├── LICENSE ├── README.md ├── clcifar.py ├── cll_experiment ├── __init__.py ├── algo.py ├── datasets.py ├── models.py ├── utils.py └── valid.py ├── clmicro_imagenet.py ├── design-layout-mturk.html ├── requirement.txt ├── run.sh ├── run_all_experiments.sh ├── train.py └── worker_ids ├── clcifar10_workerids.txt ├── clcifar20_workerids.txt ├── clmin10_annotatorID_annotatorTime.csv ├── clmin10_workerids.txt └── clmin20_workerids.txt /LICENSE: -------------------------------------------------------------------------------- 1 | MIT License 2 | 3 | Copyright (c) 2023 NTUCSIE CLLab 4 | 5 | Permission is hereby granted, free of charge, to any person obtaining a copy 6 | of this software and associated documentation files (the "Software"), to deal 7 | in the Software without restriction, including without limitation the rights 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell 9 | copies of the Software, and to permit persons to whom the Software is 10 | furnished to do so, subject to the following conditions: 11 | 12 | The above copyright notice and this permission notice shall be included in all 13 | copies or substantial portions of the Software. 14 | 15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR 16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, 17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE 18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER 19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, 20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE 21 | SOFTWARE. 22 | -------------------------------------------------------------------------------- /README.md: -------------------------------------------------------------------------------- 1 | # CLImage Dataset 2 | 3 | The dataset repo of "CLImage: Human-Annotated Datasets for Complementary-Label Learning" 4 | 5 | ## Abstract 6 | This repo contains four datasets: CLCIFAR10, CLCIFAR20, CLMicroImageNet10, and CLMicroImageNet20 with human annotated complementary labels for complementary label learning tasks. 7 | 8 | TL;DR: the download links to CLCIFAR and CLMicroImageNet dataset 9 | * CLCIFAR10: [clcifar10.pkl](https://drive.google.com/file/d/1uNLqmRUkHzZGiSsCtV2-fHoDbtKPnVt2/view?usp=sharing) (148MB) 10 | * CLCIFAR20: [clcifar20.pkl](https://drive.google.com/file/d/1PhZsyoi1dAHDGlmB4QIJvDHLf_JBsFeP/view?usp=sharing) (151MB) 11 | * CLMicroImageNet10 Train: [clmicro_imagenet10_train.pkl](https://drive.google.com/file/d/1k02mwMpnBUM9de7TiJLBaCuS8myGuYFx/view?usp=sharing) (55MB) 12 | * CLMicroImageNet10 Test: [clmicro_imagenet10_test.pkl](https://drive.google.com/file/d/1e8fZN8swbg9wc6BSOC0A5KHIqCY2C7me/view?usp=sharing) (6MB) 13 | * CLMicroImageNet20 Train: [clmicro_imagenet20_train.pkl](https://drive.google.com/file/d/1Urdxs_QTxbb1gDBpmjP09Q35btckI3_d/view?usp=sharing) (119MB) 14 | * CLMicroImageNet20 Test: [clmicro_imagenet20_test.pkl](https://drive.google.com/file/d/1EdBCrifSrIIUg1ioPWA-ZLEHO53P4NPl/view?usp=sharing) (11MB) 15 | 16 | ## Annotation Task Design and Deployment on Amazon MTurk 17 | To collect human-annotated labels, we used Amazon Mechanical Turk (MTurk) to deploy our annotation task. The layout and interface design for the MTurk task can be found in the file `design-layout-mturk.html`. 18 | 19 | In each task, a single image was presented alongside the question: `Choose any one "incorrect" label for this image`? Annotators were given four example labels to choose from (e.g., `dog, cat, ship, bird`), and were instructed to select the one that does not correctly describe the image. 20 | 21 | ## Reproduce Code 22 | 23 | The python version should be 3.8.10 or above. 24 | 25 | ```bash 26 | pip3 install -r requirement.txt 27 | bash run.sh 28 | ``` 29 | 30 | ## CLCIFAR10 31 | 32 | This Complementary labeled CIFAR10 dataset contains 3 human-annotated complementary labels for all 50000 images in the training split of CIFAR10. The workers are from Amazon Mechanical Turk(https://www.mturk.com). We randomly sampled 4 different labels for 3 different annotators, so each image would have 3 (probably repeated) complementary labels. 33 | 34 | For more details, please visit our paper at link. 35 | 36 | ### Dataset Structure 37 | 38 | Dataset download link: [clcifar10.pkl](https://clcifar.s3.us-west-2.amazonaws.com/clcifar10.pkl) (148MB) 39 | 40 | We use `pickle` package to save and load the dataset objects. Use the function `pickle.load` to load the dataset dictionary object `data` in Python. 41 | 42 | ```python 43 | data = pickle.load(open("clcifar10.pkl", "rb")) 44 | # keys of data: 'names', 'images', 'ord_labels', 'cl_labels' 45 | ``` 46 | 47 | `data` would be a dictionary object with four keys: `names`, `images`, `ord_labels`, `cl_labels`. 48 | 49 | * `names`: The list of filenames strings. This filenames are same as the ones in CIFAR10 50 | 51 | * `images`: A `numpy.ndarray` of size (32, 32, 3) representing the image data with 3 channels, 32*32 resolution. 