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251 lines
12 KiB
251 lines
12 KiB
# Ultralytics YOLO 🚀, AGPL-3.0 license
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from copy import copy
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import numpy as np
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import torch
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import torch.nn as nn
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from ultralytics.nn.tasks import DetectionModel
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from ultralytics.yolo import v8
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from ultralytics.yolo.data import build_dataloader, build_yolo_dataset
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from ultralytics.yolo.data.dataloaders.v5loader import create_dataloader
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from ultralytics.yolo.engine.trainer import BaseTrainer
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from ultralytics.yolo.utils import DEFAULT_CFG, LOGGER, RANK, colorstr
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from ultralytics.yolo.utils.loss import BboxLoss
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from ultralytics.yolo.utils.ops import xywh2xyxy
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from ultralytics.yolo.utils.plotting import plot_images, plot_labels, plot_results
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from ultralytics.yolo.utils.tal import TaskAlignedAssigner, dist2bbox, make_anchors
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from ultralytics.yolo.utils.torch_utils import de_parallel, torch_distributed_zero_first
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# BaseTrainer python usage
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class DetectionTrainer(BaseTrainer):
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def build_dataset(self, img_path, mode='train', batch=None):
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"""Build YOLO Dataset
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Args:
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img_path (str): Path to the folder containing images.
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mode (str): `train` mode or `val` mode, users are able to customize different augmentations for each mode.
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batch (int, optional): Size of batches, this is for `rect`. Defaults to None.
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"""
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gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
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return build_yolo_dataset(self.args, img_path, batch, self.data, mode=mode, rect=mode == 'val', stride=gs)
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def get_dataloader(self, dataset_path, batch_size, rank=0, mode='train'):
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"""TODO: manage splits differently."""
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# Calculate stride - check if model is initialized
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if self.args.v5loader:
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LOGGER.warning("WARNING ⚠️ 'v5loader' feature is deprecated and will be removed soon. You can train using "
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'the default YOLOv8 dataloader instead, no argument is needed.')
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gs = max(int(de_parallel(self.model).stride.max() if self.model else 0), 32)
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return create_dataloader(path=dataset_path,
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imgsz=self.args.imgsz,
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batch_size=batch_size,
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stride=gs,
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hyp=vars(self.args),
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augment=mode == 'train',
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cache=self.args.cache,
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pad=0 if mode == 'train' else 0.5,
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rect=self.args.rect or mode == 'val',
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rank=rank,
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workers=self.args.workers,
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close_mosaic=self.args.close_mosaic != 0,
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prefix=colorstr(f'{mode}: '),
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shuffle=mode == 'train',
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seed=self.args.seed)[0]
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assert mode in ['train', 'val']
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with torch_distributed_zero_first(rank): # init dataset *.cache only once if DDP
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dataset = self.build_dataset(dataset_path, mode, batch_size)
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shuffle = mode == 'train'
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if getattr(dataset, 'rect', False) and shuffle:
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LOGGER.warning("WARNING ⚠️ 'rect=True' is incompatible with DataLoader shuffle, setting shuffle=False")
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shuffle = False
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workers = self.args.workers if mode == 'train' else self.args.workers * 2
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dataloader = build_dataloader(dataset, batch_size, workers, shuffle, rank)
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return dataloader
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def preprocess_batch(self, batch):
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"""Preprocesses a batch of images by scaling and converting to float."""
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batch['img'] = batch['img'].to(self.device, non_blocking=True).float() / 255
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return batch
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def set_model_attributes(self):
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"""nl = de_parallel(self.model).model[-1].nl # number of detection layers (to scale hyps)."""
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# self.args.box *= 3 / nl # scale to layers
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# self.args.cls *= self.data["nc"] / 80 * 3 / nl # scale to classes and layers
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# self.args.cls *= (self.args.imgsz / 640) ** 2 * 3 / nl # scale to image size and layers
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self.model.nc = self.data['nc'] # attach number of classes to model
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self.model.names = self.data['names'] # attach class names to model
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self.model.args = self.args # attach hyperparameters to model
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# TODO: self.model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc
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def get_model(self, cfg=None, weights=None, verbose=True):
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"""Return a YOLO detection model."""
