I am currently working on a system that extracts certain features out of 3D-objects (Voxelgrids to be precise), and i would like to compare those features to automatically made features when it comes to performance (classification) in a tensorflow cNN with some other data, but that is not the point here, just for background. My idea now was, to take a dataset (modelnet10), train a tensorflow cNN to classify them, and then use what it learned there on my dataset – not to classify, but to extract features.
So i want to throw away everything the cnn does,except for what it takes from the objects.
Is there anyway to get these features? and how do i do that? i certainly have no idea.
Posted by Rose E. Wang, Student Researcher and Aleksandra Faust, Staff Research Scientist, Google Research
As robots become more ubiquitous in day-to-day life, the complexity of their interactions with each other and with the environment grows. In a controlled environment, such as a lab, multiple robots can coordinate their actions and efforts through a centralized planner that facilitates communication between individual agents. And while much research has been done to address reliable sensor-informedgoal navigation, in many real-world applications aligning goals across independent robotic agents must be done without a centralized planner, which poses non-trivial challenges.
An example of such a challenging decentralized task is the rendezvous task, in which multiple agents must agree upon a time and place at which they can meet, without explicitly communicating with one another. This goal alignment task plays an important role in real world multiagent and human-robot settings, e.g., performing object handovers or determining goals on the fly. Solving the decentralized rendezvous task in this situation depends not just on the obstacles in the environment, but also the policies and dynamics of each agent. Addressing potential miscoordination and dealing with noisy sensor data depends on the agents’ ability to model the motions of other agents as well as their own, and to adapt to diverging intentions while using limited information.
An example of two independently controlled robots separated by obstacles that share the objective of meeting each other. How should they move in order to meet? Example trajectories are illustrated in red and blue arrows for each robot. Each robot makes an independent decision of where to go based on their own observations.
In “Model-based Reinforcement Learning for Decentralized Multiagent Rendezvous”, presented at CoRL 2020, we propose an holistic approach to address the challenges of the decentralized rendezvous task, which we call hierarchical predictive planning (HPP). This is a decentralized, model-based reinforcement learning (RL) system that enables agents to align their goals on the fly in the real world. We evaluate HPP in a mixture of real-world and simulated environments and compare it to several learning-based planning and centralized baselines. In those evaluations, we show that HPP is able to more effectively predict and align trajectories, avoid miscoordinations, and directly transfer to the real world without additional fine-tuning.
Putting Together Prediction, Planning and Control Akin to a standard navigation pipeline, our learning-based system consists of three modules: prediction, planning, and control. Each agent employs the prediction model to learn agent motion and to predict the future positions of itself (the ego-agent) and others based on its own observations (e.g., from LiDAR and team position information) of other agents’ behaviors and navigation patterns. So, each agent learns two prediction models, one for its own motion and one for the other agent. These motion predictors constitute the prediction module, and are used by each agent’s planning module.
The output of the prediction module — the estimate of where each agent, both the ego-agent and the other agents, is most likely to be given the ego-agent’s own sensor observations — is useful information for the planning module, which evaluates different goal locations and maintains a belief distribution over where the team should converge. The belief distribution is periodically updated using evaluations provided by the prediction model. An agent samples from this belief distribution to update the goal to which it should navigate.
The selected goal is passed to the agent’s control module, which is equipped with a pre-trained, imperfect navigation policy that can navigate to a given location in the obstacle-laden environment. The control policy then determines what action the robot should execute.
This process of observing other agents, updating belief distributions and navigating to an updated goal repeats until agents have successfully rendezvoused. While the hierarchical planning and control setup are notunusual, our work closes the loop between the control and planning for decentralized multiagent systems by use of the sensor-informed prediction module.
Training the Prediction Models HPP trains motion predictors in simulation, assuming that each agent is controlled by a hidden, perhaps suboptimal, control policy capable of avoiding obstacles. The key difficulty lies in training prediction models without access to other agents’ sensor observations and control policies.
The predictors are trained via self-supervision. To collect the training data, we randomly place all the agents and obstacles in an environment, and each agent is given a random goal (unknown to other agents). As the agents move toward their respective goals, each agent records the experience — its sensor observations and the poses of all agents (itself and other agents). Next, from the recorded experience, the agent learns a separate predictor for each agent in the team including itself (target agent). The training dataset consists of ego-agent initial sensor observations, target agent’s pose and goal, labeled with future ego-observations and target agent poses. The goal and labels are inferred from the recorded experience.
As a result, the predictors learn temporal causality of the present and future ego-agent’s observations and target agent’s poses, conditioned on the target agent’s assumed goals — in other words the models predict where each agent will be in the future based on the present. The predictor training is done only with the information available to agents at the runtime, and in environments independent from the deployment environments.
The training environment for the model prediction models. The environment is filled with randomly filled obstacles. All agents (left in blue, upper right in red) are given the same random goal (center in green) and move with their own control modules towards it.
Selecting Goals for Alignment A model-based RL planner for each agent uses the learned predictors in the deployment environments to guide the agents towards the rendezvous point. The planner takes into account what it believes the other agents would do when also completing the rendezvous task.
HPP illustration. Each robot independently considers several potential rendezvous points, and evaluates each point based how close it believes that the agents can get.
To perform this reasoning, each agent independently samples a series of potential goals and selects the goal that it believes it would be the most likely to succeed. This process effectively simulates a centralized planner for fictitious agents by using the prediction models to predict trajectories of those agents moving to a fixed goal. Conditioned on a proposed goal, the algorithm predicts the poses of the agents in the future, which are generated from sequential roll outs of the prediction models. Each goal is then evaluated by scoring the anticipated system state using the task reward favoring goals that bring agents closer together. We use the cross-entropy method (CEM) to convert these goal evaluations into belief updates over potential rendezvous points. Finally, the agent’s planner selects a goal for itself from this new belief distribution and passes this goal to the agent’s control module.
A simple illustration of the goal evaluation. At the end of a simulated trajectory, the agents (red, left, and blue, right) are either far (top) or close (bottom) to each other. The goal in the bottom image is better than the goal on top because agents end up closer to each other.
Results We compare HPP against several baselines — MADDPG (learning-based), RRT (planning) with CEM, and centralized baselines that use heuristics for selecting the agent’s rendezvous point — in a mixture of real-world and simulated environments.
Evaluation environments, each of which are independent of the training environment for the agent’s control policy and prediction modules.
There are two main takeaways from our results. One is that HPP enables agents to predict and align trajectories, avoiding miscoordinations. For example:
The second takeaway is that HPP transfers directly into the real world without additional training. For example:
Conclusion This work presents HPP, a model-based RL approach for decentralized multiagent coordination. Agents first learn to predict where they and their teammates are going to be from their own sensors and decide and navigate to a common goal. Our experiments demonstrate the method generalizes to new environments and handles miscoordination while making no assumptions about the dynamics of other agents. This may be of interest to the larger multiagent research community as a real-world example of a decentralized task using noisy sensors and imperfect controllers, to the motion planning community as an example of a learning-based planning system that closes the loop between the planner and controller, and to the RL community as an example of model-based RL as feedback in a hierarchical, self-supervised prediction setting.
Acknowledgements This research was done by Rose E. Wang, J. Chase Kew, Dennis Lee, Tsang-Wei Edward Lee, Tingnan Zhang, Brian Ichter, Jie Tan, Aleksandra Faust with special thanks to Michael Everett, Oscar Ramirez and Igor Mordatch for the insightful discussions.
Today, NVIDIA is announcing the availability of cuTENSOR version 1.3.0. This software can be downloaded now free for members of the NVIDIA Developer Program.
Today, NVIDIA is announcing the availability of cuTENSOR version 1.3.0. This software can be downloaded now free for members of the NVIDIA Developer Program.
NVIDIA announced its latest update to the HPL-AI Benchmark version 2.0.0, which will reside in the HPC-Benchmarks container version 21.4.
NVIDIA announced its latest update to the HPL-AI Benchmark version 2.0.0, which will reside in the HPC-Benchmarks container version 21.4. The HPL-AI (High Performance Linpack – Artificial Intelligence) benchmark helps evaluate the convergence of HPC and data-driven AI workloads.
Historically, HPC workloads are benchmarked at double-precision, representing the accuracy requirements in computational astrophysics, computational fluid dynamics, nuclear engineering, and quantum computing. AI workloads on the other hand can deliver acceptable results using much lower precision for training and inference. This distinctive characteristic led to the development of technology such as the Tensor Cores. Tensor Cores provide substantial speedups in mixed-precision workloads by accelerating precisions such as TF32, BF16, FP16, INT8, and INT4.
