def process_batch(predictions, labels, iouv):
# Evaluate 1 batch of predictions
correct = torch.zeros(predictions.shape[0],
len(iouv),
dtype=torch.bool,
device=iouv.device)
detected = [] # label indices
tcls, pcls = labels[:, 0], predictions[:, 5]
nl = labels.shape[0] # number of labels
for cls in torch.unique(tcls):
ti = (cls == tcls).nonzero().view(-1) # label indices
pi = (cls == pcls).nonzero().view(-1) # prediction indices
if pi.shape[0]: # find detections
ious, i = box_iou(predictions[pi, 0:4],
labels[ti, 1:5]).max(1) # best ious, indices
detected_set = set()
for j in (ious > iouv[0]).nonzero():
d = ti[i[j]] # detected label
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d) # append detections
correct[pi[j]] = ious[j] > iouv # iou_thres is 1xn
if len(detected
) == nl: # all labels already located in image
break
return correct
def process_batch(detections, labels, iouv):
"""
Return correct predictions matrix. Both sets of boxes are in (x1, y1, x2, y2) format.
Arguments:
detections (Array[N, 6]), x1, y1, x2, y2, conf, class
labels (Array[M, 5]), class, x1, y1, x2, y2
Returns:
correct (Array[N, 10]), for 10 IoU levels
"""
correct = torch.zeros(detections.shape[0],
iouv.shape[0],
dtype=torch.bool,
device=iouv.device)
iou = box_iou(labels[:, 1:], detections[:, :4])
x = torch.where(
(iou >= iouv[0]) &
(labels[:, 0:1]
== detections[:, 5])) # IoU above threshold and classes match
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy() # [label, detection, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1], return_index=True)[1]]
# matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0], return_index=True)[1]]
matches = torch.Tensor(matches).to(iouv.device)
correct[matches[:, 1].long()] = matches[:, 2:3] >= iouv
return correct
def compute_stats_cuda(self,output):
whwh = torch.Tensor([self.w,self.h,self.w,self.h]).to(device = self.device)
for si, pred in enumerate(output):
labels = self.targets[self.targets[:,0] == si,1:] #label for corresponding index
cuda_preds = self.cuda_preds[self.cuda_preds[:,0] == si,1:]
cuda_preds = cuda_preds.to(self.device)
nl = len(labels)
tcls = labels[:,0].tolist() if nl else []
self.seen += 1
if cuda_preds is None :
if nl :
self.stats.append((torch.zeros(0,self.niou,dtype=torch.bool),torch.Tensor(),torch.Tensor(),tcls))
continue
# Assign all predictions as incorrect
correct = torch.zeros(cuda_preds.shape[0], self.niou, dtype=torch.bool, device=self.device)
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
pred = torch.zeros((cuda_preds.shape[0],6),device=self.device)
tbox = xywh2xyxy(labels[:, 1:5]) * whwh
pred[:,:4] = xywh2xyxy(cuda_preds[:, 1:5]) * whwh
pred[:,4] = 1
pred[:,5] = cuda_preds[:,0]
# 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
ind = i
detected_set = set()
for j in (ious > self.iouv[0]).nonzero(as_tuple=False):
d = ti[ind[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct[pi[j]] = ious[j] > self.iouv # iou_thres is 1xn
if len(detected) == nl: # all targets already located in image
break
# Append statistics (correct, conf, pcls, tcls)
self.stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
def process_batch(self, detections, labels):
"""
Return intersection-over-union (Jaccard index) of boxes.
Both sets of boxes are expected to be in (x1, y1, x2, y2) format.
Arguments:
detections (Array[N, 6]), x1, y1, x2, y2, conf, class
labels (Array[M, 5]), class, x1, y1, x2, y2
Returns:
None, updates confusion matrix accordingly
"""
detections = detections[detections[:, 4] > self.conf]
gt_classes = labels[:, 0].int()
detection_classes = detections[:, 5].int()
iou = general.box_iou(labels[:, 1:], detections[:, :4])
x = torch.where(iou > self.iou_thres)
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]),
1).cpu().numpy()
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 1],
return_index=True)[1]]
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[np.unique(matches[:, 0],
return_index=True)[1]]
else:
matches = np.zeros((0, 3))
n = matches.shape[0] > 0
m0, m1, _ = matches.transpose().astype(np.int16)
for i, gc in enumerate(gt_classes):
j = m0 == i
if n and sum(j) == 1:
self.matrix[gc, detection_classes[m1[j]]] += 1 # correct
else:
self.matrix[gc, self.nc] += 1 # background FP
if n:
for i, dc in enumerate(detection_classes):
if not any(m1 == i):
self.matrix[self.nc, dc] += 1 # background FN
def compute_stats_tags(self,output):
whwh = torch.Tensor([self.w,self.h,self.w,self.h]).to(device = self.device)
c = 0
for si, pred in enumerate(output):
labels = self.targets[self.targets[:,0] == si,1:] #label for corresponding index
nl = len(labels)
self.tags[c] = [3 if i==-1 else i for i in self.tags[c]]
tcls = self.tags[c] if nl else []
self.seen += 1
if pred is None :
if nl :
self.stats.append((torch.zeros(0,self.niou,dtype=torch.bool),torch.Tensor(),torch.Tensor(),tcls))
continue
#clip boxes to image bounds
clip_coords(pred,(self.h,self.w))
# Assign all predictions as incorrect
correct = torch.zeros(pred.shape[0], self.niou, dtype=torch.bool, device=self.device)
if nl:
detected = [] # target indices
tcls_tensor = torch.Tensor(tcls).to(self.device)
# target boxes
tbox = xywh2xyxy(labels[:, 1:5]) * whwh
# Per target class
box_ious = []
box_ious1 = []
for cls in torch.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # prediction indices
pi = (pred[:, 5] != 10).nonzero(as_tuple=False).view(-1) # target indices
# Search for detections
if pi.shape[0]:
ious, i = box_iou(tbox[ti],pred[pi, :4]).max(1) # best ious, indices
# Prediction to target ious
for iou in ious :
print(cls)
self.tags_ious[int(cls.item())].append(iou.item())
c += 1
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 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
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)
# Data
with open(data) as f:
data = yaml.safe_load(f)
check_dataset(data) # check
# Half
half &= device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half()
# Configure
model.eval()
is_coco = type(data['val']) is 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()
# 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 = str(
Path(data.get('path', '../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 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
plots=True):
# Initialize/load model and set device
# 判断是否在训练时调用test，如果是则获取训练时的设备
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
if save_txt:
out = Path('inference/output')
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Remove previous
# 删除之前的test_batch0_gt.jpg和test_batch0_pred.jpg
for f in glob.glob(str(save_dir / 'test_batch*.jpg')):
os.remove(f)
# 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
# 如果设备不是cpu，则将模型由Float32转为Float16，提高前向传播的速度
half = device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half()
# Configure
# 将模型字符串转变为函数
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
# 设置iou阈值，从0.5~0.95，每间隔0.05取一次
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for [email protected]:0.95
# iou个数
niou = iouv.numel()
# Dataloader
if not training:
# 创建一个全0数组测试一下前向传播是否正常运行
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
# 获取图片路径
path = data['test'] if opt.task == 'test' else data['val'] # path to val/test images
# 创建dataloader
# 注意这里rect参数为True，yolov5的测试评估是基于矩形推理的
dataloader = create_dataloader(path, imgsz, batch_size, model.stride.max(), opt,
hyp=None, augment=False, cache=False, pad=0.5, rect=True)[0]
# 初始化测试的图片数量
seen = 0
# 获取类别的名字
names = model.names if hasattr(model, 'names') else model.module.names
"""
获取coco数据集的类别索引
这里要说明一下，coco数据集有80个类别(索引范围应该为0~79)，
但是他的索引却属于0~90(笔者是通过查看coco数据测试集的json文件发现的，具体原因不知)
coco80_to_coco91_class()就是为了与上述索引对应起来，返回一个范围在0~90的索引数组
"""
coco91class = coco80_to_coco91_class()
# 设置tqdm进度条的显示信息
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', '[email protected]', '[email protected]:.95')
# 初始化指标，时间
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
# 初始化测试集的损失
loss = torch.zeros(4, device=device)
# 初始化json文件的字典，统计信息，ap
jdict, stats, ap, ap_class = [], [], [], []
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
'''
i: batch_index, 第i个batch
imgs : torch.Size([batch_size, 3, weights, heights])
targets : torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, Θ])
paths : List['img1_path','img2_path',......,'img-1_path'] len(paths)=batch_size
shape :
'''
img = img.to(device, non_blocking=True)
# 图片也由Float32->Float16
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()
'''
Detect层在的输出：(z,x)
if training :
x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, no)
else :
(z,x)
z tensor: [small+medium+large_inference] size=(batch_size, 3 * (small_size1*small_size2 + medium_size1*medium_size2 + large_size1*large_size2), no) 真实坐标
x list: [small_forward, medium_forward, large_forward] eg:small_forward.size=( batch_size, 3种scale框, size1, size2, no)
'''
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][:4] # box, obj, cls, angle
# Run NMS
t = time_synchronized()
# output : size = (batch_size, num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179]
#output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres)
output = rotate_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):
'''
targets : torch.Size = (该batch中的目标数量, [该image属于该batch的第几个图片, class, xywh, θ]) θ∈[0,179]
pred : shape=(num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179]
si : 该batch中的第几张图
'''
# labels: shape= (num, [class, xywh, θ])
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
# with open(str(out / Path(paths[si]).stem) + '.txt', 'a') as f:
# f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
# 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)
# pred : shape=(num_conf_nms, [xywhθ,conf,classid]) θ∈[0,179]
# labels: shape= (num, [class, xywh, θ])
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0] # torch.size(num)
# target boxes -> orignal shape
tbox = labels[:, 1:5] * whwh # torch.size(num,[xywh]) 1024*1024 无所谓顺序
#ttheta = labels[:, 5] # torch.size(num,[Θ])
# Per target class
for cls in torch.unique(tcls_tensor): # unique函数去除其中重复的元素，并按元素（类别）由大到小返回一个新的无元素重复的元组或者列表
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(-1) # target indices
pi = (cls == pred[:, 6]).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
#rious, i = rbox_iou(pred[:, :4], pred[:, 4].unsqueeze(1), tbox, ttheta.unsqueeze(1)).max(1) # best rious, 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[:, 5].cpu(), pred[:, 6].cpu(), tcls))
# Plot images
if plots and batch_i < 1:
f = save_dir / ('test_batch%g_gt.jpg' % batch_i) # filename
plot_images(img, targets, paths, str(f), names) # ground truth
f = save_dir / ('test_batch%g_pred.jpg' % batch_i)
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=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):
f = 'detections_val2017_%s_results.json' % \
(weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename
print('\nCOCO mAP with pycocotools... saving %s...' % f)
with open(f, 'w') as file:
json.dump(jdict, file)
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(f) # 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
# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
scale_coords(img[si].shape[1:], tbox, shapes[si][0],
shapes[si][1]) # native-space labels
# 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
def test(
self,
weights=None,
batch_size=16,
imgsz=640,
conf_thres=0.001,
iou_thres=0.5, # 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
losses = {} #keep track of images with worst mAP
training = True
data = self.opt.data
print("IOU Threshold", iou_thres)
print("Conf Threshold", conf_thres)
if training: # called by train.py
device = next(self.model.parameters()).device # get model device
"""
else: # called directly
set_logging()
device = self.model.device #get model device
save_txt = self.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 self.save_txt:
out = self.save_dir / 'autolabels'
if os.path.exists(out):
shutil.rmtree(out) # delete dir
os.makedirs(out) # make new dir
# Load 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
half = False
if half:
self.model.half()
# Configure
self.model.eval()
with open(data) as f:
data = yaml.load(f, Loader=yaml.FullLoader) # model dict
check_dataset(data) # check
nc = 1 if self.opt.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()
seen = 0
names = self.model.names if hasattr(
self.model, 'names') else self.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(self.test_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 = self.forward(
img) # 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,
self.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 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=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)
#set model back to train mode
self.model.float()
self.model.train()
maps = np.zeros(self.nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, map50, map,
*(loss.cpu() / len(self.test_dataloader)).tolist()), maps, t
def test(
data,
weights=None,
batch_size=32,
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_hybrid=False, # for hybrid auto-labelling
save_conf=False, # save auto-label confidences
plots=True,
log_imgs=0, # number of logged images
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(opt.device, batch_size=batch_size)
# Directories
save_dir = Path(
increment_path(Path(opt.project) / opt.name,
exist_ok=opt.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
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()
is_coco = data.endswith('coco.yaml') # is COCO dataset
with open(data) as f:
data = yaml.load(f, Loader=yaml.SafeLoader) # model dict
check_dataset(data) # check
nc1 = 1 if single_cls else int(data['nc1']) # number of classes # edit
nc2 = 1 if single_cls else int(data['nc2']) # number of classes # edit
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:
if device.type != 'cpu':
model(
torch.zeros(1, 3, imgsz, imgsz).to(device).type_as(
next(model.parameters()))) # 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,
pad=0.5,
rect=True,
prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0]
seen = 0
confusion_matrix1 = ConfusionMatrix(nc=nc1)
confusion_matrix2 = ConfusionMatrix(nc=nc2) # edit
names1 = {
k: v
for k, v in enumerate(
model.names1 if hasattr(model, 'names1') else model.module.names1)
} # edit
names2 = {
k: v
for k, v in enumerate(
model.names2 if hasattr(model, 'names2') else model.module.names2)
} # edit
coco91class = coco80_to_coco91_class()
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R',
'[email protected]', '[email protected]:.95')
t0, t1 = 0., 0.
p_1, r_1, f1_1, mp_1, mr_1, map50_1, map_1 = 0., 0., 0., 0., 0., 0., 0.
