You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
I was able to convert the pytorch model into an onnx file using,
python models/export.py --weights yolov5s.pt --img 640 --batch 1
i also was able to convert .onnx into a tensorflow model using the following code,
`import onnx
from onnx_tf.backend import prepare
import tensorflow as tf
looks like the error is because the conversion from onnx to tensorflow model is the saved model and not the frozen tf file. By finding the signature of the saved model using saved_model_cli show --dir yolov5_trial2 --all
gives me weird signature def for input and output arrays
signature_def['__saved_model_init_op']:
The given SavedModel SignatureDef contains the following input(s):
The given SavedModel SignatureDef contains the following output(s):
outputs['__saved_model_init_op'] tensor_info:
dtype: DT_INVALID
shape: unknown_rank
name: NoOp
Method name is:
signature_def['serving_default']:
The given SavedModel SignatureDef contains the following input(s):
inputs['images'] tensor_info:
dtype: DT_FLOAT
shape: (1, 3, 640, 640)
name: serving_default_images:0
The given SavedModel SignatureDef contains the following output(s):
outputs['output_0'] tensor_info:
dtype: DT_FLOAT
shape: (1, 25200, 85)
name: StatefulPartitionedCall:0
outputs['output_1'] tensor_info:
dtype: DT_FLOAT
shape: (1, 3, 80, 80, 85)
name: StatefulPartitionedCall:1
outputs['output_2'] tensor_info:
dtype: DT_FLOAT
shape: (1, 3, 40, 40, 85)
name: StatefulPartitionedCall:2
outputs['output_3'] tensor_info:
dtype: DT_FLOAT
shape: (1, 3, 20, 20, 85)
name: StatefulPartitionedCall:3
Method name is: tensorflow/serving/predict
Defined Functions:
Function Name: 'call'
Named Argument #1
images
Function Name: 'gen_tensor_dict'
Please help.
The text was updated successfully, but these errors were encountered:
Hello @Roshnee, thank you for your interest in 🚀 YOLOv5! Please visit our ⭐️ Tutorials to get started, where you can find quickstart guides for simple tasks like Custom Data Training all the way to advanced concepts like Hyperparameter Evolution.
If this is a 🐛 Bug Report, please provide screenshots and minimum viable code to reproduce your issue, otherwise we can not help you.
If this is a custom training ❓ Question, please provide as much information as possible, including dataset images, training logs, screenshots, and a public link to online W&B logging if available.
Python 3.8 or later with all requirements.txt dependencies installed, including torch>=1.7. To install run:
$ pip install -r requirements.txt
Environments
YOLOv5 may be run in any of the following up-to-date verified environments (with all dependencies including CUDA/CUDNN, Python and PyTorch preinstalled):
If this badge is green, all YOLOv5 GitHub Actions Continuous Integration (CI) tests are currently passing. CI tests verify correct operation of YOLOv5 training (train.py), testing (test.py), inference (detect.py) and export (export.py) on MacOS, Windows, and Ubuntu every 24 hours and on every commit.
This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. Thank you for your contributions.
❔Question
I have tensorflow 2.3.1 installed.
I was able to convert the pytorch model into an onnx file using,
python models/export.py --weights yolov5s.pt --img 640 --batch 1
i also was able to convert .onnx into a tensorflow model using the following code,
`import onnx
from onnx_tf.backend import prepare
import tensorflow as tf
onnx_model = onnx.load('yolov5s.onnx')
tf_rep = prepare(onnx_model)
tf_rep.export_graph("yolov5.pb") `
This yolov5.pb directory consists of the saved_model.pb file and 2 other folders: variables (2 files) and assets (empty folder)
I couldnt further convert it to a tflite model. I used the following code,
`import tensorflow as tf
saved_model_dir = 'yolov5.pb'
Convert the model.
converter = tf.lite.TFLiteConverter.from_saved_model(saved_model_dir)
tflite_model = converter.convert()
Save the TF Lite model.
with tf.io.gfile.GFile('model.tflite', 'wb') as f:
f.write(tflite_model)`
This causes a strange error:
I also used:
tflite_convert --saved_model_dir=yolov5 --output_file=yolo.tflite
which gives me the same error.
Additional context
UPDATE:
looks like the error is because the conversion from onnx to tensorflow model is the saved model and not the frozen tf file. By finding the signature of the saved model using
saved_model_cli show --dir yolov5_trial2 --all
gives me weird signature def for input and output arrays
Please help.
The text was updated successfully, but these errors were encountered: