functions will not be directly callable after loading.
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpmcmvxqfr/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpmcmvxqfr/assets
2022-12-15 01:11:01.438667: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-12-15 01:11:01.438717: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
=== TFLite ModelAnalyzer ===
Your TFLite model has '1' subgraph(s). In the subgraph description below,
T# represents the Tensor numbers. For example, in Subgraph#0, the MUL op takes
tensor #0 and tensor #133 as input and produces tensor #136 as output.
Subgraph#0 main(T#0) -> [T#263]
Op#0 MUL(T#0, T#133) -> [T#136]
Op#1 ADD(T#136, T#134) -> [T#137]
Op#2 CONV_2D(T#137, T#80, T#37) -> [T#138]
Op#3 HARD_SWISH(T#138) -> [T#139]
Op#4 DEPTHWISE_CONV_2D(T#139, T#38, T#1) -> [T#140]
Op#5 CONV_2D(T#140, T#81, T#39) -> [T#141]
Op#6 ADD(T#139, T#141) -> [T#142]
Op#7 CONV_2D(T#142, T#82, T#2) -> [T#143]
Op#8 PAD(T#143, T#129[0, 0, 0, 1, 0, ...]) -> [T#144]
Op#9 DEPTHWISE_CONV_2D(T#144, T#40, T#3) -> [T#145]
Op#10 CONV_2D(T#145, T#83, T#41) -> [T#146]
Op#11 CONV_2D(T#146, T#84, T#4) -> [T#147]
Op#12 DEPTHWISE_CONV_2D(T#147, T#42, T#5) -> [T#148]
Op#13 CONV_2D(T#148, T#85, T#43) -> [T#149]
Op#14 ADD(T#146, T#149) -> [T#150]
Op#15 CONV_2D(T#150, T#86, T#6) -> [T#151]
Op#16 PAD(T#151, T#131[0, 0, 1, 2, 1, ...]) -> [T#152]
Op#17 DEPTHWISE_CONV_2D(T#152, T#44, T#7) -> [T#153]
Op#18 MEAN(T#153, T#130[1, 2]) -> [T#154]
Op#19 CONV_2D(T#154, T#87, T#8) -> [T#155]
Op#20 CONV_2D(T#155, T#88, T#9) -> [T#156]
Op#21 MUL(T#156, T#135) -> [T#157]
Op#22 MUL(T#153, T#157) -> [T#158]
Op#23 CONV_2D(T#158, T#89, T#45) -> [T#159]
Op#24 CONV_2D(T#159, T#90, T#10) -> [T#160]
Op#25 DEPTHWISE_CONV_2D(T#160, T#46, T#11) -> [T#161]
Op#26 MEAN(T#161, T#130[1, 2]) -> [T#162]
Op#27 CONV_2D(T#162, T#91, T#12) -> [T#163]
Op#28 CONV_2D(T#163, T#92, T#13) -> [T#164]
Op#29 MUL(T#164, T#135) -> [T#165]
Op#30 MUL(T#161, T#165) -> [T#166]
Op#31 CONV_2D(T#166, T#93, T#47) -> [T#167]
Op#32 ADD(T#159, T#167) -> [T#168]
Op#33 CONV_2D(T#168, T#94, T#14) -> [T#169]
Op#34 DEPTHWISE_CONV_2D(T#169, T#48, T#15) -> [T#170]
Op#35 MEAN(T#170, T#130[1, 2]) -> [T#171]
Op#36 CONV_2D(T#171, T#95, T#16) -> [T#172]
Op#37 CONV_2D(T#172, T#96, T#17) -> [T#173]
Op#38 MUL(T#173, T#135) -> [T#174]
Op#39 MUL(T#170, T#174) -> [T#175]
Op#40 CONV_2D(T#175, T#97, T#49) -> [T#176]
Op#41 ADD(T#168, T#176) -> [T#177]
Op#42 CONV_2D(T#177, T#98, T#50) -> [T#178]
Op#43 HARD_SWISH(T#178) -> [T#179]
Op#44 PAD(T#179, T#129[0, 0, 0, 1, 0, ...]) -> [T#180]
Op#45 DEPTHWISE_CONV_2D(T#180, T#51, T#18) -> [T#181]
Op#46 HARD_SWISH(T#181) -> [T#182]
Op#47 CONV_2D(T#182, T#99, T#52) -> [T#183]
Op#48 CONV_2D(T#183, T#100, T#53) -> [T#184]
Op#49 HARD_SWISH(T#184) -> [T#185]
Op#50 DEPTHWISE_CONV_2D(T#185, T#54, T#19) -> [T#186]
Op#51 HARD_SWISH(T#186) -> [T#187]
Op#52 CONV_2D(T#187, T#101, T#55) -> [T#188]
Op#53 ADD(T#183, T#188) -> [T#189]
Op#54 CONV_2D(T#189, T#102, T#56) -> [T#190]
Op#55 HARD_SWISH(T#190) -> [T#191]
Op#56 DEPTHWISE_CONV_2D(T#191, T#57, T#20) -> [T#192]
Op#57 HARD_SWISH(T#192) -> [T#193]
Op#58 CONV_2D(T#193, T#103, T#58) -> [T#194]
Op#59 ADD(T#189, T#194) -> [T#195]
Op#60 CONV_2D(T#195, T#104, T#59) -> [T#196]
Op#61 HARD_SWISH(T#196) -> [T#197]
Op#62 DEPTHWISE_CONV_2D(T#197, T#60, T#21) -> [T#198]
Op#63 HARD_SWISH(T#198) -> [T#199]
Op#64 CONV_2D(T#199, T#105, T#61) -> [T#200]
Op#65 ADD(T#195, T#200) -> [T#201]
Op#66 CONV_2D(T#201, T#106, T#62) -> [T#202]
Op#67 HARD_SWISH(T#202) -> [T#203]
Op#68 DEPTHWISE_CONV_2D(T#203, T#63, T#22) -> [T#204]
Op#69 HARD_SWISH(T#204) -> [T#205]
Op#70 MEAN(T#205, T#130[1, 2]) -> [T#206]
Op#71 CONV_2D(T#206, T#107, T#23) -> [T#207]
Op#72 CONV_2D(T#207, T#108, T#24) -> [T#208]
Op#73 MUL(T#208, T#135) -> [T#209]
Op#74 MUL(T#205, T#209) -> [T#210]
Op#75 CONV_2D(T#210, T#109, T#64) -> [T#211]
Op#76 CONV_2D(T#211, T#110, T#65) -> [T#212]
Op#77 HARD_SWISH(T#212) -> [T#213]
Op#78 DEPTHWISE_CONV_2D(T#213, T#66, T#25) -> [T#214]
Op#79 HARD_SWISH(T#214) -> [T#215]
Op#80 MEAN(T#215, T#130[1, 2]) -> [T#216]
Op#81 CONV_2D(T#216, T#111, T#26) -> [T#217]
Op#82 CONV_2D(T#217, T#112, T#27) -> [T#218]
Op#83 MUL(T#218, T#135) -> [T#219]
Op#84 MUL(T#215, T#219) -> [T#220]
Op#85 CONV_2D(T#220, T#113, T#67) -> [T#221]
Op#86 ADD(T#211, T#221) -> [T#222]
Op#87 CONV_2D(T#222, T#114, T#68) -> [T#223]
Op#88 HARD_SWISH(T#223) -> [T#224]
Op#89 PAD(T#224, T#131[0, 0, 1, 2, 1, ...]) -> [T#225]
Op#90 DEPTHWISE_CONV_2D(T#225, T#69, T#28) -> [T#226]
Op#91 HARD_SWISH(T#226) -> [T#227]
Op#92 MEAN(T#227, T#130[1, 2]) -> [T#228]
Op#93 CONV_2D(T#228, T#115, T#29) -> [T#229]
Op#94 CONV_2D(T#229, T#116, T#30) -> [T#230]
Op#95 MUL(T#230, T#135) -> [T#231]
Op#96 MUL(T#227, T#231) -> [T#232]
Op#97 CONV_2D(T#232, T#117, T#70) -> [T#233]
Op#98 CONV_2D(T#233, T#118, T#71) -> [T#234]
Op#99 HARD_SWISH(T#234) -> [T#235]
Op#100 DEPTHWISE_CONV_2D(T#235, T#72, T#31) -> [T#236]
