解释这段代码for i, pred in enumerate(preds): shape = orig_img[i].shape if isinstance(orig_img, list) else orig_img.shape if not len(pred): results.append({"det": [], "segment": []}) continue if proto is None: pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round() results.append({"det": pred}) continue if retina_mask: pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round() masks = ops.process_mask_native(proto[i], pred[:, 6:], pred[:, :4], shape[:2]) # HWC segments = [ops.scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)] else: masks = ops.process_mask(proto[i], pred[:, 6:], pred[:, :4], input_hw, upsample=True) pred[:, :4] = ops.scale_boxes(input_hw, pred[:, :4], shape).round() segments = [ops.scale_segments(input_hw, x, shape, normalize=False) for x in ops.masks2segments(masks)] results.append({"det": pred[:, :6].numpy(), "segment": segments}) return results

if cfg.deepsupervision: masks_preds = net(imgs) loss = 0 for masks_pred in masks_preds: tot_cross_entropy = 0 for true_mask, pred in zip(true_masks, masks_pred): pred = (pred > cfg.out_threshold).float() #二值化处理 if cfg.n_classes > 1: sub_cross_entropy = F.cross_entropy(pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0).squeeze(1)).item()#计算损失 else: sub_cross_entropy = dice_coeff(pred, true_mask.squeeze(dim=1)).item()#预测分割掩码和真实标签相似度，将两个结果转化为二值化的掩码，然后计算交集并集 tot_cross_entropy += sub_cross_entropy #计算总损失 tot_cross_entropy = tot_cross_entropy / len(masks_preds) #计算平均损失 tot += tot_cross_entropy #计算总平均损失 else: masks_pred = net(imgs) for true_mask, pred in zip(true_masks, masks_pred): pred = (pred > cfg.out_threshold).float() if cfg.n_classes > 1: tot += F.cross_entropy(pred.unsqueeze(dim=0), true_mask.unsqueeze(dim=0).squeeze(1)).item() else: tot += dice_coeff(pred, true_mask.squeeze(dim=1)).item() pbar.update(imgs.shape[0])

这段代码是语义分割任务中的损失计算部分，其中使用了深监督（Deep Supervision）的方法。在深监督中，一个模型通常会产生多个输出，每个输出都对应着不同的层或分支，在训练过程中，每个输出都会计算一次损失，并...

def _forward_pass(self, img_in1,img_in2): img_in1=img_in1.unsqueeze(0) img_in2 = img_in2.unsqueeze(0) img_in1 = img_in1.to(self.device) img_in2 = img_in2.to(self.device) self.shape_h = img_in1.shape[-2] self.shape_w = img_in1.shape[-1] self.G_pred = self.net_G(img_in1, img_in2) return self._visualize_pred()什么意思

这是一个私有函数 _forward_pass，在输入 img_in1 和 img_in2 之后，使用 self.device 指定设备后，通过 net_G 进行图像处理并输出 G_pred。最后，将 G_pred 可视化后返回结果。

解读这段代码def val(val_set, max_iter=100, flag=False): print('Start val') data_loader = torch.utils.data.DataLoader( val_set, shuffle=True, batch_size=opt.batchSize, num_workers=int(opt.workers)) val_iter = iter(data_loader) n_correct = 0 loss_avg = utils.averager() if not flag: max_iter = min(max_iter, len(data_loader)) else: max_iter = max(max_iter, len(data_loader)) for i in range(max_iter): # data = val_iter.next() # # data = next(val_iter) # try: # data=next(val_iter) # except StopIteration: # return try: data = val_iter.next() except: val_iter = iter(data_loader) # 再次读取，获取数据 data = val_iter.next() cpu_images, cpu_texts = data batch_size = cpu_images.size(0) utils.loadData(image, cpu_images) t, l = converter.encode(cpu_texts) utils.loadData(text, t) utils.loadData(length, l) with torch.no_grad(): crnn.eval() preds = crnn(image) crnn.train() preds_size = torch.IntTensor([preds.size(0)] * batch_size) cost = criterion(preds, text, preds_size, length) loss_avg.add(cost) _, preds = preds.max(2) preds = preds.transpose(1, 0).contiguous().view(-1) sim_preds = converter.decode(preds.data, preds_size.data, raw=False) for pred, target in zip(sim_preds, cpu_texts): if pred == target: n_correct += 1

这段代码是一个用于验证模型的函数。它接收一个数据集 val_set，将其封装成一个 DataLoader 对象，并迭代 max_iter 次进行验证。如果 flag 为 False，则 max_iter 不得超过数据集的长度；否则 max_iter...

