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TensorFlow Lite Model Analyzer API 能够通过列出模型的结构，帮助您分析 TensorFlow Lite 格式的模型。
Model Analyzer API
以下 API 可用于 TensorFlow Lite Model Analyzer。
tf.lite.experimental.Analyzer.analyze(model_path=None,
model_content=None,
gpu_compatibility=False)
您可以在 https://tensorflow.google.cn/api_docs/python/tf/lite/experimental/Analyzer 查看 API 详细信息，也可以在 Python 终端运行 help(tf.lite.experimental.Analyzer.analyze)。
简单 Keras 模型的基本用法
以下代码显示了 Model Analyzer 的基本用法。它在 TFLite 模型内容中显示转换后的 Keras 模型的内容，格式化为平面缓冲区对象。
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)
2022-08-11 19:22:44.889506: E tensorflow/stream_executor/cuda/cuda_blas.cc:2981] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered
2022-08-11 19:22:45.711168: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory
2022-08-11 19:22:45.711456: W tensorflow/stream_executor/platform/default/dso_loader.cc:64] Could not load dynamic library 'libnvinfer_plugin.so.7'; dlerror: libnvrtc.so.11.1: cannot open shared object file: No such file or directory
2022-08-11 19:22:45.711470: 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/tmp50gum7h1/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, data:[-1, 16384]
T#2(sequential/dense/MatMul1) shape:[256, 16384], type:FLOAT32 RO 16777216 bytes, data:[]
T#3(sequential/dense_1/MatMul) shape:[10, 256], type:FLOAT32 RO 10240 bytes, data:[]
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: 16789044 bytes
Non-data buffer size: 1476 bytes (00.01 %)
Total data buffer size: 16787568 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
2022-08-11 19:22:51.163111: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 19:22:51.163156: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
MobileNetV3Large Keras 模型的基本用法
此 API 适用于 MobileNetV3Large 等大型模型。由于输出很大，您可能希望使用您最喜欢的文本编辑器来浏览它。
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/tmpmq1hheb9/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpmq1hheb9/assets
2022-08-11 19:23:27.887684: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 19:23:27.887733: 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, data:[]
T#2(MobilenetV3large/expanded_conv_1/expand/BatchNorm/FusedBatchNormV3) shape:[64], type:FLOAT32 RO 256 bytes, data:[]
T#3(MobilenetV3large/expanded_conv_1/depthwise/BatchNorm/FusedBatchNormV3) shape:[64], type:FLOAT32 RO 256 bytes, data:[]
T#4(MobilenetV3large/expanded_conv_2/expand/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, data:[]
T#5(MobilenetV3large/expanded_conv_2/depthwise/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, data:[]
T#6(MobilenetV3large/expanded_conv_3/expand/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, data:[]
T#7(MobilenetV3large/expanded_conv_3/depthwise/BatchNorm/FusedBatchNormV3) shape:[72], type:FLOAT32 RO 288 bytes, data:[]
T#8(MobilenetV3large/expanded_conv_3/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[24], type:FLOAT32 RO 96 bytes, data:[]
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, data:[]
T#10(MobilenetV3large/expanded_conv_4/expand/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, data:[]
T#11(MobilenetV3large/expanded_conv_4/depthwise/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, data:[]
T#12(MobilenetV3large/expanded_conv_4/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[32], type:FLOAT32 RO 128 bytes, data:[]
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, data:[]
T#14(MobilenetV3large/expanded_conv_5/expand/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, data:[]
T#15(MobilenetV3large/expanded_conv_5/depthwise/BatchNorm/FusedBatchNormV3) shape:[120], type:FLOAT32 RO 480 bytes, data:[]
T#16(MobilenetV3large/expanded_conv_5/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[32], type:FLOAT32 RO 128 bytes, data:[]
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, data:[]
T#18(MobilenetV3large/expanded_conv_6/depthwise/BatchNorm/FusedBatchNormV3) shape:[240], type:FLOAT32 RO 960 bytes, data:[]
T#19(MobilenetV3large/expanded_conv_7/depthwise/BatchNorm/FusedBatchNormV3) shape:[200], type:FLOAT32 RO 800 bytes, data:[]
T#20(MobilenetV3large/expanded_conv_8/depthwise/BatchNorm/FusedBatchNormV3) shape:[184], type:FLOAT32 RO 736 bytes, data:[]
T#21(MobilenetV3large/expanded_conv_9/depthwise/BatchNorm/FusedBatchNormV3) shape:[184], type:FLOAT32 RO 736 bytes, data:[]
T#22(MobilenetV3large/expanded_conv_10/depthwise/BatchNorm/FusedBatchNormV3) shape:[480], type:FLOAT32 RO 1920 bytes, data:[]
T#23(MobilenetV3large/expanded_conv_10/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[120], type:FLOAT32 RO 480 bytes, data:[]
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, data:[]
T#25(MobilenetV3large/expanded_conv_11/depthwise/BatchNorm/FusedBatchNormV3) shape:[672], type:FLOAT32 RO 2688 bytes, data:[]
T#26(MobilenetV3large/expanded_conv_11/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[168], type:FLOAT32 RO 672 bytes, data:[]
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, data:[]
T#28(MobilenetV3large/expanded_conv_12/depthwise/BatchNorm/FusedBatchNormV3) shape:[672], type:FLOAT32 RO 2688 bytes, data:[]
T#29(MobilenetV3large/expanded_conv_12/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[168], type:FLOAT32 RO 672 bytes, data:[]
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, data:[]
T#31(MobilenetV3large/expanded_conv_13/depthwise/BatchNorm/FusedBatchNormV3) shape:[960], type:FLOAT32 RO 3840 bytes, data:[]
T#32(MobilenetV3large/expanded_conv_13/squeeze_excite/Conv/BiasAdd/ReadVariableOp) shape:[240], type:FLOAT32 RO 960 bytes, data:[]
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, data:[]
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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: 21944024 bytes
Non-data buffer size: 60500 bytes (00.28 %)
Total data buffer size: 21883524 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
检查 GPU 委托兼容性
Model Analyzer API 通过提供 gpu_compatibility=True 选项，提供了一种检查给定模型的 GPU 委托兼容性的方法。
第 1 种情况：当模型不兼容时
以下代码展示了将 gpu_compatibility=True 选项用于简单 tf.function 的方式，该函数使用与 GPU 委托不兼容的带有二维张量的 tf.slice 和 tf.cosh。
对于每个存在兼容性问题的节点，您将看到 GPU COMPATIBILITY WARNING。
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 2.10.0-rc0
Tensors of Subgraph#0
T#0(x) shape:[4, 4], type:FLOAT32
T#1(Slice/begin) shape:[2], type:INT32 RO 8 bytes, 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: 1128 bytes
Non-data buffer size: 1008 bytes (89.36 %)
Total data buffer size: 120 bytes (10.64 %)
(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
2022-08-11 19:23:30.270593: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 19:23:30.270630: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
2022-08-11 19:23:30.290075: W tensorflow/compiler/mlir/lite/flatbuffer_export.cc:1918] 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
第 2 种情况：当模型兼容时
在本示例中，给定的模型与 GPU 委托兼容。
注：即使该工具没有发现任何兼容性问题，它也不能保证您的模型在每台设备上都能很好地使用 GPU 委托。可能会发生一些运行时不兼容的情况，例如目标 OpenGL 后端缺少 CL_DEVICE_IMAGE_SUPPORT 功能。
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/tmp9h0p_lcj/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmp9h0p_lcj/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, data:[-1, 16384]
T#2(sequential_1/dense_2/MatMul1) shape:[256, 16384], type:FLOAT32 RO 16777216 bytes, data:[]
T#3(sequential_1/dense_3/MatMul) shape:[10, 256], type:FLOAT32 RO 10240 bytes, data:[]
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 compatibile with GPU delegate with TFLite runtime version 2.10.0-rc0.
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: 16789072 bytes
Non-data buffer size: 1504 bytes (00.01 %)
Total data buffer size: 16787568 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
2022-08-11 19:23:31.163988: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-08-11 19:23:31.164046: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.