Op#101 HARD_SWISH(T#236) -> [T#237]
Op#102 MEAN(T#237, T#130[1, 2]) -> [T#238]
Op#103 CONV_2D(T#238, T#119, T#32) -> [T#239]
Op#104 CONV_2D(T#239, T#120, T#33) -> [T#240]
Op#105 MUL(T#240, T#135) -> [T#241]
Op#106 MUL(T#237, T#241) -> [T#242]
Op#107 CONV_2D(T#242, T#121, T#73) -> [T#243]
Op#108 ADD(T#233, T#243) -> [T#244]
Op#109 CONV_2D(T#244, T#122, T#74) -> [T#245]
Op#110 HARD_SWISH(T#245) -> [T#246]
Op#111 DEPTHWISE_CONV_2D(T#246, T#75, T#34) -> [T#247]
Op#112 HARD_SWISH(T#247) -> [T#248]
Op#113 MEAN(T#248, T#130[1, 2]) -> [T#249]
Op#114 CONV_2D(T#249, T#123, T#35) -> [T#250]
Op#115 CONV_2D(T#250, T#124, T#36) -> [T#251]
Op#116 MUL(T#251, T#135) -> [T#252]
Op#117 MUL(T#248, T#252) -> [T#253]
Op#118 CONV_2D(T#253, T#125, T#76) -> [T#254]
Op#119 ADD(T#244, T#254) -> [T#255]
Op#120 CONV_2D(T#255, T#126, T#77) -> [T#256]
Op#121 HARD_SWISH(T#256) -> [T#257]
Op#122 MEAN(T#257, T#130[1, 2]) -> [T#258]
Op#123 CONV_2D(T#258, T#127, T#78) -> [T#259]
Op#124 HARD_SWISH(T#259) -> [T#260]
Op#125 CONV_2D(T#260, T#128, T#79) -> [T#261]
Op#126 RESHAPE(T#261, T#132[-1, 1000]) -> [T#262]
Op#127 SOFTMAX(T#262) -> [T#263]
Tensors of Subgraph#0
T#0(serving_default_input_1:0) shape_signature:[-1, -1, -1, 3], type:FLOAT32
T#1(MobilenetV3large/expanded_conv/depthwise/BatchNorm/FusedBatchNormV3) shape:[16], type:FLOAT32 RO 64 bytes, buffer: 2, data:[1.62813, 33.7453, 4.72859, 8.78206, 17.5393, ...]
T#2(MobilenetV3large/expanded_conv_1/expand/BatchNorm/FusedBatchNormV3) shape:[64], type:FLOAT32 RO 256 bytes, buffer: 3, data:[5.83326, 7.79689, 5.9951, -0.769312, 8.54113, ...]
T#3(MobilenetV3large/expanded_conv_1/depthwise/BatchNorm/FusedBatchNormV3) shape:[64], type:FLOAT32 RO 256 bytes, buffer: 4, data:[6.24156, 0.981198, 2.53471, -0.0248699, 25.7691, ...]
T#4(MobilenetV3large/expanded_conv_2/expand/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, buffer: 5, data:[3.17699, 2.28101, 1.58534, 2.71796, 1.68366, ...]
T#5(MobilenetV3large/expanded_conv_2/depthwise/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, buffer: 6, data:[0.586533, 0.863577, 0.484086, -8.43705, 7.50718, ...]
T#6(MobilenetV3large/expanded_conv_3/expand/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, buffer: 7, data:[-0.498766, -0.309574, 0.104518, 2.44678, 1.72927, ...]
T#7(MobilenetV3large/expanded_conv_3/depthwise/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, buffer: 8, data:[1.70499, 18.0012, 1.05503, 10.0129, -2.74094, ...]
T#8(MobilenetV3large/expanded_conv_3/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[24], type:FLOAT32 RO 96 bytes, buffer: 9, data:[1.14102, -0.02167, -0.01928, -0.0118068, 0.218227, ...]
T#9(MobilenetV3large/re_lu_8/Relu6;MobilenetV3large/tf.__operators__.add_1/AddV2;MobilenetV3large/expanded_conv_3/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_3/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_3/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[72], type:FLOAT32 RO 288 bytes, buffer: 10, data:[5.06759, 6.06202, 5.33617, 6.0275, 4.7227, ...]
T#10(MobilenetV3large/expanded_conv_4/expand/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, buffer: 11, data:[3.30378, 2.60396, 2.83121, -4.14912, 2.59554, ...]
T#11(MobilenetV3large/expanded_conv_4/depthwise/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, buffer: 12, data:[-0.219226, 0.464636, -0.288737, -2.38097, -0.334142, ...]
T#12(MobilenetV3large/expanded_conv_4/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[32], type:FLOAT32 RO 128 bytes, buffer: 13, data:[-0.0122205, 1.39665, 0.193353, 1.20499, -0.000705811, ...]
T#13(MobilenetV3large/re_lu_11/Relu6;MobilenetV3large/tf.__operators__.add_2/AddV2;MobilenetV3large/expanded_conv_4/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_4/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_4/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[120], type:FLOAT32 RO 480 bytes, buffer: 14, data:[3.12207, 4.98045, 2.80049, 2.3461, 3.47311, ...]
T#14(MobilenetV3large/expanded_conv_5/expand/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, buffer: 15, data:[1.19221, -1.76372, 2.7938, 3.13965, -0.732204, ...]
T#15(MobilenetV3large/expanded_conv_5/depthwise/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, buffer: 16, data:[-2.55795, -2.85519, -0.168461, 3.99681, -2.29523, ...]
T#16(MobilenetV3large/expanded_conv_5/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[32], type:FLOAT32 RO 128 bytes, buffer: 17, data:[0.920288, -0.00382053, -0.0567493, 1.97454, 3.35371, ...]
T#17(MobilenetV3large/re_lu_14/Relu6;MobilenetV3large/tf.__operators__.add_3/AddV2;MobilenetV3large/expanded_conv_5/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_5/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_5/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[120], type:FLOAT32 RO 480 bytes, buffer: 18, data:[1.03397, -0.18951, 3.24036, 1.176, 2.22316, ...]
