def val(args,eval_data_loader, model):
model.eval()
batch_time = AverageMeter()
dice = AverageMeter()
end = time.time()
dice_list = []
Dice_1 = AverageMeter()
Dice_2 = AverageMeter()
Dice_3 = AverageMeter()
Dice_4 = AverageMeter()
Dice_5 = AverageMeter()
Dice_6 = AverageMeter()
Dice_7 = AverageMeter()
Dice_8 = AverageMeter()
Dice_9 = AverageMeter()
ret_segmentation = []
for iter, (image, label, _) in enumerate(eval_data_loader):
# batchsize = 1 ,so squeeze dim 1
image = image.squeeze(dim=0)
label = label.squeeze(dim=0)
target_seg = label.numpy()
#target_cls = target_seg2target_cls(target_seg)
with torch.no_grad():
# batch test for memory reduce
batch = 1
pred_seg = torch.zeros(image.shape[0],image.shape[2],image.shape[3])
pred_cls = torch.zeros(image.shape[0],9)
for i in range(0,image.shape[0],batch):
start_id = i
end_id = i + batch
if end_id > image.shape[0]:
end_id = image.shape[0]
image_batch = image[start_id:end_id,:,:,:]
image_var = Variable(image_batch).cuda()
# model forward
output_seg = model(image_var)
_, pred_batch = torch.max(output_seg, 1)
pred_seg[start_id:end_id,:,:] = pred_batch.cpu().data
#pred_cls[start_id:end_id,:] = output_cls.cpu().data
# merice dice for seg
pred_seg = pred_seg.numpy().astype('uint8')
batch_time.update(time.time() - end)
label_seg = label.numpy().astype('uint8')
ret = aic_fundus_lesion_segmentation(label_seg,pred_seg)
ret_segmentation.append(ret)
dice_score = compute_single_segment_score(ret)
dice_list.append(dice_score)
dice.update(dice_score)
Dice_1.update(ret[1])
Dice_2.update(ret[2])
Dice_3.update(ret[3])
Dice_4.update(ret[4])
Dice_5.update(ret[5])
Dice_6.update(ret[6])
Dice_7.update(ret[7])
Dice_8.update(ret[8])
Dice_9.update(ret[9])
# metric auc for cls
#ground_truth = target_cls.numpy().astype('float32')
#prediction = pred_cls.numpy().astype('float32')
#if iter == 0:
# detection_ref_all = ground_truth
# detection_pre_all = prediction
#else:
# detection_ref_all = np.concatenate((detection_ref_all, ground_truth), axis=0)
# detection_pre_all = np.concatenate((detection_pre_all, prediction), axis=0)
end = time.time()
logger_vis.info('Eval: [{0}/{1}]\t'
'Dice {dice.val:.3f} ({dice.avg:.3f})\t'
'Dice_1 {dice_1.val:.3f} ({dice_1.avg:.3f})\t'
'Dice_2 {dice_2.val:.3f} ({dice_2.avg:.3f})\t'
'Dice_3 {dice_3.val:.3f} ({dice_3.avg:.3f})\t'
'Dice_4 {dice_4.val:.6f} ({dice_4.avg:.4f})\t'
'Dice_5 {dice_5.val:.6f} ({dice_5.avg:.4f})\t'
'Dice_6 {dice_6.val:.6f} ({dice_6.avg:.4f})\t'
'Dice_7 {dice_7.val:.6f} ({dice_7.avg:.4f})\t'
'Dice_8 {dice_8.val:.6f} ({dice_8.avg:.4f})\t'
'Dice_9 {dice_9.val:.6f} ({dice_9.avg:.4f})\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
.format(iter, len(eval_data_loader), dice = dice,dice_1 = Dice_1,dice_2 = Dice_2,dice_3 = Dice_3,dice_4=Dice_4,dice_5=Dice_5,dice_6=Dice_6,dice_7=Dice_7,dice_8=Dice_8,dice_9=Dice_9,batch_time=batch_time))
