img_pairs = [] for ext in args.img_exts: test_files = data_dir.files('*1.{}'.format(ext)) for file in test_files: img_pair = file.parent / (file.stem[:-1] + '2.{}'.format(ext)) if img_pair.isfile(): img_pairs.append([file, img_pair]) print('{} samples found'.format(len(img_pairs))) # create model network_data = torch.load(args.pretrained) print("=> using pre-trained model '{}'".format(args.arch)) model = models.__dict__[args.arch](network_data).to(device) model.eval() cudnn.benchmark = True

create_pairs_for_valid.py

facenet训练时人脸验证测试文件生成流程，完整代码，可直接执行，两个参数，一个为需要验证的人脸数据集（经过检测后的），一个为输出txt文件，输出文件可直接用于facenet训练验证用

PairsTrading_FEX.zip_Pairs_PairsTrading_FEX_pairs trading_tradin

用Matlab实现古典双贸易源代码-Classical Pairs Trading

My Complete Genome_6k Base-Pairs of Phenotype SNPs_Complete Raw Data.zip

@torch.no_grad() def main(): global args, save_path args = parser.parse_args() data_dir = Path(args.data) print("=> fetching img pairs in '{}'".format(args.data)) if args.output is None: save_path = data_dir/'flow' else: save_path = Path(args.output) print('=> will save everything to {}'.format(save_path)) save_path.makedirs_p() # Data loading code input_transform = transforms.Compose([transforms.Normalize(mean=[0,0,0], std=[255,255,255]) ])

main 函数首先使用 parser.parse_args() 解析命令行参数，并将结果保存在全局变量 args 中。接下来，代码通过 Path 类将命令行参数中的数据文件夹路径转换为 data_dir 对象，并打印一条消息指示正在获取...

详细解释一下这段代码，每一句都要进行注解：for dataset in datasets: print(dataset) if dataset not in out_results: out_results[dataset] = {} for scene in data_dict[dataset]: print(scene) # Fail gently if the notebook has not been submitted and the test data is not populated. # You may want to run this on the training data in that case? img_dir = f'{src}/test/{dataset}/{scene}/images' if not os.path.exists(img_dir): continue # Wrap the meaty part in a try-except block. try: out_results[dataset][scene] = {} img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]] print (f"Got {len(img_fnames)} images") feature_dir = f'featureout/{dataset}{scene}' if not os.path.isdir(feature_dir): os.makedirs(feature_dir, exist_ok=True) t=time() index_pairs = get_image_pairs_shortlist(img_fnames, sim_th = 0.5644583, # should be strict min_pairs = 33, # we select at least min_pairs PER IMAGE with biggest similarity exhaustive_if_less = 20, device=device) t=time() -t timings['shortlisting'].append(t) print (f'{len(index_pairs)}, pairs to match, {t:.4f} sec') gc.collect() t=time() if LOCAL_FEATURE != 'LoFTR': detect_features(img_fnames, 2048, feature_dir=feature_dir, upright=True, device=device, resize_small_edge_to=600 ) gc.collect() t=time() -t timings['feature_detection'].append(t) print(f'Features detected in {t:.4f} sec') t=time() match_features(img_fnames, index_pairs, feature_dir=feature_dir,device=device) else: match_loftr(img_fnames, index_pairs, feature_dir=feature_dir, device=device, resize_to=(600, 800)) t=time() -t timings['feature_matching'].append(t) print(f'Features matched in {t:.4f} sec') database_path = f'{feature_dir}/colmap.db' if os.path.isfile(database_path): os.remove(database_path) gc.collect() import_into_colmap(img_dir, feature_dir=feature_dir,database_path=database_path) output_path = f'{feature_dir}/colmap_rec_{LOCAL_FEATURE}' t=time() pycolmap.match_exhaustive(database_path) t=time() - t timings['RANSAC'].append(t) print(f'RANSAC in {t:.4f} sec')

img_fnames = [f'{src}/test/{x}' for x in data_dict[dataset][scene]] # 获取当前场景中的图像文件名列表 print(f"Got {len(img_fnames)} images") feature_dir = f'featureout/{dataset}{scene}' # 设置特征...

详细解释一下这段代码，每一句给出详细注解：results_df = pd.DataFrame(columns=['image_path', 'dataset', 'scene', 'rotation_matrix', 'translation_vector']) for dataset_scene in tqdm(datasets_scenes, desc='Running pipeline'): dataset, scene = dataset_scene.split('/') img_dir = f"{INPUT_ROOT}/{'train' if DEBUG else 'test'}/{dataset}/{scene}/images" if not os.path.exists(img_dir): continue feature_dir = f"{DATA_ROOT}/featureout/{dataset}/{scene}" os.system(f"rm -rf {feature_dir}") os.makedirs(feature_dir) fnames = sorted(glob(f"{img_dir}/*")) print('fnames',len(fnames)) # Similarity pipeline if sim_th: index_pairs, h_w_exif = get_image_pairs_filtered(similarity_model, fnames=fnames, sim_th=sim_th, min_pairs=20, all_if_less=20) else: index_pairs, h_w_exif = get_img_pairs_all(fnames=fnames) # Matching pipeline matching_pipeline(matching_model=matching_model, fnames=fnames, index_pairs=index_pairs, feature_dir=feature_dir) # Colmap pipeline maps = colmap_pipeline(img_dir, feature_dir, h_w_exif=h_w_exif) # Postprocessing results = postprocessing(maps, dataset, scene) # Create submission for fname in fnames: image_id = '/'.join(fname.split('/')[-4:]) if image_id in results: R = results[image_id]['R'].reshape(-1) T = results[image_id]['t'].reshape(-1) else: R = np.eye(3).reshape(-1) T = np.zeros((3)) new_row = pd.DataFrame({'image_path': image_id, 'dataset': dataset, 'scene': scene, 'rotation_matrix': arr_to_str(R), 'translation_vector': arr_to_str(T)}, index=[0]) results_df = pd.concat([results_df, new_row]).reset_index(drop=True)

img_dir = f"{INPUT_ROOT}/{'train' if DEBUG else 'test'}/{dataset}/{scene}/images" if not os.path.exists(img_dir): continue feature_dir = f"{DATA_ROOT}/featureout/{dataset}/{scene}" os.system(f"rm...

all_pairs = dict(nx.all_pairs_dijkstra_path_length(G))如何输出？

for start_node, dist_dict in all_pairs.items(): for end_node, dist in dist_dict.items(): print(f"起始节点：{start_node}，终止节点：{end_node}，最短路径长度：{dist}") 这样就可以将all_pairs字典...

from pdb import set_trace as st import os import numpy as np import cv2 import argparse parser = argparse.ArgumentParser('create image pairs') parser.add_argument('--fold_A', dest='fold_A', help='input directory for image A', type=str, default='./dataset/blurred') parser.add_argument('--fold_B', dest='fold_B', help='input directory for image B', type=str, default='./dataset/sharp') parser.add_argument('--fold_AB', dest='fold_AB', help='output directory', type=str, default='../dataset/out') parser.add_argument('--num_imgs', dest='num_imgs', help='number of images',type=int, default=1000000) parser.add_argument('--use_AB', dest='use_AB', help='if true: (0001_A, 0001_B) to (0001_AB)',action='store_true') args = parser.parse_args() for arg in vars(args): print('[%s] = ' % arg, getattr(args, arg)) splits = os.listdir(args.fold_A) for sp in splits: img_fold_A = os.path.join(args.fold_A, sp) img_fold_B = os.path.join(args.fold_B, sp) img_list = os.listdir(img_fold_A) if args.use_AB: img_list = [img_path for img_path in img_list if '_A.' in img_path] num_imgs = min(args.num_imgs, len(img_list)) print('split = %s, use %d/%d images' % (sp, num_imgs, len(img_list))) img_fold_AB = os.path.join(args.fold_AB, sp) if not os.path.isdir(img_fold_AB): os.makedirs(img_fold_AB) print('split = %s, number of images = %d' % (sp, num_imgs)) for n in range(num_imgs): name_A = img_list[n] path_A = os.path.join(img_fold_A, name_A) if args.use_AB: name_B = name_A.replace('_A.', '_B.') else: name_B = name_A path_B = os.path.join(img_fold_B, name_B) if os.path.isfile(path_A) and os.path.isfile(path_B): name_AB = name_A if args.use_AB: name_AB = name_AB.replace('_A.', '.') # remove _A path_AB = os.path.join(img_fold_AB, name_AB) im_A = cv2.imread(path_A, cv2.IMREAD_COLOR) im_B = cv2.imread(path_B, cv2.IMREAD_COLOR) im_AB = np.concatenate([im_A, im_B], 1) cv2.imwrite(path_AB, im_AB),运行上述代码，提示错误：NotADirectoryError: [WinError 267] 目录名称无效。: 'D:\Users\Administrator\PycharmProjects\pythonProject\DeblurGAN-master\datasets\blurred\1.jpg'

具体来说，是出现了一个名为'1.jpg'的文件，而在代码中尝试将其作为目录来处理。你需要检查代码中是否有将文件名误认为是目录名的情况，可以使用os.path.isdir()函数来判断是否为目录。同时，你也可以手动检查一下...

