Using the tutorial here: https://github.com/tensorflow/models/tree/master/inception, I fined-tuned Inception V3 for a provided set of flower photos. After training, I am left with the following files:
I would like to convert the .ckpt files to .pb but all the scripts I have found for doing so err.
Help would be greatly appreciated.
Related
I have used this project from Github: https://github.com/nicknochnack/TFODCourse
The project contains a model that can detect License Plate on a given Vehicle image. The Github repo also contains code for the conversion of model into Tensorflow Lite file.
I used that code to generate TFLite file.
And then, I followed this link: https://developers.google.com/codelabs/tflite-object-detection-android
Where I downloaded the sample Application of Object detection model and following the instructions, I copied my TFLite files into the Android Application.
Now, if I run the application and take a photo, it gives me this error,
/TaskJniUtils: Error getting native address of native library: task_vision_jni
java.lang.RuntimeException: Error occurred when initializing ObjectDetector: Input tensor has type kTfLiteFloat32: it requires specifying NormalizationOptions metadata to preprocess input images.
at org.tensorflow.lite.task.vision.detector.ObjectDetector
I understand that I have to add Metadata in my TFLite model. so, I searched about it and ended up on this link: https://www.tensorflow.org/lite/models/convert/metadata#model_with_metadata_format
But I didn't understand at all what exactly should I be doing. Can anyone please help me in pointing to the right direction that for my problem specifically, what exactly do I need to do?
Trying to use custom folders with images instead of X, y = sklearn.datasets.load_digits(return_X_y=True) for sklearn image classification tasks.
load_files does what I need, but it seems to be created for text files. Any tips for working with image files, would be appreciated.
I have the image files stored in following structure
DataSet/label1/image1.png
DataSet/label1/image2.png
DataSet/label1/image3.png
DataSet/label2/image1.png
DataSet/label2/image2.png
I had the same task and found this thread: Using sklearn load_files() to load images from png as data
Hopefully, this helps you too.
Having followed Chris McCormick's tutorial for creating a BERT Fake News Detector (link here), at the end he saves the PyTorch model using the following code:
output_dir = './model_save/'
if not os.path.exists(output_dir):
os.makedirs(output_dir)
# Save a trained model, configuration and tokenizer using `save_pretrained()`.
# They can then be reloaded using `from_pretrained()`
model_to_save = model.module if hasattr(model, 'module') else model
model_to_save.save_pretrained(output_dir)
tokenizer.save_pretrained(output_dir)
As he says himself, it can be reloaded using from_pretrained(). Currently, what the code does is create an output directory with 6 files:
config.json
merges.txt
pytorch_model.bin
special_tokens_map.json
tokenizer_config.json
vocab.json
So how can I use the from_pretrained() method to load the model with all of its arguments and respective weights, and which files do I use from the six?
I understand that a model can be loaded as such (from PyTorch documentation):
model = TheModelClass(*args, **kwargs)
model.load_state_dict(torch.load(PATH))
model.eval()
but how can I make use of the files in the output directory to do this?
Any help is appreciated!
I am running into the problem whenever I try to load custom trained NER model of spacy inside docker container.
Note:
I am using latest spacy version 3.0 and trained that NER model using CLI commands of spacy, first by converting Train data format into .spacy format
The error throws as following(You can check error in image as hyperlinked):
config validation error
My trained model file structure looks like this:
custom ner model structure
But while run that model without docker it works perfectly. What wrong I have done in this process. Plz help me to resolve the error.
Thank you in advance.
I am trying to load a densenet121 model in Kaggle kernel without switching on the internet.
I have done the required steps such as adding the pre-trained weights to my input directory and moving it to '.cache/torch/checkpoints/'. It still would not work and throws a gaierror.
The following the is code SNIPPET:
!mkdir -p /tmp/.cache/torch/checkpoints
!cp ../input/fastai-pretrained-models/densenet121-a639ec97.pth /tmp/.cache/torch/checkpoints/densenet121-a639ec97.pth
learn_cd = create_cnn(data_cd, models.densenet121, metrics=[error_rate, accuracy],model_dir = Path('../kaggle/working/models'),path=Path('.'),).to_fp16()
I have been struggling with this for a long time. Any help would be immensely helpful
so input path "../input/" in kaggle kernel is read only. create a folder in "kaggle/working" rather and copy the model weights there. Example below
if not os.path.exists('/root/.cache/torch/hub/checkpoints/'):
os.makedirs('/root/.cache/torch/hub/checkpoints/')
!mkdir '/kaggle/working/resnet34'
!cp '/root/.cache/torch/hub/checkpoints/resnet34-333f7ec4.pth' '/kaggle/working/resnet34/resnet34.pth'