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ultralytics / yolov5

YOLOv5 πŸš€ in PyTorch > ONNX > CoreML > TFLite
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How can I convert .pt weight to tflite? #4586

Closed jaskiratsingh2000 closed 3 years ago

jaskiratsingh2000 commented 3 years ago

Hi @glenn-jocher I want to convert PyTorch weights to tflite how can I do that?

glenn-jocher commented 3 years ago

@jaskiratsingh2000 see tf.py: https://github.com/ultralytics/yolov5/blob/bbfafeabdbf7785f8da5e4f9880df27869a71218/models/tf.py#L1-L12

jaskiratsingh2000 commented 3 years ago

@glenn-jocher Hey, Thanks for referring me to this issue. I appreciate that.

Can you also let me know that instead of "yolov5s.pt" weights can I add custom trained weights file as well like "best.pt" or "last.pt"

@glenn-jocher another quick question that will this get exported or what? Where can I find after that?

glenn-jocher commented 3 years ago

@jaskiratsingh2000 you can export any YOLOv5 model, that's the main purpose of the function. It wouldn't be much use if it only exported official models.

Exported models are placed in same parent directory as source model.

jaskiratsingh2000 commented 3 years ago

But I couldn't find a way to export

On Sun, 29 Aug 2021, 5:40 pm Glenn Jocher, @.***> wrote:

@jaskiratsingh2000 https://github.com/jaskiratsingh2000 you can export any YOLOv5 model, that's the main purpose of the function. It wouldn't be much use if it only exported official models.

Exported models are placed in same parent directory as source model.

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JNaranjo-Alcazar commented 3 years ago

Following the usage indications:

python models/tf.py --weights runs/train/exp6/weights/best.pt --cfg yolov5s.yaml

I got: 

Starting TensorFlow GraphDef export with TensorFlow 2.6.0...
TensorFlow GraphDef export failure: name 'keras_model' is not defined
Traceback (most recent call last):
  File "models/tf.py", line 491, in <module>
    tf.TensorSpec(keras_model.inputs[0].shape, keras_model.inputs[0].dtype))
NameError: name 'keras_model' is not defined

Starting TFLite export with TensorFlow 2.6.0...

TFLite export failure: name 'keras_model' is not defined
Traceback (most recent call last):
  File "models/tf.py", line 521, in <module>
    converter = tf.lite.TFLiteConverter.from_keras_model(keras_model)
NameError: name 'keras_model' is not defined

However, when running the same script as:

python models/tf.py --weights runs/train/exp6/weights/best.pt --cfg models/yolov5s.yaml

the conversion is run correctly. This may be a typo in the documentation or an code issue...

Hope this feedback helps!

Thanks for the amazing work!

glenn-jocher commented 3 years ago

@JNaranjo-Alcazar thanks for the bug report! I'm not able to reproduce this, when I run the usage example in Colab everything works well:

# Setup
!git clone https://github.com/ultralytics/yolov5  # clone repo
%cd yolov5
%pip install -qr requirements.txt  # install dependencies

import torch
from IPython.display import Image, clear_output  # to display images

clear_output()
print(f"Setup complete. Using torch {torch.__version__} ({torch.cuda.get_device_properties(0).name if torch.cuda.is_available() else 'CPU'})")

# Reproduce
!python models/tf.py --weights yolov5s.pt --cfg yolov5s.yaml
JNaranjo-Alcazar commented 3 years ago

I had this bug using a Docker container (not the one available in the repository) with python 3.7 and installing all the requirements and tensorflow.

I think I should use Colab then.

Thanks for the quick reply

Ronald-Kray commented 3 years ago

@glenn-jocher After running tf.py, I can't find the result file. Where can I find tflite file? image

glenn-jocher commented 3 years ago

@Ronald-Kray TF exports are handled by export.py now:

python export.py --weights yolov5s.pt --include tflite
github-actions[bot] commented 3 years ago

πŸ‘‹ Hello, this issue has been automatically marked as stale because it has not had recent activity. Please note it will be closed if no further activity occurs.