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model = DetectionModel(cfg, nc=self.data['nc'], verbose=verbose and RANK == -1)
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if weights:
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model.load(weights)
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return model
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def get_validator(self):
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"""Returns a DetectionValidator for YOLO model validation."""
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self.loss_names = 'box_loss', 'cls_loss', 'dfl_loss'
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return v8.detect.DetectionValidator(self.test_loader, save_dir=self.save_dir, args=copy(self.args))
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def criterion(self, preds, batch):
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"""Compute loss for YOLO prediction and ground-truth."""
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if not hasattr(self, 'compute_loss'):
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self.compute_loss = Loss(de_parallel(self.model))
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return self.compute_loss(preds, batch)
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def label_loss_items(self, loss_items=None, prefix='train'):
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"""
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Returns a loss dict with labelled training loss items tensor
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"""
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# Not needed for classification but necessary for segmentation & detection
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keys = [f'{prefix}/{x}' for x in self.loss_names]
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if loss_items is not None:
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loss_items = [round(float(x), 5) for x in loss_items] # convert tensors to 5 decimal place floats
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return dict(zip(keys, loss_items))
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else:
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return keys
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def progress_string(self):
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"""Returns a formatted string of training progress with epoch, GPU memory, loss, instances and size."""
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return ('\n' + '%11s' *
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(4 + len(self.loss_names))) % ('Epoch', 'GPU_mem', *self.loss_names, 'Instances', 'Size')
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def plot_training_samples(self, batch, ni):
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"""Plots training samples with their annotations."""
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plot_images(images=batch['img'],
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batch_idx=batch['batch_idx'],
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cls=batch['cls'].squeeze(-1),
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bboxes=batch['bboxes'],
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paths=batch['im_file'],
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fname=self.save_dir / f'train_batch{ni}.jpg')
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def plot_metrics(self):
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"""Plots metrics from a CSV file."""
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plot_results(file=self.csv) # save results.png
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def plot_training_labels(self):
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"""Create a labeled training plot of the YOLO model."""
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boxes = np.concatenate([lb['bboxes'] for lb in self.train_loader.dataset.labels], 0)
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cls = np.concatenate([lb['cls'] for lb in self.train_loader.dataset.labels], 0)
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plot_labels(boxes, cls.squeeze(), names=self.data['names'], save_dir=self.save_dir)
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# Criterion class for computing training losses
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class Loss:
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def __init__(self, model): # model must be de-paralleled
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device = next(model.parameters()).device # get model device
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h = model.args # hyperparameters
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m = model.model[-1] # Detect() module
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self.bce = nn.BCEWithLogitsLoss(reduction='none')
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self.hyp = h
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self.stride = m.stride # model strides
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self.nc = m.nc # number of classes
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self.no = m.no
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self.reg_max = m.reg_max
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self.device = device
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self.use_dfl = m.reg_max > 1
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self.assigner = TaskAlignedAssigner(topk=10, num_classes=self.nc, alpha=0.5, beta=6.0)
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self.bbox_loss = BboxLoss(m.reg_max - 1, use_dfl=self.use_dfl).to(device)
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self.proj = torch.arange(m.reg_max, dtype=torch.float, device=device)
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def preprocess(self, targets, batch_size, scale_tensor):
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"""Preprocesses the target counts and matches with the input batch size to output a tensor."""
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if targets.shape[0] == 0:
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out = torch.zeros(batch_size, 0, 5, device=self.device)
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else:
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i = targets[:, 0] # image index
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_, counts = i.unique(return_counts=True)
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counts = counts.to(dtype=torch.int32)
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out = torch.zeros(batch_size, counts.max(), 5, device=self.device)
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for j in range(batch_size):
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matches = i == j
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n = matches.sum()
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if n:
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out[j, :n] = targets[matches, 1:]
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out[..., 1:5] = xywh2xyxy(out[..., 1:5].mul_(scale_tensor))
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return out
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def bbox_decode(self, anchor_points, pred_dist):
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"""Decode predicted object bounding box coordinates from anchor points and distribution."""