解释一下下面一段代码 def detect(self, name_list, img): showimg = img with torch.no_grad(): img = letterbox(img, new_shape=self.opt.img_size)[0] img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() if self.half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) pred = self.model(img, augment=self.opt.augment)[0] pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) info_show = "" for i, det in enumerate(pred): if det is not None and len(det): det[:, :4] = scale_coords(img.shape[2:], det[:, :4], showimg.shape).round() for *xyxy, conf, cls in reversed(det): label = '%s %.2f' % (self.names[int(cls)], conf) name_list.append(self.names[int(cls)]) single_info = plot_one_box2(xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=2) # print(single_info) info_show = info_show + single_info + "\n" return info_show

这是一个函数，名称为 detect，其输入参数有 name_list 和 img，其中 name_list 是一个列表，img 是一个图片。该函数的作用是使用 YOLOv5 检测算法对输入的图片进行目标检测，并返回检测结果的字符串形式。具体...

def detect(self, name_list, img): showimg = img with torch.no_grad(): img = letterbox(img, new_shape=self.opt.img_size)[0] img = img[:, :, ::-1].transpose(2, 0, 1) # BGR to RGB, to 3x416x416 img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() if self.half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) pred = self.model(img, augment=self.opt.augment)[0] pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) info_show = "" for i, det in enumerate(pred): if det is not None and len(det): det[:, :4] = scale_coords(img.shape[2:], det[:, :4], showimg.shape).round() for *xyxy, conf, cls in reversed(det): label = '%s %.2f' % (self.names[int(cls)], conf) name_list.append(self.names[int(cls)]) single_info = plot_one_box2(xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=2) # print(single_info) info_show = info_show + single_info + "\n" return info_show解释代码

这个代码是对输入的图片进行目标检测的，其中包含以下步骤： 1. 将输入的图片进行缩放，使其宽度和高度都等于opt.img_size（一般情况下为416），并将其转换为RGB格式。 2. 将缩放后的图片转换为PyTorch的tensor，...

解析代码：img = cv2.imread(img_name) print(img_name) showimg = img with torch.no_grad(): img = letterbox(img, new_shape=self.opt.img_size)[0] # Convert # BGR to RGB, to 3x416x416 img = img[:, :, ::-1].transpose(2, 0, 1) img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() if self.half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference pred = self.model(img, augment=self.opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) print(pred) # Process detections for i, det in enumerate(pred): if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords( img.shape[2:], det[:, :4], showimg.shape).round() for *xyxy, conf, cls in reversed(det): label = '%s %.2f' % (self.names[int(cls)], conf) name_list.append(self.names[int(cls)]) plot_one_box(xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=10) cv2.imwrite('prediction.jpg', showimg) self.result = cv2.cvtColor(showimg, cv2.COLOR_BGR2BGRA) self.result = cv2.resize( self.result, (640, 480), interpolation=cv2.INTER_AREA) self.QtImg = QtGui.QImage( self.result.data, self.result.shape[1], self.result.shape[0], QtGui.QImage.Format_RGB32) self.label.setPixmap(QtGui.QPixmap.fromImage(self.QtImg))

这段代码主要是进行目标检测的推理过程，并将检测结果展示在界面上。首先，使用OpenCV读取图片，然后对图片进行预处理，包括缩放、转换颜色空间、转换数据类型等。然后，将处理后的图片输入模型进行推理，得到检测...