Many vendors provide specially tuned versions of HPL for their hardware. NVIDIA is releasing the NVIDIA HPC-Benchmarks container on NGC. This container includes three different versions of the HPL benchmark for double precision arithmetic; HPL-AI, applied to mixed precision workloads, and HPCG, which performs a fixed number of multigrid preconditioned conjugate gradient (PCG) iterations at double precision.
With this latest release, the HPL-AI benchmark deliveries double the performance over the initial container released in Fall 2020. This is largely due to major improvements to load balancing at the communication layers. Multi-node communications are usually handled by the CPU, which can lead to performance bottlenecks if GPU become idle. MPI-aware communication between GPUs allows the CPU to be bypassed and keep the GPU busy. When possible data transfers are sent at a lower precision reducing the time GPUs are waiting for data. After minimizing communication, GPUs can process larger datasets to provide maximum compute efficiency. Lastly, the latest version of the NVIDIA Math Libraries are used to deliver optimal performance on an A100.
The plot below shows that with 128 DGX A100s, or 1024 NVIDIA A100 GPUs, the latest release of HPL-AI performs 2.6 times faster.
To checkout these improvements, download the latest NVIDIA HPC-Benchmark container, version 21.4, from NGC. The HPC-Benchmark landing page includes detailed instructions and additional resources. If you have any questions or issues, please send email to HPCBenchmarks@nvidia.com.
Torch is not just for deep learning. Its L-BFGS optimizer, complete with Strong-Wolfe line search, is a powerful tool in unconstrained as well as constrained optimization.
Torch is not just for deep learning. Its L-BFGS optimizer, complete with Strong-Wolfe line search, is a powerful tool in unconstrained as well as constrained optimization.
Recommender systems (RecSys) have become a key component in many online services, such as e-commerce, social media, news service, or online video streaming. However with the growth in importance, the growth in scale of industry datasets, and more sophisticated models, the bar has been raised for computational resources required for recommendation systems. To meet the … Continued
Recommender systems (RecSys) have become a key component in many online services, such as e-commerce, social media, news service, or online video streaming. However with the growth in importance, the growth in scale of industry datasets, and more sophisticated models, the bar has been raised for computational resources required for recommendation systems.
To meet the computational demands for large-scale DL recommender systems, NVIDIA introduced Merlin – a Framework for Deep Recommender Systems. Now NVIDIA teams have won two consecutive RecSys competitions in a row: the ACM RecSys Challenge 2020, and more recently the WSDM WebTour 21 Challenge organized by Booking.com. The Booking.com challenge focused on predicting the last city destination for a traveler’s trip given their previous booking history within the trip. NVIDIA’s interdisciplinary team included colleagues from NVIDIA’s KGMON (Kaggle Grandmasters), NVIDIA’s RAPIDS (Data Science), and NVIDIA’s Merlin (Recommender Systems) who collaborated on the winning solution.
This post is the second of a three-part series that gives an overview of the NVIDIA team’s first place solution for the booking.com challenge. The first post gives an overview of recommender system concepts. This second post discusses deep learning for recommender systems. The third post will discuss the winning solution, the steps involved, and also what made a difference in the outcome.
Deep Learning for Recommendation
As the growth in the volume of data available to power recommender systems accelerates rapidly, data scientists are increasingly turning from more traditional machine learning methods to highly expressive deep learning models to improve the quality of their recommendations.
Broadly, the life-cycle of deep learning for recommendation can be split into two phases: training and inference. In the training phase, the model is trained to predict user-item interaction probabilities (calculate a preference score) by presenting it with examples of interactions (or non-interactions) between users and items from the past.
Figure 1: Deep learning for recommendation training.
Once it has learned to make predictions with a sufficient level of accuracy, the model is deployed as a service to infer the likelihood of new interactions.
Figure 2: Deep learning for recommendation inference.
This inference stage utilizes a different pattern of data consumption than during training:
Candidate generation: pair a user with hundreds or thousands of candidate items based on learned user-item similarity.
Candidate ranking: rank the likelihood that the user enjoys each item.
