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ultralytics 8.0.167 Tuner updates and HUB Pose and Classify fixes (#4656)

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Glenn Jocher 2023-08-31 01:38:42 +02:00 committed by GitHub
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21 changed files with 174 additions and 144 deletions
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3
.github/workflows/ci.yaml vendored
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@ -112,6 +112,9 @@ jobs:
run: |
python -m pip install --upgrade pip wheel
pip install -e ".[export]" coverage --extra-index-url https://download.pytorch.org/whl/cpu
# Fix SavedModel issue "partially initialized module 'jax' has no attribute 'version' (most likely due to a circular import)" in https://github.com/google/jax/discussions/14036
# pip install -U 'jax!=0.4.15' 'jaxlib!=0.4.15'
# yolo settings tensorboard=False
yolo export format=tflite imgsz=32 || true
- name: Check environment
run: |

4
ultralytics/__init__.py
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@ -1,6 +1,6 @@
# Ultralytics YOLO 🚀, AGPL-3.0 license
__version__ = '8.0.166'
__version__ = '8.0.167'
from ultralytics.models import RTDETR, SAM, YOLO
from ultralytics.models.fastsam import FastSAM
@ -9,4 +9,4 @@ from ultralytics.utils import SETTINGS as settings
from ultralytics.utils.checks import check_yolo as checks
from ultralytics.utils.downloads import download
__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'settings' # allow simpler import
__all__ = '__version__', 'YOLO', 'NAS', 'SAM', 'FastSAM', 'RTDETR', 'checks', 'download', 'settings'

46
ultralytics/data/utils.py
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@ -202,6 +202,28 @@ def polygons2masks_overlap(imgsz, segments, downsample_ratio=1):
return masks, index
def find_dataset_yaml(path: Path) -> Path:
"""
Find and return the YAML file associated with a Detect, Segment or Pose dataset.
This function searches for a YAML file at the root level of the provided directory first, and if not found, it
performs a recursive search. It prefers YAML files that have the samestem as the provided path. An AssertionError
is raised if no YAML file is found or if multiple YAML files are found.
Args:
path (Path): The directory path to search for the YAML file.
Returns:
(Path): The path of the found YAML file.
"""
files = list(path.glob('*.yaml')) or list(path.rglob('*.yaml')) # try root level first and then recursive
assert files, f"No YAML file found in '{path.resolve()}'"
if len(files) > 1:
files = [f for f in files if f.stem == path.stem] # prefer *.yaml files that match
assert len(files) == 1, f"Expected 1 YAML file in '{path.resolve()}', but found {len(files)}.\n{files}"
return files[0]
def check_det_dataset(dataset, autodownload=True):
"""
Download, verify, and/or unzip a dataset if not found locally.
@ -223,8 +245,8 @@ def check_det_dataset(dataset, autodownload=True):
# Download (optional)
extract_dir = ''
if isinstance(data, (str, Path)) and (zipfile.is_zipfile(data) or is_tarfile(data)):
new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False, curl=False)
data = next((DATASETS_DIR / new_dir).rglob('*.yaml'))
new_dir = safe_download(data, dir=DATASETS_DIR, unzip=True, delete=False)
data = find_dataset_yaml(DATASETS_DIR / new_dir)
extract_dir, autodownload = data.parent, False
# Read YAML (optional)
@ -316,6 +338,10 @@ def check_cls_dataset(dataset, split=''):
- 'names' (dict): A dictionary of class names in the dataset.
"""
# Download (optional if dataset=https://file.zip is passed directly)
if str(dataset).startswith(('http:/', 'https:/')):
dataset = safe_download(dataset, dir=DATASETS_DIR, unzip=True, delete=False)
dataset = Path(dataset)
data_dir = (dataset if dataset.is_dir() else (DATASETS_DIR / dataset)).resolve()
if not data_dir.is_dir():
@ -329,8 +355,8 @@ def check_cls_dataset(dataset, split=''):
s = f"Dataset download success ✅ ({time.time() - t:.1f}s), saved to {colorstr('bold', data_dir)}\n"
LOGGER.info(s)
train_set = data_dir / 'train'
val_set = data_dir / 'val' if (data_dir / 'val').exists() else data_dir / 'validation' if (
data_dir / 'validation').exists() else None # data/test or data/val
val_set = data_dir / 'val' if (data_dir / 'val').exists() else data_dir / 'validation' if \
(data_dir / 'validation').exists() else None # data/test or data/val
test_set = data_dir / 'test' if (data_dir / 'test').exists() else None # data/val or data/test
if split == 'val' and not val_set:
LOGGER.warning("WARNING ⚠️ Dataset 'split=val' not found, using 'split=test' instead.")