T#18(MobilenetV3large/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3) shape:[240], type:FLOAT32 RO 960 bytes, buffer: 19, data:[2.15248, 1.62511, 4.58976, 2.86807, 1.67084, ...]
T#19(MobilenetV3large/expanded_conv_7/depthwise/BatchNorm/FusedBatchNormV3) shape:[200], type:FLOAT32 RO 800 bytes, buffer: 20, data:[-1.90742, -1.52078, 4.21307, -1.51046, -1.52174, ...]
T#20(MobilenetV3large/expanded_conv_8/depthwise/BatchNorm/FusedBatchNormV3) shape:[184], type:FLOAT32 RO 736 bytes, buffer: 21, data:[-2.47649, -2.20832, -1.40136, -0.623928, -1.61101, ...]
T#21(MobilenetV3large/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3) shape:[184], type:FLOAT32 RO 736 bytes, buffer: 22, data:[-1.82527, -1.90425, -0.864828, -1.20905, 1.78948, ...]
T#22(MobilenetV3large/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3) shape:[480], type:FLOAT32 RO 1920 bytes, buffer: 23, data:[-1.14594, -1.2222, 0.493229, -0.806949, -0.123236, ...]
T#23(MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[120], type:FLOAT32 RO 480 bytes, buffer: 24, data:[0.162616, 0.0211225, -0.00731861, 0.275613, 0.465336, ...]
T#24(MobilenetV3large/re_lu_25/Relu6;MobilenetV3large/tf.__operators__.add_14/AddV2;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[480], type:FLOAT32 RO 1920 bytes, buffer: 25, data:[0.765333, 0.628963, 5.4054, 4.91936, 2.86523, ...]
T#25(MobilenetV3large/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3) shape:[672], type:FLOAT32 RO 2688 bytes, buffer: 26, data:[-2.30358, -1.0415, -1.02916, -2.42349, -0.143203, ...]
T#26(MobilenetV3large/expanded_conv_11/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[168], type:FLOAT32 RO 672 bytes, buffer: 27, data:[-0.0489284, 0.178251, -0.0412987, -0.205209, 0.0695921, ...]
T#27(MobilenetV3large/re_lu_28/Relu6;MobilenetV3large/tf.__operators__.add_17/AddV2;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[672], type:FLOAT32 RO 2688 bytes, buffer: 28, data:[0.291311, 1.62599, 0.179997, 0.249016, 2.76901, ...]
T#28(MobilenetV3large/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3) shape:[672], type:FLOAT32 RO 2688 bytes, buffer: 29, data:[1.35255, 0.0874219, 0.716237, 0.865584, 1.82332, ...]
T#29(MobilenetV3large/expanded_conv_12/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[168], type:FLOAT32 RO 672 bytes, buffer: 30, data:[-0.499907, 0.0375283, -0.0576132, -0.243811, -0.391691, ...]
T#30(MobilenetV3large/re_lu_31/Relu6;MobilenetV3large/tf.__operators__.add_20/AddV2;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[672], type:FLOAT32 RO 2688 bytes, buffer: 31, data:[2.06113, 0.736983, 4.40858, 2.36386, 0.687798, ...]
T#31(MobilenetV3large/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3) shape:[960], type:FLOAT32 RO 3840 bytes, buffer: 32, data:[-1.22443, -0.854031, 1.91604, -3.2009, 0.110498, ...]
T#32(MobilenetV3large/expanded_conv_13/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[240], type:FLOAT32 RO 960 bytes, buffer: 33, data:[-0.295283, -0.171183, -0.491539, -0.201764, -0.0582549, ...]
T#33(MobilenetV3large/re_lu_34/Relu6;MobilenetV3large/tf.__operators__.add_23/AddV2;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[960], type:FLOAT32 RO 3840 bytes, buffer: 34, data:[0.195665, 0.217341, 0.114345, -0.0316076, 0.281505, ...]
T#34(MobilenetV3large/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3) shape:[960], type:FLOAT32 RO 3840 bytes, buffer: 35, data:[-1.81109, 1.68503, 1.58476, 1.70023, 0.342517, ...]
T#35(MobilenetV3large/expanded_conv_14/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[240], type:FLOAT32 RO 960 bytes, buffer: 36, data:[-0.275301, -0.0277678, -0.411228, -0.3586, -0.220745, ...]
T#36(MobilenetV3large/re_lu_37/Relu6;MobilenetV3large/tf.__operators__.add_26/AddV2;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y) shape:[960], type:FLOAT32 RO 3840 bytes, buffer: 37, data:[0.283771, 0.24407, 0.243922, 1.221, 0.460753, ...]
T#37(MobilenetV3large/Conv/BatchNorm/FusedBatchNormV3) shape:[16], type:FLOAT32 RO 64 bytes, buffer: 38, data:[26.8229, 27.4359, 2.7004, 6.57344, 25.2757, ...]
T#38(MobilenetV3large/expanded_conv/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv/depthwise/depthwise;MobilenetV3large/expanded_conv/project/Conv2D) shape:[1, 3, 3, 16], type:FLOAT32 RO 576 bytes, buffer: 39, data:[1.22061, -0.810988, -0.59552, -0.12323, 0.128769, ...]
T#39(MobilenetV3large/expanded_conv/project/BatchNorm/FusedBatchNormV3) shape:[16], type:FLOAT32 RO 64 bytes, buffer: 40, data:[-0.0141129, 49.9822, 9.52096, -9.69061, -4.32951, ...]
T#40(MobilenetV3large/expanded_conv_1/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_1/depthwise/depthwise) shape:[1, 3, 3, 64], type:FLOAT32 RO 2304 bytes, buffer: 41, data:[-7.61981, 0.609866, -0.72154, 1.24176, -0.446165, ...]
T#41(MobilenetV3large/expanded_conv_1/project/BatchNorm/FusedBatchNormV3) shape:[24], type:FLOAT32 RO 96 bytes, buffer: 42, data:[29.5271, -13.7881, -51.1199, 3.50073, -7.02167, ...]
T#42(MobilenetV3large/expanded_conv_2/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_2/depthwise/depthwise;MobilenetV3large/expanded_conv_3/squeeze_excite/Conv_1/Conv2D) shape:[1, 3, 3, 72], type:FLOAT32 RO 2592 bytes, buffer: 43, data:[-0.196813, -0.0441104, -0.806084, 0.0801485, 0.182848, ...]
T#43(MobilenetV3large/expanded_conv_2/project/BatchNorm/FusedBatchNormV3) shape:[24], type:FLOAT32 RO 96 bytes, buffer: 44, data:[-35.7347, 31.8145, 7.77917, 11.8099, 10.6855, ...]
T#44(MobilenetV3large/expanded_conv_3/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_3/depthwise/depthwise;MobilenetV3large/expanded_conv_3/squeeze_excite/Conv_1/Conv2D) shape:[1, 5, 5, 72], type:FLOAT32 RO 7200 bytes, buffer: 45, data:[0.0879386, -0.0954128, 0.0937833, -0.0427546, -0.253503, ...]
T#45(MobilenetV3large/expanded_conv_3/project/BatchNorm/FusedBatchNormV3) shape:[40], type:FLOAT32 RO 160 bytes, buffer: 46, data:[-21.1494, -0.469508, 14.1144, -5.10523, -9.47186, ...]
