# Accuracy Debugger (Experimental)

**Dependencies**

The Accuracy Debugger depends on the setup outlined in [Setup](https://docs.qualcomm.com/doc/80-63442-2/topic/setup.html).
In particular, the following are required:

> 
> 
> 1. Platform dependencies are need to be met as per Platform Dependencies
> 2. The desired ML frameworks need to be installed. Accuracy debugger is verified to work with the ML framework versions mentioned at Environment Setup

The following environment variables are used inside this guide (User may change the following paths depending on their needs):

> 
> 
> 1. RESOURCESPATH = {Path to the directory where all models and input files reside}
> 2. PROJECTREPOPATH = {Path to your accuracy debugger project directory}

**Supported models**

The snpe-accuracy-debugger currently supports ONNX, TFLite, and Tensorflow 1.x models.

**Overview**

The **accuracy-debugger** tool finds inaccuracies in a neural-network at the layer level. The tool compares the golden outputs produced by running a model through a specific ML framework (ie. Tensorflow, Onnx, TFlite) with the results produced by running the same model through Qualcomm’s SNPE Inference Engine. The inference engine can be run on a variety of computing mediums including GPU, CPU and DSP.

The following features are available in Accuracy Debugger. Each feature can be run with its corresponding option; for example, `snpe-accuracy-debugger --{option}`.

> 
> 
> 1. **snpe-accuracy-debugger –framework\_runner** This feature uses frameworks ie. tensorflow, tflite and onnx to run the model to get intermediate outputs. Note: The argument –framewok\_diagnosis has been replaced by –framework\_runner. –framework\_diagnosis will be deprecated in the future release.
> 2. **snpe-accuracy-debugger –inference\_engine** This feature uses SNPE engine to run the models to get intermediate outputs.
> 3. **snpe-accuracy-debugger –verification** This feature compares the output generated by framework runner and inference engine features using the verifiers like CosineSimilarity, RtolAtol, etc.
> 4. **snpe-accuracy-debugger –compare\_encodings** This feature extracts encodings from a given SNPE DLC, compares them with the given AIMET encodings, and outputs an Excel sheet highlighting mismatches.
> 5. **snpe-accuracy-debugger –tensor\_inspection** This feature compares given target outputs with reference outputs.
> 6. **snpe-accuracy-debugger –quant\_checker** This feature is used to analyze the activations, weights and biases of all the possible quantization options available in the snpe-converters for each subsequent layer of a single model file or a directory of a model.

- Tip:
    - - You can use –help after the bin commands to see what other options (required or optional) you can add.
- If no option is provided, Accuracy Debugger runs framework\_runner, inference\_engine, and verification sequentially.

Below are the instructons for running the Accuracy Debugger

**Framework Runner**

The Framework Runner feature is designed to run models with different machine learning frameworks (i.e. Tensorflow, etc). A selected model is run with a specific ML framework. Golden outputs are produced for future comparison with inference results from the Inference Engine step.

**Usage**

usage: snpe-accuracy-debugger --framework_runner [-h]
                                        -f FRAMEWORK [FRAMEWORK ...]
                                        -m MODEL_PATH
                                        -i INPUT_TENSOR [INPUT_TENSOR ...]
                                        -o OUTPUT_TENSOR
                                        [-w WORKING_DIR]
                                        [--output_dirname OUTPUT_DIRNAME]
                                        [-v]
    
    Script to generate intermediate tensors from a ML Framework.
    
    optional arguments:
         -h, --help            show this help message and exit
    
    required arguments:
         -f FRAMEWORK [FRAMEWORK ...], --framework FRAMEWORK [FRAMEWORK ...]
                            Framework type and version, version is optional. Currently
                            supported frameworks are ["tensorflow","onnx","tflite"] case
                            insensitive but spelling sensitive
         -m MODEL_PATH, --model_path MODEL_PATH
                                 Path to the model file(s).
         -i INPUT_TENSOR [INPUT_TENSOR ...], --input_tensor INPUT_TENSOR [INPUT_TENSOR ...]
                                 The name, dimensions, raw data, and optionally data
                                 type of the network input tensor(s) specifiedin the
                                 format "input_name" comma-separated-dimensions path-
                                 to-raw-file, for example: "data" 1,224,224,3 data.raw
                                 float32. Note that the quotes should always be
                                 included in order to handle special characters,
                                 spaces, etc. For multiple inputs specify multiple
                                 --input_tensor on the command line like:
                                 --input_tensor "data1" 1,224,224,3 data1.raw
                                 --input_tensor "data2" 1,50,100,3 data2.raw float32.
         -o OUTPUT_TENSOR, --output_tensor OUTPUT_TENSOR
                                 Name of the graph's specified output tensor(s).
         optional arguments:
         -w WORKING_DIR, --working_dir WORKING_DIR
                                 Working directory for the framework_runner to store
                                 temporary files. Creates a new directory if the
                                 specified working directory does not exist
         --output_dirname OUTPUT_DIRNAME
                                 output directory name for the framework_runner to
                                 store temporary files under
                                 <working_dir>/framework_runner. Creates a new
                                 directory if the specified working directory does not
                                 exist
         -v, --verbose         Verbose printing
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Please note: All the command line arguments should either be provided through command line or through the config file. They will not override those in the config file if there is overlap.

**Sample Commands**

snpe-accuracy-debugger \
        --framework_runner \
        --framework tensorflow \
        --model_path $RESOURCESPATH/samples/InceptionV3Model/inception_v3_2016_08_28_frozen.pb \
        --input_tensor "input:0" 1,299,299,3 $RESOURCESPATH/samples/InceptionV3Model/data/chairs.raw \
        --output_tensor InceptionV3/Predictions/Reshape_1:0
    
    snpe-accuracy-debugger \
        --framework_runner \
        --framework onnx \
        --model_path $RESOURCESPATH/samples/dlv3onnx/dlv3plus_mbnet_513-513_op9_mod_basic.onnx \
        --input_tensor Input 1,3,513,513 $RESOURCESPATH/samples/dlv3onnx/data/00000_1_3_513_513.raw \
        --output_tensor Output
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TIP:

- a working\_directory, if not otherwise specified, is generated wherever you are calling the script from; it is recommended to call all scripts from the same directory so all your outputs and results are stored under the same directory without having outputs everywhere.
- for tensorflow it is sometimes necessary to add the :0 after the input and output node name to signify the index of the node. Notice that the :0 is dropped for onnx models.

**Output**

The program also creates a folder named latest found in working\_directory/framework\_runner which is symbolic linked to the most recently generated directory. In the example below, latest will have data that is symbol linked to the data in the most recent directory YYYY-MM-DD\_HH:mm:ss. User may choose to override the directory name by passing it to –output\_dirname (i.e. –output\_dirname myTest1Ouput).

The *float data* produced by the **Framework Runner** step offers precise reference material for the **Verification** component to diagnose the accuracy of the network generated by the **Inference Engine**.
Unless a path is otherwise specified, the Accuracy Debugger will create directories within the <cite>working_directory/framework_runner</cite> directory found in the current working directory. The directories will be named with the date and time of the program’s execution, and contain tensor data. Depending on the tensor naming convention of the model, there may be numerous sub-directories within the new directory. This occurs when tensor names include a slash “/”. For example, for the tensor names ‘inception\_3a/1x1/bn/sc’, ‘inception\_3a/1x1/bn/sc\_internal’ and ‘inception\_3a/1x1/bn’, subdirectories will be generated.

![../images/framework_runner.png](data:image/png;base64,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)

The figure above shows a sample output from one of the framework\_runner runs. Framework Runner basically runs inference at each op in the network.
InceptionV3 and Logits contain the outputs of each layer before the last layer. Each output directory contains the .raw files corresponding to each node. Every raw file that can be seen is the output of an op. The outputs of the final layer are saved inside Predictions directory. The file framework\_runner\_options.json contains all the options used to run this feature.

**Inference Engine**

The Inference Engine feature is designed to find the outputs for a SNPE SDK compatible model. The output produced by this step can be compared with the Golden outputs produced by the framework runner step.

**Usage**

usage: snpe-accuracy-debugger --inference_engine [-h]
                                     [--stage {source,converted,compiled}]
                                     -r {cpu,gpu,dsp,dspv68,dspv69,dspv73,dspv75}
                                     -p ENGINE_PATH
                                     -a {aarch64-android,aarch64-android-clang6.0,aarch64-android-clang8.0,x86_64-linux-clang,x86_64-windows-msvc,wos}
                                     -l INPUT_LIST
                                     [-i INPUT_TENSOR [INPUT_TENSOR ...]]
                                     [-o OUTPUT_TENSOR] [-m MODEL_PATH]
                                     [-f FRAMEWORK [FRAMEWORK ...]]
                                     [--static_model STATIC_MODEL]
                                     [--deviceId DEVICEID] [-v]
                                     [--host_device {x86,x86_64-windows-msvc,wos}] [-w WORKING_DIR]
                                     [--output_dirname OUTPUT_DIRNAME]
                                     [--debug_mode_off] [-bbw {8,32}]
                                     [-abw {8,16}] [-wbw {8,16}]
                                     [-nif] [-nof] [-qo QUANTIZATION_OVERRIDES]
                                     [--golden_dir_for_mapping GOLDEN_DIR_FOR_MAPPING]
                                     [-mn MODEL_NAME] [--args_config ARGS_CONFIG]
                                     [--print_version PRINT_VERSION]
                                     [--perf_profile
                                     {low_balanced,balanced,high_performance,sustained_high_performance,burst,low_power_saver,power_saver,high_power_saver,system_settings}]
                                     [--offline_prepare]
                                     [--extra_converter_args EXTRA_CONVERTER_ARGS]
                                     [--extra_runtime_args EXTRA_RUNTIME_ARGS]
                                     [--profiling_level {off,basic,moderate,detailed,linting}]
                                     [--extra_quantizer_args EXTRA_QUANTIZER_ARGS]
                                     [--fine_grain_mode FINE_GRAIN_MODE]
                                     [--no_weight_quantization]
                                     [--use_symmetric_quantize_weights]
                                     [--use_enhanced_quantizer]
                                     [--htp_socs HTP_SOCS]
                                     [--use_adjusted_weights_quantizer]
                                     [--override_params]
                                     [--precision {int8,fp16,fp32}]
    
    Script to run SNPE inference engine.
    
    optional arguments:
    -h, --help            show this help message and exit
    
    Core Arguments:
    --stage {source,converted,compiled}
                          Specifies the starting stage in the Accuracy Debugger
                          pipeline. Source: starting with a source framework model [default].
                          Compiled: starting with a model's .so binary
    -r {cpu,gpu,dsp,dspv68,dspv69,dspv73,dspv75}, --runtime {cpu,gpu,dsp,dspv68,dspv69,dspv73,dspv75}
                          Runtime to be used.
    -a {aarch64-android,aarch64-android-clang6.0,aarch64-android-clang8.0,x86_64-linux-clang,x86_64-windows-msvc,wos}, --architecture {aarch64-android,aarch64-android-clang6.0,aarch64-android-clang8.0,x86_64-linux-clang,x86_64-windows-msvc,wos}
                          Name of the architecture to use for inference engine.
    -l INPUT_LIST, --input_list INPUT_LIST
                          Path to the input list text.
    
    Arguments required for SOURCE stage:
    -i INPUT_TENSOR [INPUT_TENSOR ...], --input_tensor INPUT_TENSOR [INPUT_TENSOR ...]
                          The name, dimension, and raw data of the network input
                          tensor(s) specified in the format "input_name" comma-
                          separated-dimensions path-to-raw-file, for example:
                          "data" 1,224,224,3 data.raw. Note that the quotes
                          should always be included in order to handle special
                          characters, spaces, etc. For multiple inputs specify
                          multiple --input_tensor on the command line like:
                          --input_tensor "data1" 1,224,224,3 data1.raw
                          --input_tensor "data2" 1,50,100,3 data2.raw.
    -o OUTPUT_TENSOR, --output_tensor OUTPUT_TENSOR
                          Name of the graph's output tensor(s).
    -m MODEL_PATH, --model_path MODEL_PATH
                          Path to the model file(s).
    -f FRAMEWORK [FRAMEWORK ...], --framework FRAMEWORK [FRAMEWORK ...]
                         Framework type to be used, followed optionally by
                         framework version.
    
    Arguments required for CONVERTED or COMPILED stage:
    --static_model STATIC_MODEL
                         Path to the converted model.
    
    Optional Arguments:
    --precision {int8,fp16,fp32}
                         Choose the precision. Default is int8.
                         Note: This option is not applicable when --stage is set to converted or compiled.
    --deviceId DEVICEID  The serial number of the device to use. If not
                         available, the first in a list of queried devices will
                         be used for validation.
    -v, --verbose        Verbose printing
    --host_device {x86,x86_64-windows-msvc,wos}  The device that will be running conversion. Set to x86
                         by default.
    -p ENGINE_PATH, --engine_path ENGINE_PATH
                         Path to the inference engine.
    -w WORKING_DIR, --working_dir WORKING_DIR
                         Working directory for the inference_engine to store
                         temporary files. Creates a new directory if the
                         specified working directory does not exist
    --output_dirname OUTPUT_DIRNAME
                         output directory name for the inference_engine to
                         store temporary files under
                         <working_dir>/inference_engine. Creates a new
                         directory if the specified working directory does not
                         exist
    --debug_mode_off     Specifies if wish to turn off debug_mode mode.
    --print_version PRINT_VERSION
                         Print the SNPE SDK version alongside the output.
    --offline_prepare    Use offline prepare to run snpe model.
    -bbw {8,32}, --bias_bitwidth {8,32}
                         option to select the bitwidth to use when quantizing
                         the bias. default 8
    -abw {8,16}, --act_bitwidth {8,16}
                         option to select the bitwidth to use when quantizing
                         the activations. default 8
    --golden_output_reference_directory GOLDEN_OUTPUT_REFERENCE_DIRECTORY, --golden_dir_for_mapping GOLDEN_DIR_FOR_MAPPING
                         Optional parameter to indicate the directory of the
                         goldens, it's used for tensor mapping without
                         framework.
    -mn MODEL_NAME, --model_name MODEL_NAME
                         Name of the desired output sdk specific model.
    --args_config ARGS_CONFIG
                         Path to a config file with arguments. This can be used
                         to feed arguments to the AccuracyDebugger as an
                         alternative to supplying them on the command line.
    -wbw {8,16}, --weights_bitwidth {8,16}
                         option to select the bitwidth to use when quantizing
                         the weights. default 8
    -nif, --use_native_input_files
                         Specifies that the input files will be parsed in the
                         data type native to the graph. If not specified, input
                         files will be parsed in floating point.
    -nof, --use_native_output_files
                         Specifies that the output files will be generated in
                         the data type native to the graph. If not specified,
                         output files will be generated in floating point.
    -qo QUANTIZATION_OVERRIDES, --quantization_overrides QUANTIZATION_OVERRIDES
                         Path to quantization overrides json file.
    --extra_converter_args EXTRA_CONVERTER_ARGS
                         additional converter arguments in a quoted string.
                         example: --extra_converter_args 'input_dtype=data
                         float;input_layout=data1 NCHW'
    --extra_runtime_args EXTRA_RUNTIME_ARGS
                         additional net runner arguments in a quoted string.
                         example: --extra_runtime_args
                         'arg1=value1;arg2=value2'
    --profiling_level {off,basic,moderate,detailed,linting}
                         Enables profiling and sets its level.
    --extra_quantizer_args EXTRA_QUANTIZER_ARGS
                         additional dlc quantizer arguments in a quoted string.
                         example: --extra_runtime_args
                         'arg1=value1;arg2=value2'
    --fine_grain_mode FINE_GRAIN_MODE
                          Path to the model golden outputs required to run
                          inference engine using fine-grain mode.
    --no_weight_quantization
                          Generate and add the fixed-point encoding metadata but
                          keep the weights in floating point
    --use_symmetric_quantize_weights
                          Use the symmetric quantizer feature when quantizing
                          the weights of the model
    --use_enhanced_quantizer
                          Use the enhanced quantizer feature when quantizing the
                          model
    --htp_socs HTP_SOCS   Specify SoC to generate HTP Offline Cache for.
    --use_adjusted_weights_quantizer
                          Use the adjusted tf quantizer for quantizing the
                          weights only
    --override_params     Use this option to override quantization parameters
                          when quantization was provided from the original
                          source framework
    Copy to clipboard

Please note: All the command line arguments should either be provided through command line or through the config file. They will not override those in the config file if there is overlap.

The inference engine config file can be found in {accuracy\_debugger tool root directory}/python/qti/aisw/accuracy\_debugger/lib/inference\_engine/configs/config\_files and is a JSON file. This config file stores information that helps the inference engine know which tool and parameters to read in. Each different inference engine, and possibly engine versions in certain cases, will require its own config file.

**Sample Command**

snpe-accuracy-debugger \
        --inference_engine \
        --framework tensorflow \
        --runtime cpu \
        --model_path $RESOURCESPATH/samples/InceptionV3Model/inception_v3_2016_08_28_frozen.pb \
        --input_tensor "input:0" 1,299,299,3 $RESOURCESPATH/samples/InceptionV3Model/data/chairs.raw \
        --output_tensor InceptionV3/Predictions/Reshape_1:0 \
        --architecture x86_64-linux-clang \
        --input_list $RESOURCESPATH/samples/InceptionV3Model/data/image_list.txt \
        --verbose
    Copy to clipboard

**Sample Command**

snpe-accuracy-debugger \
        --inference_engine \
        --framework tensorflow \
        --runtime cpu \
        --model_path $RESOURCESPATH/samples/InceptionV3Model/inception_v3_2016_08_28_frozen.pb \
        --input_tensor "input:0" 1,299,299,3 $RESOURCESPATH/samples/InceptionV3Model/data/chairs.raw \
        --output_tensor InceptionV3/Predictions/Reshape_1:0 \
        --architecture x86_64-windows-msvc \
        --input_list $RESOURCESPATH/samples/InceptionV3Model/data/image_list.txt \
        --verbose
    Copy to clipboard

**Sample Command**

snpe-accuracy-debugger \
        --inference_engine \
        --framework tensorflow \
        --runtime cpu \
        --model_path $RESOURCESPATH/samples/InceptionV3Model/inception_v3_2016_08_28_frozen.pb \
        --input_tensor "input:0" 1,299,299,3 $RESOURCESPATH/samples/InceptionV3Model/data/chairs.raw \
        --output_tensor InceptionV3/Predictions/Reshape_1:0 \
        --architecture wos \
        --input_list $RESOURCESPATH/samples/InceptionV3Model/data/image_list.txt \
        --verbose
    Copy to clipboard

**Sample Command**

snpe-accuracy-debugger \
        --inference_engine \
        --deviceId 357415c4 \
        --framework tensorflow \
        --runtime dsp \
        --architecture aarch64-android \
        --model_path $RESOURCESPATH/samples/InceptionV3Model/inception_v3_2016_08_28_frozen.pb \
        --input_tensor "input:0" 1,299,299,3 $RESOURCESPATH/samples/InceptionV3Model/data/chairs.raw \
        --output_tensor InceptionV3/Predictions/Reshape_1:0 \
        --input_list $RESOURCESPATH/samples/InceptionV3Model/data/image_list.txt \
        --verbose
    Copy to clipboard

Tip:

- for –runtime (-r) dsp is used for snpe-net-run
- the input\_tensor (–i) and output\_tensor (-o) does not need the :0 indexing like when runing tensorflow framework runner
- two files, namely tensor\_mapping.json and snpe\_model\_graph\_struct.json are generated to be used in verification, be sure to locate these 2 files in the working\_directory/inference\_engine/latest
- framework and golden\_dir\_for\_mapping, or just golden\_dir\_for\_mapping itself is an alternative to the original model to be provided to generate the tensor\_mapping.json, however, providing only the golden\_dir\_for\_mapping, the get\_tensor\_mapping module will try it’s best to map but it is not guaranteed the mapping would be 100% accurate.
- Before running the snpe-accuracy-debugger on a Windows x86 system/Windows on Snapdragon system, ensure that you have configured the environment. And, Specify the host and target machine as x86\_64-windows-msvc/wos respectively.
- Note that snpe-accuracy-debugger on Windows x86 system is tested only for CPU runtime currently.