52 | 53 | * `ord_labels`: The ordinary labels of the images, and they are labeled from 0 to 9 as follows: 54 | 55 | 0: airplane 56 | 1: automobile 57 | 2: bird 58 | 3: cat 59 | 4: deer 60 | 5: dog 61 | 6: frog 62 | 7: horse 63 | 8: ship 64 | 9: truck 65 | 66 | * `cl_labels`: Three complementary labels for each image from three different workers. 67 | 68 | ### HIT Design 69 | 70 | Human Intelligence Task (HIT) is the unit of works in Amazon mTurk. We have several designs to make the submission page friendly: 71 | 72 | * Enlarge the tiny 32\*32 pixels images to 200\*200 pixels for clarity. 73 | 74 | ![](https://i.imgur.com/SGVCVXV.mp4) 75 | 76 | ## CLCIFAR20 77 | 78 | This Complementary labeled CIFAR100 dataset contains 3 human annotated complementary labels for all 50000 images in the training split of CIFAR100. We group 4-6 categories as a superclass according to [[1]](https://arxiv.org/abs/2110.12088) and collect the complementary labels of these 20 superclasses. The workers are from Amazon Mechanical Turk(https://www.mturk.com). We randomly sampled 4 different labels for 3 different annotators, so each image would have 3 (probably repeated) complementary labels. 79 | 80 | ### Dataset Structure 81 | 82 | Dataset download link: [clcifar20.pkl](https://clcifar.s3.us-west-2.amazonaws.com/clcifar20.pkl) (151MB) 83 | 84 | We use `pickle` package to save and load the dataset objects. Use the function `pickle.load` to load the dataset dictionary object `data` in Python. 85 | 86 | ```python 87 | data = pickle.load(open("clcifar20.pkl", "rb")) 88 | # keys of data: 'names', 'images', 'ord_labels', 'cl_labels' 89 | ``` 90 | 91 | `data` would be a dictionary object with four keys: `names`, `images`, `ord_labels`, `cl_labels`. 92 | 93 | * `names`: The list of filenames strings. This filenames are same as the ones in CIFAR20 94 | 95 | * `images`: A `numpy.ndarray` of size (32, 32, 3) representing the image data with 3 channels, 32*32 resolution. 96 | 97 | * `ord_labels`: The ordinary labels of the images, and they are labeled from 0 to 19 as follows: 98 | 99 | 0: aquatic_mammals 100 | 1: fish 101 | 2: flowers 102 | 3: food_containers 103 | 4: fruit, vegetables and mushrooms 104 | 5: household electrical devices 105 | 6: household furniture 106 | 7: insects 107 | 8: large carnivores and bear 108 | 9: large man-made outdoor things 109 | 10: large natural outdoor scenes 110 | 11: large omnivores and herbivores 111 | 12: medium-sized mammals 112 | 13: non-insect invertebrates 113 | 14: people 114 | 15: reptiles 115 | 16: small mammals 116 | 17: trees 117 | 18: transportation vehicles 118 | 19: non-transportation vehicles 119 | 120 | * `cl_labels`: Three complementary labels for each image from three different workers. 121 | 122 | ### HIT Design 123 | 124 | Human Intelligence Task (HIT) is the unit of works in Amazon mTurk. We have several designs to make the submission page friendly: 125 | 126 | * Hyperlink to all the 10 problems that decrease the scrolling time 127 | * Example images of the superclasses for better understanding of the categories 128 | * Enlarge the tiny 32\*32 pixels images to 200\*200 pixels for clarity. 129 | 130 | ![](https://i.imgur.com/wg5pV2S.mp4) 131 | 132 | ## CLMicroImageNet10 133 | 134 | This Complementary labeled MicroImageNet10 dataset contains 3 human annotated complementary labels for all 5000 images in the training split of TinyImageNet200. The workers are from Amazon Mechanical Turk(https://www.mturk.com). We randomly sampled 4 different labels for 3 different annotators, so each image would have 3 (probably repeated) complementary labels. 135 | 136 | For more details, please visit our paper at link. 137 | 138 | ### Dataset Structure 139 | 140 | Training set download link: [clmicro_imagenet10_train.pkl](https://drive.google.com/file/d/1k02mwMpnBUM9de7TiJLBaCuS8myGuYFx/view?usp=sharing) (55MB) 141 | 142 | Testing set download link: [clmicro_imagenet10_test.pkl](https://drive.google.com/file/d/1e8fZN8swbg9wc6BSOC0A5KHIqCY2C7me/view?usp=sharing) (6MB) 143 | 144 | We use `pickle` package to save and load the dataset objects. Use the function `pickle.load` to load the dataset dictionary object `data` in Python. 145 | 146 | ```python 147 | data = pickle.load(open("clmicro_imagenet10_train.pkl", "rb")) 148 | # keys of data: 'names', 'images', 'ord_labels', 'cl_labels' 149 | ``` 150 | 151 | `data` would be a dictionary object with four keys: `names`, `images`, `ord_labels`, `cl_labels`. 152 | 153 | * `names`: The list of filenames strings. This filenames are same as the ones in MicroImageNet10 154 | 155 | * `images`: A `numpy.ndarray` of size (32, 32, 3) representing the image data with 3 channels, 64*64 resolution. 156 | 157 | * `ord_labels`: The ordinary labels of the images, and they are labeled from 0 to 9 as follows: 158 | 159 | 0: sulphur-butterfly 160 | 1: backpack 161 | 2: cardigan 162 | 3: kimono 163 | 4: magnetic-compass 164 | 5: oboe 165 | 6: scandal 166 | 7: torch 167 | 8: pizza 168 | 9: alp 169 | 170 | * `cl_labels`: Three complementary labels for each image from three different workers. 171 | 172 | ### HIT Design 173 | 174 | Human Intelligence Task (HIT) is the unit of works in Amazon mTurk. We have several designs to make the submission page friendly: 175 | 176 | * Enlarge the tiny 64\*64 pixels images to 200\*200 pixels for clarity. 177 | 178 | ## CLMicroImageNet20 179 | 180 | This Complementary labeled MicroImageNet20 dataset contains 3 human annotated complementary labels for all 10000 images in the training split of TinyImageNet200. The workers are from Amazon Mechanical Turk(https://www.mturk.com). We randomly sampled 4 different labels for 3 different annotators, so each image would have 3 (probably repeated) complementary labels. 181 | 182 | For more details, please visit our paper at link. 183 | 184 | ### Dataset Structure 185 | 186 | Training set download link: [clmicro_imagenet20_train.pkl](https://drive.google.com/file/d/1Urdxs_QTxbb1gDBpmjP09Q35btckI3_d/view?usp=sharing) (119MB) 187 | 188 | Testing set download link: [clmicro_imagenet20_test.pkl](https://drive.google.com/file/d/1EdBCrifSrIIUg1ioPWA-ZLEHO53P4NPl/view?usp=sharing) (11MB) 189 | 190 | We use `pickle` package to save and load the dataset objects. Use the function `pickle.load` to load the dataset dictionary object `data` in Python. 191 | 192 | ```python 193 | data = pickle.load(open("clmicro_imagenet20_train.pkl", "rb")) 194 | # keys of data: 'names', 'images', 'ord_labels', 'cl_labels' 195 | ``` 196 | 197 | `data` would be a dictionary object with four keys: `names`, `images`, `ord_labels`, `cl_labels`. 198 | 199 | * `names`: The list of filenames strings. This filenames are same as the ones in MicroImageNet20 200 | 201 | * `images`: A `numpy.ndarray` of size (32, 32, 3) representing the image data with 3 channels, 64*64 resolution. 202 | 203 | * `ord_labels`: The ordinary labels of the images, and they are labeled from 0 to 19 as follows: 204 | 205 | 0: tailed frog 206 | 1: scorpion 207 | 2: snail 208 | 3: american lobster 209 | 4: tabby 210 | 5: persian cat 211 | 6: gazelle 212 | 7: chimpanzee 213 | 8: bannister 214 | 9: barrel 215 | 10: christmas stocking 216 | 11: gasmask 217 | 12: hourglass 218 | 13: iPod 219 | 14: scoreboard 220 | 15: snorkel 221 | 16: suspension bridge 222 | 17: torch 223 | 18: tractor 224 | 19: triumphal arch 225 | 226 | * `cl_labels`: Three complementary labels for each image from three different workers. 227 | 228 | ### HIT Design 229 | 230 | Human Intelligence Task (HIT) is the unit of works in Amazon mTurk. We have several designs to make the submission page friendly: 231 | 232 | * Enlarge the tiny 64\*64 pixels images to 200\*200 pixels for clarity. 233 | 234 | ### Worker IDs 235 | 236 | We are also sharing the list of worker IDs that contributed to labeling our CLImage_Dataset. To protect the privacy of the worker IDs, we hashed the original *worker IDs* using SHA-1 encryption. For further details, please refer to the **worker_ids** folder, which contains the worker IDs for each dataset. 237 | 238 | ### Reference 239 | 240 | [[1]](https://arxiv.org/abs/2110.12088) Jiaheng Wei, Zhaowei Zhu, and Hao Cheng. Learning with Noisy Labels Revisited: A Study Using Real-World Human Annotations. arXiv preprint arXiv:2110.12088, 2021. 241 | -------------------------------------------------------------------------------- /clcifar.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import Dataset 2 | import os 3 | import urllib.request 4 | from tqdm import tqdm 5 | import pickle 6 | import gdown 7 | 8 | class CLCIFAR10(Dataset): 9 | """CLCIFAR10 training set 10 | 11 | The training set of CIFAR10 with human annotated complementary labels. 12 | Containing 50000 samples, each with one ordinary label and the first one of the three complementary labels 13 | 14 | Args: 15 | root: the path to store the dataset 16 | transform: feature transformation function 17 | """ 18 | def __init__(self, root="./data", transform=None): 19 | 20 | os.makedirs(os.path.join(root, 'clcifar10'), exist_ok=True) 21 | dataset_path = os.path.join(root, 'clcifar10', f"clcifar10.pkl") 22 | 23 | if not os.path.exists(dataset_path): 24 | gdown.download( 25 | id="1uNLqmRUkHzZGiSsCtV2-fHoDbtKPnVt2", output=dataset_path 26 | ) 27 | 28 | data = pickle.load(open(dataset_path, "rb")) 29 | 30 | self.transform = transform 31 | self.input_dim = 32 * 32 * 3 32 | self.num_classes = 10 33 | 34 | self.targets = [labels[0] for labels in data["cl_labels"]] 35 | self.data = data["images"] 36 | self.ord_labels = data["ord_labels"] 37 | 38 | def __len__(self): 39 | return len(self.data) 40 | 41 | def __getitem__(self, index): 42 | image = self.data[index] 43 | if self.transform is not None: 44 | image = self.transform(image) 45 | return image, self.targets[index] 46 | 47 | class CLCIFAR20(Dataset): 48 | """CLCIFAR20 training set 49 | 50 | The training set of CIFAR20 with human annotated complementary labels. 