def __init__(self, weights,service_name, image_size=640, nms_conf_thresh=0.4, nms_iou_thresh=0.5, device=""):
try:
from yolov5.utils.general import set_logging
from yolov5.utils.torch_utils import select_device
from yolov5.models.experimental import attempt_load
from yolov5.utils.general import check_img_size
from yolov5.utils.general import non_max_suppression
from yolov5.utils.general import scale_coords
from yolov5.utils.datasets import letterbox
except ImportError:
raise
self.device = select_device(device)
self.conf_thresh = nms_conf_thresh
self.iou_thresh = nms_iou_thresh
self.half = self.device.type != 'cpu'
self.model = attempt_load(weights, map_location=self.device)
self.image_size = check_img_size(image_size, s=self.model.stride.max())
if self.half:
self.model.half() # to FP16
self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
self.currently_busy = Event()
# Base class must be called at the end due to self.service_server.spin()
BaseDetectionServer.__init__(self, service_name=service_name)
def process_sample(image_filepath):
rospy.loginfo('Starting yolo sample node...')
rospy.init_node('sample', anonymous=True)
rospy.loginfo('Loading model...')
device = select_device('')
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
imgsz = 224
model = attempt_load(f'{os.path.dirname(__file__)}/yolov5/runs/train/exp4/weights/best.pt', map_location=device)
imgsz = check_img_size(imgsz, s=model.stride.max())
if half:
model.half()
rospy.loginfo('Reading image...')
im = cv2.imread(image_filepath)
if (im is None):
rospy.loginfo('Failed to read image.')
return
rospy.loginfo('Running inference...')
preds = detect(model, im)
rospy.loginfo('Predictions:')
for pred in preds:
rospy.loginfo(f' Class: {pred[0]}')
rospy.loginfo(f' Confidence: {pred[1]}')
rospy.loginfo(f' Bounds: {pred[2]}')
def opts(self):
parser = argparse.ArgumentParser()
parser.add_argument('--weights',
type=str,
default='yolov5/weights/yolov5x.pt',
help='model.pt path')
parser.add_argument(
'--source',
type=str,
default='rtsp://*****:*****@192.168.100.176:554/Streaming'
'/Channels/101',
help='source') # file/folder, 0 for webcam
parser.add_argument('--output',
type=str,
default='inference/output',
help='output folder') # output folder
parser.add_argument('--img-size',
type=int,
default=640,
help='inference size (pixels)')
parser.add_argument('--conf-thres',
type=float,
default=0.4,
help='object confidence threshold')
parser.add_argument('--iou-thres',
type=float,
default=0.5,
help='IOU threshold for NMS')
parser.add_argument('--fourcc',
type=str,
default='mp4v',
help='output video codec (verify ffmpeg support)')
parser.add_argument('--device',
default='',
help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img',
action='store_true',
help='display results')
parser.add_argument('--save-txt',
action='store_true',
help='save results to *.txt')
# class 0 is person
parser.add_argument('--classes',
nargs='+',
type=int,
default=range(0, 1),
help='filter by class')
parser.add_argument('--agnostic-nms',
action='store_true',
help='class-agnostic NMS')
parser.add_argument('--augment',
action='store_true',
help='augmented inference')
parser.add_argument("--config_deepsort",
type=str,
default="deep_sort/configs/deep_sort.yaml")
args = parser.parse_args()
args.img_size = check_img_size(args.img_size)
return args
def detect(self, source, img_size=640, conf=None, iou=None):
conf = self.model.conf if not conf else conf
iou = self.model.iou if not iou else iou
img_size = check_img_size(img_size, s=self.model.stride.max()) # check img_size
# Set Dataloader
cudnn.benchmark = True
dataset = LoadImages(source, img_size=img_size)
names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
img = torch.zeros((1, 3, img_size, img_size), device=self.device) # init img
_ = self.model(img.half() if self.half else img) if self.device.type != 'cpu' else None # run once
detections = []
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(self.device)
img = img.half() if self.half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
pred = self.model(img, augment=False)[0]
pred = non_max_suppression_torch_ops(pred, conf, iou, classes=None)
# Process detections
for i, det in enumerate(pred): # detections per image
p, s, im0 = path, '', im0s
detection_result = {"entities": [], "detections": [], "src": path}
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
# Calling detach is necessary
for c in det[:, -1].detach().unique():
n = (det[:, -1] == c).sum() # detections per class
detection_result['entities'].append((names[int(c)], int(n)))
# Write results
for *xyxy, conf, cls in reversed(det):
t_xyxy = torch.tensor(xyxy).view(1, 4)
xywh = (xyxy2xywh(t_xyxy) / gn).view(-1).tolist() # normalized xywh
detection_result['detections'].append(dict(xyxy=t_xyxy.view(-1).tolist(), xywh=xywh,
cls=names[int(cls)],
confidence="{:.2%}".format(float(conf))))
detections.append(detection_result)
return detections
def detect_displacement(self, img_path, img_size=None):
"""
基于yolo v5中detect.py的run函数改造此函数即可
方法一: 直接调用detect.run()方法并设置结果写出到txt文件 通过读取txt文件解析结果(此方法每次调用都需要重新加载模型 适合一次性大批量处理)
方法二: 复用detect.run中代码，将模型加载放到 self._load_models中 将探测代码放到 detect_displacement中
:param img_path 图片路径
:param img_size 图片(高 宽)
:return 类别，置信度，边框(x, y, w, h)
"""
stride = max(int(self.model.stride.max()), 32)
imgsz = general.check_img_size(img_size, s=stride)
dataset = datasets.LoadImages(img_path,
img_size=imgsz,
stride=stride,
auto=True)
for path, im, im0s, vid_cap, s in dataset:
im = torch.from_numpy(im).to(self.device)
# uint8 to fp16/32
im = im.float()
# 0 - 255 to 0.0 - 1.0
im /= 255
if len(im.shape) == 3:
# expand for batch 4-dim
im = im[None]
pred = self.model(im, augment=False, visualize=False)
# 非极大值抑制
pred = general.non_max_suppression(pred[0],
0.25,
0.4,
None,
False,
max_det=1000)
# Process predictions
# per image
for _, det in enumerate(pred):
if len(det):
# process result
det[:, :4] = general.scale_coords(im.shape[2:], det[:, :4],
im0s.shape).round()
for *xyxy, conf, cls in reversed(det):
box = torch.tensor(xyxy).view(1, 4).view(-1).tolist()
box[2] = (box[2] - box[0])
box[3] = (box[3] - box[1])
confidence_value = conf.item()
class_index = cls.item()
return class_index, confidence_value, box
#print('conf %s, class %s, box: %s' % (confidence_value, class_index, box))
"""
ann = plots.Annotator(im0s.copy())
ann.box_label(xyxy, 'dis')
im0 = ann.result()
cv2.imshow('dis', im0)
cv2.waitKey(5000)
"""
return None, None, None
def load_model(self,dev="cpu"):
# Initialize
set_logging()
device = select_device(dev)
# Load model
# model = attempt_load("yolov5_model/yolov5x.pt", map_location=device) # load FP32 model
model = torch.hub.load('ultralytics/yolov5', 'custom', path_or_model='yolov5_model/yolov5x.pt')
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(640, s=stride) # check img_size
# Run inference
if device.type != 'cpu':
model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters())))
return model,stride,device,imgsz
def load_model(self):
""" Load model """
Path(self.opt.weights).parent.mkdir(parents=True, exist_ok=True)
model = attempt_load(self.opt.weights,
map_location=self.device) # load FP32 model
if self.half:
model.half() # to FP16
imgsz = check_img_size(self.opt.img_size,
s=model.stride.max()) # check img_size
class_names = model.module.names if hasattr(model,
'module') else model.names
return model, imgsz, class_names
def __init__(self):
rospy.loginfo('Starting yolo video node...')
rospy.init_node('yolo', anonymous=True)
self.bridge = CvBridge()
self.pointcloud = None
self.saved = False
rospy.loginfo('Loading model...')
device = select_device('')
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
imgsz = 224
self.model = attempt_load(
f'{os.path.dirname(__file__)}/yolov5/runs/train/exp4/weights/best.pt',
map_location=device)
imgsz = check_img_size(imgsz, s=self.model.stride.max())
if half:
self.model.half()
# Can you recycle dict
class_dict = dict()
class_dict['Cardboard'] = True
class_dict['Chips Bag'] = False
class_dict['Disposable Cup'] = False
class_dict['Napkin'] = False
class_dict['Plastic Bottle'] = True
class_dict['Plastic Container'] = True
class_dict['Soda Can'] = True
self.class_dict = class_dict
# Subscribe to pointcloud to make predictions
rospy.loginfo('Subscribing to camera pointcloud topic...')
rospy.Subscriber('camera/depth_registered/points', PointCloud2,
self.process_pointcloud)
rospy.loginfo('Awaiting images...')
# Create publisher to publish predictions
self.predict_pub = rospy.Publisher('yolo/detections',
String,
queue_size=10)
# Create publisher to publish markers to RViz
self.rviz_pub = rospy.Publisher('visualization_marker',
Marker,
queue_size=10)
def __init__(self):
super().__init__()
_path = Config().data.data_path
if not os.path.exists(_path):
os.makedirs(_path)
logging.info(
"Downloading the YOLO dataset. This may take a while.")
urls = Config().data.download_urls
for url in urls:
if not os.path.exists(_path + url.split('/')[-1]):
DataSource.download(url, _path)
assert 'grid_size' in Config().params
self.grid_size = Config().params['grid_size']
self.image_size = check_img_size(Config().data.image_size,
self.grid_size)
self.train_set = None
self.test_set = None
def opts(self):
parser = argparse.ArgumentParser()
parser.add_argument('--weights', nargs='+', type=str, default='yolov5s.pt', help='model.pt path(s)')
parser.add_argument('--source', default='yolov5/data/images', help='source') # file/folder, 0 for webcam
parser.add_argument('--img-size', type=int, default=640, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.25, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.45, help='IOU threshold for NMS')
parser.add_argument('--device', default='', help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img', action='store_true', help='display results')
parser.add_argument('--save-txt', action='store_true', help='save results to *.txt')
parser.add_argument('--save-conf', action='store_true', help='save confidences in --save-txt labels')
parser.add_argument('--save-crop', action='store_true', help='save cropped prediction boxes')
parser.add_argument('--nosave', action='store_true', help='do not save images/videos')
parser.add_argument('--classes', nargs='+', type=int, help='filter by class: --class 0, or --class 0 2 3')
parser.add_argument('--agnostic-nms', action='store_true', help='class-agnostic NMS')
parser.add_argument('--augment', action='store_true', help='augmented inference')
parser.add_argument('--update', action='store_true', help='update all models')
parser.add_argument('--project', default='runs/detect', help='save results to project/name')
parser.add_argument('--name', default='exp', help='save results to project/name')
parser.add_argument('--exist-ok', action='store_true', help='existing project/name ok, do not increment')
args = parser.parse_args()
args.img_size = check_img_size(args.img_size)
return args
def __init__(self,
weights_path,
img_size=640,
device='',
conf_thres=0.5,
iou_thres=0.5,
classes=None):
self._weights_path = weights_path
self._img_size = img_size
self._device = select_device(device)
self._conf_thres = conf_thres
self._iou_thres = iou_thres
self._classes = classes
self._half = self._device.type != 'cpu' # half precision only supported on CUDA
# Load model
self._model = attempt_load(
self._weights_path, map_location=self._device) # load FP32 model
self._img_size = check_img_size(
self._img_size, s=self._model.stride.max()) # check img_size
if self._half:
self._model.half() # to FP16
def train(self, log_dir: str, epochs: int = 300,
batch_size: int = 2, img_size=(640, 640)):
"""
Parameters
----------
log_dir : str
Directory to store results to
epochs : int
batch_size : int
img_size
Returns
-------
"""
self.load_model()
log_dir = os.path.expanduser(log_dir)
os.makedirs(log_dir, exist_ok=True)
results_file = os.path.join(log_dir, 'results.txt')
weights_dir = os.path.join(log_dir, "weights")
os.makedirs(weights_dir, exist_ok=True)
last_weight_fp = os.path.join(weights_dir, "last.pt")
best_weight_fp = os.path.join(weights_dir, "best.pt")
scheduler, lf = self.load_scheduler(epochs)
gs = int(max(self.model.stride))
imgsz, imgsz_test = [check_img_size(x, gs) for x in img_size] # verify imgsz are gs-multiples
ema = ModelEMA(self.model)
dataloader, dataset = create_dataloader(self.train_path, imgsz, batch_size, gs,
hyp=self.params, augment=True, opt=OptShim)
n_batches = len(dataloader)
ema.updates = self.start_epoch * n_batches // self.accumulate
testloader = create_dataloader(self.val_path, imgsz_test, batch_size, gs,
hyp=self.params, augment=False, rect=True, opt=OptShim)[0]
self.cls = self.cls * (self.n_classes / 80)
self.model.nc = self.n_classes
self.model.hyp = self.params
self.model.gr = 1.0
self.model.class_weights = labels_to_class_weights(dataset.labels, self.n_classes).to(self.device)
self.model.names = self.classes
# Start Training
nw = max(round(self.warmup_epochs * n_batches), 1000) # number of warmup iterations
results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = self.start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=self.cuda)
s = ''
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'targets', 'img_size'))
for epoch in range(self.start_epoch, epochs):
self.model.train()
mloss = torch.zeros(4, device=self.device) # Mean losses
self.optimizer.zero_grad()
pbar = tqdm(enumerate(dataloader), total=n_batches)
for i, (imgs, targets, paths, _) in pbar:
ni = i + n_batches * epoch # number integrated batches (since train start)
imgs = imgs.to(self.device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw]
self.accumulate = max(1, np.interp(ni, xi, [1, self.NBS / batch_size]).round())
for j, x in enumerate(self.optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi,
[self.warmup_bias_lr if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [self.warmup_momentum, self.momentum])
# Forward
with amp.autocast(enabled=self.cuda):
pred = self.model(imgs) # forward
# loss scaled by batch_size
loss, loss_items = compute_loss(pred, targets.to(self.device), self.model)
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % self.accumulate == 0:
scaler.step(self.optimizer) # optimizer.step
scaler.update()
self.optimizer.zero_grad()
ema.update(self.model)
# Print
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_allocated() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Scheduler
scheduler.step()
# mAP
ema.update_attr(self.model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if final_epoch: # Calculate mAP
results, maps, times = yolov5.test.test(self.data_path,
batch_size=batch_size,
imgsz=imgsz_test,
model=ema.ema,
single_cls=False,
dataloader=testloader,
save_dir=log_dir,
plots=False) # plot first and last
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # P, R, [email protected], [email protected], val_loss(box, obj, cls)
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > self.best_fitness:
self.best_fitness = fi
# Save model
if final_epoch:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch': epoch,
'best_fitness': self.best_fitness,
'training_results': f.read(),
'model': ema.ema,
'optimizer': None if final_epoch else self.optimizer.state_dict()
}
# Save last, best and delete
torch.save(ckpt, last_weight_fp)
if self.best_fitness == fi:
torch.save(ckpt, best_weight_fp)
del ckpt
# End Training
torch.cuda.empty_cache()
return results
def train(hyp, opt, device, tb_writer=None, wandb=None):
logger.info(f"Hyperparameters {hyp}")
save_dir, epochs, batch_size, total_batch_size, weights, rank = (
Path(opt.save_dir),
opt.epochs,
opt.batch_size,
opt.total_batch_size,
opt.weights,
opt.global_rank,
)
# Directories
wdir = save_dir / "weights"
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / "last.pt"
best = wdir / "best.pt"
results_file = save_dir / "results.txt"
# Save run settings
with open(save_dir / "hyp.yaml", "w") as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / "opt.yaml", "w") as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != "cpu"
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict["train"]
test_path = data_dict["val"]
nc = 1 if opt.single_cls else int(data_dict["nc"]) # number of classes
names = (
["item"]
if opt.single_cls and len(data_dict["names"]) != 1
else data_dict["names"]
) # class names
assert len(names) == nc, "%g names found for nc=%g dataset in %s" % (
len(names),
nc,
opt.data,
) # check
# Model
pretrained = weights.endswith(".pt")
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
# add yolov5 folder to system path
here = Path(__file__).parent.absolute()
yolov5_folder_dir = str(here)
sys.path.insert(0, yolov5_folder_dir)
# load checkpoint
ckpt = torch.load(weights, map_location=device)
# remove yolov5 folder from system path
sys.path.remove(yolov5_folder_dir)
if hyp.get("anchors"):
ckpt["model"].yaml["anchors"] = round(hyp["anchors"]) # force autoanchor
model = Model(opt.cfg or ckpt["model"].yaml, ch=3, nc=nc).to(device) # create
exclude = ["anchor"] if opt.cfg or hyp.get("anchors") else [] # exclude keys
state_dict = ckpt["model"].float().state_dict() # to FP32
state_dict = intersect_dicts(
state_dict, model.state_dict(), exclude=exclude
) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info(
"Transferred %g/%g items from %s"
% (len(state_dict), len(model.state_dict()), weights)
) # report
else:
model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print("freezing %s" % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(
round(nbs / total_batch_size), 1
) # accumulate loss before optimizing
hyp["weight_decay"] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, "bias") and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, "weight") and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(
pg0, lr=hyp["lr0"], betas=(hyp["momentum"], 0.999)
) # adjust beta1 to momentum
else:
optimizer = optim.SGD(
pg0, lr=hyp["lr0"], momentum=hyp["momentum"], nesterov=True
)
optimizer.add_param_group(
{"params": pg1, "weight_decay": hyp["weight_decay"]}
) # add pg1 with weight_decay
optimizer.add_param_group({"params": pg2}) # add pg2 (biases)
logger.info(
"Optimizer groups: %g .bias, %g conv.weight, %g other"
% (len(pg2), len(pg1), len(pg0))
)
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = one_cycle(1, hyp["lrf"], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Logging
if rank in [-1, 0] and wandb and wandb.run is None:
opt.hyp = hyp # add hyperparameters
wandb_run = wandb.init(
config=opt,
resume="allow",
project="YOLOv5" if opt.project == "runs/train" else Path(opt.project).stem,
name=save_dir.stem,
id=ckpt.get("wandb_id") if "ckpt" in locals() else None,
)
loggers = {"wandb": wandb} # loggers dict
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt["optimizer"] is not None:
optimizer.load_state_dict(ckpt["optimizer"])
best_fitness = ckpt["best_fitness"]
# Results
if ckpt.get("training_results") is not None:
with open(results_file, "w") as file:
file.write(ckpt["training_results"]) # write results.txt
# Epochs
start_epoch = ckpt["epoch"] + 1
if opt.resume:
assert (
start_epoch > 0
), "%s training to %g epochs is finished, nothing to resume." % (
weights,
epochs,
)
if epochs < start_epoch:
logger.info(
"%s has been trained for %g epochs. Fine-tuning for %g additional epochs."
% (weights, ckpt["epoch"], epochs)
)
epochs += ckpt["epoch"] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(model.stride.max()) # grid size (max stride)
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [
check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info("Using SyncBatchNorm()")
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
# Trainloader
dataloader, dataset = create_dataloader(
train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights,
quad=opt.quad,
)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert (
mlc < nc
), "Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g" % (
mlc,
nc,
opt.data,
nc - 1,
)
# Process 0
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(
test_path,
imgsz_test,
total_batch_size,
gs,
opt, # testloader
hyp=hyp,
cache=opt.cache_images and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers,
pad=0.5,
)[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram("classes", c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp["anchor_t"], imgsz=imgsz)
# Model parameters
hyp["cls"] *= nc / 80.0 # scale hyp['cls'] to class count
hyp["obj"] *= (
imgsz ** 2 / 640.0 ** 2 * 3.0 / nl
) # scale hyp['obj'] to image size and output layers
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = (
labels_to_class_weights(dataset.labels, nc).to(device) * nc
) # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(
round(hyp["warmup_epochs"] * nb), 1000
) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info(
"Image sizes %g train, %g test\n"
"Using %g dataloader workers\nLogging results to %s\n"
"Starting training for %g epochs..."
% (imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs)
)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = (
model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc
) # class weights
iw = labels_to_image_weights(
dataset.labels, nc=nc, class_weights=cw
) # image weights
dataset.indices = random.choices(
range(dataset.n), weights=iw, k=dataset.n
) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (
torch.tensor(dataset.indices)
if rank == 0
else torch.zeros(dataset.n)
).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
("\n" + "%10s" * 8)
% ("Epoch", "gpu_mem", "box", "obj", "cls", "total", "targets", "img_size")
)
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs,
targets,
paths,
_,
) in (
pbar
): # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = (
imgs.to(device, non_blocking=True).float() / 255.0
) # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1, np.interp(ni, xi, [1, nbs / total_batch_size]).round()
)
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x["lr"] = np.interp(
ni,
xi,
[
hyp["warmup_bias_lr"] if j == 2 else 0.0,
x["initial_lr"] * lf(epoch),
],
)
if "momentum" in x:
x["momentum"] = np.interp(
ni, xi, [hyp["warmup_momentum"], hyp["momentum"]]
)
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [
math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(
imgs, size=ns, mode="bilinear", align_corners=False
)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device), model
) # loss scaled by batch_size
if rank != -1:
loss *= (
opt.world_size
) # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.0
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = "%.3gG" % (
torch.cuda.memory_reserved() / 1e9
if torch.cuda.is_available()
else 0
) # (GB)
s = ("%10s" * 2 + "%10.4g" * 6) % (
"%g/%g" % (epoch, epochs - 1),
mem,
*mloss,
targets.shape[0],
imgs.shape[-1],
)
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f"train_batch{ni}.jpg" # filename
Thread(
target=plot_images, args=(imgs, targets, paths, f), daemon=True
).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
elif plots and ni == 3 and wandb:
wandb.log(
{
"Mosaics": [
wandb.Image(str(x), caption=x.name)
for x in save_dir.glob("train*.jpg")
]
}
)
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x["lr"] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(
model,
include=[
"yaml",
"nc",
"hyp",
"gr",
"names",
"stride",
"class_weights",
],
)
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test(
data=opt.data,
batch_size=total_batch_size,
image_size=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
plots=plots and final_epoch,
log_imgs=opt.log_imgs if wandb else 0,
)
# Write
with open(results_file, "a") as f:
f.write(
s + "%10.4g" * 7 % results + "\n"
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
if len(opt.name) and opt.bucket:
os.system(
"gsutil cp %s gs://%s/results/results%s.txt"
% (results_file, opt.bucket, opt.name)
)
# Log
tags = [
"train/box_loss",
"train/obj_loss",
"train/cls_loss", # train loss
"metrics/precision",
"metrics/recall",
"metrics/mAP_0.5",
"metrics/mAP_0.5:0.95",
"val/box_loss",
"val/obj_loss",
"val/cls_loss", # val loss
"x/lr0",
"x/lr1",
"x/lr2",
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb:
wandb.log({tag: x}) # W&B
# Update best mAP
fi = fitness(
np.array(results).reshape(1, -1)
) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, "r") as f: # create checkpoint
ckpt = {
"epoch": epoch,
"best_fitness": best_fitness,
"training_results": f.read(),
"model": ema.ema,
"optimizer": None if final_epoch else optimizer.state_dict(),
"wandb_id": wandb_run.id if wandb else None,
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
final = best if best.exists() else last # final model
for f in [last, best]:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f"gsutil cp {final} gs://{opt.bucket}/weights") # upload
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb:
files = [
"results.png",
"precision_recall_curve.png",
"confusion_matrix.png",
]
wandb.log(
{
"Results": [
wandb.Image(str(save_dir / f), caption=f)
for f in files
if (save_dir / f).exists()
]
}
)
if opt.log_artifacts:
wandb.log_artifact(
artifact_or_path=str(final), type="model", name=save_dir.stem
)
# Test best.pt
logger.info(
"%g epochs completed in %.3f hours.\n"
% (epoch - start_epoch + 1, (time.time() - t0) / 3600)
)
if opt.data.endswith("coco.yaml") and nc == 80: # if COCO
for conf, iou, save_json in (
[0.25, 0.45, False],
[0.001, 0.65, True],
): # speed, mAP tests
results, _, _ = test(
data=opt.data,
batch_size=total_batch_size,
image_size=imgsz_test,
conf_thres=conf,
iou_thres=iou,
model=attempt_load(final, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=save_json,
plots=False,
)
else:
dist.destroy_process_group()
wandb.run.finish() if wandb and wandb.run else None
torch.cuda.empty_cache()
return results
def detect(
weights="yolov5s.pt",
source="yolov5/data/images",
img_size=640,
conf_thres=0.75,
iou_thres=0.45,
device="",
view_img=False,
save_txt=False,
save_conf=False,