all_pairs = dict(nx.all_pairs_dijkstra_path_length(G))如何写入新的excel文件

for start_node, dist_dict in all_pairs.items(): for end_node, dist in dist_dict.items(): rows.append({'起始节点': start_node, '终止节点': end_node, '最短路径长度': dist}) df_result = pd.DataFrame...

all_pairs = dict(nx.all_pairs_dijkstra_path_length(G))如何写入新的csv文件

for start_node, dist_dict in all_pairs.items(): for end_node, dist in dist_dict.items(): writer.writerow({'起始节点': start_node, '终止节点': end_node, '最短路径长度': dist}) 这段代码会将all_...

self.entity_vec.weight.data = normalize_emb(self.entity_vec.weight.data) self.relation_vec.weight.data = normalize_emb(self.relation_vec.weight.data) self.concept_vec.weight.data[:, :-1] = normalize_emb(self.concept_vec.weight.data[:, :-1]) self.concept_vec.weight.data[:, -1] = normalize_radius(self.concept_vec.weight.data[:, -1]) self.optimizer.zero_grad() for k in range(batchSize): i = random.randint(0, self.D.trainSize - 1) if i < len(self.D.fb_r): cut = 1 - epoch * self.args.hrt_cut / nepoch pairs[0].append(self.trainHLR(i, cut)) elif i < len(self.D.fb_r) + len(self.D.instanceOf): cut = 1 - epoch * self.args.ins_cut / nepoch pairs[1].append(self.trainInstanceOf(i, cut)) else: cut = 1 - epoch * self.args.sub_cut / nepoch pairs[2].append(self.trainSubClassOf(i, cut))

这段代码看起来像是在进行一些实体关系的训练，其中包括对实体向量、关系向量和概念向量进行归一化操作，然后进行随机选择训练数据并根据不同的类型进行训练。具体来说，如果选择的数据是三元组（头实体、关系、尾...

classified_ids = np.array(classified_ids) classified_ids = classified_ids.flatten() max_view = 20 current_view = 1 fig = plt.figure() fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.2, wspace=0.2) mis_pairs = {} for i, val in enumerate(classified_ids == t_test): if not val: ax = fig.add_subplot(4, 5, current_view, xticks=[], yticks=[]) ax.imshow(x_test[i].reshape(28, 28), cmap=plt.cm.gray_r, interpolation='nearest') mis_pairs[current_view] = (t_test[i], classified_ids[i]) current_view += 1 if current_view > max_view: break

这段代码的作用是将分类错误的样本可视化，并将其真实标签和预测标签保存到 mis_pairs 字典中。具体来说，代码首先将 classified_ids 列表转换为形状为 (x_test.shape[0],) 的一维数组，并将其展平为一维，方便后续...

这段代码无法运行，请为我修改一下并添加注释：import numpy as np import pandas as pd import matplotlib.pyplot as plt # 读入鸢尾花数据集 df = pd.read_csv('iris_pca.csv', header=None) # 将数据转换为NumPy数组 X = df.iloc[:, :-1].values y = df.iloc[:, -1].values # 对所有样本进行中心化 X_mean = np.mean(X, axis=0) X_centered = X - X_mean # 计算样本的协方差矩阵 cov_matrix = np.cov(X_centered, rowvar=False) # 对协方差矩阵做特征值分解 eigenvalues, eigenvectors = np.linalg.eigh(cov_matrix) # 将特征向量按照对应的特征值从大到小排序 eig_pairs = [(np.abs(eigenvalues[i]), eigenvectors[:, i]) for i in range(len(eigenvalues))] eig_pairs.sort(reverse=True) # 取最大的d个特征值所对应的特征向量 d = 2 w = np.hstack((eig_pairs[i][1].reshape(4, 1)) for i in range(d)) # 计算投影矩阵 X_new = X_centered.dot(w) # 将降维后的数据和标记合并 data_new = np.hstack((X_new, y.reshape(len(y), 1))) # 将降维后的数据可视化呈现 plt.scatter(X_new[:, 0], X_new[:, 1], c=y) plt.xlabel('PC1') plt.ylabel('PC2') plt.show()

w = np.hstack((eig_pairs[i][1].reshape(4, 1)) for i in range(d)) # 计算投影矩阵 X_new = X_centered.dot(w) # 将降维后的数据和标记合并 data_new = np.hstack((X_new, y.reshape(len(y), 1))) # 将降维后的...

