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ardianumam / Tensorflow-TensorRT

This repository is for my YT video series about optimizing a Tensorflow deep learning model using TensorRT. We demonstrate optimizing LeNet-like model and YOLOv3 model, and get 3.7x and 1.5x faster for the former and the latter, respectively, compared to the original models.
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How to implement TensorRT on custom number of classes #19

Closed Mahaaveer closed 5 years ago

Mahaaveer commented 5 years ago

Hi, I have a weights file trained on darknet for two classes. I used your repo: https://github.com/ardianumam/tensorflow-yolov3 to convert my weights file to frozen graph. Since num_classes is read from coco.names, i changed the coco.names to the classes i have. I successfully generated frozen model and copied it over to your TensorRT repo to generate tensorrt model. It created the TensorRT model successfully. When i run the inference block of the code, it returns the following error:

2019-07-15 13:15:25.604401: I tensorflow/core/platform/cpu_feature_guard.cc:141] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX2 FMA 2019-07-15 13:15:25.719409: I tensorflow/stream_executor/cuda/cuda_gpu_executor.cc:964] successful NUMA node read from SysFS had negative value (-1), but there must be at least one NUMA node, so returning NUMA node zero 2019-07-15 13:15:25.719738: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1411] Found device 0 with properties: name: GeForce GTX 970 major: 5 minor: 2 memoryClockRate(GHz): 1.2405 pciBusID: 0000:02:00.0 totalMemory: 3.94GiB freeMemory: 3.70GiB 2019-07-15 13:15:25.719751: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1490] Adding visible gpu devices: 0 2019-07-15 13:15:26.679452: I tensorflow/core/common_runtime/gpu/gpu_device.cc:971] Device interconnect StreamExecutor with strength 1 edge matrix: 2019-07-15 13:15:26.679482: I tensorflow/core/common_runtime/gpu/gpu_device.cc:977] 0 2019-07-15 13:15:26.679488: I tensorflow/core/common_runtime/gpu/gpu_device.cc:990] 0: N 2019-07-15 13:15:26.679607: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1103] Created TensorFlow device (/job:localhost/replica:0/task:0/device:GPU:0 with 2017 MB memory) -> physical GPU (device: 0, name: GeForce GTX 970, pci bus id: 0000:02:00.0, compute capability: 5.2) Segmentation fault (core dumped)

Since my number of classes are different from coco's 80, should i be following any additional steps ?
Please excuse my limited knowledge on tensorflow as i am not from computer science background.

Thanks.

Mahaaveer commented 5 years ago

I am closing this. As it turns out, it was due to cudnn version mismatch I solved this by upgrading cudnn 7.0.X to 7.5.X

Thanks.