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 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 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 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, # 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
def run(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 COCO-JSON results file project=ROOT / 'runs/val', # 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(''), callbacks=Callbacks(), 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 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 check_suffix(weights, '.pt') 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) # Data data = check_dataset(data) # check # Half half &= device.type != 'cpu' # half precision only supported on CUDA model.half() if half else model.float() # Configure model.eval() is_coco = isinstance(data.get('val'), str) and 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() # Dataloader if not training: if device.type != 'cpu': model(torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(next(model.parameters()))) # run once pad = 0.0 if task == 'speed' else 0.5 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=pad, 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)} class_map = coco80_to_coco91_class() if is_coco else list(range(1000)) s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', '[email protected]', '[email protected]:.95') dt, p, r, f1, mp, mr, map50, map = [0.0, 0.0, 0.0], 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0 loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): t1 = time_sync() 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 t2 = time_sync() dt[0] += t2 - t1 # Run model out, train_out = model(img, augment=augment) # inference and training outputs dt[1] += time_sync() - t2 # Compute loss if compute_loss: loss += compute_loss([x.float() for x in train_out], targets)[1] # 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 t3 = time_sync() out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls) dt[2] += time_sync() - t3 # 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, shape = Path(paths[si]), shapes[si][0] 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], shape, shapes[si][1]) # native-space pred # Evaluate if nl: tbox = xywh2xyxy(labels[:, 1:5]) # target boxes scale_coords(img[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels correct = process_batch(predn, labelsn, iouv) else: correct = torch.zeros(pred.shape[0], niou, dtype=torch.bool) stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls)) # (correct, conf, pcls, tcls) # Save/log if save_txt: save_one_txt(predn, save_conf, shape, file=save_dir / 'labels' / (path.stem + '.txt')) if save_json: save_one_json(predn, jdict, path, class_map) # append to COCO-JSON dictionary callbacks.run('on_val_image_end', pred, predn, path, names, img[si]) # 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, 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 LOGGER.info(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): LOGGER.info(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i])) # Print speeds t = tuple(x / seen * 1E3 for x in dt) # speeds per image if not training: shape = (batch_size, 3, imgsz, imgsz) LOGGER.info(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t) # 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 = str(Path(data.get('path', '../coco')) / 'annotations/instances_val2017.json') # annotations json pred_json = str(save_dir / f"{w}_predictions.json") # predictions json LOGGER.info(f'\nEvaluating pycocotools mAP... saving {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: LOGGER.info(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 '' LOGGER.info(f"Results saved to {colorstr('bold', 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 test( weights=None, data="yolov5/data/coco128.yaml", batch_size=32, image_size=640, conf_thres=0.001, iou_thres=0.6, # for NMS task="val", device="", single_cls=False, augment=False, verbose=False, save_txt=False, # for auto-labelling save_hybrid=False, # for hybrid auto-labelling save_conf=False, # save auto-label confidences save_json=False, project="runs/test", name="exp", exist_ok=False, model=None, dataloader=None, save_dir=Path(""), # for saving images plots=True, log_imgs=0, # number of logged images ): arguments = locals() # 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 = 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 # Load model model = attempt_load(weights, map_location=device) # load FP32 model image_size = check_img_size(image_size, s=model.stride.max()) # check img_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() is_coco = data.endswith("coco.yaml") # is COCO dataset with open(data) as f: data = yaml.load(f, Loader=yaml.FullLoader) # model dict check_dataset(data) # check 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, wandb = min(log_imgs, 100), None # ceil try: import wandb # Weights & Biases except ImportError: log_imgs = 0 # Dataloader if not training: img = torch.zeros((1, 3, image_size, image_size), device=device) # init img _ = (model(img.half() if half else img) if device.type != "cpu" else None) # run once path = (data["test"] if task == "test" else data["val"] ) # path to val/test images opt = OptFactory(arguments) dataloader = create_dataloader(path, image_size, batch_size, model.stride.max(), opt, pad=0.5, rect=True)[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" + "%12s" * 6) % ( "Class", "Images", "Targets", "P", "R", "[email protected]", "[email protected]:.95", ) p, r, f1, mp, mr, map50, map, t0, t1 = 0.0, 0.0, 0.0, 0.0, 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)): 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 with torch.no_grad(): # Run model t = time_synchronized() inf_out, train_out = model( img, augment=augment) # inference and training outputs t0 += time_synchronized() - t # Compute loss if training: loss += compute_loss([x.float() for x in train_out], targets, model)[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() output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres, labels=lb) t1 += time_synchronized() - t # Statistics per image for si, pred in enumerate(output): 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 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 if plots and len(wandb_images) < log_imgs: 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.Image(img[si], boxes=boxes, caption=path.name)) # 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( pred, 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) ) # prediction indices pi = ((cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) ) # target 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(output), 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) p, r, ap50, ap = ( p[:, 0], r[:, 0], ap[:, 0], ap.mean(1), ) # [P, R, [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" + "%12.3g" * 6 # print format print(pf % ("all", seen, nt.sum(), mp, mr, map50, map)) # Print results per class if verbose 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, t0 + t1)) + ( image_size, image_size, batch_size, ) # tuple if not training: print( "Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g" % t) # Plots if plots: confusion_matrix.plot(save_dir=save_dir, names=list(names.values())) if wandb and wandb.run: wandb.log({"Images": wandb_images}) wandb.log({ "Validation": [ wandb.Image(str(f), caption=f.name) for f in sorted(save_dir.glob("test*.jpg")) ] }) # 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 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 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}") model.float() # for training 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 test(data, weights=None, batch_size=16, imgsz=640, conf_thres=0.001, iou_thres=0.6, # for NMS save_json=False, single_cls=False, augment=False, verbose=False, model=None, dataloader=None, save_dir=Path(''), # for saving images save_txt=False, # for auto-labelling save_conf=False, plots=True): # 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(opt.device, batch_size=batch_size) save_txt = opt.save_txt # save *.txt labels # Remove previous if os.path.exists(save_dir): shutil.rmtree(save_dir) # delete dir os.makedirs(save_dir) # make new dir if save_txt: out = save_dir / 'autolabels' if os.path.exists(out): shutil.rmtree(out) # delete dir os.makedirs(out) # make new dir # Load model model = attempt_load(weights, map_location=device) # load FP32 model imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_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() with open(data) as f: data = yaml.load(f, Loader=yaml.FullLoader) # model dict check_dataset(data) # check 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() # Dataloader if not training: 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 path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt, hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0] seen = 0 names = model.names if hasattr(model, 'names') else model.module.names coco91class = coco80_to_coco91_class() s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', '[email protected]', '[email protected]:.95') p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. loss = torch.zeros(3, device=device) jdict, stats, ap, ap_class = [], [], [], [] for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)): 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 whwh = torch.Tensor([width, height, width, height]).to(device) # Disable gradients with torch.no_grad(): # Run model t = time_synchronized() inf_out, train_out = model(img, augment=augment) # inference and training outputs t0 += time_synchronized() - t # Compute loss if training: # if model has loss hyperparameters loss += compute_loss([x.float() for x in train_out], targets, model)[1][:3] # box, obj, cls # Run NMS t = time_synchronized() output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres) t1 += time_synchronized() - t # Statistics per image for si, pred in enumerate(output): labels = targets[targets[:, 0] == si, 1:] nl = len(labels) tcls = labels[:, 0].tolist() if nl else [] # target class seen += 1 if pred is None: if nl: stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls)) continue # Append to text file if save_txt: gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0]] # normalization gain whwh x = pred.clone() x[:, :4] = scale_coords(img[si].shape[1:], x[:, :4], shapes[si][0], shapes[si][1]) # to original for *xyxy, conf, cls in x: xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) / gn).view(-1).tolist() # normalized xywh line = (cls, conf, *xywh) if save_conf else (cls, *xywh) # label format with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f: f.write(('%g ' * len(line) + '\n') % line) # Clip boxes to image bounds clip_coords(pred, (height, width)) # 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 = Path(paths[si]).stem box = pred[:, :4].clone() # xyxy scale_coords(img[si].shape[1:], box, shapes[si][0], shapes[si][1]) # to original shape box = xyxy2xywh(box) # 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': int(image_id) if image_id.isnumeric() else image_id, 