Fix PaddleDetection InvalidArgumentError On Mcfairmot Export

Aug 4, 2025 by ADMIN 61 views

Troubleshooting PaddleDetection: Exporting and Using mcfairmot Static Graph Model

Hey guys! Ever run into a tricky error when trying to deploy your PaddleDetection models? Today, we're diving deep into a common issue faced when exporting the mcfairmot model to a static graph and using it with pipeline.py and test_det.py. Let's break it down and find some solutions!

The Problem: InvalidArgumentError with mcfairmot Model

So, the user was trying to use the mcfairmot_dla34_30e_1088x608_visdrone.pdparams model because it’s great at identifying both vehicles and non-vehicles. They exported the model using:

python tools/export_model.py -c configs/mot/mcfairmot/mcfairmot_dla34_30e_1088x608_visdrone.yml -o weights=deploy/model/mcfairmot_dla34_30e_1088x608_visdrone.pdparams

Then, when running pipeline.py with the exported model:

python deploy/pipeline/pipeline.py --config deploy/pipeline/config/infer_cfg_ppvehicle.yml --video_file=demo/road_1.mp4 --run_mode trt_int8 --device=gpu --do_break_in_counting

They encountered this error:

Traceback (most recent call last):
  File "C:\Users\Administrator\anaconda3\envs\paddle\lib\threading.py", line 953, in run
    self._target(*self._args, **self._kwargs)
  File "E:/project/PaddleDetection-release-2.8.1/deploy/pipeline/pipeline.py", line 633, in run
    self.predict_video(thread_idx=thread_idx)
  File "E:/project/PaddleDetection-release-2.8.1/deploy/pipeline/pipeline.py", line 970, in predict_video
    res = self.mot_predictor.predict_image(
  File "E:\project\PaddleDetection-release-2.8.1\deploy\pptracking\python\mot_sde_infer.py", line 540, in predict_image
    result = self.predict()
  File "E:\project\PaddleDetection-release-2.8.1\deploy\pptracking\python\det_infer.py", line 169, in predict
    self.predictor.run()
ValueError: In user code:


    InvalidArgumentError: The dims of input in ArrayToTensor must be equal, but received
      [Hint: Expected (vec_dims.back() == dims) == true, but received (vec_dims.back() == dims):0 != true:1.] (at C:\home\workspace\Paddle\paddle\phi\infermeta\unary.cc:213)
      [operator < pd_kernel.phi_kernel > error]

This InvalidArgumentError indicates a mismatch in the dimensions of the input tensors, a common hiccup when dealing with static graph models. Let's dive into why this happens and how to fix it. This error typically arises from the input tensor's dimensions not aligning with the model's expected input shape after the export process. Understanding the root cause is crucial for troubleshooting. When exporting models to static graphs, it is essential to ensure that the input dimensions are correctly handled during the transformation. The static graph transformation process can sometimes introduce discrepancies, leading to this error when running inference.

To resolve this, we need to explore several potential solutions. First, verify the input shape requirements of the deployed model. PaddlePaddle models often have specific input dimensions they expect, and a mismatch can trigger this error. Second, check the export configuration to ensure that the input shapes are correctly specified during the export process. Misconfigured input shapes during export are a common source of this issue. Third, examine the preprocessing steps applied to the input data. Incorrect preprocessing can alter the input dimensions, leading to the error. Finally, if you're using TensorRT for acceleration, ensure that the TensorRT engine is built with compatible input shapes. TensorRT requires precise input dimensions to optimize the model effectively. By systematically investigating these areas, you can pinpoint the exact cause of the InvalidArgumentError and implement the appropriate fix, ensuring smooth deployment and inference with your mcfairmot model. If the input dimensions are not correctly aligned, this can lead to the error. Pay close attention to the configuration files used for both the export and inference processes, as these often contain crucial details about input shapes and data preprocessing.

Why This Happens: Tensor Dimension Mismatch

The core of the issue lies in the dimensions of the input tensors. When you export a model to a static graph, PaddlePaddle optimizes the model structure for inference. This optimization sometimes requires precise input dimensions. The error message InvalidArgumentError: The dims of input in ArrayToTensor must be equal tells us that the dimensions of the input array provided to the model during inference don't match what the model expects.

Specifically, the message indicates that the expected dimension (vec_dims.back() == dims) is not equal, suggesting a mismatch in the shape of the input tensor. This can occur due to several reasons, such as incorrect resizing, padding, or data format conversion during preprocessing or within the model itself after the export process. To resolve this, it is essential to carefully examine the data preprocessing pipeline and ensure that the input tensor shapes align with the model's requirements. A systematic approach to debugging, including checking input dimensions at various stages of the pipeline, can help pinpoint the exact location where the mismatch occurs. Additionally, verifying the export configuration and the model's expected input shape in the deployment documentation can provide valuable clues. In some cases, custom operators or layers within the model might have specific input shape requirements that need to be addressed during the export and deployment phases. By thoroughly investigating these aspects, you can effectively diagnose and resolve the dimension mismatch, ensuring smooth and accurate inference with your PaddlePaddle model.

Potential Solutions: A Deep Dive

Alright, let's get our hands dirty and explore some ways to tackle this error. Here’s a breakdown of potential solutions, ranging from the simplest checks to more involved debugging steps.

1. Double-Check Your Configuration Files

First things first, let's make sure your configuration files (.yml) are in order. Specifically, the infer_cfg_ppvehicle.yml file used in pipeline.py and the mcfairmot_dla34_30e_1088x608_visdrone.yml file used during export. Verify Input Shapes: Ensure that the input shape defined in these configs matches the actual input shape of your data. Look for parameters like image_shape or input_size and make sure they are consistent. Consistent Preprocessing: Check if the preprocessing steps defined in the configs (e.g., resizing, normalization) are consistent between the training and inference stages. Discrepancies here can lead to dimension mismatches. Data Format: Ensure that the data format (e.g., RGB vs. BGR, channel order) is correctly specified in the configs. Incorrect data format settings can result in unexpected tensor shapes. Configuration files are the backbone of PaddleDetection, so ensure that every parameter is accurately set to match your model's expectations. This meticulous approach can save you from many common errors down the line. When reviewing your configuration files, consider using a validator tool to catch any syntax errors or inconsistencies. This can help you identify issues that might not be immediately obvious. Also, it's a good practice to keep your configuration files organized and well-documented, making it easier to troubleshoot and maintain your deployments. By paying close attention to detail in your configuration, you can ensure that your model operates smoothly and accurately in different environments and scenarios. Accurate configurations are the foundation for reliable model deployment.

2. Examine the Export Process

The export_model.py script is where the magic (or sometimes, the mishaps) happens. Input Shape Specification: When exporting, make sure you're explicitly specifying the input shape if necessary. Some models require this to be explicitly set during export. Dynamic vs. Static Shapes: Understand whether your model supports dynamic input shapes or requires fixed shapes. If it needs fixed shapes, ensure that the export process reflects this. Version Compatibility: Verify that the version of PaddlePaddle used for exporting the model is compatible with the version used for inference. Incompatibilities can lead to unexpected errors. Pay attention to any warnings or error messages generated during the export process, as these can provide valuable clues about potential issues. Review the export script and ensure that all necessary steps are executed correctly. If you're using custom layers or operators, ensure that they are properly handled during the export. Also, consider the optimization levels used during export, as aggressive optimizations can sometimes introduce compatibility issues. Regularly testing your exported models in a controlled environment can help you catch problems early and prevent them from escalating. By thoroughly understanding and validating the export process, you can ensure that your models are properly prepared for deployment and inference.