sample = next(iter(train_ds)) inputs_shape = sample[0].shape[1:] inputs = keras.Input(shape=inputs_shape) # 输入层 def mcp_loss(y_true, y_pred, delta_p, model): p = y_pred[:, :, 0] # 取出所有样本和时间步的输入 p f = y_true[:, :, 0] # 取出所有样本和时间步的标签 f p_new = tf.expand_dims(p + delta_p, axis=-1) f_new = model(p_new)[:, :, 0] delta_f = f - f_new relu = tf.nn.relu(delta_f) return tf.reduce_mean(tf.square(relu)) def combined_loss(y_true, y_pred, delta_p, model, alpha=0.5): mse_loss = tf.keras.losses.mean_squared_error(y_true, y_pred) mcp_loss1 = mcp_loss(y_true, y_pred, delta_p, model) return alpha * mse_loss + (1-alpha) * mcp_loss1 model = model_attention_applied_before_lstm() combined_loss1=combined_loss() model.compile(optimizer=keras.optimizers.Adam(0.001), loss=combined_loss1) epochs = 80 # 网络训练 history = model.fit(train_ds, epochs=epochs, validation_data=val_ds)结合前面的训练数据集将这段代码的combined_loss1补充完整

根据代码中的定义，combined_loss函数接收5个参数：y_true表示真实标签，y_pred表示模型的预测结果，delta_p是一个标量，model是一个Keras模型，alpha是一个可选的参数，默认值为0.5。 combined_loss...

import ast from dataclasses import dataclass from typing import List import pandas as pd import json ["text", "六十一岁还能办什么保险"] @dataclass class FAQ: title: str sim_questions: List[str] answer: str faq_id: int ori_data = pd.read_csv('baoxianzhidao_filter.csv') data = [] exist_titles = set() for index, row in enumerate(ori_data.iterrows()): row_dict = row[1] title = row_dict['title'] if title not in exist_titles: data.append(FAQ(title=title, answer=row_dict['reply'], sim_questions=[title], faq_id=index)) exist_titles.add(title) from modelscope.pipelines import pipeline from modelscope.utils.constant import Tasks pipeline_ins = pipeline(Tasks.faq_question_answering, 'damo/nlp_mgimn_faq-question-answering_chinese-base') bsz = 32 all_sentence_vecs = [] batch = [] sentence_list = [faq.title for faq in data] for i,sent in enumerate(sentence_list): batch.append(sent) if len(batch) == bsz or (i == len(sentence_list)-1 and len(batch)>0): # if i == len(sentence_list)-1 and len(batch)>0: sentence_vecs = pipeline_ins.get_sentence_embedding(batch) all_sentence_vecs.extend(sentence_vecs) batch.clear() import faiss import numpy as np hidden_size = pipeline_ins.model.network.bert.config.hidden_size # hidden_size = pipeline_ins.model.bert.config.hidden_size index = faiss.IndexFlatIP(hidden_size) vecs = np.asarray(all_sentence_vecs, dtype='float32') index.add(vecs) from modelscope.outputs import OutputKeys def ask_faq(input, history=[]): # step1: get sentence vector of query query_vec = pipeline_ins.get_sentence_embedding([input])[0] query_vec = np.asarray(query_vec, dtype='float32').reshape([1, -1]) # step2: faq dense retrieval _, indices = index.search(query_vec, k=30) # step3: build support set support_set = [] for i in indices.tolist()[0]: faq = data[i] support_set.append({"text": faq.title, "label": faq.faq_id, "index": i}) # step4: faq ranking rst = pipeline_ins(input={"query_set": input, "support_set": support_set}) rst = rst[OutputKeys.OUTPUT][0][0] pred_label = rst['label'] pred_score = rst['score'] # get answer by faq_id pred_answer = "" pred_title = "" for faq in data: if faq.faq_id == pred_label: pred_answer = faq.answer pred_title = faq.title break history.append((f'{pred_answer}|(pred_title:{pred_title},pred_score:{pred_score:.3f})')) return history优化这段代码

这段代码是一个Python脚本，用于读取CSV文件中的保险相关问题和答案，构建一个FAQ对象（包含问题、答案、相似问题和FAQ ID），并使用modelscope库中的pipeline进行常见问题解答。其中用到了ast、dataclass、List、...

for k in k_choices: k_to_accuracies[k] = [] for i in range(num_folds): X_train_fold = np.concatenate([ fold for j, fold in enumerate(X_train_folds) if i != j ]) y_train_fold = np.concatenate([ fold for j, fold in enumerate(y_train_folds) if i != j ]) X_val = X_train_folds[i] y_val = y_train_folds[i] classifier.train(X_train_fold, y_train_fold) y_pred_fold = classifier.predict(X_val, k=k, num_loops=0) num_correct = np.sum(y_pred_fold == y_val) accuracy = float(num_correct) / X_val.shape[0] k_to_accuracies[k].append(accuracy)

这段代码是一个 k-fold 交叉验证的过程，用于评估分类器在不同 k 值下的准确率。其中，k_choices 是一个包含不同 k 值的列表，k_to_accuracies 是一个字典，用于存储每个 k 值对应的准确率列表。在每个 k 值的循环...

def button_image_open(self): print('button_image_open') name_list = [] img_name, _ = QtWidgets.QFileDialog.getOpenFileName( self, "打开图片", "", ".jpg;;.png;;All Files()") if not img_name: return img = cv2.imread(img_name) print(img_name) showimg = img with torch.no_grad(): img = letterbox(img, new_shape=self.opt.img_size)[0] # Convert # BGR to RGB, to 3x416x416 img = img[:, :, ::-1].transpose(2, 0, 1) img = np.ascontiguousarray(img) img = torch.from_numpy(img).to(self.device) img = img.half() if self.half else img.float() # uint8 to fp16/32 img /= 255.0 # 0 - 255 to 0.0 - 1.0 if img.ndimension() == 3: img = img.unsqueeze(0) # Inference pred = self.model(img, augment=self.opt.augment)[0] # Apply NMS pred = non_max_suppression(pred, self.opt.conf_thres, self.opt.iou_thres, classes=self.opt.classes, agnostic=self.opt.agnostic_nms) print(pred) # Process detections for i, det in enumerate(pred): if det is not None and len(det): # Rescale boxes from img_size to im0 size det[:, :4] = scale_coords( img.shape[2:], det[:, :4], showimg.shape).round() for xyxy, conf, cls in reversed(det): label = '%s %.2f' % (self.names[int(cls)], conf) name_list.append(self.names[int(cls)]) plot_one_box(xyxy, showimg, label=label, color=self.colors[int(cls)], line_thickness=10)

这是一个用于打开图片并进行目标检测的函数，使用了OpenCV和PyTorch进行图像处理和模型推理。具体流程如下： 1. 使用QtWidgets.QFileDialog打开一个图片选择对话框，选中需要检测的图片。 2. 使用OpenCV的cv2....

def predict_one_img(model, img_dataset, args): # def predict_one_img(model, img_dataset, data, target, args): dataloader = DataLoader(dataset=img_dataset, batch_size=1, num_workers=0, shuffle=False) model.eval() test_dice = DiceAverage(args.n_labels) # target = to_one_hot_3d(label, args.n_labels) with torch.no_grad(): for data ,target in tqdm(dataloader, total=len(dataloader)): # data = data.to(device) data, target = data.float(), target.long() target = to_one_hot_3d(target, args.n_labels) data, target = data.to(device), target.to(device) # print(data.shape) # print(target.shape) output = model(data) # output = nn.functional.interpolate(output, scale_factor=(1//args.slice_down_scale,1//args.xy_down_scale,1//args.xy_down_scale), mode='trilinear', align_corners=False) # 空间分辨率恢复到原始size img_dataset.update_result(output.detach().cpu()) pred = img_dataset.recompone_result() pred = torch.argmax(pred, dim=1) pred_img = to_one_hot_3d(pred, args.n_labels) pred_img=pred_img.to(device) test_dice.update(pred_img, target) test_dice = OrderedDict({'Dice_liver': test_dice.avg[1]}) if args.n_labels == 3: test_dice.update({'Dice_tumor': test_dice.avg[2]}) pred = np.asarray(pred.numpy(), dtype='uint8') if args.postprocess: pass # TO DO pred = sitk.GetImageFromArray(np.squeeze(pred, axis=0)) return test_dice, pred

这是一个用于预测单张图像的函数，接收一个模型、一个图像数据集和一些参数作为输入。函数首先将图像数据集加载到一个Dataloader中，然后将模型设置为评估模式并初始化一个DiceAverage对象，该对象用于计算评估指标...

请详细解释一下这段代码，每一句给上相应的详细注解：def custom_average_precision(y_true, y_pred): score = average_precision_score(y_true, y_pred) return 'average_precision', score, True class LGBMMultiOutputRegressor(MultiOutputRegressor): def fit(self, X, y, eval_set=None, fit_params): self.estimators_ = [clone(self.estimator) for _ in range(y.shape[1])] for i, estimator in enumerate(self.estimators_): if eval_set: fit_params['eval_set'] = [(eval_set[0], eval_set[1][:, i])] estimator.fit(X, y[:, i], fit_params) return self

这段代码定义了一个名为custom_average_precision的自定义函数和一个名为LGBMMultiOutputRegressor的自定义类，并重写了MultiOutputRegressor类的fit方法。 custom_average_precision函数用于计算平均...

def call(self): # 图像转换 data_transorform = torchvision.transforms.Compose([ torchvision.transforms.Resize((224, 224)), torchvision.transforms.CenterCrop((224, 224)), torchvision.transforms.ToTensor(), torchvision.transforms.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]) ]) img_list = glob.glob(self.img_dir + os.sep + "*.png") for imgpath in img_list: img = cv2.imread(imgpath) new_img = self.expend_img(img) # 补边 img = Image.fromarray(new_img) img = data_transorform(img) # 转换 img = torch.reshape(img, (-1, 3, self.imgsz, self.imgsz)).to(device) # 维度转换[B,C,H,W] pred = self.model(img) _, pred = torch.max(pred, 1) outputs = self.class_name[pred] print("Image path:", imgpath, " pred:", outputs) # 补边为正方形 def expend_img(self, img, fill_pix=122): ''' :param img: 图片数据 :param fill_pix: 填充像素，默认为灰色，自行更改 :return: ''' h, w = img.shape[:2] # 获取图像的宽高 if h >= w: # 左右填充 padd_width = int(h - w) // 2 padd_h, padd_b, padd_l, padd_r = 0, 0, padd_width, padd_width # 获取上下左右四个方向需要填充的像素 elif h < w: # 上下填充 padd_high = int(w - h) // 2 padd_h, padd_b, padd_l, padd_r = padd_high, padd_high, 0, 0 new_img = cv2.copyMakeBorder(img, padd_h, padd_b, padd_l, padd_r, borderType=cv2.BORDER_CONSTANT, value=[fill_pix, fill_pix, fill_pix]) return new_img if name == 'main': opt = parser_opt() test_img = Test_model(opt) test_img()

这段代码是一个使用PyTorch实现的图像分类模型的测试程序。主要实现了以下功能： 1.读取指定目录下的所有png格式图像文件。 2.对读取到的图像进行补边操作，使其变为正方形。 3.将图像进行预处理，包括图像尺寸的...