classes=None,
agnostic_nms=False,
augment=False,
update=False,
project="runs/detect",
name="exp",
exist_ok=False,
save_img=False,
):
"""
Args:
weights: str
model.pt path(s)
source: str
file/folder, 0 for webcam
img_size: int
inference size (pixels)
conf_thres: float
object confidence threshold
iou_thres: float
IOU threshold for NMS
device: str
cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img: bool
display results
save_txt: bool
save results to *.txt
save_conf: bool
save confidences in save_txt labels
classes: int
filter by class: [0], or [0, 2, 3]
agnostic-nms: bool
class-agnostic NMS
augment: bool
augmented inference
update: bool
update all models
project: str
save results to project/name
name: str
save results to project/name
exist_ok: bool
existing project/name ok, do not increment
"""
source, weights, view_img, save_txt, imgsz = (
source,
weights,
view_img,
save_txt,
img_size,
)
webcam = (
source.isnumeric()
or source.endswith(".txt")
or source.lower().startswith(("rtsp://", "rtmp://", "http://"))
)
# Directories
save_dir = Path(
increment_path(Path(project) / name, exist_ok=exist_ok)
) # increment run
(save_dir / "labels" if save_txt else save_dir).mkdir(
parents=True, exist_ok=True
) # make dir
# Initialize
set_logging()
device = select_device(device)
half = device.type != "cpu" # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name="resnet101", n=2) # initialize
modelc.load_state_dict(
torch.load("weights/resnet101.pt", map_location=device)["model"]
).to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, "module") else model.names
colors = [[random.randint(0, 255) for _ in range(3)] for _ in names]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img) if device.type != "cpu" else None # run once
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=augment)[0]
# Apply NMS
pred = non_max_suppression(
pred,
conf_thres,
iou_thres,
classes=classes,
agnostic=agnostic_nms,
)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0, frame = path[i], "%g: " % i, im0s[i].copy(), dataset.count
else:
p, s, im0, frame = path, "", im0s, getattr(dataset, "frame", 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # img.jpg
txt_path = str(save_dir / "labels" / p.stem) + (
"" if dataset.mode == "image" else f"_{frame}"
) # img.txt
s += "%gx%g " % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1, 0]] # normalization gain whwh
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4], im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}s, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (
(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(txt_path + ".txt", "a") as f:
f.write(("%g " * len(line)).rstrip() % line + "\n")
if save_img or view_img: # Add bbox to image
label = f"{names[int(cls)]} {conf:.2f}"
plot_one_box(
xyxy,
im0,
label=label,
color=colors[int(cls)],
line_thickness=3,
)
# Print time (inference + NMS)
print(f"{s}Done. ({t2 - t1:.3f}s)")
# Stream results
if view_img:
cv2.imshow(str(p), im0)
# Save results (image with detections)
if save_img:
if dataset.mode == "image":
cv2.imwrite(save_path, im0)
else: # 'video'
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release() # release previous video writer
fourcc = "mp4v" # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h)
)
vid_writer.write(im0)
if save_txt or save_img:
s = (
f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}"
if save_txt
else ""
)
print(f"Results saved to {save_dir}{s}")
print(f"Done. ({time.time() - t0:.3f}s)")
def detect(save_img=False):
out, source, weights, view_img, save_txt, imgsz = \
opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.endswith(
'.txt') or source.lower().startswith(
('rtsp://', 'rtmp://',
'http://')) or source.lower().startswith('intel')
# Initialize
set_logging()
device = select_device(opt.device)
folder_main = out.split('/')[0]
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
folder_features = folder_main + '/features'
if os.path.exists(folder_features):
shutil.rmtree(folder_features) # delete features output folder
folder_crops = folder_main + '/image_crops'
if os.path.exists(folder_crops):
shutil.rmtree(folder_crops) # delete output folder with object crops
os.makedirs(out) # make new output folder
os.makedirs(folder_features) # make new output folder
os.makedirs(folder_crops) # make new output folder
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = torch.load(weights[0],
map_location=device)['model'].float() # load to FP32
model.to(device).eval()
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(
torch.load('weights/resnet101.pt',
map_location=device)['model']) # load weights
modelc.to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
if source.lower().startswith('intel'):
dataset = LoadRealSense2()
save_img = True
else:
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)]
for _ in range(len(names))]
# frames per second
# TODO if use intel or if use given footage
fps = 30 # dataset.cap.get(cv2.CAP_PROP_FPS)
critical_time_frames = opt.time * fps
# COUNTER: initialization
counter = VoteCounter(critical_time_frames, fps)
print('CRITICAL TIME IS ', opt.time, 'sec, or ', counter.critical_time,
' frames')
# Find index corresponding to a person
idx_person = names.index("person")
# Deep SORT: initialize the tracker
cfg = get_config()
cfg.merge_from_file(opt.config_deepsort)
deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
max_dist=cfg.DEEPSORT.MAX_DIST,
min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP,
max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE,
n_init=cfg.DEEPSORT.N_INIT,
nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
# AlphaPose: initialization
# args_p = update_config(opt.config_alphapose)
# cfg_p = update_config(args_p.ALPHAPOSE.cfg)
#
# args_p.ALPHAPOSE.tracking = args_p.ALPHAPOSE.pose_track or args_p.ALPHAPOSE.pose_flow
#
# demo = SingleImageAlphaPose(args_p.ALPHAPOSE, cfg_p, device)
# output_pose = opt.output.split('/')[0] + '/pose'
# if not os.path.exists(output_pose):
# os.mkdir(output_pose)
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img
) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
classes=opt.classes,
agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# TODO => COUNTER: draw queueing ROI
# compute urn centoid (1st frame only) and plot a bounding box around it
# if dataset.frame == 1:
# counter.read_urn_coordinates(opt.urn, im0s, opt.radius)
# counter.plot_urn_bbox(im0s)
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
if source.lower().startswith('intel'):
p, s, im0, frame = path, '%g: ' % i, im0s[i].copy(
), dataset.count
else:
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
print(save_path)
txt_path = str(Path(out) / Path(p).stem) + (
'_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1,
0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Deep SORT: person class only
idxs_ppl = (
det[:, -1] == idx_person
).nonzero(as_tuple=False).squeeze(
dim=1) # 1. List of indices with 'person' class detections
dets_ppl = det[idxs_ppl, :
-1] # 2. Torch.tensor with 'person' detections
print('\n {} people were detected!'.format(len(idxs_ppl)))
# Deep SORT: convert data into a proper format
xywhs = xyxy2xywh(dets_ppl[:, :-1]).to("cpu")
confs = dets_ppl[:, 4].to("cpu")
# Deep SORT: feed detections to the tracker
if len(dets_ppl) != 0:
trackers, features = deepsort.update(xywhs, confs, im0)
# tracks inside a critical sphere
trackers_inside = []
for i, d in enumerate(trackers):
plot_one_box(d[:-1],
im0,
label='ID' + str(int(d[-1])),
color=colors[1],
line_thickness=1)
# TODO: queue COUNTER
# d_include = counter.centroid_distance(d, im0, colors[1], dataset.frame)
# if d_include:
# trackers_inside.append(d)
# ALPHAPOSE: show skeletons for bounding boxes inside the critical sphere
# if len(trackers_inside) > 0:
# pose = demo.process('frame_'+str(dataset.frame), im0, trackers_inside)
# im0 = demo.vis(im0, pose)
# demo.writeJson([pose], output_pose, form=args_p.ALPHAPOSE.format, for_eval=args_p.ALPHAPOSE.eval)
#
# counter.save_features_and_crops(im0, dataset.frame, trackers_inside, features, folder_main)
cv2.putText(im0, 'Voted ' + str(len(counter.voters_count)),
(50, 50), cv2.FONT_HERSHEY_SIMPLEX, 1.0, (0, 0, 255),
2)
print('NUM VOTERS', len(counter.voters))
print(list(counter.voters.keys()))
# COUNTER
if len(counter.voters) > 0:
counter.save_voter_trajectory(dataset.frame, folder_main)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release(
) # release previous video writer
fourcc = 'mp4v' # output video codec
if type(vid_cap
) is dict: # estimate distance_in_meters
# TODO hard code
w, h, fps = 640, 480, 6
else:
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*fourcc), fps,
(w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % Path(out))
if platform.system() == 'Darwin' and not opt.update: # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
def detect(opt, save_img=False):
out, source, weights, view_img, save_txt, imgsz = \
opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source == '0' or source.startswith('rtsp') or source.startswith(
'http') or source.endswith('.txt')
# initialize deepsort
cfg = get_config()
cfg.merge_from_file(opt.config_deepsort)
deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
max_dist=cfg.DEEPSORT.MAX_DIST,
min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP,
max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE,
n_init=cfg.DEEPSORT.N_INIT,
nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
# Initialize
device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
names = model.module.names if hasattr(
model, 'module') else model.names # get class names
if half:
model.half() # to FP16
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
view_img = True
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
# run once
_ = model(img.half() if half else img) if device.type != 'cpu' else None
save_path = str(Path(out))
txt_path = str(Path(out)) + '/results.txt'
for frame_idx, (path, img, im0s, vid_cap) in enumerate(dataset):
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
classes=opt.classes,
agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
s += '%gx%g ' % img.shape[2:] # print string
save_path = str(Path(out) / Path(p).name)
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
bbox_xywh = []
confs = []
# Adapt detections to deep sort input format
for *xyxy, conf, cls in det:
x_c, y_c, bbox_w, bbox_h = bbox_rel(*xyxy)
obj = [x_c, y_c, bbox_w, bbox_h]
bbox_xywh.append(obj)
confs.append([conf.item()])
xywhs = torch.Tensor(bbox_xywh)
confss = torch.Tensor(confs)
# Pass detections to deepsort
outputs = deepsort.update(xywhs, confss, im0)
# draw boxes for visualization
if len(outputs) > 0:
bbox_xyxy = outputs[:, :4]
identities = outputs[:, -1]
draw_boxes(im0, bbox_xyxy, identities)
# Write MOT compliant results to file
if save_txt and len(outputs) != 0:
for j, output in enumerate(outputs):
bbox_left = output[0]
bbox_top = output[1]
bbox_w = output[2]
bbox_h = output[3]
identity = output[-1]
with open(txt_path, 'a') as f:
f.write(('%g ' * 10 + '\n') %
(frame_idx, identity, bbox_left, bbox_top,
bbox_w, bbox_h, -1, -1, -1,
-1)) # label format
else:
deepsort.increment_ages()
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
print('saving img!')
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
print('saving video!')
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release(
) # release previous video writer
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*opt.fourcc),
fps, (w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % os.getcwd() + os.sep + out)
if platform == 'darwin': # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
def test(
data,
weights=None, # model.pt path(s)
batch_size=32, # batch size
imgsz=640, # inference size (pixels)
conf_thres=0.001, # confidence threshold
iou_thres=0.6, # NMS IoU threshold
task='val', # train, val, test, speed or study
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
single_cls=False, # treat as single-class dataset
augment=False, # augmented inference
verbose=False, # verbose output
save_txt=False, # save results to *.txt
save_hybrid=False, # save label+prediction hybrid results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_json=False, # save a cocoapi-compatible JSON results file
project='runs/test', # save to project/name
name='exp', # save to project/name
exist_ok=False, # existing project/name ok, do not increment
half=True, # use FP16 half-precision inference
model=None,
dataloader=None,
save_dir=Path(''),
plots=True,
wandb_logger=None,
compute_loss=None,
):
# Initialize/load model and set device
training = model is not None
if training: # called by train.py
device = next(model.parameters()).device # get model device
else: # called directly
set_logging()
device = select_device(device, batch_size=batch_size)
# Directories
save_dir = increment_path(Path(project) / name,
exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(
parents=True, exist_ok=True) # make dir
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
gs = max(int(model.stride.max()), 32) # grid size (max stride)
imgsz = check_img_size(imgsz, s=gs) # check image size
# Multi-GPU disabled, incompatible with .half() https://github.com/ultralytics/yolov5/issues/99
# if device.type != 'cpu' and torch.cuda.device_count() > 1:
# model = nn.DataParallel(model)
# Half
half &= device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half()
# Configure
model.eval()
if isinstance(data, str):
with open(data) as f:
data = yaml.safe_load(f)
check_dataset(data) # check
is_coco = data['val'].endswith('coco/val2017.txt') # COCO dataset
nc = 1 if single_cls else int(data['nc']) # number of classes
iouv = torch.linspace(0.5, 0.95,
10).to(device) # iou vector for [email protected]:0.95
niou = iouv.numel()
# Logging
log_imgs = 0
if wandb_logger and wandb_logger.wandb:
log_imgs = min(wandb_logger.log_imgs, 100)
# Dataloader
if not training:
if device.type != 'cpu':
model(
torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(
next(model.parameters()))) # run once
task = task if task in (
'train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task],
imgsz,
batch_size,
gs,
single_cls,
pad=0.5,
rect=True,
prefix=colorstr(f'{task}: '))[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = {
k: v
for k, v in enumerate(
model.names if hasattr(model, 'names') else model.module.names)
}
coco91class = coco80_to_coco91_class()
s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R',
'[email protected]', '[email protected]:.95')
p, r, f1, mp, mr, map50, map, t0, t1, t2 = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
for batch_i, (img, targets, paths,
shapes) in enumerate(tqdm(dataloader, desc=s)):
t_ = time_synchronized()
img = img.to(device, non_blocking=True)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = img.shape # batch size, channels, height, width
t = time_synchronized()
t0 += t - t_
# Run model
out, train_out = model(
img, augment=augment) # inference and training outputs
t1 += time_synchronized() - t
# Compute loss
if compute_loss:
loss += compute_loss([x.float() for x in train_out],
targets)[1][:3] # box, obj, cls
# Run NMS
targets[:, 2:] *= torch.Tensor([width, height, width,
height]).to(device) # to pixels
lb = [targets[targets[:, 0] == i, 1:]
for i in range(nb)] if save_hybrid else [] # for autolabelling
t = time_synchronized()
out = non_max_suppression(out,
conf_thres,
iou_thres,
labels=lb,
multi_label=True,
agnostic=single_cls)
t2 += time_synchronized() - t
# Statistics per image
for si, pred in enumerate(out):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
path = Path(paths[si])
seen += 1
if len(pred) == 0:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), tcls))
continue
# Predictions
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
scale_coords(img[si].shape[1:], predn[:, :4], shapes[si][0],
shapes[si][1]) # native-space pred
# Append to text file
if save_txt:
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0
]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (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(save_dir / 'labels' / (path.stem + '.txt'),
'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
# W&B logging - Media Panel plots
if len(
wandb_images
) < log_imgs and wandb_logger.current_epoch > 0: # Check for test operation
if wandb_logger.current_epoch % wandb_logger.bbox_interval == 0:
box_data = [{
"position": {
"minX": xyxy[0],
"minY": xyxy[1],
"maxX": xyxy[2],
"maxY": xyxy[3]
},
"class_id": int(cls),
"box_caption": "%s %.3f" % (names[cls], conf),
"scores": {
"class_score": conf
},
"domain": "pixel"
} for *xyxy, conf, cls in pred.tolist()]
boxes = {
"predictions": {
"box_data": box_data,
"class_labels": names
}
} # inference-space
wandb_images.append(
wandb_logger.wandb.Image(img[si],
boxes=boxes,
caption=path.name))
wandb_logger.log_training_progress(
predn, path,
names) if wandb_logger and wandb_logger.wandb_run else None
# Append to pycocotools JSON dictionary
if save_json:
# [{"image_id": 42, "category_id": 18, "bbox": [258.15, 41.29, 348.26, 243.78], "score": 0.236}, ...
image_id = int(
path.stem) if path.stem.isnumeric() else path.stem
box = xyxy2xywh(predn[:, :4]) # xywh
box[:, :2] -= box[:, 2:] / 2 # xy center to top-left corner
for p, b in zip(pred.tolist(), box.tolist()):
jdict.append({
'image_id':
image_id,
'category_id':
coco91class[int(p[5])] if is_coco else int(p[5]),
'bbox': [round(x, 3) for x in b],
'score':
round(p[4], 5)
})
# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0],
niou,
dtype=torch.bool,
device=device)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
scale_coords(img[si].shape[1:], tbox, shapes[si][0],
shapes[si][1]) # native-space labels
if plots:
confusion_matrix.process_batch(
predn, torch.cat((labels[:, 0:1], tbox), 1))
# Per target class
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(
-1) # target indices
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(
-1) # prediction indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
ious, i = box_iou(predn[pi, :4], tbox[ti]).max(
1) # best ious, indices
# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct[
pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(
detected
) == nl: # all targets already located in image
break
# Append statistics (correct, conf, pcls, tcls)
stats.append(
(correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# Plot images
if plots and batch_i < 3:
f = save_dir / f'test_batch{batch_i}_labels.jpg' # labels
Thread(target=plot_images,
args=(img, targets, paths, f, names),
daemon=True).start()
f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions
Thread(target=plot_images,
args=(img, output_to_target(out), paths, f, names),
daemon=True).start()
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
p, r, ap, f1, ap_class = ap_per_class(*stats,
plot=plots,
save_dir=save_dir,
names=names)
ap50, ap = ap[:, 0], ap.mean(1) # [email protected], [email protected]:0.95
mp, mr, map50, map = p.mean(), r.mean(), ap50.mean(), ap.mean()
nt = np.bincount(stats[3].astype(np.int64),
minlength=nc) # number of targets per class
else:
nt = torch.zeros(1)
# Print results
pf = '%20s' + '%11i' * 2 + '%11.3g' * 4 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map))
# Print results per class
if (verbose or (nc < 50 and not training)) and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
# Print speeds
t = tuple(x / seen * 1E3 for x in (t0, t1, t2)) # speeds per image
if not training:
shape = (batch_size, 3, imgsz, imgsz)
print(
f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}'
% t)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
if wandb_logger and wandb_logger.wandb:
val_batches = [
wandb_logger.wandb.Image(str(f), caption=f.name)
for f in sorted(save_dir.glob('test*.jpg'))
]
wandb_logger.log({"Validation": val_batches})
if wandb_images:
wandb_logger.log({"Bounding Box Debugger/Images": wandb_images})
# Save JSON
if save_json and len(jdict):
w = Path(weights[0] if isinstance(weights, list) else weights
).stem if weights is not None else '' # weights
anno_json = '../coco/annotations/instances_val2017.json' # annotations json
pred_json = str(save_dir / f"{w}_predictions.json") # predictions json
print('\nEvaluating pycocotools mAP... saving %s...' % pred_json)
with open(pred_json, 'w') as f:
json.dump(jdict, f)
try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
check_requirements(['pycocotools'])
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
anno = COCO(anno_json) # init annotations api
pred = anno.loadRes(pred_json) # init predictions api
eval = COCOeval(anno, pred, 'bbox')
if is_coco:
eval.params.imgIds = [
int(Path(x).stem) for x in dataloader.dataset.img_files
] # image IDs to evaluate
eval.evaluate()
eval.accumulate()
eval.summarize()
map, map50 = eval.stats[:
2] # update results ([email protected]:0.95, [email protected])
except Exception as e:
print(f'pycocotools unable to run: {e}')
# Return results
model.float() # for training
if not training:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
print(f"Results saved to {save_dir}{s}")
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, map50, map,
*(loss.cpu() / len(dataloader)).tolist()), maps, t
def mainFunc(args):
# Set the main function flag
print("Main Function Start...")
# Check the GPU device
print("Number of available GPUs: {}".format(torch.cuda.device_count()))
# Check whether using the distributed runing for the network
is_distributed = initDistributed(args)
master = True
if is_distributed and os.environ["RANK"]:
master = int(
os.environ["RANK"]) == 0 # check whether this node is master node
# Configuration for device setting
set_logging()
if is_distributed:
device = torch.device('cuda:{}'.format(args.local_rank))
else:
device = select_device(args.device)
half = device.type != 'cpu' # half precision only supported on CUDA
# Load the configuration
config = loadConfig(args.config)
# CuDNN related setting
if torch.cuda.is_available():
cudnn.benchmark = config.DEVICE.CUDNN.BENCHMARK
cudnn.deterministic = config.DEVICE.CUDNN.DETERMINISTIC
cudnn.enabled = config.DEVICE.CUDNN.ENABLED
# Configurations for dirctories
save_img, save_dir, source, yolov5_weights, view_img, save_txt, imgsz = \
False, Path(args.save_dir), args.source, args.weights, args.view_img, args.save_txt, args.img_size
webcam = source.isnumeric() or source.startswith(
('rtsp://', 'rtmp://', 'http://')) or source.endswith('.txt')
if save_dir == Path('runs/detect'): # if default
os.makedirs('runs/detect', exist_ok=True) # make base
save_dir = Path(increment_dir(save_dir / 'exp',
args.name)) # increment run
os.makedirs(save_dir / 'labels' if save_txt else save_dir,
exist_ok=True) # make new dir
# Load yolov5 model for human detection
model_yolov5 = attempt_load(config.MODEL.PRETRAINED.YOLOV5,
map_location=device)
imgsz = check_img_size(imgsz,
s=model_yolov5.stride.max()) # check img_size
if half:
model_yolov5.half() # to FP16
# Second-stage classifier
classify = False
if classify:
model_classifier = load_classifier(name='resnet101', n=2) # initialize
model_classifier.load_state_dict(
torch.load('weights/resnet101.pt',
map_location=device)['model']) # load weights
model_classifier.to(device).eval()
# Load resnet model for human keypoints estimation
model_resnet = eval('pose_models.' + config.MODEL.NAME.RESNET +
'.get_pose_net')(config, is_train=False)
if config.EVAL.RESNET.MODEL_FILE:
print('=> loading model from {}'.format(config.EVAL.RESNET.MODEL_FILE))
model_resnet.load_state_dict(torch.load(config.EVAL.RESNET.MODEL_FILE),
strict=False)
else:
print('expected model defined in config at EVAL.RESNET.MODEL_FILE')
model_resnet.to(device)
model_resnet.eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
pose_transform = transforms.Compose(
[ # input transformation for 2d human pose estimation
transforms.ToTensor(),
transforms.Normalize(mean=[0.485, 0.456, 0.406],
std=[0.229, 0.224, 0.225])
])
# Get names and colors
names = model_yolov5.module.names if hasattr(
model_yolov5, 'module') else model_yolov5.names
colors = [[random.randint(0, 255) for _ in range(3)]
for _ in range(len(names))]
# Construt filters for filtering 2D/3D human keypoints
# filters_2d = constructFilters((1,16,2), freq=25, mincutoff=1, beta=0.01) # for test
# filters_3d = constructFilters((1,16,3), freq=25, mincutoff=1, beta=0.01)
# Run the yolov5 and resnet for 2d human pose estimation
# with torch.no_grad():
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model_yolov5(img.half() if half else img
) if device.type != 'cpu' else None # run once
# Process every video frame
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred_boxes = model_yolov5(img, augment=args.augment)[0]
# Apply NMS
pred_boxes = non_max_suppression(pred_boxes,
args.conf_thres,
args.iou_thres,
classes=args.classes,
agnostic=args.agnostic_nms)
t2 = time_synchronized()
# Can not find people and move to next frame
if pred_boxes[0] is None:
# show the frame with no human detected
cv2.namedWindow("2D Human Pose Estimation", cv2.WINDOW_NORMAL)
cv2.imshow("2D Human Pose Estimation", im0s[0].copy())
# wait manual operations
# with kb.Listener(on_press=on_press) as listener:
# listener.join()
# return
# if kb.is_pressed('t'):
# return
print("No Human Detected and Move on.")
print("-" * 30)
continue
# Print time (inference + NMS)
detect_time = t2 - t1
detect_fps = 1.0 / detect_time
print("Human Detection Time: {}, Human Detection FPS: {}".format(
detect_time, detect_fps))
# Apply Classifier
if classify: # false
pred_boxes = apply_classifier(pred_boxes, model_classifier, img,
im0s)
# Estimate 2d human pose(multiple person)
centers = []
scales = []
for id, boxes in enumerate(pred_boxes):
if boxes is not None and len(boxes):
boxes[:, :4] = scale_coords(img.shape[2:], boxes[:, :4],
im0s[id].copy().shape).round()
# convert tensor to list format
boxes = np.delete(boxes.cpu().numpy(), [-2, -1], axis=1).tolist()
for l in range(len(boxes)):
boxes[l] = [tuple(boxes[l][0:2]), tuple(boxes[l][2:4])]
# convert box to center and scale
for box in boxes:
center, scale = box_to_center_scale(box, imgsz, imgsz)
centers.append(center)
scales.append(scale)
t3 = time_synchronized()
pred_pose_2d = get_pose_estimation_prediction(config,
model_resnet,
im0s[0],
centers,
scales,
transform=pose_transform,
device=device)
t4 = time_synchronized()
# Print time (2d human pose estimation)
estimate_time = t4 - t3
estimate_fps = 1.0 / estimate_time
print("Pose Estimation Time: {}, Pose Estimation FPS: {}".format(
estimate_time, estimate_fps))
# Filter the predicted 2d human pose(multiple person)
t5 = time_synchronized()
# if False: # for test
if config.EVAL.RESNET.USE_FILTERS_2D:
# construct filters for every keypoints of every person in 2D
filters_2d = constructFilters(pred_pose_2d.shape,
freq=1,
mincutoff=1,
beta=0.01)
print("Shape of filters_2d: ({}, {}, {})".format(
len(filters_2d), len(filters_2d[0]),
len(filters_2d[0][0]))) # for test
for per in range(pred_pose_2d.shape[0]):
for kp in range(pred_pose_2d.shape[1]):
for coord in range(pred_pose_2d.shape[2]):
pred_pose_2d[per][kp][coord] = filters_2d[per][kp][
coord](pred_pose_2d[per][kp][coord])
t6 = time_synchronized()
# Print time (filter 2d human pose)
filter_time_2d = t6 - t5
filter_fps_2d = 1.0 / filter_time_2d
print("Filter 2D Pose Time: {}, Filter 2D Pose FPS: {}".format(
filter_time_2d, filter_fps_2d))
# Process detections and estimations in 2D
for i, box in enumerate(pred_boxes):
if webcam: # batch_size >= 1
p, s, im0 = Path(path[i]), '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = Path(path), '', im0s
save_path = str(save_dir / p.name)
txt_path = str(save_dir / 'labels' / p.stem) + (
'_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1,
0]] # normalization gain whwh
if box is not None and len(box):
# Rescale boxes from img_size to im0 size
box[:, :4] = scale_coords(img.shape[2:], box[:, :4],
im0.shape).round()
# Print results
for c in box[:, -1].unique():
n = (box[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(box):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
gn).view(-1).tolist() # normalized xywh
line = (cls, *xywh, conf) if args.save_conf else (
cls, *xywh) # label format
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line) + '\n') % line)
# Add bbox to image
if save_img or view_img:
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy,
im0,
label=label,
color=colors[int(cls)],
line_thickness=3)
# Draw joint keypoints, number orders and human skeletons for every detected people in 2D
for person in pred_pose_2d:
# draw the human keypoints
for idx, coord in enumerate(person):
x_coord, y_coord = int(coord[0]), int(coord[1])
cv2.circle(im0, (x_coord, y_coord), 1, (0, 0, 255), 5)
cv2.putText(im0, str(idx), (x_coord, y_coord),
cv2.FONT_HERSHEY_SIMPLEX, 0.6,
(255, 255, 255), 2, cv2.LINE_AA)
# draw the human skeletons in PACIFIC mode
for skeleton in PACIFIC_SKELETON_INDEXES:
cv2.line(im0, (int(person[skeleton[0]][0]),
int(person[skeleton[0]][1])),
(int(person[skeleton[1]][0]),
int(person[skeleton[1]][1])), skeleton[2], 2)
# Print time (inference + NMS + estimation)
print('%sDone. (%.3fs)' % (s, t4 - t1))
# Stream results
if view_img:
detect_text = "Detect FPS:{0:0>5.2f}/{1:0>6.2f}ms".format(
detect_fps, detect_time * 1000)
estimate_text = "Estimate FPS:{0:0>5.2f}/{1:0>6.2f}ms".format(
estimate_fps, estimate_time * 1000)
cv2.putText(im0, detect_text, (10, 30),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.putText(im0, estimate_text, (10, 60),
cv2.FONT_HERSHEY_SIMPLEX, 0.6, (0, 0, 255), 2,
cv2.LINE_AA)
cv2.namedWindow("2D Human Pose Estimation", cv2.WINDOW_NORMAL)
cv2.imshow("2D Human Pose Estimation", im0)
if cv2.waitKey(1) & 0xFF == ord('q'): # q to quit
return
# goto .mainFunc
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release(
) # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*fourcc), fps,
(w, h))
vid_writer.write(im0)
# Print time (inference + NMS + estimation + 2d filtering)
all_process_time = t6 - t1
all_process_fps = 1.0 / all_process_time
print("All Process Time: {}, All Process FPS: {}".format(
all_process_time, all_process_fps))
print("-" * 30)
# Goto label
# label .mainFunc
# Print saving results
if save_txt or save_img:
print('Results saved to %s' % save_dir)
# Release video reader and writer, then destory all opencv windows
dataset.vid_cap.release()
vid_writer.release()
cv2.destroyAllWindows()
print('Present 2D Human Pose Inference Done. Total Time:(%.3f seconds)' %
(time.time() - t0))
def detect(opt, device, save_img=False):
out, source, weights, view_img, save_txt, imgsz = \
opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source == '0' or source.startswith('rtsp') or source.startswith(
'http') or source.endswith('.txt')
colorOrder = ['red', 'purple', 'blue', 'green', 'yellow', 'orange']
frame_num = 0
framestr = 'Frame {frame}'
fpses = []
frame_catch_pairs = []
ball_person_pairs = {}
for color in colorDict:
ball_person_pairs[color] = 0
# Read Class Name Yaml
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader)
names = data_dict['names']
# initialize deepsort
cfg = get_config()
cfg.merge_from_file(opt.config_deepsort)
deepsort = DeepSort(cfg.DEEPSORT.REID_CKPT,
max_dist=cfg.DEEPSORT.MAX_DIST,
min_confidence=cfg.DEEPSORT.MIN_CONFIDENCE,
nms_max_overlap=cfg.DEEPSORT.NMS_MAX_OVERLAP,
max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE,
n_init=cfg.DEEPSORT.N_INIT,
nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
# Initialize
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
stride = int(model.stride.max()) # model stride
imgsz = check_img_size(imgsz, s=stride) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(
torch.load('weights/resnet101.pt',
map_location=device)['model']).to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride)
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)]
for _ in range(len(names))]
# Run inference
if device.type != 'cpu':
model(
torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(
next(model.parameters()))) # run once
t0 = time.time()
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
classes=opt.classes,
agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
txt_path = str(Path(out) / Path(p).stem) + (
'_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1,
0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
bbox_xywh = []
confs = []
clses = []
# Write results
for *xyxy, conf, cls in det:
img_h, img_w, _ = im0.shape # get image shape
x_c, y_c, bbox_w, bbox_h = bbox_rel(img_w, img_h, *xyxy)
obj = [x_c, y_c, bbox_w, bbox_h]
bbox_xywh.append(obj)
confs.append([conf.item()])
clses.append([cls.item()])
xywhs = torch.Tensor(bbox_xywh)
confss = torch.Tensor(confs)
clses = torch.Tensor(clses)
# Pass detections to deepsort
outputs = []
global groundtruths_path
if not 'disable' in groundtruths_path:
# print('\nenabled', groundtruths_path)
groundtruths = solution.load_labels(
groundtruths_path, img_w, img_h, frame_num)
if (groundtruths.shape[0] == 0):
outputs = deepsort.update(xywhs, confss, clses, im0)
else:
# print(groundtruths)
xywhs = groundtruths[:, 2:]
tensor = torch.tensor((), dtype=torch.int32)
confss = tensor.new_ones((groundtruths.shape[0], 1))
clses = groundtruths[:, 0:1]
outputs = deepsort.update(xywhs, confss, clses, im0)
if frame_num >= 2:
for real_ID in groundtruths[:, 1:].tolist():
for DS_ID in xyxy2xywh(outputs[:, :5]):
if (abs(DS_ID[0] - real_ID[1]) / img_w < 0.005
) and (abs(DS_ID[1] - real_ID[2]) / img_h <
0.005) and (
abs(DS_ID[2] - real_ID[3]) /
img_w < 0.005) and (
abs(DS_ID[3] - real_ID[4]) /
img_w < 0.005):
id_mapping[DS_ID[4]] = int(real_ID[0])
else:
outputs = deepsort.update(xywhs, confss, clses, im0)
# draw boxes for visualization
if len(outputs) > 0:
bbox_xyxy = outputs[:, :4]
identities = outputs[:, 4]
clses = outputs[:, 5]
scores = outputs[:, 6]
#Temp solution to get correct id's
mapped_id_list = []
for ids in identities:
if (ids in id_mapping):
mapped_id_list.append(int(id_mapping[ids]))
else:
mapped_id_list.append(ids)
ball_detect, frame_catch_pairs, ball_person_pairs = solution.detect_catches(
im0, bbox_xyxy, clses, mapped_id_list, frame_num,
colorDict, frame_catch_pairs, ball_person_pairs,
colorOrder, save_img)
t3 = time_synchronized()
draw_boxes(im0, bbox_xyxy, [names[i] for i in clses],
scores, ball_detect, identities)
else:
t3 = time_synchronized()
#Draw frame number
tmp = framestr.format(frame=frame_num)
t_size = cv2.getTextSize(tmp, cv2.FONT_HERSHEY_PLAIN, 2, 2)[0]
cv2.putText(im0, tmp, (0, (t_size[1] + 10)),
cv2.FONT_HERSHEY_PLAIN, 2, [255, 255, 255], 2)
#Inference Time
fps = (1 / (t3 - t1))
fpses.append(fps)
print('FPS=%.2f' % fps)
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release(
) # release previous video writer
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*opt.fourcc),
fps, (w, h))
vid_writer.write(im0)
frame_num += 1
#t4 = time_synchronized()
avgFps = (sum(fpses) / len(fpses))
print('Average FPS = %.2f' % avgFps)
#print('Total Runtime = %.2f' % (t4 - t0))
outpath = os.path.basename(source)
outpath = outpath[:-4]
outpath = out + '/' + outpath + '_out.csv'
solution.write_catches(outpath, frame_catch_pairs, colorOrder)
if save_txt or save_img:
print('Results saved to %s' % os.getcwd() + os.sep + out)
if platform == 'darwin': # MacOS
os.system('open ' + save_path)
def detect(save_img=False):
out, source, weights, view_img, save_txt, imgsz = \
opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.startswith(
'rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
set_logging()
device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(
torch.load('weights/resnet101.pt',
map_location=device)['model']) # load weights
modelc.to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)]
for _ in range(len(names))]
# Find index corresponding to a person
idx_person = names.index("person")
# SORT: initialize the tracker
mot_tracker = sort_module.Sort(max_age=opt.max_age,
min_hits=opt.min_hits,
iou_threshold=opt.iou_threshold)
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img
) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
classes=opt.classes,
agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
txt_path = str(Path(out) / Path(p).stem) + (
'_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1,
0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# SORT: number of people detected
idxs_ppl = (
det[:, -1] == idx_person
).nonzero(as_tuple=False).squeeze(
dim=1) # 1. List of indices with 'person' class detections
dets_ppl = det[idxs_ppl, :-1].to(
"cpu") # 2. Torch.tensor with 'person' detections
print('\n {} people were detected!'.format(len(idxs_ppl)))
# SORT: feed detections to the tracker
if len(dets_ppl) != 0:
trackers = mot_tracker.update(dets_ppl)
for d in trackers:
plot_one_box(d[:-1],
im0,
label='ID' + str(int(d[-1])),
color=colors[1],
line_thickness=1)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
cv2.imshow(p, im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIteration
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release(
) # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
vid_writer = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*fourcc), fps,
(w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % Path(out))
if platform.system() == 'Darwin' and not opt.update: # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
def run(
weights=ROOT / 'yolov5s.pt', # model.pt path(s)
source=ROOT / 'data/images', # file/dir/URL/glob, 0 for webcam
data=ROOT / 'data/coco128.yaml', # dataset.yaml path
imgsz=(640, 640), # inference size (height, width)
conf_thres=0.25, # confidence threshold
iou_thres=0.45, # NMS IOU threshold
max_det=1000, # maximum detections per image
device='', # cuda device, i.e. 0 or 0,1,2,3 or cpu
view_img=False, # show results
save_txt=False, # save results to *.txt
save_conf=False, # save confidences in --save-txt labels
save_crop=False, # save cropped prediction boxes
nosave=False, # do not save images/videos
classes=None, # filter by class: --class 0, or --class 0 2 3
agnostic_nms=False, # class-agnostic NMS
augment=False, # augmented inference
visualize=False, # visualize features
update=False, # update all models
project=ROOT / 'runs/detect', # save results to project/name
name='exp', # save results to project/name
exist_ok=False, # existing project/name ok, do not increment
line_thickness=3, # bounding box thickness (pixels)
hide_labels=False, # hide labels
hide_conf=False, # hide confidences
half=False, # use FP16 half-precision inference
dnn=False, # use OpenCV DNN for ONNX inference
):
source = str(source)
save_img = not nosave and not source.endswith(
'.txt') # save inference images
is_file = Path(source).suffix[1:] in (IMG_FORMATS + VID_FORMATS)
is_url = source.lower().startswith(
('rtsp://', 'rtmp://', 'http://', 'https://'))
webcam = source.isnumeric() or source.endswith('.txt') or (is_url
and not is_file)
if is_url and is_file:
source = check_file(source) # download
# Directories
save_dir = increment_path(Path(project) / name,
exist_ok=exist_ok) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(
parents=True, exist_ok=True) # make dir
# Load model
device = select_device(device)
model = DetectMultiBackend(weights, device=device, dnn=dnn, data=data)
stride, names, pt, jit, onnx, engine = model.stride, model.names, model.pt, model.jit, model.onnx, model.engine
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= (
pt or jit or onnx or engine
) and device.type != 'cpu' # FP16 supported on limited backends with CUDA
if pt or jit:
model.model.half() if half else model.model.float()
# Dataloader
if webcam:
view_img = check_imshow()
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz, stride=stride, auto=pt)
bs = len(dataset) # batch_size
else:
dataset = LoadImages(source, img_size=imgsz, stride=stride, auto=pt)
bs = 1 # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Run inference
model.warmup(imgsz=(1 if pt else bs, 3, *imgsz), half=half) # warmup
dt, seen = [0.0, 0.0, 0.0], 0
for path, im, im0s, vid_cap, s in dataset:
t1 = time_sync()
im = torch.from_numpy(im).to(device)
im = im.half() if half else im.float() # uint8 to fp16/32
im /= 255 # 0 - 255 to 0.0 - 1.0
if len(im.shape) == 3:
im = im[None] # expand for batch dim
t2 = time_sync()
dt[0] += t2 - t1
# Inference
visualize = increment_path(save_dir / Path(path).stem,
mkdir=True) if visualize else False
pred = model(im, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
# NMS
pred = non_max_suppression(pred,
conf_thres,
iou_thres,
classes,
agnostic_nms,
max_det=max_det)
dt[2] += time_sync() - t3
# Second-stage classifier (optional)
# pred = utils.general.apply_classifier(pred, classifier_model, im, im0s)
# Process predictions
for i, det in enumerate(pred): # per image
seen += 1
if webcam: # batch_size >= 1
p, im0, frame = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, frame = path, im0s.copy(), getattr(dataset, 'frame', 0)
p = Path(p) # to Path
save_path = str(save_dir / p.name) # im.jpg
txt_path = str(save_dir / 'labels' / p.stem) + (
'' if dataset.mode == 'image' else f'_{frame}') # im.txt
s += '%gx%g ' % im.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1,
0]] # normalization gain whwh
imc = im0.copy() if save_crop else im0 # for save_crop
annotator = Annotator(im0,
line_width=line_thickness,
example=str(names))
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(im.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (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(txt_path + '.txt', 'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
if save_img or save_crop or view_img: # Add bbox to image
c = int(cls) # integer class
label = None if hide_labels else (
names[c]
if hide_conf else f'{names[c]} {conf:.2f}')
annotator.box_label(xyxy, label, color=colors(c, True))
if save_crop:
save_one_box(xyxy,
imc,
file=save_dir / 'crops' / names[c] /
f'{p.stem}.jpg',
BGR=True)
# Stream results
im0 = annotator.result()
if view_img:
cv2.imshow(str(p), im0)
cv2.waitKey(1) # 1 millisecond
# Save results (image with detections)
if save_img:
if dataset.mode == 'image':
cv2.imwrite(save_path, im0)
else: # 'video' or 'stream'
if vid_path[i] != save_path: # new video
vid_path[i] = save_path
if isinstance(vid_writer[i], cv2.VideoWriter):
vid_writer[i].release(
) # release previous video writer
if vid_cap: # video
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
else: # stream
fps, w, h = 30, im0.shape[1], im0.shape[0]
save_path = str(Path(save_path).with_suffix(
'.mp4')) # force *.mp4 suffix on results videos
vid_writer[i] = cv2.VideoWriter(
save_path, cv2.VideoWriter_fourcc(*'mp4v'), fps,
(w, h))
vid_writer[i].write(im0)
# Print time (inference-only)
LOGGER.info(f'{s}Done. ({t3 - t2:.3f}s)')
# Print results
t = tuple(x / seen * 1E3 for x in dt) # speeds per image
LOGGER.info(
f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {(1, 3, *imgsz)}'
% t)
if save_txt or save_img:
s = f"\n{len(list(save_dir.glob('labels/*.txt')))} labels saved to {save_dir / 'labels'}" if save_txt else ''
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}{s}")
if update:
strip_optimizer(weights) # update model (to fix SourceChangeWarning)
def detect(save_img=False):
out, source, weights, view_img, save_txt, imgsz = \
opt.output, opt.source, opt.weights, opt.view_img, opt.save_txt, opt.img_size
webcam = source.isnumeric() or source.startswith(
'rtsp') or source.startswith('http') or source.endswith('.txt')
# Initialize
set_logging()
device = select_device(opt.device)
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
half = device.type != 'cpu' # half precision only supported on CUDA
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
if half:
model.half() # to FP16
# Second-stage classifier
classify = False
if classify:
modelc = load_classifier(name='resnet101', n=2) # initialize
modelc.load_state_dict(
torch.load('weights/resnet101.pt',
map_location=device)['model']) # load weights
modelc.to(device).eval()
# Set Dataloader
vid_path, vid_writer = None, None
if webcam:
view_img = True
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source, img_size=imgsz)
else:
save_img = True
dataset = LoadImages(source, img_size=imgsz)
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
colors = [[random.randint(0, 255) for _ in range(3)]
for _ in range(len(names))]
# Run inference
t0 = time.time()
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
_ = model(img.half() if half else img
) if device.type != 'cpu' else None # run once
for path, img, im0s, vid_cap in dataset:
img = torch.from_numpy(img).to(device)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
# Inference
t1 = time_synchronized()
pred = model(img, augment=opt.augment)[0]
# Apply NMS
pred = non_max_suppression(pred,
opt.conf_thres,
opt.iou_thres,
classes=opt.classes,
agnostic=opt.agnostic_nms)
t2 = time_synchronized()
# Apply Classifier
if classify:
pred = apply_classifier(pred, modelc, img, im0s)
# Process detections
for i, det in enumerate(pred): # detections per image
if webcam: # batch_size >= 1
p, s, im0 = path[i], '%g: ' % i, im0s[i].copy()
else:
p, s, im0 = path, '', im0s
save_path = str(Path(out) / Path(p).name)
txt_path = str(Path(out) / Path(p).stem) + (
'_%g' % dataset.frame if dataset.mode == 'video' else '')
s += '%gx%g ' % img.shape[2:] # print string
gn = torch.tensor(im0.shape)[[1, 0, 1,
0]] # normalization gain whwh
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += '%g %ss, ' % (n, names[int(c)]) # add to string
# Write results
for *xyxy, conf, cls in reversed(det):
if save_txt: # Write to file
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
gn).view(-1).tolist() # normalized xywh
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * 5 + '\n') %
(cls, *xywh)) # label format
if save_img or view_img: # Add bbox to image
label = '%s %.2f' % (names[int(cls)], conf)
plot_one_box(xyxy,
im0,
label=label,
color=colors[int(cls)],
line_thickness=3)
# Print time (inference + NMS)
print('%sDone. (%.3fs)' % (s, t2 - t1))
# Stream results
if view_img:
# cv2.imshow(p, im0)
cv2.imwrite("C:/Users/lenovo/Desktop/server/output/camera.jpg",
im0)
if cv2.waitKey(1) == ord('q'): # q to quit
raise StopIterationq
# Save results (image with detections)
if save_img:
if dataset.mode == 'images':
cv2.imwrite(save_path, im0)
else:
if vid_path != save_path: # new video
vid_path = save_path
if isinstance(vid_writer, cv2.VideoWriter):
vid_writer.release(
) # release previous video writer
fourcc = 'mp4v' # output video codec
fps = vid_cap.get(cv2.CAP_PROP_FPS)
w = int(vid_cap.get(cv2.CAP_PROP_FRAME_WIDTH))
h = int(vid_cap.get(cv2.CAP_PROP_FRAME_HEIGHT))
# vid_writer = cv2.VideoWriter(save_path, cv2.VideoWriter_fourcc(*fourcc), fps, (w, h))
vid_writer = cv2.VideoWriter(
save_path,
cv2.VideoWriter_fourcc('X', '2', '6', '4'), fps,
(w, h))
vid_writer.write(im0)
if save_txt or save_img:
print('Results saved to %s' % Path(out))
if platform.system() == 'Darwin' and not opt.update: # MacOS
os.system('open ' + save_path)
print('Done. (%.3fs)' % (time.time() - t0))
def rtsp_to_mongodb():
with open("/home/asyed/airflow/dags/parameters.json") as f:
parms = json.load(f)
agnostic_nms = parms["agnostic_nms"]
augment = parms["augment"]
classes = parms["classes"]
conf_thres = parms["conf_thres"]
config_deepsort = parms["config_deepsort"]
deep_sort_model = parms["deep_sort_model"]
device = parms["device"]
dnn = False
evaluate = parms["evaluate"]
exist_ok = parms["exist_ok"]
fourcc = parms["fourcc"]
half = False
print(device)
imgsz = parms["imgsz"]
iou_thres = parms["iou_thres"]
max_det = parms["max_det"]
name = parms["name"]
# save_vid = parms["save_vid"]
#show_vid = parms["show_vid"]
source = parms["source"]
visualize = parms["visualize"]
yolo_model = parms["yolo_model"]
webcam = parms["webcam"]
save_txt = parms["save_txt"]
homography = np.array(parms["homography"])
url = "mongodb://localhost:27017"
client = MongoClient(url)
db = client.trajectory_database
today_date = date.today().strftime("%m-%d-%y")
new = "file_image_coordinates_" + today_date
collection = db[new]
cfg = get_config()
cfg.merge_from_file(config_deepsort)
deepsort = DeepSort(deep_sort_model,
max_dist=cfg.DEEPSORT.MAX_DIST,
max_iou_distance=cfg.DEEPSORT.MAX_IOU_DISTANCE,
max_age=cfg.DEEPSORT.MAX_AGE,
n_init=cfg.DEEPSORT.N_INIT,
nn_budget=cfg.DEEPSORT.NN_BUDGET,
use_cuda=True)
device = select_device(device)
half &= device.type != 'cpu' # half precision only supported on CUDA
# The MOT16 evaluation runs multiple inference streams in parallel, each one writing to
# its own .txt file. Hence, in that case, the output folder is not restored
# make new output folder
# Load model
device = select_device(device)
model = DetectMultiBackend(yolo_model, device=device, dnn=dnn)
stride, names, pt, jit, _ = model.stride, model.names, model.pt, model.jit, model.onnx
imgsz = check_img_size(imgsz, s=stride) # check image size
# Half
half &= pt and device.type != 'cpu' # half precision only supported by PyTorch on CUDA
if pt:
model.model.half() if half else model.model.float()
# Set Dataloader
vid_path, vid_writer = None, None
# Check if environment supports image displays
cudnn.benchmark = True # set True to speed up constant image size inference
dataset = LoadStreams(source,
img_size=imgsz,
stride=stride,
auto=pt and not jit)
bs = len(dataset) # batch_size
vid_path, vid_writer = [None] * bs, [None] * bs
# Get names and colors
names = model.module.names if hasattr(model, 'module') else model.names
if pt and device.type != 'cpu':
model(
torch.zeros(1, 3, *imgsz).to(device).type_as(
next(model.model.parameters()))) # warmup
# global framess_im2
dt, seen = [0.0, 0.0, 0.0, 0.0], 0
# arr = None
past = []
for frame_idx, (path, img, im0s, vid_cap, s) in enumerate(dataset):
t1 = time_sync()
img = torch.from_numpy(img).to(device)
# print("raw_frame",img.shape)
img = img.half() if half else img.float() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
if img.ndimension() == 3:
img = img.unsqueeze(0)
t2 = time_sync()
dt[0] += t2 - t1
pred = model(img, augment=augment, visualize=visualize)
t3 = time_sync()
dt[1] += t3 - t2
pred = non_max_suppression(pred,
conf_thres,
iou_thres,
classes,
agnostic_nms,
max_det=max_det)
dt[2] += time_sync() - t3
# Process detections
# dets_per_img = []
for i, det in enumerate(pred): # detections per image
seen += 1
if webcam: # batch_size >= 1
p, im0, _ = path[i], im0s[i].copy(), dataset.count
s += f'{i}: '
else:
p, im0, _ = path, im0s.copy(), getattr(dataset, 'frame', 0)
annotator = Annotator(im0, line_width=2, pil=not ascii)
if det is not None and len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(img.shape[2:], det[:, :4],
im0.shape).round()
# Print results
for c in det[:, -1].unique():
n = (det[:, -1] == c).sum() # detections per class
s += f"{n} {names[int(c)]}{'s' * (n > 1)}, " # add to string
xywhs = xyxy2xywh(det[:, 0:4])
confs = det[:, 4]
clss = det[:, 5]
# pass detections to deepsort
t4 = time_sync()
outputs = deepsort.update(xywhs.cpu(), confs.cpu(),
clss.cpu(), im0)
t5 = time_sync()
dt[3] += t5 - t4
if len(outputs) > 0:
for j, (output, conf) in enumerate(zip(outputs,
confs)):
bboxes = output[0:4]
id = output[4]
cls = output[5]
c = int(cls) # integer class
label = f'{id} {names[c]} {conf:.2f}'
annotator.box_label(bboxes,
label,
color=colors(c, True))
if save_txt:
# to MOT format
bbox_left = output[0]
bbox_top = output[1]
bbox_w = output[2] - output[0]
bbox_h = output[3] - output[1]
# bbox_left = bbox_left + bbox_h
bbox_top = bbox_top + bbox_h
agent_data = {
'frame': int(frame_idx + 1),
'agent_id': int(id),
"labels": str(names[c]),
"x": int(bbox_left),
"y": int(bbox_top)
}
print("agent", agent_data)
collection.insert_one(agent_data)
#db.object_detection.insert_one(agent_data)
#db.pedestrian_detection_15_june.insert_one(agent_data)
#db.test_21_july.insert_one(agent_data)
LOGGER.info(
f'{s}Done. YOLO:({t3 - t2:.3f}s), DeepSort:({t5 - t4:.3f}s)'
)
else:
deepsort.increment_ages()
LOGGER.info('No detections')
im0 = annotator.result()
def train(hyp, opt, device, tb_writer=None):
logger.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
is_coco = opt.data.endswith('coco.yaml')
# Logging- Doing this before checking the dataset. Might update data_dict
loggers = {'wandb': None} # loggers dict
if rank in [-1, 0]:
opt.hyp = hyp # add hyperparameters
run_id = torch.load(weights).get('wandb_id') if weights.endswith('.pt') and os.path.isfile(weights) else None
wandb_logger = WandbLogger(opt, Path(opt.save_dir).stem, run_id, data_dict)
loggers['wandb'] = wandb_logger.wandb
data_dict = wandb_logger.data_dict
if wandb_logger.wandb:
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if opt.single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(opt.cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
exclude = ['anchor'] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info('Transferred %g/%g items from %s' % (len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# EMA
if ema and ckpt.get('ema'):
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
ema.updates = ckpt['updates']
# Results
if ckpt.get('training_results') is not None:
results_file.write_text(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (weights, epochs)
if epochs < start_epoch:
logger.info('%s has been trained for %g epochs. Fine-tuning for %g additional epochs.' %
(weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = max(int(model.stride.max()), 32) # grid size (max stride)
nl = model.model[-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Trainloader
dataloader, dataset = create_dataloader(train_path, imgsz, batch_size, gs, opt,
hyp=hyp, augment=True, cache=opt.cache_images, rect=opt.rect, rank=rank,
world_size=opt.world_size, workers=opt.workers,
image_weights=opt.image_weights, quad=opt.quad, prefix=colorstr('train: '))
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
testloader = create_dataloader(test_path, imgsz_test, batch_size * 2, gs, opt, # testloader
hyp=hyp, cache=opt.cache_images and not opt.notest, rect=True, rank=-1,
world_size=opt.world_size, workers=opt.workers,
pad=0.5, prefix=colorstr('val: '))[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
# DDP mode
if cuda and rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank)
# Model parameters
hyp['box'] *= 3. / nl # scale to layers
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640) ** 2 * 3. / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
f'Using {dataloader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices) if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'total', 'labels', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.4g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f'train_batch{ni}.jpg' # filename
Thread(target=plot_images, args=(imgs, targets, paths, f), daemon=True).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
elif plots and ni == 10 and wandb_logger.wandb:
wandb_logger.log({"Mosaics": [wandb_logger.wandb.Image(str(x), caption=x.name) for x in
save_dir.glob('train*.jpg') if x.exists()]})
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
wandb_logger.current_epoch = epoch + 1
results, maps, times = test.test(data_dict,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
verbose=nc < 50 and final_epoch,
plots=plots and final_epoch,
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results + '\n') # append metrics, val_loss
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' % (results_file, opt.bucket, opt.name))
# Log
tags = ['train/box_loss', 'train/obj_loss', 'train/cls_loss', # train loss
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/mAP_0.5:0.95',
'val/box_loss', 'val/obj_loss', 'val/cls_loss', # val loss
'x/lr0', 'x/lr1', 'x/lr2'] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb_logger.wandb:
wandb_logger.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
wandb_logger.end_epoch(best_result=best_fitness == fi)
# Save model
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
ckpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': results_file.read_text(),
'model': deepcopy(model.module if is_parallel(model) else model).half(),
'ema': deepcopy(ema.ema).half(),
'updates': ema.updates,
'optimizer': optimizer.state_dict(),
'wandb_id': wandb_logger.wandb_run.id if wandb_logger.wandb else None}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if wandb_logger.wandb:
if ((epoch + 1) % opt.save_period == 0 and not final_epoch) and opt.save_period != -1:
wandb_logger.log_model(
last.parent, opt, epoch, fi, best_model=best_fitness == fi)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb_logger.wandb:
files = ['results.png', 'confusion_matrix.png', *[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]]
wandb_logger.log({"Results": [wandb_logger.wandb.Image(str(save_dir / f), caption=f) for f in files
if (save_dir / f).exists()]})
# Test best.pt
logger.info('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
for m in (last, best) if best.exists() else (last): # speed, mAP tests
results, _, _ = test.test(opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
conf_thres=0.001,
iou_thres=0.7,
model=attempt_load(m, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=True,
plots=False,
is_coco=is_coco)
# Strip optimizers
final = best if best.exists() else last # final model
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
wandb_logger.wandb.log_artifact(str(final), type='model',
name='run_' + wandb_logger.wandb_run.id + '_model',
aliases=['last', 'best', 'stripped'])
wandb_logger.finish_run()
else:
dist.destroy_process_group()
torch.cuda.empty_cache()
return results
def train(hyp, opt, device, tb_writer=None):
logger.info(f'Hyperparameters {hyp}')
log_dir = Path(tb_writer.log_dir) if tb_writer else Path(
opt.logdir) / 'evolve' # logging directory
wdir = log_dir / 'weights' # weights directory
os.makedirs(wdir, exist_ok=True)
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = str(log_dir / 'results.txt')
epochs, batch_size, total_batch_size, weights, rank = \
opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Save run settings
with open(log_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(log_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(
data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
if hyp.get('anchors'):
ckpt['model'].yaml['anchors'] = round(
hyp['anchors']) # force autoanchor
model = Model(opt.cfg or ckpt['model'].yaml, ch=3,
nc=nc).to(device) # create
exclude = ['anchor'] if opt.cfg or hyp.get('anchors') else [
] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info(
'Transferred %g/%g items from %s' %
(len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
# Freeze
freeze = [
'',
] # parameter names to freeze (full or partial)
if any(freeze):
for k, v in model.named_parameters():
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
v.requires_grad = True
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': hyp['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp[
'lrf']) + hyp['lrf'] # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (
weights, epochs)
shutil.copytree(wdir, wdir.parent /
f'weights_backup_epoch{start_epoch - 1}'
) # save previous weights
if epochs < start_epoch:
logger.info(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Exponential moving average
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=opt.local_rank)
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path,
imgsz_test,
total_batch_size,
gs,
opt,
hyp=hyp,
augment=False,
cache=opt.cache_images
and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers)[0] # testloader
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
plot_labels(labels, save_dir=log_dir)
if tb_writer:
# tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(
device) # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb),
1e3) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test\n'
'Using %g dataloader workers\nLogging results to %s\n'
'Starting training for %g epochs...' %
(imgsz, imgsz_test, dataloader.num_workers, log_dir, epochs))
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (
1 - maps)**2 # class weights
iw = labels_to_image_weights(dataset.labels,
nc=nc,
class_weights=cw) # image weights
dataset.indices = random.choices(
range(dataset.n), weights=iw,
k=dataset.n) # rand weighted idx
# Broadcast if DDP
if rank != -1:
indices = (torch.tensor(dataset.indices)
if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls',
'total', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [
hyp['warmup_bias_lr'] if j == 2 else 0.0,
x['initial_lr'] * lf(epoch)
])
if 'momentum' in x:
x['momentum'] = np.interp(
ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]
] # new shape (stretched to gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device),
model) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9
if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 +
'%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if ni < 3:
f = str(log_dir / f'train_batch{ni}.jpg') # filename
result = plot_images(images=imgs,
targets=targets,
paths=paths,
fname=f)
# if tb_writer and result is not None:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(
model,
include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(
opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=log_dir,
plots=epoch == 0 or final_epoch) # plot first and last
# Write
with open(results_file, 'a') as f:
f.write(
s + '%10.4g' * 7 % results +
'\n') # P, R, [email protected], [email protected], val_loss(box, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' %
(results_file, opt.bucket, opt.name))
# Tensorboard
if tb_writer:
tags = [
'train/box_loss',
'train/obj_loss',
'train/cls_loss', # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95',
'val/box_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
tb_writer.add_scalar(tag, x, epoch)
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
ema.ema,
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
n = opt.name if opt.name.isnumeric() else ''
fresults, flast, fbest = log_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file],
[flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
if str(f2).endswith('.pt'): # is *.pt
strip_optimizer(f2) # strip optimizer
os.system(
'gsutil cp %s gs://%s/weights' %
(f2, opt.bucket)) if opt.bucket else None # upload
# Finish
if not opt.evolve:
plot_results(save_dir=log_dir) # save as results.png
logger.info('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if rank not in [-1, 0] else None
torch.cuda.empty_cache()
return results
def model(
backbone: YoloV5BackboneConfig,
num_classes: int,
img_size: int, # must be multiple of 32
device: Optional[torch.device] = None,
) -> nn.Module:
model_name = backbone.model_name
pretrained = backbone.pretrained
# this is to remove background from ClassMap as discussed
# here: https://github.com/ultralytics/yolov5/issues/2950
# and here: https://discord.com/channels/735877944085446747/782062040168267777/836692604224536646
# so we should pass `num_classes=parser.class_map.num_classes`
num_classes -= 1
device = (torch.device("cuda" if torch.cuda.is_available() else "cpu")
if device is None else device)
if model_name in ["yolov5s", "yolov5m", "yolov5l", "yolov5x"]:
cfg_filepath = Path(
yolov5.__file__).parent / f"models/{model_name}.yaml"
else:
cfg_filepath = Path(
yolov5.__file__).parent / f"models/hub/{model_name}.yaml"
if pretrained:
weights_path = yolo_dir / f"{model_name}.pt"
with open(
Path(yolov5.__file__).parent /
"data/hyps/hyp.finetune.yaml") as f:
hyp = yaml.load(f, Loader=yaml.SafeLoader)
attempt_download(weights_path) # download if not found locally
sys.path.insert(0, str(Path(yolov5.__file__).parent))
ckpt = torch.load(weights_path, map_location=device) # load checkpoint
sys.path.remove(str(Path(yolov5.__file__).parent))
if hyp.get("anchors"):
ckpt["model"].yaml["anchors"] = round(
hyp["anchors"]) # force autoanchor
model = Model(cfg_filepath or ckpt["model"].yaml, ch=3,
nc=num_classes).to(device) # create
exclude = [] # exclude keys
state_dict = ckpt["model"].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
else:
with open(Path(yolov5.__file__).parent /
"data/hyps/hyp.scratch.yaml") as f:
hyp = yaml.load(f, Loader=yaml.SafeLoader)
model = Model(cfg_filepath,
ch=3,
nc=num_classes,
anchors=hyp.get("anchors")).to(device) # create
gs = int(model.stride.max()) # grid size (max stride)
nl = model.model[
-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz = check_img_size(img_size, gs) # verify imgsz are gs-multiples
hyp["box"] *= 3.0 / nl # scale to layers
hyp["cls"] *= num_classes / 80.0 * 3.0 / nl # scale to classes and layers
hyp["obj"] *= (imgsz / 640)**2 * 3.0 / nl # scale to image size and layers
model.nc = num_classes # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou)
def param_groups_fn(model: nn.Module) -> List[List[nn.Parameter]]:
spp_index = [
i + 1 for i, layer in enumerate(model.model.children())
if layer._get_name() == "SPPF"
][0]
backbone = list(model.model.children())[:spp_index]
neck = list(model.model.children())[spp_index:-1]
head = list(model.model.children())[-1]
layers = [
nn.Sequential(*backbone),
nn.Sequential(*neck),
nn.Sequential(head)
]
param_groups = [list(group.parameters()) for group in layers]
check_all_model_params_in_groups2(model.model, param_groups)
return param_groups
model.param_groups = MethodType(param_groups_fn, model)
return model
parser.add_argument('--device',
default='',
help='cuda device, i.e. 0 or 0,1,2,3 or cpu')
parser.add_argument('--view-img',
action='store_true',
help='display results')
parser.add_argument('--save-txt',
action='store_true',
help='save results to *.txt')
# class 0 is person
parser.add_argument('--classes',
nargs='+',
type=int,
default=[0],
help='filter by class')
parser.add_argument('--agnostic-nms',
action='store_true',
help='class-agnostic NMS')
parser.add_argument('--augment',
action='store_true',
help='augmented inference')
parser.add_argument("--config_deepsort",
type=str,
default="deep_sort_pytorch/configs/deep_sort.yaml")
args = parser.parse_args()
args.img_size = check_img_size(args.img_size)
print(args)
with torch.no_grad():
detect(args)
def predict(self, src_image):
param = self.getParam()
# Initialize
init_logging()