p_2, r_2, f1_2, mp_2, mr_2, map50_2, map_2 = 0., 0., 0., 0., 0., 0., 0. # edit
loss = torch.zeros(4, device=device) # edit
jdict, stats1, stats2, ap_1, ap_2, ap50_1, ap50_2, ap_class_1, ap_class_2, wandb_images =\
[], [], [], [], [], [], [], [], [], [] # edit
# targets: img_id, cls1, cls2, xywh # edit
for batch_i, (img, targets, paths,
shapes) in enumerate(tqdm(dataloader, desc=s)):
# print("targets 105:", targets.shape, targets) # todo
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 compute_loss:
# print("new_loss:", new_loss.shape) # todo
loss += compute_loss(
[x.float() for x in train_out],
targets)[1][:4] # box, obj, cls1, cls2 # edit
# Run NMS
targets[:, 3:] *= torch.Tensor([width, height, width, height
]).to(device) # to pixels # edit
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,
nc1=nc1,
nc2=nc2) # edit
t1 += time_synchronized() - t
# Statistics per image
for si, pred in enumerate(
output): # pred: xyxy, conf1, cls1, conf2, cls2 # edit
labels = targets[targets[:, 0] == si,
1:] # labels: cls1, cls2, xywh # edit
# print("labels 130:", targets.shape, pred.shape, labels.shape, targets, labels) # todo
nl = len(labels)
tcls1 = labels[:, 0].tolist() if nl else [] # target class 1
tcls2 = labels[:,
1].tolist() if nl else [] # target class 2 # edit
path = Path(paths[si])
seen += 1
if len(pred) == 0:
if nl:
stats1.append((torch.zeros(0, niou, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), tcls1))
stats2.append(
(torch.zeros(0, niou,
dtype=torch.bool), torch.Tensor(),
torch.Tensor(), tcls2)) # edit
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, conf1, cls1, conf2, cls2 in predn.tolist():
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
gn).view(-1).tolist() # normalized xywh
line = (cls1, cls2, *xywh, conf1,
conf2) if save_conf else (cls1, cls2, *xywh
) # label format # edit
with open(save_dir / 'labels' / (path.stem + '.txt'),
'a') as f:
f.write(('%g ' * len(line)).rstrip() % line + '\n')
# W&B logging # edit
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(cls1),
"box_caption":
"%s (%s) %.3f" % (names1[cls1], names2[cls2], conf1),
"scores": {
"class_score": conf1
},
"domain":
"pixel"
} for *xyxy, conf1, cls1, conf2, cls2 in pred.tolist()]
boxes = {
"predictions": {
"box_data": box_data,
"class_labels": names1
}
} # inference-space
wandb_images.append(
wandb.Image(img[si], boxes=boxes, caption=path.name))
box_data = [{
"position": {
"minX": xyxy[0],
"minY": xyxy[1],
"maxX": xyxy[2],
"maxY": xyxy[3]
},
"class_id":
int(cls2),
"box_caption":
"(%s) %s %.3f" % (names1[cls1], names2[cls2], conf2),
"scores": {
"class_score": conf2
},
"domain":
"pixel"
} for *xyxy, conf1, cls1, conf2, cls2 in pred.tolist()]
boxes = {
"predictions": {
"box_data": box_data,
"class_labels": names2
}
} # inference-space
wandb_images.append(
wandb.Image(img[si], boxes=boxes, caption=path.name))
# Append to pycocotools JSON dictionary
if save_json: # todo ~
# [{"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
correct1 = torch.zeros(pred.shape[0],
niou,
dtype=torch.bool,
device=device)
correct2 = torch.zeros(pred.shape[0],
niou,
dtype=torch.bool,
device=device) # edit
if nl:
detected = [] # target indices
tcls_tensor_1 = labels[:, 0]
tcls_tensor_2 = labels[:, 1] # edit
# target boxes
tbox = xywh2xyxy(labels[:, 2:6]) # edit
scale_coords(img[si].shape[1:], tbox, shapes[si][0],
shapes[si][1]) # native-space labels
if plots:
confusion_matrix1.process_batch(predn,
torch.cat(
(labels[:, 0:1], tbox),
1)) # edit
confusion_matrix2.process_batch(predn,
torch.cat(
(labels[:, 0:1], tbox),
1)) # edit
# Per target class
unique_classes = torch.unique(torch.stack(
(tcls_tensor_1, tcls_tensor_2)),
dim=1).T
# print("unique_classes", unique_classes.shape, unique_classes) # todo
# print("tcls tensors", tcls_tensor_1, tcls_tensor_2) # todo
for cls1, cls2 in unique_classes:
ti1 = (cls1 == tcls_tensor_1).nonzero(as_tuple=False).view(
-1) # prediction indices
pi1 = (cls1 == pred[:, 5]).nonzero(as_tuple=False).view(
-1) # target indices
ti2 = (cls2 == tcls_tensor_2).nonzero(as_tuple=False).view(
-1) # prediction indices #edit
pi2 = (cls2 == pred[:, 7]).nonzero(as_tuple=False).view(
-1) # target indices #edit
# print("ti/pi", ti1, pi1, ti2, pi2) # todo
# Search for detections
if pi1.shape[0]:
# Prediction to target ious
ious, i = box_iou(predn[pi1, :4], tbox[ti1]).max(
1) # best ious, indices
# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti1[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct1[pi1[
j]] = ious[j] > iouv # iou_thres is 1xn
if len(
detected
) == nl: # all targets already located in image
break
if pi2.shape[0]: # edit
# Prediction to target ious
ious, i = box_iou(predn[pi2, :4], tbox[ti2]).max(
1) # best ious, indices
# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero(as_tuple=False):
d = ti2[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
correct2[pi2[
j]] = ious[j] > iouv # iou_thres is 1xn
if len(
detected
) == nl: # all targets already located in image
break
# Append statistics (correct, conf, predicted class, target class)
stats1.append(
(correct1.cpu(), pred[:, 4].cpu(), pred[:, 6].cpu(), tcls1))
stats2.append(
(correct2.cpu(), pred[:,
5].cpu(), pred[:,
7].cpu(), tcls2)) # edit
# 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, names1, names2),
daemon=True).start() # edit
f = save_dir / f'test_batch{batch_i}_pred.jpg' # predictions
Thread(target=plot_images,
args=(img, output_to_target(output), paths, f, names1,
names2),
daemon=True).start() # edit
# Compute statistics
stats1 = [np.concatenate(x, 0) for x in zip(*stats1)] # to numpy
stats2 = [np.concatenate(x, 0) for x in zip(*stats2)] # to numpy # edit
# s1 = [np.count_nonzero(ba) for ba in stats1[0]] # todo
# s2 = [np.count_nonzero(ba) for ba in stats2[0]] # todo
# print("stats 1:", np.sum(s1), stats1[1:]) # todo
# print("stats 2:", np.sum(s2), stats2[1:]) # todo
if len(stats1) and stats1[0].any():
p_1, r_1, ap_1, f1_1, ap_class_1 = ap_per_class(*stats1,
plot=plots,
save_dir=save_dir,
names=names1,
suffix='_1')
ap50_1, ap_1 = ap_1[:, 0], ap_1.mean(1) # [email protected], [email protected]:0.95
mp_1, mr_1, map50_1, map_1 = p_1.mean(), r_1.mean(), ap50_1.mean(
), ap_1.mean()
nt_1 = np.bincount(stats1[3].astype(np.int64),
minlength=nc1) # number of targets per class
else:
nt_1 = torch.zeros(1)
if len(stats2) and stats2[0].any():
p_2, r_2, ap_2, f1_2, ap_class_2 = ap_per_class(*stats2,
plot=plots,
save_dir=save_dir,
names=names2,
suffix='_2') # edit
ap50_2, ap_2 = ap_2[:, 0], ap_2.mean(1) # [email protected], [email protected]:0.95 # edit
mp_2, mr_2, map50_2, map_2 = p_2.mean(), r_2.mean(), ap50_2.mean(
), ap_2.mean() # edit
nt_2 = np.bincount(stats2[3].astype(
np.int64), minlength=nc2) # number of targets per class # edit
else:
nt_2 = torch.zeros(1) # edit
# Print results
pf = '%20s' + '%12.3g' * 6 # print format
print(pf % ('all_1', seen, nt_1.sum(), mp_1, mr_1, map50_1, map_1))
print(pf % ('all_2', seen, nt_2.sum(), mp_2, mr_2, map50_2, map_2)) # edit
