-
-#### For SageMaker Notebook instance
-
-
-## Installation Guide
-For both cases, the installation process is exactly the same, which is by **searching ```neural-compressor``` in the Extension Manager**.
-
-1. Search and Install
-
-
-
-2. Rebuild
-
-
-
-3. Save and Reload
-
-
-
-4. Done!
-
-
diff --git a/neural_coder/docs/BigDLNanoSupport.md b/neural_coder/docs/BigDLNanoSupport.md
deleted file mode 100644
index 0ea0e4dc849..00000000000
--- a/neural_coder/docs/BigDLNanoSupport.md
+++ /dev/null
@@ -1,37 +0,0 @@
-BigDL Nano Support
-===========================
-
-Neural Coder collaborates with [BigDL-Nano](https://bigdl.readthedocs.io/en/latest/doc/Nano/Overview/nano.html), a Python library that automatically applies modern CPU optimizations, to further democratize ease-of-use BigDL-Nano APIs as a **no-code** solution for PyTorch Deep Learning programmers.
-
-## Example
-For instance, to perform BF16 + Channels Last optimizations with BigDL-Nano API using Neural Coder on the [example code](../examples/nano/resnet18.py) and run this code with the enabled optimizations, users can simply execute this command:
-```
-python -m neural_coder -o nano_bf16_channels_last ../examples/nano/resnet18.py
-```
-The alias for each optimization set is documented in the below Support Matrix. Note that you need to ```pip install bigdl``` first following [BigDL-Nano documentation](https://github.com/intel-analytics/BigDL#installing).
-
-## Support Matrix
-
-| Optimization Set | API Alias |
-| ------------- | ------------- |
-| BF16 + Channels Last | `nano_bf16_channels_last` |
-| BF16 + IPEX + Channels Last | `nano_bf16_ipex_channels_last` |
-| BF16 + IPEX | `nano_bf16_ipex` |
-| BF16 | `nano_bf16` |
-| Channels Last | `nano_fp32_channels_last` |
-| IPEX + Channels Last | `nano_fp32_ipex_channels_last` |
-| IPEX | `nano_fp32_ipex` |
-| Convert CUDA TO GPU | `nano_gpu_to_cpu` |
-| INT8 | `nano_int8` |
-| JIT + BF16 + Channels Last | `nano_jit_bf16_channels_last` |
-| JIT + BF16 + IPEX + Channels Last | `nano_jit_bf16_ipex_channels_last` |
-| JIT + BF16 + IPEX | `nano_jit_bf16_ipex` |
-| JIT + BF16 | `nano_jit_bf16` |
-| JIT + Channels Last | `nano_jit_fp32_channels_last` |
-| JIT + IPEX + Channels Last | `nano_jit_fp32_ipex_channels_last` |
-| JIT + IPEX | `nano_jit_fp32_ipex` |
-| JIT | `nano_jit_fp32` |
-| ONNX Runtime | `nano_onnxruntime_fp32` |
-| ONNX Runtime + INT8 | `nano_onnxruntime_int8_qlinear` |
-| OpenVINO | `nano_openvino_fp32` |
-| OpenVINO + INT8 | `nano_openvino_int8` |
diff --git a/neural_coder/docs/IntelCPU_PerformanceSetting.md b/neural_coder/docs/IntelCPU_PerformanceSetting.md
deleted file mode 100644
index a112fd3516f..00000000000
--- a/neural_coder/docs/IntelCPU_PerformanceSetting.md
+++ /dev/null
@@ -1,51 +0,0 @@
-## Intel CPU Platforms: Best Performance Setting
-### Install MKL, OpenMP and JEMALLOC
-The simplest way for installation is through ```conda install```:
-```bash
-conda install -y mkl mkl-include jemalloc
-```
-
-### Install NUMA Controller
-```bash
-apt-get update && apt-get install bc numactl
-```
-
-### Environment Variables
-Check if your ```CONDA_PREFIX``` has a value by:
-```bash
-echo ${CONDA_PREFIX}
-```
-If it is empty, it means that you are not in a traditional CONDA environment, you need to find the location of the ```.so``` files by:
-```bash
-find / -name "libjemalloc.so"
-find / -name "libiomp5.so"
-```
-It will show the path these files were installed into. For example:
-```bash
-/home/name/lib/libjemalloc.so
-/home/name/lib/libiomp5.so
-```
-And then you should ```export``` this path as ```CONDA_PREFIX```:
-```bash
-export CONDA_PREFIX="/home/name"
-```
-Finally:
-```bash
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libjemalloc.so
-export LD_PRELOAD=${LD_PRELOAD}:${CONDA_PREFIX}/lib/libiomp5.so
-export MALLOC_CONF="oversize_threshold:1,background_thread:true,metadata_thp:auto,dirty_decay_ms:9000000000,muzzy_decay_ms:9000000000"
-export KMP_AFFINITY="granularity=fine,compact,1,0"
-export KMP_BLOCKTIME=1
-export DNNL_PRIMITIVE_CACHE_CAPACITY=1024
-```
-
-### Frequency Governers
-Check the frequency governor state on your machine:
-```bash
-cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
-```
-If it shows ```powersave``` instead of ```performance```, execute:
-```bash
-echo "performance" | sudo tee /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
-cat /sys/devices/system/cpu/cpu*/cpufreq/scaling_governor
-```
diff --git a/neural_coder/docs/PythonAPI.md b/neural_coder/docs/PythonAPI.md
deleted file mode 100644
index dfc7567fe43..00000000000
--- a/neural_coder/docs/PythonAPI.md
+++ /dev/null
@@ -1,58 +0,0 @@
-Neural Coder as Python API
-===========================
-
-Neural Coder can be used as Python APIs. We currently provide 3 main user-facing APIs for Neural Coder: enable, bench and superbench.
-
-#### Enable
-Users can use ```enable()``` to enable specific features into DL scripts:
-```
-from neural_coder import enable
-enable(
- code="neural_coder/examples/vision/resnet50.py",
- features=[
- "pytorch_jit_script",
- "pytorch_channels_last",
- ],
-)
-```
-To run benchmark directly on the optimization together with the enabling:
-```
-from neural_coder import enable
-enable(
- code="neural_coder/examples/vision/resnet50.py",
- features=[
- "pytorch_jit_script",
- "pytorch_channels_last"
- ],
- run_bench=True,
-)
-```
-
-#### Bench
-To run benchmark on your code with an existing patch:
-```
-from neural_coder import bench
-bench(
- code="neural_coder/examples/vision/resnet50.py",
- patch_path="${your_patch_path}",
-)
-```
-
-#### SuperBench
-To sweep on optimization sets with a fixed benchmark configuration:
-```
-from neural_coder import superbench
-superbench(code="neural_coder/examples/vision/resnet50.py")
-```
-To sweep on benchmark configurations for a fixed optimization set:
-```
-from neural_coder import superbench
-superbench(
- code="neural_coder/examples/vision/resnet50.py",
- sweep_objective="bench_config",
- bench_feature=[
- "pytorch_jit_script",
- "pytorch_channels_last",
- ],
-)
-```
diff --git a/neural_coder/docs/PythonLauncher.md b/neural_coder/docs/PythonLauncher.md
deleted file mode 100644
index 74ad230e1f9..00000000000
--- a/neural_coder/docs/PythonLauncher.md
+++ /dev/null
@@ -1,40 +0,0 @@
-Python Launcher
-===========================
-
-Neural Coder can be used as a Python **Launcher**. Users can run the Python model code as it is with automatic enabling of Deep Learning optimizations by using Neural Coder's inline Python **Launcher** design.
-
-## Quick-Start
-
-Example: Let's say you are running an NLP model using ```run_glue.py``` from HuggingFace transformers [examples](https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py).
-
-Pre-requisites:
-```bash
-pip install transformers==4.21.0 torch datasets
-```
-
-Generally we run this code with a Python command line like this:
-```bash
-python run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result
-```
-
-With Neural Coder's **Launcher**, users can easily enjoy Deep Learning optimizations (e.g. default - INT8 dynamic quantization by Intel® Neural Compressor for PyTorch models) by simply adding an inline prefix
-```bash
--m neural_coder
-```
-to the Python command line, and everything else remains the same:
-```bash
-python -m neural_coder run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result
-```
-
-This will run ```run_glue.py``` with the Deep Learning optimization automatically enabled, while everything else (e.g. your input arguments for the code itself) remains the same as the original code. You can also check out the optimized code ```run_glue_optimized.py``` auto-generated by the **Launcher** under the same folder if you want to learn the code enabling.
-
-Note: Any modification on the optimized code ```run_glue_optimized.py``` will be overwritten every time you run Neural Coder **Launcher** on ```run_glue.py```, so please make any modification on the original code ```run_glue.py``` instead of the optimized one. The optimized code is only saved for your reference.
-
-## Launcher Arguments (Optional)
-
-Users can specify which Deep Learning optimization they want to conduct using ```--opt``` argument. The list of supported Deep Learning optimization features can be found [here](SupportMatrix.md).
-
-Note that if specifically optimizing with INT8 quantization by Intel® Neural Compressor, to choose a quantization approach (strategy), ```--approach``` argument can be specified with either ```static```, ```static_ipex``` or ```dynamic```. For example, to run INT8 static quantization by Intel® Neural Compressor:
-```bash
-python -m neural_coder --approach static run_glue.py --model_name_or_path bert-base-cased --task_name mrpc --do_eval --output_dir result
-```
diff --git a/neural_coder/docs/Quantization.md b/neural_coder/docs/Quantization.md
deleted file mode 100644
index 555834c74a8..00000000000
--- a/neural_coder/docs/Quantization.md
+++ /dev/null
@@ -1,39 +0,0 @@
-Neural Coder for Quantization
-===========================
-This feature helps automatically enable quantization on Deep Learning models and automatically evaluates for the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a model with no manual coding needed. Supported features include Post-Training Static Quantization, Post-Training Dynamic Quantization, and Mixed Precision.
-
-
-## Features Supported
-- Post-Training Static Quantization for [Stock PyTorch](https://pytorch.org/tutorials/prototype/fx_graph_mode_ptq_static.html) (with FX backend)
-- Post-Training Static Quantization for [IPEX](https://github.com/intel/intel-extension-for-pytorch/blob/v1.12.0/docs/tutorials/features/int8.md)
-- Post-Training Dynamic Quantization for [Stock PyTorch](https://pytorch.org/tutorials/recipes/recipes/dynamic_quantization.html)
-- Mixed Precision for [Stock PyTorch](https://pytorch.org/tutorials/recipes/recipes/amp_recipe.html)
-
-## Models Supported
-- HuggingFace [Transformers](https://github.com/huggingface/transformers) models
-- [torchvision](https://pytorch.org/vision/stable/index.html) models
-- Broad models (under development)
-
-## Usage
-- PyPI distribution with a one-line API call
-- [JupyterLab extension](../extensions/neural_compressor_ext_lab/README.md)
-
-## Example
-### PyPI distribution:
-HuggingFace [Transformers](https://github.com/huggingface/transformers) models: [text-classification/run_glue.py](https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py)
-```
-from neural_coder import auto_quant
-auto_quant(
- code="https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py",
- args="--model_name_or_path albert-base-v2 --task_name sst2 --do_eval --output_dir result",
-)
-```
-
-[torchvision](https://pytorch.org/vision/stable/index.html) models: [imagenet/main.py](https://github.com/pytorch/examples/blob/main/imagenet/main.py)
-```
-from neural_coder import auto_quant
-auto_quant(
- code="https://github.com/pytorch/examples/blob/main/imagenet/main.py",
- args="-a alexnet --pretrained -e /path/to/imagenet/",
-)
-```
diff --git a/neural_coder/docs/SupportMatrix.md b/neural_coder/docs/SupportMatrix.md
deleted file mode 100644
index be2a7fea308..00000000000
--- a/neural_coder/docs/SupportMatrix.md
+++ /dev/null
@@ -1,26 +0,0 @@
-Supported Optimization Features
-===========================
-
-| Category | Optimization | API Alias |
-| ------------- | ------------- | ------------- |
-| PyTorch | [Mixed Precision](https://pytorch.org/docs/stable/amp.html) | `pytorch_amp` |
-| PyTorch | [Channels Last](https://pytorch.org/tutorials/intermediate/memory_format_tutorial.html) | `pytorch_channels_last` |
-| PyTorch | [JIT (Just-In-Time) Script/Trace](https://pytorch.org/docs/stable/jit.html) & [optimize_for_inference](https://pytorch.org/docs/stable/generated/torch.jit.optimize_for_inference.html) | `pytorch_jit_script`, `pytorch_jit_trace`, `pytorch_jit_script_ofi`, `pytorch_jit_trace_ofi` |
-| PyTorch | JIT with [TorchDynamo](https://github.com/pytorch/torchdynamo) | `pytorch_torchdynamo_jit_script`, `pytorch_torchdynamo_jit_trace`, `pytorch_torchdynamo_jit_script_ofi`, `pytorch_torchdynamo_jit_trace_ofi` |
-| PyTorch | [Intel Neural Compressor (INC) Mixed Precision](https://github.com/intel/neural-compressor/blob/master/docs/source/mixed_precision.md) | `pytorch_inc_bf16` |
-| PyTorch | [INC INT8 Static Quantization (FX/IPEX)](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `pytorch_inc_static_quant_fx`, `pytorch_inc_static_quant_ipex`, `pytorch_inc_static_quant_ipex_xpu` |
-| PyTorch | [INC INT8 Dynamic Quantization](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `pytorch_inc_dynamic_quant` |
-| PyTorch | [Intel Extension for PyTorch (FP32, BF16, INT8 Static/Dynamic Quantization)](https://github.com/intel/intel-extension-for-pytorch) | `pytorch_ipex_fp32`, `pytorch_ipex_bf16`, `pytorch_ipex_int8_static_quant`, `pytorch_ipex_int8_dynamic_quant` |
-| PyTorch | [Alibaba Blade-DISC](https://github.com/alibaba/BladeDISC) | `pytorch_aliblade` |
-| PyTorch Lightning | [Mixed Precision](https://pytorch-lightning.readthedocs.io/en/latest/guides/speed.html) | `pytorch_lightning_bf16_cpu` |
-| TensorFlow | [Mixed Precision](https://www.intel.com/content/www/us/en/developer/articles/guide/getting-started-with-automixedprecisionmkl.html) | `tensorflow_amp` |
-| Keras | [Mixed Precision](https://www.tensorflow.org/guide/mixed_precision) | `keras_amp` |
-| TensorFlow/Keras Model | [INC Quantization](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `tensorflow_inc` |
-| Keras Script | [INC Quantization](https://github.com/intel/neural-compressor/tree/master/examples/keras/mnist) | `keras_inc` |
-| ONNX Runtime | [INC Static Quantization (QLinear)](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `onnx_inc_static_quant_qlinear` |
-| ONNX Runtime | [INC Static Quantization (QDQ)](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `onnx_inc_static_quant_qdq` |
-| ONNX Runtime | [INC Dynamic Quantization](https://github.com/intel/neural-compressor/blob/master/docs/source/quantization.md#supported-feature-matrix) | `onnx_inc_dynamic_quant` |
-| [HuggingFace Optimum-Intel](https://huggingface.co/docs/optimum/intel/index) | INC Quantization | `pytorch_inc_huggingface_optimum_static`, `pytorch_inc_huggingface_optimum_dynamic` |
-| [Intel Extension for Transformers](https://github.com/intel/intel-extension-for-transformers/) | INC Quantization | `intel_extension_for_transformers` |
-| [BigDL Nano](https://bigdl.readthedocs.io/en/latest/doc/PythonAPI/Nano/pytorch.html#bigdl-nano-pytorch-inferenceoptimizer) | [Optimization List](./BigDLNanoSupport.md) | `nano_` + [specific alias](./BigDLNanoSupport.md) |
-| Auto-Detect | [INC Quantization](https://github.com/intel/neural-compressor) | `inc_auto` |
diff --git a/neural_coder/docs/cloud_autobench/CloudAutobench.MD b/neural_coder/docs/cloud_autobench/CloudAutobench.MD
deleted file mode 100644
index d906a80292f..00000000000
--- a/neural_coder/docs/cloud_autobench/CloudAutobench.MD
+++ /dev/null
@@ -1,95 +0,0 @@
-# Cloud Auto-bench
-This is a user guide for the automated bash script for creating a cloud instance, configuring the environment, running the benchmark code, and terminating the instance. The script supports AWS and Ali Yun for now and will support more cloud vendors in the future.
-
-## Prerequisite
-## AWS
-#### Install AWS CLI
-Install the latest AWS CLI according to https://docs.aws.amazon.com/cli/latest/userguide/getting-started-install.html.
-Example: install AWS CLI on Linux x86 (64-bit) by the following commands.
-```
-curl "https://awscli.amazonaws.com/awscli-exe-linux-x86_64.zip" -o "awscliv2.zip"
-unzip awscliv2.zip
-./aws/install -i ~/.Awscli2 -b ~/.Awscli2/bin
-export PATH=/.Awscli2/bin:$PATH
-```
-Confirm the installation with the following command.
-```
-export PATH=/.Awscli2/bin:$PATH
-```
-
-#### Configure IAM role
-Configure IAM role to access AWS according to https://us-east-2.console.aws.amazon.com/iamv2/home#/users.
-
-#### Configure AWS clients
-Configure AWS clients via "aws configure" which fetch from step 2.
-```
-aws --configure
-AWS Access Key ID [None]: x
-AWS Secret Access Key [None]: x
-Default region name [None]: us-east-2
-Default output format [None]: text
-```
-
-#### Create a key pair
-Create a key pair x.pem via https://us-east-2.console.aws.amazon.com/ec2/v2/home?region=us-east-2#KeyPairs:, add x.pem to the current folder, and modify its permission by
-```
-chmod 400 x.pem
-```
-
-## Ali Yun
-#### Install Alibaba Cloud CLI
-Download the installation package for Linux from https://www.alibabacloud.com/help/en/alibaba-cloud-cli/latest/linux
-
-Decompress the downloaded file to obtain the executable file named aliyun
-```
-tar xzvf (aliyun-cli-linux-latest-amd64.tgz)
-```
-In the "()" is the file name of the installation file you have downloaded
-
-Set environment variables
-add this line into the ~/.bash_profile file
-```
-export PATH=/home/your_directory/.Awscli2/bin:$PATH
-```
-
-Your directory is the directory of bin in the ALi Yun installation directory)
-source environment variables
-
-#### Configure Ali Yun clients
-run
-```
-aliyun configure
-```
-
-Input the key ID, key secret, region ID and default language of your Ali Yun account
-```
-Access Key Id [************************]:
-Access Key Secret [******************************]:
-Default Region Id [**-****]:
-Default Output Format [json]: json (Only support json)
-Default Language [zh|en] en
-```
-
-### Create a key pair of Ali Yun according to page https://ecs.console.aliyun.com/#/keyPair/
-add x.pem to the current folder, and modify its permission by
-```
-chmod 400 x.pem
-```
-
-## Launch the cloud benchmark script
-### Modify the permission of bench.sh
-```
-chmod 755 bench.sh
-```
-
-### Input your task config information in the config.conf file
-You need to input the information of your task in the config.conf file
-You can choose the cloud vendor(AWS or Ali Yun), Instance number, type and other information with the comments as a reference
-
-### Launch the script
-```
-./bench.sh
-```
-
-### Output
-Example Cloud auto-bench report: ```superbench_report_aws_icx.pdf```
diff --git a/neural_coder/docs/cloud_autobench/bench.sh b/neural_coder/docs/cloud_autobench/bench.sh
deleted file mode 100644
index b28b2de9ed2..00000000000
--- a/neural_coder/docs/cloud_autobench/bench.sh
+++ /dev/null
@@ -1,497 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-#!/bin/bash
-
-# Read the user input
-
-check_input()
-{
-if [ $whether_launch_new -eq "1" ]
-then
- if [ $vendor -eq "1" ]
- then
- if [ -z $security_id_aws ]
- then
- echo "[ERROR] There is no security group ID, you must specify security ID in config file when creating a new instance"
- fi
-
- if [ -z $subnet_id_aws ]
- then
- echo "[ERROR] There is no subnet ID, you must specify subnet ID in config file when creating a new instance"
- fi
- elif [ $vendor -eq "2" ]
- then
- if [ -z $security_id_ali ]
- then
- echo "[ERROR] There is no security group ID, you must specify security ID in config file when creating a new instance"
- fi
-
- if [ -z $region_id_ali ]
- then
- echo "[ERROR] There is no region ID, you must specify region ID in config file when creating a new instance"
- fi
- else
- echo "[ERROR] There is no this vendor"
- fi
-else
- if [ -z $instance_id ]
- then
- echo "[ERROR] There is no instance ID, you must specify instance ID in config file when using an existed instance"
- fi
-fi
-
-}
-
-create_AWS_instance()
-{
-if [ $os -eq "1" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ami-02f3416038bdb17fb"
- else
- ami_ID="ami-0ff596d41505819fd"
- fi
-elif [ $os -eq "2" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ami-02d1e544b84bf7502"
- else
- ami_ID="ami-03e57de632660544c"
- fi
-elif [ $os -eq "3" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ami-092b43193629811af"
- else
- ami_ID="ami-0082f8c86a7132597"
- fi
-elif [ $os -eq "4" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ami-0f7cb53c916a75006"
- else
- ami_ID="ami-075a486be6269029f"
- fi
-else
- echo "[ERROR] The operating system is invalid"
- exit 0
-fi
-
-echo "[INFO] Starting creating AMS instance ..."
-
-instance_id=$(aws ec2 run-instances --image-id $ami_ID --count $count --instance-type $i_type --key-name $key_name --security-group-ids $security_id_aws --subnet-id $subnet_id_aws --block-device-mappings 'DeviceName=/dev/sda1, Ebs={VolumeSize=30}' --query "Instances[0].InstanceId")
-result=$?
-if [ $result -ne '0' ]
-then
- echo "[ERROR] Create AWS Instance failed"
- exit 0
-else
- echo "[INFO] Create AWS instance success"
- echo "[INFO] Your Instance Id is $instance_id"
- echo "[INFO] Waiting for instance to initialize ..."
- echo "[INFO] 15s left ..."
- sleep 5s
- echo "[INFO] 10s left ..."
- sleep 5s
- echo "[INFO] 5s left ..."
- sleep 5s
-fi
-}
-
-create_Ali_Yun_instance()
-{
-if [ $os -eq "1" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- else
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- fi
-elif [ $os -eq "2" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- else
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- fi
-elif [ $os -eq "3" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- else
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- fi
-elif [ $os -eq "4" ]
-then
- if [ $arch -eq "1" ]
- then
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- else
- ami_ID="ubuntu_20_04_x64_20G_alibase_20220524.vhd"
- fi
-else
- echo "[ERROR] The operating system is invalid"
- exit 0
-fi
-
-i_type="ecs.$i_type_family.$i_type_size"
-
-echo "[INFO] Starting creating Ali Yun instance ..."
-
-instance_id=$(aliyun ecs RunInstances --RegionId $region_id_ali --InstanceType $i_type --InstanceChargeType PostPaid --ImageId $ami_ID --KeyPairName $key_name --SecurityGroupId $security_id_ali --VSwitchId vsw-m5ethlhigvonp2kuyzhjw --InternetMaxBandwidthIn 1 --InternetMaxBandwidthOut 1 |grep "i-")
-result=$?
-
-instance_id="${instance_id:4:22}"
-if [ $result -ne '0' ]
-then
- echo "[ERROR] Create Ali Yun Instance failed"
- exit 0
-else
- echo "[INFO] Create Ali Yun instance successfully"
- echo "[INFO] The Ali Yun instance id is: $instance_id"
- echo "[INFO] Waiting for instance to initialize ..."
- echo "[INFO] 35s left ..."
- sleep 5s
- echo "[INFO] 30s left ..."
- sleep 5s
- echo "[INFO] 25s left ..."
- sleep 5s
- echo "[INFO] 20s left ..."
- sleep 5s
- echo "[INFO] 15s left ..."
- sleep 5s
- echo "[INFO] 10s left ..."
- sleep 5s
- echo "[INFO] 5s left ..."
- sleep 5s
-fi
-}
-
-connect_AWS()
-{
-dns_name=$(aws ec2 describe-instances --instance-ids $instance_id --query "Reservations[0].Instances[0].PublicDnsName")
-result=$?
-if [ $result -ne '0' ]
-then
- echo "[ERROR] Can not find this instance, please check"
- exit 0
-fi
-
-host_name=$dns_name
-
-if [ $os -eq "1" ]
-then
- host_name="ubuntu@$dns_name"
-else
- host_name="ec2-user@$dns_name"
-fi
-
-key_name="$key_name.pem"
-echo "[INFO] Your instance host name is: $host_name"
-echo "[INFO] Connecting to AWS Instance ..."
-ssh -i $key_name $host_name -o "StrictHostKeyChecking no" "uname -a ; exit"
-result=$?
-if [ $result -ne '0' ]
-then
- echo "[ERROR] SSH connection failed"
- echo "[INFO] Start terminating the Instance"
- aws ec2 terminate-instances --instance-ids $instance_id
- result=$?
- if [ $result -ne '0' ]
- then
- echo "[ERROR] Instance termination failed"
- else
- echo "[INFO] Instance termination success"
- fi
- exit 0
-else
- echo "[INFO] Connect to AWS Instance success"
-fi
-
-echo "[INFO] Start to transferring benchmark files"
-scp -i $key_name -r ./code/ $host_name:/tmp
-result=$?
-if [ $result -ne '0' ]
-then
- echo "[ERROR] SSH connection failed"
- exit 0
-else
- echo "[INFO] File transferring success"
-fi
-
-if [ $whether_launch_new -eq "1" ]
-then
- ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./config.sh; ./config.sh; exit"
- echo "[INFO] Install dependencies finished"
-else
- echo "[INFO] Configured environment"
-fi
-
-echo "[INFO] Start launching the task ..."
-
-ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./launch.sh; ./launch.sh; exit"
-
-echo "[INFO] Benchmark Execution finished"
-}
-
-connect_Ali_Yun()
-{
-public_ip=$(aliyun ecs DescribeInstances --output cols=InstanceId,PublicIpAddress.IpAddress rows=Instances.Instance[] |grep $instance_id)
-result=$?
-if [ $result -ne '0' ]
-then
- echo "[ERROR] Can not find this instance, please check"
- exit 0
-fi
-
-public_ip="${public_ip:25}"
-length=${#public_ip}
-public_ip="${public_ip:1:$length-2}"
-host_name="root@$public_ip"
-key_name="$key_name.pem"
-echo "[INFO] Your instance host name is: $host_name"
-
-echo "[INFO] Start to connecting Ali Yun instance"
-ssh -i $key_name $host_name -o "StrictHostKeyChecking no" "uname -a ; exit"
-result=$?
-if [ $result -ne '0' ]
-then
- echo "[ERROR] SSH connection failed"
- echo "[INFO] Start to delete instance $instance_id"
- sleep 60s
- aliyun ecs DeleteInstance --InstanceId $instance_id --Force true
- result=$?
- if [ $result -ne '0' ]
- then
- echo "[ERROR] Instance termination failed"
- exit 0
- else
- echo "[INFO] Instance termination success"
- fi
- exit 0
-else
- echo "[INFO] Connect to Ali Yun Instance success"
-fi
-
-echo "[INFO] Start to transferring benchmark files"
-scp -i $key_name -r ./code/ $host_name:/tmp
-result=$?
-if [ $result -ne '0' ]
-then
- echo "[ERROR] SSH connection failed"
- exit 0
-else
- echo "[INFO] File transferring success"
-fi
-
-if [ $whether_launch_new -eq "1" ]
-then
- ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./config.sh; ./config.sh; exit"
- echo "[INFO] Install dependencies finished"
-else
- echo "[INFO] Configured environment"
-fi
-
-echo "[INFO] Start launching the task ..."
-
-ssh -i $key_name $host_name "cd /tmp/code; chmod +x ./launch.sh; ./launch.sh; exit"
-
-echo "[INFO] Benchmark Execution finished"
-}
-
-close_AWS()
-{
-
-if [ $whether_retain -eq "1" ]
-then
- echo "[INFO] Start stopping the Instance"
-
- aws ec2 stop-instances --instance-ids $instance_id
- result=$?
- if [ $result -ne '0' ]
- then
- echo "[ERROR] Instance stop failed"
- exit 0
- else
- echo "[INFO] Instance stop success"
- echo "[INFO] The instance id is $instance_id, Please record this $instance_id for next use"
- fi
-else
- echo "[INFO] Start terminating the Instance"
-
- aws ec2 terminate-instances --instance-ids $instance_id
- result=$?
- if [ $result -ne '0' ]
- then
- echo "[ERROR] Instance termination failed"
- exit 0
- else
- echo "[INFO] Instance termination success"
- fi
-fi
-}
-
-close_Ali_Yun()
-{
-
-if [ $whether_retain -eq "1" ]
-then
- echo "[INFO] Start stopping the Instance"
-
- aliyun ecs StopInstance --InstanceId $instance_id
- result=$?
- if [ $result -ne '0' ]
- then
- echo "[ERROR] Instance stop failed"
- exit 0
- else
- echo "[INFO] Instance stop success"
- echo "[INFO] The instance id is $instance_id, Please record this $instance_id for next use"
- fi
-elif [ $whether_retain -eq "2" ]
-then
- echo "[INFO] Start terminating the Instance"
-
- aliyun ecs DeleteInstance --InstanceId $instance_id --Force true
- result=$?
- if [ $result -ne '0' ]
- then
- echo "[ERROR] Instance termination failed"
- exit 0
- else
- echo "[INFO] Instance termination success"
- fi
-fi
-}
-
-
-main()
-{
-vendor=$(sed '/^cloud_vendor=/!d; s/.*=//' config.conf)
-os=$(sed '/^OS=/!d; s/.*=//' config.conf)
-arch=$(sed '/^arch=/!d; s/.*=//' config.conf)
-count=$(sed '/^count=/!d; s/.*=//' config.conf)
-i_type_family=$(sed '/^i_type_family=/!d; s/.*=//' config.conf)
-i_type_size=$(sed '/^i_type_size=/!d; s/.*=//' config.conf)
-key_name=$(sed '/^key_name=/!d; s/.*=//' config.conf)
-instance_id=$(sed '/^instance_id=/!d; s/.*=//' config.conf)
-security_id_aws=$(sed '/^security_id_aws=/!d; s/.*=//' config.conf)
-subnet_id_aws=$(sed '/^subnet_id_aws=/!d; s/.*=//' config.conf)
-security_id_ali=$(sed '/^security_id_ali=/!d; s/.*=//' config.conf)
-region_id_ali=$(sed '/^region_id_ali=/!d; s/.*=//' config.conf)
-
-whether_retain=$(sed '/^whether_retain=/!d; s/.*=//' config.conf)
-whether_launch_new=$(sed '/^whether_launch_new=/!d; s/.*=//' config.conf)
-
-i_type="$i_type_family.$i_type_size"
-
-check_input
-
-if [ ! -f "$key_name.pem" ]; then
- echo "[ERROR] Can not find the key pair file $key_name.pem, please put the $key_name.pem file in this folder"
- exit 0
-else
- chmod 400 ./"$key_name.pem"
-fi
-
-if [ ! -f "./code/benchmark.py" ]; then
- echo "[ERROR] Can not find the benchmark file, please put the benchmark file in code folder"
- exit 0
-fi
-
-
-if [ $whether_launch_new -eq "1" ]
-then
- echo "[INFO] Your instance info:"
- echo "[INFO] Instance key name: $key_name"
- echo "[INFO] Instance count: $count"
- echo "[INFO] Instance_type: $i_type"
-else
- echo "[INFO] The existed instance you choose: $instance_id"
-fi
-
-if [ $whether_launch_new -eq "1" ]
-then
- if [ $vendor -eq "1" ]
- then
- create_AWS_instance
- elif [ $vendor -eq "2" ]
- then
- create_Ali_Yun_instance
- else
- echo "Tencent Cloud"
- fi
-else
- if [ $vendor -eq "1" ]
- then
- aws ec2 start-instances --instance-ids $instance_id
- echo "[INFO] Waiting for instance to Start ..."
- echo "[INFO] 15s left ..."
- sleep 5s
- echo "[INFO] 10s left ..."
- sleep 5s
- echo "[INFO] 5s left ..."
- sleep 5s
- elif [ $vendor -eq "2" ]
- then
- aliyun ecs StartInstance --InstanceId $instance_id
- echo "[INFO] Waiting for instance to Start ..."
- echo "[INFO] 45s left ..."
- sleep 15s
- echo "[INFO] 30s left ..."
- sleep 15s
- echo "[INFO] 15s left ..."
- sleep 15s
- else
- echo "Tencent Cloud"
- fi
-fi
-
-if [ $vendor -eq "1" ]
-then
- connect_AWS
-elif [ $vendor -eq "2" ]
-then
- connect_Ali_Yun
-else
- echo "Tencent Cloud"
-fi
-
-if [ $vendor -eq "1" ]
-then
- close_AWS
-elif [ $vendor -eq "2" ]
-then
- close_Ali_Yun
-else
- echo "Tencent Cloud"
-fi
-
-exit 0
-
-}
-
-main
-
-
-
-
diff --git a/neural_coder/docs/cloud_autobench/code/config.sh b/neural_coder/docs/cloud_autobench/code/config.sh
deleted file mode 100644
index 10e9449a12f..00000000000
--- a/neural_coder/docs/cloud_autobench/code/config.sh
+++ /dev/null
@@ -1,47 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-set -x
-
-echo "[INFO] Start installing software packages and dependencies"
-
-# install machine packages
-sudo apt-get -y update
-sudo apt-get install -y wget
-sudo apt-get install -y git
-sudo apt-get install -y build-essential
-sudo apt-get install -y htop aha html2text numactl bc
-sudo apt-get install -y ffmpeg libsm6 libxext6
-sudo apt-get install -y automake libtool
-sudo apt-get install -y python3 pip
-
-# install conda
-wget https://repo.continuum.io/archive/Anaconda3-5.0.0-Linux-x86_64.sh -O anaconda3.sh
-chmod +x anaconda3.sh
-sudo ./anaconda3.sh -b -p /home/anaconda3
-export PATH=/home/anaconda3/bin:$PATH
-conda create -yn test python=3.9
-source activate test
-
-# install pip modules
-pip install numpy
-pip install pyyaml
-pip install typing_extensions
-pip install psutil
-pip install neural_compressor intel_extension_for_pytorch
-
-# install torch
-pip3 install torch torchvision torchaudio
-pip3 install torchdynamo
-pip3 install transformers
diff --git a/neural_coder/docs/cloud_autobench/code/launch.sh b/neural_coder/docs/cloud_autobench/code/launch.sh
deleted file mode 100644
index 1521301e1c3..00000000000
--- a/neural_coder/docs/cloud_autobench/code/launch.sh
+++ /dev/null
@@ -1,21 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-set -x
-
-export PATH=/home/anaconda3/bin:$PATH
-source activate test
-echo "[INFO] Start running auto benchmark..."
-python -c "from neural_coder import superreport; superreport(code='resnet50.py')"
-# Note: you need to uncomment superreport in neural_coder/interface.py and neural_coder/__init__.py to use this API.
diff --git a/neural_coder/docs/cloud_autobench/code/resnet50.py b/neural_coder/docs/cloud_autobench/code/resnet50.py
deleted file mode 100644
index 13caa61334a..00000000000
--- a/neural_coder/docs/cloud_autobench/code/resnet50.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import torch
-import torchvision.models as models
-
-model = models.resnet50(pretrained=True)
-model.eval()
-batch_size = 1
-input = torch.rand(batch_size, 3, 224, 224)
-with torch.no_grad():
- model(input)
diff --git a/neural_coder/docs/cloud_autobench/config.conf b/neural_coder/docs/cloud_autobench/config.conf
deleted file mode 100644
index d598178debb..00000000000
--- a/neural_coder/docs/cloud_autobench/config.conf
+++ /dev/null
@@ -1,62 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-# This is an example of providing Cloud configs for cloud benchmark
-
-# Choose Cloud Vendor
-# 1 AWS 2 Ali Yun 3 Tencent Cloud
-cloud_vendor=1
-
-# Choose Instance Operating System
-# 1 Ubuntu 2 Amazon Linux 3 Red Hat 4 SUSE Linux
-OS=1
-
-# Choose Instance CPU architecture
-# 1 x86 2 ARM
-arch=1
-
-# Number of Instances need to be launched
-count=1
-
-# Family of instance type
-i_type_family=c6i
-
-# Size of instance type
-i_type_size=2xlarge
-
-# Private key name
-key_name=huangyu
-
-# Instance id (Required if use an existing instance)
-instance_id=i-0ecdba84ca1ea5d68
-
-# Security Group id AWS (Required)
-security_id_aws=sg-00139ed15a90e83e0
-
-# Subnet id AWS (Required)
-subnet_id_aws=subnet-010cad6a296e44f8b
-
-# Security Group id Ali Yun (Required)
-security_id_ali=sg-m5e61j9zh9hivx3k9iwp
-
-# RegionId Ali Yun (Required)
-region_id_ali=cn-qingdao
-
-# Whether retain the instance after this task
-# 1 retain 2 Not retain
-whether_retain=2
-
-# Create a new instance or use an existing instance
-# 1 Creat new 2 Use an existing one
-whether_launch_new=2
diff --git a/neural_coder/docs/cloud_autobench/superbench_report_aws_icx.pdf b/neural_coder/docs/cloud_autobench/superbench_report_aws_icx.pdf
deleted file mode 100644
index cdb6d01608c..00000000000
Binary files a/neural_coder/docs/cloud_autobench/superbench_report_aws_icx.pdf and /dev/null differ
diff --git a/neural_coder/docs/release_notes/v0.4.md b/neural_coder/docs/release_notes/v0.4.md
deleted file mode 100644
index 933c02900a3..00000000000
--- a/neural_coder/docs/release_notes/v0.4.md
+++ /dev/null
@@ -1,25 +0,0 @@
-v0.4
-=====
-
-## Highlights
-- **Visual Studio Code extension**: We are delighted to announce the release of Neural Coder's [Visual Studio Code extension](https://marketplace.visualstudio.com/items?itemName=IntelNeuralCompressor.neural-coder-ext-vscode). VS Code programmers can enjoy one-click automatic enabling of Deep Learning optimization API and accelerate their Deep Learning models without manual coding.
-
-- **HuggingFace Transformers**:
- - We supported **all** HuggingFace Transformers [examples](https://github.com/huggingface/transformers/tree/main/examples/pytorch) that calls ```Trainer``` class, and validated over **500** models from HuggingFace Transformers [model hub](https://huggingface.co/models). The models are able to be accelerated automatically with Neural Coder with minimum loss of prediction accuracy.
- - We enabled the support of [HuggingFace Optimum-Intel](https://huggingface.co/docs/optimum/intel/index). User scripts of HuggingFace Transformers models will by default be optimized with Optimum-Intel API to enjoy performance speed-up brought by INT8 quantization.
- - We enabled the support of [Intel® Extension for Transformers](https://github.com/intel/intel-extension-for-transformers), an innovative toolkit to accelerate Transformer-based models on Intel platforms. For more details, please refer to the updated [support matrix](../SupportMatrix.md).
-
-- **Support of BigDL Nano**: We are delighted to announce the collaboration between Neural Coder and [BigDL Nano](https://bigdl.readthedocs.io/en/latest/doc/Nano/index.html). Users can now one-click enable BigDL Nano optimizations for PyTorch in Neural Coder. For detailed support matrix for BigDL Nano features, please refer to this [guide](../BigDLNanoSupport.md).
-
-- **Amazon AWS SageMaker**: We provided a user [tutorial](../AWSSageMakerSupport.md) for installing Neural Coder's JupyterLab extension in AWS SageMaker platform. Users are able to one-click install the extension in Amazon AWS SageMaker with Jupyter 3 and enjoy Neural Coder's functionalities.
-
-- **Python Launcher**: We added the implementation of [Python Launcher](../PythonLauncher.md) usage for Neural Coder, which will be one of the recommended user interfaces in the future as a replacement of Python API. Users can run the Python model code as it is with automatic enabling of Deep Learning optimizations by using Neural Coder's inline Python Launcher design: ```-m neural_coder```.
-
-- **Device Detection**: We enabled the capability of detecting running device and its ISA automatically and adjusting applied optimization features accordingly. For instance, when running Neural Coder on Intel GPU instead of Intel CPU, the PyTorch Mixed Precision optimization feature will adapt ```xpu``` instead of ```cpu```, and ```torch.half``` instead of ```torch.bfloat16```.
-
-## Others
-- **INT8 Accuracy Evaluation**: We enabled accuracy evaluation for INT8 quantizations in Neural Coder. Users are able to view the accuracy delta for each quantization optimization in Neural Coder's auto-benchmark output log. The calculation is ```acc_delta = (int8_acc - fp32_acc)/(fp32_acc)```.
-
-- **Auto-quantize TensorFlow/Keras scripts**: We enabled the support of auto-quantizing TensorFlow/Keras script-based models with Intel® Neural Compressor. The default quantization scheme will be applied. For more details, please refer to the updated [support matrix](../SupportMatrix.md).
-
-- **Auto-quantize ONNX Runtime scripts**: We enabled the support of auto-quantizing ONNX Runtime script-based models with Intel® Neural Compressor. We support [dynamic quantization](https://github.com/intel/neural-compressor/tree/master/examples/onnxrt#dynamic-quantization), static quantization ([QDQ](https://github.com/intel/neural-compressor/tree/master/examples/onnxrt#tensor-oriented-qdq-format)), and static quantization ([QLinearOps](https://github.com/intel/neural-compressor/tree/master/examples/onnxrt#operator-oriented-with-qlinearops)). For more details, please refer to the updated [support matrix](../SupportMatrix.md).
diff --git a/neural_coder/examples/keras/mnist.py b/neural_coder/examples/keras/mnist.py
deleted file mode 100644
index 22ba9d66876..00000000000
--- a/neural_coder/examples/keras/mnist.py
+++ /dev/null
@@ -1,97 +0,0 @@
-#!/usr/bin/env python
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-import os
-import tensorflow as tf
-import numpy as np
-from tensorflow import keras
-from tensorflow.keras import layers
-import time
-from torch.utils.data import DataLoader
-
-num_classes = 10
-
-def build_dataset():
- # Load the data and split it between train and test sets
- (x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data()
-
- # Scale images to the [0, 1] range
- x_train = x_train.astype("float32") / 255
- x_test = x_test.astype("float32") / 255
- # Make sure images have shape (28, 28, 1)
- x_train = np.expand_dims(x_train, -1)
- x_test = np.expand_dims(x_test, -1)
-
- # convert class vectors to binary class matrices
- y_train = keras.utils.to_categorical(y_train, num_classes)
- y_test = keras.utils.to_categorical(y_test, num_classes)
- return x_train, y_train, x_test, y_test
-
-class Dataset():
- def __init__(self, ):
- _, _ , self.inputs, self.labels = build_dataset()
-
- def __getitem__(self, idx):
- return self.inputs[idx], self.labels[idx]
-
- def __len__(self):
- assert len(self.inputs) == len(self.labels), 'inputs should have equal len with labels'
- return len(self.inputs)
-
-def build_model(x_train, y_train, x_test, y_test):
- if os.path.exists('fp32_model'):
- model = keras.models.load_model('fp32_model')
- return model
- # Model / data parameters
- input_shape = (28, 28, 1)
- model = keras.Sequential(
- [
- keras.Input(shape=input_shape),
- layers.Conv2D(32, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Conv2D(64, kernel_size=(3, 3), activation="relu"),
- layers.MaxPooling2D(pool_size=(2, 2)),
- layers.Flatten(),
- layers.Dropout(0.5),
- layers.Dense(num_classes, activation="softmax"),
- ]
- )
-
- batch_size = 128
- epochs = 1
-
- model.compile(loss="categorical_crossentropy", optimizer="adam",
- metrics=["accuracy"], run_eagerly=True)
- model.fit(x_train, y_train, batch_size=batch_size, epochs=epochs, validation_split=0.1)
- model.summary()
- if not os.path.exists('fp32_model'):
- model.save('fp32_model')
- return model
-
-def eval_func(model):
- x_train, y_train, x_test, y_test = build_dataset()
- model.compile(metrics=["accuracy"], run_eagerly=False)
- score = model.evaluate(x_test, y_test)
- return score[1]
-
-def main():
- x_train, y_train, x_test, y_test = build_dataset()
- model = build_model(x_train, y_train, x_test, y_test)
- calib_dataloader = DataLoader(Dataset(), batch_size=10)
-
-if __name__ == '__main__':
- main()
diff --git a/neural_coder/examples/nano/resnet18.py b/neural_coder/examples/nano/resnet18.py
deleted file mode 100644
index 8d189bf75b2..00000000000
--- a/neural_coder/examples/nano/resnet18.py
+++ /dev/null
@@ -1,26 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import torch
-from torchvision.models import resnet18
-
-if __name__ == "__main__":
-
- model_ft = resnet18(pretrained=True)
-
- x = torch.rand(2, 3, 224, 224)
- y_hat = model_ft(x)
- predictions = y_hat.argmax(dim=1)
- print(predictions)
diff --git a/neural_coder/examples/nlp/distilbert.py b/neural_coder/examples/nlp/distilbert.py
deleted file mode 100644
index eab5513a51a..00000000000
--- a/neural_coder/examples/nlp/distilbert.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-from transformers import (
- AutoModelForSequenceClassification,
- AutoTokenizer
-)
-
-finetuned_model = "distilbert-base-uncased-finetuned-sst-2-english"
-
-
-class MyDataLoader(object):
- def __init__(self):
- self.tokenizer = AutoTokenizer.from_pretrained(finetuned_model)
- self.sequence = "Shanghai is a beautiful city!"
- self.encoded_input = self.tokenizer(
- self.sequence,
- return_tensors='pt'
- )
- self.label = 1 # negative sentence: 0; positive sentence: 1
- self.batch_size = 1
-
- def __iter__(self):
- yield self.encoded_input, self.label
-
-
-my_nlp_model = AutoModelForSequenceClassification.from_pretrained(
- finetuned_model,
-)
-
-my_nlp_dataloader = MyDataLoader()
-
-output = my_nlp_model(**my_nlp_dataloader.encoded_input)
diff --git a/neural_coder/examples/nlp/run_glue.py b/neural_coder/examples/nlp/run_glue.py
deleted file mode 100644
index f3a8ccfd75c..00000000000
--- a/neural_coder/examples/nlp/run_glue.py
+++ /dev/null
@@ -1,617 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-# code source
-# https://github.com/huggingface/transformers/blob/v4.21-release/examples/pytorch/text-classification/run_glue.py
-
-import logging
-import os
-import random
-import sys
-from dataclasses import dataclass, field
-from typing import Optional
-
-import datasets
-import numpy as np
-from datasets import load_dataset, load_metric
-
-import transformers
-from transformers import (
- AutoConfig,
- AutoModelForSequenceClassification,
- AutoTokenizer,
- DataCollatorWithPadding,
- EvalPrediction,
- HfArgumentParser,
- PretrainedConfig,
- Trainer,
- TrainingArguments,
- default_data_collator,
- set_seed,
-)
-from transformers.trainer_utils import get_last_checkpoint
-from transformers.utils import check_min_version, send_example_telemetry
-from transformers.utils.versions import require_version
-
-
-# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
-check_min_version("4.21.0")
-
-require_version("datasets>=1.8.0", "To fix: pip install -r examples/pytorch/text-classification/requirements.txt")
-
-task_to_keys = {
- "cola": ("sentence", None),
- "mnli": ("premise", "hypothesis"),
- "mrpc": ("sentence1", "sentence2"),
- "qnli": ("question", "sentence"),
- "qqp": ("question1", "question2"),
- "rte": ("sentence1", "sentence2"),
- "sst2": ("sentence", None),
- "stsb": ("sentence1", "sentence2"),
- "wnli": ("sentence1", "sentence2"),
-}
-
-logger = logging.getLogger(__name__)
-
-
-@dataclass
-class DataTrainingArguments:
- """
- Arguments pertaining to what data we are going to input our model for training and eval.
-
- Using `HfArgumentParser` we can turn this class
- into argparse arguments to be able to specify them on
- the command line.
- """
-
- task_name: Optional[str] = field(
- default=None,
- metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())},
- )
- dataset_name: Optional[str] = field(
- default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
- )
- dataset_config_name: Optional[str] = field(
- default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
- )
- max_seq_length: int = field(
- default=128,
- metadata={
- "help": (
- "The maximum total input sequence length after tokenization. Sequences longer "
- "than this will be truncated, sequences shorter will be padded."
- )
- },
- )
- overwrite_cache: bool = field(
- default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."}
- )
- pad_to_max_length: bool = field(
- default=True,
- metadata={
- "help": (
- "Whether to pad all samples to `max_seq_length`. "
- "If False, will pad the samples dynamically when batching to the maximum length in the batch."
- )
- },
- )
- max_train_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of training examples to this "
- "value if set."
- )
- },
- )
- max_eval_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of evaluation examples to this "
- "value if set."
- )
- },
- )
- max_predict_samples: Optional[int] = field(
- default=None,
- metadata={
- "help": (
- "For debugging purposes or quicker training, truncate the number of prediction examples to this "
- "value if set."
- )
- },
- )
- train_file: Optional[str] = field(
- default=None, metadata={"help": "A csv or a json file containing the training data."}
- )
- validation_file: Optional[str] = field(
- default=None, metadata={"help": "A csv or a json file containing the validation data."}
- )
- test_file: Optional[str] = field(default=None, metadata={"help": "A csv or a json file containing the test data."})
-
- def __post_init__(self):
- if self.task_name is not None:
- self.task_name = self.task_name.lower()
- if self.task_name not in task_to_keys.keys():
- raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys()))
- elif self.dataset_name is not None:
- pass
- elif self.train_file is None or self.validation_file is None:
- raise ValueError("Need either a GLUE task, a training/validation file or a dataset name.")
- else:
- train_extension = self.train_file.split(".")[-1]
- assert train_extension in ["csv", "json"], "`train_file` should be a csv or a json file."
- validation_extension = self.validation_file.split(".")[-1]
- assert (
- validation_extension == train_extension
- ), "`validation_file` should have the same extension (csv or json) as `train_file`."
-
-
-@dataclass
-class ModelArguments:
- """
- Arguments pertaining to which model/config/tokenizer we are going to fine-tune from.
- """
-
- model_name_or_path: str = field(
- metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"}
- )
- config_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
- )
- tokenizer_name: Optional[str] = field(
- default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
- )
- cache_dir: Optional[str] = field(
- default=None,
- metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
- )
- use_fast_tokenizer: bool = field(
- default=True,
- metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
- )
- model_revision: str = field(
- default="main",
- metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
- )
- use_auth_token: bool = field(
- default=False,
- metadata={
- "help": (
- "Will use the token generated when running `transformers-cli login` (necessary to use this script "
- "with private models)."
- )
- },
- )
- ignore_mismatched_sizes: bool = field(
- default=False,
- metadata={"help": "Will enable to load a pretrained model whose head dimensions are different."},
- )
-
-
-def main():
- # See all possible arguments in src/transformers/training_args.py
- # or by passing the --help flag to this script.
- # We now keep distinct sets of args, for a cleaner separation of concerns.
-
- parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
- if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
- # If we pass only one argument to the script and it's the path to a json file,
- # let's parse it to get our arguments.
- model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
- else:
- model_args, data_args, training_args = parser.parse_args_into_dataclasses()
-
- # Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
- # information sent is the one passed as arguments along with your Python/PyTorch versions.
- send_example_telemetry("run_glue", model_args, data_args)
-
- # Setup logging
- logging.basicConfig(
- format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
- datefmt="%m/%d/%Y %H:%M:%S",
- handlers=[logging.StreamHandler(sys.stdout)],
- )
-
- log_level = training_args.get_process_log_level()
- logger.setLevel(log_level)
- datasets.utils.logging.set_verbosity(log_level)
- transformers.utils.logging.set_verbosity(log_level)
- transformers.utils.logging.enable_default_handler()
- transformers.utils.logging.enable_explicit_format()
-
- # Log on each process the small summary:
- logger.warning(
- f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}"
- + f"distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}"
- )
- logger.info(f"Training/evaluation parameters {training_args}")
-
- # Detecting last checkpoint.
- last_checkpoint = None
- if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
- last_checkpoint = get_last_checkpoint(training_args.output_dir)
- if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
- raise ValueError(
- f"Output directory ({training_args.output_dir}) already exists and is not empty. "
- "Use --overwrite_output_dir to overcome."
- )
- elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
- logger.info(
- f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
- "the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
- )
-
- # Set seed before initializing model.
- set_seed(training_args.seed)
-
- # Get the datasets: you can either provide your own CSV/JSON training and evaluation files (see below)
- # or specify a GLUE benchmark task (the dataset will be downloaded automatically from the datasets Hub).
- #
- # For CSV/JSON files, this script will use as labels the column called 'label' and as pair of sentences the
- # sentences in columns called 'sentence1' and 'sentence2' if such column exists or the first two columns not named
- # label if at least two columns are provided.
- #
- # If the CSVs/JSONs contain only one non-label column, the script does single sentence classification on this
- # single column. You can easily tweak this behavior (see below)
- #
- # In distributed training, the load_dataset function guarantee that only one local process can concurrently
- # download the dataset.
- if data_args.task_name is not None:
- # Downloading and loading a dataset from the hub.
- raw_datasets = load_dataset(
- "glue",
- data_args.task_name,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- elif data_args.dataset_name is not None:
- # Downloading and loading a dataset from the hub.
- raw_datasets = load_dataset(
- data_args.dataset_name,
- data_args.dataset_config_name,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- # Loading a dataset from your local files.
- # CSV/JSON training and evaluation files are needed.
- data_files = {"train": data_args.train_file, "validation": data_args.validation_file}
-
- # Get the test dataset: you can provide your own CSV/JSON test file (see below)
- # when you use `do_predict` without specifying a GLUE benchmark task.
- if training_args.do_predict:
- if data_args.test_file is not None:
- train_extension = data_args.train_file.split(".")[-1]
- test_extension = data_args.test_file.split(".")[-1]
- assert (
- test_extension == train_extension
- ), "`test_file` should have the same extension (csv or json) as `train_file`."
- data_files["test"] = data_args.test_file
- else:
- raise ValueError("Need either a GLUE task or a test file for `do_predict`.")
-
- for key in data_files.keys():
- logger.info(f"load a local file for {key}: {data_files[key]}")
-
- if data_args.train_file.endswith(".csv"):
- # Loading a dataset from local csv files
- raw_datasets = load_dataset(
- "csv",
- data_files=data_files,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- else:
- # Loading a dataset from local json files
- raw_datasets = load_dataset(
- "json",
- data_files=data_files,
- cache_dir=model_args.cache_dir,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- # See more about loading any type of standard or custom dataset at
- # https://huggingface.co/docs/datasets/loading_datasets.html.
-
- # Labels
- if data_args.task_name is not None:
- is_regression = data_args.task_name == "stsb"
- if not is_regression:
- label_list = raw_datasets["train"].features["label"].names
- num_labels = len(label_list)
- else:
- num_labels = 1
- else:
- # Trying to have good defaults here, don't hesitate to tweak to your needs.
- is_regression = raw_datasets["train"].features["label"].dtype in ["float32", "float64"]
- if is_regression:
- num_labels = 1
- else:
- # A useful fast method:
- # https://huggingface.co/docs/datasets/package_reference/main_classes.html#datasets.Dataset.unique
- label_list = raw_datasets["train"].unique("label")
- label_list.sort() # Let's sort it for determinism
- num_labels = len(label_list)
-
- # Load pretrained model and tokenizer
- #
- # In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
- # download model & vocab.
- config = AutoConfig.from_pretrained(
- model_args.config_name if model_args.config_name else model_args.model_name_or_path,
- num_labels=num_labels,
- finetuning_task=data_args.task_name,
- cache_dir=model_args.cache_dir,
- revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- tokenizer = AutoTokenizer.from_pretrained(
- model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path,
- cache_dir=model_args.cache_dir,
- use_fast=model_args.use_fast_tokenizer,
- revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
- )
- model = AutoModelForSequenceClassification.from_pretrained(
- model_args.model_name_or_path,
- from_tf=bool(".ckpt" in model_args.model_name_or_path),
- config=config,
- cache_dir=model_args.cache_dir,
- revision=model_args.model_revision,
- use_auth_token=True if model_args.use_auth_token else None,
- ignore_mismatched_sizes=model_args.ignore_mismatched_sizes,
- )
-
- # Preprocessing the raw_datasets
- if data_args.task_name is not None:
- sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
- else:
- # Again, we try to have some nice defaults but don't hesitate to tweak to your use case.
- non_label_column_names = [name for name in raw_datasets["train"].column_names if name != "label"]
- if "sentence1" in non_label_column_names and "sentence2" in non_label_column_names:
- sentence1_key, sentence2_key = "sentence1", "sentence2"
- else:
- if len(non_label_column_names) >= 2:
- sentence1_key, sentence2_key = non_label_column_names[:2]
- else:
- sentence1_key, sentence2_key = non_label_column_names[0], None
-
- # Padding strategy
- if data_args.pad_to_max_length:
- padding = "max_length"
- else:
- # We will pad later, dynamically at batch creation, to the max sequence length in each batch
- padding = False
-
- # Some models have set the order of the labels to use, so let's make sure we do use it.
- label_to_id = None
- if (
- model.config.label2id != PretrainedConfig(num_labels=num_labels).label2id
- and data_args.task_name is not None
- and not is_regression
- ):
- # Some have all caps in their config, some don't.
- label_name_to_id = {k.lower(): v for k, v in model.config.label2id.items()}
- if list(sorted(label_name_to_id.keys())) == list(sorted(label_list)):
- label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)}
- else:
- logger.warning(
- "Your model seems to have been trained with labels, but they don't match the dataset: ",
- f"model labels: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(sorted(label_list))}."
- "\nIgnoring the model labels as a result.",
- )
- elif data_args.task_name is None and not is_regression:
- label_to_id = {v: i for i, v in enumerate(label_list)}
-
- if label_to_id is not None:
- model.config.label2id = label_to_id
- model.config.id2label = {id: label for label, id in config.label2id.items()}
- elif data_args.task_name is not None and not is_regression:
- model.config.label2id = {l: i for i, l in enumerate(label_list)}
- model.config.id2label = {id: label for label, id in config.label2id.items()}
-
- if data_args.max_seq_length > tokenizer.model_max_length:
- logger.warning(
- f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the"
- f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}."
- )
- max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length)
-
- def preprocess_function(examples):
- # Tokenize the texts
- args = (
- (examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key])
- )
- result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True)
-
- # Map labels to IDs (not necessary for GLUE tasks)
- if label_to_id is not None and "label" in examples:
- result["label"] = [(label_to_id[l] if l != -1 else -1) for l in examples["label"]]
- return result
-
- with training_args.main_process_first(desc="dataset map pre-processing"):
- raw_datasets = raw_datasets.map(
- preprocess_function,
- batched=True,
- load_from_cache_file=not data_args.overwrite_cache,
- desc="Running tokenizer on dataset",
- )
- if training_args.do_train:
- if "train" not in raw_datasets:
- raise ValueError("--do_train requires a train dataset")
- train_dataset = raw_datasets["train"]
- if data_args.max_train_samples is not None:
- max_train_samples = min(len(train_dataset), data_args.max_train_samples)
- train_dataset = train_dataset.select(range(max_train_samples))
-
- if training_args.do_eval:
- if "validation" not in raw_datasets and "validation_matched" not in raw_datasets:
- raise ValueError("--do_eval requires a validation dataset")
- eval_dataset = raw_datasets["validation_matched" if data_args.task_name == "mnli" else "validation"]
- if data_args.max_eval_samples is not None:
- max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
- eval_dataset = eval_dataset.select(range(max_eval_samples))
-
- if training_args.do_predict or data_args.task_name is not None or data_args.test_file is not None:
- if "test" not in raw_datasets and "test_matched" not in raw_datasets:
- raise ValueError("--do_predict requires a test dataset")
- predict_dataset = raw_datasets["test_matched" if data_args.task_name == "mnli" else "test"]
- if data_args.max_predict_samples is not None:
- max_predict_samples = min(len(predict_dataset), data_args.max_predict_samples)
- predict_dataset = predict_dataset.select(range(max_predict_samples))
-
- # Log a few random samples from the training set:
- if training_args.do_train:
- for index in random.sample(range(len(train_dataset)), 3):
- logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
-
- # Get the metric function
- if data_args.task_name is not None:
- metric = load_metric("glue", data_args.task_name)
- else:
- metric = load_metric("accuracy")
-
- # You can define your custom compute_metrics function. It takes an `EvalPrediction` object (a namedtuple with a
- # predictions and label_ids field) and has to return a dictionary string to float.
- def compute_metrics(p: EvalPrediction):
- preds = p.predictions[0] if isinstance(p.predictions, tuple) else p.predictions
- preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1)
- if data_args.task_name is not None:
- result = metric.compute(predictions=preds, references=p.label_ids)
- if len(result) > 1:
- result["combined_score"] = np.mean(list(result.values())).item()
- return result
- elif is_regression:
- return {"mse": ((preds - p.label_ids) ** 2).mean().item()}
- else:
- return {"accuracy": (preds == p.label_ids).astype(np.float32).mean().item()}
-
- # Data collator will default to DataCollatorWithPadding when the tokenizer is passed to Trainer, so we change it if
- # we already did the padding.
- if data_args.pad_to_max_length:
- data_collator = default_data_collator
- elif training_args.fp16:
- data_collator = DataCollatorWithPadding(tokenizer, pad_to_multiple_of=8)
- else:
- data_collator = None
-
- # Initialize our Trainer
- trainer = Trainer(
- model=model,
- args=training_args,
- train_dataset=train_dataset if training_args.do_train else None,
- eval_dataset=eval_dataset if training_args.do_eval else None,
- compute_metrics=compute_metrics,
- tokenizer=tokenizer,
- data_collator=data_collator,
- )
-
- # Training
- if training_args.do_train:
- checkpoint = None
- if training_args.resume_from_checkpoint is not None:
- checkpoint = training_args.resume_from_checkpoint
- elif last_checkpoint is not None:
- checkpoint = last_checkpoint
- train_result = trainer.train(resume_from_checkpoint=checkpoint)
- metrics = train_result.metrics
- max_train_samples = (
- data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
- )
- metrics["train_samples"] = min(max_train_samples, len(train_dataset))
-
- trainer.save_model() # Saves the tokenizer too for easy upload
-
- trainer.log_metrics("train", metrics)
- trainer.save_metrics("train", metrics)
- trainer.save_state()
-
- # Evaluation
- if training_args.do_eval:
- logger.info("*** Evaluate ***")
-
- # Loop to handle MNLI double evaluation (matched, mis-matched)
- tasks = [data_args.task_name]
- eval_datasets = [eval_dataset]
- if data_args.task_name == "mnli":
- tasks.append("mnli-mm")
- eval_datasets.append(raw_datasets["validation_mismatched"])
- combined = {}
-
- for eval_dataset, task in zip(eval_datasets, tasks):
- metrics = trainer.evaluate(eval_dataset=eval_dataset)
-
- max_eval_samples = (
- data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
- )
- metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
-
- if task == "mnli-mm":
- metrics = {k + "_mm": v for k, v in metrics.items()}
- if task is not None and "mnli" in task:
- combined.update(metrics)
-
- trainer.log_metrics("eval", metrics)
- trainer.save_metrics("eval", combined if task is not None and "mnli" in task else metrics)
-
- if training_args.do_predict:
- logger.info("*** Predict ***")
-
- # Loop to handle MNLI double evaluation (matched, mis-matched)
- tasks = [data_args.task_name]
- predict_datasets = [predict_dataset]
- if data_args.task_name == "mnli":
- tasks.append("mnli-mm")
- predict_datasets.append(raw_datasets["test_mismatched"])
-
- for predict_dataset, task in zip(predict_datasets, tasks):
- # Removing the `label` columns because it contains -1 and Trainer won't like that.
