from dface.core.image_reader import TrainImageReader import datetime import os from dface.core.models import PNet,RNet,ONet,LossFn import torch from torch.autograd import Variable import dface.core.image_tools as image_tools def compute_accuracy(prob_cls, gt_cls): prob_cls = torch.squeeze(prob_cls) gt_cls = torch.squeeze(gt_cls) #we only need the detection which >= 0 mask = torch.ge(gt_cls,0) #get valid element valid_gt_cls = torch.masked_select(gt_cls,mask) valid_prob_cls = torch.masked_select(prob_cls,mask) size = min(valid_gt_cls.size()[0], valid_prob_cls.size()[0]) prob_ones = torch.ge(valid_prob_cls,0.6).float() right_ones = torch.eq(prob_ones,valid_gt_cls).float() return torch.div(torch.mul(torch.sum(right_ones),float(1.0)),float(size)) def train_pnet(model_store_path, end_epoch,imdb, batch_size,frequent=50,base_lr=0.01,use_cuda=True): if not os.path.exists(model_store_path): os.makedirs(model_store_path) lossfn = LossFn() net = PNet(is_train=True, use_cuda=use_cuda) net.train() if use_cuda: net.cuda() optimizer = torch.optim.Adam(net.parameters(), lr=base_lr) train_data=TrainImageReader(imdb,12,batch_size,shuffle=True) for cur_epoch in range(1,end_epoch+1): train_data.reset() accuracy_list=[] cls_loss_list=[] bbox_loss_list=[] # landmark_loss_list=[] for batch_idx,(image,(gt_label,gt_bbox,gt_landmark))in enumerate(train_data): im_tensor = [ image_tools.convert_image_to_tensor(image[i,:,:,:]) for i in range(image.shape[0]) ] im_tensor = torch.stack(im_tensor) im_tensor = Variable(im_tensor) gt_label = Variable(torch.from_numpy(gt_label).float()) gt_bbox = Variable(torch.from_numpy(gt_bbox).float()) # gt_landmark = Variable(torch.from_numpy(gt_landmark).float()) if use_cuda: im_tensor = im_tensor.cuda() gt_label = gt_label.cuda() gt_bbox = gt_bbox.cuda() # gt_landmark = gt_landmark.cuda() cls_pred, box_offset_pred = net(im_tensor) # all_loss, cls_loss, offset_loss = lossfn.loss(gt_label=label_y,gt_offset=bbox_y, pred_label=cls_pred, pred_offset=box_offset_pred) cls_loss = lossfn.cls_loss(gt_label,cls_pred) box_offset_loss = lossfn.box_loss(gt_label,gt_bbox,box_offset_pred) # landmark_loss = lossfn.landmark_loss(gt_label,gt_landmark,landmark_offset_pred) all_loss = cls_loss*1.0+box_offset_loss*0.5 if batch_idx%frequent==0: accuracy=compute_accuracy(cls_pred,gt_label) show1 = accuracy.data.tolist()[0] show2 = cls_loss.data.tolist()[0] show3 = box_offset_loss.data.tolist()[0] show5 = all_loss.data.tolist()[0] print("%s : Epoch: %d, Step: %d, accuracy: %s, det loss: %s, bbox loss: %s, all_loss: %s, lr:%s "%(datetime.datetime.now(),cur_epoch,batch_idx, show1,show2,show3,show5,base_lr)) accuracy_list.append(accuracy) cls_loss_list.append(cls_loss) bbox_loss_list.append(box_offset_loss) optimizer.zero_grad() all_loss.backward() optimizer.step() accuracy_avg = torch.mean(torch.cat(accuracy_list)) cls_loss_avg = torch.mean(torch.cat(cls_loss_list)) bbox_loss_avg = torch.mean(torch.cat(bbox_loss_list)) # landmark_loss_avg = torch.mean(torch.cat(landmark_loss_list)) show6 = accuracy_avg.data.tolist()[0] show7 = cls_loss_avg.data.tolist()[0] show8 = bbox_loss_avg.data.tolist()[0] print("Epoch: %d, accuracy: %s, cls loss: %s, bbox loss: %s" % (cur_epoch, show6, show7, show8)) torch.save(net.state_dict(), os.path.join(model_store_path,"pnet_epoch_%d.pt" % cur_epoch)) torch.save(net, os.path.join(model_store_path,"pnet_epoch_model_%d.pkl" % cur_epoch)) def train_rnet(model_store_path, end_epoch,imdb, batch_size,frequent=50,base_lr=0.01,use_cuda=True): if not os.path.exists(model_store_path): os.makedirs(model_store_path) lossfn = LossFn() net = RNet(is_train=True, use_cuda=use_cuda) net.train() if use_cuda: net.cuda() optimizer = torch.optim.Adam(net.parameters(), lr=base_lr) train_data=TrainImageReader(imdb,24,batch_size,shuffle=True) for cur_epoch in range(1,end_epoch+1): train_data.reset() accuracy_list=[] cls_loss_list=[] bbox_loss_list=[] landmark_loss_list=[] for batch_idx,(image,(gt_label,gt_bbox,gt_landmark))in enumerate(train_data): im_tensor = [ image_tools.convert_image_to_tensor(image[i,:,:,:]) for i in range(image.shape[0]) ] im_tensor = torch.stack(im_tensor) im_tensor = Variable(im_tensor) gt_label = Variable(torch.from_numpy(gt_label).float()) gt_bbox = Variable(torch.from_numpy(gt_bbox).float()) gt_landmark = Variable(torch.from_numpy(gt_landmark).float()) if use_cuda: im_tensor = im_tensor.cuda() gt_label = gt_label.cuda() gt_bbox = gt_bbox.cuda() gt_landmark = gt_landmark.cuda() cls_pred, box_offset_pred = net(im_tensor) # all_loss, cls_loss, offset_loss = lossfn.loss(gt_label=label_y,gt_offset=bbox_y, pred_label=cls_pred, pred_offset=box_offset_pred) cls_loss = lossfn.cls_loss(gt_label,cls_pred) box_offset_loss = lossfn.box_loss(gt_label,gt_bbox,box_offset_pred) # landmark_loss = lossfn.landmark_loss(gt_label,gt_landmark,landmark_offset_pred) all_loss = cls_loss*1.0+box_offset_loss*0.5 if batch_idx%frequent==0: accuracy=compute_accuracy(cls_pred,gt_label) show1 = accuracy.data.tolist()[0] show2 = cls_loss.data.tolist()[0] show3 = box_offset_loss.data.tolist()[0] # show4 = landmark_loss.data.tolist()[0] show5 = all_loss.data.tolist()[0] print("%s : Epoch: %d, Step: %d, accuracy: %s, det loss: %s, bbox loss: %s, all_loss: %s, lr:%s "%(datetime.datetime.now(), cur_epoch, batch_idx, show1, show2, show3, show5, base_lr)) accuracy_list.append(accuracy) cls_loss_list.append(cls_loss) bbox_loss_list.append(box_offset_loss) # landmark_loss_list.append(landmark_loss) optimizer.zero_grad() all_loss.backward() optimizer.step() accuracy_avg = torch.mean(torch.cat(accuracy_list)) cls_loss_avg = torch.mean(torch.cat(cls_loss_list)) bbox_loss_avg = torch.mean(torch.cat(bbox_loss_list)) # landmark_loss_avg = torch.mean(torch.cat(landmark_loss_list)) show6 = accuracy_avg.data.tolist()[0] show7 = cls_loss_avg.data.tolist()[0] show8 = bbox_loss_avg.data.tolist()[0] # show9 = landmark_loss_avg.data.tolist()[0] print("Epoch: %d, accuracy: %s, cls loss: %s, bbox loss: %s" % (cur_epoch, show6, show7, show8)) torch.save(net.state_dict(), os.path.join(model_store_path,"rnet_epoch_%d.pt" % cur_epoch)) torch.save(net, os.path.join(model_store_path,"rnet_epoch_model_%d.pkl" % cur_epoch)) def train_onet(model_store_path, end_epoch,imdb, batch_size,frequent=50,base_lr=0.01,use_cuda=True): if not os.path.exists(model_store_path): os.makedirs(model_store_path) lossfn = LossFn() net = ONet(is_train=True) net.train() if use_cuda: net.cuda() optimizer = torch.optim.Adam(net.parameters(), lr=base_lr) train_data=TrainImageReader(imdb,48,batch_size,shuffle=True) for cur_epoch in range(1,end_epoch+1): train_data.reset() accuracy_list=[] cls_loss_list=[] bbox_loss_list=[] landmark_loss_list=[] for batch_idx,(image,(gt_label,gt_bbox,gt_landmark))in enumerate(train_data): im_tensor = [ image_tools.convert_image_to_tensor(image[i,:,:,:]) for i in range(image.shape[0]) ] im_tensor = torch.stack(im_tensor) im_tensor = Variable(im_tensor) gt_label = Variable(torch.from_numpy(gt_label).float()) gt_bbox = Variable(torch.from_numpy(gt_bbox).float()) gt_landmark = Variable(torch.from_numpy(gt_landmark).float()) if use_cuda: im_tensor = im_tensor.cuda() gt_label = gt_label.cuda() gt_bbox = gt_bbox.cuda() gt_landmark = gt_landmark.cuda() cls_pred, box_offset_pred, landmark_offset_pred = net(im_tensor) # all_loss, cls_loss, offset_loss = lossfn.loss(gt_label=label_y,gt_offset=bbox_y, pred_label=cls_pred, pred_offset=box_offset_pred) cls_loss = lossfn.cls_loss(gt_label,cls_pred) box_offset_loss = lossfn.box_loss(gt_label,gt_bbox,box_offset_pred) landmark_loss = lossfn.landmark_loss(gt_label,gt_landmark,landmark_offset_pred) all_loss = cls_loss*0.8+box_offset_loss*0.6+landmark_loss*1.5 if batch_idx%frequent==0: accuracy=compute_accuracy(cls_pred,gt_label) show1 = accuracy.data.tolist()[0] show2 = cls_loss.data.tolist()[0] show3 = box_offset_loss.data.tolist()[0] show4 = landmark_loss.data.tolist()[0] show5 = all_loss.data.tolist()[0] print("%s : Epoch: %d, Step: %d, accuracy: %s, det loss: %s, bbox loss: %s, landmark loss: %s, all_loss: %s, lr:%s "%(datetime.datetime.now(),cur_epoch,batch_idx, show1,show2,show3,show4,show5,base_lr)) accuracy_list.append(accuracy) cls_loss_list.append(cls_loss) bbox_loss_list.append(box_offset_loss) landmark_loss_list.append(landmark_loss) optimizer.zero_grad() all_loss.backward() optimizer.step() accuracy_avg = torch.mean(torch.cat(accuracy_list)) cls_loss_avg = torch.mean(torch.cat(cls_loss_list)) bbox_loss_avg = torch.mean(torch.cat(bbox_loss_list)) landmark_loss_avg = torch.mean(torch.cat(landmark_loss_list)) show6 = accuracy_avg.data.tolist()[0] show7 = cls_loss_avg.data.tolist()[0] show8 = bbox_loss_avg.data.tolist()[0] show9 = landmark_loss_avg.data.tolist()[0] print("Epoch: %d, accuracy: %s, cls loss: %s, bbox loss: %s, landmark loss: %s " % (cur_epoch, show6, show7, show8, show9)) torch.save(net.state_dict(), os.path.join(model_store_path,"onet_epoch_%d.pt" % cur_epoch)) torch.save(net, os.path.join(model_store_path,"onet_epoch_model_%d.pkl" % cur_epoch))