Hey i can't download pre-trained weight even yu provide a link of TFLite (mirnet-fixed/dr) but didn't work should have the pretrained model please help me i need this for my studies

Hello, I successfully enhanced a part of an image with your tool however even with 32Go of ram I can't use it on a photo, is htere a way to automaticaly process the image in several parts ?

hello,
the Pre-trained Weights are not available anymore, pls recheck it!
can you send it to my email: [email protected]

thank you very much!

I have watch the youtube video https://www.youtube.com/watch?v=b5Uz_c0JLMs from Bhavesh Bhatt and copy the notebook.
I only used my own picture (see attachment)

The code

from glob import glob
import tensorflow as tf
import numpy as np
from matplotlib import pyplot as plt
from mirnet.inference import Inferer
from mirnet.utils import download_dataset, plot_result
from PIL import Image

inferer = Inferer()
inferer.download_weights('1sUlRD5MTRKKGxtqyYDpTv7T3jOW6aVAL')
inferer.build_model(
    num_rrg=3, num_mrb=2, channels=64,
    weights_path='low_light_weights_best.h5'
)

#inferer.model.summary()

inferer.model.save('mirnet')

IMAGE_LOC = 'IMG_3598_resize3.jpg'

print("image converteren..")
original_image, output_image = inferer.infer(IMAGE_LOC)
plot_result(original_image, output_image)

But i get an error on the code -> original_image, output_image = inferer.infer(IMAGE_LOC)
I used a jpeg file of 22.8 KB with width=600 and height=337 pixels and I get the following error:

InvalidArgumentError Traceback (most recent call last)

in ()
----> 1 original_image, output_image = inferer.infer(IMAGE_LOC)
2 plot_result(original_image, output_image)

7 frames

/usr/local/lib/python3.6/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
58 ctx.ensure_initialized()
59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 60 inputs, attrs, num_outputs)
61 except core._NotOkStatusException as e:
62 if name is not None:

InvalidArgumentError: Incompatible shapes: [1,337,600,64] vs. [1,336,600,64]
[[node model_1/add_180/add (defined at /content/MIRNet/mirnet/inference.py:39) ]] [Op:__inference_predict_function_126722]

Function call stack:
predict_function

I have also try with different sizes but I get similar errors.

I used google colab

Any idea why it is not working..

Thanks sofar.

Henk

Hi, when I pass my image without resizing, then I get error,
But after doing resizing to (1560, 2080) it's working fine but image quality gets reduced.
How I will solve this issue??

Tried saving with .h5 as well as the .pb extension.

`---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
Cell In[21], line 1
----> 1 model.save('mirnet.h5')

File ~\anaconda3\envs\tf\lib\site-packages\keras\utils\traceback_utils.py:70, in filter_traceback.<locals>.error_handler(*args, **kwargs)
     67     filtered_tb = _process_traceback_frames(e.__traceback__)
     68     # To get the full stack trace, call:
     69     # `tf.debugging.disable_traceback_filtering()`
---> 70     raise e.with_traceback(filtered_tb) from None
     71 finally:
     72     del filtered_tb

File ~\anaconda3\envs\tf\lib\site-packages\keras\utils\generic_utils.py:545, in serialize_keras_object(instance)
    543     raise e
    544 serialization_config = {}
--> 545 for key, item in config.items():
    546     if isinstance(item, str):
    547         serialization_config[key] = item

AttributeError: 'NoneType' object has no attribute 'items`