@ -414,16 +440,6 @@ class HUBDatasetStats:
self.stats = {'nc': len(data['names']), 'names': list(data['names'].values())} # statistics dictionary
self.data = data
@staticmethod
def _find_yaml(dir):
"""Return data.yaml file."""
files = list(dir.glob('*.yaml')) or list(dir.rglob('*.yaml')) # try root level first and then recursive
assert files, f"No *.yaml file found in '{dir.resolve()}'"
if len(files) > 1:
files = [f for f in files if f.stem == dir.stem] # prefer *.yaml files that match dir name
assert len(files) == 1, f"Expected 1 *.yaml file in '{dir.resolve()}', but found {len(files)}.\n{files}"
return files[0]
def _unzip(self, path):
"""Unzip data.zip."""
if not str(path).endswith('.zip'): # path is data.yaml
@ -431,7 +447,7 @@ class HUBDatasetStats:
unzip_dir = unzip_file(path, path=path.parent)
assert unzip_dir.is_dir(), f'Error unzipping {path}, {unzip_dir} not found. ' \
f'path/to/abc.zip MUST unzip to path/to/abc/'
return True, str(unzip_dir), self._find_yaml(unzip_dir) # zipped, data_dir, yaml_path
return True, str(unzip_dir), find_dataset_yaml(unzip_dir) # zipped, data_dir, yaml_path
def _hub_ops(self, f):
"""Saves a compressed image for HUB previews."""

11
ultralytics/engine/exporter.py
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@ -438,18 +438,16 @@ class Exporter:
Path(asset).unlink() # delete zip
pnnx.chmod(0o777) # set read, write, and execute permissions for everyone
use_ncnn = True
ncnn_args = [
f'ncnnparam={f / "model.ncnn.param"}',
f'ncnnbin={f / "model.ncnn.bin"}',
f'ncnnpy={f / "model_ncnn.py"}', ] if use_ncnn else []
f'ncnnpy={f / "model_ncnn.py"}', ]
use_pnnx = False
pnnx_args = [
f'pnnxparam={f / "model.pnnx.param"}',
f'pnnxbin={f / "model.pnnx.bin"}',
f'pnnxpy={f / "model_pnnx.py"}',
f'pnnxonnx={f / "model.pnnx.onnx"}', ] if use_pnnx else []
f'pnnxonnx={f / "model.pnnx.onnx"}', ]
cmd = [
str(pnnx),
@ -462,7 +460,10 @@ class Exporter:
f.mkdir(exist_ok=True) # make ncnn_model directory
LOGGER.info(f"{prefix} running '{' '.join(cmd)}'")
subprocess.run(cmd, check=True)
for f_debug in 'debug.bin', 'debug.param', 'debug2.bin', 'debug2.param': # remove debug files
# Remove debug files
pnnx_files = [x.split('=')[-1] for x in pnnx_args]
for f_debug in ('debug.bin', 'debug.param', 'debug2.bin', 'debug2.param', *pnnx_files):
Path(f_debug).unlink(missing_ok=True)
yaml_save(f / 'metadata.yaml', self.metadata) # add metadata.yaml

7
ultralytics/engine/model.py
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@ -341,7 +341,8 @@ class Model:
self.trainer.train()
# Update model and cfg after training
if RANK in (-1, 0):
self.model, _ = attempt_load_one_weight(str(self.trainer.best))
ckpt = self.trainer.best if self.trainer.best.exists() else self.trainer.last
self.model, _ = attempt_load_one_weight(ckpt)
self.overrides = self.model.args
self.metrics = getattr(self.trainer.validator, 'metrics', None) # TODO: no metrics returned by DDP
return self.metrics
@ -360,9 +361,9 @@ class Model:
else:
from .tuner import Tuner
custom = {} # method defaults
custom = {'plots': False, 'save': False} # method defaults
args = {**self.overrides, **custom, **kwargs, 'mode': 'train'} # highest priority args on the right
return Tuner(args=args, _callbacks=self.callbacks)(model=self.model, iterations=iterations)
return Tuner(args=args, _callbacks=self.callbacks)(model=self, iterations=iterations)
def to(self, device):
"""

9
ultralytics/engine/trainer.py
View File