- predict_dataset = predict_dataset.remove_columns("label")
- predictions = trainer.predict(predict_dataset, metric_key_prefix="predict").predictions
- predictions = np.squeeze(predictions) if is_regression else np.argmax(predictions, axis=1)
-
- output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt")
- if trainer.is_world_process_zero():
- with open(output_predict_file, "w") as writer:
- logger.info(f"***** Predict results {task} *****")
- writer.write("index\tprediction\n")
- for index, item in enumerate(predictions):
- if is_regression:
- writer.write(f"{index}\t{item:3.3f}\n")
- else:
- item = label_list[item]
- writer.write(f"{index}\t{item}\n")
-
- kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"}
- if data_args.task_name is not None:
- kwargs["language"] = "en"
- kwargs["dataset_tags"] = "glue"
- kwargs["dataset_args"] = data_args.task_name
- kwargs["dataset"] = f"GLUE {data_args.task_name.upper()}"
-
- if training_args.push_to_hub:
- trainer.push_to_hub(**kwargs)
- else:
- trainer.create_model_card(**kwargs)
-
-
-def _mp_fn(index):
- # For xla_spawn (TPUs)
- main()
-
-
-if __name__ == "__main__":
- main()
diff --git a/neural_coder/examples/onnx/onnx_model.py b/neural_coder/examples/onnx/onnx_model.py
deleted file mode 100644
index 8e2c7b2d928..00000000000
--- a/neural_coder/examples/onnx/onnx_model.py
+++ /dev/null
@@ -1,211 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import logging
-import argparse
-
-import onnx
-import yaml
-
-from pycocotools.coco import COCO
-from pycocotools.mask import iou, encode
-import numpy as np
-from torchvision import transforms
-from PIL import Image
-from onnx import numpy_helper
-import os
-import onnxruntime
-
-logger = logging.getLogger(__name__)
-logging.basicConfig(format = '%(asctime)s - %(levelname)s - %(name)s - %(message)s',
- datefmt = '%m/%d/%Y %H:%M:%S',
- level = logging.WARN)
-logger.info("Evaluating ONNXRuntime full precision accuracy and performance:")
-parser = argparse.ArgumentParser(
- formatter_class=argparse.ArgumentDefaultsHelpFormatter
-)
-parser.add_argument(
- '--model_path',
- type=str,
- help="Pre-trained model on onnx file"
-)
-parser.add_argument(
- '--label_path',
- type=str,
- help="Annotation file path"
-)
-parser.add_argument(
- '--data_path',
- type=str,
- help="Path to val2017 of COCO"
-)
-parser.add_argument(
- '--benchmark',
- action='store_true', \
- default=False
-)
-parser.add_argument(
- '--tune',
- action='store_true', \
- default=False,
- help="whether quantize the model"
-)
-parser.add_argument(
- '--config',
- type=str,
- help="config yaml path"
-)
-parser.add_argument(
- '--output_model',
- type=str,
- help="output model path"
-)
-parser.add_argument(
- '--mode',
- type=str,
- help="benchmark mode of performance or accuracy"
-)
-args = parser.parse_args()
-
-# key = COCO id, value = Pascal VOC id
-COCO_TO_VOC = {
- 1: 15, # person
- 2: 2, # bicycle
- 3: 7, # car
- 4: 14, # motorbike
- 5: 1, # airplane
- 6: 6, # bus
- 7: 19, # train
- 9: 4, # boat
- 16: 3, # bird
- 17: 8, # cat
- 18: 12, # dog
- 19: 13, # horse
- 20: 17, # sheep
- 21: 10, # cow
- 44: 5, # bottle
- 62: 9, # chair
- 63: 18, # couch/sofa
- 64: 16, # potted plant
- 67: 11, # dining table
- 72: 20, # tv
-}
-VOC_CAT_IDS = list(COCO_TO_VOC.keys())
-cocoGt = COCO(str(args.label_path))
-
-preprocess = transforms.Compose([
- transforms.ToTensor(),
- transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
-])
-
-class Dataset:
- def __init__(self):
- imgIds = self.getImgIdsUnion(cocoGt, VOC_CAT_IDS)
- self.data = []
- for imgId in imgIds:
- img_path = os.path.join(args.data_path, cocoGt.imgs[imgId]['file_name'])
- if os.path.exists(img_path):
- input_tensor = self.load_image(img_path)
-
- _, height, width = input_tensor.shape
- output_tensor = np.zeros((21, height, width), dtype=np.uint8)
-
- annIds = cocoGt.getAnnIds(imgId, VOC_CAT_IDS)
- for ann in cocoGt.loadAnns(annIds):
- mask = cocoGt.annToMask(ann)
- output_tensor[COCO_TO_VOC[ann['category_id']]] |= mask
-
- # Set everything not labeled to be background
- output_tensor[0] = 1 - np.max(output_tensor, axis=0)
- self.data.append((input_tensor, output_tensor))
-
- def __len__(self):
- return len(self.data)
-
- def __getitem__(self, index):
- return self.data[index]
-
- def getImgIdsUnion(self, gt, catIds):
- """
- Returns all the images that have *any* of the categories in `catIds`,
- unlike the built-in `gt.getImgIds` which returns all the images containing
- *all* of the categories in `catIds`.
- """
- imgIds = set()
- for catId in catIds:
- imgIds |= set(gt.catToImgs[catId])
- return list(imgIds)
-
- def load_image(self, img_path):
- input_image = Image.open(img_path).convert('RGB')
- input_tensor = preprocess(input_image)
- input_tensor = input_tensor.detach().cpu().numpy()
- return input_tensor
-
-def iou(model_tensor, target_tensor):
- # Don't include the background when summing
- model_tensor = model_tensor[:, 1:, :, :]
- target_tensor = target_tensor[:, 1:, :, :]
-
- intersection = np.sum(np.logical_and(model_tensor, target_tensor))
- union = np.sum(np.logical_or(model_tensor, target_tensor))
-
- if union == 0:
- # Can only happen if nothing was there and nothing was predicted,
- # which is a perfect score
- return 1
- else:
- return intersection / union
-
-def evaluate(model, dataloader):
- totalIoU = 0
- sess = onnxruntime.InferenceSession(model.SerializeToString(), None)
- idx = 1
- for input_tensor, target_tensor in dataloader:
- input_tensor = input_tensor[np.newaxis, ...]
- target_tensor = target_tensor[np.newaxis, ...]
- model_tensor = sess.run(["out"], {"input": input_tensor})[0]
-
- batch_size, nclasses, height, width = model_tensor.shape
- raw_labels = np.argmax(model_tensor, axis=1).astype(np.uint8)
-
- output_tensor = np.zeros((nclasses, batch_size, height, width), dtype=np.uint8)
- for c in range(nclasses):
- output_tensor[c][raw_labels==c] = 1
-
- output_tensor = np.transpose(output_tensor, [1, 0, 2, 3])
- totalIoU += iou(output_tensor, target_tensor)
- idx += 1
- return totalIoU / idx
-
-if __name__ == "__main__":
- from neural_compressor.experimental import common
- ds = Dataset()
- dataloader = common.DataLoader(ds)
- model = onnx.load(args.model_path)
- def eval(model):
- return evaluate(model, ds)
-
- if args.benchmark and args.mode == "accuracy":
- results = eval(model)
- print("Batch size = 1")
- print("Accuracy: %.5f" % results)
-
- if args.benchmark and args.mode == "performance":
- from neural_compressor.experimental import Benchmark, common
- evaluator = Benchmark(args.config)
- evaluator.model = common.Model(model)
- evaluator.b_dataloader = common.DataLoader(ds)
- evaluator(args.mode)
diff --git a/neural_coder/examples/vision/alexnet.py b/neural_coder/examples/vision/alexnet.py
deleted file mode 100644
index 10ac6d8b080..00000000000
--- a/neural_coder/examples/vision/alexnet.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import torch
-import torchvision.models as models
-model = models.alexnet(pretrained=True)
-model.eval()
-batch_size = 1
-input = torch.rand(batch_size, 3, 224, 224)
-with torch.no_grad():
- model(input)
diff --git a/neural_coder/examples/vision/main.py b/neural_coder/examples/vision/main.py
deleted file mode 100644
index 3b9bfd65298..00000000000
--- a/neural_coder/examples/vision/main.py
+++ /dev/null
@@ -1,504 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-# code source
-# https://github.com/pytorch/examples/blob/main/imagenet/main.py
-
-import argparse
-import os
-import random
-import shutil
-import time
-import warnings
-from enum import Enum
-
-import torch
-import torch.nn as nn
-import torch.nn.parallel
-import torch.backends.cudnn as cudnn
-import torch.distributed as dist
-import torch.optim
-from torch.optim.lr_scheduler import StepLR
-import torch.multiprocessing as mp
-import torch.utils.data
-import torch.utils.data.distributed
-import torchvision.transforms as transforms
-import torchvision.datasets as datasets
-import torchvision.models as models
-from torch.utils.data import Subset
-
-model_names = models.list_models(module=models)
-
-parser = argparse.ArgumentParser(description='PyTorch ImageNet Training')
-parser.add_argument('data', metavar='DIR', nargs='?', default='imagenet',
- help='path to dataset (default: imagenet)')
-parser.add_argument('-a', '--arch', metavar='ARCH', default='resnet18',
- choices=model_names,
- help='model architecture: ' +
- ' | '.join(model_names) +
- ' (default: resnet18)')
-parser.add_argument('-j', '--workers', default=4, type=int, metavar='N',
- help='number of data loading workers (default: 4)')
-parser.add_argument('--epochs', default=90, type=int, metavar='N',
- help='number of total epochs to run')
-parser.add_argument('--start-epoch', default=0, type=int, metavar='N',
- help='manual epoch number (useful on restarts)')
-parser.add_argument('-b', '--batch-size', default=256, type=int,
- metavar='N',
- help='mini-batch size (default: 256), this is the total '
- 'batch size of all GPUs on the current node when '
- 'using Data Parallel or Distributed Data Parallel')
-parser.add_argument('--lr', '--learning-rate', default=0.1, type=float,
- metavar='LR', help='initial learning rate', dest='lr')
-parser.add_argument('--momentum', default=0.9, type=float, metavar='M',
- help='momentum')
-parser.add_argument('--wd', '--weight-decay', default=1e-4, type=float,
- metavar='W', help='weight decay (default: 1e-4)',
- dest='weight_decay')
-parser.add_argument('-p', '--print-freq', default=10, type=int,
- metavar='N', help='print frequency (default: 10)')
-parser.add_argument('--resume', default='', type=str, metavar='PATH',
- help='path to latest checkpoint (default: none)')
-parser.add_argument('-e', '--evaluate', dest='evaluate', action='store_true',
- help='evaluate model on validation set')
-parser.add_argument('--pretrained', dest='pretrained', action='store_true',
- help='use pre-trained model')
-parser.add_argument('--world-size', default=-1, type=int,
- help='number of nodes for distributed training')
-parser.add_argument('--rank', default=-1, type=int,
- help='node rank for distributed training')
-parser.add_argument('--dist-url', default='tcp://224.66.41.62:23456', type=str,
- help='url used to set up distributed training')
-parser.add_argument('--dist-backend', default='nccl', type=str,
- help='distributed backend')
-parser.add_argument('--seed', default=None, type=int,
- help='seed for initializing training. ')
-parser.add_argument('--gpu', default=None, type=int,
- help='GPU id to use.')
-parser.add_argument('--multiprocessing-distributed', action='store_true',
- help='Use multi-processing distributed training to launch '
- 'N processes per node, which has N GPUs. This is the '
- 'fastest way to use PyTorch for either single node or '
- 'multi node data parallel training')
-parser.add_argument('--dummy', action='store_true', help="use fake data to benchmark")
-
-best_acc1 = 0
-
-
-def main():
- args = parser.parse_args()
-
- if args.seed is not None:
- random.seed(args.seed)
- torch.manual_seed(args.seed)
- cudnn.deterministic = True
- warnings.warn('You have chosen to seed training. '
- 'This will turn on the CUDNN deterministic setting, '
- 'which can slow down your training considerably! '
- 'You may see unexpected behavior when restarting '
- 'from checkpoints.')
-
- if args.gpu is not None:
- warnings.warn('You have chosen a specific GPU. This will completely '
- 'disable data parallelism.')
-
- if args.dist_url == "env://" and args.world_size == -1:
- args.world_size = int(os.environ["WORLD_SIZE"])
-
- args.distributed = args.world_size > 1 or args.multiprocessing_distributed
-
- ngpus_per_node = torch.cuda.device_count()
- if args.multiprocessing_distributed:
- # Since we have ngpus_per_node processes per node, the total world_size
- # needs to be adjusted accordingly
- args.world_size = ngpus_per_node * args.world_size
- # Use torch.multiprocessing.spawn to launch distributed processes: the
- # main_worker process function
- mp.spawn(main_worker, nprocs=ngpus_per_node, args=(ngpus_per_node, args))
- else:
- # Simply call main_worker function
- main_worker(args.gpu, ngpus_per_node, args)
-
-
-def main_worker(gpu, ngpus_per_node, args):
- global best_acc1
- args.gpu = gpu
-
- if args.gpu is not None:
- print("Use GPU: {} for training".format(args.gpu))
-
- if args.distributed:
- if args.dist_url == "env://" and args.rank == -1:
- args.rank = int(os.environ["RANK"])
- if args.multiprocessing_distributed:
- # For multiprocessing distributed training, rank needs to be the
- # global rank among all the processes
- args.rank = args.rank * ngpus_per_node + gpu
- dist.init_process_group(backend=args.dist_backend, init_method=args.dist_url,
- world_size=args.world_size, rank=args.rank)
- # create model
- if args.pretrained:
- print("=> using pre-trained model '{}'".format(args.arch))
- model = models.__dict__[args.arch](pretrained=True)
- else:
- print("=> creating model '{}'".format(args.arch))
- model = models.__dict__[args.arch]()
-
- if not torch.cuda.is_available():
- print('using CPU, this will be slow')
- elif args.distributed:
- # For multiprocessing distributed, DistributedDataParallel constructor
- # should always set the single device scope, otherwise,
- # DistributedDataParallel will use all available devices.
- if args.gpu is not None:
- torch.cuda.set_device(args.gpu)
- model.cuda(args.gpu)
- # When using a single GPU per process and per
- # DistributedDataParallel, we need to divide the batch size
- # ourselves based on the total number of GPUs of the current node.
- args.batch_size = int(args.batch_size / ngpus_per_node)
- args.workers = int((args.workers + ngpus_per_node - 1) / ngpus_per_node)
- model = torch.nn.parallel.DistributedDataParallel(model, device_ids=[args.gpu])
- else:
- model.cuda()
- # DistributedDataParallel will divide and allocate batch_size to all
- # available GPUs if device_ids are not set
- model = torch.nn.parallel.DistributedDataParallel(model)
- elif args.gpu is not None:
- torch.cuda.set_device(args.gpu)
- model = model.cuda(args.gpu)
- else:
- # DataParallel will divide and allocate batch_size to all available GPUs
- if args.arch.startswith('alexnet') or args.arch.startswith('vgg'):
- model.features = torch.nn.DataParallel(model.features)
- model.cuda()
- else:
- model = torch.nn.DataParallel(model).cuda()
-
- # define loss function (criterion), optimizer, and learning rate scheduler
- criterion = nn.CrossEntropyLoss().cuda(args.gpu)
-
- optimizer = torch.optim.SGD(model.parameters(), args.lr,
- momentum=args.momentum,
- weight_decay=args.weight_decay)
-
- """Sets the learning rate to the initial LR decayed by 10 every 30 epochs"""
- scheduler = StepLR(optimizer, step_size=30, gamma=0.1)
-
- # optionally resume from a checkpoint
- if args.resume:
- if os.path.isfile(args.resume):
- print("=> loading checkpoint '{}'".format(args.resume))
- if args.gpu is None:
- checkpoint = torch.load(args.resume)
- else:
- # Map model to be loaded to specified single gpu.
- loc = 'cuda:{}'.format(args.gpu)
- checkpoint = torch.load(args.resume, map_location=loc)
- args.start_epoch = checkpoint['epoch']
- best_acc1 = checkpoint['best_acc1']
- if args.gpu is not None:
- # best_acc1 may be from a checkpoint from a different GPU
- best_acc1 = best_acc1.to(args.gpu)
- model.load_state_dict(checkpoint['state_dict'])
- optimizer.load_state_dict(checkpoint['optimizer'])
- scheduler.load_state_dict(checkpoint['scheduler'])
- print("=> loaded checkpoint '{}' (epoch {})"
- .format(args.resume, checkpoint['epoch']))
- else:
- print("=> no checkpoint found at '{}'".format(args.resume))
-
- cudnn.benchmark = True
-
- # Data loading code
- if args.dummy:
- print("=> Dummy data is used!")
- train_dataset = datasets.FakeData(1281167, (3, 224, 224), 1000, transforms.ToTensor())
- val_dataset = datasets.FakeData(50000, (3, 224, 224), 1000, transforms.ToTensor())
- else:
- traindir = os.path.join(args.data, 'train')
- valdir = os.path.join(args.data, 'val')
- normalize = transforms.Normalize(mean=[0.485, 0.456, 0.406],
- std=[0.229, 0.224, 0.225])
-
- train_dataset = datasets.ImageFolder(
- traindir,
- transforms.Compose([
- transforms.RandomResizedCrop(224),
- transforms.RandomHorizontalFlip(),
- transforms.ToTensor(),
- normalize,
- ]))
-
- val_dataset = datasets.ImageFolder(
- valdir,
- transforms.Compose([
- transforms.Resize(256),
- transforms.CenterCrop(224),
- transforms.ToTensor(),
- normalize,
- ]))
-
- if args.distributed:
- train_sampler = torch.utils.data.distributed.DistributedSampler(train_dataset)
- val_sampler = torch.utils.data.distributed.DistributedSampler(val_dataset, shuffle=False, drop_last=True)
- else:
- train_sampler = None
- val_sampler = None
-
- train_loader = torch.utils.data.DataLoader(
- train_dataset, batch_size=args.batch_size, shuffle=(train_sampler is None),
- num_workers=args.workers, pin_memory=True, sampler=train_sampler)
-
- val_loader = torch.utils.data.DataLoader(
- val_dataset, batch_size=args.batch_size, shuffle=False,
- num_workers=args.workers, pin_memory=True, sampler=val_sampler)
-
- if args.evaluate:
- validate(val_loader, model, criterion, args)
- return
-
- for epoch in range(args.start_epoch, args.epochs):
- if args.distributed:
- train_sampler.set_epoch(epoch)
-
- # train for one epoch
- train(train_loader, model, criterion, optimizer, epoch, args)
-
- # evaluate on validation set
- acc1 = validate(val_loader, model, criterion, args)
-
- scheduler.step()
-
-
- # remember best acc@1 and save checkpoint
- is_best = acc1 > best_acc1
- best_acc1 = max(acc1, best_acc1)
-
- if not args.multiprocessing_distributed or (args.multiprocessing_distributed
- and args.rank % ngpus_per_node == 0):
- save_checkpoint({
- 'epoch': epoch + 1,
- 'arch': args.arch,
- 'state_dict': model.state_dict(),
- 'best_acc1': best_acc1,
- 'optimizer' : optimizer.state_dict(),
- 'scheduler' : scheduler.state_dict()
- }, is_best)
-
-
-def train(train_loader, model, criterion, optimizer, epoch, args):
- batch_time = AverageMeter('Time', ':6.3f')
- data_time = AverageMeter('Data', ':6.3f')
- losses = AverageMeter('Loss', ':.4e')
- top1 = AverageMeter('Acc@1', ':6.2f')
- top5 = AverageMeter('Acc@5', ':6.2f')
- progress = ProgressMeter(
- len(train_loader),
- [batch_time, data_time, losses, top1, top5],
- prefix="Epoch: [{}]".format(epoch))
-
- # switch to train mode
- model.train()
-
- end = time.time()
- for i, (images, target) in enumerate(train_loader):
- # measure data loading time
- data_time.update(time.time() - end)
-
- if args.gpu is not None:
- images = images.cuda(args.gpu, non_blocking=True)
- if torch.cuda.is_available():
- target = target.cuda(args.gpu, non_blocking=True)
-
- # compute output
- output = model(images)
- loss = criterion(output, target)
-
- # measure accuracy and record loss
- acc1, acc5 = accuracy(output, target, topk=(1, 5))
- losses.update(loss.item(), images.size(0))
- top1.update(acc1[0], images.size(0))
- top5.update(acc5[0], images.size(0))
-
- # compute gradient and do SGD step
- optimizer.zero_grad()
- loss.backward()
- optimizer.step()
-
- # measure elapsed time
- batch_time.update(time.time() - end)
- end = time.time()
-
- if i % args.print_freq == 0:
- progress.display(i + 1)
-
-
-def validate(val_loader, model, criterion, args):
-
- def run_validate(loader, base_progress=0):
- with torch.no_grad():
- end = time.time()
- for i, (images, target) in enumerate(loader):
- i = base_progress + i
- if args.gpu is not None:
- images = images.cuda(args.gpu, non_blocking=True)
- if torch.cuda.is_available():
- target = target.cuda(args.gpu, non_blocking=True)
-
- # compute output
- output = model(images)
- loss = criterion(output, target)
-
- # measure accuracy and record loss
- acc1, acc5 = accuracy(output, target, topk=(1, 5))
- losses.update(loss.item(), images.size(0))
- top1.update(acc1[0], images.size(0))
- top5.update(acc5[0], images.size(0))
-
- # measure elapsed time
- batch_time.update(time.time() - end)
- end = time.time()
-
- if i % args.print_freq == 0:
- progress.display(i + 1)
-
- batch_time = AverageMeter('Time', ':6.3f', Summary.NONE)
- losses = AverageMeter('Loss', ':.4e', Summary.NONE)
- top1 = AverageMeter('Acc@1', ':6.2f', Summary.AVERAGE)
- top5 = AverageMeter('Acc@5', ':6.2f', Summary.AVERAGE)
- progress = ProgressMeter(
- len(val_loader) + (args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset))),
- [batch_time, losses, top1, top5],
- prefix='Test: ')
-
- # switch to evaluate mode
- model.eval()
-
- run_validate(val_loader)
- if args.distributed:
- top1.all_reduce()
- top5.all_reduce()
-
- if args.distributed and (len(val_loader.sampler) * args.world_size < len(val_loader.dataset)):
- aux_val_dataset = Subset(val_loader.dataset,
- range(len(val_loader.sampler) * args.world_size, len(val_loader.dataset)))
- aux_val_loader = torch.utils.data.DataLoader(
- aux_val_dataset, batch_size=args.batch_size, shuffle=False,
- num_workers=args.workers, pin_memory=True)
- run_validate(aux_val_loader, len(val_loader))
-
- progress.display_summary()
-
- return top1.avg
-
-
-def save_checkpoint(state, is_best, filename='checkpoint.pth.tar'):
- torch.save(state, filename)
- if is_best:
- shutil.copyfile(filename, 'model_best.pth.tar')
-
-class Summary(Enum):
- NONE = 0
- AVERAGE = 1
- SUM = 2
- COUNT = 3
-
-class AverageMeter(object):
- """Computes and stores the average and current value"""
- def __init__(self, name, fmt=':f', summary_type=Summary.AVERAGE):
- self.name = name
- self.fmt = fmt
- self.summary_type = summary_type
- self.reset()
-
- def reset(self):
- self.val = 0
- self.avg = 0
- self.sum = 0
- self.count = 0
-
- def update(self, val, n=1):
- self.val = val
- self.sum += val * n
- self.count += n
- self.avg = self.sum / self.count
-
- def all_reduce(self):
- total = torch.FloatTensor([self.sum, self.count])
- dist.all_reduce(total, dist.ReduceOp.SUM, async_op=False)
- self.sum, self.count = total.tolist()
- self.avg = self.sum / self.count
-
- def __str__(self):
- fmtstr = '{name} {val' + self.fmt + '} ({avg' + self.fmt + '})'
- return fmtstr.format(**self.__dict__)
-
- def summary(self):
- fmtstr = ''
- if self.summary_type is Summary.NONE:
- fmtstr = ''
- elif self.summary_type is Summary.AVERAGE:
- fmtstr = '{name} {avg:.3f}'
- elif self.summary_type is Summary.SUM:
- fmtstr = '{name} {sum:.3f}'
- elif self.summary_type is Summary.COUNT:
- fmtstr = '{name} {count:.3f}'
- else:
- raise ValueError('invalid summary type %r' % self.summary_type)
-
- return fmtstr.format(**self.__dict__)
-
-
-class ProgressMeter(object):
- def __init__(self, num_batches, meters, prefix=""):
- self.batch_fmtstr = self._get_batch_fmtstr(num_batches)
- self.meters = meters
- self.prefix = prefix
-
- def display(self, batch):
- entries = [self.prefix + self.batch_fmtstr.format(batch)]
- entries += [str(meter) for meter in self.meters]
- print('\t'.join(entries))
-
- def display_summary(self):
- entries = [" *"]
- entries += [meter.summary() for meter in self.meters]
- print(' '.join(entries))
-
- def _get_batch_fmtstr(self, num_batches):
- num_digits = len(str(num_batches // 1))
- fmt = '{:' + str(num_digits) + 'd}'
- return '[' + fmt + '/' + fmt.format(num_batches) + ']'
-
-def accuracy(output, target, topk=(1,)):
- """Computes the accuracy over the k top predictions for the specified values of k"""
- with torch.no_grad():
- maxk = max(topk)
- batch_size = target.size(0)
-
- _, pred = output.topk(maxk, 1, True, True)
- pred = pred.t()
- correct = pred.eq(target.view(1, -1).expand_as(pred))
-
- res = []
- for k in topk:
- correct_k = correct[:k].reshape(-1).float().sum(0, keepdim=True)
- res.append(correct_k.mul_(100.0 / batch_size))
- return res
-
-
-if __name__ == '__main__':
- main()
diff --git a/neural_coder/examples/vision/resnet18.py b/neural_coder/examples/vision/resnet18.py
deleted file mode 100644
index a7fadf9c70b..00000000000
--- a/neural_coder/examples/vision/resnet18.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import torch
-import torchvision.models as models
-model = models.resnet18(pretrained=True)
-model.eval()
-batch_size = 1
-input = torch.rand(batch_size, 3, 224, 224)
-with torch.no_grad():
- model(input)
diff --git a/neural_coder/examples/vision/resnet50.py b/neural_coder/examples/vision/resnet50.py
deleted file mode 100644
index c7091e2e7bc..00000000000
--- a/neural_coder/examples/vision/resnet50.py
+++ /dev/null
@@ -1,23 +0,0 @@
-# Copyright (c) 2022 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-
-
-import torch
-import torchvision.models as models
-model = models.resnet50(pretrained=True)
-model.eval()
-batch_size = 1
-input = torch.rand(batch_size, 3, 224, 224)
-with torch.no_grad():
- model(input)
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.eslintignore b/neural_coder/extensions/neural_compressor_ext_lab/.eslintignore
deleted file mode 100644
index fffa32fdf63..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/.eslintignore
+++ /dev/null
@@ -1,8 +0,0 @@
-node_modules
-dist
-coverage
-**/*.d.ts
-tests
-
-**/__tests__
-ui-tests
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.eslintrc.js b/neural_coder/extensions/neural_compressor_ext_lab/.eslintrc.js
deleted file mode 100644
index c64b3721828..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/.eslintrc.js
+++ /dev/null
@@ -1,44 +0,0 @@
-module.exports = {
- extends: [
- 'eslint:recommended',
- 'plugin:@typescript-eslint/eslint-recommended',
- 'plugin:@typescript-eslint/recommended',
- 'plugin:prettier/recommended'
- ],
- parser: '@typescript-eslint/parser',
- parserOptions: {
- project: 'tsconfig.json',
- sourceType: 'module'
- },
- plugins: ['@typescript-eslint'],
- rules: {
- '@typescript-eslint/naming-convention': [
- 'error',
- {
- selector: 'interface',
- format: ['PascalCase'],
- custom: {
- regex: '^I[A-Z]',
- match: true
- }
- }
- ],
- '@typescript-eslint/no-unused-vars': ['warn', { args: 'none' }],
- '@typescript-eslint/no-explicit-any': 'off',
- '@typescript-eslint/no-namespace': 'off',
- '@typescript-eslint/no-use-before-define': 'off',
- '@typescript-eslint/quotes': [
- 'error',
- 'single',
- { avoidEscape: true, allowTemplateLiterals: false }
- ],
- node: {
- fs: 'empty',
- net:'empty',
- tls:'empty',
-},
- curly: ['error', 'all'],
- eqeqeq: 'error',
- 'prefer-arrow-callback': 'error'
- }
-};
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.prettierignore b/neural_coder/extensions/neural_compressor_ext_lab/.prettierignore
deleted file mode 100644
index 0de58a6f50b..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/.prettierignore
+++ /dev/null
@@ -1,5 +0,0 @@
-node_modules
-**/node_modules
-**/lib
-**/package.json
-neural_compressor_ext_lab
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.prettierrc b/neural_coder/extensions/neural_compressor_ext_lab/.prettierrc
deleted file mode 100644
index d0824a69c14..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/.prettierrc
+++ /dev/null
@@ -1,6 +0,0 @@
-{
- "singleQuote": true,
- "trailingComma": "none",
- "arrowParens": "avoid",
- "endOfLine": "auto"
-}
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/.stylelintrc b/neural_coder/extensions/neural_compressor_ext_lab/.stylelintrc
deleted file mode 100644
index 0e1ff30327c..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/.stylelintrc
+++ /dev/null
@@ -1,12 +0,0 @@
-{
- "extends": [
- "stylelint-config-recommended",
- "stylelint-config-standard",
- "stylelint-prettier/recommended"
- ],
- "rules": {
- "property-no-vendor-prefix": null,
- "selector-no-vendor-prefix": null,
- "value-no-vendor-prefix": null
- }
-}
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/CHANGELOG.md b/neural_coder/extensions/neural_compressor_ext_lab/CHANGELOG.md
deleted file mode 100644
index 2d352af421a..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/CHANGELOG.md
+++ /dev/null
@@ -1,5 +0,0 @@
-# Changelog
-
-
-
-
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/DEVELOP.md b/neural_coder/extensions/neural_compressor_ext_lab/DEVELOP.md
deleted file mode 100644
index 7141373e783..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/DEVELOP.md
+++ /dev/null
@@ -1,75 +0,0 @@
-# neural_compressor_ext_lab
-
-A JupyterLab extension.
-
-## Requirements
-
-- JupyterLab >= 3.0
-
-## Install
-
-To install the extension, execute:
-
-```bash
-pip install neural_compressor_ext_lab
-```
-
-## Uninstall
-
-To remove the extension, execute:
-
-```bash
-pip uninstall neural_compressor_ext_lab
-```
-
-## Contributing
-
-### Development install
-
-Note: You will need NodeJS to build the extension package.
-
-The `jlpm` command is JupyterLab's pinned version of
-[yarn](https://yarnpkg.com/) that is installed with JupyterLab. You may use
-`yarn` or `npm` in lieu of `jlpm` below.
-
-```bash
-# Clone the repo to your local environment
-# Change directory to the neural_compressor_ext_lab directory
-# Install package in development mode
-pip install -e .
-# Link your development version of the extension with JupyterLab
-jupyter labextension develop . --overwrite
-# Rebuild extension Typescript source after making changes
-jlpm build
-```
-
-You can watch the source directory and run JupyterLab at the same time in different terminals to watch for changes in the extension's source and automatically rebuild the extension.
-
-```bash
-# Watch the source directory in one terminal, automatically rebuilding when needed
-jlpm watch
-# Run JupyterLab in another terminal
-jupyter lab
-```
-
-With the watch command running, every saved change will immediately be built locally and available in your running JupyterLab. Refresh JupyterLab to load the change in your browser (you may need to wait several seconds for the extension to be rebuilt).
-
-By default, the `jlpm build` command generates the source maps for this extension to make it easier to debug using the browser dev tools. To also generate source maps for the JupyterLab core extensions, you can run the following command:
-
-```bash
-jupyter lab build --minimize=False
-```
-
-### Development uninstall
-
-```bash
-pip uninstall neural_compressor_ext_lab
-```
-
-In development mode, you will also need to remove the symlink created by `jupyter labextension develop`
-command. To find its location, you can run `jupyter labextension list` to figure out where the `labextensions`
-folder is located. Then you can remove the symlink named `neural_compressor_ext_lab` within that folder.
-
-### Packaging the extension
-
-See [RELEASE](RELEASE.md)
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/LICENSE b/neural_coder/extensions/neural_compressor_ext_lab/LICENSE
deleted file mode 100644
index d3f814da892..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/LICENSE
+++ /dev/null
@@ -1,29 +0,0 @@
-BSD 3-Clause License
-
-Copyright (c) 2022, neural_coder[C[C[C[C[C
-All rights reserved.
-
-Redistribution and use in source and binary forms, with or without
-modification, are permitted provided that the following conditions are met:
-
-1. Redistributions of source code must retain the above copyright notice, this
- list of conditions and the following disclaimer.
-
-2. Redistributions in binary form must reproduce the above copyright notice,
- this list of conditions and the following disclaimer in the documentation
- and/or other materials provided with the distribution.
-
-3. Neither the name of the copyright holder nor the names of its
- contributors may be used to endorse or promote products derived from
- this software without specific prior written permission.
-
-THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
-AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
-IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
-DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE
-FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
-DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
-SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
-CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY,
-OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE
-OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/MANIFEST.in b/neural_coder/extensions/neural_compressor_ext_lab/MANIFEST.in
deleted file mode 100644
index 99115494fae..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/MANIFEST.in
+++ /dev/null
@@ -1,24 +0,0 @@
-include LICENSE
-include *.md
-include pyproject.toml
-
-include package.json
-include install.json
-include ts*.json
-include yarn.lock
-
-graft neural_compressor_ext_lab/labextension
-
-# Javascript files
-graft src
-graft style
-prune **/node_modules
-prune lib
-prune binder
-
-# Patterns to exclude from any directory
-global-exclude *~
-global-exclude *.pyc
-global-exclude *.pyo
-global-exclude .git
-global-exclude .ipynb_checkpoints
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/README.md b/neural_coder/extensions/neural_compressor_ext_lab/README.md
deleted file mode 100644
index edd9e1e53a6..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/README.md
+++ /dev/null
@@ -1,45 +0,0 @@
-Intel® Neural Compressor as JupyterLab Extension
-===========================
-A JupyterLab Extension library supporting Neural Coder, a novel feature powered by Intel® Neural Compressor providing automatic quantization to further simplify computing performance optimizations of Deep Learning models.
