File size: 11,325 Bytes
8813b45 edc7860 420d591 8813b45 edc7860 8813b45 edc7860 8813b45 420d591 8813b45 420d591 8813b45 b9b3598 8813b45 b9b3598 8813b45 724cc25 420d591 724cc25 8813b45 edc7860 8813b45 edc7860 8813b45 edc7860 8813b45 edc7860 8813b45 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 292 293 294 295 296 297 298 299 300 301 302 303 304 305 306 307 308 309 310 311 312 313 314 315 316 317 318 319 320 321 322 323 324 325 326 327 328 329 330 331 332 333 334 335 336 337 338 |
import os
import json
import shutil
from pathlib import Path
from typing import Dict
import warnings
import contextlib
import tempfile
from PIL import Image
import io
import webdataset as wds
import numpy as np
import importlib
import subprocess
from PIL import ImageFile
from huggingface_hub.utils._headers import build_hf_headers # note: using _headers
ImageFile.LOAD_TRUNCATED_IMAGES = True
LOCAL_DATADIR = None
def setup(local_dir='./data/usm-training-data/data'):
# If we are in the test environment, we need to link the data directory to the correct location
tmp_datadir = Path('/tmp/data/data')
local_test_datadir = Path('./data/usm-test-data-x/data')
local_val_datadir = Path(local_dir)
os.system('pwd')
os.system('ls -lahtr .')
if tmp_datadir.exists() and not local_test_datadir.exists():
global LOCAL_DATADIR
LOCAL_DATADIR = local_test_datadir
# shutil.move(datadir, './usm-test-data-x/data')
print(f"Linking {tmp_datadir} to {LOCAL_DATADIR} (we are in the test environment)")
LOCAL_DATADIR.parent.mkdir(parents=True, exist_ok=True)
LOCAL_DATADIR.symlink_to(tmp_datadir)
else:
LOCAL_DATADIR = local_val_datadir
print(f"Using {LOCAL_DATADIR} as the data directory (we are running locally)")
if not LOCAL_DATADIR.exists():
warnings.warn(f"Data directory {LOCAL_DATADIR} does not exist: creating it...")
LOCAL_DATADIR.mkdir(parents=True)
return LOCAL_DATADIR
def download_package(package_name, path_to_save='packages'):
"""
Downloads a package using pip and saves it to a specified directory.
Parameters:
package_name (str): The name of the package to download.
path_to_save (str): The path to the directory where the package will be saved.
"""
try:
# pip download webdataset -d packages/webdataset --platform manylinux1_x86_64 --python-version 38 --only-binary=:all:
subprocess.check_call([subprocess.sys.executable, "-m", "pip", "download", package_name,
"-d", str(Path(path_to_save)/package_name), # Download the package to the specified directory
"--platform", "manylinux1_x86_64", # Specify the platform
"--python-version", "38", # Specify the Python version
"--only-binary=:all:"]) # Download only binary packages
print(f'Package "{package_name}" downloaded successfully')
except subprocess.CalledProcessError as e:
print(f'Failed to downloaded package "{package_name}". Error: {e}')
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}")
def importt(module_name, as_name=None):
"""
Imports a module and returns it.
Parameters:
module_name (str): The name of the module to import.
as_name (str): The name to use for the imported module. If None, the original module name will be used.
Returns:
The imported module.
"""
for _ in range(2):
try:
if as_name is None:
print(f'imported {module_name}')
return importlib.import_module(module_name)
else:
print(f'imported {module_name} as {as_name}')
return importlib.import_module(module_name, as_name)
except ModuleNotFoundError as e:
install_package_from_local_file(module_name)
print(f"Failed to import module {module_name}. Error: {e}")
def prepare_submission():
# Download packages from requirements.txt
if Path('requirements.txt').exists():
print('downloading packages from requirements.txt')
Path('packages').mkdir(exist_ok=True)
with open('requirements.txt') as f:
packages = f.readlines()
for p in packages:
download_package(p.strip())
print('all packages downloaded. Don\'t foget to include the packages in the submission by adding them with git lfs.')
def Rt_to_eye_target(im, K, R, t):
height = im.height
focal_length = K[0,0]
fov = 2.0 * np.arctan2((0.5 * height), focal_length) / (np.pi / 180.0)
x_axis, y_axis, z_axis = R
eye = -(R.T @ t).squeeze()
z_axis = z_axis.squeeze()
target = eye + z_axis
up = -y_axis
return eye, target, up, fov
########## general utilities ##########
@contextlib.contextmanager
def working_directory(path):
"""Changes working directory and returns to previous on exit."""
prev_cwd = Path.cwd()
os.chdir(path)
try:
yield
finally:
os.chdir(prev_cwd)
@contextlib.contextmanager
def temp_working_directory():
with tempfile.TemporaryDirectory(dir='.') as D:
with working_directory(D):
yield
############# Dataset #############
def proc(row, split='train'):
out = {}
out['__key__'] = None
out['__imagekey__'] = []
for k, v in row.items():
key_parts = k.split('.')