import os import random import numpy as np import cv2 import keras from create_unet import create_model img_path = 'data_enh/img' mask_path = 'data_enh/mask' # 训练集与测试集的切分 img_files = np.array(os.listdir(img_path)) data_num = len(img_files) train_num = int(data_num * 0.8) train_ind = random.sample(range(data_num), train_num) test_ind = list(set(range(data_num)) - set(train_ind)) train_ind = np.array(train_ind) test_ind = np.array(test_ind) train_img = img_files[train_ind] # 训练的数据 test_img = img_files[test_ind] # 测试的数据 def get_mask_name(img_name): mask = [] for i in img_name: mask_name = i.replace('.jpg', '.png') mask.append(mask_name) return np.array(mask) train_mask = get_mask_name(train_img) test_msak = get_mask_name(test_img) def generator(img, mask, batch_size): num = len(img) while True: IMG = [] MASK = [] for i in range(batch_size): index = np.random.choice(num) img_name = img[index] mask_name = mask[index] img_temp = os.path.join(img_path, img_name) mask_temp = os.path.join(mask_path, mask_name) temp_img = cv2.imread(img_temp) temp_mask = cv2.imread(mask_temp, 0)/255 temp_mask = np.reshape(temp_mask, [256, 256, 1]) IMG.append(temp_img) MASK.append(temp_mask) IMG = np.array(IMG) MASK = np.array(MASK) yield IMG, MASK # train_data = generator(train_img, train_mask, 32) # temp_data = train_data.next() # 计算dice系数 def dice_coef(y_true, y_pred): y_true_f = keras.backend.flatten(y_true) y_pred_f = keras.backend.flatten(y_pred) intersection = keras.backend.sum(y_true_f * y_pred_f) area_true = keras.backend.sum(y_true_f * y_true_f) area_pred = keras.backend.sum(y_pred_f * y_pred_f) dice = (2 * intersection + 1)/(area_true + area_pred + 1) return dice # 自定义损失函数，dice_loss def dice_coef_loss(y_true, y_pred): return 1 - dice_coef(y_true, y_pred) # 模型的创建 model = create_model() # 模型的编译 model.compile(optimizer='Adam', loss=dice_coef_loss, metrics=[dice_coef]) # 模型的训练 history = model.fit_generator(generator(train_img, train_mask, 4), steps_per_epoch=100, epochs=10, validation_data=generator(test_img, test_msak, 4), validation_steps=4 ) # 模型的保存 model.save('unet_model.h5') # 模型的读取 model = keras.models.load_model('unet_model.h5', custom_objects={'dice_coef_loss': dice_coef_loss, 'dice_coef': dice_coef}) # 获取测试数据 test_generator = generator(test_img, test_msak, 32) img, mask = test_generator.next() # 模型的测试 model.evaluate(img, mask) # [0.11458712816238403, 0.885412871837616] 94%

上面这段代码是在导入一些库。它导入了 OS 库，Random 库，NumPy 库，CV2 库，Keras 库，以及一个叫做 Create_unet 的自定义模块。它还定义了两个字符串变量：img_path 和 mask_path，分别存储了图像数据和掩码数据...

def crossentropy_loss(x_true,x_pred): pred_items = tf.reshape(x_pred[:,:n_notesn_durations],[tf.shape(x_pred)[0],n_notes,n_durations]) true_items = tf.reshape(x_true[:,:n_notesn_durations],[tf.shape(x_pred)[0],n_notes,n_durations])#bz,n_notes,n_durations items_loss = categorical_crossentropy(true_items,pred_items,True,axis=-1)#bz,n_notes items_weights = tf.gather(durations_weights,tf.argmax(true_items,axis=-1),axis=0)#bz,n_notes items_loss = tf.reduce_mean(items_loss * items_weights,axis=1)#bz pred_offsets = x_pred[:,-n_offsets:] true_offsets = x_true[:,-n_offsets:] offset_loss = categorical_crossentropy(true_offsets,pred_offsets,True,axis=-1)#bz loss = items_loss + offset_loss return tf.reduce_mean(loss)

这段代码定义了一个名为crossentropy_loss()的函数，用于计算模型的交叉熵损失。该函数接受两个参数，x_true和x_pred，分别表示真实标签和模型预测结果。具体来说，该函数首先通过tf.reshape()函数将预测结果和真实...

def calculate_fowlkes_mallows(y_true, y_pred): tp = 0 fp = 0 fn = 0 for i in range(len(y_true)): for j in range(i + 1, len(y_true)): if y_true[i] == y_true[j] and y_pred[i] == y_pred[j]: tp += 1 elif y_true[i] != y_true[j] and y_pred[i] == y_pred[j]: fp += 1 elif y_true[i] == y_true[j] and y_pred[i] != y_pred[j]: fn += 1 fm = ((tp / (tp + fp)) * (tp / (tp + fn))) ** 0.5 return fm 以上代码中为什么要遍历标签列表

在Fowlkes-Mallows指数中，...在这个函数中，遍历标签列表可以将所有可能的样本对找出来，然后逐一比较它们的真实标签和预测标签，从而计算出真阳性、假阳性、假阴性的数量，最终用这些数值来计算Fowlkes-Mallows指数。

掌握GTEST_ASSERT_断言：C++单元测试的核心

文件列表中包含了C和C++源代码文件以及头文件，这表明这是一个跨语言的项目，可能在C++项目中嵌入了C语言代码以进行特定的优化或与遗留代码交互。总结来看，这个资源可能是一个涉及测试框架核心功能以及特定平台和...

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