# compute average dice for seg
final_seg,seg_1,seg_2,seg_3,seg_4,seg_5,seg_6,seg_7,seg_8,seg_9, = compute_segment_score(ret_segmentation)
print('### Seg ###')
print('Final Seg Score:{}'.format(final_seg))
print('Final Seg_1 Score:{}'.format(seg_1))
print('Final Seg_2 Score:{}'.format(seg_2))
print('Final Seg_3 Score:{}'.format(seg_3))
print('Final Seg_4 Score:{}'.format(seg_4))
print('Final Seg_5 Score:{}'.format(seg_5))
print('Final Seg_6 Score:{}'.format(seg_6))
print('Final Seg_7 Score:{}'.format(seg_7))
print('Final Seg_8 Score:{}'.format(seg_8))
print('Final Seg_9 Score:{}'.format(seg_9))
# compute average auc for cls
#ret_detection = aic_fundus_lesion_classification(detection_ref_all, detection_pre_all, num_samples=len(eval_data_loader)*56) ###############
#print(ret_detection)
#auc = np.array(ret_detection).mean()
#print('AUC :',auc)
#auc_1 = ret_detection[0]
#auc_2 = ret_detection[1]
#auc_3 = ret_detection[2]
#auc_4 = ret_detection[3]
#auc_5 = ret_detection[4]
#auc_6 = ret_detection[5]
#auc_7 = ret_detection[6]
#auc_8 = ret_detection[7]
#auc_9 = ret_detection[8]
return final_seg,seg_1,seg_2,seg_3,seg_4,seg_5,seg_6,seg_7,seg_8,seg_9,dice_list
def val(args,eval_data_loader, model):
model.eval()
batch_time = AverageMeter()
dice = AverageMeter()
end = time.time()
dice_list = []
Dice_1 = AverageMeter()
Dice_2 = AverageMeter()
Dice_3 = AverageMeter()
ret_segmentation = []
for iter, (image, label, _) in enumerate(eval_data_loader):
# batchsize = 1 ,so squeeze dim 1
image = image.squeeze(dim=0)
label = label.squeeze(dim=0)
target_seg = label.numpy()
target_cls = target_seg2target_cls(target_seg)
with torch.no_grad():
# batch test for memory reduce
batch = 16
pred_seg = torch.zeros(image.shape[0],image.shape[2],image.shape[3])
pred_cls = torch.zeros(image.shape[0],3)
for i in range(0,image.shape[0],batch):
start_id = i
end_id = i + batch
if end_id > image.shape[0]:
end_id = image.shape[0]
image_batch = image[start_id:end_id,:,:,:]
image_var = Variable(image_batch).cuda()
# model forward
output_seg,output_cls = model(image_var)
_, pred_batch = torch.max(output_seg, 1)
pred_seg[start_id:end_id,:,:] = pred_batch.cpu().data
pred_cls[start_id:end_id,:] = output_cls.cpu().data
# merice dice for seg
pred_seg = pred_seg.numpy().astype('uint8')
batch_time.update(time.time() - end)
label_seg = label.numpy().astype('uint8')
ret = aic_fundus_lesion_segmentation(label_seg,pred_seg)
ret_segmentation.append(ret)
dice_score = compute_single_segment_score(ret)
dice_list.append(dice_score)
dice.update(dice_score)
Dice_1.update(ret[1])
Dice_2.update(ret[2])
Dice_3.update(ret[3])
# metric auc for cls
ground_truth = target_cls.numpy().astype('float32')
prediction = pred_cls.numpy().astype('float32')