# Print results per class
if (verbose or (nc1 < 50 and not training)) and nc1 > 1 and len(stats1):
for i, c in enumerate(ap_class_1):
print(
pf %
(names1[c], seen, nt_1[c], p_1[i], r_1[i], ap50_1[i], ap_1[i]))
if (verbose or
(nc2 < 50 and not training)) and nc2 > 1 and len(stats2): # edit
for i, c in enumerate(ap_class_2):
print(
pf %
(names2[c], seen, nt_2[c], p_2[i], r_2[i], ap50_2[i], ap_2[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)
# Plots
if plots:
confusion_matrix1.plot(save_dir=save_dir,
names=list(names1.values()),
suffix='_1')
confusion_matrix2.plot(save_dir=save_dir,
names=list(names2.values()),
suffix='_2') # edit
if wandb and wandb.run:
val_batches = [
wandb.Image(str(f), caption=f.name)
for f in sorted(save_dir.glob('test*.jpg'))
]
wandb.log({
"Images": wandb_images,
"Validation": val_batches
},
commit=False)
# Save JSON
if save_json and len(jdict): # todo ~
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_1 = np.zeros(nc1) + map_1
for i, c in enumerate(ap_class_1):
maps_1[c] = ap_1[i]
maps_2 = np.zeros(nc2) + map_2
for i, c in enumerate(ap_class_2):
maps_2[c] = ap_2[i]
res_loss = (loss.cpu() / len(dataloader)).tolist()
return maps_1, maps_2, t,\
(mp_1, mr_1, map50_1, map_1, *res_loss),\
(mp_2, mr_2, map50_2, map_2, *res_loss)
def test(cfg = None,
data = None,
weights=None,
batch_size=32,
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_hybrid=False, # for hybrid auto-labelling
save_conf=False, # save auto-label confidences
plots=True):
# Initialize/load model and set device
training = model is not None
if not training: # called by train.py
# called directly
set_logging()
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.exist_ok)) # increment run
(save_dir / 'labels' if save_txt else save_dir).mkdir(parents=True, exist_ok=True) # make dir
# Load model
model = Model(cfg)
model.load(weights)
model = model.fuse()
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
# 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 = jt.linspace(0.5, 0.95, 10) # iou vector for [email protected]:0.95
niou = iouv.numel()
# Dataloader
if not training:
img = jt.zeros((1, 3, imgsz, imgsz)) # init img
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, pad=0.5, rect=True,
prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))
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.
loss = jt.zeros((3,))
jdict, stats, ap, ap_class = [], [], [], []
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
img = img.float32() # uint8 to fp16/32
img /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets
nb, _, height, width = img.shape # batch size, channels, height, width
with jt.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:] *= jt.array([width, height, width, height]) # 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((jt.zeros((0, niou), dtype="bool"), jt.array([]), jt.array([]), tcls))
continue
# Predictions
predn = pred.clone()
predn[:, :4] = 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 = jt.array(shapes[si][0])[jt.array([1, 0, 1, 0])] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
xywh = (xyxy2xywh(jt.array(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')
# 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 = jt.zeros((pred.shape[0], niou), dtype="bool")
if nl:
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
tbox = xywh2xyxy(labels[:, 1:5])
tbox = scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
if plots:
confusion_matrix.process_batch(predn, jt.contrib.concat((labels[:, 0:1], tbox), 1))
# Per target class
for cls in jt.unique(tcls_tensor):
ti = (cls == tcls_tensor).nonzero().view(-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero().view(-1) # target indices
# Search for detections
if pi.shape[0]:
# Prediction to target ious
i ,ious = box_iou(predn[pi, :4], tbox[ti]).argmax(1) # best ious, indices
# Append detections
detected_set = set()
for j in (ious > iouv[0]).nonzero():
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.numpy(), pred[:, 4].numpy(), pred[:, 5].numpy(), 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)
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 = np.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 or (nc <= 20 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, 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)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=list(names.values()))
# 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}")
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, map50, map, *(loss.numpy() / len(dataloader)).tolist()), maps, t
def test_ddp(opt, test_model, testloader, rank, device, names):
if rank in [-1, 0]:
# t1 = time.time()
stats, seen = [],0
iou_thres = 0.25
iouv = torch.arange(iou_thres, 1, 0.05).to(device) # iou_thres : 0.95 : 0.05
niou = iouv.numel()
## for now this won't work, unless deepcopy of model is passed in from all processes
## because this would reduce precision on training model as well...
half = False
#################################
test_model.eval()
if half: test_model.half()
for batch_i, (imgs, targets, paths, shapes) in enumerate(testloader):
imgs = imgs.to(device, non_blocking=True)
imgs = imgs.half() if half else imgs.float() # uint8 to fp16/32
imgs /= 255.0 # 0 - 255 to 0.0 - 1.0
nb, _, height, width = imgs.shape # batch size, channels, height, width
targets = targets.to(device)
targets[:, 2:] *= torch.Tensor([width, height, width, height]).to(device) # to pixels
## inference step ----------------------
output = test_model(imgs, augment=False)[0]
## -------------------------------------
output = non_max_suppression(output, multi_label=False, agnostic=True)
output = output[0] ## only works with batch_size==1 (for now...)
## gather outputs from all DDP processes...
all_output = gather_tensors(output, device, rank, opt.world_size)
all_targets = gather_tensors(targets, device, rank, opt.world_size)
## imgs[0].shape
hw = torch.tensor([height, width]).to(device)
all_hw = [torch.zeros_like(hw, device=device) for _ in range(opt.world_size)]
dist.all_gather(all_hw, hw)
## shapes
s = shapes[0]
t = torch.tensor(s[0] + s[1][0] + s[1][1]).to(device)
all_shapes = [torch.zeros_like(t, device=device) for _ in range(opt.world_size)]
dist.all_gather(all_shapes, t)
if rank in [-1, 0]:
output = all_output
for j,targets in enumerate(all_targets):
targets[:,0] = j ## restore indices
targets = torch.cat(all_targets, 0)
shapes = []
for t in all_shapes:
s = list(t.cpu().numpy())
s = [[int(s[0]), int(s[1])],[s[2:4],s[4:]]]
shapes.append(s)
## METRICS
idx = []
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:
idx.append(None)
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue
idx.append(None)
predn = pred.clone()
scale_coords(all_hw[si], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
# 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(all_hw[si], 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... index into target array....
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
k = pi[j] ## index into pred array.....