T#46(MobilenetV3large/expanded_conv_4/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_4/depthwise/depthwise;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/Conv2D) shape:[1, 5, 5, 120], type:FLOAT32 RO 12000 bytes, buffer: 47, data:[0.0256352, 0.00875715, -0.00830248, 0.0426244, 0.00442088, ...]
T#47(MobilenetV3large/expanded_conv_4/project/BatchNorm/FusedBatchNormV3) shape:[40], type:FLOAT32 RO 160 bytes, buffer: 48, data:[-7.63688, 1.21586, -22.5861, 0.739685, -3.0402, ...]
T#48(MobilenetV3large/expanded_conv_5/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_5/depthwise/depthwise;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/Conv2D) shape:[1, 5, 5, 120], type:FLOAT32 RO 12000 bytes, buffer: 49, data:[-0.0563561, -0.887496, 0.0099917, 0.166615, 0.101625, ...]
T#49(MobilenetV3large/expanded_conv_5/project/BatchNorm/FusedBatchNormV3) shape:[40], type:FLOAT32 RO 160 bytes, buffer: 50, data:[-3.93807, 1.26509, -0.947863, 31.8655, 3.26632, ...]
T#50(MobilenetV3large/expanded_conv_6/expand/BatchNorm/FusedBatchNormV3) shape:[240], type:FLOAT32 RO 960 bytes, buffer: 51, data:[-3.03785, -3.20833, -1.26339, -0.875435, -0.410649, ...]
T#51(MobilenetV3large/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_6/depthwise/depthwise;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv/Conv2D) shape:[1, 3, 3, 240], type:FLOAT32 RO 8640 bytes, buffer: 52, data:[0.507291, 0.915944, 0.881445, 0.338672, -0.261484, ...]
T#52(MobilenetV3large/expanded_conv_6/project/BatchNorm/FusedBatchNormV3) shape:[80], type:FLOAT32 RO 320 bytes, buffer: 53, data:[-15.6729, 13.1146, 9.85148, 15.7407, -16.4922, ...]
T#53(MobilenetV3large/expanded_conv_7/expand/BatchNorm/FusedBatchNormV3) shape:[200], type:FLOAT32 RO 800 bytes, buffer: 54, data:[-0.0180047, 0.000351542, 2.84978, 0.00512768, -0.0474478, ...]
T#54(MobilenetV3large/expanded_conv_7/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_7/depthwise/depthwise) shape:[1, 3, 3, 200], type:FLOAT32 RO 7200 bytes, buffer: 55, data:[-0.0930532, 1.35916, 0.0699976, 2.08309, -0.714721, ...]
T#55(MobilenetV3large/expanded_conv_7/project/BatchNorm/FusedBatchNormV3) shape:[80], type:FLOAT32 RO 320 bytes, buffer: 56, data:[2.75995, -0.155923, 2.06222, -4.97617, -12.3297, ...]
T#56(MobilenetV3large/expanded_conv_8/expand/BatchNorm/FusedBatchNormV3) shape:[184], type:FLOAT32 RO 736 bytes, buffer: 57, data:[1.78543, 1.23138, -0.31343, -2.65884, 2.16531, ...]
T#57(MobilenetV3large/expanded_conv_8/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_8/depthwise/depthwise;MobilenetV3large/expanded_conv_9/depthwise/depthwise) shape:[1, 3, 3, 184], type:FLOAT32 RO 6624 bytes, buffer: 58, data:[0.186774, 0.198745, -0.694211, 0.182543, -0.045065, ...]
T#58(MobilenetV3large/expanded_conv_8/project/BatchNorm/FusedBatchNormV3) shape:[80], type:FLOAT32 RO 320 bytes, buffer: 59, data:[-1.10751, -3.36157, 0.340627, 2.23085, -0.46187, ...]
T#59(MobilenetV3large/expanded_conv_9/expand/BatchNorm/FusedBatchNormV3) shape:[184], type:FLOAT32 RO 736 bytes, buffer: 60, data:[0.213268, 0.0483445, -0.11253, 0.0761342, -1.73988, ...]
T#60(MobilenetV3large/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_9/depthwise/depthwise) shape:[1, 3, 3, 184], type:FLOAT32 RO 6624 bytes, buffer: 61, data:[4.97419, -6.57637, 0.814417, 1.46725, 0.457797, ...]
T#61(MobilenetV3large/expanded_conv_9/project/BatchNorm/FusedBatchNormV3) shape:[80], type:FLOAT32 RO 320 bytes, buffer: 62, data:[-0.435609, -2.97176, 2.74412, -6.65204, 10.2386, ...]
T#62(MobilenetV3large/expanded_conv_10/expand/BatchNorm/FusedBatchNormV3) shape:[480], type:FLOAT32 RO 1920 bytes, buffer: 63, data:[2.50857, 0.0973693, -0.563608, -1.45203, 3.44066, ...]
T#63(MobilenetV3large/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_10/depthwise/depthwise;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/Conv2D) shape:[1, 3, 3, 480], type:FLOAT32 RO 17280 bytes, buffer: 64, data:[-0.0212238, 6.44594, 0.0537825, 0.22657, -0.0316337, ...]
T#64(MobilenetV3large/expanded_conv_10/project/BatchNorm/FusedBatchNormV3) shape:[112], type:FLOAT32 RO 448 bytes, buffer: 65, data:[-4.96088, -3.39939, 4.19718, 2.48631, -1.34157, ...]
T#65(MobilenetV3large/expanded_conv_11/expand/BatchNorm/FusedBatchNormV3) shape:[672], type:FLOAT32 RO 2688 bytes, buffer: 66, data:[2.41973, -1.5073, -0.00963159, -0.640254, 0.684952, ...]
T#66(MobilenetV3large/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_11/depthwise/depthwise;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D) shape:[1, 3, 3, 672], type:FLOAT32 RO 24192 bytes, buffer: 67, data:[0.0253853, 0.0641128, 1.57708, 0.0533236, -0.00350431, ...]
T#67(MobilenetV3large/expanded_conv_11/project/BatchNorm/FusedBatchNormV3) shape:[112], type:FLOAT32 RO 448 bytes, buffer: 68, data:[0.365523, 1.10257, -1.63187, 0.706468, 0.487061, ...]
T#68(MobilenetV3large/expanded_conv_12/expand/BatchNorm/FusedBatchNormV3) shape:[672], type:FLOAT32 RO 2688 bytes, buffer: 69, data:[0.573135, -0.726054, 0.0182186, -0.206486, -1.48872, ...]
T#69(MobilenetV3large/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_12/depthwise/depthwise;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D) shape:[1, 5, 5, 672], type:FLOAT32 RO 67200 bytes, buffer: 70, data:[-0.00694154, 0.0356305, -0.195693, -0.0262144, 0.114805, ...]
T#70(MobilenetV3large/expanded_conv_12/project/BatchNorm/FusedBatchNormV3) shape:[160], type:FLOAT32 RO 640 bytes, buffer: 71, data:[-3.44684, -0.768017, -0.969108, 1.23336, -2.86966, ...]
