img += mean
return img
CanChinese = False
for i, sample in enumerate(previewed):
# if i > 30:
# break
utils.progress_bar(i, len(previewed), 'Handling...')
if opt.debug:
ipdb.set_trace()
image = sample['input'][0].detach().cpu().numpy().transpose([1, 2, 0])
# image = (image.copy()
image = (denormalize(image, max_pixel_value=1.0) * 255).astype(
np.uint8).copy()
label = sample['label'][0].item()
if CanChinese:
name = names[str(label)]
image = cv2ImgAddText(image, name, 7, 3, (255, 0, 0), textSize=24)
else:
cv2.putText(image, 'label: ' + str(label), (10, 30), 0, 1, (255, 0, 0),
2)
write_image(writer, f'preview_{opt.dataset}/{i}', '0_input', image, 0,
'HWC')
writer.flush()
def eval_mAP(self, dataloader, epoch, writer, logger, data_name='val'):
# eval_yolo(self.detector, dataloader, epoch, writer, logger, dataname=data_name)
pred_bboxes = []
pred_labels = []
pred_scores = []
gt_bboxes = []
gt_labels = []
gt_difficults = []
with torch.no_grad():
for i, sample in enumerate(dataloader):
utils.progress_bar(i, len(dataloader), 'Eva... ')
image = sample['image'].to(opt.device)
gt_bbox = sample['bboxes']
labels = sample['labels']
paths = sample['path']
batch_bboxes, batch_labels, batch_scores = self.forward(image)
pred_bboxes.extend(batch_bboxes)
pred_labels.extend(batch_labels)
pred_scores.extend(batch_scores)
for b in range(len(gt_bbox)):
gt_bboxes.append(gt_bbox[b].detach().cpu().numpy())
gt_labels.append(labels[b].int().detach().cpu().numpy())
gt_difficults.append(np.array([False] * len(gt_bbox[b])))
if opt.vis: # 可视化预测结果
img = tensor2im(image).copy()
# for x1, y1, x2, y2 in gt_bbox[0]:
# cv2.rectangle(img, (x1,y1), (x2,y2), (0, 255, 0), 2) # 绿色的是gt
num = len(batch_scores[0])
visualize_boxes(image=img,
boxes=batch_bboxes[0],
labels=batch_labels[0].astype(np.int32),
probs=batch_scores[0],
class_labels=opt.class_names)
write_image(writer, f'{data_name}/{i}', 'image', img,
epoch, 'HWC')
result = []
for iou_thresh in [0.5, 0.55, 0.6, 0.65, 0.7, 0.75]:
AP = eval_detection_voc(pred_bboxes,
pred_labels,
pred_scores,
gt_bboxes,
gt_labels,
gt_difficults=None,
iou_thresh=iou_thresh,
use_07_metric=False)
APs = AP['ap']
mAP = AP['map']
result.append(mAP)
logger.info(
f'Eva({data_name}) epoch {epoch}, IoU: {iou_thresh}, APs: {str(APs[:opt.num_classes])}, mAP: {mAP}'
)
write_loss(writer, f'val/{data_name}', 'mAP', mAP, epoch)
logger.info(
f'Eva({data_name}) epoch {epoch}, mean of (AP50-AP75): {sum(result)/len(result)}'
)
# class_names = config.DATA.CLASS_NAMES
class_names = config.DATA.CLASS_NAMES
preview = train_dataloader # train_dataloader, val_dataloader
for i, sample in enumerate(preview):
# if i > 30:
# break
utils.progress_bar(i, len(preview), 'Handling...')
if opt.debug:
ipdb.set_trace()
image = sample['image'][0].detach().cpu().numpy().transpose([1, 2, 0])
image = (image.copy() * 255).astype(np.uint8)
bboxes = sample['bboxes'][0].cpu().numpy()
labels = sample['labels'][0].cpu().numpy().astype(np.int32)
visualize_boxes(image=image,
boxes=bboxes,
labels=labels,
probs=np.array(
np.random.randint(100, 101, size=[len(bboxes)]) / 100),
class_labels=class_names)
write_image(writer, f'preview_{config.DATA.DATASET}/{i}', 'image', image,
0, 'HWC')
writer.flush()