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))
if rank in [-1, 0]:
# Compute statistics
nc = len(names) ## number of classes
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
conf_best = -1
ct=None
max_by_class=True
conf_thres=-1
if len(stats) and stats[0].any():
mp, mr, map50, map, mf1, ap_class, conf_best, nt, (p, r, ap50, ap, f1, cc) = ap_per_class(*stats, max_by_class=max_by_class, conf_thres=conf_thres)
else:
nt = torch.zeros(1)
# Print results
pfunc('------------------------------------------- Validation Set -----------------------------------------------')
fmt = '%{}s'.format(2+max([len(s) for s in names]))
s = (fmt + '%12s' * 7) % ('Class', 'Images', 'Targets', 'P', 'R', 'F1', '[email protected]', '[email protected]:.95')
pfunc(s)
pf = fmt + '%12.3g' * 7 # print format
# Print results per class
if nc < 50 and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
pfunc(pf % (names[c], seen, nt[c], p[i], r[i], f1[i], ap50[i], ap[i])) #log
## Print averages
if nc>1:
pfunc('')
pfunc(pf % ('AVG', seen, nt.sum(), p.mean(), r.mean(), f1.mean(), ap50.mean(), ap.mean())) ## unweighted average
ss = 'WEIGHTED AVG' if nc>1 else names[0]
pfunc(pf % (ss, seen, nt.sum(), mp, mr, mf1, map50, map)) ## weighted average (if nc>1)
if conf_best>-1:
pfunc('\nOptimal Confidence Threshold: {0:0.3f}'.format(conf_best)) #log
if max_by_class:
pfunc('Optimal Confidence Thresholds (Per-Class): {}'.format(list(cc.round(3)))) #log
# pfunc(f'DDP Validation Time: {(time.time()-t1)/60:0.2f} min')
results = (mp, mr, mf1, map50, map)#, *(loss.cpu() / len(dataloader)).tolist())
return results #, maps, times
return None
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='',
merge=False,
save_txt=False):
# 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)
merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels
if save_txt:
out = Path('inference/output')
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Remove previous
for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')):
os.remove(f)
# 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] # GIoU, obj, cls
# Run NMS
t = time_synchronized()
output = non_max_suppression(inf_out,
conf_thres=conf_thres,
iou_thres=iou_thres,
merge=merge)
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
with open(str(out / Path(paths[si]).stem) + '.txt',
'a') as f:
f.write(
('%g ' * 5 + '\n') % (cls, *xywh)) # label format
# 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 batch_i < 1:
f = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename
plot_images(img, targets, paths, str(f), names) # ground truth
f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i)
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)
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):
f = 'detections_val2017_%s_results.json' % \
(weights.split(os.sep)[-1].replace('.pt', '') if isinstance(weights, str) else '') # filename
print('\nCOCO mAP with pycocotools... saving %s...' % f)
with open(f, 'w') as file:
json.dump(jdict, file)
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(f) # 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 test(
data,
weights=None,
batch_size=16,
imgsz=640,
conf_thres=0.3,
iou_thres=0.5, # for NMS
save_json=False,
single_cls=False,
augment=False,
verbose=False,
model=None,
dataloader=None,
save_dir='',
merge=False,
emb_dim=256,
save_txt=False):
# 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(opt.device, batch_size=batch_size)
merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels
if save_txt:
out = Path('inference/output')
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Remove previous
for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')):
os.remove(f)
# Load model
model = attempt_load(weights, map_location=device) # load FP32 model
imgsz = [check_img_size(x, model.stride.max()) for x in imgsz]
# 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
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 len(imgsz) == 1:
img = torch.zeros((1, 3, imgsz, imgsz), device=device) # init img
else:
img = torch.zeros((1, 3, imgsz[1], imgsz[0]), device=device)
_ = model(img.half() if half else img
) if device.type != 'cpu' else None # run once
root = data['root']
path = data['test'] if opt.task == 'test' else data[
'test_emb'] # path to val/test images
dataloader = create_dataloader(root,
path,
imgsz,
batch_size,
model.stride.max(),
opt,
hyp=None,
augment=False,
cache=False,
pad=0.5,
rect=False)[0]
seen = 0
names = model.names if hasattr(model, 'names') else model.module.names
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.
jdict, stats, ap, ap_class = [], [], [], []
loss = torch.zeros(4, device=device)
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_p, train_out_pemb = 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_p],
[x.float()
for x in train_out_pemb], targets,
model)[1][:4] # GIoU, obj, cls, lid
# Run NMS
t = time_synchronized()
output = non_max_suppression(inf_out,
conf_thres=conf_thres,
iou_thres=iou_thres,
merge=merge,
emb_dim=emb_dim)
t1 += time_synchronized() - t
'''
images = letterbox(cv2.imread(paths[1]), [608,1088], auto=False, scaleup=False)[0]
d = output[1]
if d is None:
continue
for i in range(len(d)):
cv2.rectangle(images, (int(d[i][0]), int(d[i][1])), (int(d[i][2]), int(d[i][3])), (0, 0, 255), 2)
cv2.imshow("image", images)
cv2.waitKey(0)
'''
# 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
# 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[:, 2:6]) * 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
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 = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename
plot_images(img, targets, paths, str(f), names) # ground truth
f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i)
plot_test_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)
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[0], imgsz[1], batch_size)
if not training:
print(
'Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g'
% t)
# 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 evaluation(self):
"""
# run inference
"""
for batch_i, (img, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
img = img.to(device, non_blocking=True)
img = img.half() # 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
inf_out, train_out = model(img, augment=opt.augment) # inference and training outputs
# 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 opt.save_txt else [] # for autolabelling
output = non_max_suppression(inf_out, conf_thres=opt.conf_thres, iou_thres=opt.iou_thres, labels=lb)
# 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
# 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
# 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))
# 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=None, save_dir=None, names=names)
p, r, f1, ap50, ap = p[:, 0], r[:, 0], f1[:,0], ap[:, 0], ap.mean(1) # [P, R, [email protected], [email protected]:0.95]
mp, mr, mf1, map50, map = p.mean(), r.mean(), f1.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
print(s)
pf = '%20s' + '%12.3g' * 7 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map50, map, mf1))
def test(
data,
weights=None,
batch_size=32,
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_hybrid=False, # for hybrid auto-labelling
save_conf=False, # save auto-label confidences
plots=False,
log_imgs=0, # number of logged images
compute_loss=None):
# Initialize/load model and set device
logger = setup_logger('Test', './')
write_info(logger, True)
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)
# Directories
save_dir = Path(
increment_path(Path(opt.project) / opt.name,
exist_ok=opt.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
imgsz = check_img_size(imgsz, s=model.stride.max()) # check img_size
# 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()
# 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, 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,
pad=0.5,
rect=True,
prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0]
# write_imglist(path, logger)
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)
}
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, wandb_images = [], [], [], [], []
