embedding_cpu = embedding_cuda.cpu().detach().numpy()
anchor, pos, neg = select_triplet(embedding_cpu, y_train, 0.2)
#for x in range(len(anchor)):
#print(anchor[x], pos[x], neg[x],"++",y_train[anchor[x]], y_train[pos[x]], y_train[neg[x]])
#print('~'*5)
if len(anchor) > 0:
anchor_train_cuda = x_train_cuda[anchor + [0] * (8 - len(anchor))]
anchor_embedding = model(anchor_train_cuda)
pos_train_cuda = x_train_cuda[pos + [0] * (8 - len(pos))]
pos_embedding = model(pos_train_cuda)
neg_train_cuda = x_train_cuda[neg + [0] * (8 - len(neg))]
neg_embedding = model(neg_train_cuda)
optimizer.zero_grad()
loss = cost.forward(anchor_embedding, pos_embedding, neg_embedding,
0.2)
loss = loss.cpu()
loss.backward()
optimizer.step()
#print(str(time)+"****")
#time += 1
#print(loss.data)
running_loss += loss.data
#running_correct += torch.sum(pred == y_train.data)
#print(anchor_train_cuda.shape)
#print(anchor_embedding)
#anchor_embedding = embedding_cuda[anchor]
#pos_embedding = embedding_cuda[pos]
#neg_embedding = embedding_cuda[neg]
neg_img = batch_sample['neg_img']
# 模型运算
# 前向传播过程-拿模型分别跑三张图，生成embedding和loss（在训练阶段的输入是两张图，输出带loss，而验证阶段输入一张图，输出只有embedding）
anc_embedding = model(anc_img.cuda())
pos_embedding = model(pos_img.cuda())
neg_embedding = model(neg_img.cuda())
anc_embedding = torch.div(anc_embedding, torch.norm(anc_embedding))
pos_embedding = torch.div(pos_embedding, torch.norm(pos_embedding))
neg_embedding = torch.div(neg_embedding, torch.norm(neg_embedding))
# 损失计算
# 计算这个批次困难样本的三元损失
# 在159行处，调用triplet_loss完成loss的计算
triplet_loss = loss_fun.forward(anc_embedding.cpu(), pos_embedding.cpu(), neg_embedding.cpu())
loss = triplet_loss
# 反向传播过程
optimizer_model.zero_grad()
loss.backward()
optimizer_model.step()
# update the optimizer learning rate
adjust_learning_rate(optimizer_model, epoch)
# 计算这个epoch内的总三元损失和计算损失所用的样本个数
triplet_loss_sum += triplet_loss.item()
sample_num +=anc_embedding.shape[0]