3. Debugging Input Data

Let's get down to the nitty-gritty of the data itself. Print Input Shape: Add print statements in your pipeline.py script to display the shape of the input tensor right before it's fed to the model. This will help you see exactly what the model is receiving. Visualize Input: If possible, try visualizing the input data (e.g., the image frame) to ensure it looks as expected. Sometimes, issues like incorrect color channels or image corruption can be visually identified. Check Data Types: Verify that the data type of the input tensor (e.g., float32, int8) matches the model's expectation. Inconsistent data types can lead to errors during inference. Examine the preprocessing steps applied to the input data. Ensure that the resizing, normalization, and other transformations are performed correctly. If you're using a custom data loader, thoroughly test it to ensure it's producing the expected output. Consider using a debugger to step through the data processing pipeline and inspect the tensors at each stage. This can help you pinpoint the exact location where the input data is deviating from the expected format. Regularly validating your input data is a crucial step in ensuring the accuracy and reliability of your model deployments. By taking the time to thoroughly inspect and debug your input data, you can prevent many common errors and ensure smooth operation of your models.

4. TensorRT Considerations

If you're using TensorRT for inference acceleration, there are a few extra things to keep in mind. TRT Compatibility: Ensure that the TensorRT version you're using is compatible with both PaddlePaddle and your hardware. Explicit Input Shapes (Again!): TensorRT often requires explicit input shapes to be defined when building the inference engine. Engine Rebuild: If you change the input shape or model structure, you might need to rebuild the TensorRT engine. TensorRT is a powerful tool, but it can also be finicky. Pay close attention to the logs generated during the TensorRT engine building process, as these often contain valuable information about potential issues. If you're encountering errors, try reducing the optimization level or disabling certain TensorRT optimizations to see if that resolves the problem. Consider using the TensorRT inspector to examine the structure of your TensorRT engine and identify any potential bottlenecks or inefficiencies. Regularly profiling your TensorRT deployments can help you ensure that you're getting the best possible performance. By carefully managing your TensorRT settings and configurations, you can harness its power while minimizing the risk of errors and compatibility issues.

5. Environment Mismatch

Sometimes, the gremlins are hiding in your environment. PaddlePaddle Version: Double-check that the PaddlePaddle version used for training, exporting, and inference are consistent. CUDA/CUDNN Versions: Ensure that your CUDA and CUDNN versions are compatible with your PaddlePaddle version. Python Dependencies: Verify that all necessary Python packages are installed and that there are no version conflicts. Environment inconsistencies can be subtle but impactful. Consider using a virtual environment (e.g., conda, venv) to isolate your project dependencies and ensure consistency. Document your environment setup to make it easier to reproduce your results on different machines. If you're using Docker, make sure that your Dockerfile accurately captures your environment requirements. Regularly testing your deployments in different environments can help you catch and resolve environment-related issues early on. By paying close attention to your environment setup, you can minimize the risk of unexpected errors and ensure that your models perform consistently across different platforms.

Specific to the User's Case

The user mentioned that mot_ppyoloe_l_36e_ppvehicle9cls.pdparams and fairmot_dla34_30e_1088x608_bdd100kmot_vehicle.pdparams worked fine. This suggests the issue is specific to the mcfairmot_dla34_30e_1088x608_visdrone.pdparams model or its export configuration. Input Shape Anomaly: It's possible that this specific model has a unique input shape requirement that's not being met during export or inference. Model-Specific Preprocessing: There might be specific preprocessing steps required for this model that are different from the others. Layer Compatibility: Certain layers or operations within this model might not be fully compatible with the static graph export process or TensorRT. Investigate the model's configuration file for any unique requirements or settings. If you have access to the model's training code, review it to understand its input expectations and preprocessing steps. Consider reaching out to the PaddleDetection community or the model's authors for specific guidance. They might be able to provide insights into the model's quirks and potential solutions. By focusing on the unique characteristics of this particular model, you can narrow down the potential causes of the error and find a targeted solution.

Final Thoughts

Debugging these kinds of errors can be a bit of a detective game, but by systematically checking each area, you'll get there! Remember, the key is to break down the problem, verify each component, and don't be afraid to dig into the details. Happy coding, and let me know if you have more questions!

By methodically addressing each of these areas, you'll be well-equipped to tackle this pesky error and get your mcfairmot model up and running smoothly. Remember, persistence and a systematic approach are your best friends when debugging! Also, don't hesitate to tap into the PaddlePaddle community – they are a wealth of knowledge and can provide invaluable assistance.