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kashishgoyal31 commented 2 years ago

PYTHONPATH=. python3 models/tf.py --weights weights/yolov5s.pt --cfg models/yolov5s.yaml --img 320 --tfl-int8 --source /data/dataset/coco/coco2017/train2017 --ncalib 100

I am trying to convert my trained model to tflite. Please guide about weights. Will I be using my model trained best.pt ?? and what is to be added in source?

kashishgoyal31 commented 2 years ago

Also I am getting illegal instruction as the error

glenn-jocher commented 2 years ago

@kashishgoyal31

python export.py --weights yolov5s.pt --include tflite
kashishgoyal31 commented 2 years ago

Do we need to give weights of our trained custom model best.pt and what should be the --source? Should it be training dataset of images?? I tried but I am getting error permission denied!!

Please advise.

Regards Kashish Goyal

On Wed, 17 Nov, 2021, 6:33 pm Glenn Jocher, @.***> wrote:

@kashishgoyal31 https://github.com/kashishgoyal31

python export.py --weights yolov5s.pt --include tflite

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glenn-jocher commented 2 years ago

@kashishgoyal31 --source can be anything you want. See detect.py for Usage examples instead of asking: https://github.com/ultralytics/yolov5/blob/562191f5756273aca54225903f5933f7683daade/detect.py#L5-L12

kashishgoyal31 commented 2 years ago

I tried but I am getting error permission denied.

On Wed, 17 Nov, 2021, 9:10 pm Glenn Jocher, @.***> wrote:

@kashishgoyal31 https://github.com/kashishgoyal31 --source can be anything you want. See detect.py for Usage examples instead of asking:

https://github.com/ultralytics/yolov5/blob/562191f5756273aca54225903f5933f7683daade/detect.py#L5-L12

β€” You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/ultralytics/yolov5/issues/4586#issuecomment-971701148, or unsubscribe https://github.com/notifications/unsubscribe-auth/AVHEQRO5E52JNWU4IDJEILLUMPEFFANCNFSM5DADDT4Q . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

glenn-jocher commented 2 years ago

@kashishgoyal31 πŸ‘‹ hi, thanks for letting us know about this possible problem with YOLOv5 πŸš€. We've created a few short guidelines below to help users provide what we need in order to get started investigating a possible problem.

How to create a Minimal, Reproducible Example

When asking a question, people will be better able to provide help if you provide code that they can easily understand and use to reproduce the problem. This is referred to by community members as creating a minimum reproducible example. Your code that reproduces the problem should be:

For Ultralytics to provide assistance your code should also be:

If you believe your problem meets all the above criteria, please close this issue and raise a new one using the πŸ› Bug Report template with a minimum reproducible example to help us better understand and diagnose your problem.

Thank you! πŸ˜ƒ

kashishgoyal31 commented 2 years ago

I have converted the weights from best.pt to tflite using below command !python3 export.py --weights /content/best.pt --img 320 --include tflite and then tried detect.py using command !python3 detect.py --weights /content/best-fp16.tflite --img 320 --source /content/freshapple546.jpeg

The output image does not consider my class details in output and gives class 0 instead of my custom class name

glenn-jocher commented 2 years ago

@kashishgoyal31 class names are not in tflite files. You can manually add them here: https://github.com/ultralytics/yolov5/blob/7a39803476f8ae55fb25ed93a400a3bba998d5e7/detect.py#L80

kashishgoyal31 commented 2 years ago

The weights work fine for me with best.pt and when I use detect.py I get the required results. But after converting weights there is problem in class information and in Android app does not work fine. Please tell me the correct way to get through.

