HongChuang/ultralytics-ascend
1
0
Fork 0
Code Issues Pull requests Projects Releases Packages Wiki Activity Actions 3

ultralytics 8.0.169TQDM, INTERP_LINEAR and RTDETR load_image() updates (#4704)

Browse source 
Co-authored-by: Rustem Galiullin <rustemgal@gmail.com>
Co-authored-by: Rustem Galiullin <rustem.galiullin@bayanat.ai>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
This commit is contained in:
Glenn Jocher 2023-09-02 20:01:57 +02:00 • committed by GitHub
parent a4fabfdacf
commit 187b504d68
No known key found for this signature in database
GPG key ID: 4AEE18F83AFDEB23
23 changed files with 101 additions and 120 deletions
Download patch file Download diff file Expand all files Collapse all files

10
ultralytics/engine/validator.py
View file

@ -24,12 +24,11 @@ from pathlib import Path
import numpy as np
import torch
from tqdm import tqdm
from ultralytics.cfg import get_cfg, get_save_dir
from ultralytics.data.utils import check_cls_dataset, check_det_dataset
from ultralytics.nn.autobackend import AutoBackend
from ultralytics.utils import LOGGER, TQDM_BAR_FORMAT, callbacks, colorstr, emojis
from ultralytics.utils import LOGGER, TQDM, callbacks, colorstr, emojis
from ultralytics.utils.checks import check_imgsz
from ultralytics.utils.ops import Profile
from ultralytics.utils.torch_utils import de_parallel, select_device, smart_inference_mode
@ -154,12 +153,7 @@ class BaseValidator:
model.warmup(imgsz=(1 if pt else self.args.batch, 3, imgsz, imgsz)) # warmup
dt = Profile(), Profile(), Profile(), Profile()
n_batches = len(self.dataloader)
desc = self.get_desc()
# NOTE: keeping `not self.training` in tqdm will eliminate pbar after segmentation evaluation during training,
# which may affect classification task since this arg is in yolov5/classify/val.py.
# bar = tqdm(self.dataloader, desc, n_batches, not self.training, bar_format=TQDM_BAR_FORMAT)
bar = tqdm(self.dataloader, desc, n_batches, bar_format=TQDM_BAR_FORMAT)
bar = TQDM(self.dataloader, desc=self.get_desc(), total=len(self.dataloader))
self.init_metrics(de_parallel(model))
self.jdict = [] # empty before each val
for batch_i, batch in enumerate(bar):