Filter: show the user the item they are rated most likely to enjoy.
Figure 3: Deep learning for recommendation inference: candidate generation, ranking and filtering.
Deep Neural Network Models for Recommendation
Deep learning (DL) recommender models build upon existing techniques such as factorization to model the interactions between variables and embeddings to handle categorical variables. An embedding is a learned vector of numbers representing entity features so that similar entities (users or items) have similar distances in the vector space. For example, a deep learning approach to collaborative filtering learns the user and item embeddings (latent feature vectors) based on user and item interactions with a neural network.
Figure 4: A deep learning approach to collaborative filtering learns the user and item embeddings based on user and item interactions.
DL techniques also tap into the vast and rapidly growing novel network architectures and optimization algorithms to train on large amounts of data, use the power of deep learning for feature extraction, and build more expressive models. DL–based models build upon the different variations of artificial neural networks (ANNs), such as the following:
Multilayer perceptrons (MLPs) are a type of feedforward ANN consisting of at least three layers of nodes: an input layer, a hidden layer, and an output layer. MLPs are flexible networks that can be applied to a variety of scenarios.
Recurrent neural networks are the mathematical engines to parse language patterns and sequenced data.
GPUs, with their massively parallel architecture, are driving the advancement of deep learning (DL) and RecSys DL in the past several years. With GPUs, you can exploit data parallelism through columnar data processing instead of traditional row-based reading designed initially for CPUs. This provides higher performance and cost savings. Current DL–based models for recommender systems like DLRM, Wide and Deep (W&D), Neural Collaborative Filtering (NCF), Variational AutoEncoder (VAE) are part of the NVIDIA GPU-accelerated DL model portfolio that covers a wide range of network architectures and applications in many different domains beyond recommender systems, including image, text and speech analysis.
Neutral Collaborative Filtering
The Neural Collaborative Filtering (NCF) model is a neural network that provides collaborative filtering based on user and item interactions. The NCF model treats matrix factorization from a non-linearity perspective. NCF TensorFlow takes in a sequence of (user ID, item ID) pairs as inputs, then feeds them separately into a matrix factorization step (where the embeddings are multiplied) and into a multilayer perceptron (MLP) network.
The outputs of the matrix factorization and the MLP network are then combined and fed into a single dense layer which predicts whether the input user is likely to interact with the input item.
Figure 5: NCF model.
Variational Autoencoder for Collaborative Filtering
An autoencoder neural network reconstructs the input layer at the output layer by using the representation obtained in the hidden layer. An autoencoder for collaborative filtering learns a non-linear representation of a user-item matrix and reconstructs it by determining missing values.
The NVIDIA GPU-accelerated Variational Autoencoder for Collaborative Filtering (VAE-CF) is an optimized implementation of the architecture first described in Variational Autoencoders for Collaborative Filtering. VAE-CF is a neural network that provides collaborative filtering based on user and item interactions. The training data for this model consists of pairs of user-item IDs for each interaction between a user and an item.
The model consists of two parts: the encoder and the decoder. The encoder is a feedforward, fully connected neural network that transforms the input vector, containing the interactions for a specific user, into an n-dimensional variational distribution. This variational distribution is used to obtain a latent feature representation of a user (or embedding). This latent representation is then fed into the decoder, which is also a feedforward network with a similar structure to the encoder. The result is a vector of item interaction probabilities for a particular user.
Figure 6: VAE-CF model.
Wide and Deep
Wide & Deep refers to a class of networks that use the output of two parts working in parallel—wide model and deep model—whose outputs are summed to create an interaction probability. The wide model is a generalized linear model of features together with their transforms. The deep model is a Dense Neural Network (DNN), a series of hidden MLP layers, each beginning with a dense embedding of features. Categorical variables are embedded into continuous vector spaces before being fed to the DNN via learned or user-determined embeddings.
Figure 7: Wide and Deep model
What makes this model so successful for recommendation tasks is that it provides two avenues of learning patterns in the data, “deep” and “shallow”. The complex, nonlinear DNN is capable of learning rich representations of relationships in the data and generalizing to similar items via embeddings but needs to see many examples of these relationships in order to do so well. The linear piece, on the other hand, is capable of “memorizing” simple relationships that may only occur a handful of times in the training set.
