This project aims to explore the deployment of SwinTransformer based on TensorRT, including the test results of FP16 and INT8. Introduction(Quoted from the Original Project ) Swin Transformer original github repo (the name Swin stands for Shifted window) is initially described in arxiv, which capably serves as a general-purpose backbone for computer vision. It is basically a hierarchical Transformer whose representation is computed with shifted windows. The shifted windowing scheme brings greater efficiency by limiting self-attention computation to non-overlapping local […]

Pre-trained Deep Convolution Neural Network Model With Attention for Speech Emotion RecognitionHua Zhang, Ruoyun Gou, Jili Shang, Fangyao Shen, Yifan Wu and Guojun Dai Abstract: Speech emotion recognition (SER) is a difficult and challenging task because of the affective variances between different speakers. The performances of SER are extremely reliant on the extracted features from speech signals. To establish an effective features extracting and classification model is still a challenging task. In this paper, we propose a new method for […]

This is a TensorFlow implementation for C2-Rec We refer to the repo SASRec. Requirements requirement.txt Datasets This repo includes Amazon Beauty dataset as an example. you could also download Amazon review data from here. Model Training To train model on Beauty: python main.py –dataset=Beauty –train_dir=model_train –maxlen=50 –dropout_rate=0.5 –con_alpha=5.0 –rd_alpha=1.0 –neg_test=500 –user_reg_type=kl –lr=0.001 –rd_reduce=mean –neg_sample_n=50 GitHub View Github    

Fast Style Transfer in TensorFlow Add styles from famous paintings to any photo in a fraction of a second! It takes 100ms on a 2015 Titan X to style the MIT Stata Center (1024×680) like Udnie, by Francis Picabia. Our implementation is based off of a combination of Gatys’ A Neural Algorithm of Artistic Style, Johnson’s Perceptual Losses for Real-Time Style Transfer and Super-Resolution, and Ulyanov’s Instance Normalization.THe repository i based on https://github.com/lengstrom/fast-style-transfer.git. Image Stylization We added styles from various […]

HiddenMarkovModel implements hidden Markov models with Gaussian mixtures asdistributions on top of TensorFlow 2.0 Installation pip install –upgrade git+https://gitlab.com/kesmarag/hmm-gmm-tf2 HiddenMarkovModel(p0, tp, em_w, em_mu, em_var)

Deploy tensorflow graphs for fast evaluation and export to tensorflow-less environments running numpy. Now with tensorflow 1.0 support. Evaluation usage import tfdeploy as td import numpy as np model = td.Model(“/path/to/model.pkl”) inp, outp = model.get(“input”, “output”) batch = np.random.rand(10000, 784) result = outp.eval({inp: batch}) Installation and dependencies    

Open-AI’s DALL-E in Mesh-Tensorflow. If this is similarly efficient to GPT-Neo, this repo should be able to train models up to, and larger than, the size of Open-AI’s DALL-E (12B params). No pretrained models… Yet. Thanks to Ben Wang for the tf vae implementation as well as getting the mtf version working, and Aran Komatsuzaki for help building the mtf VAE and input pipeline. git clone https://github.com/EleutherAI/GPTNeo cd GPTNeo pip3 install -r requirements.txt Training Setup Runs on TPUs, untested on […]

GitHub – ibaiGorordo/HITNET-Stereo-Depth-estimation: Python scripts for performing stereo depth estimation using the HITNET Tensorflow model. Python scripts for performing stereo depth estimation using the HITNET Tensorflow model. – GitHub – ibaiGorordo/HITNET-Stereo-Depth-estimation: Python scripts for performing stereo depth estimation…    

Note:  TensorFrames is deprecated. You can use pandas UDF instead. Experimental TensorFlow binding for Scala andApache Spark. TensorFrames (TensorFlow on Spark DataFrames) lets you manipulate Apache Spark’s DataFrames withTensorFlow programs. This package is experimental and is provided as a technical preview only. While theinterfaces are all implemented and working, there are still some areas of low performance. Supported platforms: This package only officially supports linux 64bit platforms as a target.Contributions are welcome for other platforms. See the file project/Dependencies.scala for […]