for batch_i, (img, targets, paths, shapes) in enumerate(dataloader):
stats_perimg = []
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 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()
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))
stats_perimg.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
# 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))
stats_perimg.append(
(correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# # f1 per image
# mf1_perimg = 0
# mp_perimg = 0
# mr_perimg = 0
# stats_perimg = [np.concatenate(x, 0) for x in zip(*stats_perimg)]
# if len(stats_perimg) and stats_perimg[0].any():
# p_perimg, r_perimg, _, f1_perimg, ap_class_perimg = ap_per_class(*stats_perimg, plot=plots, save_dir=save_dir, names=names)
# p_perimg, r_perimg, f1_perimg = p_perimg[:, 0], r_perimg[:, 0], f1_perimg[:, 0] # [P, R, [email protected], [email protected]:0.95]
# nt_perimg = np.bincount(stats_perimg[3].astype(np.int64), minlength=nc) # number of targets per class
# for ind, c in enumerate(ap_class_perimg):
# mf1_perimg += f1_perimg[ind] * nt_perimg[c]
# mp_perimg += p_perimg[ind] * nt_perimg[c]
# mr_perimg += r_perimg[ind] * nt_perimg[c]
# mf1_perimg = mf1_perimg/nt_perimg.sum()
# mp_perimg = mp_perimg / nt_perimg.sum()
# mr_perimg = mr_perimg / nt_perimg.sum()
#
# logger.info('[{}] {} [F1 score:{:4f} (Prec: {:4f}, Rec: {:4f})]'.format(str(batch_i + 1), paths[0].split('/')[-1], mf1_perimg, mp_perimg, mr_perimg))
# save GPS log
# Logger_System('./xmls', './Logger', output, paths, names)
# 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, f1 = p[:, 0], r[:, 0], ap[:, 0], ap.mean(
1), f1[:, 0] # [P, R, [email protected], [email protected]:0.95]
mp, mr, map50, map, mf1 = p.mean(), r.mean(), ap50.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)
meanf1 = 0
meanp = 0
meanr = 0
meanap = 0
# Print results per class
if (verbose or (nc <= 20)) and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
meanf1 += f1[i] * nt[c]
meanp += p[i] * nt[c]
meanr += r[i] * nt[c]
meanap += ap50[i] * nt[c]
meanf1 = meanf1 / nt.sum()
meanp = meanp / nt.sum()
meanr = meanr / nt.sum()
meanap = meanap / nt.sum()
logger.info('[Final] F1 score:{:4f} (Prec: {:4f}, Rec: {:4f})\n'.format(
meanf1, meanp, meanr))
# 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)
# Return results
model.float() # for training
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
write_info(logger, False)
return (meanf1, mp, mr, map50, meanap,
*(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='',
merge=False,
save_txt=False):
# 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(opt.device, batch_size=batch_size)
merge, save_txt = opt.merge, opt.save_txt # use Merge NMS, save *.txt labels
if save_txt:
out = Path('inference/output')
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Remove previous
for f in glob.glob(str(Path(save_dir) / 'test_batch*.jpg')):
os.remove(f)
# Load model
model = Darknet(opt.cfg).to(device)
# load model
try:
ckpt = torch.load(weights[0],
map_location=device) # load checkpoint
ckpt['model'] = {
k: v
for k, v in ckpt['model'].items()
if model.state_dict()[k].numel() == v.numel()
}
model.load_state_dict(ckpt['model'], strict=False)
except:
load_darknet_weights(model, weights[0])
imgsz = check_img_size(imgsz, s=32) # check img_size
# 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
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,
32,
opt,
hyp=None,
augment=False,
cache=False,
pad=0.5,
rect=True)[0]
seen = 0
'''
try:
names = model.names if hasattr(model, 'names') else model.module.names
except:
names = load_classes(opt.names)
'''
results = []
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] # GIoU, obj, cls
# Run NMS
t = time_synchronized()
output = non_max_suppression(inf_out,
conf_thres=conf_thres,
iou_thres=iou_thres,
merge=merge)
t1 += time_synchronized() - t
for si, pred in enumerate(output):
image_id = Path(paths[si]).stem
print(image_id)
if pred is None:
print("???")
result = {'image_id': image_id, 'PredictionString': ''}
results.append(result)
continue
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
scores = pred.cpu().numpy()[:, 4]
result = {
'image_id':
image_id,
'PredictionString':
format_prediction_string(box.cpu().numpy(), scores)
}
results.append(result)
#-----------------------------------------------------------------------
# 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:
continue
# Append to text file
if save_txt:
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0
]] # normalization gain whwh
txt_path = str(out / Path(paths[si]).stem)
pred[:, :4] = scale_coords(img[si].shape[1:], pred[:, :4],
shapes[si][0],
shapes[si][1]) # to original
for *xyxy, conf, cls in pred:
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
gn).view(-1).tolist() # normalized xywh
with open(txt_path + '.txt', 'a') as f:
f.write(
('%g ' * 5 + '\n') % (cls, *xywh)) # label format
# 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()):
print("score: ", round(p[4], 5), "bbox:",
[round(x, 3) for x in b])
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)
})
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()):
pass
#print("score: ", round(p[4], 5), "bbox:", [round(x, 3) for x in b])
# 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
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))
test_df = pd.DataFrame(results, columns=['image_id', 'PredictionString'])
test_df.to_csv('submission.csv', index=False)
def process_image(img, processing_model):
(model, names) = processing_model
# 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] # GIoU, obj, cls
# Run NMS
t = time_synchronized()
output = non_max_suppression(inf_out,
conf_thres=conf_thres,
iou_thres=iou_thres,
merge=merge)
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
txt_path = str(out / Path(paths[si]).stem)
pred[:, :4] = scale_coords(img[si].shape[1:], pred[:, :4],
shapes[si][0],
shapes[si][1]) # to original
for *xyxy, conf, cls in pred:
xywh = (xyxy2xywh(torch.tensor(xyxy).view(1, 4)) /
gn).view(-1).tolist() # normalized xywh
with open(txt_path + '.txt', 'a') as f:
f.write(('%g ' * 5 + '\n') % (cls, *xywh)) # label format
# 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
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 = Path(save_dir) / ('test_batch%g_gt.jpg' % batch_i) # filename
plot_images(img, targets, paths, str(f), names) # ground truth
f = Path(save_dir) / ('test_batch%g_pred.jpg' % batch_i)
plot_images(img, output_to_target(output, width, height), paths,
str(f), names) # predictions
return img
def test(
data,
weights=None,
batch_size=32,
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_hybrid=False, # for hybrid auto-labelling
save_conf=False, # save auto-label confidences
plots=True,
log_imgs=0): # number of logged images
# Initialize/load model and set device
# 判断是否在训练时调用test，如果是则获取训练时的设备
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)
# Directories
save_dir = Path(
increment_path(Path(opt.project) / opt.name,
exist_ok=opt.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
# 检查输入图片分辨率是否能被模型的最大步长（默认32）整除
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
# 如果设备不是cpu并且gpu数目为1，则将模型由Float32转为Float16，提高前向传播的速度
half = device.type != 'cpu' # half precision only supported on CUDA
if half:
model.half() # to FP16
# Configure
# 加载数据配置信息
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
# 设置iou阈值，从0.5~0.95，每间隔0.05取一次
iouv = torch.linspace(0.5, 0.95,
10).to(device) # iou vector for [email protected]:0.95
# iou个数
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:
# 创建一个全0数组测试一下前向传播是否正常运行
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
# 获取图片路径
path = data['test'] if opt.task == 'test' else data[
'val'] # path to val/test images
# 创建dataloader
# 注意这里rect参数为True，yolov5的测试评估是基于矩形推理的
dataloader = create_dataloader(
path,
imgsz,
batch_size,
model.stride.max(),
opt,
pad=0.5,
rect=True,
prefix=colorstr('test: ' if opt.task == 'test' else 'val: '))[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
# 获取类别的名字
names = {
k: v
for k, v in enumerate(
model.names if hasattr(model, 'names') else model.module.names)
}
"""
获取coco数据集的类别索引
这里要说明一下，coco数据集有80个类别(索引范围应该为0~79)，
但是他的索引却属于0~90(笔者是通过查看coco数据测试集的json文件发现的，具体原因不知)