-
-## Installation
-**By Extension Manager in JupyterLab (Recommended)**
-
-Search for ```jupyter-lab-neural-compressor``` in the Extension Manager in JupyterLab.
-
-**By Linux Terminal**
-```bash
-npm i jupyter-lab-neural-compressor
-jupyter labextension install jupyter-lab-neural-compressor
-```
-
-## Getting Started!
-
-As shown in the drop-down list, the supported features include "INT8 (Static Quantization)", "INT8 (Dynamic Quantization)", "BF16", and "Auto Enable & Benchmark". Each of the first three options enables a specific quantization feature into your Deep Learning scripts. The last option automatically enables all quantization features on a Deep Learning script and automatically evaluates the best performance on the model. It is a code-free solution that can help users enable quantization algorithms on a Deep Learning model with no manual coding needed.
-
-
-
-### Auto-enable a feature
-Click the run button on the left side of the drop-down list to start. After finishing, you can see the code changes for the specific optimization enabling as shown in the figure below:
-
-
-
-### Or let us help you auto-select the best feature
-The last option automatically enables each quantization feature on your Deep Learning script and automatically evaluates for the best performance among all features on your Deep Learning model. Since it will automatically run the Python script for benchmark, it requires you to enter additional parameters needed to run your Python script. If there is no additional parameter needed, you can just leave it blank:
-
-
-
-In the new cell box appeared below your Code cell boxes, you can see the execution progress, and at the end you can see which one turns out to be the best optimization and how much performance gain can it bring to your Deep Learning model:
-
-
-
-When it is finished, you can also see that the code changes for the best optimization are automatically enabled into your script:
-
-
-
-## Pre-requisites
-```bash
-apt-get update && apt-get install bc numactl
-conda install mkl mkl-include jemalloc
-pip3 install neural-compressor opencv-python-headless
-```
\ No newline at end of file
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/RELEASE.md b/neural_coder/extensions/neural_compressor_ext_lab/RELEASE.md
deleted file mode 100644
index bd51b356f28..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/RELEASE.md
+++ /dev/null
@@ -1,61 +0,0 @@
-# Making a new release of neural_compressor_ext_lab
-
-The extension can be published to `PyPI` and `npm` manually or using the [Jupyter Releaser](https://github.com/jupyter-server/jupyter_releaser).
-
-## Manual release
-
-### Python package
-
-This extension can be distributed as Python
-packages. All of the Python
-packaging instructions in the `pyproject.toml` file to wrap your extension in a
-Python package. Before generating a package, we first need to install `build`.
-
-```bash
-pip install build twine
-```
-
-To create a Python source package (`.tar.gz`) and the binary package (`.whl`) in the `dist/` directory, do:
-
-```bash
-python -m build
-```
-
-> `python setup.py sdist bdist_wheel` is deprecated and will not work for this package.
-
-Then to upload the package to PyPI, do:
-
-```bash
-twine upload dist/*
-```
-
-### NPM package
-
-To publish the frontend part of the extension as a NPM package, do:
-
-```bash
-npm login
-npm publish --access public
-```
-
-## Automated releases with the Jupyter Releaser
-
-The extension repository should already be compatible with the Jupyter Releaser.
-
-Check out the [workflow documentation](https://github.com/jupyter-server/jupyter_releaser#typical-workflow) for more information.
-
-Here is a summary of the steps to cut a new release:
-
-- Fork the [`jupyter-releaser` repo](https://github.com/jupyter-server/jupyter_releaser)
-- Add `ADMIN_GITHUB_TOKEN`, `PYPI_TOKEN` and `NPM_TOKEN` to the Github Secrets in the fork
-- Go to the Actions panel
-- Run the "Draft Changelog" workflow
-- Merge the Changelog PR
-- Run the "Draft Release" workflow
-- Run the "Publish Release" workflow
-
-## Publishing to `conda-forge`
-
-If the package is not on conda forge yet, check the documentation to learn how to add it: https://conda-forge.org/docs/maintainer/adding_pkgs.html
-
-Otherwise a bot should pick up the new version publish to PyPI, and open a new PR on the feedstock repository automatically.
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/binder/environment.yml b/neural_coder/extensions/neural_compressor_ext_lab/binder/environment.yml
deleted file mode 100644
index 23bed128c24..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/binder/environment.yml
+++ /dev/null
@@ -1,21 +0,0 @@
-# a mybinder.org-ready environment for demoing neural_compressor_ext_lab
-# this environment may also be used locally on Linux/MacOS/Windows, e.g.
-#
-# conda env update --file binder/environment.yml
-# conda activate neural-compressor-ext-lab-demo
-#
-name: neural-compressor-ext-lab-demo
-
-channels:
- - conda-forge
-
-dependencies:
- # runtime dependencies
- - python >=3.8,<3.9.0a0
- - jupyterlab >=3,<4.0.0a0
- # labextension build dependencies
- - nodejs >=14,<15
- - pip
- - wheel
- # additional packages for demos
- # - ipywidgets
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/binder/postBuild b/neural_coder/extensions/neural_compressor_ext_lab/binder/postBuild
deleted file mode 100644
index 95eabd91874..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/binder/postBuild
+++ /dev/null
@@ -1,47 +0,0 @@
-#!/usr/bin/env python3
-""" perform a development install of neural_compressor_ext_lab
-
- On Binder, this will run _after_ the environment has been fully created from
- the environment.yml in this directory.
-
- This script should also run locally on Linux/MacOS/Windows:
-
- python3 binder/postBuild
-"""
-import subprocess
-import sys
-from pathlib import Path
-
-
-ROOT = Path.cwd()
-
-def _(*args, **kwargs):
- """ Run a command, echoing the args
-
- fails hard if something goes wrong
- """
- print("\n\t", " ".join(args), "\n")
- return_code = subprocess.call(args, **kwargs)
- if return_code != 0:
- print("\nERROR", return_code, " ".join(args))
- sys.exit(return_code)
-
-# verify the environment is self-consistent before even starting
-_(sys.executable, "-m", "pip", "check")
-
-# install the labextension
-_(sys.executable, "-m", "pip", "install", "-e", ".")
-_(sys.executable, "-m", "jupyter", "labextension", "develop", "--overwrite", ".")
-
-# verify the environment the extension didn't break anything
-_(sys.executable, "-m", "pip", "check")
-
-# list the extensions
-_("jupyter", "server", "extension", "list")
-
-# initially list installed extensions to determine if there are any surprises
-_("jupyter", "labextension", "list")
-
-
-print("JupyterLab with neural_compressor_ext_lab is ready to run with:\n")
-print("\tjupyter lab\n")
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/install.json b/neural_coder/extensions/neural_compressor_ext_lab/install.json
deleted file mode 100644
index 24f9a58e094..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/install.json
+++ /dev/null
@@ -1,5 +0,0 @@
-{
- "packageManager": "python",
- "packageName": "neural_compressor_ext_lab",
- "uninstallInstructions": "Use your Python package manager (pip, conda, etc.) to uninstall the package neural_compressor_ext_lab"
-}
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.d.ts b/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.d.ts
deleted file mode 100644
index 071e86ae2c1..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.d.ts
+++ /dev/null
@@ -1,11 +0,0 @@
-export declare namespace Constants {
- const SHORT_PLUGIN_NAME = "neural_compressor_ext_lab";
- const WORK_PATH = "neural_coder_workspace/";
- const ICON_FORMAT_ALL_SVG = "";
- const ICON_RUN = "";
- const SVG = "";
- const LONG_PLUGIN_NAME: string;
- const SETTINGS_SECTION: string;
- const COMMAND_SECTION_NAME = "Jupyterlab Code Optimizer";
- const PLUGIN_VERSION = "0.1.0";
-}
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.js b/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.js
deleted file mode 100644
index 13acd1a7ad3..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/lib/constants.js
+++ /dev/null
@@ -1,12 +0,0 @@
-export var Constants;
-(function (Constants) {
- Constants.SHORT_PLUGIN_NAME = 'neural_compressor_ext_lab';
- Constants.WORK_PATH = "neural_coder_workspace/";
- Constants.ICON_FORMAT_ALL_SVG = '';
- Constants.ICON_RUN = '';
- Constants.SVG = '';
- Constants.LONG_PLUGIN_NAME = `@rya/${Constants.SHORT_PLUGIN_NAME}`;
- Constants.SETTINGS_SECTION = `${Constants.LONG_PLUGIN_NAME}:settings`;
- Constants.COMMAND_SECTION_NAME = 'Jupyterlab Code Optimizer';
- Constants.PLUGIN_VERSION = '0.1.0';
-})(Constants || (Constants = {}));
diff --git a/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.d.ts b/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.d.ts
deleted file mode 100644
index dcf8ddfba2a..00000000000
--- a/neural_coder/extensions/neural_compressor_ext_lab/lib/deepcoder.d.ts
+++ /dev/null
@@ -1,27 +0,0 @@
-import { Cell, CodeCell } from '@jupyterlab/cells';
-import { ToolbarButton } from '@jupyterlab/apputils';
-import { Widget } from '@lumino/widgets';
-import { INotebookTracker, NotebookPanel, Notebook } from '@jupyterlab/notebook';
-declare class JupyterlabCodeOptimizer {
- protected working: boolean;
- protected panel: NotebookPanel;
- private tmp_path;
- log_path: string;
- tmp_log_path: string;
- rand: number;
- markdown: Cell | undefined;
- cells: CodeCell[];
- constructor(panel: NotebookPanel);
- optimizeCode(code: string[], formatter: string, name: string, next: string, options: string | undefined, notebook: boolean, panel: NotebookPanel, cell: CodeCell, run?: ToolbarButton | undefined): Promise
- {msg.map((s: string, i: number) => {
- return (
-
-
-
- );
- })}
-
- );
- }
-
- /**
- * Opens a pop-up dialog in JupyterLab to display a simple message.
- * @param title The title for the message popup
- * @param msg The message as an array of strings
- * @param buttonLabel The label to use for the button. Default is 'OK'
- * @param buttonClassName The classname to give to the 'ok' button
- * @returns Promise-
- {msg.map((s: string, i: number) => {
- return (
-
-
-
- );
- })}
-
- );
- }
-
- /**
- * Opens a pop-up dialog in JupyterLab to display a simple message.
- * @param title The title for the message popup
- * @param msg The message as an array of strings
- * @param buttonLabel The label to use for the button. Default is 'OK'
- * @param buttonClassName The classname to give to the 'ok' button
- * @returns Promise-
-
- 
-
- 
-
- 
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-Neural Insights
-===========================
-
-
-Neural Insights is a web application for easier use of Intel® Neural Compressor [diagnosis](/docs/source/diagnosis.md) feature.
-It provides the capability to show the model graph, histograms of weights and activations, quantization configs, etc.
-The workflow shows the relationship of Neural Insights and diagnosis.
-
-
-## Installation
-
-Installation of Neural Insights is possible in one of following ways:
-
-### Install from pypi
-```Shell
-pip install neural-insights
-```
-
-### Install from Source
-
- ```Shell
- # Install Neural Compressor
- git clone https://github.com/intel/neural-compressor.git
- cd neural-compressor
- pip install -r requirements.txt
- python setup.py install
-
- # Install Neural Insights
- pip install -r neural_insights/requirements.txt
- python setup.py install neural_insights
- ```
-
-
-## Getting Started
-
-### Start the Neural Insights
-
-To start the Neural Insights server execute `neural_insights` command:
-
-```shell
-neural_insights
-```
-The server generates a self-signed TLS certificate and prints instruction how to access the Web UI.
-
-```text
-Neural Insights Server started.
-
-Open address https://10.11.12.13:5000/?token=338174d13706855fc6924cec7b3a8ae8
-
-```
-
-Server generated certificate is not trusted by your web browser, you will need to accept usage of such certificate.
-
-
-You might also use additional parameters and settings:
-* Neural Insights listens on port 5000.
-Make sure that port 5000 is accessible to your browser (you might need to open it in your firewall),
-or specify different port that is already opened, for example 8080:
- ```shell
- neural_insights -p 8080
- ```
-
-
-* To start the Neural Insights server with your own TLS certificate add `--cert` and `--key` parameters:
-
- ```shell
- neural_insights --cert path_to_cert.crt --key path_to_private_key.key
- ```
-
-* To start the Neural Insights server without TLS encryption use `--allow-insecure-connections` parameter:
-
- ```shell
- neural_insights --allow-insecure-connections
- ```
-
- This enables access to the server from any machine in your local network (or the whole Internet if your server is exposed to it).
-
- You are forfeiting security, confidentiality and integrity of all client-server communication. Your server is exposed to external threats.
-### Quantization with Python API
-
-```shell
-# Install Intel Neural Compressor and TensorFlow
-pip install neural-compressor
-pip install neural-insights
-pip install tensorflow
-# Prepare fp32 model
-wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/mobilenet_v1_1.0_224_frozen.pb
-```
-
-```python
-from neural_compressor import Metric
-from neural_compressor.config import PostTrainingQuantConfig
-from neural_compressor.data import DataLoader
-from neural_compressor.data import Datasets
-
-top1 = Metric(name="topk", k=1)
-dataset = Datasets("tensorflow")["dummy"](shape=(1, 224, 224, 3))
-dataloader = DataLoader(framework="tensorflow", dataset=dataset)
-
-from neural_compressor.quantization import fit
-
-q_model = fit(
- model="./mobilenet_v1_1.0_224_frozen.pb",
- conf=PostTrainingQuantConfig(diagnosis=True),
- calib_dataloader=dataloader,
- eval_dataloader=dataloader,
- eval_metric=top1,
-)
-```
-
-When the quantization is started, the workload should appear on the Neural Insights page and successively, new information should be available while quantization is in progress (such as weights distribution and accuracy data).
-
-> Note that above example uses dummy data which is used to describe usage of Neural Insights. For diagnosis purposes you should use real dataset specific for your use case.
-
-## Tensor dump examples
-- [Step by step example how to dump weights data for PyTorch model with Neural Insights](docs/source/pytorch_nlp_cli_mode.md)
-
-## Step by Step Diagnosis Example
-Refer to [Step by Step Diagnosis Example with TensorFlow](https://github.com/intel/neural-compressor/tree/master/neural_insights/docs/source/tf_accuracy_debug.md) and [Step by Step Diagnosis Example with ONNXRT](https://github.com/intel/neural-compressor/tree/master/neural_insights/docs/source/onnx_accuracy_debug.md) to get started with some basic quantization accuracy diagnostic skills.
-
-## Research Collaborations
-
-Welcome to raise any interesting research ideas on model compression techniques and feel free to reach us (inc.maintainers@intel.com). Look forward to our collaborations on Neural Insights!
diff --git a/neural_insights/__init__.py b/neural_insights/__init__.py
deleted file mode 100644
index 0c1671f07de..00000000000
--- a/neural_insights/__init__.py
+++ /dev/null
@@ -1,20 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-# flake8: noqa
-"""The neural_insights package contains all components required for running Neural Insights."""
-
-from neural_insights.ni import NeuralInsights
-from neural_insights.version import __version__
diff --git a/neural_insights/bin/__init__.py b/neural_insights/bin/__init__.py
deleted file mode 100644
index 4cdc25edc13..00000000000
--- a/neural_insights/bin/__init__.py
+++ /dev/null
@@ -1,16 +0,0 @@
-# -*- coding: utf-8 -*-
-#
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The bin package contains executable for Neural Insights."""
diff --git a/neural_insights/bin/neural_insights.py b/neural_insights/bin/neural_insights.py
deleted file mode 100644
index 388038027cf..00000000000
--- a/neural_insights/bin/neural_insights.py
+++ /dev/null
@@ -1,29 +0,0 @@
-#!/usr/bin/env python3
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""WSGI Web Server."""
-import gevent.monkey
-
-gevent.monkey.patch_all()
-
-
-def execute():
- from neural_insights.main import main
-
- main()
-
-
-if __name__ == "__main__":
- execute()
diff --git a/neural_insights/components/__init__.py b/neural_insights/components/__init__.py
deleted file mode 100644
index de458e5ca97..00000000000
--- a/neural_insights/components/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The components package contains all components used in Neural Insights server."""
diff --git a/neural_insights/components/diagnosis/__init__.py b/neural_insights/components/diagnosis/__init__.py
deleted file mode 100644
index 2f9c45f5db8..00000000000
--- a/neural_insights/components/diagnosis/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The diagnosis package contains all components used for debugging of optimizations."""
diff --git a/neural_insights/components/diagnosis/diagnosis.py b/neural_insights/components/diagnosis/diagnosis.py
deleted file mode 100644
index 9283d279cb5..00000000000
--- a/neural_insights/components/diagnosis/diagnosis.py
+++ /dev/null
@@ -1,307 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The diagnosis class."""
-import os
-import pickle
-from abc import abstractmethod
-from typing import Any, List, Optional
-
-from neural_insights.components.diagnosis.op_details import OpDetails
-from neural_insights.components.diagnosis.op_entry import OpEntry
-from neural_insights.components.diagnosis.weights_details import WeightsDetails
-from neural_insights.components.model.model import Model
-from neural_insights.components.workload_manager.workload import Workload
-from neural_insights.utils.exceptions import ClientErrorException, InternalException
-from neural_insights.utils.logger import log
-from neural_insights.utils.utils import check_module
-
-
-class Diagnosis:
- """Diagnosis class."""
-
- def __init__(self, workload: Workload):
- """Initialize diagnosis."""
- self.workload_location = workload.workload_location
- self.model_path = workload.model_path
-
- @abstractmethod
- def model(self) -> Model:
- """Neural Insights model instance."""
- raise NotImplementedError
-
- def get_tensors_info(self, model_type: str = "optimized") -> dict:
- """Get information about tensors."""
- tensors_filenames = {
- "input": os.path.join("fp32", "inspect_result.pkl"),
- "optimized": os.path.join("quan", "inspect_result.pkl"),
- }
-
- tensors_filename = tensors_filenames.get(model_type, None)
- if tensors_filename is None:
- raise InternalException(f"Could not find tensors data for {model_type} model.")
- tensors_path = os.path.join(
- self.workload_location,
- "inspect_saved",
- tensors_filename,
- )
- if not os.path.exists(tensors_path):
- raise ClientErrorException("Could not find tensor data for specified optimization.")
- with open(tensors_path, "rb") as tensors_pickle:
- dump_tensor_result = pickle.load(tensors_pickle)
- return dump_tensor_result
-
- def load_quantization_config(self) -> dict:
- """Get config quantization data."""
- config_path = os.path.join(
- self.workload_location,
- "inspect_saved",
- "cfg.pkl",
- )
- if not os.path.exists(config_path):
- log.debug("Could not find config data for specified optimization. Getting data from inspect files.")
- input_model_tensors: dict = self.get_tensors_info(model_type="input")["activation"][0]
- optimized_model_tensors: dict = self.get_tensors_info(model_type="optimized")["activation"][0]
- common_ops = list(set(input_model_tensors.keys()) & set(optimized_model_tensors.keys()))
- config_data = {
- "op": {},
- }
- for op in common_ops:
- config_data["op"].update({(op,): {}})
- return config_data
-
- with open(config_path, "rb") as config_pickle:
- config_data = pickle.load(config_pickle)
- return config_data
-
- def get_op_list(self) -> List[dict]:
- """Get OP list for model."""
- check_module("numpy")
- import numpy as np
-
- op_list: List[dict] = []
-
- input_model_tensors: dict = self.get_tensors_info(model_type="input")["activation"][0]
- optimized_model_tensors: dict = self.get_tensors_info(model_type="optimized")["activation"][0]
-
- minmax_file_path = os.path.join(
- self.workload_location,
- "inspect_saved",
- "activation_min_max.pkl",
- )
-
- try:
- with open(minmax_file_path, "rb") as min_max_file:
- min_max_data: dict = pickle.load(min_max_file)
- except FileNotFoundError:
- log.debug("Could not find minmax file.")
- common_ops = list(set(input_model_tensors.keys()) & set(optimized_model_tensors.keys()))
- min_max_data = dict(zip(common_ops, [{"min": None, "max": None}] * len(common_ops)))
-
- for op_name, min_max in min_max_data.items():
- mse = self.calculate_mse(op_name, input_model_tensors, optimized_model_tensors)
- if mse is None or np.isnan(mse):
- continue
- min = min_max.get("min", None)
- max = min_max.get("max", None)
-
- if min is not None:
- min = float(min)
- if max is not None:
- max = float(max)
-
- op_entry = OpEntry(op_name, mse, min, max)
- op_list.append(op_entry.serialize())
- return op_list
-
- def get_weights_details(self, inspect_type: str) -> List[WeightsDetails]:
- """Get weights details for model."""
- weights_details = []
-
- minmax_file_path = os.path.join(
- self.workload_location,
- "inspect_saved",
- "activation_min_max.pkl",
- )
- with open(minmax_file_path, "rb") as min_max_file:
- min_max_data: dict = pickle.load(min_max_file)
-
- input_model_tensors: dict = self.get_tensors_info(model_type="input")[inspect_type]
- optimized_model_tensors: dict = self.get_tensors_info(model_type="optimized")[inspect_type]
- if inspect_type == "activation":
- input_model_tensors = input_model_tensors[0]
- optimized_model_tensors = optimized_model_tensors[0]
- common_ops = list(set(input_model_tensors.keys()) & set(optimized_model_tensors.keys()))
- for op_name in common_ops:
- input_model_op_tensors = input_model_tensors[op_name]
- optimized_model_op_tensors = optimized_model_tensors[op_name]
-
- if op_name not in min_max_data.keys():
- continue
-
- if isinstance(input_model_op_tensors, dict):
- for (input_op_name, input_op_values), (optimized_op_name, optimized_op_values) in zip(
- input_model_op_tensors.items(), optimized_model_op_tensors.items()
- ):
- if input_op_values.ndim != 4 or optimized_op_values.ndim != 4:
- continue
-
- weights_entry = WeightsDetails(
- input_op_name,
- input_op_values,
- optimized_op_values,
- )
- weights_details.append(weights_entry)
- return weights_details
-
- def calculate_mse(
- self,
- op_name: str,
- input_model_tensors: dict,
- optimized_model_tensors: dict,
- ) -> Optional[float]:
- """Calculate MSE for specified tensors."""
- input_model_op_data = input_model_tensors.get(op_name, None)
- optimized_model_op_data = optimized_model_tensors.get(op_name, None)
-
- if input_model_op_data is None or optimized_model_op_data is None:
- return None
-
- mse: float = self.mse_metric_gap(
- next(iter(input_model_op_data.values()))[0],
- next(iter(optimized_model_op_data.values()))[0],
- )
-
- return mse
-
- def get_op_details(self, name: str) -> Optional[OpDetails]:
- """Get details of specific OP."""
- config_data = self.load_quantization_config()
- for op_tuple, op_details in config_data["op"].items():
- if op_tuple[0] == name:
- return OpDetails(name, op_details)
- return None
-
- def get_histogram_data(self, op_name: str, inspect_type: str) -> list:
- """Get data to draw histogram."""
- tensors = self.get_tensors_info(model_type="optimized").get(inspect_type, None)
- if tensors is None:
- raise ClientErrorException(
- f"Could not get tensor information for {inspect_type} type.",
- )
-
- if inspect_type == "activation":
- tensors = tensors[0]
-
- op_tensors: Optional[dict] = tensors.get(op_name, None)
- if op_tensors is None:
- raise ClientErrorException(
- f"Could not get tensor information for {op_name} OP.",
- )
-
- op_histograms = []
- for tensor_name, tensor_data_raw in op_tensors.items():
- tensor_histograms = []
-
- if tensor_data_raw.ndim == 1:
- tensor_data = [tensor_data_raw]
- elif tensor_data_raw.ndim == 2:
- tensor_data = tensor_data_raw[0]
- else:
- tensor_data = tensor_data_raw[0]
- for tensor_channel_data in tensor_data:
- tensor_histograms.append(
- {
- "data": tensor_channel_data.flatten().tolist(),
- },
- )
-
- op_histograms.append(
- {
- "name": f"{tensor_name} {inspect_type} histogram",
- "histograms": tensor_histograms,
- },
- )
-
- return op_histograms
-
- @staticmethod
- def mse_metric_gap(fp32_tensor: Any, dequantize_tensor: Any) -> float:
- """Calculate the euclidean distance between fp32 tensor and int8 dequantize tensor.
-
- Args:
- fp32_tensor (tensor): The FP32 tensor.
- dequantize_tensor (tensor): The INT8 dequantize tensor.
- """
- check_module("numpy")
- import numpy as np
-
- # Get input tensor min max
- fp32_max = np.max(fp32_tensor) # type: ignore
- fp32_min = np.min(fp32_tensor) # type: ignore
-
- # Get input dequantize tensor min max
- dequantize_max = np.max(dequantize_tensor) # type: ignore
- dequantize_min = np.min(dequantize_tensor) # type: ignore
-
- # Normalize tensor values
- fp32_tensor = (fp32_tensor - fp32_min) / (fp32_max - fp32_min)
- dequantize_tensor = (dequantize_tensor - dequantize_min) / (dequantize_max - dequantize_min)
-
- diff_tensor = fp32_tensor - dequantize_tensor
- euclidean_dist = np.sum(diff_tensor**2) # type: ignore
- return euclidean_dist / fp32_tensor.size
-
- def get_weights_data(self, op_name: str, channel_normalization=True) -> list:
- """Get weights data for optimized model."""
- from PIL import Image
-
- check_module("numpy")
- import numpy as np
-
- tensors = self.get_tensors_info(model_type="optimized").get("weight", None)
- if tensors is None:
- raise ClientErrorException(
- "Could not get tensor information to display activations.",
- )
-
- op_tensors: Optional[dict] = tensors.get(op_name, None)
- if op_tensors is None:
- raise ClientErrorException(
- f"Could not get tensor information for {op_name} OP.",
- )
-
- weights = []
- for tensor_name, tensor_data_raw in op_tensors.items():
- if tensor_data_raw.ndim != 4:
- continue
- tensor_data = tensor_data_raw[0]
- shapes_order = self.model.shape_elements_order # pylint: disable=no-member
- channels_index = shapes_order.index("channels")
- new_order = [channels_index]
- new_order.extend([x for x in range(len(shapes_order)) if x != channels_index])
-
- tensor_data = np.transpose(tensor_data, new_order)
-
- if tensor_data.shape[1] != tensor_data.shape[2]:
- continue
-
- for tensor in tensor_data:
- if channel_normalization:
- tensor = 255 * (tensor - np.min(tensor)) / (np.max(tensor) - np.min(tensor))
- img = Image.fromarray(tensor)
- img = img.convert("L")
- img.show()
- weights.append(tensor.tolist())
- return weights
diff --git a/neural_insights/components/diagnosis/factory.py b/neural_insights/components/diagnosis/factory.py
deleted file mode 100644
index 544ec72a430..00000000000
--- a/neural_insights/components/diagnosis/factory.py
+++ /dev/null
@@ -1,45 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Diagnosis class factory."""
-from neural_insights.components.diagnosis.diagnosis import Diagnosis
-from neural_insights.components.diagnosis.onnx_diagnosis.onnxrt_diagnosis import OnnxRtDiagnosis
-from neural_insights.components.diagnosis.pytorch_diagnosis.pytorch_diagnosis import PyTorchDiagnosis
-from neural_insights.components.diagnosis.tensorflow_diagnosis.tensorflow_diagnosis import TensorflowDiagnosis
-from neural_insights.components.workload_manager.workload import Workload
-from neural_insights.utils.consts import Frameworks
-from neural_insights.utils.exceptions import InternalException
-from neural_insights.utils.logger import log
-
-
-class DiagnosisFactory:
- """Optimization factory."""
-
- @staticmethod
- def get_diagnosis(
- workload: Workload,
- ) -> Diagnosis:
- """Get diagnosis for specified framework."""
- diagnosis_map = {
- Frameworks.ONNX: OnnxRtDiagnosis,
- Frameworks.TF: TensorflowDiagnosis,
- Frameworks.PT: PyTorchDiagnosis,
- }
- diagnosis = diagnosis_map.get(workload.framework, None)
- if diagnosis is None:
- raise InternalException(
- f"Could not find diagnosis class for {workload.framework.value} framework.",
- )
- log.debug(f"Initializing {diagnosis.__name__} class.")
- return diagnosis(workload)
diff --git a/neural_insights/components/diagnosis/onnx_diagnosis/__init__.py b/neural_insights/components/diagnosis/onnx_diagnosis/__init__.py
deleted file mode 100644
index 1b80ab83a9f..00000000000
--- a/neural_insights/components/diagnosis/onnx_diagnosis/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The onnxrt_diagnosis package contains all components used for onnxrt optimizations debug."""
diff --git a/neural_insights/components/diagnosis/onnx_diagnosis/onnxrt_diagnosis.py b/neural_insights/components/diagnosis/onnx_diagnosis/onnxrt_diagnosis.py
deleted file mode 100644
index 540c9957ce9..00000000000
--- a/neural_insights/components/diagnosis/onnx_diagnosis/onnxrt_diagnosis.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The OnnxRT diagnosis class."""
-from typing import Optional
-
-from neural_insights.components.diagnosis.diagnosis import Diagnosis
-from neural_insights.components.model.onnxrt.model import OnnxrtModel
-from neural_insights.components.model.repository import ModelRepository
-from neural_insights.components.workload_manager.workload import Workload
-
-
-class OnnxRtDiagnosis(Diagnosis):
- """OnnxRuntime diagnosis class."""
-
- def __init__(self, workload: Workload):
- """Initialize OnnxRtDiagnosis."""
- super().__init__(workload)
- self._model: Optional[OnnxrtModel] = None
-
- @property
- def model(self) -> OnnxrtModel:
- """Get Neural Compressor Model instance."""
- self._ensure_model()
- return self._model # type: ignore
-
- def _ensure_model(self) -> None:
- """Create Neural Insights Model instance if needed."""
- if self._model is not None:
- return
- model_repository = ModelRepository()
- self._model = model_repository.get_model(self.model_path) # type: ignore
diff --git a/neural_insights/components/diagnosis/op_details.py b/neural_insights/components/diagnosis/op_details.py
deleted file mode 100644
index 6b29da74307..00000000000
--- a/neural_insights/components/diagnosis/op_details.py
+++ /dev/null
@@ -1,71 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The OpDetails class."""
-from typing import Any, Dict, List
-
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class OpDetails(JsonSerializer):
- """OP details class."""
-
- def __init__(self, op_name: str, op_details: dict) -> None:
- """Initialize OP details."""
- super().__init__()
- self.op_name: str = op_name
- self.pattern: OpPattern = OpPattern(op_details.get("pattern", {}))
- self.weight: OpWeights = OpWeights(op_details.get("weight", {}))
- self.activation: OpActivation = OpActivation(op_details.get("activation", {}))
-
- def serialize(
- self,
- serialization_type: str = "default",
- ) -> Dict[str, Any]:
- """Serialize OpDetails."""
- return {
- "OP name": self.op_name,
- "Pattern": self.pattern.serialize(),
- "Weights": self.weight.serialize(),
- "Activation": self.activation.serialize(),
- }
-
-
-class OpPattern(JsonSerializer):
- """OP pattern class."""
-
- def __init__(self, pattern_data: dict):
- """Initialize OP pattern."""
- super().__init__()
- self.sequence: List[str] = pattern_data.get("sequence", "").split(",")
- self.precision: str = pattern_data.get("precision", None)
-
-
-class OpWeights(JsonSerializer):
- """OP weights class."""
-
- def __init__(self, weights_data: dict):
- """Initialize OP weights."""
- super().__init__()
- self.dtype: str = weights_data.get("dtype", None)
- self.granularity: str = weights_data.get("granularity", None)
-
-
-class OpActivation(JsonSerializer):
- """OP activation class."""
-
- def __init__(self, op_activation: dict):
- """Initialize OP activation."""
- super().__init__()
- self.dtype: str = op_activation.get("dtype", None)
diff --git a/neural_insights/components/diagnosis/op_entry.py b/neural_insights/components/diagnosis/op_entry.py
deleted file mode 100644
index f889774a43b..00000000000
--- a/neural_insights/components/diagnosis/op_entry.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The OpEntry class."""
-from typing import Any, Dict
-
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class OpEntry(JsonSerializer):
- """OP entry class."""
-
- def __init__(self, op_name: str, mse: float, activation_min: float, activation_max: float):
- """Initialize OP entry."""
- super().__init__()
- self.op_name: str = op_name
- self.mse: float = mse
- self.activation_min: float = activation_min
- self.activation_max: float = activation_max
-
- def serialize(
- self,
- serialization_type: str = "default",
- ) -> Dict[str, Any]:
- """Serialize OP entry."""