'category_id': coco91class[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]) * whwh # Per target class for cls in torch.unique(tcls_tensor): ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(-1) # target indices # Search for detections if pi.shape[0]: # Prediction to target ious ious, i = box_iou(pred[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 save_dir and batch_i < 1: f = save_dir / f'test_batch{batch_i}_gt.jpg' # filename plot_images(img, targets, paths, str(f), names) # ground truth f = save_dir / f'test_batch{batch_i}_pred.jpg' plot_images(img, output_to_target(output, width, height), paths, str(f), names) # predictions # 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, fname=os.path.join(save_dir, 'precision-recall_curve.png')) p, r, ap50, ap = p[:, 0], r[:, 0], ap[:, 0], ap.mean(1) # [P, R, [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' + '%12.3g' * 6 # print format print(pf % ('all', seen, nt.sum(), mp, mr, map50, map)) # Print results per class if verbose 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, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple if not training: print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) # 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 file = save_dir / f"detections_val2017_{w}_results.json" # predicted annotations file print('\nCOCO mAP with pycocotools... saving %s...' % file) with open(file, 'w') as f: json.dump(jdict, f) try: # https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb from pycocotools.coco import COCO from pycocotools.cocoeval import COCOeval imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files] cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api cocoDt = cocoGt.loadRes(str(file)) # initialize COCO pred api cocoEval = COCOeval(cocoGt, cocoDt, 'bbox') cocoEval.params.imgIds = imgIds # image IDs to evaluate cocoEval.evaluate() cocoEval.accumulate() cocoEval.summarize() map, map50 = cocoEval.stats[:2] # update results ([email protected]:0.95, [email protected]) except Exception as e: print('ERROR: pycocotools unable to run: %s' % e) # Return results model.float() # for training 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 evaluate( data: Path, weights: Path, conf_threshold: float = None, batch_size: int = 256, img_size: int = 640, iou_thres: float = 0.6, ) -> None: """Evaluate model on dataset Args: data_dir: Dataset dir path weights: Path to model.pt conf_threshold: Confidence threshold batch_size: Batch size img_size: Inference size (pixels) iou_thres: Non-max supression IoU threshold """ device = select_device(batch_size=batch_size) # cuda | cpu save_dir = config_paths.temp_dir_path / "yolo" / "eval" / str(int(time())) save_dir.mkdir(parents=True) model = DetectMultiBackend(weights, device) model.model.float() model.eval() img_size = check_img_size(img_size, s=model.stride) # type:ignore model.warmup(imgsz=(1, 3, img_size, img_size)) metadata = pd.read_csv(data / "metadata.csv") dataloader = create_dataloader( data, img_size, batch_size, model.stride, single_cls=False, pad=0.5, rect=True, )[0] # Configure iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector 0.5:0.95 seen = 0 stats: List[ImgRes] = [] augmented_res = {} for batch_i, (im, targets, paths, shapes) in enumerate(tqdm(dataloader)): # Preprocess im = im.to(device, non_blocking=True) im = im.float() # uint8 to fp16/32 im /= 255 # 0 - 255 to 0.0 - 1.0 *_, height, width = im.shape # batch size, channels, height, width # [Total targetx x 6] = Tensor[[img_index, class, ...xcycwh-rel]] targets = targets.to(device) # Inference out, _ = model(im, val=True) # inference, loss outputs # NMS # Out format (after NMS), sligthly unclear before NMS... # [Images x Predictions x [...xyxy-abs, conf, class]] # List[Tensor[Tensor[]]] # Looks like bboxes are in xyxy-abs format out = non_max_suppression(prediction=out, conf_thres=0.001, iou_thres=iou_thres) targets[:, 2:] *= torch.Tensor([width, height, width, height]).to( device) # to pixels i.e. Tensor[img_index, class, ...xcycwh-abs] # Metrics # Pred: Tensor[Predictions x [...xyxy-abs, conf class]] for si, pred in enumerate(out): # for each image seen += 1 path = Path(paths[si]) # path to img, shape = shapes[si][0] # original img dimensions # Labels: Each target where image_index matches current index excluding image_index col # i.e. all targets in current image [Targets x [class, ...xcyxcwh-abs]] labels = targets[targets[:, 0] == si, 1:] # target class, one entry for each labeled object labelsn = torch.Tensor() if len(labels): tbox = xywh2xyxy(labels[:, 1:5]) # target boxes [xyxy-abs] scale_coords(im[si].shape[1:], tbox, shape, shapes[si][1]) # native-space labels labelsn = torch.cat((labels[:, 0:1], tbox), 1) # native-space labels predn = pred.clone() if len(pred): # Predictions # native-space pred # bbox was predicted on rescaled image (i.e. multiple of 32) # now we scale the predicted bbox to match the orignal image shape scale_coords(im[si].shape[1:], predn[:, :4], shape, shapes[si][1]) img_res = evaluate_single_img(labelsn, predn, iouv) if img_res is not None: stats.append(img_res) append_augmented_img_res(augmented_res, path, metadata, labelsn, predn, img_res) # Plot prediction examples if batch_i < 3: plot_batch_predictions(batch_i, save_dir, im, targets, out, paths) augmented_res_df = pd.DataFrame.from_dict(augmented_res, orient="index") augmented_res_df.sort_values(by=["run_id", "frame_index"], inplace=True) augmented_res_df.reset_index(inplace=True, drop=True) augmented_res_df.to_pickle(save_dir / f"{data.stem}-augmented-res.pkl") results_by_slice(augmented_res_df, save_dir, conf_threshold)