T#71(MobilenetV3large/expanded_conv_13/expand/BatchNorm/FusedBatchNormV3) shape:[960], type:FLOAT32 RO 3840 bytes, buffer: 72, data:[-0.0174746, 0.0162077, -1.22728, 0.279187, -0.554711, ...]
T#72(MobilenetV3large/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_13/depthwise/depthwise;MobilenetV3large/Conv_1/Conv2D) shape:[1, 5, 5, 960], type:FLOAT32 RO 96000 bytes, buffer: 73, data:[4.83438, -1.51938, -0.324659, -0.391306, -0.01447, ...]
T#73(MobilenetV3large/expanded_conv_13/project/BatchNorm/FusedBatchNormV3) shape:[160], type:FLOAT32 RO 640 bytes, buffer: 74, data:[1.56244, -9.28569, -6.53591, 2.84496, -5.46389, ...]
T#74(MobilenetV3large/expanded_conv_14/expand/BatchNorm/FusedBatchNormV3) shape:[960], type:FLOAT32 RO 3840 bytes, buffer: 75, data:[-0.213042, -1.64993, -1.58605, 3.29836, -0.697594, ...]
T#75(MobilenetV3large/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_14/depthwise/depthwise;MobilenetV3large/Conv_1/Conv2D) shape:[1, 5, 5, 960], type:FLOAT32 RO 96000 bytes, buffer: 76, data:[-0.152749, -0.152966, 0.23392, 0.00429554, -0.286706, ...]
T#76(MobilenetV3large/expanded_conv_14/project/BatchNorm/FusedBatchNormV3) shape:[160], type:FLOAT32 RO 640 bytes, buffer: 77, data:[-1.10576, -6.84556, -0.464385, 3.1173, -3.98359, ...]
T#77(MobilenetV3large/Conv_1/BatchNorm/FusedBatchNormV3) shape:[960], type:FLOAT32 RO 3840 bytes, buffer: 78, data:[-16.2774, -9.93764, -6.33422, -11.2148, -6.04787, ...]
T#78(MobilenetV3large/Conv_2/BiasAdd/ReadVariableOp) shape:[1280], type:FLOAT32 RO 5120 bytes, buffer: 79, data:[0.144575, 0.590702, 0.13199, 0.725449, -0.299175, ...]
T#79(MobilenetV3large/Logits/BiasAdd/ReadVariableOp) shape:[1000], type:FLOAT32 RO 4000 bytes, buffer: 80, data:[-0.073695, -0.0658332, -0.00686596, 0.0479387, 0.0198878, ...]
T#80(MobilenetV3large/Conv/Conv2D) shape:[16, 3, 3, 3], type:FLOAT32 RO 1728 bytes, buffer: 81, data:[2.35286, -1.02746, -1.03095, 3.50268, -1.58557, ...]
T#81(MobilenetV3large/expanded_conv/project/Conv2D) shape:[16, 1, 1, 16], type:FLOAT32 RO 1024 bytes, buffer: 82, data:[1.92682e-06, -1.08435e-05, -4.07661e-05, 7.29718e-05, -4.40744e-07, ...]
T#82(MobilenetV3large/expanded_conv_1/expand/Conv2D) shape:[64, 1, 1, 16], type:FLOAT32 RO 4096 bytes, buffer: 83, data:[-0.000504434, 0.00025894, -0.000541954, 0.00443714, 0.00453425, ...]
T#83(MobilenetV3large/expanded_conv_1/project/Conv2D) shape:[24, 1, 1, 64], type:FLOAT32 RO 6144 bytes, buffer: 84, data:[-0.0101949, 0.105657, -0.0099966, -0.140536, 0.0846852, ...]
T#84(MobilenetV3large/expanded_conv_2/expand/Conv2D) shape:[72, 1, 1, 24], type:FLOAT32 RO 6912 bytes, buffer: 85, data:[-0.135531, 0.0593861, -0.00241981, 0.0486889, 0.00179526, ...]
T#85(MobilenetV3large/expanded_conv_2/project/Conv2D) shape:[24, 1, 1, 72], type:FLOAT32 RO 6912 bytes, buffer: 86, data:[-0.291106, 0.053235, 0.518672, -1.19898, 0.418507, ...]
T#86(MobilenetV3large/expanded_conv_3/expand/Conv2D) shape:[72, 1, 1, 24], type:FLOAT32 RO 6912 bytes, buffer: 87, data:[0.0293405, -0.0246265, 0.0406672, -0.019213, 0.0562144, ...]
T#87(MobilenetV3large/expanded_conv_3/squeeze_excite/Conv/Conv2D) shape:[24, 1, 1, 72], type:FLOAT32 RO 6912 bytes, buffer: 88, data:[-0.00278714, -0.00356496, -0.00289361, -0.00207177, -0.000909253, ...]
T#88(MobilenetV3large/expanded_conv_3/squeeze_excite/Conv_1/Conv2D) shape:[72, 1, 1, 24], type:FLOAT32 RO 6912 bytes, buffer: 89, data:[0.00195266, 1.24883e-32, 1.24923e-32, 1.2486e-32, 0.000132618, ...]
T#89(MobilenetV3large/expanded_conv_3/project/Conv2D) shape:[40, 1, 1, 72], type:FLOAT32 RO 11520 bytes, buffer: 90, data:[0.00945878, -0.0363797, 0.0769495, 0.00295284, 0.0174633, ...]
T#90(MobilenetV3large/expanded_conv_4/expand/Conv2D) shape:[120, 1, 1, 40], type:FLOAT32 RO 19200 bytes, buffer: 91, data:[0.0915326, -0.0077282, 0.0208179, 0.013002, -0.0156502, ...]
T#91(MobilenetV3large/expanded_conv_4/squeeze_excite/Conv/Conv2D) shape:[32, 1, 1, 120], type:FLOAT32 RO 15360 bytes, buffer: 92, data:[-1.27762e-32, -1.26314e-32, -1.26755e-32, 1.25007e-32, -1.25637e-32, ...]
T#92(MobilenetV3large/expanded_conv_4/squeeze_excite/Conv_1/Conv2D) shape:[120, 1, 1, 32], type:FLOAT32 RO 15360 bytes, buffer: 93, data:[-1.24865e-32, 0.0671542, 0.0129759, 0.0901768, -1.7092e-28, ...]
T#93(MobilenetV3large/expanded_conv_4/project/Conv2D) shape:[40, 1, 1, 120], type:FLOAT32 RO 19200 bytes, buffer: 94, data:[-1.25906, 0.497161, -0.0669599, 0.622439, -0.657298, ...]
T#94(MobilenetV3large/expanded_conv_5/expand/Conv2D) shape:[120, 1, 1, 40], type:FLOAT32 RO 19200 bytes, buffer: 95, data:[0.00788043, -0.00202614, 0.0314182, 0.00642282, 0.0341095, ...]
T#95(MobilenetV3large/expanded_conv_5/squeeze_excite/Conv/Conv2D) shape:[32, 1, 1, 120], type:FLOAT32 RO 15360 bytes, buffer: 96, data:[0.0396201, 0.0105455, 0.0124103, 0.0153921, 0.0817555, ...]
T#96(MobilenetV3large/expanded_conv_5/squeeze_excite/Conv_1/Conv2D) shape:[120, 1, 1, 32], type:FLOAT32 RO 15360 bytes, buffer: 97, data:[0.0233749, 1.24947e-32, 1.26503e-32, 0.0765244, -0.00840057, ...]