coco80_to_coco91_class()就是为了与上述索引对应起来，返回一个范围在0~90的索引数组
"""
coco91class = coco80_to_coco91_class()
# 设置tqdm进度条的显示信息
s = ('%20s' + '%12s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R',
'[email protected]', '[email protected]:.95')
# 初始化指标，时间
p, r, f1, mp, mr, map50, map, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
# 初始化测试集的损失
loss = torch.zeros(3, device=device)
# 初始化json文件的字典，统计信息，ap
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)
# 图片也由Float32->Float16
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
"""
time_synchronized()函数里面进行了torch.cuda.synchronize()，再返回的time.time()
torch.cuda.synchronize()等待gpu上完成所有的工作
总的来说就是这样测试时间会更准确
"""
t = time_synchronized()
# inf_out为预测结果, train_out训练结果
inf_out, train_out = model(
img, augment=augment) # inference and training outputs
t0 += time_synchronized() - t
# Compute loss
# 如果是在训练时进行的test，则通过训练结果计算并返回测试集的GIoU, obj, cls损失
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()
"""
non_max_suppression进行非极大值抑制;
conf_thres为置信度阈值，iou_thres为iou阈值
merge为是否合并框
"""
output = non_max_suppression(inf_out,
conf_thres=conf_thres,
iou_thres=iou_thres,
labels=lb)
t1 += time_synchronized() - t
# Statistics per image
# 为每一张图片做统计, 写入预测信息到txt文件, 生成json文件字典, 统计tp等
for si, pred in enumerate(output):
# 获取第si张图片的标签信息, 包括class,x,y,w,h
# targets[:, 0]为标签属于哪一张图片的编号
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
# 获取标签类别
tcls = labels[:, 0].tolist() if nl else [] # target class
path = Path(paths[si])
# 统计测试图片数量
seen += 1
# 如果预测为空，则添加空的信息到stats里
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
# 保存预测结果为txt文件
if save_txt:
# 获得对应图片的长和宽
gn = torch.tensor(shapes[si][0])[[1, 0, 1, 0
]] # normalization gain whwh
for *xyxy, conf, cls in predn.tolist():
# xyxy格式->xywh, 并对坐标进行归一化处理
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
# 初始化预测评定，niou为iou阈值的个数
correct = torch.zeros(pred.shape[0],
niou,
dtype=torch.bool,
device=device)
if nl:
# detected用来存放已检测到的目标
detected = [] # target indices
tcls_tensor = labels[:, 0]
# target boxes
# 获得xyxy格式的框并乘以wh
tbox = xywh2xyxy(labels[:, 1:5])
# 将预测框的坐标调整到基于其原本长宽的坐标
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
# 对图片中的每个类单独处理
for cls in torch.unique(tcls_tensor):
# 标签框该类别的索引
ti = (cls == tcls_tensor).nonzero(as_tuple=False).view(
-1) # prediction indices
# 预测框该类别的索引
pi = (cls == pred[:, 5]).nonzero(as_tuple=False).view(
-1) # target indices
# Search for detections
if pi.shape[0]:
"""
Prediction to target ious
box_iou计算预测框与标签框的iou值，max(1)选出最大的ious值,i为对应的索引
pred shape[N, 4]
tbox shape[M, 4]
box_iou shape[N, M]
ious shape[N, 1]
i shape[N, 1], i里的值属于0~M
"""
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):
# 获得检测到的目标
d = ti[i[j]] # detected target
if d.item() not in detected_set:
detected_set.add(d.item())
detected.append(d)
# iouv为以0.05为步长 0.5到0.95的序列
# 获得不同iou阈值下的true positive
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)
# 每张图片的结果统计到stats里
stats.append(
(correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls))
# Plot images
# 画出第1个batch的图片的ground truth和预测框并保存
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
# 将stats列表的信息拼接到一起
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats) and stats[0].any():
# 根据上面得到的tp等信息计算指标
# 精准度TP/TP+FP，召回率TP/P，map，f1分数，类别
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是一个列表，测试集每个类别有多少个标签框
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 or (nc <= 20 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, 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)
# 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
# 采用之前保存的json格式预测结果，通过cocoapi评估指标
# 需要注意的是 测试集的标签也需要转成coco的json格式
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=32,
imgsz=640,
conf_thres=-1, # not for NMS
iou_thres=0.25, # not 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_hybrid=False, # for hybrid auto-labelling
save_conf=False, # save auto-label confidences
plots=True,
wandb_logger=None,
compute_loss=None,
half_precision=True, # dsv
is_coco=False,
max_by_class=True,
opt=None):
print_size, print_batches = 640, 3
log_errors = -1
if opt is not None:
print_size = opt.print_size
print_batches = opt.print_batches
max_by_class = opt.max_by_class
log_errors = opt.log_errors
ct = ast.literal_eval(opt.ct)
if len(ct) == 0:
ct = None
else:
ct = None
if print_batches < 0:
print_batches = 1000
# Initialize/load model and set device
training = model is not None
if training: # i.e. 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)
device = select_device(opt.device, batch_size=2)
# Directories
save_dir = increment_path(Path(opt.project) / opt.name, exist_ok=opt.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 img_size
# Half
half = device.type != 'cpu' and half_precision # half precision only supported on CUDA
if half:
model.half()
# Configure
model.eval()
if isinstance(data, str):
is_coco = data.endswith('coco.yaml')
with open(data) as f:
data = yaml.safe_load(f)
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
iouv = torch.arange(iou_thres, 1, 0.05).to(device) # iou_thres : 0.95 : 0.05
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 = opt.task if opt.task in ('train', 'val', 'test') else 'val' # path to train/val/test images
dataloader = create_dataloader(data[task], imgsz, batch_size, gs, opt, pad=0.5, rect=True, training=False,
prefix=colorstr(f'{task}: '))[0]
seen = 0
confusion_matrix = ConfusionMatrix(nc=nc)
names = data['names'] # names = {k: v for k, v in enumerate(model.names if hasattr(model, 'names') else model.module.names)}
names_dict = {k: v for k, v in enumerate(names)}
coco91class = coco80_to_coco91_class()
fmt = '%{}s'.format(2 + max([len(s) for s in names]))
s = (fmt + '%12s' * 7) % ('Class', 'Images', 'Targets', 'P', 'R', 'F1', '[email protected]', '[email protected]:.95')
p, r, f1, mp, mr, map50, map, t0, t1, mf1 = 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class, wandb_images = [], [], [], [], []
error_log = []
for batch_i, (imgs, targets, paths, shapes) in enumerate(dataloader):
imgs = imgs.to(device, non_blocking=True)
imgs = imgs.half() if half else imgs.float() # uint8 to fp16/32
imgs /= 255.0 # 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = imgs.shape # batch size, channels, height, width
# Run model
t = time_synchronized()
inf_out, train_out = model(imgs, augment=augment) # inference and training outputs
t0 += 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()
output = non_max_suppression(inf_out, labels=lb, multi_label=False, agnostic=True) # , conf_thres=conf_thres, iou_thres=iou_thres)
t1 += time_synchronized() - t
# pfunc(f'test_batch_size=={len(output)}')
# Statistics per image
idx = []
fn,fp = 0,0
for si, pred in enumerate(output):
labels = targets[targets[:, 0] == si, 1:]
# Dims of all target boxes (in pixels)
target_dims = targets[targets[:, 0] == si, -2:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
path = Path(paths[si])
seen += 1
if len(pred) == 0:
idx.append(None)
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue
# Filter out-of-frame predictions (in padding)
gain, img_box = shapes[si][1][0][0], None
if gain == 1:
img_box = torch.zeros([1, 4])
img_box[0, :2] = torch.FloatTensor(shapes[si][1][1])
img_box[0, 2:] = torch.FloatTensor(shapes[si][0]) + torch.FloatTensor(shapes[si][1][1])
img_box = img_box.to(device)
io2s = box_io2(img_box, pred[:, :4])
k = (io2s > 0.95).nonzero(as_tuple=True)[1]
pred = pred[k, :]
idx.append(k)
else:
idx.append(None)
# Predictions
if single_cls:
pred[:, 5] = 0
predn = pred.clone()
scale_coords(imgs[si].shape[1:], predn[:, :4], shapes[si][0], shapes[si][1]) # native-space pred
# Dims of all pred boxes (in pixels)
pred_target_dims = torch.zeros(pred.shape[0], 4, dtype=torch.float32, device=device)
pred_target_dims[:, :2] = pred[:, 2:4] - pred[:, :2]
# 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[int(cls)], conf),
"scores": {"class_score": conf},
"domain": "pixel"} for *xyxy, conf, cls in pred.tolist()]
boxes = {"predictions": {"box_data": box_data, "class_labels": names_dict}} # inference-space
wandb_images.append(wandb_logger.wandb.Image(imgs[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(imgs[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... index into target array....