- return {
- "OP name": self.op_name,
- "MSE": self.mse,
- "Activation Min": self.activation_min,
- "Activation Max": self.activation_max,
- }
diff --git a/neural_insights/components/diagnosis/pytorch_diagnosis/__init__.py b/neural_insights/components/diagnosis/pytorch_diagnosis/__init__.py
deleted file mode 100644
index 1ef0aa3b00b..00000000000
--- a/neural_insights/components/diagnosis/pytorch_diagnosis/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The pytorch_diagnosis package contains all components used for pytorch optimizations debug."""
diff --git a/neural_insights/components/diagnosis/pytorch_diagnosis/pytorch_diagnosis.py b/neural_insights/components/diagnosis/pytorch_diagnosis/pytorch_diagnosis.py
deleted file mode 100644
index 994e7e2d722..00000000000
--- a/neural_insights/components/diagnosis/pytorch_diagnosis/pytorch_diagnosis.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The PyTorch diagnosis class."""
-from typing import Optional
-
-from neural_insights.components.diagnosis.diagnosis import Diagnosis
-from neural_insights.components.model.pytorch.model import PyTorchModel
-from neural_insights.components.model.repository import ModelRepository
-from neural_insights.components.workload_manager.workload import Workload
-
-
-class PyTorchDiagnosis(Diagnosis):
- """PyTorch diagnosis class."""
-
- def __init__(self, workload: Workload):
- """Initialize PyTorchDiagnosis."""
- super().__init__(workload)
- self._model: Optional[PyTorchModel] = None
-
- @property
- def model(self) -> PyTorchModel:
- """Get Neural Compressor Model instance."""
- self._ensure_model()
- return self._model # type: ignore
-
- def _ensure_model(self) -> None:
- """Create Neural Insights Model instance if needed."""
- if self._model is not None:
- return
- model_repository = ModelRepository()
- self._model = model_repository.get_model(self.model_path) # type: ignore
diff --git a/neural_insights/components/diagnosis/tensorflow_diagnosis/__init__.py b/neural_insights/components/diagnosis/tensorflow_diagnosis/__init__.py
deleted file mode 100644
index 391a72fd766..00000000000
--- a/neural_insights/components/diagnosis/tensorflow_diagnosis/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The tensorflow_diagnosis package contains components used for tensorflow optimizations debug."""
diff --git a/neural_insights/components/diagnosis/tensorflow_diagnosis/tensorflow_diagnosis.py b/neural_insights/components/diagnosis/tensorflow_diagnosis/tensorflow_diagnosis.py
deleted file mode 100644
index e0c80d1febf..00000000000
--- a/neural_insights/components/diagnosis/tensorflow_diagnosis/tensorflow_diagnosis.py
+++ /dev/null
@@ -1,43 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The Tensorflow diagnosis class."""
-from typing import Optional
-
-from neural_insights.components.diagnosis.diagnosis import Diagnosis
-from neural_insights.components.model.repository import ModelRepository
-from neural_insights.components.model.tensorflow.model import TensorflowModel
-from neural_insights.components.workload_manager.workload import Workload
-
-
-class TensorflowDiagnosis(Diagnosis):
- """OnnxRuntime diagnosis class."""
-
- def __init__(self, workload: Workload):
- """Initialize TensorflowDiagnosis."""
- super().__init__(workload)
- self._model: Optional[TensorflowModel] = None
-
- @property
- def model(self) -> TensorflowModel:
- """Get Neural Compressor Model instance."""
- self._ensure_model()
- return self._model # type: ignore
-
- def _ensure_model(self) -> None:
- """Create Neural Insights Model instance if needed."""
- if self._model is not None:
- return
- model_repository = ModelRepository()
- self._model = model_repository.get_model(self.model_path) # type: ignore
diff --git a/neural_insights/components/diagnosis/weights_details.py b/neural_insights/components/diagnosis/weights_details.py
deleted file mode 100644
index 68a64662668..00000000000
--- a/neural_insights/components/diagnosis/weights_details.py
+++ /dev/null
@@ -1,76 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The Weights details class."""
-from typing import Any, Dict, Optional
-
-from neural_compressor.utils.utility import mse_metric_gap
-from neural_insights.utils.json_serializer import JsonSerializer
-from neural_insights.utils.utils import check_module
-
-
-class WeightsDetails(JsonSerializer):
- """Weights details class."""
-
- def __init__(self, op_name: str, input_tensor_data, optimized_tensor_data):
- """Initialize Weights details."""
- super().__init__()
- self.op_name: str = op_name
- self.mse: Optional[float] = mse_metric_gap(
- input_tensor_data,
- optimized_tensor_data,
- )
- self.input_stats: WeightsStatistics = WeightsStatistics(input_tensor_data)
- self.optimized_stats: WeightsStatistics = WeightsStatistics(optimized_tensor_data)
-
- def serialize(
- self,
- serialization_type: str = "default",
- ) -> Dict[str, Any]:
- """Serialize Weights details."""
- return {
- "OP name": self.op_name,
- "MSE": self.mse,
- "Input model": self.input_stats.serialize(),
- "Optimized model": self.optimized_stats.serialize(),
- }
-
-
-class WeightsStatistics(JsonSerializer):
- """Weights statistics class."""
-
- def __init__(self, tensor_data) -> None:
- """Initialize Weights details."""
- check_module("numpy")
- import numpy as np
-
- super().__init__()
- self.min: float = np.min(tensor_data)
- self.max: float = np.max(tensor_data)
- self.mean: float = np.mean(tensor_data)
- self.std: float = np.std(tensor_data)
- self.var: float = np.var(tensor_data)
-
- def serialize(
- self,
- serialization_type: str = "default",
- ) -> Dict[str, Any]:
- """Serialize Weights details."""
- return {
- "Min weight": self.min,
- "Max weight": self.max,
- "Mean": self.mean,
- "Standard deviation": self.std,
- "Variance": self.var,
- }
diff --git a/neural_insights/components/graph/__init__.py b/neural_insights/components/graph/__init__.py
deleted file mode 100644
index 602e3be0736..00000000000
--- a/neural_insights/components/graph/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Package graph contains all packages required to display model graph."""
diff --git a/neural_insights/components/graph/attribute.py b/neural_insights/components/graph/attribute.py
deleted file mode 100644
index 8697a3ed875..00000000000
--- a/neural_insights/components/graph/attribute.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Graph Node Attribute."""
-
-from typing import Any
-
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class Attribute(JsonSerializer):
- """Graph Node Attribute."""
-
- def __init__(self, name: str, attribute_type: str, value: Any):
- """Construct the object."""
- super().__init__()
- self.name = name
- self.attribute_type = attribute_type
- self.value = value
diff --git a/neural_insights/components/graph/collapser.py b/neural_insights/components/graph/collapser.py
deleted file mode 100644
index 7a2abcb3790..00000000000
--- a/neural_insights/components/graph/collapser.py
+++ /dev/null
@@ -1,132 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Graph collapser."""
-
-from typing import Dict, List, Set
-
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.graph.node import GroupNode, Node
-
-
-class Collapser:
- """Graph collapser."""
-
- GROUP_NAME_PREFIX = "node_group_"
-
- def __init__(self, expanded_groups: List[str] = []) -> None:
- """Construct the collapser."""
- restored_group_names = [self._unprepare_group_name(name) for name in expanded_groups]
- self.expanded_groups: Set[str] = set(restored_group_names)
- self.group_sizes: Dict[str, int] = {}
-
- def collapse(self, graph: Graph) -> Graph:
- """Return graph with collapsed nodes."""
- collapsed_graph = Graph()
-
- self._calculate_group_sizes(graph)
- self._add_nodes_to_collapsed_graph(collapsed_graph, graph)
- self._add_edges_to_collapsed_graph(collapsed_graph, graph)
-
- return collapsed_graph
-
- def _calculate_group_sizes(self, graph: Graph) -> None:
- """Create dict with number of nodes in each group."""
- self.group_sizes = {}
- for node in graph.nodes:
- for group in node.groups:
- if not self.group_sizes.get(group):
- self.group_sizes[group] = 0
- self.group_sizes[group] += 1
-
- def _add_nodes_to_collapsed_graph(self, collapsed_graph: Graph, graph: Graph) -> None:
- """Add Nodes from source graph to collapsed graph."""
- for node in graph.nodes:
- collapsed_graph.add_node(self._get_node_for_collapsed_graph(node))
-
- def _add_edges_to_collapsed_graph(self, collapsed_graph: Graph, graph: Graph) -> None:
- """Add Edges from source graph to collapsed graph."""
- collapsed_edges_repository: Dict[str, bool] = {}
-
- for edge in graph.edges:
- edge_added = collapsed_graph.add_edge(
- source_id=edge.source,
- target_id=edge.target,
- )
- if edge_added:
- continue
-
- source_node_id = self._get_collapsed_node_id(graph.get_node(edge.source))
- target_node_id = self._get_collapsed_node_id(graph.get_node(edge.target))
-
- if source_node_id == target_node_id:
- # skip edges inside collapsed node
- continue
-
- collapsed_edge_designation = f"collapsed edge between {source_node_id} and {target_node_id}"
- is_collapsed_already_added = collapsed_edges_repository.get(
- collapsed_edge_designation,
- False,
- )
- if not is_collapsed_already_added:
- collapsed_graph.add_edge(
- source_id=source_node_id,
- target_id=target_node_id,
- )
- collapsed_edges_repository[collapsed_edge_designation] = True
-
- def _get_node_for_collapsed_graph(self, node: Node) -> Node:
- """Return a node for collapsed graph."""
- if self._should_collapse_node(node):
- return self._collapse_node(node)
- return node
-
- def _should_collapse_node(self, node: Node) -> bool:
- """Decide if given node should be collapsed."""
- if not node.groups:
- return False
-
- for group in node.groups:
- if group not in self.expanded_groups:
- return self.group_sizes.get(group, 0) > 1
-
- return False
-
- def _collapse_node(self, node: Node) -> Node:
- """Collapse node into group node."""
- collapsed_node_id = self._get_collapsed_node_id(node)
- group_name = self._unprepare_group_name(collapsed_node_id)
- return GroupNode(id=collapsed_node_id, group_name=group_name)
-
- def _get_collapsed_node_id(self, node: Node) -> str:
- """Collapse node into group node."""
- if not self._should_collapse_node(node):
- return node.id
-
- for group in node.groups:
- if group not in self.expanded_groups:
- break
-
- return self._prepare_group_name(group)
-
- def _prepare_group_name(self, name: str) -> str:
- """Convert group name to unique one."""
- return f"{self.GROUP_NAME_PREFIX}{name}"
-
- def _unprepare_group_name(self, name: str) -> str:
- """Convert group name to unique one."""
- if not name.startswith(self.GROUP_NAME_PREFIX):
- return name
-
- return name[len(self.GROUP_NAME_PREFIX) :]
diff --git a/neural_insights/components/graph/edge.py b/neural_insights/components/graph/edge.py
deleted file mode 100644
index 3f0a10f2e5e..00000000000
--- a/neural_insights/components/graph/edge.py
+++ /dev/null
@@ -1,38 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Edge class."""
-
-from neural_insights.components.graph.node import Node
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class Edge(JsonSerializer):
- """Edge in graph."""
-
- def __init__(self, source: Node, target: Node) -> None:
- """Construct object."""
- super().__init__()
- self._source = source
- self._target = target
-
- @property
- def source(self) -> str:
- """Return source Node id."""
- return self._source.id
-
- @property
- def target(self) -> str:
- """Return target Node id."""
- return self._target.id
diff --git a/neural_insights/components/graph/graph.py b/neural_insights/components/graph/graph.py
deleted file mode 100644
index 3d3093bb92f..00000000000
--- a/neural_insights/components/graph/graph.py
+++ /dev/null
@@ -1,88 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Graph class."""
-
-from typing import Dict, List
-
-from neural_insights.components.graph.edge import Edge
-from neural_insights.components.graph.node import Node
-from neural_insights.utils.exceptions import NotFoundException
-from neural_insights.utils.json_serializer import JsonSerializer
-from neural_insights.utils.logger import log
-
-
-class Graph(JsonSerializer):
- """Model graph definition."""
-
- def __init__(self) -> None:
- """Construct object."""
- super().__init__()
- self._nodes: Dict[str, Node] = {}
- self._edges: List[Edge] = []
-
- def add_node(self, node: Node) -> None:
- """Add a Node to graph."""
- self._nodes[node.id] = node
-
- @property
- def nodes(self) -> List[Node]:
- """Return nodes as a list."""
- return list(self._nodes.values())
-
- @property
- def edges(self) -> List[Edge]:
- """Return edges as a list."""
- return self._edges
-
- def add_edge(self, source_id: str, target_id: str) -> bool:
- """Add an Edge to graph."""
- try:
- source = self.get_node(source_id)
- target = self.get_node(target_id)
- except NotFoundException as err:
- log.debug(
- f"Got an error: {str(err)} while attempted " f"to add an Edge from {source_id} to {target_id}",
- )
- return False
- self._edges.append(Edge(source, target))
- return True
-
- def get_node(self, id: str) -> Node:
- """Get a node by id."""
- if id not in self._nodes:
- raise NotFoundException(f"Node id: {id} not found in Graph")
- return self._nodes[id]
-
- def highlight_pattern(self, op_name: str, pattern: List[str]) -> None:
- """Highlight pattern in graph."""
- source_op = op_name
- self.get_node(source_op).highlight = True
- for op in pattern[1:]:
- target_nodes = self.get_target_nodes(source_op)
- for target_node in target_nodes:
- if target_node.label == op:
- self.get_node(target_node.id).highlight = True
- source_op = target_node.id
- continue
-
- def get_target_nodes(self, op_name: str) -> List[Node]:
- """Get target nodes from specified op."""
- target_nodes: List[Node] = []
-
- for edge in self.edges:
- if edge.source == op_name:
- target_nodes.append(self.get_node(edge.target))
-
- return target_nodes
diff --git a/neural_insights/components/graph/graph_reader.py b/neural_insights/components/graph/graph_reader.py
deleted file mode 100644
index 6228d94aeeb..00000000000
--- a/neural_insights/components/graph/graph_reader.py
+++ /dev/null
@@ -1,66 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Graph reader."""
-from typing import List, Optional, Tuple
-
-from neural_insights.components.graph.collapser import Collapser
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.graph.node import Node
-from neural_insights.components.model.repository import ModelRepository
-from neural_insights.utils.exceptions import ClientErrorException
-
-
-class GraphReader:
- """Model graph reader."""
-
- def read(self, model_path: str, expanded_groups: List[str]) -> Graph:
- """Return Graph for given model path."""
- model_repository = ModelRepository()
- model = model_repository.get_model(model_path)
- graph = model.get_model_graph()
-
- collapser = Collapser(expanded_groups)
- collapsed_graph = collapser.collapse(graph)
-
- return collapsed_graph
-
- def find_pattern_in_graph(
- self,
- model_path: str,
- op_name: str,
- pattern: List[str],
- ) -> Tuple[Graph, List[str]]:
- """Search graph for specific nodes pattern."""
- model_repository = ModelRepository()
- model = model_repository.get_model(model_path)
- graph = model.get_model_graph()
-
- op_data: Optional[Node] = None
- for node in graph.nodes:
- if node.id == op_name:
- op_data = node
- break
-
- if op_data is None:
- raise ClientErrorException(f"Could not find {op_name} in graph.")
-
- expanded_groups = op_data.groups
-
- collapser = Collapser(expanded_groups)
- collapsed_graph = collapser.collapse(graph)
-
- collapsed_graph.highlight_pattern(op_name, pattern)
-
- return collapsed_graph, expanded_groups
diff --git a/neural_insights/components/graph/node.py b/neural_insights/components/graph/node.py
deleted file mode 100644
index 59d8e263684..00000000000
--- a/neural_insights/components/graph/node.py
+++ /dev/null
@@ -1,65 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Node class."""
-
-from typing import Any, Dict, List
-
-from neural_insights.components.graph.attribute import Attribute
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class Node(JsonSerializer):
- """Single Node in graph."""
-
- TYPE_SIMPLE_NODE = "node"
-
- def __init__(
- self,
- id: str,
- label: str,
- highlight: bool = False,
- properties: Dict[str, Any] = {},
- attributes: List[Attribute] = [],
- groups: List[str] = [],
- ) -> None:
- """Construct object."""
- super().__init__()
- self.id = id
- self.label = label
- self.highlight = highlight
- self.properties = properties
- self.attributes = attributes
- self.groups = groups
- self.node_type = self.TYPE_SIMPLE_NODE
- self._skip.append("groups")
-
-
-class GroupNode(Node):
- """Node being a group of Nodes in Graph."""
-
- TYPE_GROUP_NODE = "group_node"
-
- def __init__(
- self,
- id: str,
- group_name: str,
- ) -> None:
- """Construct object."""
- super().__init__(
- id=id,
- label=group_name,
- )
- self.group = group_name
- self.node_type = self.TYPE_GROUP_NODE
diff --git a/neural_insights/components/graph/reader/__init__.py b/neural_insights/components/graph/reader/__init__.py
deleted file mode 100644
index cac19a74909..00000000000
--- a/neural_insights/components/graph/reader/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Package with all graph readers."""
diff --git a/neural_insights/components/graph/reader/onnxrt_reader.py b/neural_insights/components/graph/reader/onnxrt_reader.py
deleted file mode 100644
index 4c891d79252..00000000000
--- a/neural_insights/components/graph/reader/onnxrt_reader.py
+++ /dev/null
@@ -1,244 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Onnxrt Graph reader."""
-
-import uuid
-from typing import Any, Dict, List, Optional
-
-from neural_insights.components.graph.attribute import Attribute
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.graph.node import Node
-from neural_insights.components.model.model import Model
-from neural_insights.utils.exceptions import NotFoundException
-
-
-class OnnxrtReader:
- """Graph Reader for Onnxrt."""
-
- def __init__(self, model: Model) -> None:
- """Initialize object."""
- self._hidden_node_ids: Dict[str, bool] = {}
- self.model: Model = model
-
- def read(self) -> Graph:
- """Read a graph."""
- self._hidden_node_ids = {}
-
- from neural_insights.components.model.onnxrt.model import OnnxrtModel
-
- if not isinstance(self.model, OnnxrtModel):
- raise NotFoundException(f"{self.model.path} is not Onnxrt model.")
-
- onnx_nodes = self.model.nc_model_instance.nodes()
- graph = Graph()
-
- graph = self._add_boundary_nodes(graph)
-
- edges: dict = {}
-
- for node_def in onnx_nodes:
- node_name = node_def.name
- if node_name == "":
- node_name = f"{node_def.op_type}_{uuid.uuid4()}"
-
- if self._should_hide_node(node_def):
- self._hide_node(node_name)
- continue
-
- edges = self._add_edges_from_node(edges, node_def, node_name)
-
- graph.add_node(
- Node(
- id=node_name,
- label=node_def.op_type,
- highlight=False,
- properties={
- "name": node_name,
- "type": node_def.op_type,
- },
- attributes=self._convert_attributes(node_def),
- groups=self._get_group_names(node_name),
- ),
- )
-
- self._process_edges(graph, edges)
-
- return graph
-
- def _get_group_names(self, node_id: str) -> List[str]:
- """Get a group names from Node starting from the top."""
- elements = node_id.split("/")
- names = []
- for idx in range(1, len(elements)):
- names.append("/".join(elements[0:idx]))
- return names
-
- def _should_hide_node(self, node_def: Any) -> bool:
- """Check if given node should be hidden."""
- if node_def.op_type not in ["Const", "Identity"]:
- return False
-
- if self._node_def_has_not_hidden_inputs(node_def):
- return False
-
- return True
-
- def _node_def_has_not_hidden_inputs(self, node_def: Any) -> bool:
- """Check if node has any visible input."""
- not_hidden_input_ids = list(
- filter(
- lambda input_node: input_node not in self._hidden_node_ids,
- node_def.input,
- ),
- )
- return len(not_hidden_input_ids) > 0
-
- def _hide_node(self, node_name: str) -> None:
- """Mark node as hidden."""
- self._hidden_node_ids[node_name] = True
-
- def _is_node_id_hidden(self, node_id: str) -> bool:
- """Check if provided node_id is hidden."""
- return node_id in self._hidden_node_ids
-
- def _convert_attributes(self, node_def: Any) -> List[Attribute]:
- """Convert NodeDef attributes to our format."""
- attributes = []
-
- for attribute in node_def.attribute:
- converted_attribute = self._convert_attribute(attribute)
- if converted_attribute is not None:
- attributes.append(converted_attribute)
-
- return attributes
-
- def _convert_attribute(self, attribute: Any) -> Optional[Attribute]:
- """Convert NodeDef attribute to our format."""
- from onnx.mapping import TENSOR_TYPE_TO_NP_TYPE
-
- attr_type = str(TENSOR_TYPE_TO_NP_TYPE.get(attribute.type, "UNKNOWN"))
-
- attr_float = attribute.f
- attr_floats = attribute.floats
-
- attr_int = attribute.i
- attr_ints = attribute.ints
-
- value = None
- if "int" in attr_type:
- value = attr_int
- if len(attr_ints) > 0:
- value = list(attr_ints)
- elif "float" in attr_type:
- value = attr_float
- if len(attr_floats) > 0:
- value = list(attr_floats)
- else:
- print("Attribute type not yet supported")
-
- return Attribute(
- attribute.name,
- str(TENSOR_TYPE_TO_NP_TYPE.get(attribute.type, "UNKNOWN")),
- value,
- )
-
- def _add_boundary_nodes(self, graph: Graph) -> Graph:
- """Add boundary nodes to graph."""
- from neural_insights.components.model.onnxrt.model import OnnxrtModel
-
- if not isinstance(self.model, OnnxrtModel):
- raise NotFoundException(f"{self.model.path} is not Onnxrt model.")
-
- onnx_graph = self.model.nc_model_instance.graph()
- boundary_nodes = {"Input node": onnx_graph.input, "Output node": onnx_graph.output}
- for node_type, nodes in boundary_nodes.items():
- for node in nodes:
- graph.add_node(
- Node(
- id=node.name,
- label=node_type,
- highlight=False,
- properties={
- "name": node.name,
- "type": node_type,
- },
- attributes=[],
- groups=self._get_group_names(node.name),
- ),
- )
- return graph
-
- def _add_edges_from_node(self, edges: dict, node: Any, node_name: str) -> dict:
- """Add update edges from node data."""
- for edge in node.input:
- edge_data = edges.get(edge, {})
- input_nodes = edge_data.get("inputs", [])
- input_nodes.append(node_name)
- edge_data.update({"inputs": input_nodes})
- edges.update({edge: edge_data})
-
- for edge in node.output:
- edge_data = edges.get(edge, {})
- output_nodes = edge_data.get("outputs", [])
- output_nodes.append(node_name)
- edge_data.update({"outputs": output_nodes})
- edges.update({edge: edge_data})
-
- return edges
-
- def _process_edges(self, graph: Graph, edges: dict) -> None:
- """Process edges."""
- from neural_insights.components.model.onnxrt.model import OnnxrtModel
-
- if not isinstance(self.model, OnnxrtModel):
- raise NotFoundException(f"{self.model.path} is not Onnxrt model.")
-
- for edge, edge_data in edges.items():
- # Check if edge connects to input node
- input_nodes = [node.name for node in self.model.nc_model_instance.graph().input]
- if edge in input_nodes and "inputs" in edge_data:
- source_node = edge
- for target_node in edge_data["inputs"]:
- graph.add_edge(
- source_id=source_node,
- target_id=target_node,
- )
-
- # Check if edge connects to output node
- output_nodes = [node.name for node in self.model.nc_model_instance.graph().output]
- if edge in output_nodes and "outputs" in edge_data:
- target_node = edge
- for source_node in edge_data["outputs"]:
- graph.add_edge(
- source_id=source_node,
- target_id=target_node,
- )
-
- self._process_standard_edge(graph, edge_data)
-
- def _process_standard_edge(self, graph: Graph, edge_data: dict) -> None:
- """Process standard edges that contain both inputs and outputs."""
- if "inputs" not in edge_data or "outputs" not in edge_data:
- return
- for source_node in edge_data["outputs"]:
- for target_node in edge_data["inputs"]:
- if self._is_node_id_hidden(source_node) or self._is_node_id_hidden(
- target_node,
- ):
- continue
- graph.add_edge(
- source_id=source_node,
- target_id=target_node,
- )
diff --git a/neural_insights/components/graph/reader/pytorch_reader.py b/neural_insights/components/graph/reader/pytorch_reader.py
deleted file mode 100644
index 7bcab50ca34..00000000000
--- a/neural_insights/components/graph/reader/pytorch_reader.py
+++ /dev/null
@@ -1,34 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch Graph reader."""
-
-from typing import Dict
-
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.model.model import Model
-
-
-class PyTorchReader:
- """Graph Reader for PyTorch."""
-
- def __init__(self, model: Model) -> None:
- """Initialize object."""
- self._hidden_node_ids: Dict[str, bool] = {}
- self.model: Model = model
-
- def read(self) -> Graph:
- """Read a graph."""
- graph = Graph()
- return graph
diff --git a/neural_insights/components/graph/reader/tensorflow_reader.py b/neural_insights/components/graph/reader/tensorflow_reader.py
deleted file mode 100644
index f6866785550..00000000000
--- a/neural_insights/components/graph/reader/tensorflow_reader.py
+++ /dev/null
@@ -1,150 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tensorflow Graph reader."""
-
-from typing import Any, Dict, List, Optional
-
-from neural_insights.components.graph.attribute import Attribute
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.graph.node import Node
-from neural_insights.components.model.model import Model
-from neural_insights.utils.exceptions import NotFoundException
-
-
-class TensorflowReader:
- """Graph Reader for Tensorflow."""
-
- def __init__(self, model: Model) -> None:
- """Initialize object."""
- self._hidden_node_ids: Dict[str, bool] = {}
- self.model: Model = model
-
- def read(self) -> Graph:
- """Read a graph."""
- self._hidden_node_ids = {}
-
- from neural_insights.components.model.tensorflow.model import TensorflowModel
-
- if not isinstance(self.model, TensorflowModel):
- raise NotFoundException(f"{self.model.path} is not Tensorflow model.")
-
- graph_def = self.model.nc_model_instance.graph_def
-
- graph = Graph()
-
- for node_def in graph_def.node:
- if self._should_hide_node(node_def):
- self._hide_node(node_def)
- continue
-
- current_node_id = node_def.name
-
- graph.add_node(
- Node(
- id=current_node_id,
- label=node_def.op,
- highlight=False,
- properties={
- "name": node_def.name,
- "type": node_def.op,
- },
- attributes=self._convert_attributes(node_def),
- groups=self._get_group_names(current_node_id),
- ),
- )
-
- for input_node_name in node_def.input:
- if self._is_node_id_hidden(input_node_name) or self._is_node_id_hidden(
- current_node_id,
- ):
- continue
- graph.add_edge(
- source_id=input_node_name,
- target_id=current_node_id,
- )
-
- return graph
-
- def _get_group_names(self, node_id: str) -> List[str]:
- """Get a group names from Node starting from the top."""
- elements = node_id.split("/")
- names = []
- for idx in range(1, len(elements)):
- names.append("/".join(elements[0:idx]))
- return names
-
- def _should_hide_node(self, node_def: Any) -> bool:
- """Check if given node should be hidden."""
- if node_def.op not in ["Const", "Identity"]:
- return False
-
- if self._node_def_has_not_hidden_inputs(node_def):
- return False
-
- return True
-
- def _node_def_has_not_hidden_inputs(self, node_def: Any) -> bool:
- """Check if node has any visible input."""
- not_hidden_input_ids = list(
- filter(
- lambda input_node: input_node not in self._hidden_node_ids,
- node_def.input,
- ),
- )
- return len(not_hidden_input_ids) > 0
-
- def _hide_node(self, node: Any) -> None:
- """Mark node as hidden."""
- self._hidden_node_ids[node.name] = True
-
- def _is_node_id_hidden(self, node_id: str) -> bool:
- """Check if provided node_id is hidden."""
- return node_id in self._hidden_node_ids
-
- def _convert_attributes(self, node_def: Any) -> List[Attribute]:
- """Convert NodeDef attributes to our format."""
- attributes = []
-
- for attribute_name in node_def.attr:
- converted_attribute = self._convert_attribute(
- attribute_name,
- node_def.attr[attribute_name],
- )
- if converted_attribute is not None:
- attributes.append(converted_attribute)
-
- return attributes
-
- def _convert_attribute(self, name: str, value: Any) -> Optional[Attribute]:
- """Convert NodeDef attribute to our format."""
- from tensorflow.python.framework.dtypes import _TYPE_TO_STRING
-
- if name == "shape":
- shape = [dim.size for dim in value.shape.dim]
- return Attribute(
- name,
- "shape",
- shape,
- )
-
- if 0 != len(value.s):
- return Attribute(name, "string", str(value.s.decode("utf-8")))
- if 0 != value.type:
- return Attribute(
- name,
- "type",
- _TYPE_TO_STRING.get(value.type, "UNKNOWN"),
- )
- return None
diff --git a/neural_insights/components/model/__init__.py b/neural_insights/components/model/__init__.py
deleted file mode 100644
index 32ce5f8d7cb..00000000000
--- a/neural_insights/components/model/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The model package contains all model related code used in Neural Insights server."""
diff --git a/neural_insights/components/model/domain.py b/neural_insights/components/model/domain.py
deleted file mode 100644
index 1a20138d314..00000000000
--- a/neural_insights/components/model/domain.py
+++ /dev/null
@@ -1,30 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Model domain class."""
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class Domain(JsonSerializer):
- """Model domain definition."""
-
- def __init__(self, domain: str = "", domain_flavour: str = "") -> None:
- """Object construction."""
- super().__init__()
-
- if not domain and domain_flavour:
- raise ValueError("Domain must be set when given flavour")
-
- self.domain = domain
- self.domain_flavour = domain_flavour
diff --git a/neural_insights/components/model/model.py b/neural_insights/components/model/model.py
deleted file mode 100644
index fec5aa17261..00000000000
--- a/neural_insights/components/model/model.py
+++ /dev/null
@@ -1,140 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The abstract model class."""
-
-from abc import ABC, abstractmethod
-from typing import Any, List, Optional
-
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.model.domain import Domain
-from neural_insights.components.model.shape import Shape
-from neural_insights.utils.consts import DomainFlavours, Domains
-from neural_insights.utils.logger import log
-from neural_insights.utils.utils import get_size
-
-
-class Model(ABC):
- """Abstract model class."""
-
- def __init__(self, path: str) -> None:
- """Initialize object."""
- self.ensure_supported_path(path)
- self.path = path
- self.size = get_size(self.path)
- self.framework = None
-
- def get_input_nodes(self) -> Optional[List[Any]]:
- """Get model input nodes."""
- self.guard_requirements_installed()
- return None
-
- def get_output_nodes(self) -> Optional[List[Any]]:
- """Get model output nodes."""
- self.guard_requirements_installed()
- return None
-
- def ensure_supported_path(self, path: str) -> None:
- """Make sure that provided path is to supported model."""
- if not self.supports_path(path):
- klass = self.__class__
- model_name = ".".join([klass.__module__, klass.__qualname__])
- raise AttributeError(f"Model path: {path} is not supported by {model_name} class.")
-
- def get_model_graph(self) -> Graph:
- """Get model graph."""
- raise NotImplementedError(f"Reading graph for model {self.path} is not supported.")
-
- def get_tensors_info(self) -> dict:
- """Get information about tensors."""
- raise NotImplementedError(
- f"Getting tensors informarmation for model {self.path} is not supported.",
- )
-
- @property
- def domain(self) -> Domain:
- """Get model domain."""
- try:
- node_names = {node.id for node in self.get_model_graph().nodes}
- except Exception:
- return Domain()
-
- def has_name_part(name_part: str) -> bool:
- """Check if a node exists with given name_part."""
- return bool([name for name in node_names if name_part in name])
-
- def has_all_name_parts(name_parts: List[str]) -> bool:
- """Check if there is a node for every name_part."""
- missing = [name_part for name_part in name_parts if not has_name_part(name_part)]
- return not missing
-
- if has_all_name_parts(["bboxes", "scores", "classes", "ssd"]):
- return Domain(
- domain=Domains.OBJECT_DETECTION.value,
- domain_flavour=DomainFlavours.SSD.value,
- )
- if has_all_name_parts(["boxes", "yolo"]):
- return Domain(
- domain=Domains.OBJECT_DETECTION.value,
- domain_flavour=DomainFlavours.YOLO.value,
- )
- if has_all_name_parts(["boxes", "scores", "classes"]):
- return Domain(domain=Domains.OBJECT_DETECTION.value)
- if has_all_name_parts(["resnet"]):
- return Domain(domain=Domains.IMAGE_RECOGNITION.value)
- return Domain()
-
- @property
- def input_shape(self) -> Shape:
- """Try to detect data shape."""
- return Shape(trusted=True)
-
- @property
- def shape_elements_order(self) -> List[str]:
- """Get order of input shape channels."""
- raise NotImplementedError
-
- @staticmethod
- @abstractmethod
- def get_framework_name() -> str:
- """Get the name of framework."""
