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YOLOv5 updates (#90)

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Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
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Glenn Jocher 2 years ago committed by GitHub
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commit 98815d560f
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27 changed files with 286 additions and 166 deletions
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2
requirements.txt
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@ -26,7 +26,7 @@ seaborn>=0.11.0
# Export --------------------------------------
# coremltools>=5.2 # CoreML export
# onnx>=1.9.0 # ONNX export
# onnx>=1.12.0 # ONNX export
# onnx-simplifier>=0.4.1 # ONNX simplifier
# nvidia-pyindex # TensorRT export
# nvidia-tensorrt # TensorRT export

2
ultralytics/tests/data/dataloader/yolodetection.py
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@ -55,7 +55,7 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
)
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def test(cfg):
cfg.task = "detect"
cfg.mode = "train"

2
ultralytics/tests/data/dataloader/yolosegment.py
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@ -54,7 +54,7 @@ def plot_one_box(x, img, color=None, label=None, line_thickness=None):
)
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def test(cfg):
cfg.task = "segment"
cfg.mode = "train"

19
ultralytics/yolo/data/augment.py
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@ -82,7 +82,7 @@ class BaseMixTransform:
indexes = [indexes]
# get images information will be used for Mosaic or MixUp
mix_labels = [deepcopy(dataset.get_label_info(index)) for index in indexes]
mix_labels = [dataset.get_label_info(index) for index in indexes]
if self.pre_transform is not None:
for i, data in enumerate(mix_labels):
@ -134,9 +134,8 @@ class Mosaic(BaseMixTransform):
assert len(labels.get("mix_labels", [])) > 0, "There are no other images for mosaic augment."
s = self.imgsz
yc, xc = (int(random.uniform(-x, 2 * s + x)) for x in self.border) # mosaic center x, y
mix_labels = labels["mix_labels"]
for i in range(4):
labels_patch = deepcopy(labels) if i == 0 else deepcopy(mix_labels[i - 1])
labels_patch = (labels if i == 0 else labels["mix_labels"][i - 1]).copy()
# Load image
img = labels_patch["img"]
h, w = labels_patch["resized_shape"]
@ -186,9 +185,8 @@ class Mosaic(BaseMixTransform):
"ori_shape": mosaic_labels[0]["ori_shape"],
"resized_shape": (self.imgsz * 2, self.imgsz * 2),
"im_file": mosaic_labels[0]["im_file"],
"cls": np.concatenate(cls, 0)}
final_labels["instances"] = Instances.concatenate(instances, axis=0)
"cls": np.concatenate(cls, 0),
"instances": Instances.concatenate(instances, axis=0)}
final_labels["instances"].clip(self.imgsz * 2, self.imgsz * 2)
return final_labels
@ -345,7 +343,6 @@ class RandomPerspective:
Affine images and targets.
Args:
img(ndarray): image.
labels(Dict): a dict of `bboxes`, `segments`, `keypoints`.
"""
img = labels["img"]
@ -387,7 +384,7 @@ class RandomPerspective:
return labels
def box_candidates(self, box1, box2, wh_thr=2, ar_thr=100, area_thr=0.1, eps=1e-16): # box1(4,n), box2(4,n)
# Compute candidate boxes: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
# Compute box candidates: box1 before augment, box2 after augment, wh_thr (pixels), aspect_ratio_thr, area_ratio
w1, h1 = box1[2] - box1[0], box1[3] - box1[1]
w2, h2 = box2[2] - box2[0], box2[3] - box2[1]
ar = np.maximum(w2 / (h2 + eps), h2 / (w2 + eps)) # aspect ratio
@ -609,6 +606,7 @@ class Format:
self.batch_idx = batch_idx # keep the batch indexes
def __call__(self, labels):
labels.pop("dataset", None)
img = labels["img"]
h, w = img.shape[:2]
cls = labels.pop("cls")
@ -672,10 +670,7 @@ def mosaic_transforms(imgsz, hyp):
),])
return Compose([
pre_transform,
MixUp(
pre_transform=pre_transform,
p=hyp.mixup,
),
MixUp(pre_transform=pre_transform, p=hyp.mixup),
Albumentations(p=1.0),
RandomHSV(hgain=hyp.hsv_h, sgain=hyp.hsv_s, vgain=hyp.hsv_v),
RandomFlip(direction="vertical", p=hyp.flipud),

