我们从Python开源项目中，提取了以下10个代码示例，用于说明如何使用torch.nn.functional.triplet_margin_loss()。
def test_triplet_margin_loss(self): input1 = Variable(torch.randn(4, 4), requires_grad=True) input2 = Variable(torch.randn(4, 4), requires_grad=True) input3 = Variable(torch.randn(4, 4), requires_grad=True) self.assertTrue(gradcheck(lambda x1, x2, x3: F.triplet_margin_loss( x1, x2, x3), (input1, input2, input3)))
def test_triplet_margin_swap_loss(self): input1 = Variable(torch.randn(4, 4), requires_grad=True) input2 = Variable(torch.randn(4, 4), requires_grad=True) input3 = Variable(torch.randn(4, 4), requires_grad=True) self.assertTrue(gradcheck(lambda x1, x2, x3: F.triplet_margin_loss( x1, x2, x3, swap=True), (input1, input2, input3)))
def test(model, test_loader, epoch, margin, threshlod, is_cuda=True, log_interval=1000): model.eval() test_loss = AverageMeter() accuracy = 0 num_p = 0 total_num = 0 batch_num = len(test_loader) for batch_idx, (data_a, data_p, data_n,target) in enumerate(test_loader): if is_cuda: data_a = data_a.cuda() data_p = data_p.cuda() data_n = data_n.cuda() target = target.cuda() data_a = Variable(data_a, volatile=True) data_p = Variable(data_p, volatile=True) data_n = Variable(data_n, volatile=True) target = Variable(target) out_a = model(data_a) out_p = model(data_p) out_n = model(data_n) loss = F.triplet_margin_loss(out_a,out_p,out_n, margin) dist1 = F.pairwise_distance(out_a,out_p) dist2 = F.pairwise_distance(out_a,out_n) num = ((dist1 < threshlod).float().sum() + (dist2 > threshlod).float().sum()).data[0] num_p += num num_p = 1.0 * num_p total_num += data_a.size()[0] * 2 #print('num--num_p -- total', num, num_p , total_num) test_loss.update(loss.data[0]) if (batch_idx + 1) % log_interval == 0: accuracy_tmp = num_p / total_num print('Test- Epoch {:04d}\tbatch:{:06d}/{:06d}\tAccuracy:{:.04f}\tloss:{:06f}'\ .format(epoch, batch_idx+1, batch_num, accuracy_tmp, test_loss.avg)) test_loss.reset() accuracy = num_p / total_num return accuracy
def test_vis(model, test_loader, model_path, threshlod,\ margin=1.0, is_cuda=True, output_dir='output',is_visualization=True): if not model_path is None: model.load_full_weights(model_path) print('loaded model file: {:s}'.format(model_path)) if is_cuda: model = model.cuda() model.eval() test_loss = AverageMeter() accuracy = 0 num_p = 0 total_num = 0 batch_num = len(test_loader) for batch_idx, (data_a, data_p, data_n,target, img_paths) in enumerate(test_loader): #for batch_idx, (data_a, data_p, data_n, target) in enumerate(test_loader): if is_cuda: data_a = data_a.cuda() data_p = data_p.cuda() data_n = data_n.cuda() target = target.cuda() data_a = Variable(data_a, volatile=True) data_p = Variable(data_p, volatile=True) data_n = Variable(data_n, volatile=True) target = Variable(target) out_a = model(data_a) out_p = model(data_p) out_n = model(data_n) loss = F.triplet_margin_loss(out_a,out_p,out_n, margin) dist1 = F.pairwise_distance(out_a,out_p) dist2 = F.pairwise_distance(out_a,out_n) batch_size = data_a.size()[0] pos_flag = (dist1 <= threshlod).float() neg_flag = (dist2 > threshlod).float() if is_visualization: for k in torch.arange(0, batch_size): k = int(k) if pos_flag[k].data[0] == 0: combine_and_save(img_paths[0][k], img_paths[1][k], dist1[k], output_dir, '1-1') if neg_flag[k].data[0] == 0: combine_and_save(img_paths[0][k], img_paths[2][k], dist2[k], output_dir, '1-0') num = (pos_flag.sum() + neg_flag.sum()).data[0] print('{:f}, {:f}, {:f}'.format(num, pos_flag.sum().data[0], neg_flag.sum().data[0])) num_p += num total_num += data_a.size()[0] * 2 print('num_p = {:f}, total = {:f}'.format(num_p, total_num)) print('dist1 = {:f}, dist2 = {:f}'.format(dist1[0].data[0], dist2[0].data[0])) accuracy = num_p / total_num return accuracy
