import argparse
import json
from torch.utils.data import DataLoader
from models import *
from utils.datasets import *
from utils.utils import *
def test(cfg,
data,
weights=None,
batch_size=16,
img_size=416,
conf_thres=0.001,
iou_thres=0.6, # for nms
save_json=False,
single_cls=False,
augment=False,
model=None,
dataloader=None):
# Initialize/load model and set device
if model is None:
device = torch_utils.select_device(opt.device, batch_size=batch_size)
verbose = opt.task == 'test'
# Remove previous
for f in glob.glob('test_batch*.png'):
os.remove(f)
# Initialize model
model = Darknet(cfg, img_size)
# Load weights
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
model.load_state_dict(torch.load(weights, map_location=device)['model'])
else: # darknet format
load_darknet_weights(model, weights)
# Fuse
model.fuse()
model.to(device)
if device.type != 'cpu' and torch.cuda.device_count() > 1:
model = nn.DataParallel(model)
else: # called by train.py
device = next(model.parameters()).device # get model device
verbose = False
# Configure run
data = parse_data_cfg(data)
nc = 1 if single_cls else int(data['classes']) # number of classes
path = data['valid'] # path to test images
names = load_classes(data['names']) # class names
iouv = torch.linspace(0.5, 0.95, 10).to(device) # iou vector for mAP@0.5:0.95
iouv = iouv[0].view(1) # comment for mAP@0.5:0.95
niou = iouv.numel()
# Dataloader
if dataloader is None:
dataset = LoadImagesAndLabels(path, img_size, batch_size, rect=True, single_cls=opt.single_cls)
batch_size = min(batch_size, len(dataset))
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]),
pin_memory=True,
collate_fn=dataset.collate_fn)
seen = 0
model.eval()
coco91class = coco80_to_coco91_class()
s = ('%20s' + '%10s' * 6) % ('Class', 'Images', 'Targets', 'P', 'R', 'mAP@0.5', 'F1')
p, r, f1, mp, mr, map, mf1, t0, t1 = 0., 0., 0., 0., 0., 0., 0., 0., 0.
loss = torch.zeros(3, device=device)
jdict, stats, ap, ap_class = [], [], [], []
for batch_i, (imgs, targets, paths, shapes) in enumerate(tqdm(dataloader, desc=s)):
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
targets = targets.to(device)
nb, _, height, width = imgs.shape # batch size, channels, height, width
whwh = torch.Tensor([width, height, width, height]).to(device)
# Plot images with bounding boxes
f = 'test_batch%g.png' % batch_i # filename
if batch_i < 1 and not os.path.exists(f):
plot_images(imgs=imgs, targets=targets, paths=paths, fname=f)
# Disable gradients
with torch.no_grad():
# Augment images
if augment: # https://github.com/ultralytics/yolov3/issues/931
imgs = torch.cat((imgs,
torch_utils.scale_img(imgs.flip(3), 0.9), # flip-lr and scale
torch_utils.scale_img(imgs, 0.7), # scale
), 0)
# Run model
t = torch_utils.time_synchronized()
inf_out, train_out = model(imgs) # inference and training outputs
t0 += torch_utils.time_synchronized() - t
# De-augment results
if augment:
x = torch.split(inf_out, nb, dim=0)
x[1][..., :4] /= 0.9 # scale
x[1][..., 0] = width - x[1][..., 0] # flip lr
x[2][..., :4] /= 0.7 # scale
inf_out = torch.cat(x, 1)
# Compute loss
if hasattr(model, 'hyp'): # if model has loss hyperparameters
loss += compute_loss(train_out, targets, model)[1][:3] # GIoU, obj, cls
# Run NMS
t = torch_utils.time_synchronized()
output = non_max_suppression(inf_out, conf_thres=conf_thres, iou_thres=iou_thres) # nms
t1 += torch_utils.time_synchronized() - t
# Statistics per image
for si, pred in enumerate(output):
labels = targets[targets[:, 0] == si, 1:]
nl = len(labels)
tcls = labels[:, 0].tolist() if nl else [] # target class
seen += 1
if pred is None:
if nl:
stats.append((torch.zeros(0, niou, dtype=torch.bool), torch.Tensor(), torch.Tensor(), tcls))
continue
# Append to text file
# with open('test.txt', 'a') as file:
# [file.write('%11.5g' * 7 % tuple(x) + '\n') for x in pred]
# Clip boxes to image bounds
clip_coords(pred, (height, width))
# 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(paths[si]).stem.split('_')[-1])
box = pred[:, :4].clone() # xyxy
scale_coords(imgs[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])],
'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().view(-1) # prediction indices
pi = (cls == pred[:, 5]).nonzero().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():
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))
# Compute statistics
stats = [np.concatenate(x, 0) for x in zip(*stats)] # to numpy
if len(stats):
p, r, ap, f1, ap_class = ap_per_class(*stats)
if niou > 1:
p, r, ap, f1 = p[:, 0], r[:, 0], ap.mean(1), ap[:, 0] # [P, R, AP@0.5:0.95, AP@0.5]
mp, mr, map, mf1 = p.mean(), r.mean(), ap.mean(), f1.mean()
nt = np.bincount(stats[3].astype(np.int64), minlength=nc) # number of targets per class
else:
nt = torch.zeros(1)
# Print results
pf = '%20s' + '%10.3g' * 6 # print format
print(pf % ('all', seen, nt.sum(), mp, mr, map, mf1))
# Print results per class
if verbose and nc > 1 and len(stats):
for i, c in enumerate(ap_class):
print(pf % (names[c], seen, nt[c], p[i], r[i], ap[i], f1[i]))
# Print speeds
if verbose:
t = tuple(x / seen * 1E3 for x in (t0, t1, t0 + t1)) + (img_size, img_size, batch_size) # tuple
print('Speed: %.1f/%.1f/%.1f ms inference/NMS/total per %gx%g image at batch-size %g' % t)
# Save JSON
if save_json and map and len(jdict):
print('\nCOCO mAP with pycocotools...')