T#97(MobilenetV3large/expanded_conv_5/project/Conv2D) shape:[40, 1, 1, 120], type:FLOAT32 RO 19200 bytes, buffer: 98, data:[-0.437176, 0.171119, 0.225141, -0.0630487, 1.52885, ...]
T#98(MobilenetV3large/expanded_conv_6/expand/Conv2D) shape:[240, 1, 1, 40], type:FLOAT32 RO 38400 bytes, buffer: 99, data:[-0.0238429, 0.00749496, 0.0132094, -0.0011158, 0.00737228, ...]
T#99(MobilenetV3large/expanded_conv_6/project/Conv2D) shape:[80, 1, 1, 240], type:FLOAT32 RO 76800 bytes, buffer: 100, data:[0.219832, 0.0737946, 0.457842, 0.671469, -0.385924, ...]
T#100(MobilenetV3large/expanded_conv_7/expand/Conv2D) shape:[200, 1, 1, 80], type:FLOAT32 RO 64000 bytes, buffer: 101, data:[-0.00231777, -0.000986836, 4.6371e-05, -0.00405917, -0.00202406, ...]
T#101(MobilenetV3large/expanded_conv_7/project/Conv2D) shape:[80, 1, 1, 200], type:FLOAT32 RO 64000 bytes, buffer: 102, data:[0.306236, -0.0031209, 0.0347371, -0.0932839, 0.142599, ...]
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T#192(MobilenetV3large/expanded_conv_8/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_9/depthwise/depthwise;MobilenetV3large/expanded_conv_8/depthwise/depthwise) shape_signature:[-1, -1, -1, 184], type:FLOAT32
T#193(MobilenetV3large/multiply_6/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_20/Relu6;MobilenetV3large/tf.__operators__.add_9/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_9/Mul) shape_signature:[-1, -1, -1, 184], type:FLOAT32
T#194(MobilenetV3large/expanded_conv_8/project/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_9/project/Conv2D;MobilenetV3large/expanded_conv_8/project/Conv2D) shape_signature:[-1, -1, -1, 80], type:FLOAT32
T#195(MobilenetV3large/expanded_conv_8/Add/add) shape_signature:[-1, -1, -1, 80], type:FLOAT32
T#196(MobilenetV3large/expanded_conv_9/expand/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_9/depthwise/depthwise;MobilenetV3large/expanded_conv_9/expand/Conv2D) shape_signature:[-1, -1, -1, 184], type:FLOAT32
T#197(MobilenetV3large/multiply_7/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_21/Relu6;MobilenetV3large/tf.__operators__.add_10/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_10/Mul) shape_signature:[-1, -1, -1, 184], type:FLOAT32
T#198(MobilenetV3large/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_9/depthwise/depthwise1) shape_signature:[-1, -1, -1, 184], type:FLOAT32
T#199(MobilenetV3large/multiply_8/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_22/Relu6;MobilenetV3large/tf.__operators__.add_11/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_11/Mul) shape_signature:[-1, -1, -1, 184], type:FLOAT32
T#200(MobilenetV3large/expanded_conv_9/project/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_9/project/Conv2D) shape_signature:[-1, -1, -1, 80], type:FLOAT32
T#201(MobilenetV3large/expanded_conv_9/Add/add) shape_signature:[-1, -1, -1, 80], type:FLOAT32
T#202(MobilenetV3large/expanded_conv_10/expand/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/expanded_conv_10/expand/Conv2D) shape_signature:[-1, -1, -1, 480], type:FLOAT32
T#203(MobilenetV3large/multiply_9/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_23/Relu6;MobilenetV3large/tf.__operators__.add_12/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_12/Mul) shape_signature:[-1, -1, -1, 480], type:FLOAT32
T#204(MobilenetV3large/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_10/depthwise/depthwise;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/Conv2D1) shape_signature:[-1, -1, -1, 480], type:FLOAT32
T#205(MobilenetV3large/multiply_10/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_24/Relu6;MobilenetV3large/tf.__operators__.add_13/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_13/Mul) shape_signature:[-1, -1, -1, 480], type:FLOAT32
T#206(MobilenetV3large/expanded_conv_10/squeeze_excite/AvgPool/Mean) shape_signature:[-1, 1, 1, 480], type:FLOAT32
T#207(MobilenetV3large/expanded_conv_10/squeeze_excite/Relu/Relu;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/BiasAdd;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape_signature:[-1, 1, 1, 120], type:FLOAT32
T#208(MobilenetV3large/re_lu_25/Relu6;MobilenetV3large/tf.__operators__.add_14/AddV2;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_10/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y1) shape_signature:[-1, 1, 1, 480], type:FLOAT32
T#209(MobilenetV3large/tf.math.multiply_14/Mul) shape_signature:[-1, 1, 1, 480], type:FLOAT32
T#210(MobilenetV3large/expanded_conv_10/squeeze_excite/Mul/mul) shape_signature:[-1, -1, -1, 480], type:FLOAT32
T#211(MobilenetV3large/expanded_conv_10/project/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_11/project/Conv2D;MobilenetV3large/expanded_conv_10/project/Conv2D) shape_signature:[-1, -1, -1, 112], type:FLOAT32
T#212(MobilenetV3large/expanded_conv_11/expand/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/expanded_conv_11/expand/Conv2D) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#213(MobilenetV3large/multiply_11/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_26/Relu6;MobilenetV3large/tf.__operators__.add_15/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_15/Mul) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#214(MobilenetV3large/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/expanded_conv_11/depthwise/depthwise) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#215(MobilenetV3large/multiply_12/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_27/Relu6;MobilenetV3large/tf.__operators__.add_16/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_16/Mul) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#216(MobilenetV3large/expanded_conv_11/squeeze_excite/AvgPool/Mean) shape_signature:[-1, 1, 1, 672], type:FLOAT32
T#217(MobilenetV3large/expanded_conv_11/squeeze_excite/Relu/Relu;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv/BiasAdd;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape_signature:[-1, 1, 1, 168], type:FLOAT32
T#218(MobilenetV3large/re_lu_28/Relu6;MobilenetV3large/tf.__operators__.add_17/AddV2;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/expanded_conv_11/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y1) shape_signature:[-1, 1, 1, 672], type:FLOAT32
T#219(MobilenetV3large/tf.math.multiply_17/Mul) shape_signature:[-1, 1, 1, 672], type:FLOAT32
T#220(MobilenetV3large/expanded_conv_11/squeeze_excite/Mul/mul) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#221(MobilenetV3large/expanded_conv_11/project/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_11/project/Conv2D) shape_signature:[-1, -1, -1, 112], type:FLOAT32
T#222(MobilenetV3large/expanded_conv_11/Add/add) shape_signature:[-1, -1, -1, 112], type:FLOAT32
T#223(MobilenetV3large/expanded_conv_12/expand/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/expanded_conv_12/expand/Conv2D) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#224(MobilenetV3large/multiply_13/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_29/Relu6;MobilenetV3large/tf.__operators__.add_18/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_18/Mul) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#225(MobilenetV3large/expanded_conv_12/depthwise/pad/Pad) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#226(MobilenetV3large/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_12/depthwise/depthwise;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D1) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#227(MobilenetV3large/multiply_14/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_30/Relu6;MobilenetV3large/tf.__operators__.add_19/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_19/Mul) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#228(MobilenetV3large/expanded_conv_12/squeeze_excite/AvgPool/Mean) shape_signature:[-1, 1, 1, 672], type:FLOAT32