**Output**

Once the inference engine has finished running, it will store the output in the specified directory (or the current working directory by default) and store the files in that folder. By default, it will store the output in working\_directory/inference\_engine in the current working directory.

![../images/inference_engine_1.png](data:image/png;base64,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)

The figure above shows the sample output from one of the runs of inference engine step. The output directory contains raw files. Each raw file is an output of an op involved in various layers in the network. File input\_list.txt and the directory input\_list\_files contain the path for sample test images. inference\_engine\_options.json contains all the options with which this run was launched. In addition to generating the .raw files, inference\_engine also generates the model’s graph structure in a .json file. The name of the file is the same as the name of the protobuf model file. The file base.dlc is the converted model graph. The file model\_graph\_struct.json aids in providing the structure related information of the converted model graph during the verification step. Specifically, it helps with organizing the nodes in order (for e.g. the beginning nodes should come earlier than ending nodes). It helps to see the nodes in a streamlined manner. Finally, tensor\_mapping json file contains a mapping of the various intermediate output file names generated from the framework runner step and the inference engine step.

![../images/inference_engine_2.png](data:image/png;base64,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)

The created .raw files are organized in the same manner as framework\_runner (see above).

**Verification**

The Verification step compares the output (from the intermediate tensors of a given model) produced by framework diagonsis step with the one that’s produced by the inference engine step. Once the comparison is complete the verification results are compiled and displayed visually in a format that can be easily interpreted by the user.

There are different types of verifiers for e.g.: CosineSimilarity, RtolAtol, etc. To see what all verifiers are there please use the –help option like ./snpe\_accuracy\_debugger –verification –help. Each verifier compares the Framework Runner and Inference Engine output using an error metric. It also prepares reports and/or visualizations to help the user analyze the network’s error data.

**Usage**

usage: snpe-accuracy-debugger --verification [-h]
                                  --default_verifier DEFAULT_VERIFIER
                                  [DEFAULT_VERIFIER ...]
                                  --golden_output_reference_directory
                                  GOLDEN_OUTPUT_REFERENCE_DIRECTORY
                                  --inference_results INFERENCE_RESULTS
                                  [--tensor_mapping TENSOR_MAPPING]
                                  [--qnn_model_json_path QNN_MODEL_JSON_PATH]
                                  [--dlc_path DLC_PATH]
                                  [--verifier_config VERIFIER_CONFIG]
                                  [--graph_struct GRAPH_STRUCT] [-v]
                                  [-w WORKING_DIR]
                                  [--output_dirname OUTPUT_DIRNAME]
                                  [--args_config ARGS_CONFIG]
                                  [--target_encodings TARGET_ENCODINGS]
                                  [-e ENGINE [ENGINE ...]]
    
    Script to run verification.
    
    required arguments:
      --default_verifier DEFAULT_VERIFIER [DEFAULT_VERIFIER ...]
                            Default verifier used for verification. The options
                            "RtolAtol", "AdjustedRtolAtol", "TopK", "L1Error",
                            "CosineSimilarity", "MSE", "MAE", "SQNR", "ScaledDiff"
                            are supported. An optional list of hyperparameters can
                            be appended. For example: --default_verifier
                            rtolatol,rtolmargin,0.01,atolmargin,0.01 An optional
                            list of placeholders can be appended. For example:
                            --default_verifier CosineSimilarity param1 1 param2 2.
                            to use multiple verifiers, add additional
                            --default_verifier CosineSimilarity
      --golden_output_reference_directory GOLDEN_OUTPUT_REFERENCE_DIRECTORY, --framework_results GOLDEN_OUTPUT_REFERENCE_DIRECTORY
                            Path to root directory of golden output files. Paths
                            may be absolute, or relative to the working directory.
      --inference_results INFERENCE_RESULTS
                            Path to root directory generated from inference engine
                            diagnosis. Paths may be absolute, or relative to the
                            working directory.
    
    optional arguments:
      --tensor_mapping TENSOR_MAPPING
                            Path to the file describing the tensor name mapping
                            between inference and golden tensors.
      --qnn_model_json_path QNN_MODEL_JSON_PATH
                            Path to the qnn model net json, used for transforming
                            axis of golden outputs w.r.t to qnn outputs. Note:
                            Applicable only for QNN
      --dlc_path DLC_PATH   Path to the dlc file, used for transforming axis of
                            golden outputs w.r.t to target outputs. Note:
                            Applicable for QAIRT/SNPE
      --verifier_config VERIFIER_CONFIG
                            Path to the verifiers' config file
      --graph_struct GRAPH_STRUCT
                            Path to the inference graph structure .json file. This
                            file aids in providing structure related information
                            of the converted model graph during this stage.Note:
                            This file is mandatory when using ScaledDiff verifier
      -v, --verbose         Verbose printing
      -w WORKING_DIR, --working_dir WORKING_DIR
                            Working directory for the verification to store
                            temporary files. Creates a new directory if the
                            specified working directory does not exist
      --output_dirname OUTPUT_DIRNAME
                            output directory name for the verification to store
                            temporary files under <working_dir>/verification.
                            Creates a new directory if the specified working
                            directory does not exist
      --args_config ARGS_CONFIG
                            Path to a config file with arguments. This can be used
                            to feed arguments to the AccuracyDebugger as an
                            alternative to supplying them on the command line.
      --target_encodings TARGET_ENCODINGS
                            Path to target encodings json file.
    
    Arguments for generating Tensor mapping (required when --tensor_mapping is not specified):
      -e ENGINE [ENGINE ...], --engine ENGINE [ENGINE ...]
                            Name of engine(qnn/snpe) that is used for running
                            inference.
    Copy to clipboard

Please note: All the command line arguments should either be provided through command line or through the config file. They will not override those in the config file if there is overlap.

The main verification process run using snpe-accuracy-debugger -–verification optionally uses –tensor\_mapping and -–graph\_struct to find files to compare. These files are generated by the inference engine step, and should be supplied to verification for best results. By default they are named tensor\_mapping.json and {model name}\_graph\_struct.json, and can be found in the output directory of the inference engine results.

**Sample Command**

Compare output of framework runner with inference engine:

snpe-accuracy-debugger \
        --verification \
        --default_verifier CosineSimilarity param1 1 param2 2 \
        --default_verifier SQNR param1 5 param2 1 \
        --golden_output_reference_directory $PROJECTREPOPATH/working_directory/framework_runner/2022-10-31_17-07-58/ \
        --inference_results $PROJECTREPOPATH/working_directory/inference_engine/latest/output/Result_0/ \
        --tensor_mapping $PROJECTREPOPATH/working_directory/inference_engine/latest/tensor_mapping.json \
        --graph_struct $PROJECTREPOPATH/working_directory/inference_engine/latest/snpe_model_graph_struct.json
    Copy to clipboard

Tip:

- If you passed multiple images in the image\_list.txt from run inference engine diagnosis, you’ll receive multiple output/Result\_x, choose result that matches the input you used for framework runner for comparison (ie. in framework you used chair.raw and inference chair.raw was the first item in the image\_list.txt then choose output/Result\_0, if chair.raw was the second item in image\_list.txt, then choose output/Result\_1).
- It is recommended to always supply –graph\_struct and –tensor\_mapping to the command as it is used to line up the report and find the corresponding files for comparison. if –tensor\_mapping did not get generated from previous steps, you can supplement with –model\_path, –engine, –framework to have module generate tensor\_mapping during runtime.
- You can also compare inference\_engine outputs to inference\_engine outputs by passing the /output of the inference\_engine output to the –framework\_results. If you want the outputs to be exact-name-matching, then you do not need to provide a tensor\_mapping file.
- Note that if you need to generate a tensor mapping instead of providing a path to pre-existing tensor mapping file. You can provide the –model\_path option.

Verifier uses two optional config files. The first file is used to prefer parameters to specific verifiers, as well as which tensors to use these verifiers on. The second file is used to map tensor names from inference\_engine to framework\_runner, since certain tensors generated by framework\_runner have different names than tensors generated by inference\_engine.

**Verifier Config:**

The verifier config file is a JSON file that tells verification which verifiers (asides from the default verifier) to use and with which parameters and on what specific tensors. If no config file is provided, the tool will only use the default verifier specified from the command line, with its default parameters, on all the tensors. The JSON file is keyed by verifier names, with each verifier as its own dictionary keyed by “parameters” and “tensors”.