imgIds = [int(Path(x).stem.split('_')[-1]) for x in dataloader.dataset.img_files]
with open('results.json', 'w') as file:
json.dump(jdict, file)
try:
from pycocotools.coco import COCO
from pycocotools.cocoeval import COCOeval
except:
print('WARNING: missing pycocotools package, can not compute official COCO mAP. See requirements.txt.')
# https://github.com/cocodataset/cocoapi/blob/master/PythonAPI/pycocoEvalDemo.ipynb
cocoGt = COCO(glob.glob('../coco/annotations/instances_val*.json')[0]) # initialize COCO ground truth api
cocoDt = cocoGt.loadRes('results.json') # initialize COCO pred api
cocoEval = COCOeval(cocoGt, cocoDt, 'bbox')
cocoEval.params.imgIds = imgIds # [:32] # only evaluate these images
cocoEval.evaluate()
cocoEval.accumulate()
cocoEval.summarize()
mf1, map = cocoEval.stats[:2] # update to pycocotools results (mAP@0.5:0.95, mAP@0.5)
# Return results
maps = np.zeros(nc) + map
for i, c in enumerate(ap_class):
maps[c] = ap[i]
return (mp, mr, map, mf1, *(loss.cpu() / len(dataloader)).tolist()), maps
if __name__ == '__main__':
parser = argparse.ArgumentParser(prog='test.py')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
parser.add_argument('--data', type=str, default='data/coco2014.data', help='*.data path')
parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='weights path')
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--conf-thres', type=float, default=0.001, help='object confidence threshold')
parser.add_argument('--iou-thres', type=float, default=0.6, help='IOU threshold for NMS')
parser.add_argument('--save-json', action='store_true', help='save a cocoapi-compatible JSON results file')
parser.add_argument('--task', default='test', help="'test', 'study', 'benchmark'")
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1) or cpu')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--augment', action='store_true', help='augmented testing')
opt = parser.parse_args()
opt.save_json = opt.save_json or any([x in opt.data for x in ['coco.data', 'coco2014.data', 'coco2017.data']])
print(opt)
# task = 'test', 'study', 'benchmark'
if opt.task == 'test': # (default) test normally
test(opt.cfg,
opt.data,
opt.weights,
opt.batch_size,
opt.img_size,
opt.conf_thres,
opt.iou_thres,
opt.save_json,
opt.single_cls,
opt.augment)
elif opt.task == 'benchmark': # mAPs at 320-608 at conf 0.5 and 0.7
y = []
for i in [320, 416, 512, 608]: # img-size
for j in [0.5, 0.7]: # iou-thres
t = time.time()
r = test(opt.cfg, opt.data, opt.weights, opt.batch_size, i, opt.conf_thres, j, opt.save_json)[0]
y.append(r + (time.time() - t,))
np.savetxt('benchmark.txt', y, fmt='%10.4g') # y = np.loadtxt('study.txt')
elif opt.task == 'study': # Parameter study
y = []
x = np.arange(0.4, 0.9, 0.05) # iou-thres
for i in x:
t = time.time()
r = test(opt.cfg, opt.data, opt.weights, opt.batch_size, opt.img_size, opt.conf_thres, i, opt.save_json)[0]
y.append(r + (time.time() - t,))
np.savetxt('study.txt', y, fmt='%10.4g') # y = np.loadtxt('study.txt')
# Plot
fig, ax = plt.subplots(3, 1, figsize=(6, 6))
y = np.stack(y, 0)
ax[0].plot(x, y[:, 2], marker='.', label='mAP@0.5')
ax[0].set_ylabel('mAP')
ax[1].plot(x, y[:, 3], marker='.', label='mAP@0.5:0.95')
ax[1].set_ylabel('mAP')
ax[2].plot(x, y[:, -1], marker='.', label='time')
ax[2].set_ylabel('time (s)')
for i in range(3):
ax[i].legend()
ax[i].set_xlabel('iou_thr')
fig.tight_layout()
plt.savefig('study.jpg', dpi=200)