In combination, these two representation channels often end up providing more modeling power than either on its own. NVIDIA has worked with many industry partners who reported improvements in offline and online metrics by using Wide & Deep as a replacement for more traditional machine learning models.
Figure 8: Wide and Deep model with NVIDIA GPU-accelerated TensorRT.
DLRM
DLRM is a DL-based model for recommendations introduced by Facebook research. It’s designed to make use of both categorical and numerical inputs that are usually present in recommender system training data. To handle categorical data, embedding layers map each category to a dense representation before being fed into multilayer perceptrons (MLP). Numerical features can be fed directly into an MLP.
At the next level, second-order interactions of different features are computed explicitly by taking the dot product between all pairs of embedding vectors and processed dense features. Those pairwise interactions are fed into a top-level MLP to compute the likelihood of interaction between a user and item pair.
Figure 9: DLRM model.
Compared to other DL-based approaches to recommendation, DLRM differs in two ways. First, it computes the feature interaction explicitly while limiting the order of interaction to pairwise interactions. Second, DLRM treats each embedded feature vector (corresponding to categorical features) as a single unit, whereas other methods (such as Deep and Cross) treat each element in the feature vector as a new unit that should yield different cross terms. These design choices help reduce computational/memory cost while maintaining competitive accuracy.
Contextual Sequence Learning
A Recurrent neural network (RNN) is a class of neural network that has memory or feedback loops that allow it to better recognize patterns in data. RNNs solve difficult tasks that deal with context and sequences, such as natural language processing, and are also used for contextual sequence recommendations. What distinguishes sequence learning from other tasks is the need to use models with an active data memory, such as LSTMs (Long Short-Term Memory) or GRU (Gated Recurrent Units) to learn temporal dependence in input data. This memory of past input is crucial for successful sequence learning. Transformer deep learning models, such as BERT (Bidirectional Encoder Representations from Transformers), are an alternative to RNNs that apply an attention technique—parsing a sentence by focusing attention on the most relevant words that come before and after it. Transformer-based deep learning models don’t require sequential data to be processed in order, allowing for much more parallelization and reduced training time on GPUs than RNNs.
Figure 10: Neutral machine translation Model.
In an NLP application, input text is converted into word vectors using techniques, such as word embedding. With word embedding, each word in the sentence is translated into a set of numbers before being fed into RNN variants, Transformer, or BERT to understand context. These numbers change over time while the neural net trains itself, encoding unique properties such as the semantics and contextual information for each word, so that similar words are close to each other in this number space, and dissimilar words are far apart. These DL models provide an appropriate output for a specific language task like next-word prediction and text summarization, which are used to produce an output sequence.
Figure 11: A recurrent neural network has memory of past experiences. The recurrent connection preserves these experiences and helps the network keep a notion of context.
Session-based recommendations apply the advances in sequence modeling from deep learning and NLP to recommendations. RNN models train on the sequence of user events in a session (e.g. products clicked, date and time of interactions) in order to predict the probability of a user clicking the candidate or target item. User item interactions in a session are embedded similarly to words in a sentence before being fed into RNN variants such as LSTM, GRU, or Transformer to understand the context. For example, Square’s deep learning-based product recommendation system shown below leverages the transformer-based model BERT, GRUs, and NVIDIA GPUs to create a vector representation of their sellers.
Figure 12: Square’s model architecture leverages the transformer-based model BERT and GRUs to create the vector representation of their sellers.
Figure 13: Alibaba’s recommender system model architecture.
In the more detailed model diagram below, you can see that GRUs are added to learn and capture the relations among the items in the user behavior sequences in order to predict if a user will click on an advertised product.
Figure 14: Alibaba’s recommender system model architecture uses GRUs to capture user sequence behavior.
Alibaba is using thousands of T4 GPUs across its infrastructure with TensorRT to support the entire recommendation query AI pipeline on a real-time basis.
Figure 15: Model and embedding sizes between NLP and recommender systems vary significantly.
NVIDIA GPU Accelerated, End-to-End Data Science
NVIDIA developed RAPIDS—an open-source data analytics and machine learning acceleration platform—for executing end-to-end data science training pipelines completely in GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high memory bandwidth through user-friendly Python interfaces.