T#229(MobilenetV3large/expanded_conv_12/squeeze_excite/Relu/Relu;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv/BiasAdd;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape_signature:[-1, 1, 1, 168], type:FLOAT32
T#230(MobilenetV3large/re_lu_31/Relu6;MobilenetV3large/tf.__operators__.add_20/AddV2;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/expanded_conv_12/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y1) shape_signature:[-1, 1, 1, 672], type:FLOAT32
T#231(MobilenetV3large/tf.math.multiply_20/Mul) shape_signature:[-1, 1, 1, 672], type:FLOAT32
T#232(MobilenetV3large/expanded_conv_12/squeeze_excite/Mul/mul) shape_signature:[-1, -1, -1, 672], type:FLOAT32
T#233(MobilenetV3large/expanded_conv_12/project/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_14/project/Conv2D;MobilenetV3large/expanded_conv_12/project/Conv2D) shape_signature:[-1, -1, -1, 160], type:FLOAT32
T#234(MobilenetV3large/expanded_conv_13/expand/BatchNorm/FusedBatchNormV3;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_13/expand/Conv2D) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#235(MobilenetV3large/multiply_15/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_32/Relu6;MobilenetV3large/tf.__operators__.add_21/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_21/Mul) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#236(MobilenetV3large/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_13/depthwise/depthwise) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#237(MobilenetV3large/multiply_16/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_33/Relu6;MobilenetV3large/tf.__operators__.add_22/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_22/Mul) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#238(MobilenetV3large/expanded_conv_13/squeeze_excite/AvgPool/Mean) shape_signature:[-1, 1, 1, 960], type:FLOAT32
T#239(MobilenetV3large/expanded_conv_13/squeeze_excite/Relu/Relu;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv/BiasAdd;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape_signature:[-1, 1, 1, 240], type:FLOAT32
T#240(MobilenetV3large/re_lu_34/Relu6;MobilenetV3large/tf.__operators__.add_23/AddV2;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_13/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y1) shape_signature:[-1, 1, 1, 960], type:FLOAT32
T#241(MobilenetV3large/tf.math.multiply_23/Mul) shape_signature:[-1, 1, 1, 960], type:FLOAT32
T#242(MobilenetV3large/expanded_conv_13/squeeze_excite/Mul/mul) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#243(MobilenetV3large/expanded_conv_13/project/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_14/project/Conv2D;MobilenetV3large/expanded_conv_13/project/Conv2D) shape_signature:[-1, -1, -1, 160], type:FLOAT32
T#244(MobilenetV3large/expanded_conv_13/Add/add) shape_signature:[-1, -1, -1, 160], type:FLOAT32
T#245(MobilenetV3large/expanded_conv_14/expand/BatchNorm/FusedBatchNormV3;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_14/expand/Conv2D) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#246(MobilenetV3large/multiply_17/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_35/Relu6;MobilenetV3large/tf.__operators__.add_24/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_24/Mul) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#247(MobilenetV3large/expanded_conv_14/depthwise/BatchNorm/FusedBatchNormV3;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_14/depthwise/depthwise) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#248(MobilenetV3large/multiply_18/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_36/Relu6;MobilenetV3large/tf.__operators__.add_25/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_25/Mul) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#249(MobilenetV3large/expanded_conv_14/squeeze_excite/AvgPool/Mean) shape_signature:[-1, 1, 1, 960], type:FLOAT32
T#250(MobilenetV3large/expanded_conv_14/squeeze_excite/Relu/Relu;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv/BiasAdd;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv/Conv2D;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape_signature:[-1, 1, 1, 240], type:FLOAT32
T#251(MobilenetV3large/re_lu_37/Relu6;MobilenetV3large/tf.__operators__.add_26/AddV2;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv_1/BiasAdd/ReadVariableOp;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv_1/BiasAdd;MobilenetV3large/Conv_1/Conv2D;MobilenetV3large/expanded_conv_14/squeeze_excite/Conv_1/Conv2D;MobilenetV3large/tf.__operators__.add/y1) shape_signature:[-1, 1, 1, 960], type:FLOAT32
T#252(MobilenetV3large/tf.math.multiply_26/Mul) shape_signature:[-1, 1, 1, 960], type:FLOAT32
T#253(MobilenetV3large/expanded_conv_14/squeeze_excite/Mul/mul) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#254(MobilenetV3large/expanded_conv_14/project/BatchNorm/FusedBatchNormV3;MobilenetV3large/expanded_conv_14/project/Conv2D) shape_signature:[-1, -1, -1, 160], type:FLOAT32
T#255(MobilenetV3large/expanded_conv_14/Add/add) shape_signature:[-1, -1, -1, 160], type:FLOAT32
T#256(MobilenetV3large/Conv_1/BatchNorm/FusedBatchNormV3;MobilenetV3large/Conv_1/Conv2D) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#257(MobilenetV3large/multiply_19/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_38/Relu6;MobilenetV3large/tf.__operators__.add_27/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_27/Mul) shape_signature:[-1, -1, -1, 960], type:FLOAT32
T#258(MobilenetV3large/global_average_pooling2d/Mean) shape_signature:[-1, 1, 1, 960], type:FLOAT32
T#259(MobilenetV3large/Conv_2/BiasAdd;MobilenetV3large/Conv_2/Conv2D;MobilenetV3large/Conv_2/BiasAdd/ReadVariableOp) shape_signature:[-1, 1, 1, 1280], type:FLOAT32
T#260(MobilenetV3large/multiply_20/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_39/Relu6;MobilenetV3large/tf.__operators__.add_28/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_28/Mul) shape_signature:[-1, 1, 1, 1280], type:FLOAT32
T#261(MobilenetV3large/Logits/BiasAdd;MobilenetV3large/Logits/Conv2D;MobilenetV3large/Logits/BiasAdd/ReadVariableOp) shape_signature:[-1, 1, 1, 1000], type:FLOAT32
T#262(MobilenetV3large/flatten_1/Reshape) shape_signature:[-1, 1000], type:FLOAT32
T#263(StatefulPartitionedCall:0) shape_signature:[-1, 1000], type:FLOAT32
---------------------------------------------------------------
Your TFLite model has '1' signature_def(s).
Signature#0 key: 'serving_default'
- Subgraph: Subgraph#0
- Inputs:
'input_1' : T#0
- Outputs:
'Predictions' : T#263
---------------------------------------------------------------
Model size: 21944020 bytes
Non-data buffer size: 60500 bytes (00.28 %)
Total data buffer size: 21883520 bytes (99.72 %)
(Zero value buffers): 0 bytes (00.00 %)
* Buffers of TFLite model are mostly used for constant tensors.
And zero value buffers are buffers filled with zeros.
Non-data buffers area are used to store operators, subgraphs and etc.
