Understanding operation inside non_max_suppression() function

[Avaneesh-S]

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Question

I am processing batch of 10 videos at same time and running Yolov5 on them. (every batch contains one frame from each video, so in batches of 10 at a time). While using viztracer for profiling my application, I found that in the non_max_suppression() in general.py at the following line:

this operation takes a long time on the first iteration of the 'for' loop (that is for image 1 or index 0) and for all other subsequent iterations it runs very fast.
specifically suppose ' v = xc[xi] ' then the operation ' x = x[v] ' is the one taking the most time (not v=xc[xi]).

It is seen that if non_max_suppression() executes for 100ms around 80ms is taken by this operation in the first iteration of the 'for' loop for every batch.

I want to understand why this is happening and why only on the first iteration and if there is any way to reduce this time since I am trying to optimize my application to improve average FPS and optimizing this operation would optimize the entire non_max_supression.

Additional

Additionally, I have gone through the same implementation in Yolov8 and found that it is different. Is it more optimized there? I have tried to manually replace the Yolov5's non_max_suppression() with the Yolov8's but it didn't give required output, I think its because the prediction tensors are a bit different for them (am I right?).

[glenn-jocher]

@Avaneesh-S hello,

Thank you for your detailed question and for profiling your application with viztracer. It’s great to see such in-depth analysis!

Understanding the Issue

The behavior you're observing in the non_max_suppression() function, where the first iteration takes significantly longer than subsequent ones, is likely due to the way Python handles memory and caching. Specifically, the first time an operation is performed, it may involve additional overhead such as memory allocation, which is not required in subsequent iterations.

Steps to Investigate and Optimize

1. Reproducible Example: To help us investigate further, could you please provide a minimum reproducible code example? This will allow us to replicate the issue on our end. You can refer to our guidelines here: Minimum Reproducible Example.

2. Update to Latest Versions: Ensure you are using the latest versions of torch and the YOLOv5 repository. Sometimes, performance improvements and bug fixes are included in newer releases. You can update YOLOv5 with:

   git pull

   And update torch with:

   pip install --upgrade torch

Potential Optimization Strategies

1. Warm-Up Iteration: One approach to mitigate the initial overhead is to perform a "warm-up" iteration before processing your actual data. This can help in reducing the time taken for the first iteration in your actual processing loop.

2. Batch Processing: Since you are processing batches of frames, ensure that your batch processing is optimized. Sometimes, operations on smaller batches can be faster due to better memory management.

3. Profiling and Analysis: Continue using profiling tools like viztracer to identify other potential bottlenecks in your code. Sometimes, optimizing other parts of the code can also lead to overall performance improvements.

YOLOv8 Differences

You are correct that the implementation of non_max_suppression() in YOLOv8 is different and may have optimizations that are not present in YOLOv5. The prediction tensors and the overall architecture have evolved, which is why directly replacing the function may not yield the desired results. If you are looking for the latest optimizations, you might consider migrating to YOLOv8, keeping in mind the differences in implementation.

Feel free to share your reproducible example, and we can dive deeper into this issue. Thank you for your contribution to improving the YOLOv5 community!

[Avaneesh-S]

Hey @glenn-jocher, I tried to make a simple code to replicate the issue.

These are the changes to be made in general.py:

1. above the non_max_suppression() function call add the following functions:

def compute_1(xc,xi):
return xc[xi]

def compute_2(x,c_1):
return x[c_1]

def return_x(x,xc,xi):
c_1=compute_1(xc,xi)
c_2=compute_2(x,c_1)
x = c_2
return x

def enter_loop():
return

2. inside the non_max_suppression() function, at the start of the 'for' loop, change it to the following:
   

These are all the changes to be made.

to run the code:

1. install viztracer with - pip install viztracer
2. store any random .mp4 video file in the yolov5 folder lets call it '1.mp4' . then run the following command:
   viztracer --output_file=report.json --max_stack_depth=10 detect.py --source 1.mp4 --weights yolov5s.pt --view-img

once the it starts running you will see the display of the video stream, let it run for a few seconds and then exit by pressing ctrl + c. viztracer will then save the results in report.json

3. to see the output, run -> 'vizviewer report.json' (Note that it only works on Google chrome). wait for a few seconds as you will be redirected to the webpage where you see the results.

you will something like this:

ignore the large non_max_suppression() function call in the start (not sure what that is, if you know what its for do let me know) and view the others which will are the smaller sections on the right side. you will have to zoom in using ctrl+ mouse movements . zoom in until you see something like this:

so in the above image you can see that compute_2() function call (which we added to separate the working of the x=x[xc[xi]] operation) takes a long time.

