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TensorFlow Lite Model Analyzer API helps you analyze models in TensorFlow Lite format by listing a model's structure.
Model Analyzer API
The following API is available for the TensorFlow Lite Model Analyzer.
tf.lite.experimental.Analyzer.analyze(model_path=None,
model_content=None,
gpu_compatibility=False)
You can find the API details from https://www.tensorflow.org/api_docs/python/tf/lite/experimental/Analyzer or run help(tf.lite.experimental.Analyzer.analyze) from a Python terminal.
Basic usage with simple Keras model
The following code shows basic usage of Model Analyzer. It shows contents of the converted Keras model in TFLite model content, formatted as a flatbuffer object.
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)
2022-10-20 13:42:14.949228: 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
2022-10-20 13:42:14.949325: 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
2022-10-20 13:42:14.949335: 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/tmpfm9il5ks/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:[-0.0086278, -0.00479643, -0.00869051, 0.014341, -0.00724641, ...]
T#3(sequential/dense_1/MatMul) shape:[10, 256], type:FLOAT32 RO 10240 bytes, buffer: 4, data:[0.0534109, 0.000120163, 0.124163, -0.0802587, 0.0346059, ...]
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
2022-10-20 13:42:20.320758: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-10-20 13:42:20.320800: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
Basic usage with MobileNetV3Large Keras model
This API works with large models such as MobileNetV3Large. Since the output is large, you might want to browse it with your favorite text editor.
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/tmpwcib_ubs/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpwcib_ubs/assets
2022-10-20 13:42:50.365759: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-10-20 13:42:50.365810: 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, ...]
T#102(MobilenetV3large/expanded_conv_8/expand/Conv2D) shape:[184, 1, 1, 80], type:FLOAT32 RO 58880 bytes, buffer: 103, data:[-0.000389813, -0.0035757, -0.000356501, -0.00755378, 0.0180794, ...]
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T#191(MobilenetV3large/multiply_5/mul;MobilenetV3large/tf.__operators__.add/y;MobilenetV3large/re_lu_19/Relu6;MobilenetV3large/tf.__operators__.add_8/AddV2;MobilenetV3large/tf.math.multiply/Mul/y;MobilenetV3large/tf.math.multiply_8/Mul) shape_signature:[-1, -1, -1, 184], type:FLOAT32
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: 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
Check GPU delegate compatibility
The ModelAnalyzer API provides a way to check the GPU delegate compatibility of the given model by providing gpu_compatibility=True option.
Case 1: When model is incompatibile
The following code shows a way to use gpu_compatibility=True option for simple tf.function which uses tf.slice with a 2D tensor and tf.cosh which are not compatible with GPU delegate.
You will see GPU COMPATIBILITY WARNING per every node which has compatibility issue(s).
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-rc1
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: 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
2022-10-20 13:42:52.829127: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-10-20 13:42:52.829177: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.
2022-10-20 13:42:52.846507: 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
Case 2: When model is compatibile
In this example, the given model is compatbile with GPU delegate.
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/tmpu9jwiyob/assets
INFO:tensorflow:Assets written to: /tmpfs/tmp/tmpu9jwiyob/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:[-0.0014805, -0.0166984, 0.00713065, 0.013145, 0.0179128, ...]
T#3(sequential_1/dense_3/MatMul) shape:[10, 256], type:FLOAT32 RO 10240 bytes, buffer: 4, data:[0.0867367, -0.0645691, -0.0314855, 0.0317347, -0.112443, ...]
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-rc1.
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
2022-10-20 13:42:53.569009: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:362] Ignored output_format.
2022-10-20 13:42:53.569050: W tensorflow/compiler/mlir/lite/python/tf_tfl_flatbuffer_helpers.cc:365] Ignored drop_control_dependency.