baldassarrefe/deep-koalarization

Keras/Tensorflow implementation of our paper Grayscale Image Colorization using deep CNN and Inception-ResNet-v2 (https://arxiv.org/abs/1712.03400)

deep-koalarization's Issues

Lab_Batch file is not creating Tf-records

from resizeimage import resizeimage

I con't find the model resizeimage .

Pre-trained model

Is there a pre-trained model for this? I want to do transfer learning on my dataset w/o training from scratch.

train on multiple gpu

Have you tried to train it on multiple gpu?How?

queue_single_images_from_folder

it seems the functions queue_images_from_folder in images_queue.py is out of date with tensorflow v2 and I'm not sure how to recreate its functionality. Any tips?

training on my own data

for my own dataset, I don't have any label , just color images, any instructions for this ? thank you

How do you train your neural network?

I've managed to prepare the images and run the batching tests.

When I run the main > python3 -m colorization.main. I get a couple of errors. I've used the folder structure you suggested, but I believe I have the wrong configuration. I'd appreciate if you could outline the steps to train the network. Thanks 🚀

train.py?

Hi!what does the "from colorization import Colorization" means?I did not find the Colorization....

TypeError: Can not convert a LabImageRecordReader into a Tensor or Operation.

TypeError: Fetch argument <dataset.tfrecords.images.lab_image_record.LabImageRecordReader object at 0x0000000074CF3940> has invalid type <class 'dataset.tfrecords.images.lab_image_record.LabImageRecordReader'>, must be a string or Tensor. (Can not convert a LabImageRecordReader into a Tensor or Operation.)

i got this error when i ran train.py.

the solution is below@charleshamesse .check it out

koalarization.dataset.lab_batch issue

Hi! I want to run

python -m koalarization.dataset.lab_batch -c '../data/inception_resnet_v2_2016_08_30.ckpt' '../data/resized' '../data/tfrecords' --verbose

I have the following Traceback:

Using TensorFlow backend.
2021-12-01 00:26:43.453183: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.1 instructions, but these are available on your machine and could speed up CPU computations.
2021-12-01 00:27:03.174050: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use SSE4.2 instructions, but these are available on your machine and could speed up CPU computations.
2021-12-01 00:27:03.174278: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX instructions, but these are available on your machine and could speed up CPU computations.
2021-12-01 00:27:03.174446: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use AVX2 instructions, but these are available on your machine and could speed up CPU computations.
2021-12-01 00:27:03.174590: W tensorflow/core/platform/cpu_feature_guard.cc:45] The TensorFlow library wasn't compiled to use FMA instructions, but these are available on your machine and could speed up CPU computations.
Finished writing 0 images in 0.54s
Traceback (most recent call last):
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/mnt/c/Users/Lucas Ariel Saavedra/Documents/GitHub/deep-koalarization/src/koalarization/dataset/lab_batch.py", line 255, in <module>
    ).batch_all(examples_per_record=args.batch_size)
  File "/mnt/c/Users/Lucas Ariel Saavedra/Documents/GitHub/deep-koalarization/src/koalarization/dataset/lab_batch.py", line 89, in batch_all
    self._run_session(sess, operations, examples_per_record)
  File "/mnt/c/Users/Lucas Ariel Saavedra/Documents/GitHub/deep-koalarization/src/koalarization/dataset/lab_batch.py", line 172, in _run_session
    coord.join(threads)
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/site-packages/tensorflow/python/training/coordinator.py", line 389, in join
    six.reraise(*self._exc_info_to_raise)
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/site-packages/six.py", line 719, in reraise
    raise value
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/site-packages/tensorflow/python/training/queue_runner_impl.py", line 238, in _run
    enqueue_callable()
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/site-packages/tensorflow/python/client/session.py", line 1235, in _single_operation_run
    target_list_as_strings, status, None)
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/contextlib.py", line 88, in __exit__
    next(self.gen)
  File "/home/lucas/anaconda3/envs/koalarization/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py", line 466, in raise_exception_on_not_ok_status
    pywrap_tensorflow.TF_GetCode(status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: assertion failed: [string_input_producer requires a non-null input tensor]
         [[Node: input_producer/Assert/Assert = Assert[T=[DT_STRING], summarize=3, _device="/job:localhost/replica:0/task:0/cpu:0"](input_producer/Greater, input_producer/Assert/Assert/data_0)]]

I don't know how to fix it. Thank You!

PD: I'm working without GPU

[REQ] Colab version

Hi there, congratulations for your great study/work !

It would be great to let users test its powerfulness/precision by a colab notebook (as, for example, DeOldify already allows):
https://github.com/jantic/DeOldify

I've also found this git that by @animalecs may help.

Hope that inspires.

Inception output size

Hi! I'm confused as to the fusion layer. I thought Inception Resnet v2 was supposed to have 1000 classes, so the output is supposed to be of the shape (1000, ), not 1001. Can you explain this?

Labels from Inception at the beginning of training seem incorrect

When training the network and looking at the outputted images from the validation set, i see labels from inception at the top of the images, are they supposed to be objects recognized in the images from the pretrained inception features? If so they seem very incorrect, i see objects listed which are not present in the images and vice versa. Is this because the network is untrained and doesn't utilize the features from inception correctly? Also how long does it usually take for the network (on say 10.000 images, i have a larger dataset but I've started of with a small portion to test it out) to converge to some colors being generated because after about 200 iterations all i see is a redish-brownish color on all the images. Is it possible I'm doing something wrong?

Training

Hi,
I am a student in deep learning and neural network.
Could you help me with some questions I wonder myself ?

how many lab_images_.tfrecord do we need to do before training ?
For val_lab_images_.tfrecord, do we just need to rename one lab_images_.tfrecord ?
Where are the grey images ?

Cheers

Simon