@ -115,7 +115,7 @@ class BaseTrainer:
try:
if self.args.task == 'classify':
self.data = check_cls_dataset(self.args.data)
elif self.args.data.split('.')[-1] in ('yaml', 'yml') or self.args.task in ('detect', 'segment'):
elif self.args.data.split('.')[-1] in ('yaml', 'yml') or self.args.task in ('detect', 'segment', 'pose'):
self.data = check_det_dataset(self.args.data)
if 'yaml_file' in self.data:
self.args.data = self.data['yaml_file'] # for validating 'yolo train data=url.zip' usage
@ -251,9 +251,8 @@ class BaseTrainer:
self.args.imgsz = check_imgsz(self.args.imgsz, stride=gs, floor=gs, max_dim=1)
# Batch size
if self.batch_size == -1:
if RANK == -1: # single-GPU only, estimate best batch size
self.args.batch = self.batch_size = check_train_batch_size(self.model, self.args.imgsz, self.amp)
if self.batch_size == -1 and RANK == -1: # single-GPU only, estimate best batch size
self.args.batch = self.batch_size = check_train_batch_size(self.model, self.args.imgsz, self.amp)
# Dataloaders
batch_size = self.batch_size // max(world_size, 1)
@ -262,7 +261,7 @@ class BaseTrainer:
self.test_loader = self.get_dataloader(self.testset, batch_size=batch_size * 2, rank=-1, mode='val')
self.validator = self.get_validator()
metric_keys = self.validator.metrics.keys + self.label_loss_items(prefix='val')
self.metrics = dict(zip(metric_keys, [0] * len(metric_keys))) # TODO: init metrics for plot_results()?
self.metrics = dict(zip(metric_keys, [0] * len(metric_keys)))
self.ema = ModelEMA(self.model)
if self.args.plots:
self.plot_training_labels()

45
ultralytics/engine/tuner.py
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@ -18,6 +18,7 @@ Example:
"""
import random
import time
from copy import deepcopy
import numpy as np
@ -51,7 +52,7 @@ class Tuner:
from ultralytics import YOLO
model = YOLO('yolov8n.pt')
model.tune(data='coco8.yaml', imgsz=640, epochs=100, iterations=10)
model.tune(data='coco8.yaml', imgsz=640, epochs=100, iterations=10, val=False, cache=True)
```
"""
@ -63,11 +64,11 @@ class Tuner:
args (dict, optional): Configuration for hyperparameter evolution.
"""
self.args = get_cfg(overrides=args)
self.space = {
self.space = { # key: (min, max, gain(optionaL))
# 'optimizer': tune.choice(['SGD', 'Adam', 'AdamW', 'NAdam', 'RAdam', 'RMSProp']),
'lr0': (1e-5, 1e-1),
'lrf': (0.01, 1.0), # final OneCycleLR learning rate (lr0 * lrf)
'momentum': (0.6, 0.98), # SGD momentum/Adam beta1
'momentum': (0.6, 0.98, 0.3), # SGD momentum/Adam beta1
'weight_decay': (0.0, 0.001), # optimizer weight decay 5e-4
'warmup_epochs': (0.0, 5.0), # warmup epochs (fractions ok)
'warmup_momentum': (0.0, 0.95), # warmup initial momentum
@ -86,13 +87,13 @@ class Tuner:
'mosaic': (0.0, 1.0), # image mixup (probability)
'mixup': (0.0, 1.0), # image mixup (probability)
'copy_paste': (0.0, 1.0)} # segment copy-paste (probability)
self.tune_dir = get_save_dir(self.args, name='tune')
self.tune_dir = get_save_dir(self.args, name='_tune')
self.evolve_csv = self.tune_dir / 'evolve.csv'
self.callbacks = _callbacks or callbacks.get_default_callbacks()
callbacks.add_integration_callbacks(self)
LOGGER.info(f"Initialized Tuner instance with 'tune_dir={self.tune_dir}'.")
def _mutate(self, parent='single', n=5, mutation=0.8, sigma=0.2, return_best=False):
def _mutate(self, parent='single', n=5, mutation=0.8, sigma=0.2):
"""
Mutates the hyperparameters based on bounds and scaling factors specified in `self.space`.
