def __init__(self, cfg, data, weights, output, img_size = 416):
# Initialize
self.device = torch_utils.select_device(force_cpu=ONNX_EXPORT)
torch.backends.cudnn.benchmark = False # set False for reproducible results
# Create output dir if doesn't exist
if not os.path.exists(output):
os.makedirs(output) # make new output folder
# Initialize model
self.model = Darknet(cfg, img_size)
# Load weights
_ = load_darknet_weights(self.model, weights)
# Eval mode
self.model.to(self.device).eval()
# Get classes and colors
self.classes = load_classes(parse_data_cfg(data)['names'])
self.colors = [[random.randint(0, 255) for _ in range(3)] for _ in range(len(self.classes))]
def launch_inferece(args):
device = select_device(args.device)
data_cfg = load_data_cfg(args.data_cfg)
model = build_model(args.model_cfg,
args.model_weights,
nc=data_cfg['nc'],
eval=True,
device=device)
dataloader = LoadImages(args.input, img_size=args.input_size)
visualizer = Visualizer()
check_initialization(model, args.input_size, device, args.half)
for image_path, input_image, target_image, _ in tqdm(
dataloader, desc='>>', total=len(dataloader)):
# image to tensor
input_image = torch.from_numpy(input_image).to(device)
input_image = input_image.half() if args.half else input_image.float()
input_image /= 255.0
if input_image.ndimension() == 3:
input_image = input_image.unsqueeze(0)
# forward pass
pred = model(input_image, augment=args.augment)[0]
# non-maximum suppression
pred = non_max_suppression(pred,
args.conf_thres,
args.iou_thres,
classes=args.classes,
agnostic=args.agnostic_nms,
max_det=args.max_det)
# process detections
detections = []
for det in pred:
if det is not None and len(det):
# rescale boxes to target_image size
det[:, :4] = scale_coords(input_image.shape[2:], det[:, :4],
target_image.shape).round()
# formulate detections in schema
for *xyxy, conf, class_id in det:
xyxy = torch.tensor(xyxy).view(-1).tolist()
xywh = xyxy[0], xyxy[
1], xyxy[2] - xyxy[0], xyxy[3] - xyxy[1]
score = int((conf.item() * 10**2) + 0.5) / 10**2
category_id = int(class_id.item())
detection = {
'bbox': xywh,
'score': score,
'category_id': category_id,
'segmentation': [[]],
'label': data_cfg['names'][category_id]
}
detections.append(detection)
# plot detections
save_as = os.path.join(args.output, os.path.basename(image_path))
visualizer(image_path,
detections,
exclude=['labels', 'scores'],
save_as=save_as)
def test(
data,
model=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,
dataloader=None,
save_dir='./results',
merge=False,
save_txt=False):
os.makedirs(save_dir, exist_ok=True)
with open(data) as f:
data = yaml.load(f, Loader=yaml.FullLoader)
# 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(f'{save_dir}/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)
model = build_model(args.cfg,
weights,
nc=int(data_dict['nc']),
device=device)
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()
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 dataloader:
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 = data['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 = [], [], [], []
#model = model.to(memory_format=torch.channels_last)
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
#inf_out, train_out = model(img.to(memory_format=torch.channels_last), augment=augment) # inference and training outputs
t0 += time_synchronized() - t
# Compute loss
if not dataloader: # (HACK for 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
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 = os.path.join(save_dir, 'coco_instances_results.json') # filename
print('\nCOCO mAP with pycocotools... saving %s...' % f)
with open(f, 'w') as file:
json.dump(jdict, file, indent=4)
if opt.gt_coco_file:
imgIds = [int(Path(x).stem) for x in dataloader.dataset.img_files]
cocoGt = COCO(opt.gt_coco_file) # 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])
# 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
parser.add_argument('--gt-coco-file',
default=None,
help='Ground-truth COCO annotations')
parser.add_argument('--save-txt',
default=False,
help='save results to *.txt')
parser.add_argument('--save-json',
default=True,
help='save results to *.json COCO format')
parser.add_argument('--no-verbose',
action='store_false',
help='Not to report mAP by class')
opt = parser.parse_args()
if opt.task in ['val', 'test']: # run normally
device = select_device(opt.device)
data_cfg = load_data_cfg(opt.data_cfg)
model = build_model(opt.model_cfg,
opt.model_weights,
nc=int(data_cfg['nc']),
device=device)
