Update test.py profiling (#3555)
* Update test.py profiling * half_precision to half * inplace
This commit is contained in:
Glenn Jocher
GitHub
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commit
5948f20a3d
30
test.py
30
test.py
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@ -38,7 +38,7 @@ def test(data,
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plots=True,
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wandb_logger=None,
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compute_loss=None,
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half_precision=True,
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half=True,
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opt=None):
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# Initialize/load model and set device
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training = model is not None
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@ -63,7 +63,7 @@ def test(data,
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# model = nn.DataParallel(model)
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# Half
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half = device.type != 'cpu' and half_precision # half precision only supported on CUDA
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half &= device.type != 'cpu' # half precision only supported on CUDA
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if half:
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model.half()
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@ -95,20 +95,22 @@ def test(data,
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names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
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coco91class = coco80_to_coco91_class()
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s = ('%20s' + '%11s' * 6) % ('Class', 'Images', 'Labels', 'P', 'R', 'mAP@.5', 'mAP@.5:.95')
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p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
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p, r, f1, mp, mr, map50, map, t0, t1, t2 = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
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loss = torch.zeros(3, device=device)
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jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
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for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
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t_ = time_synchronized()
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img = img.to(device, non_blocking=True)
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img = img.half() if half else img.float() # uint8 to fp16/32
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img /= 255.0 # 0 - 255 to 0.0 - 1.0
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targets = targets.to(device)
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nb, _, height, width = img.shape # batch size, channels, height, width
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t = time_synchronized()
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t0 += t - t_
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# Run model
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t = time_synchronized()
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out, train_out = model(img, augment=augment) # inference and training outputs
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t0 += time_synchronized() - t
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t1 += time_synchronized() - t
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# Compute loss
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if compute_loss:
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@ -119,7 +121,7 @@ def test(data,
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lb = [targets[targets[:, 0] == i, 1:] for i in range(nb)] if save_hybrid else [] # for autolabelling
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t = time_synchronized()
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out = non_max_suppression(out, conf_thres, iou_thres, labels=lb, multi_label=True, agnostic=single_cls)
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t1 += time_synchronized() - t
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t2 += time_synchronized() - t
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# Statistics per image
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for si, pred in enumerate(out):
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@ -236,9 +238,10 @@ def test(data,
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print(pf % (names[c], seen, nt[c], p[i], r[i], ap50[i], ap[i]))
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# Print speeds
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t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (imgsz, imgsz, batch_size) # tuple
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t = tuple(x / seen * 1E3 for x in (t0, t1, t2)) # speeds per image
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if not training:
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print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
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shape = (batch_size, 3, imgsz, imgsz)
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print(f'Speed: %.1fms pre-process, %.1fms inference, %.1fms NMS per image at shape {shape}' % t)
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# Plots
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if plots:
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@ -327,24 +330,25 @@ if __name__ == '__main__':
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save_txt=opt.save_txt | opt.save_hybrid,
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save_hybrid=opt.save_hybrid,
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save_conf=opt.save_conf,
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half_precision=opt.half,
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half=opt.half,
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opt=opt
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)
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elif opt.task == 'speed': # speed benchmarks
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for w in opt.weights:
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test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, opt=opt)
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for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
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test(opt.data, w, opt.batch_size, opt.img_size, 0.25, 0.45, save_json=False, plots=False, half=True,
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opt=opt)
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elif opt.task == 'study': # run over a range of settings and save/plot
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# python test.py --task study --data coco.yaml --iou 0.7 --weights yolov5s.pt yolov5m.pt yolov5l.pt yolov5x.pt
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x = list(range(256, 1536 + 128, 128)) # x axis (image sizes)
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for w in opt.weights:
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for w in opt.weights if isinstance(opt.weights, list) else [opt.weights]:
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f = f'study_{Path(opt.data).stem}_{Path(w).stem}.txt' # filename to save to
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y = [] # y axis
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for i in x: # img-size
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print(f'\nRunning {f} point {i}...')
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r, _, t = test(opt.data, w, opt.batch_size, i, opt.conf_thres, opt.iou_thres, opt.save_json,
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plots=False, opt=opt)
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plots=False, half=True, opt=opt)
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y.append(r + t) # results and times
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np.savetxt(f, y, fmt='%10.4g') # save
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os.system('zip -r study.zip study_*.txt')
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4
train.py
4
train.py
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@ -74,7 +74,7 @@ def train(hyp, opt, device, tb_writer=None):
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loggers['wandb'] = wandb_logger.wandb
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data_dict = wandb_logger.data_dict
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if wandb_logger.wandb:
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weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
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weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # may update weights, epochs if resuming
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nc = 1 if single_cls else int(data_dict['nc']) # number of classes
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names = ['item'] if single_cls and len(data_dict['names']) != 1 else data_dict['names'] # class names
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@ -354,7 +354,7 @@ def train(hyp, opt, device, tb_writer=None):
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final_epoch = epoch + 1 == epochs
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if not opt.notest or final_epoch: # Calculate mAP
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wandb_logger.current_epoch = epoch + 1
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results, maps, times = test.test(data_dict,
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results, maps, _ = test.test(data_dict,
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batch_size=batch_size * 2,
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imgsz=imgsz_test,
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model=ema.ema,
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@ -3,7 +3,6 @@
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import glob
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import math
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import os
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import random
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from copy import copy
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from pathlib import Path
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@ -252,21 +251,23 @@ def plot_targets_txt(): # from utils.plots import *; plot_targets_txt()
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def plot_study_txt(path='', x=None): # from utils.plots import *; plot_study_txt()
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# Plot study.txt generated by test.py
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fig, ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)
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# ax = ax.ravel()
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plot2 = False # plot additional results
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if plot2:
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ax = plt.subplots(2, 4, figsize=(10, 6), tight_layout=True)[1].ravel()
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fig2, ax2 = plt.subplots(1, 1, figsize=(8, 4), tight_layout=True)
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# for f in [Path(path) / f'study_coco_{x}.txt' for x in ['yolov5s6', 'yolov5m6', 'yolov5l6', 'yolov5x6']]:
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for f in sorted(Path(path).glob('study*.txt')):
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y = np.loadtxt(f, dtype=np.float32, usecols=[0, 1, 2, 3, 7, 8, 9], ndmin=2).T
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x = np.arange(y.shape[1]) if x is None else np.array(x)
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s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_inference (ms/img)', 't_NMS (ms/img)', 't_total (ms/img)']
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# for i in range(7):
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# ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
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# ax[i].set_title(s[i])
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if plot2:
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s = ['P', 'R', 'mAP@.5', 'mAP@.5:.95', 't_preprocess (ms/img)', 't_inference (ms/img)', 't_NMS (ms/img)']
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for i in range(7):
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ax[i].plot(x, y[i], '.-', linewidth=2, markersize=8)
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ax[i].set_title(s[i])
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j = y[3].argmax() + 1
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ax2.plot(y[6, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
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ax2.plot(y[5, 1:j], y[3, 1:j] * 1E2, '.-', linewidth=2, markersize=8,
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label=f.stem.replace('study_coco_', '').replace('yolo', 'YOLO'))
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ax2.plot(1E3 / np.array([209, 140, 97, 58, 35, 18]), [34.6, 40.5, 43.0, 47.5, 49.7, 51.5],
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