Focusing on common data preparation tasks for analytics and data science, RAPIDS offers a GPU-accelerated DataFrame (cuDF) that mimics the pandas API and is built on Apache Arrow. It integrates with scikit-learn and a variety of machine learning algorithms to maximize interoperability and performance without paying typical serialization costs. This allows acceleration for end-to-end pipelines—from data prep to machine learning to deep learning. RAPIDS also includes support for multi-node, multi-GPU deployments, enabling vastly accelerated processing and training on much larger dataset sizes.
Compared to similar CPU-based implementations, RAPIDS delivers 50x performance improvements for classical data analytics and machine learning (ML) processes at scale which drastically reduces the total cost of ownership (TCO) for large data science operations.
Figure 16: End-to-End Data science pipeline with GPUs and RAPIDS.
NVIDIA Merlin
NVIDIA Merlin is an open-source application framework for building high-performance, DL–based recommender systems, built on NVIDIA RAPIDS, NVIDIA CUDA® Deep Neural Network library (cuDNN), and Triton. Merlin facilitates and accelerates recommender systems on GPU, speeding up common ETL tasks, training of models, and inference serving by ~10x over commonly used methods.
Figure 17: NVIDIA Merlin Open Beta Recommender System Framework.
NVTabular is a feature engineering and preprocessing library for recommender systems. It provides a high-level abstraction to simplify code and accelerates computation on the GPU using the RAPIDS GPU-accelerated DataFrame cuDF library.
Figure18:Recommender system training pipeline with NVTabular.
Figure 19:An example model expressible by HugeCTR: A hybrid model with two embeddings and two different types of inputs.
NVIDIA Triton Inference Server and NVIDIA® TensorRT accelerate production inference on GPUs for feature transforms and neural network execution.
Beyond providing better performance, these libraries are also designed to be easy to use and integrate with existing recommendation pipelines.
Conclusion
In this blog, we gave an overview of deep learning models for recommender systems. Part three will discuss the NVIDIA teams’ winning solution for the ACM WSDM WebTour 21 Challenge organized by Booking.com.
Posted by Siyuan Qiao, Student Researcher and Liang-Chieh Chen, Research Scientist, Google Research
People are able to retrieve the visual information about 3D environments from a picture quite easily — we can identify objects, determine instance sizes, and reconstruct 3D scene layout, all using the limited signals contained in 2D images. This ability is commonly known as the inverse projection problem, which refers to reconstructing the ambiguous mapping from the retinal images to the sources of retinal stimulation. Real-world computer vision applications, such as autonomous driving, heavily rely on these capabilities to localize and identify 3D objects, which require vision models to infer the spatial location, semantic class, and instance label for each 3D point projected to the 2D images. The ability to reconstruct the 3D world from images can be decomposed into two disjoint computer vision tasks: monocular depth estimation (predicting depth from a single image) and video panoptic segmentation (the unification of instance segmentation and semantic segmentation, in the video domain). However, research has generally considered each task separately. Tackling these tasks jointly with a unified computer vision model could result in easier deployment and greater efficiency by sharing computation among multiple tasks.
Driven by the potential value of a model that predicts depth and video panoptic segmentation at the same time, we present “ViP-DeepLab: Learning Visual Perception with Depth-aware Video Panoptic Segmentation”, accepted to CVPR 2021. In this work, we propose a new task, depth-aware video panoptic segmentation, that aims to simultaneously tackle monocular depth estimation and video panoptic segmentation. For the new task, we present two derived datasets accompanied by a new evaluation metric called depth-aware video panoptic quality (DVPQ). This new metric includes the metrics for depth estimation and video panoptic segmentation, requiring a vision model to simultaneously tackle the two sub-tasks. To this end, we extend Panoptic-DeepLab by adding network branches for depth and video predictions to create ViP-DeepLab, a unified model that jointly performs video panoptic segmentation and monocular depth estimation for each pixel on the image plane, and achieves state-of-the-art performance on several academic datasets for the sub-tasks. This video demonstrates the new task and shows the results of ViP-DeepLab.
Depth-aware video panoptic segmentation results obtained by ViP-DeepLab. Top-left: Video frames used as input. Top-right: Video panoptic segmentation results. Bottom-left: Estimated depth. Bottom-right: Reconstructed 3D points. Each object instance has a unique and temporally consistent label, e.g., pedestrain_1, pedestrain_2, etc. Input images are from the Cityscapes dataset.
