| ### This is example of the script that will be run in the test environment. | |
| ### Some parts of the code are compulsory and you should NOT CHANGE THEM. | |
| ### They are between '''---compulsory---''' comments. | |
| ### You can change the rest of the code to define and test your solution. | |
| ### However, you should not change the signature of the provided function. | |
| ### The script would save "submission.parquet" file in the current directory. | |
| ### The actual logic of the solution is implemented in the `handcrafted_solution.py` file. | |
| ### The `handcrafted_solution.py` file is a placeholder for your solution. | |
| ### You should implement the logic of your solution in that file. | |
| ### You can use any additional files and subdirectories to organize your code. | |
| '''---compulsory---''' | |
| # import subprocess | |
| # from pathlib import Path | |
| # def install_package_from_local_file(package_name, folder='packages'): | |
| # """ | |
| # Installs a package from a local .whl file or a directory containing .whl files using pip. | |
| # Parameters: | |
| # path_to_file_or_directory (str): The path to the .whl file or the directory containing .whl files. | |
| # """ | |
| # try: | |
| # pth = str(Path(folder) / package_name) | |
| # subprocess.check_call([subprocess.sys.executable, "-m", "pip", "install", | |
| # "--no-index", # Do not use package index | |
| # "--find-links", pth, # Look for packages in the specified directory or at the file | |
| # package_name]) # Specify the package to install | |
| # print(f"Package installed successfully from {pth}") | |
| # except subprocess.CalledProcessError as e: | |
| # print(f"Failed to install package from {pth}. Error: {e}") | |
| # install_package_from_local_file('hoho') | |
| import hoho; hoho.setup() # YOU MUST CALL hoho.setup() BEFORE ANYTHING ELSE | |
| # import subprocess | |
| # import importlib | |
| # from pathlib import Path | |
| # import subprocess | |
| # ### The function below is useful for installing additional python wheels. | |
| # def install_package_from_local_file(package_name, folder='packages'): | |
| # """ | |
| # Installs a package from a local .whl file or a directory containing .whl files using pip. | |
| # Parameters: | |
| # path_to_file_or_directory (str): The path to the .whl file or the directory containing .whl files. | |
| # """ | |
| # try: | |
| # pth = str(Path(folder) / package_name) | |
| # subprocess.check_call([subprocess.sys.executable, "-m", "pip", "install", | |
| # "--no-index", # Do not use package index | |
| # "--find-links", pth, # Look for packages in the specified directory or at the file | |
| # package_name]) # Specify the package to install | |
| # print(f"Package installed successfully from {pth}") | |
| # except subprocess.CalledProcessError as e: | |
| # print(f"Failed to install package from {pth}. Error: {e}") | |
| # pip download webdataset -d packages/webdataset --platform manylinux1_x86_64 --python-version 38 --only-binary=:all: | |
| # install_package_from_local_file('webdataset') | |
| # install_package_from_local_file('tqdm') | |
| ### Here you can import any library or module you want. | |
| ### The code below is used to read and parse the input dataset. | |
| ### Please, do not modify it. | |
| import webdataset as wds | |
| from tqdm import tqdm | |
| from typing import Dict | |
| import pandas as pd | |
| from transformers import AutoTokenizer | |
| import os | |
| import time | |
| import io | |
| from PIL import Image as PImage | |
| import numpy as np | |
| from hoho.read_write_colmap import read_cameras_binary, read_images_binary, read_points3D_binary | |
| from hoho import proc, Sample | |
| def convert_entry_to_human_readable(entry): | |
| out = {} | |
| already_good = ['__key__', 'wf_vertices', 'wf_edges', 'edge_semantics', 'mesh_vertices', 'mesh_faces', 'face_semantics', 'K', 'R', 't'] | |
| for k, v in entry.items(): | |
| if k in already_good: | |
| out[k] = v | |
| continue | |
| if k == 'points3d': | |
| out[k] = read_points3D_binary(fid=io.BytesIO(v)) | |
| if k == 'cameras': | |
| out[k] = read_cameras_binary(fid=io.BytesIO(v)) | |
| if k == 'images': | |
| out[k] = read_images_binary(fid=io.BytesIO(v)) | |
| if k in ['ade20k', 'gestalt']: | |
| out[k] = [PImage.open(io.BytesIO(x)).convert('RGB') for x in v] | |
| if k == 'depthcm': | |
| out[k] = [PImage.open(io.BytesIO(x)) for x in entry['depthcm']] | |
| return out | |
| '''---end of compulsory---''' | |
| ### The part below is used to define and test your solution. | |
| from pathlib import Path | |
| def save_submission(submission, path): | |
| """ | |
| Saves the submission to a specified path. | |
| Parameters: | |
| submission (List[Dict[]]): The submission to save. | |
| path (str): The path to save the submission to. | |
| """ | |
| sub = pd.DataFrame(submission, columns=["__key__", "wf_vertices", "wf_edges"]) | |
| sub.to_parquet(path) | |
| print(f"Submission saved to {path}") | |
| if __name__ == "__main__": | |
| from handcrafted_solution import predict | |
| print ("------------ Loading dataset------------ ") | |
| params = hoho.get_params() | |
| dataset = hoho.get_dataset(decode=None, split='all', dataset_type='webdataset') | |
| print('------------ Now you can do your solution ---------------') | |
| solution = [] | |
| from concurrent.futures import ProcessPoolExecutor | |
| with ProcessPoolExecutor(max_workers=8) as pool: | |
| results = [] | |
| for i, sample in enumerate(tqdm(dataset)): | |
| results.append(pool.submit(predict, sample, visualize=False, | |
| point_radius=25, | |
| max_angle=15, | |
| extend=30, | |
| merge_th=3.0, | |
| min_missing_distance=1000000.0, | |
| scale_estimation_coefficient=4.5)) | |
| for i, result in enumerate(tqdm(results)): | |
| key, pred_vertices, pred_edges = result.result() | |
| solution.append({ | |
| '__key__': key, | |
| 'wf_vertices': pred_vertices.tolist(), | |
| 'wf_edges': pred_edges | |
| }) | |
| if i % 100 == 0: | |
| # incrementally save the results in case we run out of time | |
| print(f"Processed {i} samples") | |
| # save_submission(solution, Path(params['output_path']) / "submission.parquet") | |
| print('------------ Saving results ---------------') | |
| save_submission(solution, Path(params['output_path']) / "submission.parquet") | |
| print("------------ Done ------------ ") | |