detect GPU data-stream

[LZLwoaini]

Search before asking

• [X] I have searched the YOLOv5 issues and discussions and found no similar questions.

Question

How to check the data-stream during GPU environment inference, such as which data is parallel and which data is serial. In other words, which part of the data is accelerated by GPU. Thanks！！

Additional

No response

[UltralyticsAssistant]

👋 Hello @LZLwoaini, thank you for your interest in YOLOv5 🚀! It looks like you are asking about data streams and GPU environment inference. An Ultralytics engineer will review your question and assist you soon.

In the meantime, please note the following to assist with any debugging or inquiries:

• If this is a 🐛 Bug Report, please provide a minimum reproducible example to help us understand and debug the issue.
• If this is a custom training ❓ Question, please give as much detail as possible, including dataset image examples, training logs, and the exact steps you’ve followed. Ensure you’re adhering to best practices for training efficiency and performance.

To ensure smooth operation, make sure you’re using Python>=3.8 and have all required dependencies installed, including PyTorch>=1.8. You can install these dependencies via the repository's requirements.txt file.

We support various environments for running YOLOv5, including notebooks, cloud platforms, and Docker. Please ensure your environment is fully set up and updated for optimal GPU utilization.

Let us know if you need further clarification, and thank you for using YOLOv5 🌟!

[pderrenger]

@LZLwoaini to analyze the GPU data stream during inference and determine which operations are parallel or serial, you can use profiling tools like NVIDIA Nsight Systems or PyTorch's autograd profiler. These tools allow you to visualize GPU utilization and identify which parts of the process are GPU-accelerated. For YOLOv5 specifically, ensure you run inference with device='cuda' to leverage GPU acceleration. Let us know if you encounter any issues!

[LZLwoaini]

@LZLwoaini to analyze the GPU data stream during inference and determine which operations are parallel or serial, you can use profiling tools like NVIDIA Nsight Systems or PyTorch's autograd profiler. These tools allow you to visualize GPU utilization and identify which parts of the process are GPU-accelerated. For YOLOv5 specifically, ensure you run inference with device='cuda' to leverage GPU acceleration. Let us know if you encounter any issues!

OK!！Thank you for your answer, I will give it a try.

[LZLwoaini]

@LZLwoaini to analyze the GPU data stream during inference and determine which operations are parallel or serial, you can use profiling tools like NVIDIA Nsight Systems or PyTorch's autograd profiler. These tools allow you to visualize GPU utilization and identify which parts of the process are GPU-accelerated. For YOLOv5 specifically, ensure you run inference with device='cuda' to leverage GPU acceleration. Let us know if you encounter any issues!

Excuse me, I have another question. When I went to print the weight file - "yolov5. pt", I could only see the model structure, and I couldn't see anything else such as the convolutional kernel weights. What should I do if I want to view detailed information. thank you!

[pderrenger]

To view detailed information like the convolutional kernel weights of the YOLOv5 model, you can directly load the PyTorch .pt weight file and inspect its parameters using torch as shown below:

import torch

# Load model weights
weights_path = "yolov5s.pt"  # replace with your weight file
model = torch.load(weights_path, map_location='cpu')  # load weights

# Access model state_dict
state_dict = model['model'].state_dict()  # `model['model']` contains the neural network

# Print convolutional layer weights
for name, param in state_dict.items():
    if 'conv' in name:  # filter for convolutional layers
        print(f"{name}: {param.shape}")
        print(param)  # prints the weights
        break  # remove this to print all layers

This will allow you to inspect the weights layer by layer. Let me know if you need further assistance!

[LZLwoaini]

Excuse me, is there any way to see the transmission and changes of specific data during the inference process? If possible, I would like to print and take a look. Alternatively, how can I view the specific content of the kernel function.Thanks!

[pderrenger]

To monitor data transmission and changes during inference, you can insert print statements or use PyTorch hooks to inspect intermediate outputs. For example:

import torch
from models.common import DetectMultiBackend

model = DetectMultiBackend('yolov5s.pt')  # Load YOLOv5 model

# Register a forward hook to view intermediate outputs
def hook_fn(module, input, output):
    print(f"Layer: {module.__class__.__name__}")
    print(f"Input: {input}")
    print(f"Output: {output}")

for name, module in model.model.named_modules():
    module.register_forward_hook(hook_fn)

# Perform inference
img = torch.randn(1, 3, 640, 640)  # Example input
results = model(img)

To view kernel function details, you would need to explore PyTorch's source or use tools like NVIDIA Nsight to profile GPU operations. Let me know if you need further clarification!

[LZLwoaini]

OK,thank you! I found an issue :where when the number of input images is less than 3 during inference, the GPU does not enable multiple streams. When the number of input images is greater than or equal to 3, the GPU starts to enable multi stream parallel inference, and there is only one parallel kernel function: implicit_convolve_sgemm. I had no prior knowledge of CUDA programming, please. In addition, due to our recent exposure to the field of artificial intelligence, we have been debating a rather foolish question: whether we can change the model structure during inference, whether the changes are effective, and whether we can only retrain.

[pderrenger]

Thank you for sharing your findings! Regarding your observations on multi-stream GPU inference, this behavior is likely influenced by the GPU's internal optimization mechanisms and PyTorch's handling of small batch sizes. For batch sizes less than 3, the GPU may not fully utilize parallel streams, as smaller workloads are often executed serially to reduce overhead. This is expected and not specific to YOLOv5 but rather a property of CUDA and PyTorch.

As for modifying the model structure during inference, changes to the model architecture (e.g., adding/removing layers) generally require retraining the model, as the weights are tied to the original architecture. Without retraining, such modifications may result in errors or ineffective inference. If you need a different architecture, it's best to adjust it during training or fine-tuning.

If you have further questions or need clarification, feel free to ask!

[LZLwoaini]

Thank you for your answer！ The data is images, and the batch-size of 1 is fixed and cannot be modified, meaning that no matter how many images are passed in, only one will be inferred one by one. However, the stream kernel function is enable to parallel when the number of images in the folder is greater than or equal to 3. Is this also the reason you mentioned?

In addition, it can be seen from the inference output data that the convolutional layer and the BN layer are fused, but why is the output data from "conv2d" different from the input data from "silu"?

[pderrenger]

Yes, the behavior you described is likely influenced by the CUDA stream optimization and the GPU's workload scheduling. When the batch size is fixed to 1, inference occurs one image at a time. However, with a larger input queue (e.g., 3 or more images in the folder), parallelism in the stream kernel function can become more efficient, as the GPU has more operations to overlap. This aligns with CUDA's design to optimize throughput by leveraging multiple streams when sufficient workload exists.

Regarding the difference between the conv2d output and the SiLU input, this is expected since the SiLU (activation function) applies a non-linear transformation to the data output from the convolutional layer (conv2d). Even after fusing Conv2D and BatchNorm, the activation function remains distinct and will modify the tensor values after they are passed through the fused layers.

For more about layer fusion, see Ultralytics Docs: Model Fuse. Let me know if you have further questions!

[github-actions[bot]]

👋 Hello there! We wanted to give you a friendly reminder that this issue has not had any recent activity and may be closed soon, but don't worry - you can always reopen it if needed. If you still have any questions or concerns, please feel free to let us know how we can help.

For additional resources and information, please see the links below:

• Docs: https://docs.ultralytics.com
• HUB: https://hub.ultralytics.com
• Community: https://community.ultralytics.com

Feel free to inform us of any other issues you discover or feature requests that come to mind in the future. Pull Requests (PRs) are also always welcomed!

Thank you for your contributions to YOLO 🚀 and Vision AI ⭐