51 | Containing 50000 samples, each with one ordinary label and the first one of the three complementary labels 52 | 53 | Args: 54 | root: the path to store the dataset 55 | transform: feature transformation function 56 | """ 57 | def __init__(self, root="./data", transform=None): 58 | 59 | os.makedirs(os.path.join(root, 'clcifar20'), exist_ok=True) 60 | dataset_path = os.path.join(root, 'clcifar20', f"clcifar20.pkl") 61 | 62 | if not os.path.exists(dataset_path): 63 | gdown.download( 64 | id="1PhZsyoi1dAHDGlmB4QIJvDHLf_JBsFeP", output=dataset_path 65 | ) 66 | 67 | data = pickle.load(open(dataset_path, "rb")) 68 | 69 | self.transform = transform 70 | self.input_dim = 32 * 32 * 3 71 | self.num_classes = 20 72 | 73 | self.targets = [labels[0] for labels in data["cl_labels"]] 74 | self.data = data["images"] 75 | self.ord_labels = data["ord_labels"] 76 | 77 | def __len__(self): 78 | return len(self.data) 79 | 80 | def __getitem__(self, index): 81 | image = self.data[index] 82 | if self.transform is not None: 83 | image = self.transform(image) 84 | return image, self.targets[index] -------------------------------------------------------------------------------- /cll_experiment/__init__.py: -------------------------------------------------------------------------------- https://raw.githubusercontent.com/ntucllab/CLImage_Dataset/37e735d26d7d0aba37e35a8eb8650ab66cddf632/cll_experiment/__init__.py -------------------------------------------------------------------------------- /cll_experiment/algo.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | import numpy as np 4 | 5 | def ga_loss(outputs, labels, class_prior, T, num_classes): 6 | device = labels.device 7 | if torch.det(T) != 0: 8 | Tinv = torch.inverse(T) 9 | else: 10 | Tinv = torch.pinverse(T) 11 | batch_size = outputs.shape[0] 12 | outputs = -F.log_softmax(outputs, dim=1) 13 | loss_mat = torch.zeros([num_classes, num_classes], device=device) 14 | for k in range(num_classes): 15 | mask = k == labels 16 | indexes = torch.arange(batch_size).to(device) 17 | indexes = torch.masked_select(indexes, mask) 18 | if indexes.shape[0] > 0: 19 | outputs_k = outputs[indexes] 20 | # outputs_k = torch.gather(outputs, 0, indexes.view(-1, 1).repeat(1,num_classes)) 21 | loss_mat[k] = class_prior[k] * outputs_k.mean(0) 22 | loss_vec = torch.zeros(num_classes, device=device) 23 | for k in range(num_classes): 24 | loss_vec[k] = torch.inner(Tinv[k], loss_mat[k]) 25 | return loss_vec 26 | 27 | def l_mae(y, output): 28 | return 2 - 2 * F.softmax(output, dim=1)[:, y] 29 | 30 | def l_cce(y, output): 31 | return -F.log_softmax(output, dim=1)[:, y] 32 | 33 | def l_wmae(w): 34 | def real_l_wmae(y, output): 35 | return w[y] * l_mae(y, output) 36 | return real_l_wmae 37 | 38 | def l_gce(y, output, q=0.7): 39 | return (1-F.softmax(output, dim=1)[:, y].pow(q)) / q 40 | 41 | def l_sl(y, output, alpha=0.1, beta=1.0, A=-4): 42 | def l_rce(y, output, A): 43 | return -A * F.softmax(output, dim=1).sum(dim=1) - F.softmax(output, dim=1)[:, y] 44 | return alpha * l_cce(y, output) + beta * l_rce(y, output, A) 45 | 46 | def robust_ga_loss(outputs, labels, class_prior, T, num_classes, algo_name): 47 | device = labels.device 48 | if torch.det(T) != 0: 49 | Tinv = torch.inverse(T) 50 | else: 51 | Tinv = torch.pinverse(T) 52 | 53 | if algo_name == 'rob-mae': 54 | loss_func = l_mae 55 | elif algo_name == 'rob-cce': 56 | loss_func = l_cce 57 | elif algo_name == 'rob-wmae': 58 | loss_func = l_wmae(Tinv.sum(dim=0).squeeze()) 59 | elif algo_name == 'rob-gce': 60 | loss_func = l_gce 61 | elif algo_name == 'rob-sl': 62 | loss_func = l_sl 63 | else: 64 | raise NotImplementedError 65 | 66 | loss_vec = torch.zeros(num_classes, device=device) 67 | for k in range(num_classes): 68 | for j in range(num_classes): 69 | mask = j == labels 70 | indexes = torch.arange(outputs.shape[0]).to(device) 71 | indexes = torch.masked_select(indexes, mask) 72 | if indexes.shape[0] > 0: 73 | loss_vec[k] += class_prior[j] * Tinv[j][k] * loss_func(k, outputs[indexes]).mean() 74 | return loss_vec -------------------------------------------------------------------------------- /cll_experiment/datasets.py: -------------------------------------------------------------------------------- 1 | import os 2 | from torch.utils.data import DataLoader, Dataset 3 | import torchvision 4 | import torchvision.transforms as transforms 5 | import torch 6 | import numpy as np 7 | import torch.optim as optim 8 | import torch.nn as nn 9 | from tqdm import tqdm 10 | from copy import deepcopy 11 | import torch.nn.functional as F 12 | import urllib.request 13 | import pickle 14 | import gdown 15 | from PIL import Image 16 | 17 | class CLMicro_ImageNet10(Dataset): 18 | def __init__(self, root="./data", train=True, transform=None, data_cleaning_rate=None): 19 | os.makedirs(os.path.join(root, 'clmicro_imagenet10'), exist_ok=True) 20 | if train: 21 | dataset_path = f"{root}/clmicro_imagenet10_train.pkl" 22 | gid = "1k02mwMpnBUM9de7TiJLBaCuS8myGuYFx" 23 | else: 24 | dataset_path = f"{root}/clmicro_imagenet10_test.pkl" 25 | gid = "1e8fZN8swbg9wc6BSOC0A5KHIqCY2C7me" 26 | if not os.path.exists(dataset_path): 27 | os.makedirs(root, exist_ok=True) 28 | gdown.download(id=gid, output=dataset_path) 29 | with open(dataset_path, "rb") as f: 30 | data = pickle.load(f) 31 | 32 | 33 | if train: 34 | self.targets = [] 35 | self.data = [] 36 | self.ord_labels = [] 37 | noise = {'targets':[], 'data':[], 'ord_labels':[]} 38 | for i in range(len(data["cl_labels"])): 39 | cl = data["cl_labels"][i][0] 40 | if cl != data["ord_labels"][i]: 41 | self.targets.append(cl) 42 | self.data.append(data["images"][i]) 43 | self.ord_labels.append(data["ord_labels"][i]) 44 | else: 45 | noise['targets'].append(data["cl_labels"][i][0]) 46 | noise['data'].append(data["images"][i]) 47 | noise['ord_labels'].append(data["ord_labels"][i]) 48 | 49 | assert((0 <= data_cleaning_rate) and (data_cleaning_rate <= 1)) 50 | noise_num = int(len(noise['data']) * data_cleaning_rate) 51 | self.targets.extend(noise['targets'][noise_num:]) 52 | self.data.extend(noise['data'][noise_num:]) 53 | self.ord_labels.extend(noise['ord_labels'][noise_num:]) 54 | 55 | indexes = np.arange(len(self.data)) 56 | np.random.shuffle(indexes) 57 | self.targets = [self.targets[i] for i in indexes] 58 | self.data = [self.data[i] for i in indexes] 59 | self.ord_labels = [self.ord_labels[i] for i in indexes] 60 | else: 61 | self.data = data["images"] 62 | self.ord_labels = data["ord_labels"] 63 | self.targets = data["ord_labels"] 64 | self.transform = transform 65 | self.num_classes = 10 66 | self.input_dim = 64 * 64 * 3 67 | 68 | def __len__(self): 69 | return len(self.data) 70 | 71 | def __getitem__(self, index): 72 | image = self.data[index] 73 | if self.transform is not None: 74 | image = self.transform(image) 75 | return image, self.targets[index] 76 | 77 | class CLMicro_ImageNet20(Dataset): 78 | def __init__(self, root="./data", train=True, transform=None, data_cleaning_rate=None): 79 | os.makedirs(os.path.join(root, 'clmicro_imagenet20'), exist_ok=True) 80 | if train: 81 | dataset_path = f"{root}/clmicro_imagenet20_train.pkl" 82 | gid = "1Urdxs_QTxbb1gDBpmjP09Q35btckI3_d" 83 | else: 84 | dataset_path = f"{root}/clmicro_imagenet20_test.pkl" 85 | gid = "1EdBCrifSrIIUg1ioPWA-ZLEHO53P4NPl" 86 | if not os.path.exists(dataset_path): 87 | os.makedirs(root, exist_ok=True) 88 | gdown.download(id=gid, output=dataset_path) 89 | with open(dataset_path, "rb") as f: 90 | data = pickle.load(f) 91 | 92 | if train: 93 | self.targets = [] 94 | self.data = [] 95 | self.ord_labels = [] 96 | noise = {'targets':[], 'data':[], 'ord_labels':[]} 97 | for i in range(len(data["cl_labels"])): 98 | cl = data["cl_labels"][i][0] 99 | if cl != data["ord_labels"][i]: 100 | self.targets.append(cl) 101 | self.data.append(data["images"][i]) 102 | self.ord_labels.append(data["ord_labels"][i]) 103 | else: 104 | noise['targets'].append(data["cl_labels"][i][0]) 105 | noise['data'].append(data["images"][i]) 106 | noise['ord_labels'].append(data["ord_labels"][i]) 107 | 108 | assert((0 <= data_cleaning_rate) and (data_cleaning_rate <= 1)) 109 | noise_num = int(len(noise['data']) * data_cleaning_rate) 110 | self.targets.extend(noise['targets'][noise_num:]) 111 | self.data.extend(noise['data'][noise_num:]) 112 | self.ord_labels.extend(noise['ord_labels'][noise_num:]) 113 | 114 | indexes = np.arange(len(self.data)) 115 | np.random.shuffle(indexes) 116 | self.targets = [self.targets[i] for i in indexes] 117 | self.data = [self.data[i] for i in indexes] 118 | self.ord_labels = [self.ord_labels[i] for i in indexes] 119 | else: 120 | self.data = data["images"] 121 | self.ord_labels = data["ord_labels"] 122 | self.targets = data["ord_labels"] 123 | self.transform = transform 124 | self.num_classes = 20 125 | self.input_dim = 64 * 64 * 3 126 | 127 | def __len__(self): 128 | return len(self.data) 129 | 130 | def __getitem__(self, index): 131 | image = self.data[index] 132 | if self.transform is not None: 133 | image = self.transform(image) 134 | return image, self.targets[index] 135 | 136 | def get_dataset(args): 137 | dataset_name = args.dataset_name 138 | data_aug = args.data_aug 139 | data_cleaning_rate = args.data_cleaning_rate 140 | eta = args.eta 141 | num_classes = 10 142 | if dataset_name == "uniform-cifar10": 143 | trainset, validset, testset, ord_trainset, ord_validset = get_cifar10("uniform", data_aug=data_aug, eta=eta) 144 | elif dataset_name == "uniform-cifar20": 145 | trainset, validset, testset, ord_trainset, ord_validset = get_cifar20("uniform", data_aug=data_aug, eta=eta) 146 | num_classes = 20 147 | elif dataset_name in ["clcifar10", 'clcifar10-n']: 148 | trainset, validset, testset, ord_trainset, ord_validset = get_clcifar10(dataset_name, data_aug, data_cleaning_rate=data_cleaning_rate) 149 | elif dataset_name in ["clcifar20", 'clcifar20-n']: 150 | trainset, validset, testset, ord_trainset, ord_validset = get_clcifar20(dataset_name, data_aug, data_cleaning_rate=data_cleaning_rate) 151 | num_classes = 20 152 | elif dataset_name == 'clcifar10-noiseless': 153 | trainset, validset, testset, ord_trainset, ord_validset = get_clcifar10('clcifar10-noiseless', data_aug, data_cleaning_rate=data_cleaning_rate) 154 | elif dataset_name == 'clcifar20-noiseless': 155 | trainset, validset, testset, ord_trainset, ord_validset = get_clcifar20('clcifar20-noiseless', data_aug, data_cleaning_rate=data_cleaning_rate) 156 | num_classes = 20 157 | elif dataset_name == "noisy-uniform-cifar10": 158 | trainset, validset, testset, ord_trainset, ord_validset = get_cifar10("synthetic-noise", data_aug=data_aug, eta=eta) 