8
ultralytics/yolo/data/base.py
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@ -1,4 +1,5 @@
import glob
import math
import os
from multiprocessing.pool import ThreadPool
from pathlib import Path
@ -121,7 +122,7 @@ class BaseDataset(Dataset):
r = self.imgsz / max(h0, w0) # ratio
if r != 1: # if sizes are not equal
interp = cv2.INTER_LINEAR if (self.augment or r > 1) else cv2.INTER_AREA
im = cv2.resize(im, (int(w0 * r), int(h0 * r)), interpolation=interp)
im = cv2.resize(im, (math.ceil(w0 * r), math.ceil(h0 * r)), interpolation=interp)
return im, (h0, w0), im.shape[:2] # im, hw_original, hw_resized
return self.ims[i], self.im_hw0[i], self.im_hw[i] # im, hw_original, hw_resized
@ -179,10 +180,7 @@ class BaseDataset(Dataset):
def get_label_info(self, index):
label = self.labels[index].copy()
img, (h0, w0), (h, w) = self.load_image(index)
label["img"] = img
label["ori_shape"] = (h0, w0)
label["resized_shape"] = (h, w)
label["img"], label["ori_shape"], label["resized_shape"] = self.load_image(index)
if self.rect:
label["rect_shape"] = self.batch_shapes[self.batch[index]]
label = self.update_labels_info(label)

30
ultralytics/yolo/data/build.py
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@ -64,7 +64,7 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank
label_path=label_path,
imgsz=cfg.imgsz,
batch_size=batch_size,
augment=True if mode == "train" else False, # augmentation
augment=mode == "train", # augmentation
hyp=cfg, # TODO: probably add a get_hyps_from_cfg function
rect=cfg.rect if mode == "train" else True, # rectangular batches
cache=None if cfg.noval else cfg.get("cache", None),
@ -73,31 +73,25 @@ def build_dataloader(cfg, batch_size, img_path, stride=32, label_path=None, rank
pad=0.0 if mode == "train" else 0.5,
prefix=colorstr(f"{mode}: "),
use_segments=cfg.task == "segment",
use_keypoints=cfg.task == "keypoint",
)
use_keypoints=cfg.task == "keypoint")
batch_size = min(batch_size, len(dataset))
nd = torch.cuda.device_count() # number of CUDA devices
workers = cfg.workers if mode == "train" else cfg.workers * 2
nw = min([os.cpu_count() // max(nd, 1), batch_size if batch_size > 1 else 0, workers]) # number of workers
sampler = None if rank == -1 else distributed.DistributedSampler(dataset, shuffle=shuffle)
loader = DataLoader if cfg.image_weights else InfiniteDataLoader # only DataLoader allows for attribute updates
loader = DataLoader if cfg.image_weights or cfg.close_mosaic else InfiniteDataLoader # allow attribute updates
generator = torch.Generator()
generator.manual_seed(6148914691236517205 + RANK)
return (
loader(
dataset=dataset,
batch_size=batch_size,
shuffle=shuffle and sampler is None,
num_workers=nw,
sampler=sampler,
pin_memory=PIN_MEMORY,
collate_fn=getattr(dataset, "collate_fn", None),
worker_init_fn=seed_worker,
generator=generator,
),
dataset,
)
return loader(dataset=dataset,
batch_size=batch_size,
shuffle=shuffle and sampler is None,
num_workers=nw,
sampler=sampler,
pin_memory=PIN_MEMORY,
collate_fn=getattr(dataset, "collate_fn", None),
worker_init_fn=seed_worker,
generator=generator), dataset
# build classification

12
ultralytics/yolo/data/dataset.py
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@ -124,13 +124,9 @@ class YOLODataset(BaseDataset):
# TODO: use hyp config to set all these augmentations
def build_transforms(self, hyp=None):
mosaic = self.augment and not self.rect
# mosaic = False
if self.augment:
if mosaic:
transforms = mosaic_transforms(self.imgsz, hyp)
else:
transforms = affine_transforms(self.imgsz, hyp)
mosaic = self.augment and not self.rect
transforms = mosaic_transforms(self.imgsz, hyp) if mosaic else affine_transforms(self.imgsz, hyp)
else:
transforms = Compose([LetterBox(new_shape=(self.imgsz, self.imgsz))])
transforms.append(
@ -143,7 +139,7 @@ class YOLODataset(BaseDataset):
def update_labels_info(self, label):
"""custom your label format here"""
# NOTE: cls is not with bboxes now, since other tasks like classification and semantic segmentation need a independent cls label
# NOTE: cls is not with bboxes now, classification and semantic segmentation need an independent cls label
# we can make it also support classification and semantic segmentation by add or remove some dict keys there.
bboxes = label.pop("bboxes")
segments = label.pop("segments")
@ -206,7 +202,7 @@ class ClassificationDataset(torchvision.datasets.ImageFolder):
sample = self.album_transforms(image=cv2.cvtColor(im, cv2.COLOR_BGR2RGB))["image"]
else:
sample = self.torch_transforms(im)
return OrderedDict(img=sample, cls=j)
return {'img': sample, 'cls': j}
def __len__(self) -> int:
return len(self.samples)