@ -111,10 +112,7 @@ class Tuner:
fitness = x[:, 0] # first column
n = min(n, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness)][:n] # top n mutations
if return_best:
return {k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())}
fitness = x[:, 0] # first column
w = fitness - fitness.min() + 1E-6 # weights (sum > 0)
w = x[:, 0] - x[:, 0].min() + 1E-6 # weights (sum > 0)
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
@ -124,7 +122,7 @@ class Tuner:
# Mutate
r = np.random # method
r.seed(int(time.time()))
g = np.array([self.space[k][0] for k in self.space.keys()]) # gains 0-1
g = np.array([v[2] if len(v) == 3 else 1.0 for k, v in self.space.items()]) # gains 0-1
ng = len(self.space)
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
@ -152,7 +150,7 @@ class Tuner:
4. Log the fitness score and mutated hyperparameters to a CSV file.
Args:
model (YOLO): A pre-initialized YOLO model to be used for training.
model (Model): A pre-initialized YOLO model to be used for training.
iterations (int): The number of generations to run the evolution for.
Note:
@ -160,6 +158,7 @@ class Tuner:
Ensure this path is set correctly in the Tuner instance.
"""
t0 = time.time()
self.tune_dir.mkdir(parents=True, exist_ok=True)
for i in range(iterations):
# Mutate hyperparameters
@ -167,17 +166,27 @@ class Tuner:
LOGGER.info(f'{prefix} Starting iteration {i + 1}/{iterations} with hyperparameters: {mutated_hyp}')
# Initialize and train YOLOv8 model
model = YOLO('yolov8n.pt')
train_args = {**vars(self.args), **mutated_hyp}
results = model.train(**train_args)
try:
train_args = {**vars(self.args), **mutated_hyp}
fitness = (deepcopy(model) or YOLO(self.args.model)).train(**train_args).fitness # results.fitness
except Exception as e:
LOGGER.warning(f'WARNING ❌️ training failure for hyperparameter tuning iteration {i}\n{e}')
fitness = 0.0
# Save results and mutated_hyp to evolve_csv
log_row = [round(fitness, 5)] + [mutated_hyp[k] for k in self.space.keys()]
headers = '' if self.evolve_csv.exists() else (','.join(['fitness_score'] + list(self.space.keys())) + '\n')
log_row = [results.fitness] + [mutated_hyp[k] for k in self.space.keys()]
with open(self.evolve_csv, 'a') as f:
f.write(headers + ','.join(map(str, log_row)) + '\n')
LOGGER.info(f'{prefix} All iterations complete. Results saved to {colorstr("bold", self.tune_dir)}')
best_hyp = self._mutate(return_best=True) # best hyps
yaml_save(self.tune_dir / 'best.yaml', best_hyp)
# Print tuning results
x = np.loadtxt(self.evolve_csv, ndmin=2, delimiter=',', skiprows=1)
fitness = x[:, 0] # first column
i = np.argsort(-fitness)[0] # best fitness index
LOGGER.info(f'\n{prefix} All iterations complete ✅ ({time.time() - t0:.2f}s)\n'
f'{prefix} Results saved to {colorstr("bold", self.tune_dir)}\n'
f'{prefix} Best fitness={fitness[i]} observed at iteration {i}')
# Save turning results
yaml_save(self.tune_dir / 'best.yaml', data={k: float(x[0, i + 1]) for i, k in enumerate(self.space.keys())})
yaml_print(self.tune_dir / 'best.yaml')

11
ultralytics/engine/validator.py
View File

@ -111,12 +111,12 @@ class BaseValidator:
if self.training:
self.device = trainer.device
self.data = trainer.data
model = trainer.ema.ema or trainer.model
self.args.half = self.device.type != 'cpu' # force FP16 val during training
model = trainer.ema.ema or trainer.model
model = model.half() if self.args.half else model.float()
self.model = model
# self.model = model
self.loss = torch.zeros_like(trainer.loss_items, device=trainer.device)
self.args.plots = trainer.stopper.possible_stop or (trainer.epoch == trainer.epochs - 1)
self.args.plots &= trainer.stopper.possible_stop or (trainer.epoch == trainer.epochs - 1)
model.eval()
else:
callbacks.add_integration_callbacks(self)
@ -126,7 +126,7 @@ class BaseValidator:
dnn=self.args.dnn,
data=self.args.data,
fp16=self.args.half)
self.model = model
# self.model = model
self.device = model.device # update device
self.args.half = model.fp16 # update half
stride, pt, jit, engine = model.stride, model.pt, model.jit, model.engine
@ -297,8 +297,7 @@ class BaseValidator:
def on_plot(self, name, data=None):
"""Registers plots (e.g. to be consumed in callbacks)"""
path = Path(name)
self.plots[path] = {'data': data, 'timestamp': time.time()}
self.plots[Path(name)] = {'data': data, 'timestamp': time.time()}
# TODO: may need to put these following functions into callback
def plot_val_samples(self, batch, ni):

57
ultralytics/models/utils/loss.py
View File

@ -84,35 +84,36 @@ class DETRLoss(nn.Module):
loss[name_giou] = self.loss_gain['giou'] * loss[name_giou]
return {k: v.squeeze() for k, v in loss.items()}
def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
# masks: [b, query, h, w], gt_mask: list[[n, H, W]]
name_mask = f'loss_mask{postfix}'
name_dice = f'loss_dice{postfix}'
# This function is for future RT-DETR Segment models
# def _get_loss_mask(self, masks, gt_mask, match_indices, postfix=''):
# # masks: [b, query, h, w], gt_mask: list[[n, H, W]]
# name_mask = f'loss_mask{postfix}'
# name_dice = f'loss_dice{postfix}'
#
# loss = {}
# if sum(len(a) for a in gt_mask) == 0:
# loss[name_mask] = torch.tensor(0., device=self.device)
# loss[name_dice] = torch.tensor(0., device=self.device)
# return loss
#
# num_gts = len(gt_mask)
# src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
# src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
# # TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
# loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
# torch.tensor([num_gts], dtype=torch.float32))
# loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
# return loss
loss = {}
if sum(len(a) for a in gt_mask) == 0:
loss[name_mask] = torch.tensor(0., device=self.device)
loss[name_dice] = torch.tensor(0., device=self.device)
return loss
num_gts = len(gt_mask)
src_masks, target_masks = self._get_assigned_bboxes(masks, gt_mask, match_indices)
src_masks = F.interpolate(src_masks.unsqueeze(0), size=target_masks.shape[-2:], mode='bilinear')[0]
# TODO: torch does not have `sigmoid_focal_loss`, but it's not urgent since we don't use mask branch for now.
loss[name_mask] = self.loss_gain['mask'] * F.sigmoid_focal_loss(src_masks, target_masks,
torch.tensor([num_gts], dtype=torch.float32))
loss[name_dice] = self.loss_gain['dice'] * self._dice_loss(src_masks, target_masks, num_gts)
return loss
@staticmethod
def _dice_loss(inputs, targets, num_gts):
inputs = F.sigmoid(inputs)
inputs = inputs.flatten(1)
targets = targets.flatten(1)
numerator = 2 * (inputs * targets).sum(1)
denominator = inputs.sum(-1) + targets.sum(-1)
loss = 1 - (numerator + 1) / (denominator + 1)
return loss.sum() / num_gts
# This function is for future RT-DETR Segment models
# @staticmethod
# def _dice_loss(inputs, targets, num_gts):
# inputs = F.sigmoid(inputs).flatten(1)
# targets = targets.flatten(1)
# numerator = 2 * (inputs * targets).sum(1)
# denominator = inputs.sum(-1) + targets.sum(-1)
# loss = 1 - (numerator + 1) / (denominator + 1)
# return loss.sum() / num_gts
def _get_loss_aux(self,
pred_bboxes,

57
ultralytics/models/utils/ops.py
View File

@ -110,34 +110,35 @@ class HungarianMatcher(nn.Module):
return [(torch.tensor(i, dtype=torch.long), torch.tensor(j, dtype=torch.long) + gt_groups[k])
for k, (i, j) in enumerate(indices)]
def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
# all masks share the same set of points for efficient matching
sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
sample_points = 2.0 * sample_points - 1.0
out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
out_mask = out_mask.flatten(0, 1)
tgt_mask = torch.cat(gt_mask).unsqueeze(1)
sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
with torch.cuda.amp.autocast(False):
# binary cross entropy cost
pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
cost_mask /= self.num_sample_points
# dice cost
out_mask = F.sigmoid(out_mask)
numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
cost_dice = 1 - (numerator + 1) / (denominator + 1)
C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
return C