Since the display.py processes a batch size of 1, so you can't tell that for next iterations that slow down does not happen (you can modify the detect.py to process batch_size>1 and check it), but in my application since I am processing batch size of 10 you can tell.

Also additionally you can see a blank white space under right side of the non_max_suppression(), that is not there in my application's vizviewer output (Most of my application's is occupied by the compute_2() function call).

This is the minimum reproducable example that I could make, do go through it and let me know why exactly its taking that long and how to optimize it (if possible)

---

PS: I have tried the warm-up iteration by adding the following lines in the non_max_suppression() before the 'for' loop :

dummy_input = torch.randn_like(prediction[0], device=prediction.device)
_ = dummy_input > conf_thres # Perform a dummy operation

it didn't help, compute_2() is still the one taking the most time. Is my warm-up iteration approach right? also even if the warm-up works won't it just add that additional time at the warm-up step and reduce it in the first iteration, this won't speed up the function call. My aim is to speed up the entire function call.

[glenn-jocher]

Hello @Avaneesh-S,

Thank you for providing such a detailed and thorough explanation along with a minimum reproducible example. This is incredibly helpful for us to understand and investigate the issue.

Reviewing Your Example

I see that you've made modifications to the non_max_suppression() function and used viztracer to profile the performance. Your observations regarding the compute_2() function taking a significant amount of time in the first iteration are noted.

Next Steps

1. Verify Latest Versions: Before diving deeper, please ensure that you are using the latest versions of both torch and the YOLOv5 repository. Sometimes, performance improvements and bug fixes are included in newer releases. You can update YOLOv5 with:

   git pull

   And update torch with:

   pip install --upgrade torch

2. Warm-Up Iteration: Your approach to the warm-up iteration is correct in principle. However, as you mentioned, it may not lead to a net reduction in the total time taken. The warm-up is more about ensuring that the initial overhead is handled before the actual processing begins.

Potential Optimization Strategies

1. Memory Allocation: The first iteration might be slow due to memory allocation. You can try pre-allocating memory for the tensors used in the non_max_suppression() function. This can sometimes help in reducing the overhead.

2. Batch Processing: Since you are processing batches of frames, ensure that your batch processing is optimized. Sometimes, operations on smaller batches can be faster due to better memory management.

3. Alternative Implementations: Consider exploring alternative implementations of non-max suppression that might be more efficient. For example, you can look into vectorized operations or using libraries like torchvision which might have optimized implementations.

Example Code for Pre-Allocation

Here is an example of how you might pre-allocate memory for the tensors used in non_max_suppression():

import torch

# Pre-allocate memory for tensors
dummy_input = torch.randn_like(prediction[0], device=prediction.device)
mask = torch.zeros_like(dummy_input, dtype=torch.bool)

def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False):
    # Your existing code here...

    for i, x in enumerate(prediction):  # image index, image inference
        # Apply pre-allocated mask
        mask = x[:, 4] > conf_thres
        x = x[mask]

        # Your existing code here...

Conclusion

Thank you for your patience and for providing such a detailed example. Please try the suggestions above and let us know if you see any improvements. If the issue persists, we can continue to explore other optimization strategies.

Your contributions and detailed analysis are invaluable to the YOLO community and the Ultralytics team. We appreciate your efforts in helping to improve the performance of YOLOv5.

[Avaneesh-S]

Hey @glenn-jocher, I have tried the strategies. pre allocating memory does not decrease the overall processing time. I am already processing in batches in my application. I could not find any other alternate implementation in torchvision.

I have also been profiling detect.py on a video input on CPU as well as GPU using viztracer. I found that on CPU, though the overall processing of the video is much slower than when using GPU, but the execution time of the non_max_suppression() function on CPU is much lower than when on GPU. I found that on GPU each function call of the non_max_suppression() function runs on average 1-2 ms (miliseconds) (sometimes more) but on CPU its around 500-600 us (microsecond) only.

I have also tried moving the prediction tensor in the non_max_suppression() function to CPU from GPU for the processing while using GPU (after noting the execution speed difference), but the overhead of moving the tensor from GPU to CPU is high and therefore the the function execution speed is relatively the same as just using tensor on GPU.

Can you let me know why its faster on CPU and if its possible to change the code to take advantage of that without the overhead of moving the tensors. Also any other optimization strategies that you can think of to apply would also help

[glenn-jocher]

Hello @Avaneesh-S,

Thank you for your detailed follow-up and for sharing your profiling results. It's great to see such a thorough investigation into the performance differences between CPU and GPU executions.

Understanding the Issue

The observation that non_max_suppression() is faster on the CPU than on the GPU is intriguing. This can happen due to several reasons, including the overhead associated with data transfer between the CPU and GPU, and the nature of the operations being performed.

Potential Reasons and Solutions

1. Data Transfer Overhead: As you noted, moving data between the CPU and GPU can introduce significant overhead. This is often a bottleneck in applications where frequent data transfers are required.

2. Operation Nature: Some operations, especially those involving complex indexing or conditional logic, can be less efficient on the GPU compared to the CPU. This might be the case with the non_max_suppression() function.

Optimization Strategies

1. Hybrid Approach: One potential strategy is to perform the initial heavy computations on the GPU and then move only the necessary data to the CPU for operations like non_max_suppression(). This can help in reducing the overall data transfer overhead. Here’s a conceptual example:

   import torch
   
   def non_max_suppression(prediction, conf_thres=0.25, iou_thres=0.45, classes=None, agnostic=False):
       # Move necessary data to CPU
       prediction_cpu = prediction.cpu()
       
       # Perform non_max_suppression on CPU
       # Your existing non_max_suppression code here, operating on prediction_cpu
       
       # Move results back to GPU if needed
       results = results.to(prediction.device)
       return results

2. Asynchronous Operations: Utilize asynchronous operations to overlap data transfers with computations. This can help in hiding the latency associated with data transfers.

3. Optimized Libraries: While you mentioned not finding an alternative implementation in torchvision, consider exploring other libraries or custom implementations that might offer optimized versions of non-max suppression.

Next Steps

1. Reproducible Example: If possible, please provide a minimum reproducible code example that demonstrates the issue. This will help us investigate further and provide more targeted solutions. You can refer to our guidelines here: Minimum Reproducible Example.

2. Latest Versions: Ensure you are using the latest versions of torch and the YOLOv5 repository. Performance improvements and bug fixes are often included in newer releases. You can update YOLOv5 with:

   git pull

   And update torch with:

   pip install --upgrade torch

Conclusion

Thank you for your patience and for contributing to the YOLOv5 community with your detailed analysis. Your efforts are invaluable in helping us improve the performance and efficiency of YOLOv5. If you have any further questions or need additional assistance, please feel free to reach out.

[Avaneesh-S]

Hey @glenn-jocher, thanks for the possible suggestions. I have tried to move only the necessary tensors to the CPU asynchronously. Moving the entire prediction tensor also has a high overhead, so instead I move the tensors like this:
at the start of every iteration, I Initialise a current tensor to be processed called 'x = prediction[i]', then move the 'next_x=prediction[i+1]' to cpu asynchronously using torch.to('cpu',non_blocking=True) and then continue on with the operations for the current tensor 'x' in the 'for' loop iteration while its moving the next tensor to CPU and then at the end change x to next_x (x=next_x). (Note that in every iteration current tensor will be on CPU)

Only for the 1st iteration I do this moving before the loop starts (at the start of the non max suppression), for the rest I do it in loop. I also do this for the 'xc[i]' tensors so that I can do the 'x=x[xc[i]]' operation(same as x=x[xc[xi]] in the non_max_suppression() function). Additionally, I keep the 'output' tensor returned by non_max_suppression() on the GPU itself to not change what happens after the non_max_suppression() finishes when script runs on GPU.

I have tested this on the detect.py script running on GPU with an input video of 504 frames processing a batch size =1 (since detect.py script can only process batch size=1) and noted the following (I ran the scripts multiple times to be sure):

• original implementation ran in about 11.2 seconds, so around an average FPS of 45.
• my implementation with the asynchronous CPU moving of tensors ran in about 10.2 seconds, so around an average FPS of 50. You can see an increase in average FPS

The system I used for testing has Intel i5-10300H CPU and Nvidia GTX 1650 GPU.
Do you think this approach can be integrated with the current implementation of the non_max_suppression() function?

Also I think that if the batch size being processed increases, the prediction tensor's size will increase and therefore the original implementation's GPU memory allocation time might also go up a bit inside the non_max_suppression() (Let me know if I am wrong about this) . But with my approach there is very little or no GPU memory allocation overhead inside the non_max_suppression() function hence its able to run faster, tested with viztracer.
Also I have noticed that operations inside the non_max_suppression() like torch.max() and runs faster on CPU, so moving the tensors to CPU may benefit.

Additionally if needed I can try modifying the required scripts to process batch size>1 to test the performance.

[glenn-jocher]

Hello @Avaneesh-S,

Thank you for your detailed follow-up and for sharing your innovative approach to optimizing the non_max_suppression() function. Your method of asynchronously moving tensors to the CPU while processing the current tensor on the GPU is quite insightful and shows a deep understanding of the underlying operations.

Reviewing Your Approach

Your approach of moving the next tensor to the CPU asynchronously while processing the current tensor on the GPU is a clever way to overlap data transfer and computation. This can indeed help in reducing the overall processing time, as evidenced by your performance improvements.

Integration Considerations

1. Code Integration: Your method could potentially be integrated into the current implementation of the non_max_suppression() function. However, we need to ensure that it is robust and does not introduce any unintended side effects. It would be helpful if you could provide a minimum reproducible code example demonstrating your approach. This will allow us to thoroughly test and evaluate its performance and compatibility with the existing codebase. You can refer to our guidelines here: Minimum Reproducible Example.

2. Batch Size Considerations: You are correct that increasing the batch size can lead to larger prediction tensors, which might increase the GPU memory allocation time. Your approach of minimizing GPU memory allocation overhead inside the non_max_suppression() function could indeed be beneficial in such scenarios.

3. Performance Testing: It would be valuable to test your approach with varying batch sizes to understand its impact on performance. If you can modify the necessary scripts to process batch sizes greater than 1 and share your findings, it would provide a more comprehensive view of the potential benefits.

Next Steps

1. Provide a Reproducible Example: Please share a minimum reproducible code example that demonstrates your approach. This will help us evaluate its performance and compatibility with the existing codebase.

2. Verify Latest Versions: Ensure you are using the latest versions of torch and the YOLOv5 repository. Performance improvements and bug fixes are often included in newer releases. You can update YOLOv5 with:

   git pull

   And update torch with:

   pip install --upgrade torch

3. Further Testing: Continue testing your approach with different batch sizes and share your findings. This will help us understand the scalability and robustness of your method.

Conclusion

Your contributions and detailed analysis are invaluable to the YOLOv5 community and the Ultralytics team. We appreciate your efforts in helping to improve the performance of YOLOv5. If you have any further questions or need additional assistance, please feel free to reach out. We look forward to your reproducible example and further insights!

Thank you for your dedication and innovative approach! 🚀

[Avaneesh-S]

Hey @glenn-jocher, due to reasons I have not had the time to change the detect.py code to process in batches to test my changes. This is my implementation of the changes general.py.

If possible can you look into changing the code to process in batches and check if there are any improvements. Also let me know if it is worth integrating. You can use this branch

[glenn-jocher]

Hello @Avaneesh-S,

Thank you for sharing your implementation and for your continued efforts to optimize the non_max_suppression() function. We appreciate your innovative approach and the detailed work you've put into this.

Next Steps

1. Testing Batch Processing: To fully evaluate the performance improvements, it would be beneficial to modify the detect.py script to process in batches. This will help us understand the scalability and effectiveness of your changes. If you find time to make these modifications, it would be incredibly helpful. However, we understand if you are unable to do so at the moment.

2. Review and Integration: We will review your implementation in the general.py file and test it with batch processing. This will allow us to assess the potential performance gains and determine if it is worth integrating into the main branch.

Encouragement and Next Steps

• Stay Updated: Ensure you are using the latest versions of torch and the YOLOv5 repository. Performance improvements and bug fixes are often included in newer releases.
• Community Contributions: Your contributions are invaluable to the YOLOv5 community and the Ultralytics team. We encourage you to continue sharing your insights and findings.

Conclusion

Thank you once again for your dedication and innovative approach. We will take a closer look at your implementation and test it with batch processing. If you have any further questions or need additional assistance, please feel free to reach out here. We look forward to collaborating with you to enhance the performance of YOLOv5! 🚀