113
ultralytics/yolo/data/datasets/coco.yaml
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@ -0,0 +1,113 @@
# YOLOv5 🚀 by Ultralytics, GPL-3.0 license
# COCO 2017 dataset http://cocodataset.org by Microsoft
# Example usage: python train.py --data coco.yaml
# parent
# ├── yolov5
# └── datasets
# └── coco ← downloads here (20.1 GB)
# Train/val/test sets as 1) dir: path/to/imgs, 2) file: path/to/imgs.txt, or 3) list: [path/to/imgs1, path/to/imgs2, ..]
path: ../datasets/coco # dataset root dir
train: train2017.txt # train images (relative to 'path') 118287 images
val: val2017.txt # val images (relative to 'path') 5000 images
test: test-dev2017.txt # 20288 of 40670 images, submit to https://competitions.codalab.org/competitions/20794
# Classes
names:
0: person
1: bicycle
2: car
3: motorcycle
4: airplane
5: bus
6: train
7: truck
8: boat
9: traffic light
10: fire hydrant
11: stop sign
12: parking meter
13: bench
14: bird
15: cat
16: dog
17: horse
18: sheep
19: cow
20: elephant
21: bear
22: zebra
23: giraffe
24: backpack
25: umbrella
26: handbag
27: tie
28: suitcase
29: frisbee
30: skis
31: snowboard
32: sports ball
33: kite
34: baseball bat
35: baseball glove
36: skateboard
37: surfboard
38: tennis racket
39: bottle
40: wine glass
41: cup
42: fork
43: knife
44: spoon
45: bowl
46: banana
47: apple
48: sandwich
49: orange
50: broccoli
51: carrot
52: hot dog
53: pizza
54: donut
55: cake
56: chair
57: couch
58: potted plant
59: bed
60: dining table
61: toilet
62: tv
63: laptop
64: mouse
65: remote
66: keyboard
67: cell phone
68: microwave
69: oven
70: toaster
71: sink
72: refrigerator
73: book
74: clock
75: vase
76: scissors
77: teddy bear
78: hair drier
79: toothbrush
# Download script/URL (optional)
download: |
from utils.general import download, Path
# Download labels
segments = True # segment or box labels
dir = Path(yaml['path']) # dataset root dir
url = 'https://github.com/ultralytics/yolov5/releases/download/v1.0/'
urls = [url + ('coco2017labels-segments.zip' if segments else 'coco2017labels.zip')] # labels
download(urls, dir=dir.parent)
# Download data
urls = ['http://images.cocodataset.org/zips/train2017.zip', # 19G, 118k images
'http://images.cocodataset.org/zips/val2017.zip', # 1G, 5k images
'http://images.cocodataset.org/zips/test2017.zip'] # 7G, 41k images (optional)
download(urls, dir=dir / 'images', threads=3)

10
ultralytics/yolo/engine/predictor.py
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@ -29,16 +29,14 @@ import platform
from pathlib import Path
import cv2
import torch
from ultralytics.yolo.data.dataloaders.stream_loaders import LoadImages, LoadScreenshots, LoadStreams
from ultralytics.yolo.data.utils import IMG_FORMATS, VID_FORMATS, check_dataset, check_dataset_yaml
from ultralytics.yolo.utils import LOGGER, ROOT, TQDM_BAR_FORMAT, colorstr, ops
from ultralytics.yolo.utils import LOGGER, ROOT, colorstr, ops
from ultralytics.yolo.utils.checks import check_file, check_imshow
from ultralytics.yolo.utils.configs import get_config
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
from ultralytics.yolo.utils.plotting import Annotator
from ultralytics.yolo.utils.torch_utils import check_imgsz, select_device, smart_inference_mode
DEFAULT_CONFIG = ROOT / "yolo/utils/configs/default.yaml"
@ -125,11 +123,7 @@ class BasePredictor:
@smart_inference_mode()
def __call__(self, source=None, model=None):
if not self.done_setup:
model = self.setup(source, model)
else:
model = self.model
model = self.model if self.done_setup else self.setup(source, model)
self.seen, self.windows, self.dt = 0, [], (ops.Profile(), ops.Profile(), ops.Profile())
for batch in self.dataset:
path, im, im0s, vid_cap, s = batch

