I am running my ML inference inside a docker container. Should I include my weights in the image, or should I download them from S3 when the container starts up? From what I can see, the benefits are as follows:
Pros for including: faster startup times since I don’t need to download after startup. Less dependencies since everything is included in the container image
Pros for downloading: separation of weights and code. Easier weight tweaking since I won’t need to redeploy image when changing weights
Thoughts?
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This is a quick showcase of my first ‘real’ Machine Learning project: ‘Jumping over obstacles’:
Everything was done from scratch using the Unity3D engine (C#).
Any Feedback or Questions will be appreciated!
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In most objective functions comparing a learned and source probability distribution, KL divergence is used to measure their dissimilarity. What advantages does KL divergence have over true metrics like Wasserstein (earth mover’s distance), and Bhattacharyya? Is its asymmetry actually a desired property because the fixed source distribution should be treated differently compared to a learned distribution?
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False color maps show up in many applications in computer vision and machine learning, from visualizing depth images to more abstract uses, such as image differencing. Colorizing images helps the human visual system pick out detail, estimate quantitative values, and notice patterns in data in a more intuitive fashion. However, the choice of color map can have a significant impact on a given task. For example, interpretation of “rainbow maps” have been linked to lower accuracy in mission critical applications, such as medical imaging. Still, in many applications, “rainbow maps” are preferred since they show more detail (at the expense of accuracy) and allow for quicker visual assessment.
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| Left: Disparity image displayed as greyscale. Right: The commonly used Jet rainbow map being used to create a false color image. |
One of the most commonly used color mapping algorithms in computer vision applications is Jet, which is high contrast, making it useful for accentuating even weakly distinguished image features. However, if you look at the color map gradient, one can see distinct “bands” of color, most notably in the cyan and yellow regions. This causes sharp transitions when the map is applied to images, which are misleading when the underlying data is actually smoothly varying. Because the rate at which the color changes ‘perceptually’ is not constant, Jet is not perceptually uniform. These effects are even more pronounced for users that are color blind, to the point of making the map ambiguous:
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| The above image with simulated Protanopia |
Today there are many modern alternatives that are uniform and color blind accessible, such as Viridis or Inferno from matplotlib. While these linear lightness maps solve many important issues with Jet, their constraints may make them suboptimal for day to day tasks where the requirements are not as stringent.
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| Viridis | Inferno |
Today we are happy to introduce Turbo, a new colormap that has the desirable properties of Jet while also addressing some of its shortcomings, such as false detail, banding and color blindness ambiguity. Turbo was hand-crafted and fine-tuned to be effective for a variety of visualization tasks. You can find the color map data and usage instructions for Python here and C/C++ here, as well as a polynomial approximation here.
Development
To create the Turbo color map, we created a simple interface that allowed us to interactively adjust the sRGB curves using a 7-knot cubic spline, while comparing the result on a selection of sample images as well as other well known color maps.
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| Screenshot of the interface used to create and tune Turbo. |
This approach provides control while keeping the curve C2 continuous. The resulting color map is not “perceptually linear” in the quantitative sense, but it is more smooth than Jet, without introducing false detail.
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| Turbo |
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| Jet |
Comparison with Common Color Maps
Viridis is a linear color map that is generally recommended when false color is needed because it is pleasant to the eye and it fixes most issues with Jet. Inferno has the same linear properties of Viridis, but is higher contrast, making it better for picking out detail. However, some feel that it can be harsh on the eyes. While this isn’t a concern for publishing, it does affect people’s choice when they must spend extended periods examining visualizations.
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| Turbo | Jet |
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| Viridis | Inferno |
Because of rapid color and lightness changes, Jet accentuates detail in the background that is less apparent with Viridis and even Inferno. Depending on the data, some detail may be lost entirely to the naked eye. The background in the following images is barely distinguishable with Inferno (which is already punchier than Viridis), but clear with Turbo.
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| Inferno | Turbo |
Turbo mimics the lightness profile of Jet, going from low to high back down to low, without banding. As such, its lightness slope is generally double that of Viridis, allowing subtle changes to be more easily seen. This is a valuable feature, since it greatly enhances detail when color can be used to disambiguate the low and high ends.
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| Turbo | Jet |
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| Viridis | Inferno |
| Lightness plots generated by converting the sRGB values to CIECAM02-UCS and displaying the lightness value (J) in greyscale. The black line traces the lightness value from the low end of the color map (left) to the high end (right). |
The Viridis and Inferno plots are linear, with Inferno exhibiting a higher slope and over a broader range. Jet’s plot is erratic and peaky, and banding can be seen clearly even in the grayscale image. Turbo has a similar asymmetric profile to Jet with the lows darker than the highs.This is intentional, to make cases where low values appear next to high values more distinct. The curvature in the lower region is also different from the higher region, due to the way blues are perceived in comparison to reds.
Although this low-high-low curve increases detail, it comes at the cost of lightness ambiguity. When rendered in grayscale, the coloration will be ambiguous, since some of the lower values will look identical to higher values. Consequently, Turbo is inappropriate for grayscale printing and for people with the rare case of achromatopsia.
Semantic Layers
When examining disparity maps, it is often desirable to compare values on different sides of the image at a glance. This task is much easier when values can be mentally mapped to a distinct semantic color, such as red or blue. Thus, having more colors helps the estimation ease and accuracy.
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| Turbo | Jet |
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| Viridis | Inferno |
With Jet and Turbo, it’s easy to see which objects on the left of the frame are at the same depth as objects on the right, even though there is a visual gap in the middle. For example, you can easily spot which sphere on the left is at the same depth as the ring on the right. This is much harder to determine using Viridis or Inferno, which have far fewer distinct colors. Compared to Jet, Turbo is also much more smooth and has no “false layers” due to banding. You can see this improvement more clearly if the incoming values are quantized:
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| Left: Quantized Turbo colormap. Up to 33 quantized colors remain distinguishable and smooth in both lightness and hue change. Right: Quantized Jet color map. Many neighboring colors appear the same; Yellow and Cyan colors appear brighter than the rest. |
Quick Judging
When doing a quick comparison of two images, it’s much easier to judge the differences in color than in lightness (because our attention system prioritizes hue). For example, imagine we have an output image from a depth estimation algorithm beside the ground truth. With Turbo it’s easy to discern whether or not the two are in agreement and which regions may disagree.
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| “Output” Viridis | “Ground Truth” Viridis |
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| “Output” Turbo | “Ground Truth” Turbo |
In addition, it is easy to estimate quantitative values, since they map to distinguishable and memorable colors.
Diverging Map Use Cases
Although the Turbo color map was designed for sequential use (i.e., values [0-1]), it can be used as a diverging colormap as well, as is needed in difference images, for example. When used this way, zero is green, negative values are shades of blue, and positive values are shades of red. Note, however, that the negative minimum is darker than the positive maximum, so it is not truly balanced.
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| “Ground Truth” disparity image | Estimated disparity image |
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| Difference Image (ground truth – estimated disparity image), visualized with Turbo |
Accessibility for Color Blindness
We tested Turbo using a color blindness simulator and found that for all conditions except Achromatopsia (total color blindness), the map remains distinguishable and smooth. In the case of Achromatopsia, the low and high ends are ambiguous. Since the condition affects 1 in 30,000 individuals (or 0.00003%), Turbo should be usable by 99.997% of the population.
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| Test Image |
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| Protanomaly | Protanopia | |
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| Deuteranomaly | Deuteranopia | |
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| Tritanomaly | Tritanopia | |
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| Blue cone monochromacy | Achromatopsia |
Conclusion
Turbo is a slot-in replacement for Jet, and is intended for day-to-day tasks where perceptual uniformity is not critical, but one still wants a high contrast, smooth visualization of the underlying data. It can be used as a sequential as well as a diverging map, making it a good all-around map to have in the toolbox. You can find the color map data and usage instructions for Python here and for C/C++ here. There is also a polynomial approximation here, for cases where a look-up table may not be desirable.Our team uses it for visualizing disparity maps, error maps, and various other scalar quantities, and we hope you’ll find it useful as well.
Acknowledgements
Ambrus Csaszar stared at many color ramps with me in order to pick the right tradeoffs between uniformity and detail accentuation. Christian Haene integrated the map into our team’s tools, which caused wide usage and thus spurred further improvements. Matthias Kramm and Ruofei Du came up with closed form approximations.
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In ML projects hard links and symbolic links can help us, when setting up new experiments, to rearrange data files quickly and efficiently. However, with traditional links, we run the risk of polluting the data files with erroneous edits.