-
- @staticmethod
- @abstractmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
-
- @property
- def supports_profiling(self) -> bool:
- """Check if profiling is supported for the model."""
- return False
-
- @property
- def supports_graph(self) -> bool:
- """Check if it is possible to display model's graph."""
- try:
- self.get_model_graph()
- return True
- except Exception:
- log.debug("Could not load graph of model.")
- return False
-
- @property
- def supports_pruning(self) -> bool:
- """Check if pruning is supported for the model."""
- return False
-
- @abstractmethod
- def guard_requirements_installed(self) -> None:
- """Ensure all requirements are installed."""
diff --git a/neural_insights/components/model/model_type_getter.py b/neural_insights/components/model/model_type_getter.py
deleted file mode 100644
index b82c96266b7..00000000000
--- a/neural_insights/components/model/model_type_getter.py
+++ /dev/null
@@ -1,33 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Model type getter."""
-
-from neural_compressor.model.model import get_model_type as nc_get_model_type
-from neural_insights.utils.expiring_dict import ExpiringDict
-
-model_type_cache = ExpiringDict(ttl=600)
-
-
-def get_model_type(model_path: str) -> str:
- """Get model_type using local cache."""
- try:
- return model_type_cache[model_path]
- except KeyError:
- try:
- model_type = nc_get_model_type(model_path)
- except Exception:
- model_type = "not a model"
- model_type_cache[model_path] = model_type
- return model_type
diff --git a/neural_insights/components/model/onnxrt/__init__.py b/neural_insights/components/model/onnxrt/__init__.py
deleted file mode 100644
index e52d96d6833..00000000000
--- a/neural_insights/components/model/onnxrt/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""OnnxRt related models."""
diff --git a/neural_insights/components/model/onnxrt/model.py b/neural_insights/components/model/onnxrt/model.py
deleted file mode 100644
index e60c0a1d457..00000000000
--- a/neural_insights/components/model/onnxrt/model.py
+++ /dev/null
@@ -1,230 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Onnxrt model class."""
-# pylint: disable=import-error
-# pylint: disable=no-name-in-module
-import os
-import re
-import sys
-from typing import Any, List, Optional
-
-from neural_compressor.experimental.common.model import Model as NCModel
-from neural_compressor.model.onnx_model import ONNXModel
-from neural_compressor.utils.logger import Logger
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.graph.reader.onnxrt_reader import OnnxrtReader
-from neural_insights.components.model.domain import Domain
-from neural_insights.components.model.model import Model
-from neural_insights.components.model.shape import Shape
-from neural_insights.utils.consts import DomainFlavours, Domains, Frameworks
-from neural_insights.utils.logger import log
-from neural_insights.utils.utils import check_module, get_file_extension
-
-
-class OnnxrtModel(Model):
- """Onnxrt Model class."""
-
- def __init__(self, path: str) -> None:
- """Initialize object."""
- super().__init__(path)
- self._nc_model_instance: Optional[ONNXModel] = None
-
- @property
- def domain(self) -> Domain:
- """Get model domain."""
- try:
- input_node_names = {node.name for node in self.nc_model_instance.graph().input} # pylint: disable=E1101
- node_names = {node.name for node in self.nc_model_instance.nodes()} # pylint: disable=E1101
- boundary_nodes = [node.name for node in self.nc_model_instance.graph().input] # pylint: disable=E1101
- boundary_nodes.extend(
- [node.name for node in self.nc_model_instance.graph().output], # pylint: disable=E1101
- )
- op_names = {node.op_type for node in self.nc_model_instance.nodes()} # pylint: disable=E1101
-
- except Exception:
- return Domain()
-
- nlp_domain = self._check_nlp_domain(input_node_names)
- if nlp_domain is not None:
- return nlp_domain
-
- object_detection_domain = self._check_object_detection_domain(
- node_names,
- boundary_nodes,
- op_names,
- )
- if object_detection_domain is not None:
- return object_detection_domain
-
- image_recognition_domain = self._check_image_recognition_domain(op_names)
- if image_recognition_domain is not None:
- return image_recognition_domain
-
- return Domain()
-
- @staticmethod
- def _has_any_name_parts(nodes: set, name_parts: List[str]) -> bool:
- """Check if there is at least one node for name_parts."""
- matching_names = []
- for node in nodes:
- for partial_name in name_parts:
- search = re.match(partial_name, node)
- if search:
- matching_names.append(node)
- return bool(matching_names)
-
- def _check_nlp_domain(self, input_node_names: set) -> Optional[Domain]:
- """Check if model fits into NLP domain."""
- if self._has_any_name_parts(
- input_node_names,
- ["input_ids", "input_mask", "segment_ids"],
- ):
- return Domain(
- domain=Domains.NLP.value,
- domain_flavour=DomainFlavours.NONE.value,
- )
- return None
-
- def _check_object_detection_domain(
- self,
- node_names: set,
- boundary_nodes: list,
- op_names: set,
- ) -> Optional[Domain]:
- """Check if model fits into Object Detection domain."""
- if (
- self._has_any_name_parts(
- node_names,
- ["NonMaxSuppression", "box", "score", "class", "detection"],
- )
- or self._has_any_name_parts(op_names, ["NonMaxSuppression"])
- or self._has_any_name_parts(
- set(boundary_nodes),
- ["NonMaxSuppression", "box", "score", "class", "detection"],
- )
- ):
- return Domain(
- domain=Domains.OBJECT_DETECTION.value,
- domain_flavour=DomainFlavours.NONE.value,
- )
- return None
-
- def _check_image_recognition_domain(self, op_names: set) -> Optional[Domain]:
- """Check if model fits into Image Recognition domain."""
- if self._has_any_name_parts(op_names, ["Conv", "Relu/Clip"]):
- return Domain(
- domain=Domains.IMAGE_RECOGNITION.value,
- domain_flavour=DomainFlavours.NONE.value,
- )
- return None
-
- @property
- def input_shape(self) -> Shape:
- """Try to detect data shape."""
- from google.protobuf.json_format import MessageToDict
-
- shape = None
- trusted = False
-
- if self.domain.domain == Domains.IMAGE_RECOGNITION.value:
- shape_list = []
-
- for input_node in self.filtered_input_nodes:
- node_dict = MessageToDict(input_node)
- dimensions = node_dict.get("type", {}).get("tensorType", {}).get("shape", {}).get("dim", [])
- input_shape = []
- for dim in dimensions:
- if dim.get("dimValue", None) is not None:
- input_shape.append(dim["dimValue"])
- shape_list.append(",".join(input_shape))
-
- if len(shape_list) == 1:
- shape = shape_list[0]
- shape = remove_number_of_samples_from_shape(shape)
- trusted = True
- if len(shape_list) > 1:
- adjusted_shapes_list = []
- for sub_shape in shape_list:
- adjusted_shapes_list.append(
- remove_number_of_samples_from_shape(sub_shape),
- )
- shape = ",".join(
- ["[" + sub_shape + "]" for sub_shape in adjusted_shapes_list],
- )
- trusted = True
-
- return Shape(shape=shape, trusted=trusted)
-
- @property
- def shape_elements_order(self) -> List[str]:
- """Get order of input shape channels."""
- return ["channels", "height", "width"]
-
- @staticmethod
- def get_framework_name() -> str:
- """Get the name of framework."""
- return Frameworks.ONNX.value
-
- def get_model_graph(self) -> Graph:
- """Get model Graph."""
- graph_reader = OnnxrtReader(self)
- return graph_reader.read()
-
- @property
- def nc_model_instance(self) -> ONNXModel:
- """Get Neural Compressor Model instance."""
- self._ensure_nc_model_instance()
- return self._nc_model_instance
-
- def _ensure_nc_model_instance(self) -> None:
- """Create Neural Compressor Model instance if needed."""
- if self._nc_model_instance is not None:
- return
- model_name = os.path.splitext(os.path.basename(self.path))[0]
- Logger().get_logger().setLevel(log.level)
- self._nc_model_instance = NCModel(self.path)
- self._nc_model_instance.name = model_name
-
- @staticmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
- return "onnx" == get_file_extension(path)
-
- def guard_requirements_installed(self) -> None:
- """Ensure all requirements are installed."""
- check_module("onnx")
- check_module("onnxruntime")
- if sys.version_info < (3, 11): # pragma: no cover
- check_module("onnxruntime_extensions")
-
- @property
- def filtered_input_nodes(self) -> List[Any]:
- """Get filtered input nodes."""
- input_nodes = self.nc_model_instance.graph().input # pylint: disable=E1101
- name_to_input = {}
- for input in input_nodes:
- name_to_input[input.name] = input
- for initializer in self.nc_model_instance.graph().initializer: # pylint: disable=E1101
- if initializer.name in name_to_input:
- input_nodes.remove(name_to_input[initializer.name])
- return input_nodes
-
-
-def remove_number_of_samples_from_shape(shape: str) -> str:
- """Remove number of samples from shape if exists."""
- shape_as_list = shape.split(",")
- if len(shape_as_list) > 3:
- shape_as_list.pop(0)
- return ",".join(shape_as_list)
diff --git a/neural_insights/components/model/pytorch/__init__.py b/neural_insights/components/model/pytorch/__init__.py
deleted file mode 100644
index 0e070ced844..00000000000
--- a/neural_insights/components/model/pytorch/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch related models."""
diff --git a/neural_insights/components/model/pytorch/model.py b/neural_insights/components/model/pytorch/model.py
deleted file mode 100644
index 1d21fa823f1..00000000000
--- a/neural_insights/components/model/pytorch/model.py
+++ /dev/null
@@ -1,47 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch model class."""
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.graph.reader.pytorch_reader import PyTorchReader
-from neural_insights.components.model.model import Model
-from neural_insights.utils.consts import Frameworks
-from neural_insights.utils.utils import check_module, get_file_extension
-
-
-class PyTorchModel(Model):
- """PyTorch Script Model class."""
-
- def get_model_graph(self) -> Graph:
- """Get model summary."""
- graph_reader = PyTorchReader(self)
- return graph_reader.read()
-
- def __init__(self, path: str) -> None:
- """Initialize object."""
- super().__init__(path)
-
- @staticmethod
- def get_framework_name() -> str:
- """Get the name of framework."""
- return Frameworks.PT.value
-
- @staticmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
- return "pt" == get_file_extension(path)
-
- def guard_requirements_installed(self) -> None:
- """Ensure all requirements are installed."""
- check_module("torch")
diff --git a/neural_insights/components/model/pytorch/pytorch_script/__init__.py b/neural_insights/components/model/pytorch/pytorch_script/__init__.py
deleted file mode 100644
index 0e070ced844..00000000000
--- a/neural_insights/components/model/pytorch/pytorch_script/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch related models."""
diff --git a/neural_insights/components/model/pytorch/pytorch_script/model.py b/neural_insights/components/model/pytorch/pytorch_script/model.py
deleted file mode 100644
index 9718d0fa43b..00000000000
--- a/neural_insights/components/model/pytorch/pytorch_script/model.py
+++ /dev/null
@@ -1,54 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""PyTorch model class."""
-import re
-from typing import List
-
-from neural_insights.components.model.model import Model
-from neural_insights.utils.consts import Frameworks
-from neural_insights.utils.utils import check_module, get_file_extension
-
-
-class PyTorchScriptModel(Model):
- """PyTorch Script Model class."""
-
- def __init__(self, path: str) -> None:
- """Initialize object."""
- super().__init__(path)
-
- @staticmethod
- def _has_any_name_parts(nodes: set, name_parts: List[str]) -> bool:
- """Check if there is at least one node for name_parts."""
- matching_names = []
- for node in nodes:
- for partial_name in name_parts:
- search = re.match(partial_name, node)
- if search:
- matching_names.append(node)
- return bool(matching_names)
-
- @staticmethod
- def get_framework_name() -> str:
- """Get the name of framework."""
- return Frameworks.PT.value
-
- @staticmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
- return "py" == get_file_extension(path)
-
- def guard_requirements_installed(self) -> None:
- """Ensure all requirements are installed."""
- check_module("torch")
diff --git a/neural_insights/components/model/repository.py b/neural_insights/components/model/repository.py
deleted file mode 100644
index 2e5fa748bc7..00000000000
--- a/neural_insights/components/model/repository.py
+++ /dev/null
@@ -1,86 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Model repository."""
-
-from typing import List, Type
-
-from neural_insights.components.model.model import Model
-from neural_insights.components.model.onnxrt.model import OnnxrtModel
-from neural_insights.components.model.pytorch.model import PyTorchModel
-from neural_insights.components.model.pytorch.pytorch_script.model import PyTorchScriptModel
-from neural_insights.components.model.tensorflow.frozen_pb import FrozenPbModel
-from neural_insights.components.model.tensorflow.keras import KerasModel
-from neural_insights.components.model.tensorflow.meta_graph import MetaGraphModel
-from neural_insights.components.model.tensorflow.saved_model import SavedModelModel
-from neural_insights.utils.exceptions import NotFoundException
-from neural_insights.utils.logger import log
-
-
-class ModelRepository:
- """Model repository."""
-
- def __init__(self) -> None:
- """Initialize class."""
- self.model_types: List[Type[Model]] = [
- FrozenPbModel,
- KerasModel,
- MetaGraphModel,
- OnnxrtModel,
- SavedModelModel,
- PyTorchScriptModel,
- PyTorchModel,
- ]
-
- def get_model(self, path: str) -> Model:
- """Get Model for given path."""
- for model_type in self.model_types:
- supports_path = model_type.supports_path(path)
- log.debug(f"{model_type.__name__}: {supports_path}")
- if model_type.supports_path(path):
- return model_type(path)
-
- raise NotFoundException(f"Unable to find Model for path {path}")
-
- def get_frameworks(self) -> List[str]:
- """Get list of supported frameworks."""
- unique_frameworks = {model.get_framework_name() for model in self.model_types}
- return sorted(unique_frameworks)
-
- @staticmethod
- def get_framework_from_path(model_path: str) -> str:
- """Get framework name from model extension.
-
- :param model_path: Path to model.
- """
- model_repository = ModelRepository()
- try:
- model = model_repository.get_model(model_path)
- return model.get_framework_name()
- except NotFoundException:
- raise NotFoundException("Framework was not recognized.")
-
- @staticmethod
- def get_supported_frameworks() -> List[str]:
- """Get list of supported frameworks."""
- return ModelRepository().get_frameworks()
-
- @staticmethod
- def is_model_path(path: str) -> bool:
- """Check if provided path is for model."""
- try:
- ModelRepository().get_model(path)
- return True
- except NotFoundException:
- return False
diff --git a/neural_insights/components/model/shape.py b/neural_insights/components/model/shape.py
deleted file mode 100644
index 8c3cf546013..00000000000
--- a/neural_insights/components/model/shape.py
+++ /dev/null
@@ -1,41 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Data shape class."""
-from typing import Any, Dict, List, Optional, Union
-
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class Shape(JsonSerializer):
- """Data shape definition."""
-
- def __init__(self, shape: Optional[str] = "", trusted: bool = False) -> None:
- """Object construction."""
- super().__init__()
-
- self.shape = shape
- self.trusted = trusted
-
- def serialize(
- self,
- serialization_type: str = "default",
- ) -> Union[Dict[str, Any], List[Dict[str, Any]]]:
- """Serialize Shape class to dict."""
- result = {}
- for key, value in self.__dict__.items():
- if key in self._skip:
- continue
- result.update({key: value})
- return result
diff --git a/neural_insights/components/model/tensorflow/__init__.py b/neural_insights/components/model/tensorflow/__init__.py
deleted file mode 100644
index 61a68631837..00000000000
--- a/neural_insights/components/model/tensorflow/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tensorflow related models."""
diff --git a/neural_insights/components/model/tensorflow/frozen_pb.py b/neural_insights/components/model/tensorflow/frozen_pb.py
deleted file mode 100644
index 4bfcdc3abea..00000000000
--- a/neural_insights/components/model/tensorflow/frozen_pb.py
+++ /dev/null
@@ -1,32 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tensorflow frozen pb model."""
-
-from neural_insights.components.model.model_type_getter import get_model_type
-from neural_insights.components.model.tensorflow.model import TensorflowModel as TFModel
-
-
-class FrozenPbModel(TFModel):
- """Frozen pb model."""
-
- @staticmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
- return "frozen_pb" == get_model_type(path)
-
- @property
- def supports_profiling(self) -> bool:
- """Check if profiling is supported for the model."""
- return True
diff --git a/neural_insights/components/model/tensorflow/keras.py b/neural_insights/components/model/tensorflow/keras.py
deleted file mode 100644
index d5dbecabdb6..00000000000
--- a/neural_insights/components/model/tensorflow/keras.py
+++ /dev/null
@@ -1,37 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tensorflow keras model."""
-
-from typing import Any, List, Optional
-
-from neural_insights.components.model.model_type_getter import get_model_type
-from neural_insights.components.model.tensorflow.model import TensorflowModel as TFModel
-
-
-class KerasModel(TFModel):
- """Keras model."""
-
- def get_input_nodes(self) -> Optional[List[Any]]:
- """Get model input nodes."""
- return None
-
- def get_output_nodes(self) -> Optional[List[Any]]:
- """Get model output nodes."""
- return None
-
- @staticmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
- return "keras" == get_model_type(path)
diff --git a/neural_insights/components/model/tensorflow/meta_graph.py b/neural_insights/components/model/tensorflow/meta_graph.py
deleted file mode 100644
index d20ef3b2771..00000000000
--- a/neural_insights/components/model/tensorflow/meta_graph.py
+++ /dev/null
@@ -1,42 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tensorflow frozen pb model."""
-
-from typing import Any, List, Optional
-
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.model.model_type_getter import get_model_type
-from neural_insights.components.model.tensorflow.model import TensorflowModel as TFModel
-
-
-class MetaGraphModel(TFModel):
- """MetaGraph model."""
-
- def get_input_nodes(self) -> Optional[List[Any]]:
- """Get model input nodes."""
- return []
-
- def get_output_nodes(self) -> Optional[List[Any]]:
- """Get model output nodes."""
- return []
-
- def get_model_graph(self) -> Graph:
- """Get model graph."""
- raise NotImplementedError("Reading graph for MetaGraph is not supported.")
-
- @staticmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
- return "checkpoint" == get_model_type(path)
diff --git a/neural_insights/components/model/tensorflow/model.py b/neural_insights/components/model/tensorflow/model.py
deleted file mode 100644
index 663556bcdc3..00000000000
--- a/neural_insights/components/model/tensorflow/model.py
+++ /dev/null
@@ -1,125 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Abstract Tensorflow model class."""
-# pylint: disable=import-error
-# pylint: disable=no-name-in-module
-import os.path
-from typing import Any, List, Optional
-
-from neural_compressor.experimental.common.model import Model as NCModel
-from neural_compressor.model.model import TensorflowBaseModel
-from neural_compressor.utils.logger import Logger
-from neural_insights.components.graph.graph import Graph
-from neural_insights.components.graph.reader.tensorflow_reader import TensorflowReader
-from neural_insights.components.model.model import Model
-from neural_insights.components.model.shape import Shape
-from neural_insights.components.model.tensorflow.utils import get_input_shape
-from neural_insights.utils.consts import Domains, Frameworks
-from neural_insights.utils.logger import log
-from neural_insights.utils.utils import check_module
-
-
-class TensorflowModel(Model):
- """Abstract TensorFlow Model class."""
-
- def __init__(self, path: str) -> None:
- """Initialize object."""
- super().__init__(path)
- self._nc_model_instance: Optional[TensorflowBaseModel] = None
-
- def get_input_nodes(self) -> Optional[List[Any]]:
- """Get model input nodes."""
- self.guard_requirements_installed()
-
- # pylint: disable=maybe-no-member
- return self.nc_model_instance.input_node_names
-
- def get_output_nodes(self) -> Optional[List[Any]]:
- """Get model output nodes."""
- self.guard_requirements_installed()
-
- # pylint: disable=maybe-no-member
- return self.nc_model_instance.output_node_names + ["custom"]
-
- def get_model_graph(self) -> Graph:
- """Get model Graph."""
- graph_reader = TensorflowReader(self)
- return graph_reader.read()
-
- @property
- def nc_model_instance(self) -> TensorflowBaseModel:
- """Get Neural Compressor Model instance."""
- self._ensure_nc_model_instance()
- return self._nc_model_instance
-
- def _ensure_nc_model_instance(self) -> None:
- """Create Neural Compressor Model instance if needed."""
- if self._nc_model_instance is not None:
- return
- model_name = os.path.splitext(os.path.basename(self.path))[0]
- Logger().get_logger().setLevel(log.level)
- self._nc_model_instance = NCModel(self.path)
- self._nc_model_instance.name = model_name
-
- @property
- def input_shape(self) -> Shape:
- """Try to detect data shape."""
- try:
- domain = Domains(self.domain.domain)
- default_shapes = {
- Domains.IMAGE_RECOGNITION: 224,
- Domains.OBJECT_DETECTION: 300,
- }
- default_shape = default_shapes.get(domain, None)
- except ValueError:
- log.debug(f'Could not detect "{self.domain.domain}" domain.')
- default_shape = None
-
- if default_shape is None:
- return Shape(trusted=True)
-
- input_shape = [default_shape, default_shape, 3]
- is_trusted = False
- try:
- # pylint: disable=maybe-no-member
- input_shape = get_input_shape(self.nc_model_instance.graph_def, default_shape)
- is_trusted = True
- except Exception:
- log.debug(f"Could not get input shape from model. Using default shape: {input_shape}")
-
- return Shape(shape=", ".join(map(str, input_shape)), trusted=is_trusted)
-
- @property
- def shape_elements_order(self) -> List[str]:
- """Get order of input shape channels."""
- return ["height", "width", "channels"]
-
- @staticmethod
- def get_framework_name() -> str:
- """Get the name of framework."""
- return Frameworks.TF.value
-
- @property
- def supports_pruning(self) -> bool:
- """Check if pruning is supported for the model."""
- try:
- self.nc_model_instance.get_all_weight_names()
- return True
- except AttributeError:
- return False
-
- def guard_requirements_installed(self) -> None:
- """Ensure all requirements are installed."""
- check_module("tensorflow")
diff --git a/neural_insights/components/model/tensorflow/saved_model.py b/neural_insights/components/model/tensorflow/saved_model.py
deleted file mode 100644
index 1e59200b5db..00000000000
--- a/neural_insights/components/model/tensorflow/saved_model.py
+++ /dev/null
@@ -1,27 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""Tensorflow saved_model model."""
-
-from neural_insights.components.model.model_type_getter import get_model_type
-from neural_insights.components.model.tensorflow.model import TensorflowModel as TFModel
-
-
-class SavedModelModel(TFModel):
- """Saved_model model."""
-
- @staticmethod
- def supports_path(path: str) -> bool:
- """Check if given path is of supported model."""
- return "saved_model" == get_model_type(path)
diff --git a/neural_insights/components/model/tensorflow/utils.py b/neural_insights/components/model/tensorflow/utils.py
deleted file mode 100644
index c0cf391a4a6..00000000000
--- a/neural_insights/components/model/tensorflow/utils.py
+++ /dev/null
@@ -1,64 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""TensorFlow model utils."""
-
-from typing import Any, List
-
-
-def get_input_shape(graph_def: Any, fix_dynamic_shape: int) -> List[int]:
- """Get input shape of passed graph."""
- try:
- import tensorflow.compat.v1 as tf_v1
- except ImportError:
- import tensorflow as tf_v1
-
- graph = tf_v1.Graph()
- with graph.as_default(): # pylint: disable=not-context-manager
- tf_v1.import_graph_def(graph_def, name="")
-
- node_dict = {}
- for node in graph.as_graph_def().node: # pylint: disable=no-member
- if node.op == "Placeholder":
- node_dict = {"type": tf_v1.DType(node.attr["dtype"].type).name}
-
- if node_dict["type"] != "bool":
- # convert shape to list
- node["shape"] = _convert_shape_to_list(node, fix_dynamic_shape, tf_v1)
- else: # deal with bool dtype inputs, now assign bool dtype input False value
- node_dict["shape"] = None
- node_dict["value"] = False
-
- return node_dict["shape"]
-
-
-def _convert_shape_to_list(
- node: Any,
- fix_dynamic_shape: int,
- tf_module: Any,
-) -> list:
- """Convert tensorflow shape to list."""
- try:
- _shape = list(tf_module.TensorShape(node.attr["shape"].shape))
- if tf_module.__version__ >= "2.0.0":
- shape = [item if item is not None else fix_dynamic_shape for item in _shape]
- else:
- shape = [item.value if item.value is not None else fix_dynamic_shape for item in _shape]
- # if shape dimension > 1, suppose first dimension is batch-size
- if isinstance(shape, list) and len(shape) > 1:
- return shape[1:]
- return shape
- except ValueError:
- _shape = [fix_dynamic_shape, fix_dynamic_shape, 3]
- return _shape
diff --git a/neural_insights/components/workload_manager/__init__.py b/neural_insights/components/workload_manager/__init__.py
deleted file mode 100644
index fdec04a8c0d..00000000000
--- a/neural_insights/components/workload_manager/__init__.py
+++ /dev/null
@@ -1,15 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The workload_manager package contains code used for Neural Insights workloads management."""
diff --git a/neural_insights/components/workload_manager/quantization_workload.py b/neural_insights/components/workload_manager/quantization_workload.py
deleted file mode 100644
index 63979d61a57..00000000000
--- a/neural_insights/components/workload_manager/quantization_workload.py
+++ /dev/null
@@ -1,84 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The quantization_workload module for Neural Insights quantization workloads."""
-from typing import Any, Dict, Optional
-
-from neural_insights.components.workload_manager.workload import Workload
-from neural_insights.utils.json_serializer import JsonSerializer
-
-
-class QuantizationWorkload(Workload):
- """QuantizationWorkload class."""
-
- def __init__(self, data: Optional[dict] = None):
- """Initialize Quantization Workload."""
- super().__init__(data)
- if data is None:
- data = {}
- self.accuracy_data = AccuracyData(data.get("accuracy_data", None))
-
- def serialize(self, serialization_type: str = "default") -> Dict[str, Any]:
- """Serialize Workload class."""
- serialized_workload = super().serialize(serialization_type)
- serialized_workload.update({"accuracy_data": self.accuracy_data.serialize()})
- return serialized_workload
-
-
-class AccuracyData(JsonSerializer):
- """AccuracyData class."""
-
- def __init__(self, data: Optional[dict] = None):
- """Initialize Accuracy Data."""
- super().__init__()
- if data is None:
- data = {}
- self._baseline_accuracy: Optional[float] = data.get("baseline_accuracy", None)
- self._optimized_accuracy: Optional[float] = data.get("optimized_accuracy", None)
-
- @property
- def baseline_accuracy(self) -> Optional[float]:
- """Get baseline accuracy.
-
- Returns: baseline accuracy value
- """
- return self._baseline_accuracy
-
- @property
- def optimized_accuracy(self) -> Optional[float]:
- """Get optimized accuracy.
-
- Returns: optimized accuracy value
- """
- return self._optimized_accuracy
-
- @property
- def ratio(self) -> Optional[float]:
- """Get accuracy ratio.
-
- Returns: accuracy ratio if baseline and optimized accuracy are present
- Otherwise returns None
- """
- if self.optimized_accuracy in [None, 0] or self.baseline_accuracy in [None, 0]:
- return None
-
- return (self.optimized_accuracy - self.baseline_accuracy) / self.baseline_accuracy
-
- def serialize(self, serialization_type: str = "default") -> Dict[str, Any]:
- """Serialize AccuracyData class."""
- return {
- "baseline_accuracy": self.baseline_accuracy,
- "optimized_accuracy": self.optimized_accuracy,
- "ratio": self.ratio,
- }
diff --git a/neural_insights/components/workload_manager/workload.py b/neural_insights/components/workload_manager/workload.py
deleted file mode 100644
index a4a9f15732e..00000000000
--- a/neural_insights/components/workload_manager/workload.py
+++ /dev/null
@@ -1,87 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The workload module for Neural Insights workloads."""
-
-import os
-from datetime import datetime
-from typing import Any, Dict, Optional
-from uuid import uuid4
-
-from neural_insights.utils.consts import Frameworks, WorkloadModes, WorkloadStatus
-from neural_insights.utils.exceptions import InternalException
-from neural_insights.utils.json_serializer import JsonSerializer
-from neural_insights.utils.utils import get_framework_from_path
-
-
-class Workload(JsonSerializer):
- """Workload class."""
-
- def __init__(self, data: Optional[dict] = None):
- """Initialize Workload."""
- super().__init__()
- if data is None:
- data = {}
- self.uuid = str(data.get("uuid", uuid4()))
- self.creation_time: int = int(data.get("creation_time", datetime.now().timestamp()))
- self.workload_location: str = data.get("workload_location", None)
-
- mode = data.get("mode")
- self.workload_name = data.get("workload_name", mode)
- if not isinstance(mode, WorkloadModes) and isinstance(mode, str):
- mode = WorkloadModes(mode)
- self.mode: WorkloadModes = mode
-
- status = data.get("status", WorkloadStatus.NEW)
- if not isinstance(status, WorkloadStatus) and isinstance(status, str):
- status = WorkloadStatus(status)
- self.status: WorkloadStatus = status
-
- self._model_path: str = data.get("model_path", None)
-
- framework = data.get("framework", None)
- if not isinstance(framework, Frameworks):
- if framework is None and self.model_path is not None:
- framework = get_framework_from_path(self.model_path)
- if isinstance(framework, str):
- framework = Frameworks(framework)
- self.framework: Optional[Frameworks] = framework
-
- self.model_summary_file = data.get("model_summary_file", None)
-
- @property
- def model_path(self) -> str:
- """Get model_path."""
- return self._model_path
-
- @model_path.setter
- def model_path(self, model_path: str) -> None:
- if not os.path.exists(model_path):
- raise InternalException("Could not locate model.")
- self._model_path = model_path
- self.framework = Frameworks(get_framework_from_path(self.model_path))
-
- def serialize(self, serialization_type: str = "default") -> Dict[str, Any]:
- """Serialize Workload class."""
- return {
- "uuid": self.uuid,
- "workload_name": self.workload_name,
- "framework": self.framework.value,
- "workload_location": self.workload_location,
- "mode": self.mode.value,
- "model_path": self.model_path,
- "model_summary_file": self.model_summary_file,
- "status": self.status.value,
- "creation_time": self.creation_time,
- }
diff --git a/neural_insights/components/workload_manager/workload_manager.py b/neural_insights/components/workload_manager/workload_manager.py
deleted file mode 100644
index 8f65f9523ce..00000000000
--- a/neural_insights/components/workload_manager/workload_manager.py
+++ /dev/null
@@ -1,171 +0,0 @@
-# -*- coding: utf-8 -*-
-# Copyright (c) 2023 Intel Corporation
-#
-# Licensed under the Apache License, Version 2.0 (the "License");
-# you may not use this file except in compliance with the License.
-# You may obtain a copy of the License at
-#
-# http://www.apache.org/licenses/LICENSE-2.0
-#
-# Unless required by applicable law or agreed to in writing, software
-# distributed under the License is distributed on an "AS IS" BASIS,
-# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-# See the License for the specific language governing permissions and
-# limitations under the License.
-"""The workload_manager module contains code used for Neural Insights workloads management."""
-
-import json
-import os
-import shutil
-from os import PathLike
-from typing import List, Optional
-
-from neural_insights.components.workload_manager.quantization_workload import AccuracyData, QuantizationWorkload
-from neural_insights.components.workload_manager.workload import Workload
-from neural_insights.utils.consts import WORKDIR_LOCATION, WorkloadModes, WorkloadStatus
-from neural_insights.utils.exceptions import ClientErrorException, InternalException
-from neural_insights.utils.json_serializer import JsonSerializer
-from neural_insights.utils.logger import log
-from neural_insights.utils.singleton import Singleton
-
-
-class WorkloadManager(JsonSerializer, metaclass=Singleton):
- """Workload Manager class."""
-
- def __init__(self, workdir_location: Optional[PathLike] = None):
- """Initialize WorkloadManager."""
- super().__init__()
- if workdir_location is None:
- log.debug("Using default workdir location.")
- workdir_location = WORKDIR_LOCATION
- self.config_path = os.path.join(workdir_location, "neural_insights", "config.json")
- self._version: int = 1
- self._workloads: List[Workload] = []
-
- self.load_workloads()
- self.dump_config()
-
- def add_workload(self, workload: Workload) -> None:
- """Add workload."""
- self._workloads.append(workload)
- self.dump_config()
-
- def update_workload_status(self, workload_uuid: str, status: str) -> None:
- """Update workload status."""
- try:
- new_status = WorkloadStatus(status)
- except ValueError:
- raise ClientErrorException("Could not parse status.")
- workload = self.get_workload(workload_uuid)
- current_status = workload.status
- self.validate_status_flow(current_status, new_status)
- workload.status = new_status
- self.dump_config()
-
- def update_workload_accuracy_data(
- self,
- workload_uuid: str,
- baseline_accuracy: float,
- optimized_accuracy: float,
- ) -> None:
- """Update workload status."""