Overview While Panoptic-DeepLab is able to output semantic segmentation, center prediction, and center regression for a single frame, it lacks the capability of depth estimation and temporally consistent instance ID prediction for multiple frames. However, ViP-DeepLab accomplishes this by performing additional predictions from two consecutive frames as input. The first additional output is depth estimation for the first frame, for which it assigns an estimated depth to each pixel. In addition, ViP-DeepLab also performs center regression for two consecutive frames for only the object centers that appear in the first frame. This process is called center offset prediction, and allows ViP-DeepLab to group all the pixels in the two frames to the same object that appears in the first frame. New instances emerge if they are not grouped to the previously detected instances. This process continues for every two consecutive frames (with one overlapping frame) in a video sequence, stitching panoptic predictions together to form predictions with temporally consistent instance IDs. That is, it stitches together where objects are and how they move in a video scene with time.
Outputs of ViP-DeepLab for video panoptic segmentation. Two consecutive frames are concatenated as input. The semantic segmentation output associates each pixel with its semantic classes, while the instance segmentation outputs identify the pixels from two frames associated with an individual object in the first frame. Input images are from the Cityscapes dataset.
Visualization of stitching video panoptic predictions. ViP-DeepLab propagates IDs based on mask intersection-over-union between region pairs. It is capable of tracking objects with large movements, e.g., the cyclist in the image.
Neural Network Design Building on top of Panoptic-DeepLab, ViP-DeepLab additionally contains two prediction branches: (1) a depth prediction branch, and (2) a next-frame instance branch. Specifically, the depth prediction head is a simple design that predicts depth regression for every pixel, while the next-frame instance branch predicts the center offsets for the pixels in the second frame with respect to the centers in the first frame.
Specifically, ViP-DeepLab achieves state-of-the-art (SOTA) results, significantly outperforming previous methods by 5.1% video panoptic quality (VPQ) on the Cityscapes-VPS test set.
Monocular depth estimation comparison on KITTI Depth Prediction benchmark. Note for the depth estimation metrics, the smaller the values, the better the performance. While differences may appear small, the top-performing method on this benchmark usually has a gap in SILog smaller than 0.1.
Additionally, ViP-DeepLab was also 1st on KITTI MOTS pedestrians and 3rd on KITTI MOTS cars ranked by the metric sMOTSA, and now is 3rd for both pedestrians and cars ranked by a newer metric HOTA.
Performance comparison on KITTI Multi-Object Tracking and Segmentation.
Finally, we also present two new datasets for the new task, depth-aware video panoptic segmentation, and test ViP-DeepLab on them. We hope our ViP-DeepLab results on these two new datasets will serve as a strong baseline for the community to compare against. The results are shown below.
Dataset
DVPQAll
DVPQThings
DVPQStuff
Cityscapes-DVPS
55.1%
43.3%
63.6%
SemKITTI-DVPS
45.6%
36.6%
52.2%
ViP-DeepLab performance for the task of depth-aware video panoptic segmentation on two new datasets.
Conclusion With a simple architecture, ViP-DeepLab achieves state-of-the-art performance on video panoptic segmentation, monocular depth estimation, and multi-object tracking and segmentation. We hope that along with MaX-DeepLab, which proposes an efficient dual-path transformer module that allows for end-to-end image panoptic segmentation, ViP-DeepLab is useful to the community and furthers research into a more holistic understanding of scenes in the real world.
Acknowledgements We would like to thank the support and valuable discussions with Yukun Zhu, Hartwig Adam, and Alan Yuille (co-authors of ViP-DeepLab), as well as Maxwell Collins, and the Mobile Vision team.
Google Cloud and NVIDIA collaborated to make MLOps simple, powerful, and cost-effective by bringing together the solution elements to build, serve and dynamically scale your end-to-end ML pipelines with the right-sized GPU acceleration in one place.
Building, deploying, and managing end-to-end ML pipelines in production, particularly for applications like recommender systems is challenging. Operationalizing ML models, within enterprise applications, to deliver business value involves a lot more than developing the machine learning algorithms and models themselves – it’s a continuous process of data collection and preparation, model building, training or retraining with newer data, model validation, inference serving, and monitoring model performance to ensure the relevance of the results.