colname = key_parts[0]
if colname == 'ade20k':
out['__imagekey__'].append(key_parts[1])
if colname in {'ade20k', 'depthcm', 'gestalt'}:
if colname in out:
out[colname].append(v)
else:
out[colname] = [v]
elif colname in {'wireframe', 'mesh'}:
out.update({a: b for a,b in v.items()})
elif colname in 'kr':
out[colname.upper()] = v
else:
out[colname] = v
return Sample(out)
from . import read_write_colmap
def decode_colmap(s):
with temp_working_directory():
with open('points3D.bin', 'wb') as stream:
stream.write(s['points3d'])
with open('cameras.bin', 'wb') as stream:
stream.write(s['cameras'])
with open('images.bin', 'wb') as stream:
stream.write(s['images'])
cameras, images, points3D = read_write_colmap.read_model(
path='.', ext='.bin'
)
return cameras, images, points3D
def decode(row):
cameras, images, points3D = decode_colmap(row)
out = {}
for k, v in row.items():
# colname = k.split('.')[0]
if k in {'ade20k', 'depthcm', 'gestalt'}:
# print(k, len(v), type(v))
v = [Image.open(io.BytesIO(im)) for im in v]
if k in out:
out[k].extend(v)
else:
out[k] = v
elif k in {'wireframe', 'mesh'}:
# out.update({a: b.tolist() for a,b in v.items()})
v = dict(np.load(io.BytesIO(v)))
out.update({a: b for a,b in v.items()})
elif k in 'kr':
out[k.upper()] = v
elif k == 'cameras':
out[k] = cameras
elif k == 'images':
out[k] = images
elif k =='points3d':
out[k] = points3D
else:
out[k] = v
return Sample(out)
class Sample(Dict):
def __repr__(self):
return str({k: v.shape if hasattr(v, 'shape') else [type(v[0])] if isinstance(v, list) else type(v) for k,v in self.items()})
def get_params():
exmaple_param_dict = {
"competition_id": "usm3d/S23DR",
"competition_type": "script",
"metric": "custom",
"token": "hf_**********************************",
"team_id": "local-test-team_id",
"submission_id": "local-test-submission_id",
"submission_id_col": "__key__",
"submission_cols": [
"__key__",
"wf_edges",
"wf_vertices",
"edge_semantics"
],
"submission_rows": 180,
"output_path": ".",
"submission_repo": "<THE HF MODEL ID of THIS REPO",
"time_limit": 7200,
"dataset": "usm3d/usm-test-data-x",
"submission_filenames": [
"submission.parquet"
]
}
param_path = Path('params.json')
if not param_path.exists():
print('params.json not found (this means we probably aren\'t in the test env). Using example params.')
params = exmaple_param_dict
else:
print('found params.json (this means we are probably in the test env). Using params from file.')
with param_path.open() as f:
params = json.load(f)
print(params)
return params
SHARD_IDS = {'train': (0, 25), 'val': (25, 26), 'public': (26, 27), 'private': (27, 32)}
def get_dataset(decode='pil', proc=proc, split='train', dataset_type='webdataset', stream=True):
if LOCAL_DATADIR is None:
raise ValueError('LOCAL_DATADIR is not set. Please run setup() first.')
local_dir = Path(LOCAL_DATADIR)
if split != 'all':
local_dir = local_dir / split
paths = [str(p) for p in local_dir.rglob('*.tar.gz')]
msg = f'no tarfiles found in {local_dir}.'
if len(paths) == 0:
if stream:
if split=='all': split = 'train'
warnings.warn('streaming isn\'t using with \'all\': changing `split` to \'train\'')
warnings.warn(msg)
if split == 'val':
names = [f'data/val/inputs/hoho_v3_{i:03}-of-032.tar.gz' for i in range(*SHARD_IDS[split])]
elif split == 'train':
names = [f'data/train/hoho_v3_{i:03}-of-032.tar.gz' for i in range(*SHARD_IDS[split])]
auth = build_hf_headers()['authorization']
paths = [f"pipe:curl -L -s https://huggingface.co/datasets/usm3d/hoho-train-set/resolve/main/{name} -H 'Authorization: {auth}'" for name in names]
else:
raise FileNotFoundError(msg)
dataset = wds.WebDataset(paths)
if decode is not None:
dataset = dataset.decode(decode)
else:
dataset = dataset.decode()
dataset = dataset.map(proc)
if dataset_type == 'webdataset':
return dataset
if dataset_type == 'hf':
import datasets
from datasets import Features, Value, Sequence, Image, Array2D
if split == 'train':
return datasets.IterableDataset.from_generator(lambda: dataset.iterator())
elif split == 'val':
return datasets.IterableDataset.from_generator(lambda: dataset.iterator())
else:
raise NotImplementedError('only train and val are implemented as hf datasets')
|