On Tue, 23 Nov, 2021, 7:22 pm Glenn Jocher, @.***> wrote:

@kashishgoyal31 https://github.com/kashishgoyal31 class names are not in tflite files. You can manually add them here:

https://github.com/ultralytics/yolov5/blob/7a39803476f8ae55fb25ed93a400a3bba998d5e7/detect.py#L80

β€” You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/ultralytics/yolov5/issues/4586#issuecomment-976567195, or unsubscribe https://github.com/notifications/unsubscribe-auth/AVHEQROJL6H2IS4G3XXOJUTUNOMDHANCNFSM5DADDT4Q . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

kashishgoyal31 commented 2 years ago

When I am running detect.py it's predicting correct but it is not taking class labels instead taking default values class 0, 1, 2.

On Wed, 24 Nov, 2021, 11:11 am KASHISH GOYAL, @.***> wrote:

The weights work fine for me with best.pt and when I use detect.py I get the required results. But after converting weights there is problem in class information and in Android app does not work fine. Please tell me the correct way to get through.

On Tue, 23 Nov, 2021, 7:22 pm Glenn Jocher, @.***> wrote:

@kashishgoyal31 https://github.com/kashishgoyal31 class names are not in tflite files. You can manually add them here:

https://github.com/ultralytics/yolov5/blob/7a39803476f8ae55fb25ed93a400a3bba998d5e7/detect.py#L80

β€” You are receiving this because you were mentioned. Reply to this email directly, view it on GitHub https://github.com/ultralytics/yolov5/issues/4586#issuecomment-976567195, or unsubscribe https://github.com/notifications/unsubscribe-auth/AVHEQROJL6H2IS4G3XXOJUTUNOMDHANCNFSM5DADDT4Q . Triage notifications on the go with GitHub Mobile for iOS https://apps.apple.com/app/apple-store/id1477376905?ct=notification-email&mt=8&pt=524675 or Android https://play.google.com/store/apps/details?id=com.github.android&referrer=utm_campaign%3Dnotification-email%26utm_medium%3Demail%26utm_source%3Dgithub.

endeavorhh commented 2 years ago

python3 export.py --weights /content/best.pt --img 320 --include tflite but fire-fp16.tflite not my trained class. i trained fire,the detect result is person/bicycle like yolov5s.pt

glenn-jocher commented 2 years ago

TFLite models don't have class names attached. You can pass a --data yaml during detect if you'd like to use alternative annotations names:

python detect.py --data custom_data.yaml
ramchandra-bioenable commented 2 years ago

I want to convert .pt file to .tflite. Is it possible to set input image size to .tflite model?

glenn-jocher commented 2 years ago

@ramchandra-bioenable TFLite image sizes are fixed, you define them at export time:

pythone export.py --include tflite --imgsz 640 640
c1p31068 commented 2 years ago

!python models/tf.py --weights '/content/drive/MyDrive/best.pt'

Classes are not reflected in best-fp16.tflite. Please let me know how to reflect the classes in the .pt file. I'm running on Colab.

glenn-jocher commented 2 years ago

@c1p31068 tflite models don't have attached class names metadata, you can find the names manually in your data.yaml

c1p31068 commented 2 years ago

Thanks for the reply.γ€€What is the best way to manually name in data.yaml? Can they be done with googl colab? Furthermore, I am a beginner, is it possible to do this task?γ€€The sentence is wrong because I'm using a translation.

c1p31068 commented 2 years ago

Hi, can .tflite be used with yolov5?

glenn-jocher commented 2 years ago

@c1p31068 to export from PyTorch to TFLite

python export.py --weights yolov5s.pt --include tflite
c1p31068 commented 2 years ago

Thanks for the reply. I was able to export! However, the yolov5 labels are not reflected. I have yolov5 running and testing and in doing so it becomes a person. I can't understand some of the previous questions.

glenn-jocher commented 2 years ago

@c1p31068 if you're using YOLOv5 for inference you can pass your --data to specify your names, i.e.

python detect.py --weights model.tflite --data your_data.yaml
c1p31068 commented 2 years ago

Thank you! We've solved the problem! I am so glad to have found you. Thank you so much!

c1p31068 commented 2 years ago

Hi, is it possible to specify a version of tflite to convert?

glenn-jocher commented 2 years ago

@c1p31068 what do you mean a version of tflite?