39
ultralytics/yolo/engine/trainer.py
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@ -60,7 +60,8 @@ class BaseTrainer:
# device
self.device = utils.torch_utils.select_device(self.args.device, self.batch_size)
self.scaler = amp.GradScaler(enabled=self.device.type != 'cpu')
self.amp = self.device.type != 'cpu'
self.scaler = amp.GradScaler(enabled=self.amp)
# Model and Dataloaders.
self.model = self.args.model
@ -175,6 +176,10 @@ class BaseTrainer:
nw = max(round(self.args.warmup_epochs * nb), 100) # number of warmup iterations
last_opt_step = -1
self.trigger_callbacks("on_train_start")
self.log(f"Image sizes {self.args.imgsz} train, {self.args.imgsz} val\n"
f'Using {self.train_loader.num_workers * (world_size or 1)} dataloader workers\n'
f"Logging results to {colorstr('bold', self.save_dir)}\n"
f"Starting training for {self.epochs} epochs...")
for epoch in range(self.start_epoch, self.epochs):
self.epoch = epoch
self.trigger_callbacks("on_train_epoch_start")
@ -189,8 +194,6 @@ class BaseTrainer:
self.optimizer.zero_grad()
for i, batch in pbar:
self.trigger_callbacks("on_train_batch_start")
# forward
batch = self.preprocess_batch(batch)
# warmup
ni = i + nb * epoch
@ -204,17 +207,20 @@ class BaseTrainer:
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [self.args.warmup_momentum, self.args.momentum])
preds = self.model(batch["img"])
self.loss, self.loss_items = self.criterion(preds, batch)
if rank != -1:
self.loss *= world_size
self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
else self.loss_items
# backward
# Forward
with torch.cuda.amp.autocast(self.amp):
batch = self.preprocess_batch(batch)
preds = self.model(batch["img"])
self.loss, self.loss_items = self.criterion(preds, batch)
if rank != -1:
self.loss *= world_size
self.tloss = (self.tloss * i + self.loss_items) / (i + 1) if self.tloss is not None \
else self.loss_items
# Backward
self.scaler.scale(self.loss).backward()
# optimize
# Optimize - https://pytorch.org/docs/master/notes/amp_examples.html
if ni - last_opt_step >= self.accumulate:
self.optimizer_step()
last_opt_step = ni
@ -237,7 +243,7 @@ class BaseTrainer:
self.scheduler.step()
self.trigger_callbacks("on_train_epoch_end")
if rank in [-1, 0]:
if rank in {-1, 0}:
# validation
self.trigger_callbacks('on_val_start')
self.ema.update_attr(self.model, include=['yaml', 'nc', 'args', 'names', 'stride', 'class_weights'])
@ -245,7 +251,7 @@ class BaseTrainer:
if not self.args.noval or final_epoch:
self.metrics, self.fitness = self.validate()
self.trigger_callbacks('on_val_end')
log_vals = self.label_loss_items(self.tloss) | self.metrics | lr
log_vals = {**self.label_loss_items(self.tloss), **self.metrics, **lr}
self.save_metrics(metrics=log_vals)
# save model
@ -259,12 +265,13 @@ class BaseTrainer:
# TODO: termination condition
if rank in [-1, 0]:
if rank in {-1, 0}:
# do the last evaluation with best.pt
self.log(f'\n{epoch - self.start_epoch + 1} epochs completed in '
f'{(time.time() - self.train_time_start) / 3600:.3f} hours.')
self.final_eval()
if self.args.plots:
self.plot_metrics()
self.log(f"\nTraining complete ({(time.time() - self.train_time_start) / 3600:.3f} hours)")
self.log(f"Results saved to {colorstr('bold', self.save_dir)}")
self.trigger_callbacks('on_train_end')
dist.destroy_process_group() if world_size > 1 else None

