SubhadityaMukherjee

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Presenter Info

• Subhaditya Mukhejee : [email protected]

SubhadityaMukherjee

Freelance Projects/Blogs I have worked on

I am Subhaditya, it's nice to meet you!
Below is a list of projects I have worked on for various clients over the years.
I wanted to keep a list of them for both myself and future employers. 
That being said, I am almost always open for more freelancw work, so hit me up if you want to discuss some projects :)

• While most of the code is closed source due to client requirements, I am still open to discussing them to a certain extent
• My Github is https://github.com/SubhadityaMukherjee

SubhadityaMukherjee

"""
Run result aggregator
- Read all tensorbord logs and save as a pandas dataframe for analysis
"""

import os
import pandas as pd
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
from tqdm import tqdm
from pathlib import Path

SubhadityaMukherjee

filtered_df = combined_df[(~pd.isnull(combined_df['converted_proxy'])) & (~pd.isnull(combined_df['original_images']))] 
filtered_df.iloc[0].original_images

SubhadityaMukherjee

combined_df = process_runs(main_path=main_path)
combined_df = combined_df[(~pd.isnull(combined_df['experiment_name'])) & (~pd.isnull(combined_df['Loss/Val']))]
combined_df.head()

SubhadityaMukherjee

import pickle
with open("pickled_df.pkl", "wb+") as f:
    pickle.dump(combined_df, f)

with open("pickled_df.pkl", "rb+") as f:
    combined_df = pickle.load(f)

SubhadityaMukherjee

def process_event_acc(event_acc, save_ims=False) -> dict:
    """Process the EventAccumulator and return a dictionary of tag values"""
    all_tags = event_acc.Tags() # Get all tags
    temp_dict = {} # Store all values here
    for tag in all_tags.keys(): # Loop over all tags
        if tag == "scalars":
            # Process scalars
            for subtag in all_tags[tag]:
                try:
                    # Try to get the last value

SubhadityaMukherjee

def process_runs(main_path, save_ims=False) -> pd.DataFrame:
    """Process all runs and return a dataframe of all results"""
    all_files = get_event_files(main_path=main_path, save_ims=False)
    all_dict = {}
    for files in tqdm(all_files, total=len(all_files)):
        try:
            # Process each file using the EventAccumulator and save to a dictionary
            event_acc = EventAccumulator(files)
            event_acc.Reload()
            temp_dict = process_event_acc(event_acc, save_ims=save_ims)

SubhadityaMukherjee

def get_event_files(main_path) -> list:
    """Return a list of event files under the given directory"""
    all_files = []
    for root, _, filenames in os.walk(main_path):
        for filename in filenames:
            if "events.out.tfevents" in filename:
                all_files.append(str(Path(root) / Path(filename)))
    return all_files

SubhadityaMukherjee

"""
Run result aggregator
- Read all tensorbord logs and save as a pandas dataframe for analysis
"""

import os
import pandas as pd
from tensorboard.backend.event_processing.event_accumulator import EventAccumulator
from tqdm import tqdm
from pathlib import Path