You can find more details from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs
GPU 대리자 호환성 확인
ModelAnalyzer API는 gpu_compatibility=True 옵션을 제공하여 주어진 모델의 GPU 대리자 호환성을 확인하는 방법을 제공합니다.
사례 1: 모델이 호환되지 않는 경우
다음 코드는 GPU 대리자와 호환되지 않는 2D 텐서 및 tf.slice와 함께 tf.cosh를 사용하는 간단한 tf.function에 대해 gpu_compatibility=True 옵션을 사용하는 방법을 보여줍니다.
호환성 문제가 있는 모든 노드마다 GPU COMPATIBILITY WARNING가 표시됩니다.
import tensorflow as tf
@tf.function(input_signature=[
tf.TensorSpec(shape=[4, 4], dtype=tf.float32)
])
def func(x):
return tf.cosh(x) + tf.slice(x, [1, 1], [1, 1])
converter = tf.lite.TFLiteConverter.from_concrete_functions(
[func.get_concrete_function()], func)
converter.target_spec.supported_ops = [
tf.lite.OpsSet.TFLITE_BUILTINS,
tf.lite.OpsSet.SELECT_TF_OPS,
]
fb_model = converter.convert()
tf.lite.experimental.Analyzer.analyze(model_content=fb_model, gpu_compatibility=True)
=== TFLite ModelAnalyzer ===
Your TFLite model has '1' subgraph(s). In the subgraph description below,
T# represents the Tensor numbers. For example, in Subgraph#0, the FlexCosh op takes
tensor #0 as input and produces tensor #2 as output.
Subgraph#0 main(T#0) -> [T#4]
Op#0 FlexCosh(T#0) -> [T#2]
GPU COMPATIBILITY WARNING: Not supported custom op FlexCosh
Op#1 SLICE(T#0, T#1[1, 1], T#1[1, 1]) -> [T#3]
GPU COMPATIBILITY WARNING: SLICE supports for 3 or 4 dimensional tensors only, but node has 2 dimensional tensors.
Op#2 ADD(T#2, T#3) -> [T#4]
GPU COMPATIBILITY WARNING: Subgraph#0 has GPU delegate compatibility issues at nodes 0, 1 with TFLite runtime version 2.11.0
Tensors of Subgraph#0
T#0(x) shape:[4, 4], type:FLOAT32
T#1(Slice/begin) shape:[2], type:INT32 RO 8 bytes, buffer: 2, data:[1, 1]
T#2(Cosh) shape:[4, 4], type:FLOAT32
T#3(Slice) shape:[1, 1], type:FLOAT32
T#4(Identity) shape:[4, 4], type:FLOAT32
---------------------------------------------------------------
Model size: 1124 bytes
Non-data buffer size: 1008 bytes (89.68 %)
Total data buffer size: 116 bytes (10.32 %)
(Zero value buffers): 0 bytes (00.00 %)
* Buffers of TFLite model are mostly used for constant tensors.
And zero value buffers are buffers filled with zeros.
Non-data buffers area are used to store operators, subgraphs and etc.
You can find more details from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs
2022-12-15 01:11:03.880058: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-12-15 01:11:03.880096: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
2022-12-15 01:11:03.896718: W tensorflow/compiler/mlir/lite/flatbuffer_export.cc:2046] TFLite interpreter needs to link Flex delegate in order to run the model since it contains the following Select TFop(s):
Flex ops: FlexCosh
Details:
tf.Cosh(tensor<4x4xf32>) -> (tensor<4x4xf32>) : {device = ""}
See instructions: https://www.tensorflow.org/lite/guide/ops_select
사례 2: 모델이 호환되는 경우
이 예에서 주어진 모델은 GPU 대리자와 호환됩니다.
참고: 도구가 호환성 문제를 찾지 못하더라도 모델이 모든 장치에서 GPU 대리자와 잘 작동한다는 보장은 없습니다. 대상 OpenGL 백엔드에서 CL_DEVICE_IMAGE_SUPPORT 요소 누락과 같은 런타임 비호환성이 발생할 수 있습니다.
model = tf.keras.models.Sequential([
tf.keras.layers.Flatten(input_shape=(128, 128)),
tf.keras.layers.Dense(256, activation='relu'),
tf.keras.layers.Dropout(0.2),
tf.keras.layers.Dense(10)
])
fb_model = tf.lite.TFLiteConverter.from_keras_model(model).convert()
tf.lite.experimental.Analyzer.analyze(model_content=fb_model, gpu_compatibility=True)
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp72m3ikyw/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp72m3ikyw/assets
=== TFLite ModelAnalyzer ===
Your TFLite model has '1' subgraph(s). In the subgraph description below,
T# represents the Tensor numbers. For example, in Subgraph#0, the RESHAPE op takes
tensor #0 and tensor #1 as input and produces tensor #4 as output.
Subgraph#0 main(T#0) -> [T#6]
Op#0 RESHAPE(T#0, T#1[-1, 16384]) -> [T#4]
Op#1 FULLY_CONNECTED(T#4, T#2, T#-1) -> [T#5]
Op#2 FULLY_CONNECTED(T#5, T#3, T#-1) -> [T#6]
Tensors of Subgraph#0
T#0(serving_default_flatten_2_input:0) shape_signature:[-1, 128, 128], type:FLOAT32
T#1(sequential_1/flatten_2/Const) shape:[2], type:INT32 RO 8 bytes, buffer: 2, data:[-1, 16384]
T#2(sequential_1/dense_2/MatMul1) shape:[256, 16384], type:FLOAT32 RO 16777216 bytes, buffer: 3, data:[-0.00593336, -0.0180754, 0.00914702, 0.00351369, -0.015456, ...]
T#3(sequential_1/dense_3/MatMul) shape:[10, 256], type:FLOAT32 RO 10240 bytes, buffer: 4, data:[-0.0970062, -0.057773, 0.1411, 0.119214, -0.0340087, ...]
T#4(sequential_1/flatten_2/Reshape) shape_signature:[-1, 16384], type:FLOAT32
T#5(sequential_1/dense_2/MatMul;sequential_1/dense_2/Relu;sequential_1/dense_2/BiasAdd) shape_signature:[-1, 256], type:FLOAT32
T#6(StatefulPartitionedCall:0) shape_signature:[-1, 10], type:FLOAT32
Your model looks compatible with GPU delegate with TFLite runtime version 2.11.0.
But it doesn't guarantee that your model works well with GPU delegate.
There could be some runtime incompatibililty happen.
---------------------------------------------------------------
Your TFLite model has '1' signature_def(s).
Signature#0 key: 'serving_default'
- Subgraph: Subgraph#0
- Inputs:
'flatten_2_input' : T#0
- Outputs:
'dense_3' : T#6
---------------------------------------------------------------
Model size: 16789068 bytes
Non-data buffer size: 1504 bytes (00.01 %)
Total data buffer size: 16787564 bytes (99.99 %)
(Zero value buffers): 0 bytes (00.00 %)
* Buffers of TFLite model are mostly used for constant tensors.
And zero value buffers are buffers filled with zeros.
Non-data buffers area are used to store operators, subgraphs and etc.
You can find more details from https://github.com/tensorflow/tensorflow/blob/master/tensorflow/lite/schema/schema.fbs
2022-12-15 01:11:04.612193: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-12-15 01:11:04.612233: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.