73
ultralytics/yolo/engine/validator.py
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@ -1,4 +1,3 @@
import logging
from pathlib import Path
import torch
@ -9,10 +8,9 @@ from ultralytics.yolo.data.utils import check_dataset, check_dataset_yaml
from ultralytics.yolo.engine.trainer import DEFAULT_CONFIG
from ultralytics.yolo.utils import LOGGER, TQDM_BAR_FORMAT
from ultralytics.yolo.utils.files import increment_path
from ultralytics.yolo.utils.modeling import get_model
from ultralytics.yolo.utils.modeling.autobackend import AutoBackend
from ultralytics.yolo.utils.ops import Profile
from ultralytics.yolo.utils.torch_utils import check_imgsz, de_parallel, select_device
from ultralytics.yolo.utils.torch_utils import check_imgsz, de_parallel, select_device, smart_inference_mode
class BaseValidator:
@ -32,8 +30,9 @@ class BaseValidator:
self.training = True
self.speed = None
self.save_dir = save_dir if save_dir is not None else \
increment_path(Path(self.args.project) / self.args.name, exist_ok=self.args.exist_ok)
increment_path(Path(self.args.project) / self.args.name, exist_ok=self.args.exist_ok)
@smart_inference_mode()
def __call__(self, trainer=None, model=None):
"""
Supports validation of a pre-trained model if passed or a model being trained
@ -76,35 +75,34 @@ class BaseValidator:
dt = Profile(), Profile(), Profile(), Profile()
n_batches = len(self.dataloader)
desc = self.get_desc()
# NOTE: keeping this `not self.training` in tqdm will eliminate pbar after finishing segmantation evaluation during training,
# so I removed it, not sure if this will affect classification task cause I saw we use this arg in yolov5/classify/val.py.
# 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)
self.init_metrics(de_parallel(model))
with torch.no_grad():
for batch_i, batch in enumerate(bar):
self.batch_i = batch_i
# pre-process
with dt[0]:
batch = self.preprocess(batch)
# inference
with dt[1]:
preds = model(batch["img"])
# loss
with dt[2]:
if self.training:
self.loss += trainer.criterion(preds, batch)[1]
# pre-process predictions
with dt[3]:
preds = self.postprocess(preds)
self.update_metrics(preds, batch)
if self.args.plots and batch_i < 3:
self.plot_val_samples(batch, batch_i)
self.plot_predictions(batch, preds, batch_i)
for batch_i, batch in enumerate(bar):
self.batch_i = batch_i
# pre-process
with dt[0]:
batch = self.preprocess(batch)
# inference
with dt[1]:
preds = model(batch["img"])
# loss
with dt[2]:
if self.training:
self.loss += trainer.criterion(preds, batch)[1]
# pre-process predictions
with dt[3]:
preds = self.postprocess(preds)
self.update_metrics(preds, batch)
if self.args.plots and batch_i < 3:
self.plot_val_samples(batch, batch_i)
self.plot_predictions(batch, preds, batch_i)
stats = self.get_stats()
self.check_stats(stats)
@ -113,22 +111,21 @@ class BaseValidator:
# calculate speed only once when training
if not self.training or trainer.epoch == 0:
t = tuple(x.t / len(self.dataloader.dataset) * 1E3 for x in dt) # speeds per image
self.speed = t
self.speed = tuple(x.t / len(self.dataloader.dataset) * 1E3 for x in dt) # speeds per image
if not self.training: # print only at inference
self.logger.info(
'Speed: %.1fms pre-process, %.1fms inference, %.1fms loss, %.1fms post-process per image' % t)
if not self.training: # print only at inference
self.logger.info('Speed: %.1fms pre-process, %.1fms inference, %.1fms loss, %.1fms post-process per image' %
self.speed)
if self.training:
model.float()
# TODO: implement save json
return stats | trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix="val") \
if self.training else stats
return {**stats, **trainer.label_loss_items(self.loss.cpu() / len(self.dataloader), prefix="val")} \
if self.training else stats
def get_dataloader(self, dataset_path, batch_size):
raise Exception("get_dataloder function not implemented for this validator")
raise NotImplementedError("get_dataloader function not implemented for this validator")
def preprocess(self, batch):
return batch

2
ultralytics/yolo/utils/__init__.py
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@ -17,7 +17,7 @@ NUM_THREADS = min(8, max(1, os.cpu_count() - 1)) # number of YOLOv5 multiproces
AUTOINSTALL = str(os.getenv('YOLOv5_AUTOINSTALL', True)).lower() == 'true' # global auto-install mode
FONT = 'Arial.ttf' # https://ultralytics.com/assets/Arial.ttf
VERBOSE = str(os.getenv('YOLOv5_VERBOSE', True)).lower() == 'true' # global verbose mode
TQDM_BAR_FORMAT = '{l_bar}{bar:10}| {n_fmt}/{total_fmt} {elapsed}' # tqdm bar format
TQDM_BAR_FORMAT = '{l_bar}{bar:10}{r_bar}' # tqdm bar format
LOGGING_NAME = 'yolov5'

4
ultralytics/yolo/utils/callbacks/clearml.py
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@ -23,9 +23,9 @@ def on_train_start(trainer):
def on_val_end(trainer):
if trainer.epoch == 0:
model_info = {
"Inference speed (ms/img)": round(trainer.validator.speed[1], 1),
"Parameters": get_num_params(trainer.model),
"GFLOPs": round(get_flops(trainer.model), 1),
"Parameters": get_num_params(trainer.model)}
"Inference speed (ms/img)": round(trainer.validator.speed[1], 1)}
Task.current_task().connect(model_info, name='Model')

2
ultralytics/yolo/utils/callbacks/tb.py
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@ -11,8 +11,6 @@ def _log_scalars(scalars, step=0):
def on_train_start(trainer):
global writer
writer = SummaryWriter(str(trainer.save_dir))
trainer.console.info(f"Logging results to {trainer.save_dir}\n"
f"Starting training for {trainer.args.epochs} epochs...")
def on_batch_end(trainer):