if iter == 0:
detection_ref_all = ground_truth
detection_pre_all = prediction
else:
detection_ref_all = np.concatenate((detection_ref_all, ground_truth), axis=0)
detection_pre_all = np.concatenate((detection_pre_all, prediction), axis=0)
end = time.time()
logger_vis.info('Eval: [{0}/{1}]\t'
'Dice {dice.val:.3f} ({dice.avg:.3f})\t'
'Dice_1 {dice_1.val:.3f} ({dice_1.avg:.3f})\t'
'Dice_2 {dice_2.val:.3f} ({dice_2.avg:.3f})\t'
'Dice_3 {dice_3.val:.3f} ({dice_3.avg:.3f})\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'
.format(iter, len(eval_data_loader), dice = dice,dice_1 = Dice_1,dice_2 = Dice_2,dice_3 = Dice_3,batch_time=batch_time))
# compute average dice for seg
final_seg,seg_1,seg_2,seg_3 = compute_segment_score(ret_segmentation)
print('### Seg ###')
print('Final Seg Score:{}'.format(final_seg))
print('Final Seg_1 Score:{}'.format(seg_1))
print('Final Seg_2 Score:{}'.format(seg_2))
print('Final Seg_3 Score:{}'.format(seg_3))
# compute average auc for cls
ret_detection = aic_fundus_lesion_classification(detection_ref_all, detection_pre_all, num_samples=len(eval_data_loader)*128)
auc = np.array(ret_detection).mean()
print('AUC :',auc)
auc_1 = ret_detection[0]
auc_2 = ret_detection[1]
auc_3 = ret_detection[2]
return final_seg,seg_1,seg_2,seg_3,dice_list,auc,auc_1,auc_2,auc_3
def eval(args, eval_data_loader, model, result_path, logger):
model.eval()
batch_time = AverageMeter()
dice = AverageMeter()
end = time.time()
dice_list = []
Dice_1 = AverageMeter()
Dice_2 = AverageMeter()
Dice_3 = AverageMeter()
ret_segmentation = []
for iter, (image, label, imt) in enumerate(eval_data_loader):
# if iter > 1:
# break
# batchsize = 1 ,so squeeze dim 1
image = image.squeeze(dim=0)
label = label.squeeze(dim=0)
target_seg = label.numpy()
target_cls = target_seg2target_cls(target_seg)
with torch.no_grad():
# batch test for memory reduce
batch = 8
pred_seg = torch.zeros(image.shape[0], image.shape[2],
image.shape[3])
pred_cls = torch.zeros(image.shape[0], 3)
for i in range(0, image.shape[0], batch):
start_id = i
end_id = i + batch
if end_id > image.shape[0]:
end_id = image.shape[0]
image_batch = image[start_id:end_id, :, :, :]
image_var = Variable(image_batch).cuda()
# wangshen model forward
output_seg, output_cls = model(image_var)
_, pred_batch = torch.max(output_seg, 1)
pred_seg[start_id:end_id, :, :] = pred_batch.cpu().data
pred_cls[start_id:end_id, :] = output_cls.cpu().data
pred_seg = pred_seg.numpy().astype('uint8')
if args.vis:
imt = (imt.squeeze().numpy()).astype('uint8')
ant = label.numpy().astype('uint8')
model_name = args.seg_path.split('/')[-3]
save_dir = osp.join(result_path, 'vis', '%04d' % iter)
if not exists(save_dir): os.makedirs(save_dir)
vis_multi_class(imt, ant, pred_seg, save_dir)
print('save vis, finished!')
batch_time.update(time.time() - end)
label_seg = label.numpy().astype('uint8')
pred_seg = pred_seg.numpy().astype('uint8')
ret = aic_fundus_lesion_segmentation(label_seg, pred_seg)
ret_segmentation.append(ret)
dice_score = compute_single_segment_score(ret)
dice_list.append(dice_score)
dice.update(dice_score)
Dice_1.update(ret[1])
Dice_2.update(ret[2])
Dice_3.update(ret[3])
ground_truth = target_cls.numpy().astype('float32')
prediction = pred_cls.numpy().astype('float32') # predict label
if iter == 0:
detection_ref_all = ground_truth
detection_pre_all = prediction
else:
detection_ref_all = np.concatenate(
(detection_ref_all, ground_truth), axis=0)
detection_pre_all = np.concatenate(
(detection_pre_all, prediction), axis=0)
end = time.time()
logger_vis.info(
'Eval: [{0}/{1}]\t'
'Dice {dice.val:.3f} ({dice.avg:.3f})\t'
'Dice_1 {dice_1.val:.3f} ({dice_1.avg:.3f})\t'
'Dice_2 {dice_2.val:.3f} ({dice_2.avg:.3f})\t'
'Dice_3 {dice_3.val:.3f} ({dice_3.avg:.3f})\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'.format(
iter,
len(eval_data_loader),
dice=dice,
dice_1=Dice_1,
dice_2=Dice_2,
dice_3=Dice_3,
batch_time=batch_time))
final_seg, seg_1, seg_2, seg_3 = compute_segment_score(ret_segmentation)
print('### Seg ###')
print('Final Seg Score:{}'.format(final_seg))
print('Final Seg_1 Score:{}'.format(seg_1))
print('Final Seg_2 Score:{}'.format(seg_2))
print('Final Seg_3 Score:{}'.format(seg_3))
ret_detection = aic_fundus_lesion_classification(
detection_ref_all,
detection_pre_all,
num_samples=len(eval_data_loader) * 128)
auc = np.array(ret_detection).mean()
print('AUC :', auc)
auc_1 = ret_detection[0]
auc_2 = ret_detection[1]
auc_3 = ret_detection[2]
epoch = 0
logger.append(
[epoch, final_seg, seg_1, seg_2, seg_3, auc, auc_1, auc_2, auc_3])
def eval(args, eval_data_loader, model, result_path, logger):
model.eval()
batch_time = AverageMeter()
dice = AverageMeter()
end = time.time()
dice_list = []
Dice_1 = AverageMeter()
Dice_2 = AverageMeter()
Dice_3 = AverageMeter()
Dice_4 = AverageMeter()
Dice_5 = AverageMeter()
Dice_6 = AverageMeter()
Dice_7 = AverageMeter()
Dice_8 = AverageMeter()
Dice_9 = AverageMeter()
ret_segmentation = []
for iter, (image, label, imt) in enumerate(eval_data_loader):
# if iter > 1:
# break
# batchsize = 1 ,so squeeze dim 1
image = image.squeeze(dim=0)
label = label.squeeze(dim=0)
target_seg = label.numpy()
#target_cls = target_seg2target_cls(target_seg)
with torch.no_grad():
# batch test for memory reduce
batch = 1
pred_seg = torch.zeros(image.shape[0], image.shape[2],
image.shape[3])
#pred_cls = torch.zeros(image.shape[0],10)
for i in range(0, image.shape[0], batch):
start_id = i
end_id = i + batch
if end_id > image.shape[0]:
end_id = image.shape[0]
image_batch = image[start_id:end_id, :, :, :]
image_var = Variable(image_batch).cuda()
# wangshen model forward
output_seg = model(image_var)
#print(output_seg.shape)
#print(torch.max(output_seg, 1))
_, pred_batch = torch.max(output_seg, 1)
pred_seg[start_id:end_id, :, :] = pred_batch.cpu().data
#pred_cls[start_id:end_id,:] = output_cls.cpu().data
pred_seg = pred_seg.numpy().astype('uint8')
if args.vis:
imt = (imt.squeeze().numpy()).astype('uint8')
ant = label.numpy().astype('uint8')
model_name = args.seg_path.split('/')[-3]
save_dir = osp.join(result_path, 'vis', '%04d' % iter)
if not exists(save_dir): os.makedirs(save_dir)
vis_multi_class(imt, ant, pred_seg, save_dir)
print('save vis, finished!')
