diff --git a/test.py b/test.py index b17415431615..4e554cf1fe43 100644 --- a/test.py +++ b/test.py @@ -38,7 +38,7 @@ def test(data, plots=True, wandb_logger=None, compute_loss=None, - half_precision=True, + half=True, opt=None): # Initialize/load model and set device training = model is not None @@ -63,7 +63,7 @@ def test(data, # model = nn.DataParallel(model) # Half - half = device.type != 'cpu' and half_precision # half precision only supported on CUDA + half &= device.type != 'cpu' # half precision only supported on CUDA if half: model.half() @@ -95,20 +95,22 @@ def test(data, 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', 'mAP@.5', 'mAP@.5:.95') - p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0. + 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 - t = time_synchronized() out, train_out = model(img, augment=augment) # inference and training outputs - t0 += time_synchronized() - t + t1 += time_synchronized() - t # Compute loss if compute_loss: @@ -119,7 +121,7 @@ def test(data, 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) - t1 += time_synchronized() - t + t2 += time_synchronized() - t # Statistics per image for si, pred in enumerate(out): @@ -236,9 +238,10 @@ def test(data, 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 + t = tuple(x / seen * 1E3 for x in (t0, t1, t2)) # speeds per image if not training: - print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t) + 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: @@ -327,24 +330,25 @@ def test(data, save_txt=opt.save_txt | opt.save_hybrid, save_hybrid=opt.save_hybrid, save_conf=opt.save_conf, - half_precision=opt.half, + half=opt.half, opt=opt ) elif opt.task == 'speed': # speed benchmarks - for w in opt.weights: - test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, opt=opt) + for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]: + test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, half=True, + opt=opt) elif opt.task == 'study': # run over a range of settings and save/plot # python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt x = list(range(256, 1536 + 128, 128)) # x axis (image sizes) - for w in opt.weights: + for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]: f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to y = [] # y axis for i in x: # img-size print(f'\nRunning {f} point {i}...') r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json, - plots=False, opt=opt) + plots=False, half=True, opt=opt) y.append(r + t) # results and times np.savetxt(f, y, fmt='%10.4g') # save os.system('zip -r study.zip study_*.txt') diff --git a/train.py b/train.py index 43c63b6ff3c1..b92936d762b5 100644 --- a/train.py +++ b/train.py @@ -74,7 +74,7 @@ def train(hyp, opt, device, tb_writer=None): 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 + weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # may update weights, epochs if resuming 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 @@ -354,18 +354,18 @@ def train(hyp, opt, device, tb_writer=None): 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=single_cls, - dataloader=testloader, - save_dir=save_dir, - save_json=is_coco and final_epoch, - verbose=nc < 50 and final_epoch, - plots=plots and final_epoch, - wandb_logger=wandb_logger, - compute_loss=compute_loss) + results, maps, _ = test.test(data_dict, + batch_size=batch_size * 2, + imgsz=imgsz_test, + model=ema.ema, + single_cls=single_cls, + dataloader=testloader, + save_dir=save_dir, + save_json=is_coco and final_epoch, + verbose=nc < 50 and final_epoch, + plots=plots and final_epoch, + wandb_logger=wandb_logger, + compute_loss=compute_loss) # Write with open(results_file, 'a') as f: diff --git a/utils/plots.py b/utils/plots.py index 8313ef210f90..973b9ae19b54 100644 --- a/utils/plots.py +++ b/utils/plots.py @@ -3,7 +3,6 @@ import glob import math import os -import random from copy import copy from pathlib import Path @@ -252,21 +251,23 @@ def plot_targets_txt(): # from utils.plots import *; plot_targets_txt() def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt() # Plot study.txt generated by test.py - fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True) - # ax = ax.ravel() + plot2 = False # plot additional results + if plot2: + ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel() fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True) # for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]: for f in sorted(Path(path).glob('study*.txt')): y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T x = np.arange(y.shape[1]) if x is None else np.array(x) - s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)'] - # for i in range(7): - # ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) - # ax[i].set_title(s[i]) + if plot2: + s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)'] + for i in range(7): + ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8) + ax[i].set_title(s[i]) j = y[3].argmax() + 1 - ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, + ax2.plot(y[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8, label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO')) ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],