half = self.device.type != 'cpu' # half precision only supported on CUDA
# Load model
if self.model is None or param.update:
self.model = attempt_load(param.model_path, map_location=self.device) # load FP32 model
stride = int(self.model.stride.max()) # model stride
param.input_size = check_img_size(param.input_size, s=stride) # check img_size
if half:
self.model.half() # to FP16F
# Get names and colors
self.names = self.model.module.names if hasattr(self.model, 'module') else self.model.names
self.colors = [[random.randint(0, 255) for _ in range(3)] for _ in self.names]
param.update = False
else:
stride = int(self.model.stride.max()) # model stride
# Resize image
image = letterbox(src_image, param.input_size, stride)[0]
image = image.transpose(2, 0, 1)
image = np.ascontiguousarray(image)
self.emitStepProgress()
# Run inference
image = torch.from_numpy(image).to(self.device)
image = image.half() if half else image.float() # uint8 to fp16/32
image /= 255.0 # 0 - 255 to 0.0 - 1.0
if image.ndimension() == 3:
image = image.unsqueeze(0)
self.emitStepProgress()
# Inference
pred = self.model(image, augment=param.augment)[0]
self.emitStepProgress()
# Apply NMS
pred = non_max_suppression(pred, param.conf_thres, param.iou_thres, agnostic=param.agnostic_nms)
self.emitStepProgress()
graphics_output = self.getOutput(1)
graphics_output.setNewLayer("YoloV5")
graphics_output.setImageIndex(0)
detected_names = []
detected_conf = []
# Process detections
for i, det in enumerate(pred): # detections per image
if len(det):
# Rescale boxes from img_size to im0 size
det[:, :4] = scale_coords(image.shape[2:], det[:, :4], src_image.shape).round()
# Results
for *xyxy, conf, cls in reversed(det):
# Box
w = float(xyxy[2] - xyxy[0])
h = float(xyxy[3] - xyxy[1])
prop_rect = core.GraphicsRectProperty()
prop_rect.pen_color = self.colors[int(cls)]
graphics_box = graphics_output.addRectangle(float(xyxy[0]), float(xyxy[1]), w, h, prop_rect)
graphics_box.setCategory(self.names[int(cls)])
# Label
name = self.names[int(cls)]
prop_text = core.GraphicsTextProperty()
prop_text.font_size = 8
prop_text.color = self.colors[int(cls)]
graphics_output.addText(name, float(xyxy[0]), float(xyxy[1]), prop_text)
detected_names.append(name)
detected_conf.append(conf.item())
# Init numeric output
numeric_ouput = self.getOutput(2)
numeric_ouput.clearData()
numeric_ouput.setOutputType(dataprocess.NumericOutputType.TABLE)
numeric_ouput.addValueList(detected_conf, "Confidence", detected_names)
self.emitStepProgress()
def load_model(weights=WEIGHTS, device=DEVICE, imgsz=IMGSZ):
""" Load the YoloV5 model. """
model = attempt_load(weights, map_location=device)
stride = int(model.stride.max())
imgsz = check_img_size(imgsz, s=stride)
return (model, stride, imgsz)
def train(hyp, # path/to/hyp.yaml or hyp dictionary
opt,
device,
callbacks
):
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
# Directories
w = save_dir / 'weights' # weights dir
(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
last, best = w / 'last.pt', w / 'best.pt'
# Hyperparameters
if isinstance(hyp, str):
with open(hyp, errors='ignore') as f:
hyp = yaml.safe_load(f) # load hyps dict
LOGGER.info(colorstr('hyperparameters: ') + ', '.join(f'{k}={v}' for k, v in hyp.items()))
# Save run settings
if not evolve:
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.safe_dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.safe_dump(vars(opt), f, sort_keys=False)
# Loggers
data_dict = None
if RANK in [-1, 0]:
loggers = Loggers(save_dir, weights, opt, hyp, LOGGER) # loggers instance
if loggers.wandb:
data_dict = loggers.wandb.data_dict
if resume:
weights, epochs, hyp, batch_size = opt.weights, opt.epochs, opt.hyp, opt.batch_size
# Register actions
for k in methods(loggers):
callbacks.register_action(k, callback=getattr(loggers, k))
# Config
plots = not evolve # create plots
cuda = device.type != 'cpu'
init_seeds(1 + RANK)
with torch_distributed_zero_first(LOCAL_RANK):
data_dict = data_dict or check_dataset(data) # check if None
train_path, val_path = data_dict['train'], data_dict['val']
nc = 1 if single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check
is_coco = isinstance(val_path, str) and val_path.endswith('coco/val2017.txt') # COCO dataset
# Model
check_suffix(weights, '.pt') # check weights
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(LOCAL_RANK):
weights = attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location='cpu') # load checkpoint to CPU to avoid CUDA memory leak
model = Model(cfg or ckpt['model'].yaml, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
exclude = ['anchor'] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
csd = ckpt['model'].float().state_dict() # checkpoint state_dict as FP32
csd = intersect_dicts(csd, model.state_dict(), exclude=exclude) # intersect
model.load_state_dict(csd, strict=False) # load
LOGGER.info(f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}') # report
else:
model = Model(cfg, ch=3, nc=nc, anchors=hyp.get('anchors')).to(device) # create
# Freeze
freeze = [f'model.{x}.' for x in (freeze if len(freeze) > 1 else range(freeze[0]))] # layers to freeze
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
LOGGER.info(f'freezing {k}')
v.requires_grad = False
# Image size
gs = max(int(model.stride.max()), 32) # grid size (max stride)
imgsz = check_img_size(opt.imgsz, gs, floor=gs * 2) # verify imgsz is gs-multiple
# Batch size
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
batch_size = check_train_batch_size(model, imgsz)
loggers.on_params_update({"batch_size": batch_size})
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / batch_size), 1) # accumulate loss before optimizing
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
g0, g1, g2 = [], [], [] # optimizer parameter groups
for v in model.modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias
g2.append(v.bias)
if isinstance(v, nn.BatchNorm2d): # weight (no decay)
g0.append(v.weight)
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter): # weight (with decay)
g1.append(v.weight)
if opt.optimizer == 'Adam':
optimizer = Adam(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
elif opt.optimizer == 'AdamW':
optimizer = AdamW(g0, lr=hyp['lr0'], betas=(hyp['momentum'], 0.999)) # adjust beta1 to momentum
else:
optimizer = SGD(g0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': g1, 'weight_decay': hyp['weight_decay']}) # add g1 with weight_decay
optimizer.add_param_group({'params': g2}) # add g2 (biases)
LOGGER.info(f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
f"{len(g0)} weight (no decay), {len(g1)} weight, {len(g2)} bias")
del g0, g1, g2
# Scheduler
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp['lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
ema = ModelEMA(model) if RANK in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# EMA
if ema and ckpt.get('ema'):
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
ema.updates = ckpt['updates']
# Epochs
start_epoch = ckpt['epoch'] + 1
if resume:
assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'
if epochs < start_epoch:
LOGGER.info(f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs.")
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, csd
# DP mode
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
LOGGER.warning('WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.')
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and RANK != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
LOGGER.info('Using SyncBatchNorm()')
# Trainloader
train_loader, dataset = create_dataloader(train_path, imgsz, batch_size // WORLD_SIZE, gs, single_cls,
hyp=hyp, augment=True, cache=opt.cache, rect=opt.rect, rank=LOCAL_RANK,
workers=workers, image_weights=opt.image_weights, quad=opt.quad,
prefix=colorstr('train: '), shuffle=True)
mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class
nb = len(train_loader) # number of batches
assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
# Process 0
if RANK in [-1, 0]:
val_loader = create_dataloader(val_path, imgsz, batch_size // WORLD_SIZE * 2, gs, single_cls,
hyp=hyp, cache=None if noval else opt.cache, rect=True, rank=-1,
workers=workers, pad=0.5,
prefix=colorstr('val: '))[0]
if not resume:
labels = np.concatenate(dataset.labels, 0)
# c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
callbacks.run('on_pretrain_routine_end')
# DDP mode
if cuda and RANK != -1:
model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
# Model attributes
nl = de_parallel(model).model[-1].nl # number of detection layers (to scale hyps)
hyp['box'] *= 3 / nl # scale to layers
hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb), 1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
last_opt_step = -1
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
stopper = EarlyStopping(patience=opt.patience)
compute_loss = ComputeLoss(model) # init loss class
LOGGER.info(f'Image sizes {imgsz} train, {imgsz} val\n'
f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
f"Logging results to {colorstr('bold', save_dir)}\n"
f'Starting training for {epochs} epochs...')
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional, single-GPU only)
if opt.image_weights:
cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc # class weights
iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw) # image weights
dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
# Update mosaic border (optional)
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(3, device=device) # mean losses
if RANK != -1:
train_loader.sampler.set_epoch(epoch)
pbar = enumerate(train_loader)
LOGGER.info(('\n' + '%10s' * 7) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
if RANK in [-1, 0]:
pbar = tqdm(pbar, total=nb, bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
optimizer.zero_grad()
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float() / 255 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(1, np.interp(ni, xi, [1, nbs / batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [hyp['warmup_bias_lr'] if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5, imgsz * 1.5 + gs) // gs * gs # size
sf = sz / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to gs-multiple)
imgs = nn.functional.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(pred, targets.to(device)) # loss scaled by batch_size
if RANK != -1:
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# Backward
scaler.scale(loss).backward()
# Optimize
if ni - last_opt_step >= accumulate:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
last_opt_step = ni
# Log
if RANK in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
pbar.set_description(('%10s' * 2 + '%10.4g' * 5) % (
f'{epoch}/{epochs - 1}', mem, *mloss, targets.shape[0], imgs.shape[-1]))
callbacks.run('on_train_batch_end', ni, model, imgs, targets, paths, plots, opt.sync_bn)
if callbacks.stop_training:
return
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for loggers
scheduler.step()
if RANK in [-1, 0]:
# mAP
callbacks.run('on_train_epoch_end', epoch=epoch)
ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'names', 'stride', 'class_weights'])
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
if not noval or final_epoch: # Calculate mAP
results, maps, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz,
model=ema.ema,
single_cls=single_cls,
dataloader=val_loader,
save_dir=save_dir,
plots=False,
callbacks=callbacks,
compute_loss=compute_loss)
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
log_vals = list(mloss) + list(results) + lr
callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness, fi)
# Save model
if (not nosave) or (final_epoch and not evolve): # if save
ckpt = {'epoch': epoch,
'best_fitness': best_fitness,
'model': deepcopy(de_parallel(model)).half(),
'ema': deepcopy(ema.ema).half(),
'updates': ema.updates,
'optimizer': optimizer.state_dict(),
'wandb_id': loggers.wandb.wandb_run.id if loggers.wandb else None,
'date': datetime.now().isoformat()}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if (epoch > 0) and (opt.save_period > 0) and (epoch % opt.save_period == 0):
torch.save(ckpt, w / f'epoch{epoch}.pt')
del ckpt
callbacks.run('on_model_save', last, epoch, final_epoch, best_fitness, fi)
# Stop Single-GPU
if RANK == -1 and stopper(epoch=epoch, fitness=fi):
break
# Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
# stop = stopper(epoch=epoch, fitness=fi)
# if RANK == 0:
# dist.broadcast_object_list([stop], 0) # broadcast 'stop' to all ranks
# Stop DPP
# with torch_distributed_zero_first(RANK):
# if stop:
# break # must break all DDP ranks
# end epoch ----------------------------------------------------------------------------------------------------
# end training -----------------------------------------------------------------------------------------------------
if RANK in [-1, 0]:
LOGGER.info(f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.')
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if f is best:
LOGGER.info(f'\nValidating {f}...')
results, _, _ = val.run(data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz,
model=attempt_load(f, device).half(),
iou_thres=0.65 if is_coco else 0.60, # best pycocotools results at 0.65
single_cls=single_cls,
dataloader=val_loader,
save_dir=save_dir,
save_json=is_coco,
verbose=True,
plots=True,
callbacks=callbacks,
compute_loss=compute_loss) # val best model with plots
if is_coco:
callbacks.run('on_fit_epoch_end', list(mloss) + list(results) + lr, epoch, best_fitness, fi)
callbacks.run('on_train_end', last, best, plots, epoch, results)
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
torch.cuda.empty_cache()
return results