- workload = self.get_workload(workload_uuid)
- if not isinstance(workload, QuantizationWorkload):
- raise ClientErrorException(
- "Accuracy data is only available in quantization workloads.",
- )
-
- accuracy_data = AccuracyData(
- {
- "baseline_accuracy": baseline_accuracy,
- "optimized_accuracy": optimized_accuracy,
- },
- )
- workload.accuracy_data = accuracy_data
- self.dump_config()
-
- @staticmethod
- def validate_status_flow(
- prev_status: WorkloadStatus,
- new_status: WorkloadStatus,
- ) -> None:
- """Validate status flow."""
- status_indices = {
- WorkloadStatus.NEW: 0,
- WorkloadStatus.WIP: 1,
- WorkloadStatus.SUCCESS: 2,
- WorkloadStatus.FAILURE: 3,
- }
- status_connections = [(0, 1), (1, 2), (1, 3)]
-
- # Initialize adjacency matrix with zeros
- adjacency_matrix = [[0 for _ in status_indices] for _ in status_indices]
- for source, target in status_connections:
- adjacency_matrix[source][target] = 1
-
- prev_status_indice = status_indices[prev_status]
- new_status_indice = status_indices[new_status]
- if adjacency_matrix[prev_status_indice][new_status_indice] != 1:
- raise ClientErrorException(
- f"Changing status from {prev_status.value} to {new_status.value} is not allowed.",
- )
-
- def load_workloads(self) -> None:
- """Read workloads from config file."""
- if not os.path.exists(self.config_path):
- return
- with open(self.config_path, "r") as config_file:
- config_data = json.load(config_file)
- config_version = int(config_data.get("version"))
- if config_version != self._version:
- raise InternalException(
- f"Incompatible config version has been found. "
- f"Expected version: {self._version}, found {config_version}.",
- )
- workloads_config = config_data.get("workloads", [])
- if not isinstance(workloads_config, list):
- raise InternalException(
- "Incompatible format of workloads. Workloads should be a list.",
- )
- self._workloads = []
- for workload_config in workloads_config:
- if workload_config.get("mode") == WorkloadModes.QUANTIZATION.value:
- workload = QuantizationWorkload(workload_config)
- else:
- workload = Workload(workload_config)
- self._workloads.append(workload)
-
- def dump_config(self) -> None:
- """Dump workloads to config file."""
- serialized_workloads = [workload.serialize() for workload in self._workloads]
- data = {
- "version": self._version,
- "workloads": serialized_workloads,
- }
-
- os.makedirs(os.path.dirname(self.config_path), exist_ok=True)
- with open(self.config_path, "w") as config_file:
- json.dump(data, config_file)
-
- @property
- def workloads(self) -> List[Workload]:
- """Get workloads list."""
- self.load_workloads()
- return self._workloads
-
- def get_workload(self, workload_uuid: str) -> Workload:
- """Get workload from workloads list."""
- for workload in self.workloads:
- if workload.uuid == workload_uuid:
- return workload
- raise ClientErrorException("Could not find workload with specified ID.")
-
- def remove_workload(self, workload_uuid: str) -> str:
- """Remove workload from workloads list."""
- for index, workload in enumerate(self.workloads):
- if workload.uuid == workload_uuid:
- shutil.rmtree(workload.workload_location, ignore_errors=True)
- del self._workloads[index]
- self.dump_config()
- return workload.uuid
- raise ClientErrorException("Could not find workload with specified ID.")
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diff --git a/neural_insights/docs/source/imgs/weights_summary_onnx.jpg b/neural_insights/docs/source/imgs/weights_summary_onnx.jpg
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diff --git a/neural_insights/docs/source/onnx_accuracy_debug.md b/neural_insights/docs/source/onnx_accuracy_debug.md
deleted file mode 100644
index 78ddece14d3..00000000000
--- a/neural_insights/docs/source/onnx_accuracy_debug.md
+++ /dev/null
@@ -1,127 +0,0 @@
-# Step by step example how to debug accuracy with Neural Insights
-1. [Introduction](#introduction)
-2. [Preparation](#preparation)
-3. [Running the quantization](#running-the-quantization)
-4. [Analyzing the result of quantization](#-analyzing-the-result-of-quantization)
-
-# Introduction
-In this instruction accuracy issue will be debugged using Neural Insights. ONNX LayoutLMv3 model will be used as an example. It will be quantized and the results will be analyzed to find the cause of the accuracy loss.
-
-# Preparation
-## Requirements
-First you need to install Intel® Neural Compressor and other requirements.
-```shell
-pip install neural-compressor
-pip install datasets transformers torch torchvision
-pip install onnx onnxruntime onnxruntime-extensions
-pip install accelerate seqeval tensorboard sentencepiece timm fvcore Pillow einops textdistance shapely protobuf setuptools optimum
-```
-
-## Model
-Get the LayoutLMv3 model from Intel® Neural Compressor [LayoutLMv3 example](https://github.com/intel/neural-compressor/tree/master/examples/onnxrt/nlp/huggingface_model/token_classification/layoutlmv3/quantization/ptq_static).
-```shell
-optimum-cli export onnx --model HYPJUDY/layoutlmv3-base-finetuned-funsd layoutlmv3-base-finetuned-funsd-onnx/ --task=token-classification
-```
-
-# Running the quantization
-Generate a quantized model.
-```python
-onnx_model = onnx.load(input_model)
-calib_dataset = IncDataset(eval_dataset, onnx_model)
-config = PostTrainingQuantConfig(approach="static", quant_format="QOperator")
-q_model = quantization.fit(
- onnx_model, config, calib_dataloader=DataLoader(framework="onnxruntime", dataset=calib_dataset)
-)
-```
-
-Execute benchmark to get the F1 score of both FP32 and INT8 models and then compute the relative accuracy ratio.
-The output results indicate that the quantized model's accuracy is noticeably poor.
-
-```
-fp32 f1 = 0.9049, int8 f1 = 0.2989, accuracy ratio = -66.9631%
-```
-
-# Analyzing the result of quantization
-In this section, the diagnosis tool is used for debugging to achieve higher INT8 model accuracy.
-We need to set `diagnosis` parameter to `True` as shown below.
-```python
-config = PostTrainingQuantConfig(
- approach="static", quant_format="QOperator", quant_level=1, diagnosis=True
-) # set 'diagnosis' to True
-q_model = quantization.fit(
- onnx_model, config, eval_func=eval_func, calib_dataloader=DataLoader(framework="onnxruntime", dataset=calib_dataset)
-)
-```
-The diagnosis tool will output `Activations summary` and `Weights summary` in terminal.
-
-For easy to check, here we reload them to .csv files as shown below.
-```python
-import glob
-import pandas as pd
-
-pd.set_option("display.max_rows", None)
-pd.set_option("display.max_columns", None)
-
-subfolders = glob.glob("./nc_workspace" + "/*/")
-subfolders.sort(key=os.path.getmtime, reverse=True)
-if subfolders:
- activations_table = os.path.join(subfolders[0], "activations_table.csv")
- weights_table = os.path.join(subfolders[0], "weights_table.csv")
-
- activations_table = pd.read_csv(activations_table)
- weights_table = pd.read_csv(weights_table)
-
- print("Activations summary")
- display(activations_table)
-
- print("\nWeights summary")
- display(weights_table)
-```
-
-## Weights summary
-These are the top 10 rows from weights summary table:
-
-
-
-## Activations summary
-These are the top 10 rows from activations summary table:
-
-
-
-In the Activations summary table, there are some nodes showing dispersed activation data range. Therefore, we calculate the `Min-Max data range` for activations data and sort the results in descending order.
-
-```python
-activations_table["Min-Max data range"] = activations_table["Activation max"] - activations_table["Activation min"]
-sorted_data = activations_table.sort_values(by="Min-Max data range", ascending=False)
-display(sorted_data)
-```
-
-The results should look like below:
-
-
-
-According to the results displayed above, it is evident that the nodes of type `/layoutlmv3/encoder/layer.\d+/output/Add` and `/layoutlmv3/encoder/layer.\d+/output/dense/MatMul` have significantly higher values for `Min-Max data range` compared to other node types. This indicates that they may have caused a loss of accuracy. Therefore, we can try to fallback these nodes.
-
-Refer to [diagnosis.md](https://github.com/intel/neural-compressor/blob/master/docs/source/diagnosis.md) for more tips for diagnosis.
-
-```python
-from neural_compressor.utils.constant import FP32
-
-config = PostTrainingQuantConfig(
- approach="static",
- quant_format="QOperator",
- op_name_dict={
- "/layoutlmv3/encoder/layer.\d+/output/dense/MatMul": FP32,
- "/layoutlmv3/encoder/layer.\d+/output/Add": FP32,
- },
-)
-q_model = quantization.fit(
- onnx_model, config, calib_dataloader=DataLoader(framework="onnxruntime", dataset=calib_dataset)
-)
-q_model.save(output_model)
-```
-
-Execute benchmark on the new quantized model again and the accuracy ratio is improved to <1%.
-```
-fp32 f1 = 0.9049, int8 f1 = 0.8981, accuracy ratio = -0.7502%
-```
diff --git a/neural_insights/docs/source/pytorch_nlp_cli_mode.md b/neural_insights/docs/source/pytorch_nlp_cli_mode.md
deleted file mode 100644
index 449a66ce431..00000000000
--- a/neural_insights/docs/source/pytorch_nlp_cli_mode.md
+++ /dev/null
@@ -1,63 +0,0 @@
-# Step by step example how to dump weights data for PyTorch model with Neural Insights
-1. [Introduction](#introduction)
-2. [Preparation](#preparation)
-3. [Running the quantization](#running-the-quantization)
-
-# Introduction
-In this instruction weight data will be dumped using Neural Insights. PyTorch GPT-J-6B model will be used as an example.
-
-# Preparation
-## Source
-First you need to install Intel® Neural Compressor.
-```shell
-# Install Neural Compressor
-git clone https://github.com/intel/neural-compressor.git
-cd neural-compressor
-pip install -r requirements.txt
-python setup.py install
-
-# Install Neural Insights
-pip install -r neural_insights/requirements.txt
-python setup.py install neural_insights
-```
-
-## Requirements
-```shell
-cd /examples/pytorch/nlp/huggingface_models/language-modeling/quantization/ptq_static/fx
-pip install -r requirements.txt
-```
-
-# Running the quantization
-Before applying quantization, modify some code in `run_clm.py` file to enable Neural Insights:
-1. Set the argument `diagnosis` to be `True` in `PostTrainingQuantConfig` so that Neural Insights will dump weights of quantizable Ops in this model.
-
-```python
-conf = PostTrainingQuantConfig(
- accuracy_criterion=accuracy_criterion,
- diagnosis=True,
-)
-```
-2. Quantize the model with following command:
-```shell
-python run_clm.py \
- --model_name_or_path EleutherAI/gpt-j-6B \
- --dataset_name wikitext\
- --dataset_config_name wikitext-2-raw-v1 \
- --do_train \
- --do_eval \
- --tune \
- --output_dir saved_results
-```
-
-Results would be dumped into `nc_workspace` directory in similar structure:
-```
-├── history.snapshot
-├── input_model.pt
-├── inspect_saved
-│ ├── fp32
-│ │ └── inspect_result.pkl
-│ └── quan
-│ └── inspect_result.pkl
-├── model_summary.txt
-└── weights_table.csv
-```
diff --git a/neural_insights/docs/source/tf_accuracy_debug.md b/neural_insights/docs/source/tf_accuracy_debug.md
deleted file mode 100644
index 8422aa57f11..00000000000
--- a/neural_insights/docs/source/tf_accuracy_debug.md
+++ /dev/null
@@ -1,112 +0,0 @@
-# Step by step example how to debug accuracy with Neural Insights
-1. [Introduction](#introduction)
-2. [Preparation](#preparation)
-3. [Running the quantization](#running-the-quantization)
-4. [Analyzing the result of quantization](#-analyzing-the-result-of-quantization)
-5. [Analyzing weight histograms](#-analyzing-weight-histograms)
-
-# Introduction
-In this instruction accuracy issue will be debugged using Neural Insights. TensorFlow Inception_v3 model will be used as an example. It will be quantized and the results will be analyzed to find the cause of the accuracy loss.
-
-# Preparation
-## Source
-First you need to install Intel® Neural Compressor.
-```shell
-# Install Neural Compressor
-git clone https://github.com/intel/neural-compressor.git
-cd neural-compressor
-pip install -r requirements.txt
-python setup.py install
-
-# Install Neural Insights
-pip install -r neural_insights/requirements.txt
-python setup.py install neural_insights
-```
-
-## Requirements
-```shell
-cd examples/tensorflow/image_recognition/tensorflow_models/inception_v3/quantization/ptq
-pip install -r requirements.txt
-```
-
-## Model
-Download pre-trained PB model file.
-```shell
-wget https://storage.googleapis.com/intel-optimized-tensorflow/models/v1_6/inceptionv3_fp32_pretrained_model.pb
-```
-
-## Prepare the dataset
-Download dataset from ImageNet and process the data to TensorFlow Record format.
-```shell
-cd examples/tensorflow/image_recognition/tensorflow_models/
-bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/val/ --subset=validation
-bash prepare_dataset.sh --output_dir=./inception_v3/quantization/ptq/data --raw_dir=/PATH/TO/img_raw/train/ --subset=train
-```
-
-# Running the quantization
-Before applying quantization, modify some code to enable Neural Insights:
-1. Set the argument `diagnosis` to be `True` in `PostTrainingQuantConfig` so that Neural Insights will dump weights and activations of quantizable Ops in this model.
-2. Delete the `op_name_dict` argument because that’s the answer of our investigation.
-```python
-conf = PostTrainingQuantConfig(calibration_sampling_size=[50, 100], diagnosis=True)
-```
-3. Quantize the model with following command:
-```shell
-bash run_tuning.sh --input_model=/PATH/TO/inceptionv3_fp32_pretrained_model.pb --output_model=./nc_inception_v3.pb --dataset_location=/path/to/ImageNet/
-```
-
-The accuracy of this model will decrease a lot if all Ops are quantized to int8 as default strategy:
-
-
-
-# Analyzing the result of quantization
-Then, if you run quantization, you will find the following table:
-
-
-
-The MSE (Mean Square Error) of the Ops’ activation are listed from high to low, there are also min-max values.
-Usually, MSE can be referred as one of a typical indexes leading to accuracy loss.
-
-
-
-There are also relevant information about Ops’ weights.
-Often Op with highest MSE will cause the highest accuracy loss, but it is not always the case.
-
-Experiment with disabling the quantization of some of the Ops with top 5 highest MSE in both tables is not satisfactory, as results show in this example:
-
-
-
-Then weights histograms can be analyzed to find the reason of the accuracy loss.
-
-# Analyzing weight histograms
-## Open Neural Insights
-```shell
-neural_insights
-```
-
-Then you will get a webpage address with Neural insights GUI mode. You can find there histograms of weights and activations.
-```
-Neural Insights Server started.
-Open address [...]
-```
-
-The weights of Ops are usually distributed in one spike like the following graph:
-
-
-
-When you click on the Op in the Op list, you can get weight and activation histograms at the bottom of the page.
-One of the weights histograms looks different than the examples above.
-
-
-
-As is shown in the chart, the distribution of weights often concentrates in a small range of min-max values, when the accuracy loss of an Op is tolerable. But in this Op the min-max values of weights are significantly high (range is bigger than [-20, 20]) because of some outliers. The values near zero point, which are the majority, will be mapped to a very small range in int8, leading to a huge accuracy loss. Besides, since the min-max values vary in different channels, the accuracy will decrease without using channel-wise quantization.
-
-Therefore, you can disable this Op:
-```python
-op_name_dict = {"v0/cg/conv0/conv2d/Conv2D": {"activation": {"dtype": ["fp32"]}}}
-conf = PostTrainingQuantConfig(calibration_sampling_size=[50, 100], op_name_dict=op_name_dict)
-```
-
-After running quantization again, you can see that accuracy result has increased. The Op that caused accuracy loss was found.
-
-
diff --git a/neural_insights/gui/README.md b/neural_insights/gui/README.md
deleted file mode 100644
index 58beeaccd87..00000000000
--- a/neural_insights/gui/README.md
+++ /dev/null
@@ -1,70 +0,0 @@
-# Getting Started with Create React App
-
-This project was bootstrapped with [Create React App](https://github.com/facebook/create-react-app).
-
-## Available Scripts
-
-In the project directory, you can run:
-
-### `npm start`
-
-Runs the app in the development mode.\
-Open [http://localhost:3000](http://localhost:3000) to view it in your browser.
-
-The page will reload when you make changes.\
-You may also see any lint errors in the console.
-
-### `npm test`
-
-Launches the test runner in the interactive watch mode.\
-See the section about [running tests](https://facebook.github.io/create-react-app/docs/running-tests) for more information.
-
-### `npm run build`
-
-Builds the app for production to the `build` folder.\
-It correctly bundles React in production mode and optimizes the build for the best performance.
-
-The build is minified and the filenames include the hashes.\
-Your app is ready to be deployed!
-
-See the section about [deployment](https://facebook.github.io/create-react-app/docs/deployment) for more information.
-
-### `npm run eject`
-
-**Note: this is a one-way operation. Once you `eject`, you can't go back!**
-
-If you aren't satisfied with the build tool and configuration choices, you can `eject` at any time. This command will remove the single build dependency from your project.
-
-Instead, it will copy all the configuration files and the transitive dependencies (webpack, Babel, ESLint, etc) right into your project so you have full control over them. All of the commands except `eject` will still work, but they will point to the copied scripts so you can tweak them. At this point you're on your own.
-
-You don't have to ever use `eject`. The curated feature set is suitable for small and middle deployments, and you shouldn't feel obligated to use this feature. However we understand that this tool wouldn't be useful if you couldn't customize it when you are ready for it.
-
-## Learn More
-
-You can learn more in the [Create React App documentation](https://facebook.github.io/create-react-app/docs/getting-started).
-
-To learn React, check out the [React documentation](https://reactjs.org/).
-
-### Code Splitting
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/code-splitting](https://facebook.github.io/create-react-app/docs/code-splitting)
-
-### Analyzing the Bundle Size
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size](https://facebook.github.io/create-react-app/docs/analyzing-the-bundle-size)
-
-### Making a Progressive Web App
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app](https://facebook.github.io/create-react-app/docs/making-a-progressive-web-app)
-
-### Advanced Configuration
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/advanced-configuration](https://facebook.github.io/create-react-app/docs/advanced-configuration)
-
-### Deployment
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/deployment](https://facebook.github.io/create-react-app/docs/deployment)
-
-### `npm run build` fails to minify
-
-This section has moved here: [https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify](https://facebook.github.io/create-react-app/docs/troubleshooting#npm-run-build-fails-to-minify)
diff --git a/neural_insights/gui/generate-react-cli.json b/neural_insights/gui/generate-react-cli.json
deleted file mode 100644
index 24ce1132711..00000000000
--- a/neural_insights/gui/generate-react-cli.json
+++ /dev/null
@@ -1,15 +0,0 @@
-{
- "usesTypeScript": false,
- "usesCssModule": false,
- "cssPreprocessor": "scss",
- "testLibrary": "None",
- "component": {
- "default": {
- "path": "src/components",
- "withStyle": true,
- "withTest": false,
- "withStory": false,
- "withLazy": false
- }
- }
-}
\ No newline at end of file
diff --git a/neural_insights/gui/package-lock.json b/neural_insights/gui/package-lock.json
deleted file mode 100644
index ec4b91b3cb3..00000000000
--- a/neural_insights/gui/package-lock.json
+++ /dev/null
@@ -1,22255 +0,0 @@
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diff --git a/neural_insights/gui/package.json b/neural_insights/gui/package.json
deleted file mode 100644
index 5374ea56188..00000000000
--- a/neural_insights/gui/package.json
+++ /dev/null
@@ -1,60 +0,0 @@
-{
- "name": "app",
- "version": "0.1.0",
- "private": true,
- "dependencies": {
- "@testing-library/jest-dom": "^5.16.5",
- "@testing-library/react": "^13.4.0",
- "@testing-library/user-event": "^13.5.0",
- "axios": "^1.6.0",
- "bootstrap": "^5.3.0",
- "css-select": "^5.1.0",
- "cytoscape": "^3.25.0",
- "cytoscape-elk": "^2.2.0",
- "cytoscape-node-html-label": "^1.2.2",
- "moment": "^2.29.4",
- "plotly.js": "^2.24.3",
- "react": "^18.2.0",
- "react-bootstrap": "^2.8.0",
- "react-cytoscapejs": "^2.0.0",
- "react-dom": "^18.2.0",
- "react-plotly.js": "^2.6.0",
- "react-scripts": "^5.0.1",
- "sass": "^1.63.6",
- "socket.io": "^4.7.1",
- "socket.io-client": "^4.7.1",
- "web-vitals": "^2.1.4",
- "web-worker": "^1.2.0"
- },
- "devDependencies": {
- "@svgr/webpack": "8.0.1"
- },
- "overrides": {
- "@svgr/webpack": "$@svgr/webpack",
- "word-wrap": "npm:@aashutoshrathi/word-wrap@1.2.6"
- },
- "scripts": {
- "start": "react-scripts start",
- "build": "BUILD_PATH='./../web/app' react-scripts build",
- "test": "react-scripts test",
- "eject": "react-scripts eject"
- },
- "eslintConfig": {
- "extends": [
- "react-app",
- "react-app/jest"
- ]
- },
- "browserslist": {
- "production": [
- ">0.2%",
- "not dead",
- "not op_mini all"
- ],
- "development": [
- "last 1 chrome version",
- "last 1 firefox version",
- "last 1 safari version"
- ]
- }
-}
\ No newline at end of file
diff --git a/neural_insights/gui/public/favicon.ico b/neural_insights/gui/public/favicon.ico
deleted file mode 100644
index 39f348e6a77..00000000000
Binary files a/neural_insights/gui/public/favicon.ico and /dev/null differ
diff --git a/neural_insights/gui/public/icons/057a-trash-solid-red.svg b/neural_insights/gui/public/icons/057a-trash-solid-red.svg
deleted file mode 100644
index 389c37091bd..00000000000
--- a/neural_insights/gui/public/icons/057a-trash-solid-red.svg
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
diff --git a/neural_insights/gui/public/icons/057a-trash-solid.svg b/neural_insights/gui/public/icons/057a-trash-solid.svg
deleted file mode 100644
index 623f93c34b7..00000000000
--- a/neural_insights/gui/public/icons/057a-trash-solid.svg
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
diff --git a/neural_insights/gui/public/icons/146b-copy-outlined-gray.svg b/neural_insights/gui/public/icons/146b-copy-outlined-gray.svg
deleted file mode 100644
index 2194c1ab27e..00000000000
--- a/neural_insights/gui/public/icons/146b-copy-outlined-gray.svg
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
diff --git a/neural_insights/gui/public/icons/146b-copy-outlined.svg b/neural_insights/gui/public/icons/146b-copy-outlined.svg
deleted file mode 100644
index 9a6262f2284..00000000000
--- a/neural_insights/gui/public/icons/146b-copy-outlined.svg
+++ /dev/null
@@ -1,12 +0,0 @@
-
-
-
diff --git a/neural_insights/gui/public/index.html b/neural_insights/gui/public/index.html
deleted file mode 100644
index eba49cd08a1..00000000000
--- a/neural_insights/gui/public/index.html
+++ /dev/null
@@ -1,28 +0,0 @@
-
-
-
-
-
-
-
-
-
-
-
- );
-}
-
-export default App;
diff --git a/neural_insights/gui/src/App.scss b/neural_insights/gui/src/App.scss
deleted file mode 100644
index 32b0d37967a..00000000000
--- a/neural_insights/gui/src/App.scss
+++ /dev/null
@@ -1,137 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-@import './variables.scss';
-
-table.rounded {
- margin: auto;
- border-collapse: collapse;
-
- tr.header {
- text-align: center;
- font-weight: bold;
- }
-
- th.header {
- background: $accent-yellow;
- color: black;
- vertical-align: middle;
- }
-
- td.header {
- background: $accent-yellow;
- padding: 10px;
- color: black;
- text-align: center;
- border-bottom: 1px solid #fafafa;
- border-radius: 0px;
- }
-
- tr:nth-child(even) {
- background-color: rgba(182, 182, 182, 0.13);
- }
-
- tr:nth-child(odd) {
- background-color: rgba(231, 231, 231, 0.13);
- }
-
- tr.cell,
- td.cell {
- padding: 5px;
- border-bottom: none;
- }
-
- tr:last-child {
-
- td:last-child {
- border-radius: 0 0 0.5rem 0;
- }
-
- td:first-child {
- border-radius: 0 0 0 0.5rem;
- }
-
- td:only-child {
- border-radius: 0 0 0.5rem 0.5rem;
- }
- }
-
- tr:first-child {
-
- th:first-child {
- border-radius: 0.5rem 0 0 0;
- }
-
- th:last-child {
- border-radius: 0 0.5rem 0 0;
- }
-
- td:first-child {
- border-radius: 0.5rem 0 0 0;
- }
-
- td:last-child {
- border-radius: 0 0.5rem 0 0;
- }
-
- td:only-child {
- border-radius: 0.5rem 0.5rem 0 0;
- }
- }
-
- tr:only-child {
- td:only-child {
- border-radius: 0.5rem 0.5rem 0.5rem 0.5rem;
- }
- }
-
- tr.active {
- background-color: #ffe07ab6 !important;
- color: black;
- }
-}
-
-.right {
- text-align: right;
-}
-
-.center {
- text-align: center;
-}
-
-.btn-primary {
- background-color: $accent-yellow !important;
- color: black !important;
- border: 2px solid $accent-yellow !important;
-}
-
-.btn-primary:hover,
-.btn-primary:focus {
- color: black !important;
- border: 2px solid black !important;
-}
-
-.btn-secondary {
- background-color: white !important;
- color: black !important;
- border-color: $accent-yellow !important;
- margin-bottom: 4px;
- width: 100%;
-}
-
-.btn-secondary:hover {
- background-color: $lighter-yellow !important;
- color: black !important;
- border-color: $accent-yellow !important;
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/App.test.js b/neural_insights/gui/src/App.test.js
deleted file mode 100644
index 6739eb20554..00000000000
--- a/neural_insights/gui/src/App.test.js
+++ /dev/null
@@ -1,22 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import { render, screen } from '@testing-library/react';
-import App from './App';
-
-test('renders learn react link', () => {
- render(
-
- )
-};
-
-function NodeProperties({ selectedNode }) {
- if (selectedNode) {
- const propertyList = Object.entries(selectedNode.properties).map(([key, value]) => {
- return (
-
-
- {selectedWorkload?.mode === 'quantization' && selectedOp &&
-
-
- {selectedWorkload?.mode === 'benchmark' &&
-
-
-
-
- }
- {selectedWorkload?.mode === 'quantization' &&
-
- {selectedWorkload.framework !== 'PyTorch' &&
-
- }
- {selectedWorkload?.mode === 'quantization' &&
-
-
- }
-
-
- }
-
-
-
- {histogramType &&
-
-
-
-
-
- );
- } else {
- return;
- }
-}
-
-class NodeSearch extends React.Component {
- render() {
- return (
- Node details
-| Properties | -
| Attributes | } -
-
-
-
- )
- }
-}
-
-function AccuracyResults({ selectedWorkload }) {
- return (
- Node search
-
- {selectedWorkload.status === 'wip' &&
-
-
-
- }
- {selectedWorkload.status !== 'wip' &&
- selectedWorkload.accuracy_data.ratio &&
-
-
-
- }
-
- )
-}
-
-export const getLabel = (label) => {
- if (label.includes('/')) {
- return label.replace(/^.*[\\\/]/, '');
- } else {
- return label;
- }
-}
-
-export const customColor = [
- '#5B69FF',
- '#FF848A',
- '#EDB200',
- '#1E2EB8',
- '#FF5662',
- '#C98F00',
- '#000F8A',
- '#C81326',
- '#000864',
- '#9D79BC',
- '#A14DA0',
-];
-
-export default Diagnosis;
diff --git a/neural_insights/gui/src/components/Diagnosis/Diagnosis.scss b/neural_insights/gui/src/components/Diagnosis/Diagnosis.scss
deleted file mode 100644
index 30d29279bab..00000000000
--- a/neural_insights/gui/src/components/Diagnosis/Diagnosis.scss
+++ /dev/null
@@ -1,142 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-@import './../../variables.scss';
-
-.Diagnosis {
-
- .property-table {
- border-collapse: collapse;
- width: 100%;
- }
-
- .table-title {
- font-family: IntelClearBd;
- text-align: center;
- }
-
- .table-key {
- text-align: right;
- padding: 5px;
- }
-
- .table-value {
- text-align: left;
- background-color: $light-gray;
- padding: 5px;
- border: 2px white solid;
- }
-
- .data-panel {
- background-color: white;
- padding: 10px;
- border-radius: 0.5rem;
- margin-bottom: 6px;
- }
-
- .data-panel-top {
- background-color: white;
- padding: 10px;
- border-radius: 0.5rem;
- margin-top: 6px;
- }
-
- .flexbox {
- display: flex;
- max-height: 99vh;
- background-color: $light-gray;
- }
-
- .flexbox-inside {
- flex-shrink: 0;
- padding: 3px;
- display: flex;
- flex-direction: column;
- max-height: 90vh;
- }
-
- .workloads-flex {
- display: flex;
- flex-direction: column;
- overflow: auto;
- }
-
- .overflow-table {
- max-height: calc(90vh - 96px);
- background-color: white;
- border-radius: 0.5rem;
- overflow: auto;
- }
-
- .flex-item {
- padding: 3px;
- width: 98%;
- max-width: calc(100vw - 370px);
- }
-
- .flex-bigger {
- padding: 3px;
- width: 98%;
- }
-
- .accuracy-table {
- width: 98%;
- }
-
- .accuracy-number {
- font-size: 200%;
- color: $energy-blue;
- text-align: center;
- padding-left: 20px;
- }
-
- .accuracy-title {
- font-family: IntelOneRg;
- font-size: 120%;
- }
-
- .accuracy-subtitle {
- font-size: 80%;
- color: $dark-gray;
- text-align: center;
- padding-left: 20px;
- }
-
- .clickable {
- cursor: pointer;
- }
-
- .nowrap {
- white-space: nowrap;
- }
-
- .alert {
- position: fixed;
- top: 40vh;
- left: 25vw;
- margin: auto;
- width: 50vw;
- z-index: 100;
- }
-
- .spinner-container {
- width: 50px;
- margin: auto;
- padding: 20px;
- }
-
- .spinner {
- color: #5B69FF;
- }
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/Graph/Graph.js b/neural_insights/gui/src/components/Graph/Graph.js
deleted file mode 100644
index 90e4ebdb8f0..00000000000
--- a/neural_insights/gui/src/components/Graph/Graph.js
+++ /dev/null
@@ -1,334 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React, { useEffect, useState } from 'react';
-import './Graph.scss';
-import { getLabel, customColor } from './../Diagnosis/Diagnosis';
-import Button from 'react-bootstrap/Button';
-import { api } from './../../App';
-import Spinner from 'react-bootstrap/Spinner';
-
-const cytoscape = require('cytoscape');
-const nodeHtmlLabel = require('cytoscape-node-html-label');
-cytoscape.use(nodeHtmlLabel);
-const elk = require('cytoscape-elk');
-cytoscape.use(elk);
-
-export default function Graph({ setSelectedNode, selectedWorkload, selectedOp, selectedPattern, setWarningText }) {
- const [graph, setGraph] = useState(null);
- const [groupNode, setGroupNode] = useState([]);
- const groupNodeOpList = [];
-
- useEffect(() => {
- if (selectedWorkload) {
- const payload = {
- workload_id: selectedWorkload.uuid,
- path: [selectedWorkload.model_path],
- ...((groupNode.length || groupNodeOpList.length) && { group: [...groupNode, ...groupNodeOpList] })
- };
- api.post('api/model/graph?token=' + localStorage.getItem('token'), payload)
