def test_f1_two_class(self):
"""Test F1-score for two class
"""
correct = np.array([[False], [True], [False]])
conf = np.array([0.654, 0.702, 0.432])
pred_cls = np.array([0., 0., 1.])
target_cls = np.array([1. , 0., 0.])
_, _, _, f1, _ = ap_per_class(correct, conf, pred_cls, target_cls)
expect = np.array([[0.5], [0.]])
self.assertTrue((f1 == expect).all())
def test_f1_single_class(self):
"""Test F1-score for single class
"""
correct = np.array([[True], [True]])
conf = np.array([0.685, 0.702])
pred_cls = np.array([0., 0.])
target_cls = np.array([0., 0.])
_, _, _, f1, _ = ap_per_class(correct, conf, pred_cls, target_cls)
expect = np.array([[1.]])
self.assertTrue((f1 == expect).all())
def test_ap_two_class(self):
"""Test average precision for two class
"""
correct = np.array([[False], [True], [False]])
conf = np.array([0.654, 0.702, 0.432])
pred_cls = np.array([0., 0., 1.])
target_cls = np.array([1. , 0., 0.])
_, _, ap, _, _ = ap_per_class(correct, conf, pred_cls, target_cls)
expect = np.array([[0.5], [0.]])
self.assertTrue((ap == expect).all())
def test_ap_single_class(self):
"""Test average precision for single class
"""
correct = np.array([[True], [True]])
conf = np.array([0.685, 0.702])
pred_cls = np.array([0., 0.])
target_cls = np.array([0., 0.])
_, _, ap, _, _ = ap_per_class(correct, conf, pred_cls, target_cls)
expect = np.array([[0.995]])
self.assertTrue((ap == expect).all())
def test_precision_recall_three_class(self):
"""Test precision and recall for three class
"""
correct = np.array([[False], [True], [False]])
conf = np.array([0.654, 0.702, 0.432])
pred_cls = np.array([0., 2., 1.])
target_cls = np.array([1. , 2., 0.])
p, r, _, _, _ = ap_per_class(correct, conf, pred_cls, target_cls)
expect_p = np.array([[0.], [0.], [1.]])
expect_r = np.array([[0.], [0.], [1.]])
self.assertTrue((p == expect_p).all() and (r == expect_r).all())
def test_precision_recall_single_class(self):
"""Test precision and recall for single class
"""
correct = np.array([[True], [True]])
conf = np.array([0.685, 0.702])
pred_cls = np.array([0., 0.])
target_cls = np.array([0., 0.])
p, r, _, _, _ = ap_per_class(correct, conf, pred_cls, target_cls)
output = np.concatenate([p, r])
expect = np.array([[1.], [1.]])
self.assertTrue((output == expect).all())
def test(
cfg,
data,
weights=None,
batch_size=16,
imgsz=416,
conf_thres=0.1,
iou_thres=0.6, # for nms
single_cls=False,
augment=False,
model=None,
dataloader=None,
multi_label=True):
# Initialize/load model and set device
if model is None:
is_training = False
device = select_device(opt.device, batch_size=batch_size)
verbose = opt.task == 'test'
# Remove previous
for f in glob.glob('test_batch*.jpg'):
os.remove(f)
# Initialize model
model = Darknet(cfg, imgsz)
# Load weights
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(
torch.load(weights, map_location=device)['model'])
else: # darknet format
load_darknet_weights(model, weights)
# Fuse
model.fuse()
model.to(device)
if device.type != 'cpu' and torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
else: # called by train.py
is_training = True
device = next(model.parameters()).device # get model device
verbose = False
# Configure run
data = parse_data_cfg(data)
nc = 1 if single_cls else int(data['classes']) # number of classes
path = data['valid'] # path to test images
names = load_classes(data['names']) # class names
iouv = torch.linspace(0.5, 0.95,
10).to(device) # iou vector for [email protected]:0.95
iouv = iouv[0].view(1) # comment for [email protected]:0.95
niou = iouv.numel()
# Dataloader
if dataloader is None:
dataset = LoadImagesAndLabels(path,
imgsz,
batch_size,
rect=True,
single_cls=opt.single_cls,
pad=0.5)
batch_size = min(batch_size, len(dataset))
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=min([
os.cpu_count(),
batch_size if batch_size > 1 else 0, 8
]),
pin_memory=True,
collate_fn=dataset.collate_fn)
seen = 0
model.eval()
_ = model(torch.zeros(
(1, 3, imgsz,
imgsz), device=device)) if device.type != 'cpu' else None # run once
s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R',
'[email protected]', 'F1')
p, r, f1, mp, mr, map, mf1, 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, (imgs, targets, paths,
shapes) in enumerate(tqdm(dataloader, desc=s)):
imgs = imgs.to(
device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = imgs.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(
imgs, augment=augment) # inference and training outputs
t0 += time_synchronized() - t
# Compute loss
if is_training: # if model has loss hyperparameters
loss += compute_loss(train_out, targets,
model)[1][:3] # GIoU, obj, cls
# Run NMS
t = time_synchronized()
output = non_max_suppression(inf_out,
conf_thres=conf_thres,
iou_thres=iou_thres,
multi_label=multi_label)
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
# with open('test.txt', 'a') as file:
# [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]
# Clip boxes to image bounds
clip_coords(pred, (height, width))
# 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) # 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(pred[pi, :4], tbox[ti]).max(
1) # best ious, indices
# Append detections
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti[i[j]] # detected target
if d not in detected:
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 batch_i < 1:
f = 'test_batch%g_gt.jpg' % batch_i # filename
plot_images(imgs, targets, paths=paths, names=names,
fname=f) # ground truth
f = 'test_batch%g_pred.jpg' % batch_i
plot_images(imgs,
output_to_target(output, width, height),
paths=paths,
names=names,
fname=f) # predictions
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
# Visualize probability of predictions
# probs = np.array(stats[1])
# import seaborn as sns
# plot = sns.distplot(probs, bins=probs.shape[0] // 3)
# plot.figure.savefig("prob_%s.png" % imgsz)
if len(stats):
p, r, ap, f1, ap_class = ap_per_class(*stats)
if niou > 1:
p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(
1), ap[:, 0] # [P, R, [email protected]:0.95, [email protected]]
mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.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' + '%10.3g' * 6 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))
# 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], ap[i], f1[i]))
# Print speeds
if verbose:
t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (
imgsz, imgsz, batch_size) # tuple
print(
'Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g'
% t)
# Return results
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps