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 :)
| """ | |
| 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 |
| filtered_df = combined_df[(~pd.isnull(combined_df['converted_proxy'])) & (~pd.isnull(combined_df['original_images']))] | |
| filtered_df.iloc[0].original_images |
| 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() |
| 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) |
| 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 |
| 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) |
| 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 |
| """ | |
| 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 |
| import os | |
| import pandas as pd | |
| from tensorboard.backend.event_processing.event_accumulator import EventAccumulator | |
| from tqdm import tqdm | |
| from pathlib import Path | |
| import numpy as np | |
| import matplotlib.pyplot as plt | |
| import clipboard | |
| import base64 | |
| from io import BytesIO |