Quantized model INT8 is not able to do inference or convert to any other type of model structure

Sanath1998 commented 2 years ago

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Question

Hi @glenn-jocher

As I have quantized FLOAT32 model to INT8 model, I'am not able to convert the model to any other formats nor able to inference using detect.py and val.py

Error msg:

While doing inference

ckpt = (ckpt.get('ema') or ckpt['model']).to(device).float() # FP32 model AttributeError: 'collections.OrderedDict' object has no attribute 'to'
While converting to any other model type using export.py

[TRT] [E] ModelImporter.cpp:779: ERROR: images:232 In function importInput: [8] Assertion failed: convertDtype(onnxDtype.elem_type(), &trtDtype) && "Failed to convert ONNX date type to TensorRT data type."

@glenn-jocher Can you plz look onto this? Looking forward for your reply

Additional

No response

glenn-jocher commented 2 years ago

👋 Hello! Thanks for asking about YOLOv5 🚀 benchmarks. YOLOv5 inference is officially supported in 11 formats, and all formats are benchmarked for identical accuracy and to compare speed every 24 hours by the YOLOv5 CI.

💡 ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See CPU Benchmarks. 💡 ProTip: Export to TensorRT for up to 5x GPU speedup. See GPU Benchmarks.

Format	`export.py --include`	Model
PyTorch	-	`yolov5s.pt`
TorchScript	`torchscript`	`yolov5s.torchscript`
ONNX	`onnx`	`yolov5s.onnx`
OpenVINO	`openvino`	`yolov5s_openvino_model/`
TensorRT	`engine`	`yolov5s.engine`
CoreML	`coreml`	`yolov5s.mlmodel`
TensorFlow SavedModel	`saved_model`	`yolov5s_saved_model/`
TensorFlow GraphDef	`pb`	`yolov5s.pb`
TensorFlow Lite	`tflite`	`yolov5s.tflite`
TensorFlow Edge TPU	`edgetpu`	`yolov5s_edgetpu.tflite`
TensorFlow.js	`tfjs`	`yolov5s_web_model/`

Benchmarks

Benchmarks below run on a Colab Pro with the YOLOv5 tutorial notebook . To reproduce:

python utils/benchmarks.py --weights yolov5s.pt --imgsz 640 --device 0

Colab Pro V100 GPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=0, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 46.7/166.8 GB disk)

Benchmarks complete (458.07s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623                10.19
1             TorchScript        0.4623                 6.85
2                    ONNX        0.4623                14.63
3                OpenVINO           NaN                  NaN
4                TensorRT        0.4617                 1.89
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623                21.28
7     TensorFlow GraphDef        0.4623                21.22
8         TensorFlow Lite           NaN                  NaN
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Colab Pro CPU

benchmarks: weights=/content/yolov5/yolov5s.pt, imgsz=640, batch_size=1, data=/content/yolov5/data/coco128.yaml, device=cpu, half=False, test=False
Checking setup...
YOLOv5 🚀 v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete ✅ (8 CPUs, 51.0 GB RAM, 41.5/166.8 GB disk)

Benchmarks complete (241.20s)
                   Format  mAP@0.5:0.95  Inference time (ms)
0                 PyTorch        0.4623               127.61
1             TorchScript        0.4623               131.23
2                    ONNX        0.4623                69.34
3                OpenVINO        0.4623                66.52
4                TensorRT           NaN                  NaN
5                  CoreML           NaN                  NaN
6   TensorFlow SavedModel        0.4623               123.79
7     TensorFlow GraphDef        0.4623               121.57
8         TensorFlow Lite        0.4623               316.61
9     TensorFlow Edge TPU           NaN                  NaN
10          TensorFlow.js           NaN                  NaN

Good luck 🍀 and let us know if you have any other questions!

Sanath1998 commented 2 years ago

@glenn-jocher , my intent was to take a call on know-hows of converting model to quantized INT8 precison and doing inference on the INT8 precision mode? Could you please explain me on this point

glenn-jocher commented 2 years ago

@Sanath1998 depends on the export format. Some are already doing this, i.e. python export.py --tflite --int8

Sanath1998 commented 2 years ago

@glenn-jocher Can't we do quantization for other models which is exported into onnx, tensorrt etc?

glenn-jocher commented 2 years ago

Many formats support FP16 with the --half flag

Sanath1998 commented 2 years ago

Many formats support FP16 with the --half flag

@glenn-jocher actually the models before using --half flag and after using --half flag, the size of the model is same for both. Actually once we reduce to fp16, the model size should decrease ryt? Can u plz explain me the context on this

glenn-jocher commented 2 years ago

👋 hi, thanks for letting us know about this possible problem with YOLOv5 🚀.

Not all formats support --half and --int8. See export.py code for details.

We've created a few short guidelines below to help users provide what we need in order to start investigating a possible problem.

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✅ Current – Verify that your code is up-to-date with GitHub master, and if necessary git pull or git clone a new copy to ensure your problem has not already been solved in master.
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If you believe your problem meets all the above criteria, please close this issue and raise a new one using the 🐛 Bug Report template with a minimum reproducible example to help us better understand and diagnose your problem.

Thank you! 😃

github-actions[bot] commented 1 year ago

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