解释这行代码all_pairs_shortest_path_lengths = dict(nx.all_pairs_dijkstra_path_length(G))

这行代码的作用是使用 NetworkX 库中的 all_pairs_dijkstra_path_length 函数计算给定图 G 中所有节点对的最短路径长度，并将结果保存在一个字典 all_pairs_shortest_path_lengths 中。其中，all_pairs_dijkstra_...

sum_score = score.reduce(lambda x,y:x+y)这个会执行什么

This code snippet is using PySpark's RDD (Resilient Distributed Datasets) API to calculate the sum of all the values in the score RDD. The reduce method is a higher-order function that takes a ...

def doTrain(self): nbatches = self.args.nbatches nepoch = self.args.nepoch batchSize = int(self.D.trainSize / nbatches) allreadyindex = 0 dis_a_L, dis_b_L = [], [] dis_count = 0 for epoch in range(nepoch): res = 0 for batch in range(nbatches): losses = [] stime = time.time() pairs = [[], [], []]

在每个批次中，模型会从数据集中取出一定数量的数据对（pairs），然后计算损失函数（losses）并更新模型参数。其中，batchSize 是每个批次的数据对数量，nbatches 是训练轮数，nepoch 是训练的总轮数，...

import pandas as pd import numpy as np import networkx as nx import matplotlib.pyplot as plt df = pd.read_excel(r"C:\Users\li'yi'jie\Desktop\运筹学网络规划数据.xlsx") edges = [] for i in range(len(df)): edge = { "id": df.loc[i, "边的编号"], "tail": df.loc[i, "边的尾节点"], "head": df.loc[i, "边的头节点"], "length": df.loc[i, "长度"], "capacity": df.loc[i, "容量"] } edges.append(edge) plt.figure(figsize=(15,15)) G = nx.DiGraph() for edge in edges: G.add_edge(edge["tail"], edge["head"], weight=edge["length"]) pos = nx.spring_layout(G) nx.draw(G, pos, with_labels=True) labels = nx.get_edge_attributes(G, "weight") nx.draw_networkx_edge_labels(G, pos, edge_labels=labels) #nx.draw_networkx_edge_labels(G, pos, edge_labels=labels, label_pos=0.3) plt.show() all_pairs = dict(nx.all_pairs_dijkstra_path_length(G)) rows = [] for start_node, dist_dict in all_pairs.items(): for end_node, dist in dist_dict.items(): rows.append({'起始节点': start_node, '终止节点': end_node, '最短路径长度': dist}) df_result = pd.DataFrame(rows) df_result.to_excel('all_pairs.xlsx', index=False)请问在写入excel的时候，能否把经过的边的编号一起写入呢？麻烦给下修改后的代码，谢谢你！

for i in range(len(path_nodes)-1): tail = path_nodes[i] head = path_nodes[i+1] for edge in edges: if edge["tail"] == tail and edge["head"] == head: path_edges.append(edge["id"]) break path_...

相关推荐

create_pairs_for_valid.py

PairsTrading_FEX.zip_Pairs_PairsTrading_FEX_pairs trading_tradin

My Complete Genome_6k Base-Pairs of Phenotype SNPs_Complete Raw Data.zip

all_pairs = dict(nx.all_pairs_dijkstra_path_length(G))如何输出？

all_pairs = dict(nx.all_pairs_dijkstra_path_length(G))如何写入新的excel文件

all_pairs = dict(nx.all_pairs_dijkstra_path_length(G))如何写入新的csv文件

解释这行代码all_pairs_shortest_path_lengths = dict(nx.all_pairs_dijkstra_path_length(G))

sum_score = score.reduce(lambda x,y:x+y)这个会执行什么

def doTrain(self): nbatches = self.args.nbatches nepoch = self.args.nepoch batchSize = int(self.D.trainSize / nbatches) allreadyindex = 0 dis_a_L, dis_b_L = [], [] dis_count = 0 for epoch in range(nepoch): res = 0 for batch in range(nbatches): losses = [] stime = time.time() pairs = [[], [], []]

最新推荐

yolov5-face-landmarks-opencv

zigbee-cluster-library-specification

管理建模和仿真的文件

实现实时数据湖架构：Kafka与Hive集成

2． 通过python绘制y=e-xsin(2πx)图像

JSBSim Reference Manual

"互动学习：行动中的多样性与论文攻读经历"

实现实时监控告警系统：Kafka与Grafana整合

导入numpy库，创建两个包含9个随机数的3*3的矩阵，将两个矩阵分别打印出来，计算两个数组的点积并打印出来。（random.randn()、dot（）函数）

c++校园超市商品信息管理系统课程设计说明书(含源代码) (2).pdf

2．通过python绘制y=e-xsin(2πx)图像