Whatever version of tensorflow that is installed is used during export.

c1p31068 commented 2 years ago

Thanks for the reply. I got an error about version mismatch when I used the training data in android studio, because I thought the version of the file I converted from yolov5 to tensorflow lite did not match.

HripsimeS commented 1 year ago

@jaskiratsingh2000 Hello. Did you try to use converted/exported to tflite model weight deploy in Android Studio to do Object detection with mobile app? If yes, did you have any issues with converted/exported to tflite model ?

glenn-jocher commented 1 year ago

@HripsimeS πŸ‘‹ Hello! Thanks for asking about Export Formats. YOLOv5 πŸš€ offers export to almost all of the common export formats. See our TFLite, ONNX, CoreML, TensorRT Export Tutorial for full details.

Formats

YOLOv5 inference is officially supported in 11 formats:

πŸ’‘ ProTip: Export to ONNX or OpenVINO for up to 3x CPU speedup. See CPU Benchmarks. πŸ’‘ 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/
PaddlePaddle paddle yolov5s_paddle_model/

Benchmarks

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

python 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 πŸš€ v6.1-135-g7926afc torch 1.10.0+cu111 CUDA:0 (Tesla V100-SXM2-16GB, 16160MiB)
Setup complete βœ… (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 πŸš€ v6.1-135-g7926afc torch 1.10.0+cu111 CPU
Setup complete βœ… (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

Export a Trained YOLOv5 Model

This command exports a pretrained YOLOv5s model to TorchScript and ONNX formats. yolov5s.pt is the 'small' model, the second smallest model available. Other options are yolov5n.pt, yolov5m.pt, yolov5l.pt and yolov5x.pt, along with their P6 counterparts i.e. yolov5s6.pt or you own custom training checkpoint i.e. runs/exp/weights/best.pt. For details on all available models please see our README table.

python export.py --weights yolov5s.pt --include torchscript onnx

πŸ’‘ ProTip: Add --half to export models at FP16 half precision for smaller file sizes

Output:

export: data=data/coco128.yaml, weights=['yolov5s.pt'], imgsz=[640, 640], batch_size=1, device=cpu, half=False, inplace=False, train=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=12, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['torchscript', 'onnx']
YOLOv5 πŸš€ v6.2-104-ge3e5122 Python-3.7.13 torch-1.12.1+cu113 CPU

Downloading https://github.com/ultralytics/yolov5/releases/download/v6.2/yolov5s.pt to yolov5s.pt...
100% 14.1M/14.1M [00:00<00:00, 274MB/s]

Fusing layers... 
YOLOv5s summary: 213 layers, 7225885 parameters, 0 gradients

PyTorch: starting from yolov5s.pt with output shape (1, 25200, 85) (14.1 MB)

TorchScript: starting export with torch 1.12.1+cu113...
TorchScript: export success βœ… 1.7s, saved as yolov5s.torchscript (28.1 MB)

ONNX: starting export with onnx 1.12.0...
ONNX: export success βœ… 2.3s, saved as yolov5s.onnx (28.0 MB)

Export complete (5.5s)
Results saved to /content/yolov5
Detect:          python detect.py --weights yolov5s.onnx 
Validate:        python val.py --weights yolov5s.onnx 
PyTorch Hub:     model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.onnx')
Visualize:       https://netron.app/

The 3 exported models will be saved alongside the original PyTorch model:

Netron Viewer is recommended for visualizing exported models:

Exported Model Usage Examples

detect.py runs inference on exported models:

python detect.py --weights yolov5s.pt                 # PyTorch
                           yolov5s.torchscript        # TorchScript
                           yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                           yolov5s_openvino_model     # OpenVINO
                           yolov5s.engine             # TensorRT
                           yolov5s.mlmodel            # CoreML (macOS only)
                           yolov5s_saved_model        # TensorFlow SavedModel
                           yolov5s.pb                 # TensorFlow GraphDef
                           yolov5s.tflite             # TensorFlow Lite
                           yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                           yolov5s_paddle_model       # PaddlePaddle

val.py runs validation on exported models:

python val.py --weights yolov5s.pt                 # PyTorch
                        yolov5s.torchscript        # TorchScript
                        yolov5s.onnx               # ONNX Runtime or OpenCV DNN with --dnn
                        yolov5s_openvino_model     # OpenVINO
                        yolov5s.engine             # TensorRT
                        yolov5s.mlmodel            # CoreML (macOS Only)
                        yolov5s_saved_model        # TensorFlow SavedModel
                        yolov5s.pb                 # TensorFlow GraphDef
                        yolov5s.tflite             # TensorFlow Lite
                        yolov5s_edgetpu.tflite     # TensorFlow Edge TPU
                        yolov5s_paddle_model       # PaddlePaddle

Use PyTorch Hub with exported YOLOv5 models:

import torch

# Model
model = torch.hub.load('ultralytics/yolov5', 'custom', 'yolov5s.pt')
                                                       'yolov5s.torchscript ')       # TorchScript
                                                       'yolov5s.onnx')               # ONNX Runtime
                                                       'yolov5s_openvino_model')     # OpenVINO
                                                       'yolov5s.engine')             # TensorRT
                                                       'yolov5s.mlmodel')            # CoreML (macOS Only)
                                                       'yolov5s_saved_model')        # TensorFlow SavedModel
                                                       'yolov5s.pb')                 # TensorFlow GraphDef
                                                       'yolov5s.tflite')             # TensorFlow Lite
                                                       'yolov5s_edgetpu.tflite')     # TensorFlow Edge TPU
                                                       'yolov5s_paddle_model')       # PaddlePaddle

# Images
img = 'https://ultralytics.com/images/zidane.jpg'  # or file, Path, PIL, OpenCV, numpy, list

# Inference
results = model(img)

# Results
results.print()  # or .show(), .save(), .crop(), .pandas(), etc.

OpenCV DNN inference

OpenCV inference with ONNX models:

python export.py --weights yolov5s.pt --include onnx

python detect.py --weights yolov5s.onnx --dnn  # detect
python val.py --weights yolov5s.onnx --dnn  # validate

C++ Inference

YOLOv5 OpenCV DNN C++ inference on exported ONNX model examples:

YOLOv5 OpenVINO C++ inference examples:

Good luck πŸ€ and let us know if you have any other questions!

paramkaur10 commented 1 year ago

@glenn-jocher I am trying to convert best.pt to tflite but keep encountering the error.

export: data=data/coco128.yaml, weights=['runs/train/results_128/weights/best.pt'], imgsz=[256], batch_size=1, device=cpu, half=False, inplace=False, keras=False, optimize=False, int8=False, dynamic=False, simplify=False, opset=17, verbose=False, workspace=4, nms=False, agnostic_nms=False, topk_per_class=100, topk_all=100, iou_thres=0.45, conf_thres=0.25, include=['tflite']
YOLOv5 πŸš€ v7.0-66-g9650f16 Python-3.9.13 torch-1.12.1 CPU

Fusing layers... 
Model summary: 267 layers, 46113663 parameters, 0 gradients, 107.7 GFLOPs

PyTorch: starting from runs/train/results_128/weights/best.pt with output shape (1, 4032, 7) (88.4 MB)
TensorFlow SavedModel: export failure ❌ 1.3s: module 'tensorflow.python.util.dispatch' has no attribute 'add_fallback_dispatch_list'