159 | elif dataset_name == "noisy-uniform-cifar20": 160 | trainset, validset, testset, ord_trainset, ord_validset = get_cifar20("synthetic-noise", data_aug=data_aug, eta=eta) 161 | num_classes = 20 162 | elif dataset_name == 'b-clcifar10-n': 163 | trainset, validset, testset, ord_trainset, ord_validset = get_clcifar10('b-clcifar10-n', data_aug, data_cleaning_rate=data_cleaning_rate) 164 | elif "micro_imagenet" in dataset_name: 165 | trainset, validset, testset, ord_trainset, ord_validset = get_imagenet(dataset_name, data_aug, data_cleaning_rate=data_cleaning_rate) 166 | num_classes = 20 if "20" in dataset_name else 10 167 | else: 168 | raise NotImplementedError 169 | return trainset, validset, testset, ord_trainset, ord_validset, num_classes 170 | 171 | def get_imagenet(T_option, data_aug=False, eta=0, data_cleaning_rate=None): 172 | if data_aug == 'autoaug': 173 | train_transform = transforms.Compose( 174 | [ 175 | transforms.ToPILImage(), 176 | transforms.AutoAugment(transforms.AutoAugmentPolicy.IMAGENET), 177 | transforms.ToTensor(), 178 | ] 179 | ) 180 | else: 181 | train_transform = transforms.Compose( 182 | [ 183 | transforms.ToPILImage(), 184 | transforms.RandomCrop(64, padding=8), 185 | transforms.RandomHorizontalFlip(), 186 | transforms.ToTensor(), 187 | transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), 188 | ] 189 | ) 190 | if data_aug == 'autoaug': 191 | test_transform = transforms.Compose( 192 | [ 193 | transforms.ToPILImage(), 194 | transforms.ToTensor() 195 | ] 196 | ) 197 | else: 198 | test_transform = transforms.Compose( 199 | [ 200 | transforms.ToPILImage(), 201 | transforms.ToTensor(), 202 | transforms.Normalize((0.485, 0.456, 0.406), (0.229, 0.224, 0.225)), 203 | ] 204 | ) 205 | if "10" in T_option: 206 | dataset = CLMicro_ImageNet10( 207 | root="./data/imagenet10", 208 | train=True, 209 | transform=train_transform, 210 | data_cleaning_rate=data_cleaning_rate, 211 | ) 212 | testset = CLMicro_ImageNet10( 213 | root="./data/imagenet10", 214 | train=False, 215 | transform=test_transform, 216 | data_cleaning_rate=data_cleaning_rate, 217 | ) 218 | elif "20" in T_option: 219 | dataset = CLMicro_ImageNet20( 220 | root="./data/imagenet20", 221 | train=True, 222 | transform=train_transform, 223 | data_cleaning_rate=data_cleaning_rate, 224 | ) 225 | testset = CLMicro_ImageNet20( 226 | root="./data/imagenet20", 227 | train=False, 228 | transform=test_transform, 229 | data_cleaning_rate=data_cleaning_rate, 230 | ) 231 | n_samples = len(dataset) 232 | 233 | ord_trainset, ord_validset = torch.utils.data.random_split(dataset, [int(n_samples*0.9), n_samples - int(n_samples*0.9)]) 234 | 235 | trainset = deepcopy(ord_trainset) 236 | validset = deepcopy(ord_validset) 237 | ord_trainset.dataset.targets = ord_trainset.dataset.ord_labels 238 | ord_validset.dataset.targets = ord_validset.dataset.ord_labels 239 | num_classes = dataset.num_classes 240 | if "cl" in T_option: 241 | return trainset, validset, testset, ord_trainset, ord_validset 242 | 243 | if "uniform" in T_option: 244 | T = torch.full([num_classes, num_classes], 1/(num_classes-1)) 245 | for i in range(num_classes): 246 | T[i][i] = 0 247 | elif "noisy-uniform" in T_option: 248 | T = np.array(torch.full([num_classes, num_classes], (1-eta)/(num_classes-1))) 249 | for i in range(num_classes): 250 | T[i][i] = eta 251 | for i in range(num_classes): 252 | T[i] /= sum(T[i]) 253 | else: 254 | raise NotImplementedError 255 | 256 | for i in range(n_samples): 257 | ord_label = trainset.dataset.ord_labels[i] 258 | trainset.dataset.targets[i] = np.random.choice(list(range(num_classes)), p=T[ord_label]) 259 | 260 | for i in range(n_samples): 261 | ord_label = validset.dataset.ord_labels[i] 262 | validset.dataset.targets[i] = np.random.choice(list(range(num_classes)), p=T[ord_label]) 263 | 264 | return trainset, validset, testset, ord_trainset, ord_validset 265 | 266 | def get_cifar10(T_option, data_aug=False, eta=0): 267 | """ 268 | T_option: ["uniform", "synthetic-noise"] 269 | eta: noise rate 270 | """ 271 | if data_aug == 'std': 272 | transform = transforms.Compose( 273 | [ 274 | transforms.RandomHorizontalFlip(), 275 | transforms.RandomCrop(32, padding=4), 276 | transforms.ToTensor(), 277 | transforms.Normalize( 278 | [0.4922, 0.4832, 0.4486], [0.2456, 0.2419, 0.2605] 279 | ), 280 | ] 281 | ) 282 | elif data_aug == 'autoaug': 283 | transform = transforms.Compose( 284 | [ 285 | transforms.AutoAugment(transforms.AutoAugmentPolicy.CIFAR10), 286 | transforms.ToTensor(), 287 | ] 288 | ) 289 | else: 290 | transform = transforms.Compose( 291 | [ 292 | transforms.ToTensor(), 293 | transforms.Normalize( 294 | [0.4922, 0.4832, 0.4486], [0.2456, 0.2419, 0.2605] 295 | ), 296 | ] 297 | ) 298 | if data_aug == 'autoaug': 299 | test_transform = transforms.Compose( 300 | [ 301 | transforms.ToTensor() 302 | ] 303 | ) 304 | else: 305 | test_transform = transforms.Compose( 306 | [ 307 | transforms.ToTensor(), 308 | transforms.Normalize( 309 | [0.4922, 0.4832, 0.4486], [0.2456, 0.2419, 0.2605] 310 | ), 311 | ] 312 | ) 313 | 314 | dataset = torchvision.datasets.CIFAR10(root='./data', train=True, download=True, transform=transform) 315 | testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform) 316 | n_samples = len(dataset) 317 | 318 | ord_trainset, ord_validset = torch.utils.data.random_split(dataset, [int(n_samples*0.9), n_samples - int(n_samples*0.9)]) 319 | 320 | trainset = deepcopy(ord_trainset) 321 | validset = deepcopy(ord_validset) 322 | trainset.dataset.ord_labels = deepcopy(trainset.dataset.targets) 323 | validset.dataset.ord_labels = deepcopy(validset.dataset.targets) 324 | num_classes = 10 325 | 326 | if T_option == "uniform": 327 | T = torch.full([num_classes, num_classes], 1/(num_classes-1)) 328 | for i in range(num_classes): 329 | T[i][i] = 0 330 | elif T_option == "synthetic-noise": 331 | T = np.array(torch.full([num_classes, num_classes], (1-eta)/(num_classes-1))) 332 | for i in range(num_classes): 333 | T[i][i] = eta 334 | for i in range(num_classes): 335 | T[i] /= sum(T[i]) 336 | else: 337 | raise NotImplementedError 338 | 339 | for i in range(n_samples): 340 | ord_label = trainset.dataset.targets[i] 341 | trainset.dataset.targets[i] = np.random.choice(list(range(10)), p=T[ord_label]) 342 | 343 | for i in range(n_samples): 344 | ord_label = validset.dataset.targets[i] 345 | validset.dataset.targets[i] = np.random.choice(list(range(10)), p=T[ord_label]) 346 | 347 | return trainset, validset, testset, ord_trainset, ord_validset 348 | 349 | def get_cifar20(T_option, data_aug=False, eta=0): 350 | if data_aug == 'std': 351 | transform = transforms.Compose( 352 | [ 353 | transforms.RandomHorizontalFlip(), 354 | transforms.RandomCrop(32, padding=4), 355 | transforms.ToTensor(), 356 | transforms.Normalize( 357 | [0.5068, 0.4854, 0.4402], [0.2672, 0.2563, 0.2760] 358 | ), 359 | ] 360 | ) 361 | elif data_aug == 'autoaug': 362 | transform = transforms.Compose( 363 | [ 364 | transforms.AutoAugment(transforms.AutoAugmentPolicy.CIFAR10), 365 | transforms.ToTensor(), 366 | ] 367 | ) 368 | else: 369 | transform = transforms.Compose( 370 | [ 371 | transforms.ToTensor(), 372 | transforms.Normalize( 373 | [0.5068, 0.4854, 0.4402], [0.2672, 0.2563, 0.2760] 374 | ), 375 | ] 376 | ) 377 | if data_aug == 'autoaug': 378 | test_transform = transforms.Compose( 379 | [ 380 | transforms.ToTensor() 381 | ] 382 | ) 383 | else: 384 | test_transform = transforms.Compose( 385 | [ 386 | transforms.ToTensor(), 387 | transforms.Normalize( 388 | [0.4922, 0.4832, 0.4486], [0.2456, 0.2419, 0.2605] 389 | ), 390 | ] 391 | ) 392 | 393 | dataset = torchvision.datasets.CIFAR100(root='./data', train=True, download=True, transform=transform) 394 | testset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=test_transform) 395 | n_samples = len(dataset) 396 | num_classes = 20 397 | 398 | def _cifar100_to_cifar20(target): 399 | _dict = {0: 4, 1: 1, 2: 14, 3: 8, 4: 0, 5: 6, 6: 7, 7: 7, 8: 18, 9: 3, 10: 3, 11: 14, 12: 9, 13: 18, 14: 7, 15: 11, 16: 3, 17: 9, 18: 7, 19: 11, 20: 6, 21: 11, 22: 5, 23: 10, 24: 7, 25: 6, 26: 13, 27: 15, 28: 3, 29: 15, 30: 0, 31: 11, 32: 1, 33: 10, 34: 12, 35: 14, 36: 16, 37: 9, 38: 11, 39: 5, 40: 5, 41: 19, 42: 8, 43: 8, 44: 15, 45: 13, 46: 14, 47: 17, 48: 18, 49: 10, 50: 16, 51: 4, 52: 17, 53: 4, 54: 2, 55: 0, 56: 17, 57: 4, 58: 18, 59: 17, 60: 10, 61: 3, 62: 2, 63: 12, 64: 12, 65: 16, 66: 12, 67: 1, 68: 9, 69: 19, 70: 2, 71: 10, 72: 0, 73: 1, 74: 16, 75: 12, 76: 9, 77: 13, 78: 15, 79: 13, 80: 16, 81: 18, 82: 2, 83: 4, 84: 6, 85: 19, 86: 5, 87: 5, 88: 8, 89: 19, 90: 18, 91: 1, 92: 2, 93: 15, 94: 6, 95: 0, 96: 17, 97: 8, 98: 14, 99: 13} 400 | return _dict[target] 401 | 402 | dataset.targets = [_cifar100_to_cifar20(i) for i in dataset.targets] 403 | testset.targets = [_cifar100_to_cifar20(i) for i in testset.targets] 404 | ord_trainset, ord_validset = torch.utils.data.random_split(dataset, [int(n_samples*0.9), n_samples - int(n_samples*0.9)]) 405 | 406 | trainset = deepcopy(ord_trainset) 407 | validset = deepcopy(ord_validset) 408 | trainset.dataset.ord_labels = deepcopy(trainset.dataset.targets) 409 | validset.dataset.ord_labels = deepcopy(validset.dataset.targets) 410 | 411 | if T_option == "uniform": 412 | T = torch.full([num_classes, num_classes], 1/(num_classes-1)) 413 | for i in range(num_classes): 414 | T[i][i] = 0 415 | elif T_option == "synthetic-noise": 416 | T = np.array(torch.full([num_classes, num_classes], (1-eta)/(num_classes-1))) 417 | for i in range(num_classes): 418 | T[i][i] = eta 419 | for i in range(num_classes): 420 | T[i] /= sum(T[i]) 421 | else: 422 | raise NotImplementedError 423 | 424 | for i in range(n_samples): 425 | ord_label = trainset.dataset.targets[i] 426 | trainset.dataset.targets[i] = np.random.choice(list(range(20)), p=T[ord_label]) 427 | 428 | for i in range(n_samples): 429 | ord_label = validset.dataset.targets[i] 430 | validset.dataset.targets[i] = np.random.choice(list(range(20)), p=T[ord_label]) 431 | 432 | return trainset, validset, testset, ord_trainset, ord_validset 433 | 434 | class CustomDataset(Dataset): 435 | def __init__(self, root="./data", transform=None, dataset_name="clcifar10", data_cleaning_rate=None): 