`ultralytics 8.0.56` PyTorch 2.0 support and minor fixes (#1538)

Co-authored-by: N-Friederich <127681326+N-Friederich@users.noreply.github.com>
Co-authored-by: Uhrendoktor <36703334+Uhrendoktor@users.noreply.github.com>
Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>
Co-authored-by: Aman Agarwal <amanag.11@gmail.com>
Co-authored-by: ExtReMLapin <3909752+ExtReMLapin@users.noreply.github.com>
Co-authored-by: Nadav Eidelstein <30617226+nodeav@users.noreply.github.com>
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Glenn Jocher 2 years ago committed by GitHub
parent 8c8c43922c
commit 28e48be5b6
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@ -7,7 +7,7 @@ on:
push: push:
branches: [main] branches: [main]
pull_request: pull_request:
branches: [main] branches: [main, updates]
schedule: schedule:
- cron: '0 0 * * *' # runs at 00:00 UTC every day - cron: '0 0 * * *' # runs at 00:00 UTC every day
@ -105,7 +105,9 @@ jobs:
from ultralytics.yolo.utils.benchmarks import benchmark from ultralytics.yolo.utils.benchmarks import benchmark
benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.61) benchmark(model='${{ matrix.model }}-cls.pt', imgsz=160, half=False, hard_fail=0.61)
- name: Benchmark Summary - name: Benchmark Summary
run: cat benchmarks.log run: |
cat benchmarks.log
echo "$(cat benchmarks.log)" >> $GITHUB_STEP_SUMMARY
Tests: Tests:
timeout-minutes: 60 timeout-minutes: 60
@ -133,9 +135,9 @@ jobs:
run: | run: |
python -m pip install --upgrade pip wheel python -m pip install --upgrade pip wheel
if [ "${{ matrix.torch }}" == "1.8.0" ]; then if [ "${{ matrix.torch }}" == "1.8.0" ]; then
pip install -e '.[export]' torch==1.8.0 torchvision==0.9.0 pytest --extra-index-url https://download.pytorch.org/whl/cpu pip install -e . torch==1.8.0 torchvision==0.9.0 pytest --extra-index-url https://download.pytorch.org/whl/cpu
else else
pip install -e '.[export]' pytest --extra-index-url https://download.pytorch.org/whl/cpu pip install -e . pytest --extra-index-url https://download.pytorch.org/whl/cpu
fi fi
- name: Check environment - name: Check environment
run: | run: |

@ -29,27 +29,24 @@ To request an Enterprise License please complete the form at [Ultralytics Licens
<img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a> <img width="100%" src="https://raw.githubusercontent.com/ultralytics/assets/main/yolov8/yolo-comparison-plots.png"></a>
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</div>
</div> </div>
## <div align="center">Documentation</div> ## <div align="center">Documentation</div>
@ -262,20 +259,17 @@ the [Ultralytics Community Forum](https://community.ultralytics.com/).
<a href="https://github.com/ultralytics" style="text-decoration:none;"> <a href="https://github.com/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
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<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
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@ -25,27 +25,24 @@ SOTA 模型。它在以前成功的 YOLO 版本基础上,引入了新的功能
如果要申请企业许可证,请填写 [Ultralytics 许可](https://ultralytics.com/license)。 如果要申请企业许可证,请填写 [Ultralytics 许可](https://ultralytics.com/license)。
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>
</div>
</div> </div>
## <div align="center">文档</div> ## <div align="center">文档</div>
@ -239,20 +236,17 @@ YOLOv8 在两种不同的 License 下可用:
<a href="https://github.com/ultralytics" style="text-decoration:none;"> <a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;"> <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;"> <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="3%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="3%" alt="" />
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="3%" alt="" /></a>
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@ -7,20 +7,17 @@
<a href="https://github.com/ultralytics" style="text-decoration:none;"> <a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;"> <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;"> <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.producthunt.com/@glenn_jocher" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-producthunt.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://youtube.com/ultralytics" style="text-decoration:none;"> <a href="https://youtube.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-youtube.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://www.facebook.com/ultralytics" style="text-decoration:none;"> <a href="https://www.tiktok.com/@ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-facebook.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-tiktok.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
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<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-instagram.png" width="2%" alt="" /></a>