- .then(
- response => {
- setGraph(response.data);
- })
- .catch(error => {
- setWarningText(error.message);
- });
- }
- }, [selectedWorkload, groupNode]);
-
- useEffect(() => {
- if (selectedOp) {
- api.post('api/model/graph/highlight_pattern?token=' + localStorage.getItem('token'), {
- workload_id: selectedWorkload.uuid,
- path: [selectedWorkload.model_path],
- op_name: selectedOp,
- pattern: selectedPattern,
- ...((groupNode.length || groupNodeOpList.length) && { group: [...groupNode, ...groupNodeOpList] })
- })
- .then(
- response => {
- setGraph(response.data.graph);
- groupNodeOpList.push(...response.data.groups);
- })
- .catch(error => {
- if (error.response.status !== 400) {
- setWarningText(error.message);
- }
- });
- }
- }, [selectedPattern]);
-
- return (
- Quantization is in progress. -
-
-
- }
- {selectedWorkload.status !== 'wip' &&
- !selectedWorkload.accuracy_data.ratio &&
- | Accuracy results |
-
- N/A
- FP32
- |
-
- N/A
- INT8
- |
-
- N/A
- Ratio
- |
-
| Accuracy results |
-
- {(selectedWorkload.accuracy_data.baseline_accuracy * 100).toPrecision(3)}%
- FP32
- |
-
- {(selectedWorkload.accuracy_data.optimized_accuracy * 100).toPrecision(3)}%
- INT8
- |
-
- {(selectedWorkload.accuracy_data.ratio * 100).toPrecision(2)}%
- Ratio
- |
-
-
- )
-};
-
-class CytoGraph extends React.Component {
- constructor(props) {
- super(props);
- this.renderCytoscapeElement = this.renderCytoscapeElement.bind(this);
- }
-
- handleCallback = (childData) => {
- switch (childData) {
- case 'fit':
- this.cy.fit();
- break;
- case 'center':
- this.cy.center();
- break;
- case 'reset':
- this.cy.reset();
- break;
- default:
- break;
- }
- }
-
- renderCytoscapeElement() {
- const elements = getElements(this.props.graph);
- this.cy =
- cytoscape({
- container: document.getElementById('cy'),
- elements,
- style: [{
- selector: 'node',
- style: {
- 'background-color': 'data(color)',
- 'border-color': 'data(border_color)',
- 'border-width': '3px',
- color: '#fff',
- label: 'data(label)',
- shape: 'round-rectangle',
- 'text-valign': 'center',
- 'text-halign': 'center',
- width: (node) => node.data('label').length * 12,
- }
- },
- {
- selector: 'edge',
- style: {
- 'font-size': '10px',
- 'source-text-offset': '10px',
- 'target-text-offset': '10px',
- width: 3,
- 'line-color': '#ccc',
- 'target-arrow-color': '#ccc',
- 'target-arrow-shape': 'triangle',
- 'curve-style': 'taxi',
- }
- },
- {
- selector: 'node',
- css: {
- 'font-family': 'IntelClearRg',
- }
- },
- {
- selector: 'node.selected',
- css: {
- 'border-color': '#00c7fd'
- }
- },
- {
- selector: 'node.hover',
- css: {
- 'border-color': '#B1BABF',
- 'border-style': 'dashed',
- }
- },
- {
- selector: 'node[node_type = \'group_node\']',
- css: {
- color: 'black'
- }
- },
- {
- selector: 'node[highlight = \'true\']',
- css: {
- 'border-color': '#FEC91B'
- }
- }
- ],
- layout: {
- name: 'elk',
- animate: true,
- elk: {
- 'algorithm': 'layered',
- 'elk.direction': 'DOWN'
- }
- }
- });
-
- this.cy.nodeHtmlLabel([
- {
- query: 'node[node_type = "group_node"]',
- halign: 'right',
- valign: 'bottom',
- cssClass: 'plus-sign',
- tpl: (data) => '＋
'
- }
- ]);
-
- this.cy.on('click', (event) => {
- if (event.target._private.data.node_type === 'group_node') {
- this.props.setGroupNode([...this.props.groupNode, event.target._private.data.id]);
- }
- });
-
- this.cy.on('mouseover', 'node', e => {
- e.target.addClass('hover');
- });
-
- this.cy.on('mouseout', 'node', e => {
- e.target.removeClass('hover');
- });
-
- this.cy.on('tap', 'node', e => {
- this.cy.elements('node:selected').removeClass('selected');
- if (e.target._private.data.node_type === 'node') {
- e.target.addClass('selected');
- this.props.setSelectedNode(e.target._private.data);
- }
- });
-
- setTimeout(() => {
- if (this.cy.elements('node[highlight = \'true\']').length) {
- this.cy.reset();
- this.cy.center(this.cy.elements('node[highlight = \'true\']')[0]);
- } else {
- this.cy.zoom({
- level: 2.0
- });
- this.cy.center();
- }
- }, 1000);
- }
-
- componentDidUpdate(prevProps) {
- if (prevProps.graph !== this.props.graph) {
- this.renderCytoscapeElement();
- }
- }
-
- componentDidMount() {
- if (this.props.graph) {
- this.renderCytoscapeElement();
- }
- }
-
- collapseNode(nodeName) {
- const newExpandedNodes = this.props.groupNode.filter(x => x !== nodeName);
- this.props.setGroupNode(newExpandedNodes);
- }
-
- render() {
- return (
-
-
- )
- }
-}
-
-class GraphButtons extends React.Component {
- onTrigger = (event) => {
- this.props.parentCallback(event.target.id);
- event.preventDefault();
- }
-
- render() {
- return (
-
-
-
-
- }
- {this.props.groupNode.length > 0 &&
-
-
-
-
-
- }
- | Expanded groups | -- |
| {groupNode.replace('node_group_', '')} | -this.collapseNode(groupNode)} className="clickable" tooltip="Collapse this group">✕ | -
-
-
-
-
-
-
- )
- }
-}
-
-const getElements = (graph) => {
- const elements = [];
- if (graph.nodes && graph.edges) {
- graph.nodes.forEach(node => {
- elements.push({
- data: {
- id: node.id,
- label: getLabel(node.label),
- parent: node.parent,
- attributes: node.attributes,
- properties: node.properties,
- node_type: node.node_type,
- highlight: String(node.highlight),
- border_color: node.node_type === 'group_node' ? '#5B69FF' : customColor[getHash(node.label)],
- color: node.node_type === 'group_node' ? '#fff' : customColor[getHash(node.label)],
- },
- grabbable: false,
- });
- });
- graph.edges.forEach(edge => {
- elements.push({
- data: {
- source: edge.source,
- target: edge.target,
- }
- });
- });
- }
- return elements;
-}
-
-function getHash(input) {
- var hash = 0, len = input.length;
- for (var i = 0; i < len; i++) {
- hash = ((hash << 5) - hash) + input.charCodeAt(i);
- hash |= 0;
- }
- hash = Math.abs(hash);
- return hash % customColor.length;
-}
-
diff --git a/neural_insights/gui/src/components/Graph/Graph.scss b/neural_insights/gui/src/components/Graph/Graph.scss
deleted file mode 100644
index ef4aa6c4690..00000000000
--- a/neural_insights/gui/src/components/Graph/Graph.scss
+++ /dev/null
@@ -1,73 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-@import './../../variables.scss';
-
-* {
- font-family: IntelClearRg;
-}
-
-.Graph {
- #cy {
- height: 90vh;
- position: relative;
- margin: auto;
- background: #fafafa;
- background-image: radial-gradient(#bebebe 1px, transparent 0);
- background-size: 18px 18px;
- border-radius: 0.5rem;
- }
-
- .plus-sign {
- color: white;
- background-color: #5B69FF;
- border-radius: 50%;
- width: 25px;
- height: 25px;
- text-align: center;
- cursor: pointer;
- }
-
- .graph-buttons {
- position: absolute;
- margin-top: 6px;
- z-index: 10;
- float: right;
- }
-
- .graph-button {
- cursor: pointer;
- margin-left: 6px;
- background-color: $accent-yellow;
- color: black;
- border: none;
- }
-
- .nodes-table-container {
- position: absolute;
- top: 50px;
- left: 5px;
- background-color: rgba(182, 182, 182, 0.13);
- }
-
- .nodes-table {
- font-family: IntelClearRg;
- width: 200px;
- }
-
- .header {
- font-family: IntelClearBd;
- text-align: left;
- }
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/Histogram/Histogram.js b/neural_insights/gui/src/components/Histogram/Histogram.js
deleted file mode 100644
index b93ff8bde2a..00000000000
--- a/neural_insights/gui/src/components/Histogram/Histogram.js
+++ /dev/null
@@ -1,130 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React, { useEffect, useState } from 'react';
-import './Histogram.scss';
-import Plot from 'react-plotly.js';
-import { api } from './../../App';
-import Spinner from 'react-bootstrap/Spinner';
-
-function Histogram({ selectedWorkload, selectedOp, histogramType, setWarningText }) {
- const [histogramData, setHistogramData] = useState(null);
-
- useEffect(() => {
- if (selectedOp.length && histogramType.length) {
- setHistogramData(null);
- api.post('api/diagnosis/histogram?token=' + localStorage.getItem('token'), { workload_id: selectedWorkload.uuid, op_name: selectedOp, type: histogramType })
- .then(
- response => {
- setHistogramData(response.data);
- })
- .catch(error => {
- setWarningText(error?.response?.data);
- });
- }
- }, [histogramType, selectedOp]);
-
- return (
-
-
- )
-};
-
-const getHistogramData = (histogramData) => {
- const data = [];
- if (histogramData.length) {
- const colorPalette = generateColor(histogramData[0].histograms.length);
- histogramData[0].histograms.forEach((series, index) => {
- data.push(
- {
- x: series.data,
- type: 'violin',
- orientation: 'h',
- side: 'negative',
- y0: 'channel ' + index,
- name: 'channel ' + index,
- width: 100,
- opacity: 0.8,
- fillcolor: colorPalette[index],
- hoverinfo: 'none',
- line: {
- width: 1,
- color: series.data.length === 1 ? colorPalette[index] : '#fff',
- },
- points: false,
- spanmode: 'hard'
- }
- );
- });
- }
- return data;
-}
-
-const layout = {
- height: 450,
- responsive: true,
- yaxis: {
- autorange: 'reversed',
- showgrid: true,
- },
- legend: {
- tracegroupgap: 0,
- },
- violinmode: 'overlay',
- opacity: 1,
- margin: {
- l: 150,
- r: 50,
- b: 20,
- t: 30,
- pad: 0
- }
-};
-
-const generateColor = (num) => {
- const colorPalette = [];
- const step = 100 / num;
- for (let i = num; i > 0; --i) {
- colorPalette.push(`rgb(${20 + (step * i)}, ${100 - (step * i * 0.1)}, ${200 - (step * i * 0.1)})`);
- }
- return colorPalette;
-}
-
-Histogram.propTypes = {};
-
-Histogram.defaultProps = {};
-
-export default Histogram;
diff --git a/neural_insights/gui/src/components/Histogram/Histogram.scss b/neural_insights/gui/src/components/Histogram/Histogram.scss
deleted file mode 100644
index 77d121d95a4..00000000000
--- a/neural_insights/gui/src/components/Histogram/Histogram.scss
+++ /dev/null
@@ -1,20 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-.Histogram {
- padding: 10px;
- background-color: white;
- border-radius: 0.5rem;
- min-width: 60vw;
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/ModelSummary/ModelSummary.js b/neural_insights/gui/src/components/ModelSummary/ModelSummary.js
deleted file mode 100644
index 0e81aa20522..00000000000
--- a/neural_insights/gui/src/components/ModelSummary/ModelSummary.js
+++ /dev/null
@@ -1,53 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React, { useEffect, useState } from 'react';
-import './ModelSummary.scss';
-import { api } from './../../App';
-import Spinner from 'react-bootstrap/Spinner';
-
-export default function ModelSummary({ selectedWorkload, setWarningText }) {
- const [summary, setSummary] = useState(null);
- useEffect(() => {
- if (selectedWorkload) {
- setSummary(null);
- const payload = {
- workload_id: selectedWorkload.uuid,
- };
- api.post('api/model/summary?token=' + localStorage.getItem('token'), payload)
- .then(
- response => {
- setSummary(response);
- })
- .catch(error => {
- setWarningText(error.message);
- });
- }
- }, [selectedWorkload]);
-
- return (
- {histogramType.charAt(0).toUpperCase() + histogramType.slice(1)} histogram
- {!histogramData &&No histogram data for this OP.
} - - {histogramData?.length > 0 && -
-
- }
-
- When you hover over the chart a menu will appear in the top right corner.
- You can zoom the chart, save it as .png file or hide channels by clicking them in the legend. -
-
- - You can zoom the chart, save it as .png file or hide channels by clicking them in the legend. -
-
-
-
-
- {!summary &&
-
- )
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/ModelSummary/ModelSummary.scss b/neural_insights/gui/src/components/ModelSummary/ModelSummary.scss
deleted file mode 100644
index dcfe870d316..00000000000
--- a/neural_insights/gui/src/components/ModelSummary/ModelSummary.scss
+++ /dev/null
@@ -1,22 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-@import './../../variables.scss';
-
-.summary {
- font-family: IntelOneMono;
- white-space: pre;
- overflow: auto;
- max-height: 90vh;
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/OpDetails/OpDetails.js b/neural_insights/gui/src/components/OpDetails/OpDetails.js
deleted file mode 100644
index 8c1be7434f4..00000000000
--- a/neural_insights/gui/src/components/OpDetails/OpDetails.js
+++ /dev/null
@@ -1,133 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React, { useEffect, useState } from 'react';
-import './OpDetails.scss';
-import Button from 'react-bootstrap/esm/Button';
-import { api } from './../../App';
-
-export default function OpDetails({ selectedWorkload, selectedOp, setHistogramType, setSelectedPattern, setWarningText }) {
- const [opDetails, setOpDetails] = useState({
- "OP name": "",
- "Pattern": {
- "sequence": [],
- "precision": ""
- },
- "Weights": {
- "dtype": "",
- "granularity": ""
- },
- "Activation": {
- "dtype": ""
- }
- });
-
- useEffect(() => {
- if (selectedOp?.length) {
- api.post('api/diagnosis/op_details?token=' + localStorage.getItem('token'), { workload_id: selectedWorkload.uuid, op_name: selectedOp })
- .then(
- response => {
- setOpDetails(response.data);
- setSelectedPattern(response.data.Pattern.sequence);
- setHistogramType(null);
- })
- .catch(error => {
- setWarningText(error.message);
- });
- }
- }, [selectedOp]);
-
- return (
-
-
- }
-
-
- {summary?.data?.summary.replaceAll('\n\n', '\n')}
-
-
-
- {selectedOp &&
-
- );
-}
diff --git a/neural_insights/gui/src/components/OpDetails/OpDetails.scss b/neural_insights/gui/src/components/OpDetails/OpDetails.scss
deleted file mode 100644
index a4f8ee21345..00000000000
--- a/neural_insights/gui/src/components/OpDetails/OpDetails.scss
+++ /dev/null
@@ -1,26 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-.histogram-btn {
- float: right;
-}
-
-#opDetails {
- height: 560px;
- max-width: 40vw;
-}
-
-.table-value {
- word-break: break-all;
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/OpList/OpList.js b/neural_insights/gui/src/components/OpList/OpList.js
deleted file mode 100644
index 8f0639c39d7..00000000000
--- a/neural_insights/gui/src/components/OpList/OpList.js
+++ /dev/null
@@ -1,90 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React, { useEffect, useState } from 'react';
-import { api } from '../../App';
-import Table from 'react-bootstrap/Table';
-import Spinner from 'react-bootstrap/Spinner';
-
-export default function OpList({ selectedWorkload, setSelectedOp, selectedOp, setWarningText }) {
- const [opList, setOpList] = useState([]);
-
- useEffect(() => {
- if (selectedWorkload) {
- setOpList([]);
- api.post('api/diagnosis/op_list?token=' + localStorage.getItem('token'), { workload_id: selectedWorkload.uuid })
- .then(
- response => {
- setOpList(response.data.sort((a, b) => b.MSE - a.MSE));
- })
- .catch(error => {
- if (selectedWorkload?.status !== 'wip') {
- setWarningText(error.message + ': ' + error?.response?.data);
- }
- });
- }
- }, [selectedWorkload, selectedOp]);
-
- const tableContent =
- opList.map(opData => {
- return (
-
-
- {selectedWorkload.framework !== 'PyTorch' && -
-
-
- }
- {selectedWorkload.framework === 'PyTorch' &&
- <>
-
-
-
-
- -
-
- - - } -
- }
- OP details
-- {selectedWorkload.framework !== 'PyTorch' && -
| OP name | -{opDetails['OP name']} | -|
| Pattern | -||
| Sequence | -- {opDetails.Pattern.sequence.map( - sequence => { - return {sequence} - } - )} - | -|
| Precision | -{opDetails.Pattern.precision} | -|
| - Weights - | -||
| Dtype | -{opDetails.Weights.dtype} | -- - | -
| Granularity | -{opDetails.Weights.granularity} | -|
| - Activation - | -||
| Dtype | -{opDetails.Activation.dtype} | -- - | -
| OP name | -{opDetails['OP name']} | -
- -
-
- - - } -
- {opList.length === 0 &&
-
-
-
- }
-
- );
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/OpList/OpList.scss b/neural_insights/gui/src/components/OpList/OpList.scss
deleted file mode 100644
index 02101c1432c..00000000000
--- a/neural_insights/gui/src/components/OpList/OpList.scss
+++ /dev/null
@@ -1,14 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/Profiling/Profiling.js b/neural_insights/gui/src/components/Profiling/Profiling.js
deleted file mode 100644
index c70545be4eb..00000000000
--- a/neural_insights/gui/src/components/Profiling/Profiling.js
+++ /dev/null
@@ -1,197 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React, { useEffect, useState, useMemo } from 'react';
-import './Profiling.scss';
-import { api } from './../../App';
-import Plot from 'react-plotly.js';
-import { getLabel } from './../Diagnosis/Diagnosis';
-import Table from 'react-bootstrap/Table';
-
-export default function Profiling({ selectedWorkload, setWarningText }) {
- const [profilingTable, setProfilingTable] = useState([]);
- const [profilingChartData, setProfilingChartData] = useState([]);
- return (
-
-
- }
- {opList.length > 0 &&
- | OP Name | -MSE | - {typeof opList[0]['Activation Min'] !== 'undefined' && -Activation Min | - } - {typeof opList[0]['Activation Max'] !== 'undefined' && -Activation Max | - } -
|---|
-
- )
-}
-
-function ProfilingTable({ selectedWorkload, profilingTable, setProfilingTable, setProfilingChartData, setWarningText }) {
-
- const [checked, setChecked] = useState({});
- const [sorting, setSorting] = useState({ field: 'node_name', direction: 1 });
-
- useEffect(() => {
- if (selectedWorkload) {
- api.post('api/profiling?token=' + localStorage.getItem('token'), { workload_id: selectedWorkload.uuid })
- .then(
- response => {
- setProfilingTable(response.data);
- setSorting({ field: 'total_execution_time', direction: 1 });
- const showOnChart = {};
- const chartData = [];
- response.data.forEach((node, index) => {
- if (index < 10) {
- showOnChart[node.node_name] = true;
- chartData.push(node);
- } else {
- showOnChart[node.node_name] = false;
- }
- });
- setChecked(showOnChart);
- setProfilingChartData(chartData);
- })
- .catch(error => {
- setWarningText(error.message);
- });
- }
- }, [selectedWorkload]);
-
- let sortedProfiling = useMemo(() => {
- let sortedTable = [...profilingTable];
- if (sorting !== null) {
- sortedTable.sort((a, b) => {
- if (a[sorting.field] < b[sorting.field]) {
- return sorting.direction;
- }
- if (a[sorting.field] > b[sorting.field]) {
- return -sorting.direction;
- }
- return 0;
- });
- }
- return sortedTable;
- }, [sorting]);
-
- const requestSorting = field => {
- let direction = -sorting.direction;
- setSorting({ field, direction });
- };
-
- const getSortingClass = (name) => {
- let classes = 'header clickable';
- if (sorting.field === name) {
- return classes + (sorting.direction === 1 ? ' ascending' : ' descending');
- }
- return 'header clickable';
- };
-
- const requestChartCheck = (nodeName, value) => {
- let chartCheck = checked;
- chartCheck[nodeName] = value;
- setChecked(chartCheck);
- const newProfilingChartData = profilingTable.filter(node => checked[node.node_name] === true);
- setProfilingChartData(newProfilingChartData);
- };
-
- const tableContent = sortedProfiling?.map(profiling => {
- return (
-
-
-
-
-
- );
-}
-
-function ProfilingChart({ profilingChartData }) {
- return (| requestSorting('node_name')}>Name | -requestSorting('accelerator_execution_time')}>Accelerator execution time [μs] | -requestSorting('cpu_execution_time')}>CPU execution time [μs] | -requestSorting('op_defined')}>Op defined | -requestSorting('op_run')}>Op run | -requestSorting('total_execution_time')}>Total execution time [μs] | -Show on chart | -
-
)
-};
-
-const getChartData = (profilingData) => {
- let data = [];
- if (Object.keys(profilingData).length) {
- const colorPalette = generateColor(profilingData.length);
- profilingData.forEach((node, index) => {
- data.push({
- name: getLabel(node.node_name),
- x: [node.node_name],
- y: [node.total_execution_time],
- type: 'bar',
- marker: {
- color: colorPalette[index]
- }
- });
- });
- }
- return data;
-}
-
-const layout = {
- responsive: true,
- xaxis: {
- title: 'Total execution time [μs]',
- showticklabels: false
- },
- yaxis: {
- showgrid: true,
- },
- legend: {
- tracegroupgap: 0,
- },
- opacity: 1,
-};
-
-const generateColor = (num) => {
- const colorPalette = [];
- const step = 100 / num;
- for (let i = num; i > 0; --i) {
- colorPalette.push(`rgb(${20 + (step * i)}, ${100 - (step * i * 0.1)}, ${200 - (step * i * 0.1)})`);
- }
- return colorPalette;
-}
-
diff --git a/neural_insights/gui/src/components/Profiling/Profiling.scss b/neural_insights/gui/src/components/Profiling/Profiling.scss
deleted file mode 100644
index cddbf72cdf1..00000000000
--- a/neural_insights/gui/src/components/Profiling/Profiling.scss
+++ /dev/null
@@ -1,31 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-.Profiling {
- .data-panel {
- width: 100%;
- }
-}
-
-.ascending::after {
- content: '\2191';
- display: inline-block;
- margin-left: 1em;
-}
-
-.descending::after {
- content: '\2193';
- display: inline-block;
- margin-left: 1em;
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/Warning/Warning.js b/neural_insights/gui/src/components/Warning/Warning.js
deleted file mode 100644
index 29fd86d7137..00000000000
--- a/neural_insights/gui/src/components/Warning/Warning.js
+++ /dev/null
@@ -1,38 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React from 'react';
-import Alert from 'react-bootstrap/Alert';
-import Button from 'react-bootstrap/Button';
-import './Warning.scss';
-
-export default function Warning({ warningText, setWarningText }) {
-
- if (warningText.length) {
- return (
- - {warningText} -
-
-
-
-
- {selectedWorkload &&
-
- )
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/WorkloadDetails/WorkloadDetails.scss b/neural_insights/gui/src/components/WorkloadDetails/WorkloadDetails.scss
deleted file mode 100644
index 3d75f746787..00000000000
--- a/neural_insights/gui/src/components/WorkloadDetails/WorkloadDetails.scss
+++ /dev/null
@@ -1,25 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-@import './../../variables.scss';
-
-.details-list {
- width: 100%;
-}
-
-.delete-button {
- float: right;
- width: 25px;
- height: 25px;
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/Workloads/Workloads.js b/neural_insights/gui/src/components/Workloads/Workloads.js
deleted file mode 100644
index f65945589bf..00000000000
--- a/neural_insights/gui/src/components/Workloads/Workloads.js
+++ /dev/null
@@ -1,87 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React, { useEffect, useState } from 'react';
-import Button from 'react-bootstrap/esm/Button';
-import Spinner from 'react-bootstrap/Spinner';
-import './Workloads.scss';
-import moment from 'moment';
-import { api } from './../../App';
-import { io } from 'socket.io-client';
-
-export default function Workloads({ setSelectedWorkload, selectedWorkload, setWarningText, setSelectedOp, setSelectedNode }) {
- const [workloads, setWorkloads] = useState([]);
- const [spinner, setSpinner] = useState(true);
-
- let socket = io('/');
- socket.on('Config update', data => {
- getWorkloads(false);
- });
-
- useEffect(() => {
- getWorkloads(true);
- }, []);
-
- let getWorkloads = (changeSelectedWorkload) => {
- api.get('api/workloads?token=' + localStorage.getItem('token'))
- .then(
- response => {
- if (changeSelectedWorkload) {
- setSelectedWorkload(response.data.workloads[0]);
- }
- setWorkloads(response.data.workloads);
- setSpinner(false);
- }
- )
- .catch(error => {
- setWarningText(error.message);
- setSpinner(false);
- });
- }
-
- let workloadsList = workloads.map(workload => {
- return (
-
- Details
-
-
-
-
-
- }
- Details
-
- { deleteWorkload(selectedWorkload); setSelectedOp(null); }}>
- (e.currentTarget.src = "icons/057a-trash-solid.svg")}
- onMouseOut={e => (e.currentTarget.src = "icons/057a-trash-solid-red.svg")}
- />
-
-
-
- { deleteWorkload(selectedWorkload); setSelectedOp(null); }}>
- (e.currentTarget.src = "icons/057a-trash-solid.svg")}
- onMouseOut={e => (e.currentTarget.src = "icons/057a-trash-solid-red.svg")}
- />
-
- | Framework: | -{selectedWorkload?.framework} | -|
| - Model path: - | -
- {getLabel(selectedWorkload?.model_path)}
- |
-
- {selectedWorkload?.framework === 'TensorFlow' &&
- { navigator.clipboard.writeText(selectedWorkload.model_path) }}>
-
- |
-
{ setSelectedWorkload(workload); setSelectedOp(null); setSelectedNode(null) }}>
-
-
- );
- });
-
- return (
-
- {workloadsList.length > 0 &&
-
- )
-
-}
\ No newline at end of file
diff --git a/neural_insights/gui/src/components/Workloads/Workloads.scss b/neural_insights/gui/src/components/Workloads/Workloads.scss
deleted file mode 100644
index 0e36aae295b..00000000000
--- a/neural_insights/gui/src/components/Workloads/Workloads.scss
+++ /dev/null
@@ -1,24 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-@import './../../variables.scss';
-
-.active {
- background-color: $accent-yellow !important;
-}
-
-.date {
- color: $dark-gray;
- font-size: 80%;
-}
diff --git a/neural_insights/gui/src/index.js b/neural_insights/gui/src/index.js
deleted file mode 100644
index 62b5c4f6dc8..00000000000
--- a/neural_insights/gui/src/index.js
+++ /dev/null
@@ -1,30 +0,0 @@
-// -* - coding: utf - 8 -* -
-// Copyright(c) 2023 Intel Corporation
-//
-// Licensed under the Apache License, Version 2.0(the "License");
-// you may not use this file except in compliance with the License.
-// You may obtain a copy of the License at
-//
-// http://www.apache.org/licenses/LICENSE-2.0
-//
-// Unless required by applicable law or agreed to in writing, software
-// distributed under the License is distributed on an "AS IS" BASIS,
-// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
-// See the License for the specific language governing permissions and
-// limitations under the License.
-import React from 'react';
-import ReactDOM from 'react-dom/client';
-import './index.scss';
-import App from './App';
-import reportWebVitals from './reportWebVitals';
-import 'bootstrap/dist/css/bootstrap.min.css';
-
-const root = ReactDOM.createRoot(document.getElementById('root'));
-root.render(
-
-
- }
- {workloadsList.length === 0 &&
- Workloads
- {workloadsList} -
-
- }
- {spinner &&
- Neural Insights
-Run diagnosis or profiling process to see workloads on this page.
-
-
- }
- `-`` all receive top and bottom margins. We nuke the top\n// margin for easier control within type scales as it avoids margin collapsing.\n\n%heading {\n margin-top: 0; // 1\n margin-bottom: $headings-margin-bottom;\n font-family: $headings-font-family;\n font-style: $headings-font-style;\n font-weight: $headings-font-weight;\n line-height: $headings-line-height;\n color: var(--#{$prefix}heading-color);\n}\n\nh1 {\n @extend %heading;\n @include font-size($h1-font-size);\n}\n\nh2 {\n @extend %heading;\n @include font-size($h2-font-size);\n}\n\nh3 {\n @extend %heading;\n @include font-size($h3-font-size);\n}\n\nh4 {\n @extend %heading;\n @include font-size($h4-font-size);\n}\n\nh5 {\n @extend %heading;\n @include font-size($h5-font-size);\n}\n\nh6 {\n @extend %heading;\n @include font-size($h6-font-size);\n}\n\n\n// Reset margins on paragraphs\n//\n// Similarly, the top margin on `
`s get reset. However, we also reset the\n// bottom margin to use `rem` units instead of `em`.\n\np {\n margin-top: 0;\n margin-bottom: $paragraph-margin-bottom;\n}\n\n\n// Abbreviations\n//\n// 1. Add the correct text decoration in Chrome, Edge, Opera, and Safari.\n// 2. Add explicit cursor to indicate changed behavior.\n// 3. Prevent the text-decoration to be skipped.\n\nabbr[title] {\n text-decoration: underline dotted; // 1\n cursor: help; // 2\n text-decoration-skip-ink: none; // 3\n}\n\n\n// Address\n\naddress {\n margin-bottom: 1rem;\n font-style: normal;\n line-height: inherit;\n}\n\n\n// Lists\n\nol,\nul {\n padding-left: 2rem;\n}\n\nol,\nul,\ndl {\n margin-top: 0;\n margin-bottom: 1rem;\n}\n\nol ol,\nul ul,\nol ul,\nul ol {\n margin-bottom: 0;\n}\n\ndt {\n font-weight: $dt-font-weight;\n}\n\n// 1. Undo browser default\n\ndd {\n margin-bottom: .5rem;\n margin-left: 0; // 1\n}\n\n\n// Blockquote\n\nblockquote {\n margin: 0 0 1rem;\n}\n\n\n// Strong\n//\n// Add the correct font weight in Chrome, Edge, and Safari\n\nb,\nstrong {\n font-weight: $font-weight-bolder;\n}\n\n\n// Small\n//\n// Add the correct font size in all browsers\n\nsmall {\n @include font-size($small-font-size);\n}\n\n\n// Mark\n\nmark {\n padding: $mark-padding;\n background-color: var(--#{$prefix}highlight-bg);\n}\n\n\n// Sub and Sup\n//\n// Prevent `sub` and `sup` elements from affecting the line height in\n// all browsers.\n\nsub,\nsup {\n position: relative;\n @include font-size($sub-sup-font-size);\n line-height: 0;\n vertical-align: baseline;\n}\n\nsub { bottom: -.25em; }\nsup { top: -.5em; }\n\n\n// Links\n\na {\n color: rgba(var(--#{$prefix}link-color-rgb), var(--#{$prefix}link-opacity, 1));\n text-decoration: $link-decoration;\n\n &:hover {\n --#{$prefix}link-color-rgb: var(--#{$prefix}link-hover-color-rgb);\n text-decoration: $link-hover-decoration;\n }\n}\n\n// And undo these styles for placeholder links/named anchors (without href).\n// It would be more straightforward to just use a[href] in previous block, but that\n// causes specificity issues in many other styles that are too complex to fix.\n// See https://github.com/twbs/bootstrap/issues/19402\n\na:not([href]):not([class]) {\n &,\n &:hover {\n color: inherit;\n text-decoration: none;\n }\n}\n\n\n// Code\n\npre,\ncode,\nkbd,\nsamp {\n font-family: $font-family-code;\n @include font-size(1em); // Correct the odd `em` font sizing in all browsers.\n}\n\n// 1. Remove browser default top margin\n// 2. Reset browser default of `1em` to use `rem`s\n// 3. Don't allow content to break outside\n\npre {\n display: block;\n margin-top: 0; // 1\n margin-bottom: 1rem; // 2\n overflow: auto; // 3\n @include font-size($code-font-size);\n color: $pre-color;\n\n // Account for some code outputs that place code tags in pre tags\n code {\n @include font-size(inherit);\n color: inherit;\n word-break: normal;\n }\n}\n\ncode {\n @include font-size($code-font-size);\n color: var(--#{$prefix}code-color);\n word-wrap: break-word;\n\n // Streamline the style when inside anchors to avoid broken underline and more\n a > & {\n color: inherit;\n }\n}\n\nkbd {\n padding: $kbd-padding-y $kbd-padding-x;\n @include font-size($kbd-font-size);\n color: $kbd-color;\n background-color: $kbd-bg;\n @include border-radius($border-radius-sm);\n\n kbd {\n padding: 0;\n @include font-size(1em);\n font-weight: $nested-kbd-font-weight;\n }\n}\n\n\n// Figures\n//\n// Apply a consistent margin strategy (matches our type styles).\n\nfigure {\n margin: 0 0 1rem;\n}\n\n\n// Images and content\n\nimg,\nsvg {\n vertical-align: middle;\n}\n\n\n// Tables\n//\n// Prevent double borders\n\ntable {\n caption-side: bottom;\n border-collapse: collapse;\n}\n\ncaption {\n padding-top: $table-cell-padding-y;\n padding-bottom: $table-cell-padding-y;\n color: $table-caption-color;\n text-align: left;\n}\n\n// 1. Removes font-weight bold by inheriting\n// 2. Matches default `