test(opt.data_cfg, model, opt.batch_size, opt.img_size, opt.conf_thres,
opt.iou_thres, opt.save_json, opt.single_cls, opt.augment,
not opt.no_verbose)
elif opt.task == 'study': # run over a range of settings and save/plot
for weights in ['']:
f = 'study_%s_%s.txt' % (Path(opt.data).stem, Path(weights).stem
) # filename to save to
x = list(range(352, 832, 64)) # x axis
if last and not opt.model_weights:
print(f'Resuming training from {last}')
opt.weights = last if opt.resume and not opt.model_weights else opt.model_weights
opt.hyp = opt.train_cfg or ('./configs/hyp.finetune.yaml'
if opt.model_weights else
'./configs/hyp.scratch.yaml')
opt.data, opt.cfg, opt.hyp = check_file(opt.data_cfg), check_file(
opt.model_cfg), check_file(opt.hyp) # check files
assert len(opt.cfg) or len(
opt.weights), 'either --cfg or --weights must be specified'
opt.img_size.extend(
[opt.img_size[-1]] *
(2 - len(opt.img_size))) # extend to 2 sizes (train, test)
device = select_device(opt.device, batch_size=opt.batch_size)
opt.total_batch_size = opt.batch_size
opt.world_size = 1
opt.global_rank = -1
# DDP mode
if opt.local_rank != -1:
assert torch.cuda.device_count() > opt.local_rank
torch.cuda.set_device(opt.local_rank)
device = torch.device('cuda', opt.local_rank)
dist.init_process_group(backend='nccl',
init_method='env://') # distributed backend
opt.world_size = dist.get_world_size()
opt.global_rank = dist.get_rank()
assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
opt.batch_size = opt.total_batch_size // opt.world_size
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,
metrics_path=None,
compute_eer=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
render_dir = save_dir / 'render'
render_dir.mkdir(exist_ok=True)
# 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=64) # check img_size
# 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,
64,
opt,
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)
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 = [], [], [], [], []
error_cases = {}
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 len(pred) == 0:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool),
torch.Tensor(), torch.Tensor(), tcls))
continue
# Append to text file
path = Path(paths[si])
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, *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
}
}
wandb_images.append(
wandb.Image(img[si], boxes=boxes, caption=path.name))
# 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 = int(
path.stem) if path.stem.isnumeric() else path.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':
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
# 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)
_correct, _conf, _pcls, _tcls = (correct.cpu(), pred[:, 4].cpu(),
pred[:, 5].cpu(), tcls)
if not training and opt.save_errors:
# Save error cases for 50% iou and 0.5 confidence
valid_preds = _conf > 0.2
n_correct = _correct[valid_preds, 0].sum().item()
n_fp = valid_preds.sum().item() - n_correct
n_fn = len(tcls) - n_correct
if n_fp > 0 or n_fn > 0:
error_cases[paths[si]] = (n_fp, n_fn)
f = render_dir / Path(paths[si]).name
plot_images(img[[si]],
output_to_target([output[si]], width, height),
[f'#FP:{n_fp}, #FN:{n_fn}'], str(f),
names) # predictions
stats.append((_correct, _conf, _pcls, _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
if not training and opt.save_errors:
error_path = Path(save_dir) / 'error_cases.csv'
print(f'Saving error cases to {error_path}')
total_fp = 0
total_fn = 0
with open(error_path, 'w') as f:
f.write('path,num_false_pos,num_false_neg\n')
for path, (nfp, nfn) in error_cases.items():
f.write(f'{path},{nfp},{nfn}\n')
total_fp += nfp
total_fn += nfn
print(f'Got {total_fp} false positives and {total_fn} false negatives')
# 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, eer_precision, eer_recall, threshold = ap_per_class(
*stats,
plot=plots,
fname=save_dir / 'precision-recall_curve.png',
compute_eer=compute_eer)
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
if metrics_path is not None:
metrics_path = Path(metrics_path)
#output to json for dvc metrics
data = {
"equal_error_rate": {
"precision": eer_precision.mean(),
"recall": eer_recall.mean(),
"threshold": threshold.mean()
},
"average_precision": map50
}
metrics_path.parent.mkdir(exist_ok=True, parents=True)
with open(metrics_path, 'w+') as f:
json.dump(data, f, ensure_ascii=False)
else:
nt = torch.zeros(1)
# W&B logging
if plots and wandb:
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