This tool rapidly converts loose files scattered within any folder into a consolidated H5 file. This allows for faster read operations with lower memory requirement. Reasoning: H5 files consolidate data in contiguous memory sectors.

To learn more about how H5 files work, I refer the reader to this fantastic article.

Features:

• H5 files speed up training
• Point and go - just give the paths!
• Utilizes all your cores to read data into H5
• Easy steps to incorporate H5 file into data loader
• Allows reading custom data formats by providing your own file reading function
• Allows complex file pruning by supplying your own extension matching routine

conda install -c anaconda h5py

conda install -c conda-forge imageio

Suppose your loose files are spread across a folder. You can use this utility as such:

python converter --path_images=<PATH_TO_FOLDER> --path_output=<PATH_ENDING_WITH_H5_FILE> --ext=jpg

For example:

python converter --path_images=/media/HDD/Gaze360 --path_output=/media/HDD/H5/Gaze360.h5

This script finds all files (images or otherwise) using the os.walk utility within a folder which matches the user specified extension. These data files are then consolidated into a single H5 file. Each file can be then be read directly from the H5 file using their relative path . For example:

path_image_file = '<PATH_TO_FOLDER>/foo/boo/goo/image_0001.jpg'
data = cv2.imread(path_image_file)

can be replaced with:

path_h5 = '<PATH_ENDING_WITH_H5_FILE>'
h5_obj = h5py.File(path_h5, mode='r')
data = h5_obj['foo/boo/goo/image_0001.jpg'][:]

• Easy to manage
• H5 files improve speed of reading operation
• Lowers memory consumption by leveraging lossless compression
• Partially loads data instead of hosting on RAM - convenient for large datasets
• Utilizes caching to further improve reading speeds when reading same samples again and again

You can specify a custom file extension by specifying --ext=fancy_ext. For example:

python converter --path_images=<PATH_TO_FOLDER> --path_output=<PATH_ENDING_WITH_H5_FILE> --ext=json --custom_read_func

You may then add your own custom reading logic in my_functions.py in the function my_read. To ensure the program reads your custom read function, please add the flag --custom_read_func which tells the script to ignore the default reader.

You can provide your own file extension matching function in my_prune with the template provided by --ext flag. For example, if you want to match complex file extensions such as .FoO0345 with a template extension string foo, then you can supply the following code as your own custom prune function.

def my_prune(filename_str, ext_str):
    # Logic to verify if the extension type is present
    # within the filename
    return True if ext_str in filename_str.lower() else False

To leverage H5 files into your training data loader, please refer to benchmark.py. There are three easy steps to follow:

• Step 1. Generate a list of all files used during training in the init function.

with h5py.File(path_h5, 'r') as h5_obj:
    self.file_list = list(h5_obj.keys())  # Each key is the relative path to file

• Step 2. Open the H5 reader object within the __getitem__ call. This creates a separate reader object for each individual worker.

if  not  hasattr(self, 'h5_obj'):
   self.h5_obj = h5py.File(self.path_h5, mode='r', swmr=True)

• Step 3. Add a safe closing operation for the H5 file.

def  __del__(self, ):
    self.h5_obj.close()

Coming soon!

For more information, please feel free to reach out to me at [email protected]