TensorFlow Lite: starting export with tensorflow 2.6.0...
TensorFlow Lite: export failure ❌ 0.0s: 'NoneType' object has no attribute 'call'
Traceback (most recent call last):
  File "/home/ec2-user/SageMaker/yolov5/export.py", line 653, in <module>
    main(opt)
  File "/home/ec2-user/SageMaker/yolov5/export.py", line 648, in main
    run(**vars(opt))
  File "/home/ec2-user/anaconda3/envs/pytorch_p39/lib/python3.9/site-packages/torch/autograd/grad_mode.py", line 27, in decorate_context
    return func(*args, **kwargs)
  File "/home/ec2-user/SageMaker/yolov5/export.py", line 589, in run
    add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs))
AttributeError: 'NoneType' object has no attribute 'outputs'
YasmineeBa commented 1 year ago

hi, i want to convert the weights file 'best.pt' into tflite such as i modified the architecture of yolov5 model ( i replace some c3 function by a transformer ), but i got an error in export.py that c3tr is not defined. ''' TensorFlow SavedModel: export failure 4.4s: name 'C3STR' is not defined

TensorFlow Lite: starting export with tensorflow 2.12.0... TensorFlow Lite: export failure 0.0s: 'NoneType' object has no attribute 'call'

ext return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\jodo\Documents\yolov5\yolov5\export.py", line 610, in run add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) ^^^^^^^^^^^^^^^ AttributeError: 'NoneType' object has no attribute 'outputs' '''

YasmineeBa commented 1 year ago

hi, i want to convert the weights file 'best.pt' into tflite such as i modified the architecture of yolov5 model ( i replace the final c3 function by a transformer ), but i got an error in export.py that c3tr is not defined. please help me iw there is a solution or tell me if it is impossible to convert it into tflite when the architecture is modified ''' TensorFlow SavedModel: export failure 4.4s: name 'C3STR' is not defined

TensorFlow Lite: starting export with tensorflow 2.12.0... TensorFlow Lite: export failure 0.0s: 'NoneType' object has no attribute 'call'

ext return func(*args, **kwargs) ^^^^^^^^^^^^^^^^^^^^^ File "C:\Users\jodo\Documents\yolov5\yolov5\export.py", line 610, in run add_tflite_metadata(f[8] or f[7], metadata, num_outputs=len(s_model.outputs)) ^^^^^^^^^^^^^^^ AttributeError: 'NoneType' object has no attribute 'outputs' '''

glenn-jocher commented 1 year ago

@YasmineeBa hello, it seems like the error you're encountering is related to the modified architecture that you're using. The error message states that 'C3STR' is not defined, which suggests that the modified architecture is expecting a layer or function that is not defined.

Regarding your question about whether it is possible to convert a modified YOLOv5 architecture to TFLite, the answer is that it should be possible as long as the modified architecture can be defined using the available TensorFlow operations. However, it might require some additional customization and configuration to ensure that the conversion process is successful.

One suggestion is to try and define the 'C3STR' layer or function in your modified architecture and see if that resolves the issue. Additionally, you could try modifying the TFLite export script to account for the changes in your architecture. If the issue persists, it might be helpful to share more details about the modified architecture and the changes that were made in order to provide more specific guidance.

I hope this helps!

YasmineeBa commented 1 year ago

thank you for response, i really defined all function related with C3STR function in the common.py file, and declared in yolo.py using pytorch, and i modified the yolov5s.yaml with the new architecture but when i run the script to export weights into tflite, the code show me the default architecture of yolov5s ( summary architecture). so did you mean, that i need to modified the implementation of pytorch functions to tensorflow in the tf.py file?

glenn-jocher commented 1 year ago

@YasmineeBa hello! It's great to hear that you've defined all the necessary functions related to 'C3STR' in the common.py file and declared them in yolo.py using PyTorch. If you're modifying the yolov5s.yaml file to incorporate these changes, you should see the modified architecture when running the training script with PyTorch.

However, if you're encountering issues when exporting the model to TFLite, it's possible that some changes may need to be made in the tf.py file to ensure that the TensorFlow implementation is consistent with the changes made in the PyTorch implementation. It may be helpful to review the documentation and examples related to exporting PyTorch models to TensorFlow and TFLite to ensure that the conversion process is executed correctly.

Please feel free to provide more details about the specific issues you're encountering with the TFLite export process, and we can work together to find a solution.

YasmineeBa commented 1 year ago

Hi, Thank you for your response. I solve the problem of undefined function by defining all functions dependent on c3str on tensorflow in the tf.py file. but i got a new error. Can you please help me with this error?

Le dim. 30 avr. 2023 Γ  11:50, Glenn Jocher @.***> a Γ©crit :

@YasmineeBa https://github.com/YasmineeBa hello! It's great to hear that you've defined all the necessary functions related to 'C3STR' in the common.py file and declared them in yolo.py using PyTorch. If you're modifying the yolov5s.yaml file to incorporate these changes, you should see the modified architecture when running the training script with PyTorch.

However, if you're encountering issues when exporting the model to TFLite, it's possible that some changes may need to be made in the tf.py file to ensure that the TensorFlow implementation is consistent with the changes made in the PyTorch implementation. It may be helpful to review the documentation and examples related to exporting PyTorch models to TensorFlow and TFLite to ensure that the conversion process is executed correctly.

Please feel free to provide more details about the specific issues you're encountering with the TFLite export process, and we can work together to find a solution.

β€” Reply to this email directly, view it on GitHub https://github.com/ultralytics/yolov5/issues/4586#issuecomment-1528994233, or unsubscribe https://github.com/notifications/unsubscribe-auth/APK2TZMWHK3ALDZZPAKKDGTXDY7Y5ANCNFSM5DADDT4Q . You are receiving this because you were mentioned.Message ID: @.***>

glenn-jocher commented 1 year ago

Hi @YasmineeBa,

It's good to hear that you were able to define all the necessary functions related to 'C3STR' in the common.py file and resolved the issue related to undefined functions. However, it seems like you're encountering a new error during the TFLite export process.

In order to provide more specific guidance, it would be helpful to review the error message that you're encountering. Based on the error message, we can suggest possible solutions to resolve the issue. Please feel free to share more details about the error message that you're encountering, and we can work together to find a solution.

In the meantime, it may be helpful to review the documentation and examples related to exporting PyTorch models to TensorFlow and TFLite to ensure that the conversion process is executed correctly.

Let us know if you have further questions or concerns.

Best regards.

YasmineeBa commented 1 year ago

Thank you for your response. I was able to solve this problem, it was a miss declaration of the call function in my function defined, so when the program is compiling, it dpesn't compile the part of c3str function, i noticed this issue so i review all my functions declarations and finally I could solve the problem. thank you

Le lun. 1 mai 2023 Γ  14:40, Glenn Jocher @.***> a Γ©crit :

Hi @YasmineeBa https://github.com/YasmineeBa,

It's good to hear that you were able to define all the necessary functions related to 'C3STR' in the common.py file and resolved the issue related to undefined functions. However, it seems like you're encountering a new error during the TFLite export process.

In order to provide more specific guidance, it would be helpful to review the error message that you're encountering. Based on the error message, we can suggest possible solutions to resolve the issue. Please feel free to share more details about the error message that you're encountering, and we can work together to find a solution.

In the meantime, it may be helpful to review the documentation and examples related to exporting PyTorch models to TensorFlow and TFLite to ensure that the conversion process is executed correctly.

Let us know if you have further questions or concerns.

Best regards.

β€” Reply to this email directly, view it on GitHub https://github.com/ultralytics/yolov5/issues/4586#issuecomment-1529719892, or unsubscribe https://github.com/notifications/unsubscribe-auth/APK2TZPBGR25JHB4KBPJHL3XD64MJANCNFSM5DADDT4Q . You are receiving this because you were mentioned.Message ID: @.***>

glenn-jocher commented 1 year ago

Hi @YasmineeBa,

Thanks for updating us and letting us know that the issue has been resolved! It's great to hear that you were able to identify and fix the problem by reviewing your function declarations.

If you encounter any further issues or have any additional questions, please don't hesitate to reach out for assistance. We're always here to help.

Best regards.