In addition to the challenge of developing the pipeline, you also need to secure and manage the right compute infrastructure to accelerate these steps for a guaranteed quality-of-service (QoS) for your customers. And, each step in the pipeline is unique too so your compute requirements for data preparation and training might be completely different from what’s required to service multiple disparate inference requests. This is both a development and infrastructure management challenge also commonly referred to as the MLOps challenge.
Google Cloud and NVIDIA have collaborated to make MLOps simple, powerful, and cost-effective by bringing together the solution elements to build, serve and dynamically scale your end-to-end ML pipelines with the right-sized GPU acceleration in one place. You can focus on delivering the best value for your end customers while maximizing infrastructure utilization and minimizing operational costs for deploying your AI-enabled services.
GKE + MIG = Portability, Scalability & Productivity for MLOps
Google Kubernetes Engine (GKE) is a managed environment for deploying, scaling and managing containerized ML applications in a secure Google infrastructure. GKE facilitates easy cluster creation, load balancing, autoscaling compute requirements based on demand among other things. Most importantly GKE frees up users from having to manage their own workstations, servers, and VMs while building and deploying ML pipelines – you can focus on the most important value-add tasks of building and training your ML models for your business use-case.
The Google Kubernetes Engine (GKE) now supports the Multi-Instance GPU (MIG) feature enabling each NVIDIA A100 Tensor Core GPU in the new A2 VM instance to be partitioned into as many as seven independent GPU instances, each with its own high-bandwidth memory, cache, and compute cores. GKE can then provision GPU resources for your workloads with greater granularity, share a single GPU for multi-user, multi-model use-cases and automatically scale up or down based on changing needs of your ML pipelines.
Figure 2. Multiple AI inference requests on a single NVIDIA A100 GPU with NVIDIA Triton Inference Server and GKE
For example, GKE can provision multiple A100 GPU MIG instances to process inference requests for multiple models to be simultaneously executed on the independent MIG partitions within a single A100 GPU to maximize utilization. As the compute required for your deployed ML pipelines increase (e.g. a sudden surge in inference requests to service), GKE can automatically scale to additional node-pools with MIG partitions. In addition, the NVIDIA Collective Communication Library (NCCL) further optimizes multi-GPU, multi-node communications within the GKE cluster to ensure high bandwidth, high throughput, and low latency.
To develop ML application pipelines that are scalable and are optimized to leverage the full benefits of the MIG capabilities for GPU utilization on Google Cloud, NVIDIA offers several GPU-accelerated end-to-end application-specific frameworks – NVIDIA Merlin for end-to-end recommendation systems, NVIDIA Jarvis for multimodal conversational AI services, and NVIDIA RAPIDS for data analytics pipelines. All NVIDIA optimized frameworks, SDKs, pre-trained models, and performance-optimized libraries can be accessed from NGC Catalog, a hub for GPU-accelerated software.
Deploying the ML pipelines into production at scale on a GKE managed cluster is further simplified with NVIDIA Triton Inference Server software. This open-source inference serving software lets teams deploy trained AI models from any framework (TensorFlow, TensorRT, PyTorch, ONNX Runtime, or a custom framework), from local storage or Google Cloud’s managed storage products on any GPU- or CPU-based infrastructure. Triton Inference Server software is now directly available on the GCP Marketplace to seamlessly deploy, serve, monitor performance and dynamically scale multiple AI inference requests on MIG-enabled GKE clusters.
Bringing it All Together
GKE’s managed Kubernetes services combined with the flexibility of the A100 MIG feature and NVIDIA’s GPU-optimized solution stack for accelerating ML pipelines helps address both the development and infrastructure management challenges of productizing end-to-end ML pipelines.
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—an open-source data analytics and machine learning acceleration platform—for executing end-to-end data science training pipelines completely in GPUs. It relies on NVIDIA® CUDA® primitives for low-level compute optimization, but exposes that GPU parallelism and high memory bandwidth through user-friendly Python interfaces.
Google Cloud and NVIDIA collaborated to make MLOps simple, powerful, and cost-effective by bringing together the solution elements to build, serve and dynamically scale your end-to-end ML pipelines with the right-sized GPU acceleration in one place. 