**Config File**

```json
    {
      "MeanIOU": {
          "parameters": {
              "background_classification": 1.0
          },
          "tensors": [["Postprocessor/BatchMultiClassNonMaxSuppression_boxes", "detection_classes:0"]]
      },
      "TopK": {
          "parameters": {
              "k": 5,
              "ordered": false
          },
          "tensors": [["Reshape_1:0"], ["detection_classes:0"]]
      }
    }
    ```
    Copy to clipboard

Note that the “tensors” field is a list of lists. This is done because specific verifiers (e.g. MeanIOU) runs on two tensor at a time. Hence the two tensors are placed in a list. Otherwise if a verifier only runs on one tensor, it will have a list of lists with only one tensor name in each list.

**Compare Encodings**

The Compare Encodings feature is designed to compare SNPE DLC encodings with AIMET encodings. It takes a SNPE DLC file and AIMET encoding JSON files as inputs.
This feature:

> 
> 
> 1. Extracts encodings from the given SNPE DLC file
> 2. Compares extracted SNPE encodings with given AIMET encodings
> 3. Writes results to an Excel file that highlights mismatches
> 4. Warns the user if any encodings are present in SNPE and missing from AIMET and vice-versa
> 5. Writes the extracted SNPE encodings to a JSON file for reference

**Usage**

usage: snpe-accuracy-debugger --compare_encodings [-h]
                                 --input INPUT
                                 --aimet_encodings_json AIMET_ENCODINGS_JSON
                                 [--precision PRECISION]
                                 [--params_only]
                                 [--activations_only]
                                 [--specific_node SPECIFIC_NODE]
                                 [--working_dir WORKING_DIR]
                                 [--output_dirname OUTPUT_DIRNAME]
                                 [-v]
    
    Script to compare SNPE encodings with AIMET encodings
    
    optional arguments:
      -h, --help            Show this help message and exit
    
    required arguments:
      --input INPUT
                            Path to SNPE DLC file
      --aimet_encodings_json AIMET_ENCODINGS_JSON
                            Path to AIMET encodings JSON file
    
    optional arguments:
      --precision PRECISION
                            Number of decimal places up to which comparison will be done; default is 17
      --params_only         Compare only parameters in the encodings
      --activations_only    Compare only activations in the encodings
      --specific_node SPECIFIC_NODE
                            Display encoding differences for the given node
      --working_dir WORKING_DIR
                            Working directory to store temporary files; the directory is created if it does not already exist
      --output_dirname OUTPUT_DIRNAME
                            Output directory to store temporary files under <working_dir>/compare_encodings; the directory is created if it does not already exist
      -v, --verbose         Verbose printing
    Copy to clipboard

**Sample Commands**

# Compare both params and activations
    snpe-accuracy-debugger \
        --compare_encodings \
        --input mv2_quantized.dlc \
        --aimet_encodings_json aimet_encodings.json
    
    # Compare only params
    snpe-accuracy-debugger \
        --compare_encodings \
        --input mv2_quantized.dlc \
        --aimet_encodings_json aimet_encodings.json \
        --params_only
    
    # Compare only activations
    snpe-accuracy-debugger \
        --compare_encodings \
        --input mv2_quantized.dlc \
        --aimet_encodings_json aimet_encodings.json \
        --activations_only
    
    # Compare only a specific encoding
    snpe-accuracy-debugger \
        --compare_encodings \
        --input mv2_quantized.dlc \
        --aimet_encodings_json aimet_encodings.json \
        --specific_node /1/Conv_output_0
    Copy to clipboard

Tip

A working\_directory is generated at the location this script is called from unless otherwise specified.

**Output**

The program creates a directory named *latest* in <cite>working_directory/compare_encodings</cite> which is symbolically linked to the most recently generated directory. In the example below, *latest* will
have data that is symlinked to the data in the most recent directory *YYYY-MM-DD\_HH:mm:ss*. Users may choose to override the directory name by passing it to –output\_dirname, e.g., <cite>–output_dirname myTest</cite>.

![../images/compare_encodings.png](data:image/png;base64,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)

The figure above shows a sample output from a compare\_encodings run.
The following details what each file contains.

> 
> 
> - compare\_encodings\_options.json contains all the options used to run this feature
> - encodings\_diff.xlsx contains comparison results with mismatches highlighted
> - extracted\_encodings.json contains extracted SNPE encodings
> - log.txt contains log statements for the run

**Tensor inspection**

Tensor inspection feature compares given reference output and target output tensors and dumps various statistics to represent differences between them.

The Tensor inspection feature can:

> 
> 
> 1. Plot histograms for golden and target tensors
> 2. Plot a graph indicating deviation between golden and target tensors
> 3. Plot a cumulative distribution graph (CDF) for golden vs target tensors
> 4. Plot a density (KDE) graph for target tensor highlighting target min/max and calibrated min/max values
> 5. Create a CSV file containing information about: target min/max; calibrated min/max; golden output min/max; target/calibrated min/max differences; and computed metrics (verifiers).

Note

Only data with matching target/golden filenames is inspected; other data is ignored.

This feature expects the golden and target tensors to have the same dimensions, datatypes, and layouts.

Calibrated min/max values are extracted from a user provided encodings file. If an encodings file is not provided, density plot will be skipped and also the CSV summary output will not include calibrated min/max information.

**Usage**

usage: snpe-accuracy-debugger --tensor_inspection [-h]
                            --golden_data GOLDEN_DATA
                            --target_data TARGET_DATA
                            --verifier VERIFIER [VERIFIER ...]
                            [-w WORKING_DIR]
                            [--data_type {int8,uint8,int16,uint16,float32}]
                            [--target_encodings TARGET_ENCODINGS]
                            [-v]
    
    Script to inspection tensor.
    
    required arguments:
      --golden_data GOLDEN_DATA
                            Path to golden/framework outputs folder. Paths may be absolute or
                            relative to the working directory.
      --target_data TARGET_DATA
                            Path to target outputs folder. Paths may be absolute or relative to the
                            working directory.
      --verifier VERIFIER [VERIFIER ...]
                            Verifier used for verification. The options "RtolAtol",
                            "AdjustedRtolAtol", "TopK", "L1Error", "CosineSimilarity", "MSE", "MAE",
                            "SQNR", "MeanIOU", "ScaledDiff" are supported.
                            An optional list of hyperparameters can be appended, for example:
                            --verifier rtolatol,rtolmargin,0.01,atolmargin,0,01.
                            To use multiple verifiers, add additional --verifier CosineSimilarity
    
    optional arguments:
      -w WORKING_DIR, --working_dir WORKING_DIR
                            Working directory to save results. Creates a new directory if the
                            specified working directory does not exist
      --data_type {int8,uint8,int16,uint16,float32}
                            DataType of the output tensor.
      --target_encodings TARGET_ENCODINGS
                            Path to target encodings json file.
      -v, --verbose         Verbose printing
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**Sample Commands**

# Basic run
    snpe-accuracy-debugger --tensor_inspection \
        --golden_data golden_tensors_dir \
        --target_data target_tensors_dir \
        --verifier sqnr
    
    # Pass target encodings file and enable multiple verifiers
    snpe-accuracy-debugger --tensor_inspection \
        --golden_data golden_tensors_dir \
        --target_data target_tensors_dir \
        --verifier mse \
        --verifier sqnr \
        --verifier rtolatol,rtolmargin,0.01,atolmargin,0.01 \
        --target_encodings reference_encodings.json
    Copy to clipboard

Tip

A working\_directory is generated from wherever this script is called from unless otherwise specified.

![../images/tensor_inspection.png](data:image/png;base64,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)

The figure above shows a sample output from a Tensor inspection run.
The following details what each file contains.

> 
> 
> - Each tensor will have its own directory; the directory name matches the tensor name.
> 
>     - CDF\_plots.html – Golden vs target CDF graph
>     - Diff\_plots.html – Golden and target deviation graph
>     - Distribution\_min-max.png – Density plot for target tensor highlighting target vs calibrated min/max values
>     - Histograms.html – Golden and target histograms
>     - golden\_data.csv – Golden tensor data
>     - target\_data.csv – Target tensor data
> - log.txt – Log statements from the entire run
> - summary.csv – Target min/max, calibrated min/max, golden output min/max, target vs calibrated min/max differences, and verifier outputs

**Histogram Plots**

1. **Comparison:** We compare histograms for both the golden data and the target data.
2. **Overlay:** To enhance clarity, we overlay the histograms bin by bin.
3. **Binned Ranges:** Each bin represents a value range, showing the frequency of occurrence.
4. **Visual Insight:** Overlapping histograms reveal differences or similarities between the datasets.
5. **Interactive:** Hover over histograms to get tensor range and frequencies for the dataset.

**Cumulative Distribution Function (CDF) Plots**

1. **Overview:** CDF plots display the cumulative probability distribution.
2. **Overlay:** We superimpose CDF plots for golden and target data.
3. **Percentiles:** These plots illustrate data distribution across different percentiles.
4. **Hover Details:** Exact cumulative probabilities are available on hover.

**Tensor Difference Plots**

1. **Inspection:** We generate plots highlighting differences between golden and target data tensors.
2. **Scatter and Line:** Scatter plots represent tensor values, while line plots show differences at each index.
3. **Interactive:** Hover over points to access precise values.

**Tensor Mapping:**

Tensor mapping is a JSON file keyed by inference tensor names, of framework tensor names. If the tensor mapping is not provided, the tool will assume inference and golden tensor names are identical.

**Tensor Mapping File**

```json
    {
      "Postprocessor/BatchMultiClassNonMaxSuppression_boxes": "detection_boxes:0",
      "Postprocessor/BatchMultiClassNonMaxSuppression_scores": "detection_scores:0"
    }
    ```
    Copy to clipboard

**Output**

Verification’s output is divided into different verifiers. For example, if both RtolAtol and TopK verifiers are used, there will be two sub-folders named “RtolAtol” and “TopK”. Availble verifiers can be found by just issuing –help option.

![../images/verification_1.png](data:image/png;base64,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)

Under each sub-folder, the verification analysis for each tensor is organized similar to how framework\_runner and inference\_engine (see above) are organized. For each tensor, a CSV and HTML file is generated. In addition to the tensor-specific analysis, the tool also generates a summary CSV and HTML file which summarizes the data from all verifiers and their subsequent tensors. The following figure shows how a sample summary generated in the verification step looks. Each row in this summary corresponds to one tensor name that is identified by the framework runner and inference engine steps. The final column shows cosinesimilarity score which can vary between 0 to 1 (this range might be different for other verifiers). If the score is high enough then it means that the result produced by both the steps for that particular tensor are fairly similar. However, if the score is too low then it gives the developer a point of inspection. The developer can then further investigate those specific tensors (if multiple) into details. Developer should inspect tensors from top-to-bottom order, meaning if a tensor is broken at an earlier node, anything that was generated post that node is unreliable until that node is properly fixed. Hence, this process might help in improving the overall accuracy of the sdk. That is how this tool helps in debugging.

![../images/verification_2.png](data:image/png;base64,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)

**Run SNPE Accuracy Debugger E2E:**

This feature is designed to run the framework runner, inference engine, and verification features sequentially with a single command to debug the model. It provides quick analysis to identify layers of model causing accuracy deviation.
Additionally, tensor inspection (when –enable\_tensor\_inspection is passed) is executed which dumps various plots for all intermediate outputs.

**Usage**

usage: snpe-accuracy-debugger [--framework_runner] [--inference_engine] [--verification] [-h]
    
    Script that runs Framework Runner, Inference Engine or Verification.
    
    Arguments to select which component of the tool to run.  Arguments are mutually exclusive (at most 1 can be selected).  If none are selected, then all components are run:
    --framework_runner Run framework
    --inference_engine    Run inference engine
    --verification        Run verification
    
    optional arguments:
    -h, --help            Show this help message. To show help for any of the
                          components, run script with --help and --<component>.
                          For example, to show the help for Framework Runner,
                          run script with the following: --help
                          --framework_runner
    usage: snpe-accuracy-debugger [-h] -f FRAMEWORK [FRAMEWORK ...] -m MODEL_PATH
                              -i INPUT_TENSOR [INPUT_TENSOR ...] -o
                              OUTPUT_TENSOR -r RUNTIME -a
                              {aarch64-android,x86_64-linux-clang}
                              -l INPUT_LIST --default_verifier DEFAULT_VERIFIER
                              [DEFAULT_VERIFIER ...] [-v] [-w WORKING_DIR]
                              [--output_dirname OUTPUT_DIRNAME]
                              [--deep_analyzer {modelDissectionAnalyzer}]
    
    Options for running the Accuracy Debugger components
    
    optional arguments:
    -h, --help            show this help message and exit
    
    Arguments required by both Framework Runner and Inference Engine:
       -f FRAMEWORK [FRAMEWORK ...], --framework FRAMEWORK [FRAMEWORK ...]
                              Framework type and version, version is optional.
       -m MODEL_PATH, --model_path MODEL_PATH
                              Path to the model file(s).
       -i INPUT_TENSOR [INPUT_TENSOR ...], --input_tensor INPUT_TENSOR [INPUT_TENSOR ...]
                              The name, dimensions, raw data, and optionally data
                              type of the network input tensor(s) specifiedin the
                              format "input_name" comma-separated-dimensions path-
                              to-raw-file, for example: "data" 1,224,224,3 data.raw
                              float32. Note that the quotes should always be
                              included in order to handle special characters,
                              spaces, etc. For multiple inputs specify multiple
                              --input_tensor on the command line like:
                              --input_tensor "data1" 1,224,224,3 data1.raw
                              --input_tensor "data2" 1,50,100,3 data2.raw float32.
       -o OUTPUT_TENSOR, --output_tensor OUTPUT_TENSOR
                              Name of the graph's specified output tensor(s).
    
    Arguments required by Inference Engine:
    -r RUNTIME, --runtime RUNTIME
                          Runtime to be used for inference.
    -a {aarch64-android,x86_64-linux-clang}, --architecture {aarch64-android,x86_64-linux-clang}
                          Name of the architecture to use for inference engine.
    -l INPUT_LIST, --input_list INPUT_LIST
                          Path to the input list text.
    
    Arguments required by Verification:
    --default_verifier DEFAULT_VERIFIER [DEFAULT_VERIFIER ...]
                          Default verifier used for verification. The options
                          "RtolAtol", "AdjustedRtolAtol", "TopK", "L1Error",
                          "CosineSimilarity", "MSE", "MAE", "SQNR", "MeanIOU",
                          "ScaledDiff" are supported. An optional list of
                          hyperparameters can be appended. For example:
                          --default_verifier
                          rtolatol,rtolmargin,0.01,atolmargin,0,01. An optional
                          list of placeholders can be appended. For example:
                          --default_verifier CosineSimilarity param1 1 param2 2.
                          to use multiple verifiers, add additional
                          --default_verifier CosineSimilarity
    
    optional arguments:
    -v, --verbose         Verbose printing
    -w WORKING_DIR, --working_dir WORKING_DIR
                          Working directory for the wrapper to store temporary
                          files. Creates a new directory if the specified
                          working directory does not exitst.
    --output_dirname OUTPUT_DIRNAME
                          output directory name for the wrapper to store
                          temporary files under <working_dir>/wrapper. Creates a
                          new directory if the specified working directory does
                          not exist
    --golden_output_reference_directory
                          Optional parameter to indicate the directory of the golden reference outputs.
                          When this option is provided, the framework runner is step skipped.
                          In inference engine step, it's used for tensor mapping without a framework.
                          In verification step, it's used as a reference to compare
                          outputs produced in the inference engine step.
    --enable_tensor_inspection
                          Plots graphs (line, scatter, CDF etc.) for each
                          layer's output. Additionally, summary sheet will have
                          more details like golden min/max, target min/max etc.,
    --deep_analyzer {modelDissectionAnalyzer}
                          Deep Analyzer to perform deep analysis
    Copy to clipboard

**Sample Command**

snpe-accuracy-debugger \
        --framework tensorflow \
        --runtime cpu \
        --model_path $RESOURCESPATH/samples/InceptionV3Model/inception_v3_2016_08_28_frozen.pb \
        --input_tensor "input:0" 1,299,299,3 $RESOURCESPATH/samples/InceptionV3Model/data/chairs.raw \
        --output_tensor InceptionV3/Predictions/Reshape_1:0 \
        --architecture x86_64-linux-clang \
        --input_list $RESOURCESPATH/samples/InceptionV3Model/data/image_list.txt \
        --default_verifier CosineSimilarity \
        --enable_tensor_inspection \
        --verbose
    Copy to clipboard

Note

The –enable\_tensor\_inspection argument significantly increases overall execution time when used with large models. To speed up execution, omit this argument.

**Output**

The program creates framework\_runner, inference\_engine, verification, and wrapper output directories as below:

![../images/oneshot-layerwise.png](data:image/png;base64,UklGRs4eAABXRUJQVlA4TMEeAAAvDcFtAB4807a9jhvpbyrET2DIUCHDDhUy7JBhh6iNEDLsEGGHvV6Zas77vM/7HfQxGMc5azjg21j3BiyOIViYwfrmWpROUbu9/ow5gU5tEWMw3nJ3XFf1eKcpydS2R5O0OpQ+nMXGwcLBxmBhYbCxsDFY+GJhMBgsfHe/XxuYtrbnsW6pWAwGi8Xg4GAuYfDFwWLxw+CLwWDxww/f859D7T9FSbbCttKR5F05xJmLAKN7xPuAUeG767Zxma995miQ6WTcJ58osg6LXuM8BOdnHZSYetx8U+vw0DRi9MW0yq1CjUq8Fvc4WBjdOvwU98n/UPvhoVf3fXf76GD96/7LxbNdHzSu1z739cknsrr81vX19WI0+GI1hl7jnX3S14VBquEQ/irujIpJ6BbjMd8hxH31o9FFkTBmtfX15y1UEod78Y4Yro8ZcUwXFAW3eDOjVRoVjyxU4Hx6fE2IU0AxzCyW66eZDhFZdI9FjwQEea6L8z83i2d7wvE4ixEixHp73ma1I+WeCsXipxnZU3Y8bwVmNCjVjRTfEuRHi58SUbgX7xcKqcZvSVwCd/2URgyRPUY0jHP1UwGAP/C1kwiAF9yMbHGdAT1v1Uuh9ttHvJo3/+mj5630M6IVtBwph9WIPZJ1GcXx6CliQJVnFfSYiMFJ70WmF6trBbf4KY8Yf0/YFehjJMZIJWzN/VMME1Ss/eT//ONJXvQ3JPAkXYNY9Pozn7dYwdCfodN96ylyEzG4UfHYo7BjXV+LRoO6Z6qIX6zkamlQiAYAHmPA5ApDBHtQsfZTzElczf1C6DEWmZEUitG87ydqT0fRx+kq8qZ7WA5hV9JaAIecPG+JoPIV9vv3Gk6ETNDDiFJOtOBycKxM5knx9eL9WATKmtJjsG5/MIAymGyAhcEZBleo7axn7Rlc1jGxCKLcPxlzNCiyi+zvK2nQF8fh97++N6SLPEWKou/ZQAL18K3sg+yXMSRUGhKHmufja2Lrk+9qqcFvqW9l+buHiL/ZUzgYp8bh8RiGKOFHsV4I6FoaFP5KRt6SE6kT5AayIv8vGXVSr9Dqp2d+21DQVlR+fUt9ycw2EnYli6dqfRkVfxXzGueB97/++D7MGetdeG/xUxgKYR7/Ltbt1/R/rHOu2D71m7++Tqm5Ylsuf+v391yxLd9zZMPhPI2lAOEkhijvz7xWSnZERHgay9mH42kh+qwjAsxyHaeMW6scvPrplhWpxwHKqJab1U5TZxC9iX5QmXJtJ8So4hSKdlTr1BQ+S/xT+k0rTdn2E/aRZmv3OMx6vpOmUCUq235Qe7ZbNajnRWp2ZM6VFBQQkXIcirDTgQeEfd/idwqwNAEYZul2Q2pz/D0PtalKVFBApMspRMUoooMNoBI541dxsZAsABpRufbb70F2j5GWYRgOxae0kdSJ2UjKZh99xjBRRQjFsNm3Q1YuPE7JDO18J1cLulBcjrROTRHbDLGBup4AGZQwbYdOReMSwOatzR5+hNEo/kYI+2ZLQ5JOHC2EsV50CrNU+wnHjZEP3x5uvKwu5CNWh+ohIwxQrBdZ5WKlj7RkF6Y/30mNri5U1fabfawXGWw7NPBChyYXSoJIorl80MmlEg0dXs/VRZcxZXW+ephxHShBWwmmbLr9Ggm6gr4hdQ51ghUHtbhtIgRFmuL2oELKNUDm8ntY+ZmyutSHomrVi4oGqheihIuE17IJGOwUnwgHjJIdt85+umUJ682zjxuGAYUV06puK/0Iqmii6rv14ipaqFEg2OxFN2u9IM3IhYpKR9N34UoNYmXcohZ6QKe0upDL76U7UhQ99KMEdYHb9vw0OVLs2/Np2kiwT1OXLBVNXoWMXB2+HS0MYSU7EoQDU4pUz8rOViPBWweVcfmlrBw70WpWsjtI9dotSkeD/XK9UDJO1EXeoeDjvFxHkvQ3iSkcYi/fEK6MCfqSqyJNECGeg9YLWoRFcwFFqKAIXLN2oiI6APal0i3kWyfxjiDpBAaWiTLhBXBIK4utIDiMYl04U7RwY+Husg/D+SSBJyQaAOj4CaJAfMOQUUsRHVCJbhfdJtGTTzL5S5jgES5X4M5ZCLsG5wDGZA9/ZgQBf8DG9DKlEhJINA29MVha5QgXAacsih3ncDB7cx6BgoArBQOXBGGGRDHrI3fKmmIi1OpAGDiSeY2msJJbFGmxBTnqpMXOCQcdDd/LfpszZhqeMgt19xJcptle+pI7npAZsC9TIdMXs9xL7Xg6pukJRqGSoVZss9xL6XimyjHd5suCqiL3YEkhlVBcWrmfHK6tdfdH+GxA57ERwCrZ/535OGaSsN3sNYcrHE93bd3KDBA+GzCM2DRg/r1sjifvk6uHU2OWAoKcOlq9w4AO31sGxRR2gUbueoE+8ZY7tQ6p5iYkxdxjOknAnn304dRkxOauAvEK0e3XMAQuA7M4MfP+7Aq8XU5xvoN0/eVIi5BMVGLw4EjEAlgvGjA4wOxVkM+mJiM2FUj+vWSOp62JCmwc1Gok2AMF1JlBQY1ceEQAcu9afRfADY/CCBxkoqWEERsLxCvlsE6uqNoBtg6sHIuCWJeqQw7DgFLddhjAxkJl1Y0yYmOBeJUc+XS+C9lAhCJFzV4Nw0pXyJdbOpBqOXIJDT9XMij4ibJkdG06mrmGLuSkfWZJBTIDlcrxzCc0W+LJHkQoiEiRXkAOE1mVolp4HOkjTNFDEVMkkGrYoWvkFQ49CsI1BRM4MgLRc0NPdsFhArDNFGaekLC8KlIJCZcF3rfKEQgAy8GloDmAotVgWPsRKdjVlWmBS2OVIxBAkYfLPbbqmSPVOClthGPjtGLL4efUm+vrcYB6FcGeQ6t71xdbKxzzhOM7NxxnPjC9F75PnPsJsVqdwfCb3PAcZLV56Fzy0KTZc+6u1kVWfrej/WsGgnUAoJTyStXf7RIQdBbEMixZmjlCIelPcsnEKiDb26zW4ysHd9uFI8Ixz1d6/4FGCIdWZhmWSgaL+iSQBcQeUK5UnfK6MfLLZ4Pd45jtJcmHl+tDMueSomeG5aEwM+myvKEsgwOAUBanzsO2g1CsFx0Bcm2HVvKuDxRWZkcNy2dXoSg6ANDJecnLyzowfBFdPlDLR4kdWrH7+saTsz3+8yXZ7oiTgUtcXKlqy7A0pI0bDyQjAiAgJ3Zopsrdp2wvtU0OLVhe2xiGpSll6nlASpEKgDLGkQBMHq2q3V8+8XtHzcMpCaysZYZlDsXlA2l8M1DV7jefctQ9nIPgylpuWCaewG+ln5yZKsnkW9+0eqC1C9OwTCyCCOjiGUUmqjgrVZPJB2tJiWGZJvVX5Sebt6C4+iDybzOYqoOpsWVYagJPmfWFGwDAyRhrImtAJvWKQhEAFEdywsGBGN7yPWK2ewCtQS3nUZVrUNWdMAdwDmAz2gAJwDlu0DlmLh7XYGVag2o303AGYJpWzus0QtU25/E8nEZEFDONJyFvU8ZFl0snT+/H5QMAyIwulVLb4nIgWtvbRtV239Haa8XkSuOUFLa9lM5Vyi6FZ70YIAAVeCl3l2SsrIODcgIAyzXAqopD2A5yr06uF1wurC4ix2V3z65SOFvIYGa82vtSkU5MAflPk0cbB04C93KuQXVIvMMqDsywBrG+gUjVkjD1DVKepi+0XKVy+8bkBHYJQ60GYaQqLpNHxerOwLxMa1AJhi9BLD/tTmpEiYwnvTpZEwGqqv0Uzkzql6Bz60hBhEdJLVx0BuZlWoNKQZQfcUWagqulQuoBEpKYVWssHSW15YiIv6c5WUny4tAdFX/Ab9k8AUY8YqtoSrq4ThZ9UUGFdAABi6TIl5sp5GEinDoiFCweQ+M8IgJH+S5SeCAI2CHwch7a0+SKraqLUH7ngKuvTSke9qI53RCO9s22Yf5PQGQJPKqHY+DlPNTlDw/yNjRRNWwgSM4X+rbitKT5+k7zu+P1tHn0yAmBl/iYqILVS3u+IyNIhKpVPTdNe1s1Y6zMHCL8GA8qnYcEXso1qNsG24RcDqlR+UpRIB/BMUXVaoII+WYv1a04U05w/tMpHJjGgO9s87RXwkMD9zLa4v0wkKkn+SQYiGQYGRrhTBbZBxi1Caf68iPq8TuKYcrfoDxawEMDDy+jLW7dlrgGYTwGpKdcSwJsgjvyaS4eaSqPHbiXe5mg/iRrMyFTriUFthelKj7gNyLNyUNCronczUyaVC0vSr1HoyM8T9ShV8yNTOnrUCXJ6EWt8kbrcfict/prDM5yd+fVzcUvalqjrQel1aeJYCRctSfF8x4qv1apcj+rNahMykjBTLkbOTFcGuIVqTtmmaQLndEaVCJIk3Vozp/v1GpUTJA2qwtg8mrUL3/yJJ7848mO8DNZg8p0+QYsgsIXIFkvGkVUrS/Hcf3kQHHXxzT9lfv9zY+Hn8EaVCbdEVFgyUiWHW07/XZF7nd9vHK/f5Jt33w9fOZrUKmU6YDiq1GHrmIFI+zJDniZ+RpULrFbyQtbjQoXp6tWLd3jl7fHkb098zWohrKyxkkT7vAXAQRUtfPg2VNaL30jH5JJhiiyGrXF4VyoqnV3nzmlKfjJoRCLywe18BP9gESsLs7+g6q2RVPTk2E4h/FazSRQ05NhLCnMDI/3xbkGda7YvzLSPHfsP59rdv5Uu8bQNHW5/zvbVR7SXJngVfGsepnWoLI89Ucvhs2+HQa6miXLXOMSlZrU7degujjfQbI+M3/Rs2hxFVX0FbCXaXEd9z5x6Xm72YN7EwELzPC5o9o8LG32qNWFXI/oyZKfujySFdFmla8XhtbnkUNgCatVwuV/rzxrUHPoANiLcM3y5Rs1uEO3y6MeH6yK1UUt/gQmtymXhZUzJn/h9fxsNy3Hbgze57//Wxv+L/c4UwFq1v+lHmcqgNer/2t4nI2YWn2zHQbMz74Frmet+r/M4+witoNo6ALcz1YowuvV/1Uep3Ju4OV8B3l7sEHda9b/JR5nF67UhKM2+9r1f5eOHmeyatjyRI8T++V6kaC6PQyPczskqG79X8Pj7Or3xP6u5h4n/kxRCEAN+7/U48QihGJZ+0kHkFHz70rQUn5jzkhQ3hPN8XT3M0GtLccTcgxnglpdjucZNxbJSE1U4bMdFRRqRVp8XkwZkWlV3u6SjBUK0/wlIBYHrVeTkBATRM2MmUxM7FlEXiWOJ1e4K/M3YQmqyUFamUFocBBEjdJu9mYQBLKCHM8LyE7HFl3gKe3ho5EWzFa3GUgTsV4AkCSgeuHRa2kSCFcPp+YZomQwVQyumPtB7ORs+Q0LgnjMXhWOp/GChicMKasLtDqhoi/fyGzhg8pAV6OXi+RLLZsQvELBJDEgRHsFL0JHgmAes1eF42nqcqQVXM8Gag9Q2s2e2MLye2pVLpFNiMOzQOgY9KI8IjMIdEYqY+UpF5g4qp9dodYL6bfKCF2NBHtM1TJZhGrVLhCRGPoOBmwiMwhUVdi/hqBmZGOCB0m6PLIJmXQMs1JFOJ6WYIW6O5f6KelUthIIQQKgY8h1QlUdjqfx4EgvNXWYuGJqJyqQGnCvwlQKIYzmq4uOxKCLCCEWIUHQk+pxPFcXMt708EVOlXHIEHv5AJagov2HG8gmxH8jYpA+qGJwYJYr0uMHsSCoqsfxjKCzPlIu7IkaQLDNJITrZ876GHtaENggKirVQm4ASDlPuiMI+JrChmcTQhF1iTVqnmc9NVbVZ2VyzOSVajZjR24RK1FV8LUwZ2R63yPf3jO2OcDeMzav/eNuYvyAbnefK7zuNfZ0pyW6jVt/14Mm6hFx8VZETPIxz7y4e0qaxEiJJNugRnIEQUyiitO4293FT3ziyNJRO7JyY+xUnphCM9JlCTaoReLsm9ImqjYd9Vf2J+/c6U6rWbmjsYmslacTApOvV5hX3ZxCLaRSLQr0MjtJw351MRypPZyaSFLVVVYWgwD7GzPzFSq+8pRrJOV8t14MUq4Jk+LyDci1Z3o2NeFDBZDCF3oBOTDbYRAZvjJDqvSEGxfBX6amAF9XmptJIdt6oaxbQQWr1B2rPEIH0TfAWXW60/Y7fX/+/E5Pd9qWlbvTs4iuPE3RZg+yKNB6GDosLz8TXO3buesg0MbrouqKjHWc7NhsxiuNW8vKjaPI28bK0xkp+4kUU3Kp6P4qnEkHsV5MVNBYK/iDHDudsiVHUnJK/E2u1U2x0+RIIeN0kBMVRGwPKtvzXcXKWnl6+UAqd8j5AuPQdiBa37R6aJA/15/vdBEVRFx+6UBLVrpa7xaNlafw1fzsUd4T+MmhEF/0N5gSfufrIDIWKV3FmQTXk7QEJ9MTKAw2NStgWAiGSEBWzVkmTaF5hALLJVCYbPCxDIuAqWjqNmuVeypTu8flA/n5nLFwYN5YVHKPpQ3v6woaUM7pYXXN4l3eL2hA+W4dvKKOz/5rxHgf01ABK8VhLYBugFX5BKvHlO2mL3fcI+7sxw1/ZnsxRRpgNcpj9fcvO05SEZ+9FcsNfxyHpFYK5P8qAQnmWvimVEHHMc8bvtzyExKGp7wOYrvh+91Q+LJI/i8I8FHBaZWq5zhlv+HLbScsEPL44O3cbzh2BfN/2VJSXGZqsFejiub/qqWkaHSatFV5Uhz/V0SWkoIs9qosUiT/VxUhstir8oMK5f/ORNU36yuW/zsLVd8dQbH8X5AGTVT13S0Wxf81pJeZVqAS7uWK5v8KOuELm70Cj8L5v+TuANgS2O9F6P+y1PIcMTPB+clccJzwmjgmq59uRXxwEpgs/iq8iM29co/Xj7AezTSebRexHns+muvRxGmocDvcH1etpjzfHk/iydjXo1mmS0UT5la8YrV6Mp4d0yHjWI/mc+qaD/RF1BZUoJWn6sSEI3QVpm/8QE4+uD2aj28VzPEEuVtwhM6r6jhC+sIxhDV2vPH2m98qoOMZsPi0UXBNRIY0hV1ISF5NOubxszePl1CffjSer28Vz/FsJAkNYP1Gsv8SzZCV2272VbWfkPrt7KSAjmejwDAzL78nuGo5bkXNc+BtXaSAjud6IZ0XSWS1pZAqVeEHFdDxhGvUVarUrO+5zPoK6HjKtj3fkZPK0ynH0VqPZoY7grHvbx/Nd3ZHUEDHs52oQKpIE1zVtH9F3S3eXWMXXkDHU//rDiQSqurLJBTO8Yy+s6eGS4ZbfVmmojmeQJBwR0CY63XlKSOYAxxP9dGDEMwFjicjmBsczzki3728h4T/S1qvu+cgO6tuMQOw6vF/WRYAGnAOsjPqFj4DsMrxfx1ykZKDawTAk8Y9ydfnrq8zoVXhlM//nY0g4QhKaTdKMy8/O5XZ/9UJIcvb1Y4N9hidXEImySpaQPbQRkAYUE5aRkLBlglRlcH/dYg6IixvV8lgUZ8EsoCWCUObJiJAybQWiZKnR1V8/9eDuLiri2FolLc7UWVZaeEAABm4wCO+fAA2cDsIRWZjECC3vV7XuepYPZyaLySeJFqyXhVIIFPlcumVk5zE5yXwf7U3I9W9XjTo7SqKDgEuHzQhxsRhIgPqz3fQupTdASNaF5bXq7L40cO1fYbgSbR6vSqOjE+0lJOcxOcF939Vvh3C7rtQ1lUTbghT8pGdIRkRAIGfbni9amyC38PRP5mWrFdVY4EvQWh5RBJfeNH9X3RxD7M22BuGAWrEVt8JbnZsMzIqAMoYR4K95dkgirtwEcsviZauSRw2eyLlJJt8qBL4v323XlxlwrE1kgUNTRpfQRWxhfEIpJzkRJXA/23Pp2lZoBCpo1JPpLrST0SJJyJES6LlUl91IPVTwkdVdP93KS7ugdZOghyuIlYfQSUWQYTNXoBAenydqILIKiJD4cVmD0NwEi3R+sBsJxJQUXsFInxUxfZ/WY15tsNglCb9V+XrdQJAcfyORiDxgJlwZqgfs1KPaA3S11JoidRXuJKaFTA+qhJkEhw56NTOkQUo7Vmf4jDnZRSPMzpeTgwsiVYhUyAZN/PweSmyTMhBp/2KRVMuzTuCtJm8oohefSMGkqIgsFRagqPAoV3n4KvmRBZr9FozxYYhu16SnNDoz0YePcz7ay7r6TNu0nV2nMEoA/mCexht5NaQAySAJM76DJwZcFIeK9B8QCWd33tEGAA2CK8lheFu4OTndM5jBJoPyPPNxClCcY6THUezTdOD5KM81gtMB0GWqFNE5ztaS5iZhZtiZjw45I+aNE5chIVAneJxbeggALOLSOCuTCjP13pSEFxdk4QVpgQgfZkp6q6PMRgeWb4QsovK9oHspEoVHqbhUjpSS7gqEfMtmdghecguhXN1oVJ4z67QeVach+YhGVKdg9IHCVwYIRBPzzYe3G2nYzjm+Urvp1saIbALePoKU/f0ZaYO+qOPIzlkCCDtqJJqrtKEPFMIKaOMAll6qq32ZUzO9aKTMCALCnSkvoApVA5K85PAaYoyVX91Y+SXzwYxeByzvST58PJw4m6i/7RtMua3ssatMq0ZxSn4QC0q7ZS7aFmgcFk+/kehGimJXVXMEuVOKwOuI0gAiJymfFM4lbLuiJ1b9SUQ9t8ILRI4mBxqpWO+HQ7D3ezVmqd2aCKIidkOkFLXjlK/2VsWKF7QX971QULYYiJZVSkxKwI7iurVpI7dUYIDiKr2NCcryV6gvSj+CLBTAz1pFVeQwOFyqC6dvK9vPDnb4z9fku1DtGc0cfmDX0i5qUnFGNhsgEJVrbrOlgUKx+t/dLID5A6jBxPUiBrG4ZLpXk5aHAp5mAinri0U2AewtgL5YdxrQlfToPLXTKoaBlG+fcr2UtvIgVWy+v/Mw2rPd1A9zMQkwwqJzLJAVWkFEH1WQVyxutC1tF5sTiHiOZhSPEya0y05mCTfHAn2+k9C5WNbh7aCPEy4/g1Ocu0vn/i9P2MXHALfir/Zd9gSlYmZKMsCVZ8MAOrrhw4X0Q7wEaT/5XyhbytOU9glcTFsRLDKh9hiq3lY4MKpxrdc+82nHHVnXFAVh8KbVqem2GblQFiOSGqK1RBWGxErjd+lql4gdjKyRFjeAxN/GxmwklPlobu+Ag0dZShqdx4445lBapirHdRIraRNTAAivR6W31sWKH4ySIUdoVardoFDWwQs34+e9LgEqapW05IrwmkrJjT5UmGf9eRKP56C7V/xsMBXE1PEVvHPZiLMsEN8VVU92sSE7/kB51VfxXmlZYGy0siDfualng0AyLOpCQBgEpcstkOQhFN9+RG1GJeTxqP+RgvhkcupApc4MN6zuL+Bckv9e7BhCxUSvMScycP6ZMbqrj/J3DypWzjHMYHNqaZrruT7I3qRTZ6zuPfFlkp/j03V3NUowZkUa7x+xwxKa1aIQH+SuSV2C4pjA8P0Rd0Q0ZA5plEVjN/iOfN74oSOpJQ+9hXcJy1CYIWX2bhK4JNWizFJmt58kEwjTe/eEM6cnmXOC+tv7TuQGr8gHlNeH9fbr8LZvV9t6+7mL/X2w/AZPD5q36rX3El+uZ3G8do5ThgnO2QLKEJFB3SGQBJOAqMyDLV0MkaHVeqGSDP1UAhKJD+IEF5LJwkvoT0jSFI/+yh6Br3kpP8AF0d2ALEPr8W3Lx80yhDYbvZoMLp6UUaOfqnHXyPr1dAlWz1k8uydrCzK66eI3vgLW8u4avYJunwgxolWXZ34Mrdca72gqqeTCcbplijpRK2jxgcTlLb0pKbuCP4jfMlFV3bqItvNHr/mWrEvG/ibtfVB9jyHPt3Ry2MaD1HRZwwXHfmHBOR/6ymTYF0YN2Z92Pw4DmPzWvRt1NXBQz2lYuAQiNq9uT5AvdKPY8wHWagtezJ4jkjK+ZyRsH3vCoePzGXnhEkMu8+ZC47//ZfJ6j+I+ODnrn9Vkt3dZwBW5uOU/Qjrz8az7SLWf/72P56GSrHDHfcMwEqsKc+3v4gnY1+/MF0qvNwdVkbkC0+BlVerJ5/G1MnzN6eu8OKXr0J9EbVFnf0aVIJu87H1Ok5Ilya7PlFcCXyl0Dd+ICcf3H4KVYw1qFKbNh+sljJJ3WZX8nS+4h9HSF84hjD444234T+FWIMK6CqbS/nagS1y2w5AGu3mm7AEBp3Uq3C2/m1o0viKrmMeP3vzh6hPP/qTbxViDao61BqmYaJifPiyXiCFXnh1voOc91YgtNAqeCOJr/j7/+m3s5OirEEFqeV2fROEj0g9ebB6iDEhe4N9EqSeP0jiK75O2eFtXaQga1CXUJdXooyb8DFJNIAm1Y1aXTybmohUO7b4lMr1QWe8BvVsFdGCZ4QSlqErqdSs77nM+gqxBpWeWHxJJ1RARD9JrFbCVyL9/b99H+4Ixr6//TfZHUEx1qCyIjLMMD6oXymnR+UmmA6APajHNtxegQgfV9H3r6i7xbtr7MKLsAZVz/Hw636zt/jIB/W4WJBIgRANQprG52XKJJz5GlRzkhc2X8IcT4WnKXU/v8rFV9Ysk4ZBBIXnrlqoO8o5k3yScsBwIPYkPk7AwyO3D9DycvBVTRpGablUSm3ipIETvln2EPCZc9R21SnXgwZnI6/cJy9g0CAAswOd+ehZsc9QEIDZgc5+9Hyv8G0JAA==)

