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import os
import contextlib
import joblib
from typing import Union
from loguru import _Logger, logger
from itertools import chain
import torch
from yacs.config import CfgNode as CN
from pytorch_lightning.utilities import rank_zero_only
import cv2
import numpy as np
def lower_config(yacs_cfg):
if not isinstance(yacs_cfg, CN):
return yacs_cfg
return {k.lower(): lower_config(v) for k, v in yacs_cfg.items()}
def upper_config(dict_cfg):
if not isinstance(dict_cfg, dict):
return dict_cfg
return {k.upper(): upper_config(v) for k, v in dict_cfg.items()}
def log_on(condition, message, level):
if condition:
assert level in ["INFO", "DEBUG", "WARNING", "ERROR", "CRITICAL"]
logger.log(level, message)
def get_rank_zero_only_logger(logger: _Logger):
if rank_zero_only.rank == 0:
return logger
else:
for _level in logger._core.levels.keys():
level = _level.lower()
setattr(logger, level, lambda x: None)
logger._log = lambda x: None
return logger
def setup_gpus(gpus: Union[str, int]) -> int:
"""A temporary fix for pytorch-lighting 1.3.x"""
gpus = str(gpus)
gpu_ids = []
if "," not in gpus:
n_gpus = int(gpus)
return n_gpus if n_gpus != -1 else torch.cuda.device_count()
else:
gpu_ids = [i.strip() for i in gpus.split(",") if i != ""]
# setup environment variables
visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
if visible_devices is None:
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = ",".join(str(i) for i in gpu_ids)
visible_devices = os.getenv("CUDA_VISIBLE_DEVICES")
logger.warning(
f"[Temporary Fix] manually set CUDA_VISIBLE_DEVICES when specifying gpus to use: {visible_devices}"
)
else:
logger.warning(
"[Temporary Fix] CUDA_VISIBLE_DEVICES already set by user or the main process."
)
return len(gpu_ids)
def flattenList(x):
return list(chain(*x))
@contextlib.contextmanager
def tqdm_joblib(tqdm_object):
"""Context manager to patch joblib to report into tqdm progress bar given as argument
Usage:
with tqdm_joblib(tqdm(desc="My calculation", total=10)) as progress_bar:
Parallel(n_jobs=16)(delayed(sqrt)(i**2) for i in range(10))
When iterating over a generator, directly use of tqdm is also a solutin (but monitor the task queuing, instead of finishing)
ret_vals = Parallel(n_jobs=args.world_size)(
delayed(lambda x: _compute_cov_score(pid, *x))(param)
for param in tqdm(combinations(image_ids, 2),
desc=f'Computing cov_score of [{pid}]',
total=len(image_ids)*(len(image_ids)-1)/2))
Src: https://stackoverflow.com/a/58936697
"""
class TqdmBatchCompletionCallback(joblib.parallel.BatchCompletionCallBack):
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
def __call__(self, *args, **kwargs):
tqdm_object.update(n=self.batch_size)
return super().__call__(*args, **kwargs)
old_batch_callback = joblib.parallel.BatchCompletionCallBack
joblib.parallel.BatchCompletionCallBack = TqdmBatchCompletionCallback
try:
yield tqdm_object
finally:
joblib.parallel.BatchCompletionCallBack = old_batch_callback
tqdm_object.close()
def draw_points(img, points, color=(0, 255, 0), radius=3):
dp = [(int(points[i, 0]), int(points[i, 1])) for i in range(points.shape[0])]
for i in range(points.shape[0]):
cv2.circle(img, dp[i], radius=radius, color=color)
return img
def draw_match(
img1,
img2,
corr1,
corr2,
inlier=[True],
color=None,
radius1=1,
radius2=1,
resize=None,
):
if resize is not None:
scale1, scale2 = [img1.shape[1] / resize[0], img1.shape[0] / resize[1]], [
img2.shape[1] / resize[0],
img2.shape[0] / resize[1],
]
img1, img2 = cv2.resize(img1, resize, interpolation=cv2.INTER_AREA), cv2.resize(
img2, resize, interpolation=cv2.INTER_AREA
)
corr1, corr2 = (
corr1 / np.asarray(scale1)[np.newaxis],
corr2 / np.asarray(scale2)[np.newaxis],
)
corr1_key = [
cv2.KeyPoint(corr1[i, 0], corr1[i, 1], radius1) for i in range(corr1.shape[0])
]
corr2_key = [
cv2.KeyPoint(corr2[i, 0], corr2[i, 1], radius2) for i in range(corr2.shape[0])
]
assert len(corr1) == len(corr2)
draw_matches = [cv2.DMatch(i, i, 0) for i in range(len(corr1))]
if color is None:
color = [(0, 255, 0) if cur_inlier else (0, 0, 255) for cur_inlier in inlier]
if len(color) == 1:
display = cv2.drawMatches(
img1,
corr1_key,
img2,
corr2_key,
draw_matches,
None,
matchColor=color[0],
singlePointColor=color[0],
flags=4,
)
else:
height, width = max(img1.shape[0], img2.shape[0]), img1.shape[1] + img2.shape[1]
display = np.zeros([height, width, 3], np.uint8)
display[: img1.shape[0], : img1.shape[1]] = img1
display[: img2.shape[0], img1.shape[1] :] = img2
for i in range(len(corr1)):
left_x, left_y, right_x, right_y = (
int(corr1[i][0]),
int(corr1[i][1]),
int(corr2[i][0] + img1.shape[1]),
int(corr2[i][1]),
)
cur_color = (int(color[i][0]), int(color[i][1]), int(color[i][2]))
cv2.line(
display,
(left_x, left_y),
(right_x, right_y),
cur_color,
1,
lineType=cv2.LINE_AA,
)
return display
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