Deep Neural Network Prediction

Step 1. Genernate random parameters and Run them sequentially : $ python3 collect_data.py -gp -ep -pp -pl pooling -num 10 -shuffle -d 1080ti

Step 2. Data Timeline parser : $ python3 preprocess_data.py -pt -pl pooling -d 1080ti

Step 3. Combine all raw data : $ python3 preprocess_data.py -c -pl pooling -d 1080ti

Step 4. Split raw data to train and test data as performance prediction inputs $ python3 preprocess_data.py -sp -pl pooling -d 1080ti

Step 5. Train Model :

python3 train_model.py -ftf ./utils/Feature_Target/conv_pre.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl pre
    
python3 train_model.py -ftf ./utils/Feature_Target/conv_exe.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl exe
        
python3 train_model.py -ftf ./utils/Feature_Target/conv_post.json -log2file 1 -e 1000 -st 400 -sg 0.5 -lf maple -n perfnetA -pd 1080ti -pl convolution -psl post

Step 6. Generate model csv file :

python3 verify_model.py -gmc --model lenet -b 1

Step 7. Predict model csv file :

python3 verify_model.py -pdm -n perfnetA -d 1080ti -l -eva -lf maple --model lenet -b 1

Or you can use script to predict all models with all batches:

python3 predict_script.py -n perfnetA -d 1080ti -lf maple

Predict the latency time of the deep learning models

Related tags

Overview

Deep Neural Network Prediction

Owner

QAQ

Build upon neural radiance fields to create a scene-specific implicit 3D semantic representation, Semantic-NeRF

Model-based 3D Hand Reconstruction via Self-Supervised Learning, CVPR2021

Implementation of E(n)-Transformer, which extends the ideas of Welling's E(n)-Equivariant Graph Neural Network to attention

A PyTorch Implementation of the paper - Choi, Woosung, et al. "Investigating u-nets with various intermediate blocks for spectrogram-based singing voice separation." 21th International Society for Music Information Retrieval Conference, ISMIR. 2020.

VISNOTATE: An Opensource tool for Gaze-based Annotation of WSI Data

Exploring Simple 3D Multi-Object Tracking for Autonomous Driving (ICCV 2021)

Deep Anomaly Detection with Outlier Exposure (ICLR 2019)

Code artifacts for the submission "Mind the Gap! A Study on the Transferability of Virtual vs Physical-world Testing of Autonomous Driving Systems"

Point cloud processing tool library.

Lunar is a neural network aimbot that uses real-time object detection accelerated with CUDA on Nvidia GPUs.

PULSE: Self-Supervised Photo Upsampling via Latent Space Exploration of Generative Models

Official code for the paper "Why Do Self-Supervised Models Transfer? Investigating the Impact of Invariance on Downstream Tasks".

Code for the CVPR2021 workshop paper "Noise Conditional Flow Model for Learning the Super-Resolution Space"

Mini-hmc-jax - A simple implementation of Hamiltonian Monte Carlo in JAX

Implementation of paper "Graph Condensation for Graph Neural Networks"

Causal Imitative Model for Autonomous Driving

Face Recognize System on camera AI OAK1

Denoising images with Fourier Ring Correlation loss

Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models

Does Pretraining for Summarization Reuqire Knowledge Transfer?