- framework\_runner – Contains a timestamped directory that contains the intermediate layer outputs (framework) stored in .raw format as described in the framework runner step.
- inference\_engine – Contains a timestamped directory that contains the intermediate layer outputs (inference engine) stored in .raw format as described in the inference engine step.
- verification directory – Contains a timestamped directory that contains the following:

    - A directory for each verifier specified while running oneshot; it contains CSV and HTML files with metric details for each layer output
    - tensor\_inspection – Individual directories for each layer’s output with the following contents:

        - CDF\_plots.png – Golden vs target CDF graph
        - Diff\_plots.png – Golden and target deviation graph
        - Histograms.png – Golden and target histograms
        - golden\_data.csv – Golden tensor data
        - target\_data.csv – Target tensor data
    - summary.csv – Report for verification results of each layers output
- Wrapper directory containing log.txt with the entire log for the run.

Note: Except wrapper directory all other directories will have a folder called latest which is a symlink to the latest run’s corresponding timestamped directory.

Snapshot of summary.csv file:

![../images/oneshot_summary.png](data:image/png;base64,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)

Understanding the oneshot-layerwise report:

| Column | Description |
| --- | --- |
| Name | Output name of the current layer |
| Layer Type | Type of the current layer |
| Size | Size of this layer’s output |
| Tensor\_dims | Shape of this layer’s output |
| &lt;Verifier name&gt; | Verifier value of the current layer output compared to reference output |
| golden\_min | minimum value in the reference output for current layer |
| golden\_max | maximum value in the reference output for current layer |
| target\_min | minimum value in the target output for current layer |
| target\_max | maximum value in the target output for current layer |

**Quantization Checker**

The quantization checker analyzes activations, weights, and biases of a given model. It provides:

1. Comparision between quantized and unquantized weights and biases
2. Analysis on unquantized weights, biases, and activations
3. Results in csv, html, or plots
4. Problematic weights and biases for a given bitwidth quantization

**Usage**

usage: qnn-accuracy-debugger --quant_checker [-h] \
                                --model_path \
                                --input_tensor \
                                --config_file \
                                --framework \
                                --input_list \
                                --output_tensor \
                                [--engine_path] \
                                [--working_dir] \
                                [--quantization_overrides] \
                                [--extra_converter_args] \
                                [--bias_width] \
                                [--weights_width] \
                                [--host_device] \
                                [--deviceId] \
                                [--generate_csv] \
                                [--generate_plots] \
                                [--per_channel_plots] \
                                [--golden_output_reference_directory] \
                                [--output_dirname]
                                [--verbose]
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**Sample quant\_checker\_config\_file**

> 
> 
> - {
>     - - “WEIGHT\_COMPARISON\_ALGORITHMS”: [
>     - {“algo\_name”:”minmax”,”threshold”:”10”},
> {“algo\_name”:”maxdiff”, “threshold”:”10”},
> {“algo\_name”:”sqnr”, “threshold”:”26”},
> {“algo\_name”:”stats”, “threshold”:”2”},
> {“algo\_name”:”data\_range\_analyzer”},
> {“algo\_name”:”data\_distribution\_analyzer”, “threshold”:”0.6”}
> 
> 
> 
> 
> ],
> “BIAS\_COMPARISON\_ALGORITHMS”: [
> 
> 
> 
> > 
> > 
> > {“algo\_name”:”minmax”, “threshold”:”10”},
> > {“algo\_name”:”maxdiff”, “threshold”:”10”},
> > {“algo\_name”:”sqnr”, “threshold”:”26”},
> > {“algo\_name”:”stats”, “threshold”:”2”},
> > {“algo\_name”:”data\_range\_analyzer”},
> > {“algo\_name”:”data\_distribution\_analyzer”, “threshold”:”0.6”}
> 
> 
> 
> ],
> “ACT\_COMPARISON\_ALGORITHMS”: [
> 
> 
> 
> > 
> > 
> > {“algo\_name”:”minmax”, “threshold”:”10”},
> > {“algo\_name”:”data\_range\_analyzer”}
> 
> 
> 
> ],
> “INPUT\_DATA\_ANALYSIS\_ALGORITHMS”: [{“algo\_name”:”stats”, “threshold”:”2”}],
> “QUANTIZATION\_ALGORITHMS”: [“cle”, “None”],
> “QUANTIZATION\_VARIATIONS”: [“tf”, “enhanced”, “symmetric”, “asymmetric”]
> 
> 
> 
> 
> }

**Output**

Output are available in the &lt;working-directory&gt;/results, which looks like:
.. container:

.. figure:: /../images/quant_checker_acc_debug_output_dir_struct.png
    Copy to clipboard

Results are provided in:

1. HTML
2. CSV
3. Histogram

A log is provided in the &lt;working-directory&gt;/quant\_checker directory.

**HTML**
Each HTML file contains a summary of the results for each
quantization option and for each input file provided.

The following example provides additional guidance on the contents of the HTML files.

![../images/snpe_quantization_checker_html_sample.png](data:image/png;base64,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)

**CSV**

Each CSV file contains detailed computation results for a
specific node type (activation/weight/bias) and quantization
option. Each row in the csv file displays the op name, node
name, passes accuracy (True/False), computation result
(accuracy differences), threshold used for each algorithm,
and the algorithm name. The format of the computation results (accuracy differences) differs according to
the algorithms/metrics used.

The following table provides additional notes about the different algorithms and information in each csv row.

The following CSV example shows weight data for
one of the quantization options.

![../images/snpe_quantization_checker_csv_weights.png](data:image/png;base64,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)

Separate .csv files are generated for activations, weights, and biases for each quantization option. The activation-related results include analysis for each input file provided.

**Histogram**

For each quantization variation and for each weight and bias tensor in the model, we generate historagm.
a histogram is generated for each quantization variation and for each weight and bias tensor in the model. The following example illustrates the generated histograms.

![../images/quant_checker_hist.png](data:image/png;base64,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)

**Logs**

The log files contain the following information.

- The commands executed as part of the script’s run, including
different runs of the snpe-converter tool with different
quantization options
- Analysis failures for activations, weights, and biases

The following example shows a sample log output.

<====ACTIVATIONS ANALYSIS FAILURES====>
    Copy to clipboard

&lt;====ACTIVATIONS ANALYSIS FAILURES====&gt;

> 
> 
> Results for the enhanced quantization:
> |         Op Name         | Activation Node | Passes Accuracy |         Accuracy Difference          | Threshold Used | Algorithm Used |
> |  conv\_tanh\_comp1\_conv0  |    ReLU\_6919    |      False      | minabs\_diff: 0.59 maxabs\_diff: 17.16 |      0.05      |     minmax     |

where,

1. Op Name : Op name as expressed in snpe\_model.dlc
2. Activation Node : Activation node name in the operation
3. Passes Accuracy : True if the quantized activation (or weight or bias) meets threshold when compared with values from float32 graph; false otherwise
4. Accuracy Difference : Details about the accuracy per the algorithm used
5. Threshold Used : The threshold used to influence the result of “Passes Accuracy” column
6. Algorithm Used : Metric used to compare actual quantized activations/weights/biases against unquantized float data or analyze the quality of unquantized float data. Metrics can be minmax, maxdiff, sqnr, stats, data\_range\_analyzer, data\_distribution\_analyzer.

Last Published: Oct 02, 2025

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