5
ultralytics/yolo/utils/configs/default.yaml
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@ -27,12 +27,13 @@ local_rank: -1
single_cls: False # train multi-class data as single-class
image_weights: False # use weighted image selection for training
rect: False # support rectangular training
cos_lr: False # Use cosine LR scheduler
cos_lr: False # use cosine LR scheduler
close_mosaic: 10 # disable mosaic for final 10 epochs
# Segmentation
overlap_mask: True # masks overlap
mask_ratio: 4 # mask downsample ratio
# Classification
dropout: False # use dropout
dropout: False # use dropout
resume: False

4
ultralytics/yolo/utils/modeling/__init__.py
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@ -45,7 +45,7 @@ def attempt_load_weights(weights, device=None, inplace=True, fuse=True):
def parse_model(d, ch): # model_dict, input_channels(3)
# Parse a YOLOv5 model.yaml dictionary
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<40}{'arguments':<30}")
LOGGER.info(f"\n{'':>3}{'from':>18}{'n':>3}{'params':>10} {'module':<50}{'arguments':<30}")
nc, gd, gw, act = d['nc'], d['depth_multiple'], d['width_multiple'], d.get('activation')
if act:
Conv.default_act = eval(act) # redefine default activation, i.e. Conv.default_act = nn.SiLU()
@ -87,7 +87,7 @@ def parse_model(d, ch): # model_dict, input_channels(3)
t = str(m)[8:-2].replace('__main__.', '') # module type
m.np = sum(x.numel() for x in m_.parameters()) # number params
m_.i, m_.f, m_.type = i, f, t # attach index, 'from' index, type
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{m.np:10.0f} {t:<40}{str(args):<30}') # print
LOGGER.info(f'{i:>3}{str(f):>18}{n_:>3}{m.np:10.0f} {t:<50}{str(args):<30}') # print
save.extend(x % i for x in ([f] if isinstance(f, int) else f) if x != -1) # append to savelist
layers.append(m_)
if i == 0:

79
ultralytics/yolo/utils/ops.py
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@ -63,9 +63,9 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
gain = ratio_pad[0][0]
pad = ratio_pad[1]
boxes[:, [0, 2]] -= pad[0] # x padding
boxes[:, [1, 3]] -= pad[1] # y padding
boxes[:, :4] /= gain
boxes[..., [0, 2]] -= pad[0] # x padding
boxes[..., [1, 3]] -= pad[1] # y padding
boxes[..., :4] /= gain
clip_boxes(boxes, img0_shape)
return boxes
@ -73,13 +73,13 @@ def scale_boxes(img1_shape, boxes, img0_shape, ratio_pad=None):
def clip_boxes(boxes, shape):
# Clip boxes (xyxy) to image shape (height, width)
if isinstance(boxes, torch.Tensor): # faster individually
boxes[:, 0].clamp_(0, shape[1]) # x1
boxes[:, 1].clamp_(0, shape[0]) # y1
boxes[:, 2].clamp_(0, shape[1]) # x2
boxes[:, 3].clamp_(0, shape[0]) # y2
boxes[..., 0].clamp_(0, shape[1]) # x1
boxes[..., 1].clamp_(0, shape[0]) # y1
boxes[..., 2].clamp_(0, shape[1]) # x2
boxes[..., 3].clamp_(0, shape[0]) # y2
else: # np.array (faster grouped)
boxes[:, [0, 2]] = boxes[:, [0, 2]].clip(0, shape[1]) # x1, x2
boxes[:, [1, 3]] = boxes[:, [1, 3]].clip(0, shape[0]) # y1, y2
boxes[..., [0, 2]] = boxes[..., [0, 2]].clip(0, shape[1]) # x1, x2
boxes[..., [1, 3]] = boxes[..., [1, 3]].clip(0, shape[0]) # y1, y2
def make_divisible(x, divisor):
@ -106,6 +106,9 @@ def non_max_suppression(
list of detections, on (n,6) tensor per image [xyxy, conf, cls]
"""
# Checks
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
if isinstance(prediction, (list, tuple)): # YOLOv5 model in validation model, output = (inference_out, loss_out)
prediction = prediction[0] # select only inference output
@ -118,10 +121,6 @@ def non_max_suppression(
mi = 4 + nc # mask start index
xc = prediction[:, 4:mi].amax(1) > conf_thres # candidates
# Checks
assert 0 <= conf_thres <= 1, f'Invalid Confidence threshold {conf_thres}, valid values are between 0.0 and 1.0'
assert 0 <= iou_thres <= 1, f'Invalid IoU {iou_thres}, valid values are between 0.0 and 1.0'
# Settings
# min_wh = 2 # (pixels) minimum box width and height
max_wh = 7680 # (pixels) maximum box width and height
@ -172,17 +171,13 @@ def non_max_suppression(
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
elif n > max_nms: # excess boxes
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence
else:
x = x[x[:, 4].argsort(descending=True)] # sort by confidence
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes
# Batched NMS
c = x[:, 5:6] * (0 if agnostic else max_wh) # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes (offset by class), scores
i = torchvision.ops.nms(boxes, scores, iou_thres) # NMS
if i.shape[0] > max_det: # limit detections
i = i[:max_det]
i = i[:max_det] # limit detections
if merge and (1 < n < 3E3): # Merge NMS (boxes merged using weighted mean)
# update boxes as boxes(i,4) = weights(i,n) * boxes(n,4)
iou = box_iou(boxes[i], boxes) > iou_thres # iou matrix
@ -244,20 +239,50 @@ def scale_image(im1_shape, masks, im0_shape, ratio_pad=None):
def xyxy2xywh(x):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = (x[:, 0] + x[:, 2]) / 2 # x center
y[:, 1] = (x[:, 1] + x[:, 3]) / 2 # y center
y[:, 2] = x[:, 2] - x[:, 0] # width
y[:, 3] = x[:, 3] - x[:, 1] # height
y[..., 0] = (x[..., 0] + x[..., 2]) / 2 # x center
y[..., 1] = (x[..., 1] + x[..., 3]) / 2 # y center
y[..., 2] = x[..., 2] - x[..., 0] # width
y[..., 3] = x[..., 3] - x[..., 1] # height
return y
def xywh2xyxy(x):
# Convert nx4 boxes from [x, y, w, h] to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
y[..., 0] = x[..., 0] - x[..., 2] / 2 # top left x
y[..., 1] = x[..., 1] - x[..., 3] / 2 # top left y
y[..., 2] = x[..., 0] + x[..., 2] / 2 # bottom right x
y[..., 3] = x[..., 1] + x[..., 3] / 2 # bottom right y
return y
def xywhn2xyxy(x, w=640, h=640, padw=0, padh=0):
# Convert nx4 boxes from [x, y, w, h] normalized to [x1, y1, x2, y2] where xy1=top-left, xy2=bottom-right
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * (x[..., 0] - x[..., 2] / 2) + padw # top left x
y[..., 1] = h * (x[..., 1] - x[..., 3] / 2) + padh # top left y
y[..., 2] = w * (x[..., 0] + x[..., 2] / 2) + padw # bottom right x
y[..., 3] = h * (x[..., 1] + x[..., 3] / 2) + padh # bottom right y
return y
def xyxy2xywhn(x, w=640, h=640, clip=False, eps=0.0):
# Convert nx4 boxes from [x1, y1, x2, y2] to [x, y, w, h] normalized where xy1=top-left, xy2=bottom-right
if clip:
clip_boxes(x, (h - eps, w - eps)) # warning: inplace clip
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = ((x[..., 0] + x[..., 2]) / 2) / w # x center
y[..., 1] = ((x[..., 1] + x[..., 3]) / 2) / h # y center
y[..., 2] = (x[..., 2] - x[..., 0]) / w # width
y[..., 3] = (x[..., 3] - x[..., 1]) / h # height
return y
def xyn2xy(x, w=640, h=640, padw=0, padh=0):
# Convert normalized segments into pixel segments, shape (n,2)
y = x.clone() if isinstance(x, torch.Tensor) else np.copy(x)
y[..., 0] = w * x[..., 0] + padw # top left x
y[..., 1] = h * x[..., 1] + padh # top left y
return y

2
ultralytics/yolo/utils/torch_utils.py
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@ -29,7 +29,7 @@ WORLD_SIZE = int(os.getenv('WORLD_SIZE', 1))
@contextmanager
def torch_distributed_zero_first(local_rank: int):
# Decorator to make all processes in distributed training wait for each local_master to do something
if local_rank not in [-1, 0]:
if local_rank not in {-1, 0}:
dist.barrier(device_ids=[local_rank])
yield
if local_rank == 0:

2
ultralytics/yolo/v8/classify/predict.py
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@ -52,7 +52,7 @@ class ClassificationPredictor(BasePredictor):
return log_string
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "squeezenet1_0"
sz = cfg.imgsz

2
ultralytics/yolo/v8/classify/train.py
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@ -59,7 +59,7 @@ class ClassificationTrainer(BaseTrainer):
pass
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def train(cfg):
cfg.model = cfg.model or "resnet18"
cfg.data = cfg.data or "imagenette160" # or yolo.ClassificationDataset("mnist")

2
ultralytics/yolo/v8/classify/val.py
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@ -35,7 +35,7 @@ class ClassificationValidator(BaseValidator):
return ["top1", "top5"]
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def val(cfg):
cfg.data = cfg.data or "imagenette160"
cfg.model = cfg.model or "resnet18"