The article explain details of using links, some cool new stuff in modern file systems (reflinks), and an example of how DVC (Data Version Control) tool leverages this for managing ML project datasets and workflow: Reflinks vs symlinks vs hard links, and how they can help machine learning projects
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In the field of neuroimaging, two heads are better than one. So radiologists around the globe are exploring the use of AI tools to share their heavy workloads — and improve the consistency, speed and accuracy of brain scan analysis.
“We often refer to manual annotation as the gold standard for neuroimaging, when it’s actually probably not,” said Tim Wang, director of operations at the Sydney Neuroimaging Analysis Centre, or SNAC. “In many cases, AI provides a more consistent, less biased evaluation than manual classification or segmentation.”
An Australian company co-located with the University of Sydney’s Brain and Mind Centre, SNAC conducts neuroimaging research as well as commercial image analysis for clinical research trials. The center is building AI tools to automate laborious analysis tasks in their research workflow, like isolating brain images from head scans and segmenting brain lesions.
Additional algorithms are in development and being validated for clinical use. One compares how a patient’s brain volume and lesions change over time. Another flags critical brain scans, so radiologists can more quickly attend to urgent cases.
SNAC uses the NVIDIA DGX-1 and DGX Station, powered by NVIDIA V100 Tensor Core GPUs, as well as PC workstations with NVIDIA GeForce RTX 2080 Ti graphics cards. The researchers develop their algorithms using the NVIDIA Clara suite of medical imaging tools, as well as cuDNN libraries and TensorRT inference software.
When developing medicines, pharmaceutical companies conduct clinical trials to test how effective a new drug treatment is — often using brain imaging metrics such as brain atrophy rates and lesion changes as key indicators.
To ensure accurate and consistent measurements, pharma companies rely on centralized reading centers that evaluate trial participants’ brain scans in a blind analysis.
That’s where SNAC comes in. It analyzes patient MRI and CT scans acquired at clinical sites around the world. Its expertise in multicenter studies makes it well-positioned to develop AI tools that address challenges faced by radiologists and clinicians.
With a training dataset of more than 15,000 three-dimensional CT and MRI images, SNAC is building its deep learning algorithms using the PyTorch and TensorFlow frameworks.
One of the center’s AI models automates the time-consuming task of cleaning up MRI images to isolate the brain from other parts of the head, such as the venous sinuses and fluid-filled compartments around the brain. Using the NVIDIA DGX-1 system for inference, SNAC can speed up this process by at least 10x.
“That’s no small difference,” Wang said. “Previously, this would take our analysts 20 to 30 minutes with semi-automatic methods. Now, that’s down to 2 or 3 minutes of pure machine time, while performing better and more consistently than a human.”
Another tool tackles brain lesion analysis for multiple sclerosis cases. In research and clinical trials, image analysts typically segment brain lesions and determine their volume by manually examining scans — a process that takes up to 15 minutes.
AI can shrink the time needed to determine lesion volume to just 3 seconds. That makes it possible for these metrics to be used in clinical practice as well, where due to time constraints, radiologists often simply eyeball scans to estimate lesion volumes.
“By providing quantitative, individualized neuroimaging measurements, we can help streamline and add value to clinical radiology,” said Wang.
The center collaborates with I-MED, one of the largest imaging providers in the world, as well as the computational neuroscience team at the University of Sydney’s Brain and Mind Centre. The group also works closely with radiologists at major Australian hospitals to validate its algorithms.
SNAC plans to integrate its analysis tools with systems already used by clinicians, so that once a scan is taken, it’s automatically routed to a server and processed. The AI-evaluated scan is then passed on to radiologists’ viewers — giving them the analysis results without altering their workflow.
“Someone can develop a fantastic tool, but it’s hard to ask radiologists to use it by opening yet another application, or another browser on their workstations,” Wang said. “They don’t want to do that simply because they’re time poor, often punching through a very large volume of clinical scans a day.”
Main image shows a side-by-side comparison of multiple sclerosis lesion segmentation. Left image shows manual lesion segmentation, while right shows fully automated lesion segmentation. Image courtesy of Sydney Neuroimaging Analysis Center.
The post AI Frame of Mind: Neural Networks Bring Speed, Consistency to Brain Scan Analysis appeared first on The Official NVIDIA Blog.
My problem consists of classifying nodes of graphs (number of vertices <500) , nodes are attributed with both Continuous (some numerical features) and Categorical (CountVectorizer) features. I’ve looked in to various articles both about node embedding and graph neural networks but I’m still not sure what algorithms will be the best fit for my task.
What is your recommended approach for my problem?
Sorry for my short explanation, please ask me if you need more details.
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