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],