# This function is for future RT-DETR Segment models
# def _cost_mask(self, bs, num_gts, masks=None, gt_mask=None):
# assert masks is not None and gt_mask is not None, 'Make sure the input has `mask` and `gt_mask`'
# # all masks share the same set of points for efficient matching
# sample_points = torch.rand([bs, 1, self.num_sample_points, 2])
# sample_points = 2.0 * sample_points - 1.0
#
# out_mask = F.grid_sample(masks.detach(), sample_points, align_corners=False).squeeze(-2)
# out_mask = out_mask.flatten(0, 1)
#
# tgt_mask = torch.cat(gt_mask).unsqueeze(1)
# sample_points = torch.cat([a.repeat(b, 1, 1, 1) for a, b in zip(sample_points, num_gts) if b > 0])
# tgt_mask = F.grid_sample(tgt_mask, sample_points, align_corners=False).squeeze([1, 2])
#
# with torch.cuda.amp.autocast(False):
# # binary cross entropy cost
# pos_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.ones_like(out_mask), reduction='none')
# neg_cost_mask = F.binary_cross_entropy_with_logits(out_mask, torch.zeros_like(out_mask), reduction='none')
# cost_mask = torch.matmul(pos_cost_mask, tgt_mask.T) + torch.matmul(neg_cost_mask, 1 - tgt_mask.T)
# cost_mask /= self.num_sample_points
#
# # dice cost
# out_mask = F.sigmoid(out_mask)
# numerator = 2 * torch.matmul(out_mask, tgt_mask.T)
# denominator = out_mask.sum(-1, keepdim=True) + tgt_mask.sum(-1).unsqueeze(0)
# cost_dice = 1 - (numerator + 1) / (denominator + 1)
#
# C = self.cost_gain['mask'] * cost_mask + self.cost_gain['dice'] * cost_dice
# return C
def get_cdn_group(batch,

2
ultralytics/models/yolo/classify/val.py
View File

@ -55,7 +55,7 @@ class ClassificationValidator(BaseValidator):
def update_metrics(self, preds, batch):
"""Updates running metrics with model predictions and batch targets."""
n5 = min(len(self.model.names), 5)
n5 = min(len(self.names), 5)
self.pred.append(preds.argsort(1, descending=True)[:, :n5])
self.targets.append(batch['cls'])

1
ultralytics/utils/__init__.py
View File

@ -325,6 +325,7 @@ def yaml_load(file='data.yaml', append_filename=False):
Returns:
(dict): YAML data and file name.
"""
assert Path(file).suffix in ('.yaml', '.yml'), f'Attempting to load non-YAML file {file} with yaml_load()'
with open(file, errors='ignore', encoding='utf-8') as f:
s = f.read() # string

4
ultralytics/utils/callbacks/clearml.py
View File

@ -9,14 +9,14 @@ from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
from ultralytics.utils.torch_utils import model_info_for_loggers
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['clearml'] is True # verify integration is enabled
import clearml
from clearml import Task
from clearml.binding.frameworks.pytorch_bind import PatchPyTorchModelIO
from clearml.binding.matplotlib_bind import PatchedMatplotlib
assert hasattr(clearml, '__version__') # verify package is not directory
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['clearml'] is True # verify integration is enabled
except (ImportError, AssertionError):
clearml = None

4
ultralytics/utils/callbacks/comet.py
View File

@ -7,11 +7,11 @@ from ultralytics.utils import LOGGER, RANK, SETTINGS, TESTS_RUNNING, ops
from ultralytics.utils.torch_utils import model_info_for_loggers
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['comet'] is True # verify integration is enabled
import comet_ml
assert not TESTS_RUNNING # do not log pytest
assert hasattr(comet_ml, '__version__') # verify package is not directory
assert SETTINGS['comet'] is True # verify integration is enabled
except (ImportError, AssertionError):
comet_ml = None

5
ultralytics/utils/callbacks/dvc.py
View File

@ -10,13 +10,12 @@ from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
from ultralytics.utils.torch_utils import model_info_for_loggers
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['dvc'] is True # verify integration is enabled
from importlib.metadata import version
import dvclive
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['dvc'] is True # verify integration is enabled
ver = version('dvclive')
if pkg.parse_version(ver) < pkg.parse_version('2.11.0'):
LOGGER.debug(f'DVCLive is detected but version {ver} is incompatible (>=2.11 required).')