2
ultralytics/yolo/v8/detect/predict.py
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@ -81,7 +81,7 @@ class DetectionPredictor(BasePredictor):
return log_string
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "n.pt"
sz = cfg.imgsz

18
ultralytics/yolo/v8/detect/train.py
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@ -53,7 +53,9 @@ class DetectionTrainer(BaseTrainer):
args=self.args)
def criterion(self, preds, batch):
return Loss(self.model)(preds, batch)
if not hasattr(self, 'compute_loss'):
self.compute_loss = Loss(de_parallel(self.model))
return self.compute_loss(preds, batch)
def label_loss_items(self, loss_items=None, prefix="train"):
# We should just use named tensors here in future
@ -61,8 +63,8 @@ class DetectionTrainer(BaseTrainer):
return dict(zip(keys, loss_items)) if loss_items is not None else keys
def progress_string(self):
return ('\n' + '%11s' * 6) % \
('Epoch', 'GPU_mem', *self.loss_names, 'Size')
return ('\n' + '%11s' * 7) % \
('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
def plot_training_samples(self, batch, ni):
images = batch["img"]
@ -79,7 +81,7 @@ class DetectionTrainer(BaseTrainer):
# Criterion class for computing training losses
class Loss:
def __init__(self, model):
def __init__(self, model): # model must be de-paralleled
device = next(model.parameters()).device # get model device
h = model.args # hyperparameters
@ -90,7 +92,7 @@ class Loss:
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
self.cp, self.cn = smooth_BCE(eps=h.get("label_smoothing", 0.0)) # positive, negative BCE targets
m = de_parallel(model).model[-1] # Detect() module
m = model.model[-1] # Detect() module
self.BCEcls = BCEcls
self.hyp = h
self.stride = m.stride # model strides
@ -169,12 +171,12 @@ class Loss:
return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def train(cfg):
cfg.model = cfg.model or "models/yolov8n.yaml"
cfg.data = cfg.data or "coco128.yaml" # or yolo.ClassificationDataset("mnist")
cfg.imgsz = 160
cfg.epochs = 5
# cfg.imgsz = 160
# cfg.epochs = 5
trainer = DetectionTrainer(cfg)
trainer.train()

8
ultralytics/yolo/v8/detect/val.py
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@ -119,9 +119,9 @@ class DetectionValidator(BaseValidator):
if len(stats) and stats[0].any():
self.metrics.process(*stats)
self.nt_per_class = np.bincount(stats[-1].astype(int), minlength=self.nc) # number of targets per class
metrics = {"fitness": self.metrics.fitness()}
metrics |= zip(self.metric_keys, self.metrics.mean_results())
return metrics
fitness = {"fitness": self.metrics.fitness()}
metrics = dict(zip(self.metric_keys, self.metrics.mean_results()))
return {**metrics, **fitness}
def print_results(self):
pf = '%22s' + '%11i' * 2 + '%11.3g' * len(self.metric_keys) # print format
@ -198,7 +198,7 @@ class DetectionValidator(BaseValidator):
names=self.names) # pred
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def val(cfg):
cfg.data = cfg.data or "coco128.yaml"
validator = DetectionValidator(args=cfg)

2
ultralytics/yolo/v8/segment/predict.py
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@ -99,7 +99,7 @@ class SegmentationPredictor(DetectionPredictor):
return log_string
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def predict(cfg):
cfg.model = cfg.model or "n.pt"
sz = cfg.imgsz

6
ultralytics/yolo/v8/segment/train.py
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@ -214,8 +214,8 @@ class SegmentationTrainer(DetectionTrainer):
return dict(zip(keys, loss_items)) if loss_items is not None else keys
def progress_string(self):
return ('\n' + '%11s' * 7) % \
('Epoch', 'GPU_mem', *self.loss_names, 'Size')
return ('\n' + '%11s' * 8) % \
('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
def plot_training_samples(self, batch, ni):
images = batch["img"]
@ -230,7 +230,7 @@ class SegmentationTrainer(DetectionTrainer):
plot_results(file=self.csv, segment=True) # save results.png
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def train(cfg):
cfg.model = cfg.model or "models/yolov8n-seg.yaml"
cfg.data = cfg.data or "coco128-seg.yaml" # or yolo.ClassificationDataset("mnist")

2
ultralytics/yolo/v8/segment/val.py
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@ -211,7 +211,7 @@ class SegmentationValidator(DetectionValidator):
self.plot_masks.clear()
@hydra.main(version_base=None, config_path=DEFAULT_CONFIG.parent, config_name=DEFAULT_CONFIG.name)
@hydra.main(version_base=None, config_path=str(DEFAULT_CONFIG.parent), config_name=DEFAULT_CONFIG.name)
def val(cfg):
cfg.data = cfg.data or "coco128-seg.yaml"
validator = SegmentationValidator(args=cfg)

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