batch_time.update(time.time() - end)
label_seg = label.numpy().astype('uint8')
ret = aic_fundus_lesion_segmentation(label_seg, pred_seg)
ret_segmentation.append(ret)
dice_score = compute_single_segment_score(ret)
dice_list.append(dice_score)
dice.update(dice_score)
Dice_1.update(ret[1])
Dice_2.update(ret[2])
Dice_3.update(ret[3])
Dice_4.update(ret[4])
Dice_5.update(ret[5])
Dice_6.update(ret[6])
Dice_7.update(ret[7])
Dice_8.update(ret[8])
Dice_9.update(ret[9])
#ground_truth = target_cls.numpy().astype('float32')
#prediction = pred_cls.numpy().astype('float32') # predict label
#if iter == 0:
# detection_ref_all = ground_truth
# detection_pre_all = prediction
#else:
# detection_ref_all = np.concatenate((detection_ref_all, ground_truth), axis=0)
# detection_pre_all = np.concatenate((detection_pre_all, prediction), axis=0)
end = time.time()
logger_vis.info(
'Eval: [{0}/{1}]\t'
'Dice {dice.val:.3f} ({dice.avg:.3f})\t'
'Dice_1 {dice_1.val:.6f} ({dice_1.avg:.4f})\t'
'Dice_2 {dice_2.val:.6f} ({dice_2.avg:.4f})\t'
'Dice_3 {dice_3.val:.6f} ({dice_3.avg:.4f})\t'
'Dice_4 {dice_4.val:.6f} ({dice_4.avg:.4f})\t'
'Dice_5 {dice_5.val:.6f} ({dice_5.avg:.4f})\t'
'Dice_6 {dice_6.val:.6f} ({dice_6.avg:.4f})\t'
'Dice_7 {dice_7.val:.6f} ({dice_7.avg:.4f})\t'
'Dice_8 {dice_8.val:.6f} ({dice_8.avg:.4f})\t'
'Dice_9 {dice_9.val:.6f} ({dice_9.avg:.4f})\t'
'Time {batch_time.val:.3f} ({batch_time.avg:.3f})\t'.format(
iter,
len(eval_data_loader),
dice=dice,
dice_1=Dice_1,
dice_2=Dice_2,
dice_3=Dice_3,
dice_4=Dice_4,
dice_5=Dice_5,
dice_6=Dice_6,
dice_7=Dice_7,
dice_8=Dice_8,
dice_9=Dice_9,
batch_time=batch_time))
final_seg, seg_1, seg_2, seg_3, seg_4, seg_5, seg_6, seg_7, seg_8, seg_9 = compute_segment_score(
ret_segmentation)
print('### Seg ###')
print('Final Seg Score:{}'.format(final_seg))
print('Final Seg_1 Score:{}'.format(seg_1))
print('Final Seg_2 Score:{}'.format(seg_2))
print('Final Seg_3 Score:{}'.format(seg_3))
print('Final Seg_4 Score:{}'.format(seg_4))
print('Final Seg_5 Score:{}'.format(seg_5))
print('Final Seg_6 Score:{}'.format(seg_6))
print('Final Seg_7 Score:{}'.format(seg_7))
print('Final Seg_8 Score:{}'.format(seg_8))
print('Final Seg_9 Score:{}'.format(seg_9))
#ret_detection = aic_fundus_lesion_classification( detection_ref_all, detection_pre_all, num_samples=len(eval_data_loader)*42) #######val number
#auc = np.array(ret_detection).mean()
#print('AUC :',auc)
#auc_1 = ret_detection[0]
#auc_2 = ret_detection[1]
#auc_3 = ret_detection[2]
#auc_4 = ret_detection[3]
#auc_5 = ret_detection[4]
#auc_6 = ret_detection[5]
#auc_7 = ret_detection[6]
#auc_8 = ret_detection[7]
#auc_9 = ret_detection[8]
epoch = 0
logger.append([
epoch, final_seg, seg_1, seg_2, seg_3, seg_4, seg_5, seg_6, seg_7,
seg_8, seg_9
])