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if self.use_dfl:
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b, a, c = pred_dist.shape # batch, anchors, channels
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pred_dist = pred_dist.view(b, a, 4, c // 4).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# pred_dist = pred_dist.view(b, a, c // 4, 4).transpose(2,3).softmax(3).matmul(self.proj.type(pred_dist.dtype))
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# pred_dist = (pred_dist.view(b, a, c // 4, 4).softmax(2) * self.proj.type(pred_dist.dtype).view(1, 1, -1, 1)).sum(2)
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return dist2bbox(pred_dist, anchor_points, xywh=False)
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def __call__(self, preds, batch):
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"""Calculate the sum of the loss for box, cls and dfl multiplied by batch size."""
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loss = torch.zeros(3, device=self.device) # box, cls, dfl
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feats = preds[1] if isinstance(preds, tuple) else preds
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pred_distri, pred_scores = torch.cat([xi.view(feats[0].shape[0], self.no, -1) for xi in feats], 2).split(
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(self.reg_max * 4, self.nc), 1)
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pred_scores = pred_scores.permute(0, 2, 1).contiguous()
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pred_distri = pred_distri.permute(0, 2, 1).contiguous()
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dtype = pred_scores.dtype
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batch_size = pred_scores.shape[0]
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imgsz = torch.tensor(feats[0].shape[2:], device=self.device, dtype=dtype) * self.stride[0] # image size (h,w)
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anchor_points, stride_tensor = make_anchors(feats, self.stride, 0.5)
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# targets
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targets = torch.cat((batch['batch_idx'].view(-1, 1), batch['cls'].view(-1, 1), batch['bboxes']), 1)
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targets = self.preprocess(targets.to(self.device), batch_size, scale_tensor=imgsz[[1, 0, 1, 0]])
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gt_labels, gt_bboxes = targets.split((1, 4), 2) # cls, xyxy
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mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
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# pboxes
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pred_bboxes = self.bbox_decode(anchor_points, pred_distri) # xyxy, (b, h*w, 4)
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_, target_bboxes, target_scores, fg_mask, _ = self.assigner(
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pred_scores.detach().sigmoid(), (pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
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anchor_points * stride_tensor, gt_labels, gt_bboxes, mask_gt)
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target_bboxes /= stride_tensor
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target_scores_sum = max(target_scores.sum(), 1)
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# cls loss
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# loss[1] = self.varifocal_loss(pred_scores, target_scores, target_labels) / target_scores_sum # VFL way
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loss[1] = self.bce(pred_scores, target_scores.to(dtype)).sum() / target_scores_sum # BCE
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# bbox loss
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if fg_mask.sum():
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loss[0], loss[2] = self.bbox_loss(pred_distri, pred_bboxes, anchor_points, target_bboxes, target_scores,
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target_scores_sum, fg_mask)
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loss[0] *= self.hyp.box # box gain
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loss[1] *= self.hyp.cls # cls gain
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loss[2] *= self.hyp.dfl # dfl gain
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return loss.sum() * batch_size, loss.detach() # loss(box, cls, dfl)
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def train(cfg=DEFAULT_CFG, use_python=False):
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"""Train and optimize YOLO model given training data and device."""
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model = cfg.model or 'yolov8n.pt'
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data = cfg.data or 'coco128.yaml' # or yolo.ClassificationDataset("mnist")
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device = cfg.device if cfg.device is not None else ''
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args = dict(model=model, data=data, device=device)
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if use_python:
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from ultralytics import YOLO
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YOLO(model).train(**args)
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else:
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trainer = DetectionTrainer(overrides=args)
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trainer.train()
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if __name__ == '__main__':
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train()
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