VishalShinde16 commented 1 year ago

hey i am converting .pb to tflite of yolov7 but when i add metadata using tensorflow object detection metadata writer , in output_tensor_metadata its showing only location instead of category,score,location

!python export.py --weights /content/drive/MyDrive/ObjectDetection/yolov7/runs/train/yolov7-custom/weights/best.pt --grid --end2end --simplify \ --topk-all 100 --iou-thres 0.65 --conf-thres 0.35 --img-size 640 640 --max-wh 640

!onnx-tf convert -i /content/drive/MyDrive/ObjectDetection/yolov7/runs/train/yolov7-custom/weights/best.onnx -o /content/drive/MyDrive/ObjectDetection/yolov7/tfmodel

import tensorflow as tf converter = tf.lite.TFLiteConverter.from_saved_model('/content/drive/MyDrive/ObjectDetection/yolov7/tfmodel') tflite_model = converter.convert()

with open('/content/drive/MyDrive/ObjectDetection/yolov7/yolov7_model.tflite', 'wb') as f: f.write(tflite_model)

adding metadata

ObjectDetectorWriter = object_detector.MetadataWriter _MODEL_PATH = "/content/drive/MyDrive/ObjectDetection/yolov7/yolov7_model.tflite"

_LABEL_FILE = "/content/drive/MyDrive/ObjectDetection/yolov7/labels.txt" _SAVE_TO_PATH = "/content/drive/MyDrive/ObjectDetection/yolov7/yolov7_model_metadata.tflite"

_INPUT_NORM_MEAN = 127.5 _INPUT_NORM_STD = 127.5

Create the metadata writer.

writer = ObjectDetectorWriter.create_for_inference( writer_utils.load_file(_MODEL_PATH), [_INPUT_NORM_MEAN], [_INPUT_NORM_STD], [_LABEL_FILE])

Verify the metadata generated by metadata writer.

print(writer.get_metadata_json())

Populate the metadata into the model.

writer_utils.save_file(writer.populate(), _SAVE_TO_PATH)

output:- { "name": "ObjectDetector", "description": "Identify which of a known set of objects might be present and provide information about their positions within the given image or a video stream.", "subgraph_metadata": [ { "input_tensor_metadata": [ { "name": "image", "description": "Input image to be detected.", "content": { "content_properties_type": "ImageProperties", "content_properties": { "color_space": "RGB" } }, "process_units": [ { "options_type": "NormalizationOptions", "options": { "mean": [ 127.5 ], "std": [ 127.5 ] } } ], "stats": { "max": [ 1.0 ], "min": [ -1.0 ] } } ], "output_tensor_metadata": [ { "name": "location", "description": "The locations of the detected boxes.", "content": { "content_properties_type": "BoundingBoxProperties", "content_properties": { "index": [ 1, 0, 3, 2 ], "type": "BOUNDARIES" }, "range": { "min": 2, "max": 2 } }, "stats": { } } ], "output_tensor_groups": [ { "name": "detection_result", "tensor_names": [ "location", "category", "score" ] } ] } ] }

/usr/local/lib/python3.10/dist-packages/tensorflow_lite_support/metadata/python/metadata.py:395: UserWarning: File, 'labels.txt', does not exist in the metadata. But packing it to tflite model is still allowed. warnings.warn(

glenn-jocher commented 1 year ago

@VishalShinde16 hello,

It seems that you are encountering an issue with the TensorFlow Object Detection Metadata Writer when adding metadata to your converted .tflite model. Specifically, you mentioned that in the "output_tensor_metadata" section, only the "location" is shown instead of "category," "score," and "location".

To address this issue, one potential solution is to check your label file, "labels.txt", to ensure that it exists and is correctly formatted. The warning message you received indicates that the file might not exist, but it is still allowed to pack it into the .tflite model.

Please verify that the "labels.txt" file is present and correctly specifies the categories for your objects. Ensure that each category is listed on a separate line in the file.

Once you have confirmed the presence and correctness of the label file, rerun the process of adding metadata to your model using the TensorFlow Object Detection Metadata Writer. This should ensure that the metadata properly includes the "category" and "score" information along with the "location."

I hope this helps! Let me know if you have any further questions or concerns.