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
k = pi[j] # index into pred array.....
pred_target_dims[k, 2:] = target_dims[d]
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))
# stats.append((correct.cpu(), pred[:, 4].cpu(), pred[:, 5].cpu(), tcls, pred_target_dims.cpu(), target_dims.cpu()))
# FP/FN counts (per image)
if log_errors > -1:
corr = correct.cpu().numpy()
idx_conf = pred[:, 4].cpu() > conf_thres
if idx_conf.cpu().numpy().mean() < 1:
corr = corr[idx_conf]
tp = corr[:, 0].sum()
fp, fn = len(corr) - tp, len(tcls) - tp
if fp + fn > log_errors:
error_log.append(path.stem)
# Plot images
if plots and batch_i < print_batches:
prefix = Path(paths[0]).stem if batch_size == 1 else f'test_batch{batch_i}'
f1 = save_dir / f'{prefix}_labels.jpg' # labels
thread1 = Thread(target=plot_images, args=(imgs, targets, paths, f1, names, print_size), daemon=True)
f2 = save_dir / f'{prefix}_pred.jpg' # predictions
thread2 = Thread(target=plot_images, args=(imgs, output_to_target(output, idx), paths, f2, names, print_size), daemon=True)
thread1.start()
thread1.join()
thread2.start()
thread2.join()
##################################
# show FP and FN detections...
if log_errors > -1 and batch_size == 1:
# fn boxes...
if fn > 0:
idx_fn = np.ones(len(targets))
for d in detected:
idx_fn[d.item()] = 0
idx_fn = np.nonzero(idx_fn)[0]
f = save_dir / f'{prefix}_fn.jpg' # labels
thread = Thread(target=plot_images, args=(imgs, targets[idx_fn], paths, f, names, print_size, 16, True), daemon=True)
thread.start()
thread.join()
# fp boxes...
if fp > 0:
idx_fp = ~corr[:, 0]
f = save_dir / f'{prefix}_fp.jpg' # labels
thread = Thread(target=plot_images, args=(imgs, output_to_target(output, idx, idx_fp, idx_conf), paths, f, names, print_size, 16, True), daemon=True)
thread.start()
thread.join()
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
conf_best = -1
if len(stats) and stats[0].any():
mp, mr, map50, map, mf1, ap_class, conf_best, nt, (p, r, ap50, ap, f1, cc) = ap_per_class(*stats, plot=plots, save_dir=save_dir, names=names,
ct=ct, max_by_class=max_by_class, conf_thres=conf_thres)
else:
nt = torch.zeros(1)
# Print results
pfunc('------------------------------------------- Validation Set -----------------------------------------------')
pfunc(s)
pf = fmt + '%12.3g' * 7 # print format
# Print results per class
if (verbose or nc < 50) and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
pfunc(pf % (names[c], seen, nt[c], p[i], r[i], f1[i], ap50[i], ap[i])) # log
# Print averages
if nc > 1:
pfunc('')
pfunc(pf % ('AVG', seen, nt.sum(), p.mean(), r.mean(), f1.mean(), ap50.mean(), ap.mean())) # unweighted average
ss = 'WEIGHTED AVG' if nc > 1 else names[0]
pfunc(pf % (ss, seen, nt.sum(), mp, mr, mf1, map50, map)) # weighted average (if nc>1)
if conf_best > -1:
pfunc('\nOptimal Confidence Threshold: {0:0.3f}'.format(conf_best)) # log
if max_by_class:
pfunc('Optimal Confidence Thresholds (Per-Class): {}'.format(list(cc.round(3)))) # log
# 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)
# Plots
if plots:
confusion_matrix.plot(save_dir=save_dir, names=names)
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
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}")
# if len(error_log)>0:
# fn = f'{save_dir}/error_log.txt'
# save_list(error_log, fn)
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, mf1, 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,
log_imgs=0): # number of logged images
# 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
# Directories
save_dir = Path(increment_path(Path(opt.project) / opt.name, exist_ok=opt.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
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()
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, 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, pad=0.5, rect=True)[0]
seen = 0
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.
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
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
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]) * whwh
scale_coords(img[si].shape[1:], tbox, shapes[si][0], shapes[si][1]) # native-space labels
# 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' # filename
plot_images(img, targets, paths, f, names) # labels
f = save_dir / f'test_batch{batch_i}_pred.jpg'
plot_images(img, output_to_target(output, width, height), paths, 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, 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)
# W&B logging
if plots and wandb and wandb.run:
wandb.log({"Images": wandb_images})
wandb.log({"Validation": [wandb.Image(str(x), caption=x.name) for x in sorted(save_dir.glob('test*.jpg'))]})
# 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
anno_json = glob.glob('../coco/annotations/instances_val*.json')[0] # 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('ERROR: pycocotools unable to run: %s' % e)
# Return results
if not training:
print('Results saved to %s' % save_dir)
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
single_cls=False,
augment=False,
verbose=False,
model=None,
dataloader=None,
save_dir=Path(''), # for saving images
save_txt=False, # for auto-labelling
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
if save_txt:
out = Path('inference/output')
if os.path.exists(out):
shutil.rmtree(out) # delete output folder
os.makedirs(out) # make new output folder
# Remove previous
for f in glob.glob(str(save_dir / 'test_batch*.jpg')):
os.remove(f)
# 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
with open(str(out / Path(paths[si]).stem) + '.txt',
'a') as f:
f.write(
('%g ' * 5 + '\n') % (cls, *xywh)) # label format
# 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) # 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 batch_i < 1:
f = save_dir / ('test_batch%g_gt.jpg' % batch_i) # filename
plot_images(img, targets, paths, str(f), names) # ground truth
f = save_dir / ('test_batch%g_pred.jpg' % batch_i)
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=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)
# 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