4
ultralytics/utils/callbacks/mlflow.py
View File

@ -7,11 +7,11 @@ from pathlib import Path
from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['mlflow'] is True # verify integration is enabled
import mlflow
assert not TESTS_RUNNING # do not log pytest
assert hasattr(mlflow, '__version__') # verify package is not directory
assert SETTINGS['mlflow'] is True # verify integration is enabled
except (ImportError, AssertionError):
mlflow = None

4
ultralytics/utils/callbacks/neptune.py
View File

@ -7,12 +7,12 @@ from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING
from ultralytics.utils.torch_utils import model_info_for_loggers
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['neptune'] is True # verify integration is enabled
import neptune
from neptune.types import File
assert not TESTS_RUNNING # do not log pytest
assert hasattr(neptune, '__version__')
assert SETTINGS['neptune'] is True # verify integration is enabled
except (ImportError, AssertionError):
neptune = None

3
ultralytics/utils/callbacks/raytune.py
View File

@ -3,11 +3,10 @@
from ultralytics.utils import SETTINGS
try:
assert SETTINGS['raytune'] is True # verify integration is enabled
import ray
from ray import tune
from ray.air import session
assert SETTINGS['raytune'] is True # verify integration is enabled
except (ImportError, AssertionError):
tune = None

4
ultralytics/utils/callbacks/tensorboard.py
View File

@ -3,11 +3,9 @@
from ultralytics.utils import LOGGER, SETTINGS, TESTS_RUNNING, colorstr
try:
from torch.utils.tensorboard import SummaryWriter
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['tensorboard'] is True # verify integration is enabled
from torch.utils.tensorboard import SummaryWriter
# TypeError for handling 'Descriptors cannot not be created directly.' protobuf errors in Windows
except (ImportError, AssertionError, TypeError):
SummaryWriter = None

4
ultralytics/utils/callbacks/wb.py
View File

@ -4,11 +4,11 @@ from ultralytics.utils import SETTINGS, TESTS_RUNNING
from ultralytics.utils.torch_utils import model_info_for_loggers
try:
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['wandb'] is True # verify integration is enabled
import wandb as wb
assert hasattr(wb, '__version__')
assert not TESTS_RUNNING # do not log pytest
assert SETTINGS['wandb'] is True # verify integration is enabled
except (ImportError, AssertionError):
wb = None

33
ultralytics/utils/downloads.py
View File

@ -160,7 +160,7 @@ def unzip_file(file, path=None, exclude=('.DS_Store', '__MACOSX'), exist_ok=Fals
# Check if destination directory already exists and contains files
if path.exists() and any(path.iterdir()) and not exist_ok:
# If it exists and is not empty, return the path without unzipping
LOGGER.info(f'Skipping {file} unzip (already unzipped)')
LOGGER.warning(f'WARNING ⚠️ Skipping {file} unzip as destination directory {path} is not empty.')
return path
for f in tqdm(files,
@ -185,22 +185,25 @@ def check_disk_space(url='https://ultralytics.com/assets/coco128.zip', sf=1.5, h
Returns:
(bool): True if there is sufficient disk space, False otherwise.
"""
with contextlib.suppress(Exception):
gib = 1 << 30 # bytes per GiB
data = int(requests.head(url).headers['Content-Length']) / gib # file size (GB)
total, used, free = (x / gib for x in shutil.disk_usage('/')) # bytes
if data * sf < free:
return True # sufficient space
r = requests.head(url) # response
# Insufficient space
text = (f'WARNING ⚠️ Insufficient free disk space {free:.1f} GB < {data * sf:.3f} GB required, '
f'Please free {data * sf - free:.1f} GB additional disk space and try again.')
if hard:
raise MemoryError(text)
LOGGER.warning(text)
return False
# Check response
assert r.status_code < 400, f'URL error for {url}: {r.status_code} {r.reason}'
return True
# Check file size
gib = 1 << 30 # bytes per GiB
data = int(r.headers.get('Content-Length', 0)) / gib # file size (GB)
total, used, free = (x / gib for x in shutil.disk_usage('/')) # bytes
if data * sf < free:
return True # sufficient space
# Insufficient space
text = (f'WARNING ⚠️ Insufficient free disk space {free:.1f} GB < {data * sf:.3f} GB required, '
f'Please free {data * sf - free:.1f} GB additional disk space and try again.')
if hard:
raise MemoryError(text)
LOGGER.warning(text)
return False
def get_google_drive_file_info(link):