@ -7,20 +7,17 @@
<a href="https://github.com/ultralytics" style="text-decoration:none;"> <a href="https://github.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-github.png" width="2%" alt="" /></a>
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<a href="https://www.linkedin.com/company/ultralytics/" style="text-decoration:none;"> <a href="https://www.linkedin.com/company/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-linkedin.png" width="2%" alt="" /></a>
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" /> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-transparent.png" width="2%" alt="" />
<a href="https://twitter.com/ultralytics" style="text-decoration:none;"> <a href="https://twitter.com/ultralytics" style="text-decoration:none;">
<img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a> <img src="https://github.com/ultralytics/assets/raw/main/social/logo-social-twitter.png" width="2%" alt="" /></a>
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, GPL-3.0 license # Ultralytics YOLO 🚀, GPL-3.0 license
__version__ = '8.0.55' __version__ = '8.0.56'
from ultralytics.yolo.engine.model import YOLO from ultralytics.yolo.engine.model import YOLO
from ultralytics.yolo.utils.checks import check_yolo as checks from ultralytics.yolo.utils.checks import check_yolo as checks

@ -0,0 +1,54 @@
# Ultralytics YOLO 🚀, GPL-3.0 license
# YOLOv8 object detection model with P2-P5 outputs. For Usage examples see https://docs.ultralytics.com/tasks/detect
# Parameters
nc: 80 # number of classes
scales: # model compound scaling constants, i.e. 'model=yolov8n.yaml' will call yolov8.yaml with scale 'n'
# [depth, width, max_channels]
n: [0.33, 0.25, 1024]
s: [0.33, 0.50, 1024]
m: [0.67, 0.75, 768]
l: [1.00, 1.00, 512]
x: [1.00, 1.25, 512]
# YOLOv8.0 backbone
backbone:
# [from, repeats, module, args]
- [-1, 1, Conv, [64, 3, 2]] # 0-P1/2
- [-1, 1, Conv, [128, 3, 2]] # 1-P2/4
- [-1, 3, C2f, [128, True]]
- [-1, 1, Conv, [256, 3, 2]] # 3-P3/8
- [-1, 6, C2f, [256, True]]
- [-1, 1, Conv, [512, 3, 2]] # 5-P4/16
- [-1, 6, C2f, [512, True]]
- [-1, 1, Conv, [1024, 3, 2]] # 7-P5/32
- [-1, 3, C2f, [1024, True]]
- [-1, 1, SPPF, [1024, 5]] # 9
# YOLOv8.0-p2 head
head:
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 6], 1, Concat, [1]] # cat backbone P4
- [-1, 3, C2f, [512]] # 12
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 4], 1, Concat, [1]] # cat backbone P3
- [-1, 3, C2f, [256]] # 15 (P3/8-small)
- [-1, 1, nn.Upsample, [None, 2, 'nearest']]
- [[-1, 2], 1, Concat, [1]] # cat backbone P2
- [-1, 3, C2f, [128]] # 18 (P2/4-xsmall)
- [-1, 1, Conv, [128, 3, 2]]
- [[-1, 15], 1, Concat, [1]] # cat head P3
- [-1, 3, C2f, [256]] # 21 (P3/8-small)
- [-1, 1, Conv, [256, 3, 2]]
- [[-1, 12], 1, Concat, [1]] # cat head P4
- [-1, 3, C2f, [512]] # 24 (P4/16-medium)
- [-1, 1, Conv, [512, 3, 2]]
- [[-1, 9], 1, Concat, [1]] # cat head P5
- [-1, 3, C2f, [1024]] # 27 (P5/32-large)
- [[18, 21, 24, 27], 1, Detect, [nc]] # Detect(P2, P3, P4, P5)

@ -53,3 +53,7 @@ class BaseTrack:
def mark_removed(self): def mark_removed(self):
self.state = TrackState.Removed self.state = TrackState.Removed
@staticmethod
def reset_id():
BaseTrack._count = 0

@ -168,6 +168,7 @@ class BYTETracker:
self.args = args self.args = args
self.max_time_lost = int(frame_rate / 30.0 * args.track_buffer) self.max_time_lost = int(frame_rate / 30.0 * args.track_buffer)
self.kalman_filter = self.get_kalmanfilter() self.kalman_filter = self.get_kalmanfilter()
self.reset_id()
def update(self, results, img=None): def update(self, results, img=None):
self.frame_id += 1 self.frame_id += 1
@ -299,6 +300,9 @@ class BYTETracker:
def multi_predict(self, tracks): def multi_predict(self, tracks):
STrack.multi_predict(tracks) STrack.multi_predict(tracks)
def reset_id(self):
STrack.reset_id()
@staticmethod @staticmethod
def joint_stracks(tlista, tlistb): def joint_stracks(tlista, tlistb):
exists = {} exists = {}

@ -284,6 +284,7 @@ class LoadPilAndNumpy:
def __init__(self, im0, imgsz=640, stride=32, auto=True, transforms=None): def __init__(self, im0, imgsz=640, stride=32, auto=True, transforms=None):
if not isinstance(im0, list): if not isinstance(im0, list):
im0 = [im0] im0 = [im0]
self.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(im0)]
self.im0 = [self._single_check(im) for im in im0] self.im0 = [self._single_check(im) for im in im0]
self.imgsz = imgsz self.imgsz = imgsz
self.stride = stride self.stride = stride
@ -291,7 +292,6 @@ class LoadPilAndNumpy:
self.transforms = transforms self.transforms = transforms
self.mode = 'image' self.mode = 'image'
# generate fake paths # generate fake paths
self.paths = [getattr(im, 'filename', f'image{i}.jpg') for i, im in enumerate(self.im0)]
self.bs = len(self.im0) self.bs = len(self.im0)
@staticmethod @staticmethod

@ -125,7 +125,10 @@ class YOLODataset(BaseDataset):
self.label_files = img2label_paths(self.im_files) self.label_files = img2label_paths(self.im_files)
cache_path = Path(self.label_files[0]).parent.with_suffix('.cache') cache_path = Path(self.label_files[0]).parent.with_suffix('.cache')
try: try:
import gc
gc.disable() # reduce pickle load time https://github.com/ultralytics/ultralytics/pull/1585
cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True # load dict cache, exists = np.load(str(cache_path), allow_pickle=True).item(), True # load dict
gc.enable()
assert cache['version'] == self.cache_version # matches current version assert cache['version'] == self.cache_version # matches current version
assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash assert cache['hash'] == get_hash(self.label_files + self.im_files) # identical hash
except (FileNotFoundError, AssertionError, AttributeError): except (FileNotFoundError, AssertionError, AttributeError):

@ -129,13 +129,14 @@ class Results:
annotator = Annotator(deepcopy(self.orig_img), line_width, font_size, font, pil, example) annotator = Annotator(deepcopy(self.orig_img), line_width, font_size, font, pil, example)
boxes = self.boxes boxes = self.boxes
masks = self.masks masks = self.masks
logits = self.probs probs = self.probs
names = self.names names = self.names
hide_labels, hide_conf = False, not show_conf
if boxes is not None: if boxes is not None:
for d in reversed(boxes): for d in reversed(boxes):
cls, conf = d.cls.squeeze(), d.conf.squeeze() c, conf, id = int(d.cls), float(d.conf), None if d.id is None else int(d.id.item())
c = int(cls) name = ('' if id is None else f'id:{id} ') + names[c]
label = (f'{names[c]}' if names else f'{c}') + (f'{conf:.2f}' if show_conf else '') label = None if hide_labels else (name if hide_conf else f'{name} {conf:.2f}')
annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True)) annotator.box_label(d.xyxy.squeeze(), label, color=colors(c, True))
if masks is not None: if masks is not None:
@ -143,10 +144,10 @@ class Results:
im = F.resize(im.contiguous(), masks.data.shape[1:]) / 255 im = F.resize(im.contiguous(), masks.data.shape[1:]) / 255
annotator.masks(masks.data, colors=[colors(x, True) for x in boxes.cls], im_gpu=im) annotator.masks(masks.data, colors=[colors(x, True) for x in boxes.cls], im_gpu=im)
if logits is not None: if probs is not None:
n5 = min(len(self.names), 5) n5 = min(len(names), 5)
top5i = logits.argsort(0, descending=True)[:n5].tolist() # top 5 indices top5i = probs.argsort(0, descending=True)[:n5].tolist() # top 5 indices
text = f"{', '.join(f'{names[j] if names else j} {logits[j]:.2f}' for j in top5i)}, " text = f"{', '.join(f'{names[j] if names else j} {probs[j]:.2f}' for j in top5i)}, "
annotator.text((32, 32), text, txt_color=(255, 255, 255)) # TODO: allow setting colors annotator.text((32, 32), text, txt_color=(255, 255, 255)) # TODO: allow setting colors
return np.asarray(annotator.im) if annotator.pil else annotator.im return np.asarray(annotator.im) if annotator.pil else annotator.im

@ -445,7 +445,7 @@ def get_user_config_dir(sub_dir='Ultralytics'):
return path return path
USER_CONFIG_DIR = get_user_config_dir() # Ultralytics settings dir USER_CONFIG_DIR = os.getenv('YOLO_CONFIG_DIR', get_user_config_dir()) # Ultralytics settings dir
def emojis(string=''): def emojis(string=''):

@ -21,7 +21,7 @@ import torch
from matplotlib import font_manager from matplotlib import font_manager
from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads, from ultralytics.yolo.utils import (AUTOINSTALL, LOGGER, ONLINE, ROOT, USER_CONFIG_DIR, TryExcept, colorstr, downloads,
emojis, is_colab, is_docker, is_jupyter, is_online, is_pip_package) emojis, is_colab, is_docker, is_kaggle, is_online, is_pip_package)
def is_ascii(s) -> bool: def is_ascii(s) -> bool:
@ -292,8 +292,7 @@ def check_yaml(file, suffix=('.yaml', '.yml'), hard=True):
def check_imshow(warn=False): def check_imshow(warn=False):
# Check if environment supports image displays # Check if environment supports image displays
try: try:
assert not is_jupyter() assert not any((is_colab(), is_kaggle(), is_docker()))
assert not is_docker()
cv2.imshow('test', np.zeros((1, 1, 3))) cv2.imshow('test', np.zeros((1, 1, 3)))
cv2.waitKey(1) cv2.waitKey(1)
cv2.destroyAllWindows() cv2.destroyAllWindows()

@ -27,6 +27,7 @@ WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
TORCH_1_9 = check_version(torch.__version__, '1.9.0') TORCH_1_9 = check_version(torch.__version__, '1.9.0')
TORCH_1_11 = check_version(torch.__version__, '1.11.0') TORCH_1_11 = check_version(torch.__version__, '1.11.0')
TORCH_1_12 = check_version(torch.__version__, '1.12.0') TORCH_1_12 = check_version(torch.__version__, '1.12.0')
TORCH_2_X = check_version(torch.__version__, minimum='2.0')
@contextmanager @contextmanager
@ -95,7 +96,8 @@ def select_device(device='', batch=0, newline=False, verbose=True):
p = torch.cuda.get_device_properties(i) p = torch.cuda.get_device_properties(i)
s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB s += f"{'' if i == 0 else space}CUDA:{d} ({p.name}, {p.total_memory / (1 << 20):.0f}MiB)\n" # bytes to MB
arg = 'cuda:0' arg = 'cuda:0'
elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available(): # prefer MPS if available elif mps and getattr(torch, 'has_mps', False) and torch.backends.mps.is_available() and TORCH_2_X:
# prefer MPS if available
s += 'MPS\n' s += 'MPS\n'
arg = 'mps' arg = 'mps'
else: # revert to CPU else: # revert to CPU

@ -134,6 +134,7 @@ class Loss:
else: else:
i = targets[:, 0] # image index i = targets[:, 0] # image index
_, counts = i.unique(return_counts=True) _, counts = i.unique(return_counts=True)
counts = counts.to(dtype=torch.int32)
out = torch.zeros(batch_size, counts.max(), 5, device=self.device) out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
for j in range(batch_size): for j in range(batch_size):
matches = i == j matches = i == j

@ -108,8 +108,9 @@ class DetectionValidator(BaseValidator):
# Save # Save
if self.args.save_json: if self.args.save_json:
self.pred_to_json(predn, batch['im_file'][si]) self.pred_to_json(predn, batch['im_file'][si])
# if self.args.save_txt: if self.args.save_txt:
# save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / f'{path.stem}.txt') file = self.save_dir / 'labels' / f'{Path(batch["im_file"][si]).stem}.txt'
self.save_one_txt(predn, self.args.save_conf, shape, file)
def finalize_metrics(self, *args, **kwargs): def finalize_metrics(self, *args, **kwargs):
self.metrics.speed = self.speed self.metrics.speed = self.speed
@ -197,6 +198,14 @@ class DetectionValidator(BaseValidator):
fname=self.save_dir / f'val_batch{ni}_pred.jpg', fname=self.save_dir / f'val_batch{ni}_pred.jpg',
names=self.names) # pred names=self.names) # pred
def save_one_txt(self, predn, save_conf, shape, file):
gn = torch.tensor(shape)[[1, 0, 1, 0]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (ops.xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if save_conf else (cls, *xywh) # label format
with open(file, 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
def pred_to_json(self, predn, filename): def pred_to_json(self, predn, filename):
stem = Path(filename).stem stem = Path(filename).stem
image_id = int(stem) if stem.isnumeric() else stem image_id = int(stem) if stem.isnumeric() else stem

@ -79,7 +79,7 @@ class SegLoss(Loss):
# targets # targets
try: try:
batch_idx = batch['batch_idx'].view(-1, 1) batch_idx = batch['batch_idx'].view(-1, 1)
targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes']), 1) targets = torch.cat((batch_idx, batch['cls'].view(-1, 1), batch['bboxes'].to(dtype)), 1)
targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]]) targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0) mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)

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