436 | 437 | os.makedirs(os.path.join(root, dataset_name), exist_ok=True) 438 | dataset_path = os.path.join(root, dataset_name, f"{dataset_name}.pkl") 439 | 440 | if dataset_name == 'b-clcifar10-n': 441 | dataset_path = dataset_path = os.path.join(root, 'clcifar10', "clcifar10.pkl") 442 | 443 | if not os.path.exists(dataset_path): 444 | if dataset_name == "clcifar10" or dataset_name == "clcifar10": 445 | print("Downloading clcifar10(148.3MB)") 446 | if not os.path.exists(dataset_path): 447 | gdown.download( 448 | id="1uNLqmRUkHzZGiSsCtV2-fHoDbtKPnVt2", output=dataset_path 449 | ) 450 | elif dataset_name == "clcifar20": 451 | print("Downloading clcifar20(150.6MB)") 452 | if not os.path.exists(dataset_path): 453 | gdown.download( 454 | id="1PhZsyoi1dAHDGlmB4QIJvDHLf_JBsFeP", output=dataset_path 455 | ) 456 | elif dataset_name == 'clcifar10-n': 457 | pass 458 | elif dataset_name == 'clcifar20-n': 459 | pass 460 | else: 461 | raise NotImplementedError 462 | 463 | data = pickle.load(open(dataset_path, "rb")) 464 | 465 | self.transform = transform 466 | self.input_dim = 3 * 32 * 32 467 | 468 | self.targets = [] 469 | self.data = [] 470 | self.ord_labels = [] 471 | 472 | if dataset_name == 'b-clcifar10-un': 473 | T = np.zeros([10, 10]) 474 | for i in range(len(data['cl_labels'])): 475 | for j in range(3): 476 | T[data['ord_labels'][i]][data['cl_labels'][i][j]] += 1 477 | noise_rate = 0 478 | for i in range(10): 479 | noise_rate += T[i][i] 480 | T[i][i] = 0 481 | noise_rate *= (1 - data_cleaning_rate) 482 | for i in range(10): 483 | T[i] = sum(T[i]) 484 | 485 | for i in range(len(data['ord_labels'])): 486 | ord_label = data['ord_labels'][i] 487 | self.targets.append(np.random.choice(list(range(10)), p=T[ord_label])) 488 | self.data = data['images'] 489 | self.ord_labels = data['ord_labels'] 490 | return 491 | 492 | noise = {'targets':[], 'data':[], 'ord_labels':[]} 493 | for i in range(len(data["cl_labels"])): 494 | cl = data["cl_labels"][i][0] 495 | if cl != data["ord_labels"][i]: 496 | self.targets.append(cl) 497 | self.data.append(data["images"][i]) 498 | self.ord_labels.append(data["ord_labels"][i]) 499 | else: 500 | noise['targets'].append(data["cl_labels"][i][0]) 501 | noise['data'].append(data["images"][i]) 502 | noise['ord_labels'].append(data["ord_labels"][i]) 503 | 504 | assert((0 <= data_cleaning_rate) and (data_cleaning_rate <= 1)) 505 | noise_num = int(len(noise['data']) * data_cleaning_rate) 506 | self.targets.extend(noise['targets'][noise_num:]) 507 | self.data.extend(noise['data'][noise_num:]) 508 | self.ord_labels.extend(noise['ord_labels'][noise_num:]) 509 | 510 | indexes = np.arange(len(self.data)) 511 | np.random.shuffle(indexes) 512 | self.targets = [self.targets[i] for i in indexes] 513 | self.data = [self.data[i] for i in indexes] 514 | self.ord_labels = [self.ord_labels[i] for i in indexes] 515 | 516 | def __len__(self): 517 | return len(self.data) 518 | 519 | def __getitem__(self, index): 520 | image = self.data[index] 521 | if self.transform is not None: 522 | image = self.transform(image) 523 | return image, self.targets[index] 524 | 525 | def get_clcifar10(dataset_name, data_aug=False, data_cleaning_rate=0): 526 | """ 527 | dataset_name: ['clcifar10', 'clcifar10-n', 'clcifar10-noiseless] 528 | data_cleaning_rate: we delete N% of noisy data 529 | """ 530 | if data_aug == 'std': 531 | transform = transforms.Compose( 532 | [ 533 | transforms.RandomHorizontalFlip(), 534 | transforms.RandomCrop(32, padding=4), 535 | transforms.ToTensor(), 536 | transforms.Normalize( 537 | [0.5068, 0.4854, 0.4402], [0.2672, 0.2563, 0.2760] 538 | ), 539 | ] 540 | ) 541 | elif data_aug == 'autoaug': 542 | transform = transforms.Compose( 543 | [ 544 | transforms.ToPILImage(), 545 | transforms.AutoAugment(transforms.AutoAugmentPolicy.CIFAR10), 546 | transforms.ToTensor(), 547 | ] 548 | ) 549 | else: 550 | transform = transforms.Compose( 551 | [ 552 | transforms.ToTensor(), 553 | transforms.Normalize( 554 | [0.4914, 0.4822, 0.4465], [0.247, 0.2435, 0.2616] 555 | ), 556 | ] 557 | ) 558 | if data_aug == 'autoaug': 559 | test_transform = transforms.Compose( 560 | [ 561 | transforms.ToTensor() 562 | ] 563 | ) 564 | else: 565 | test_transform = transforms.Compose( 566 | [ 567 | transforms.ToTensor(), 568 | transforms.Normalize( 569 | [0.4922, 0.4832, 0.4486], [0.2456, 0.2419, 0.2605] 570 | ), 571 | ] 572 | ) 573 | 574 | dataset = CustomDataset(root='./data', transform=transform, dataset_name=dataset_name, data_cleaning_rate=data_cleaning_rate) 575 | testset = torchvision.datasets.CIFAR10(root='./data', train=False, download=True, transform=test_transform) 576 | 577 | n_samples = len(dataset) 578 | validset_size = int(n_samples * 0.1) 579 | trainset_size = n_samples - validset_size 580 | 581 | ord_trainset, ord_validset = torch.utils.data.random_split(dataset, [trainset_size, validset_size]) 582 | 583 | trainset = deepcopy(ord_trainset) 584 | validset = deepcopy(ord_validset) 585 | 586 | ord_trainset.dataset.targets = ord_trainset.dataset.ord_labels 587 | ord_validset.dataset.targets = ord_validset.dataset.ord_labels 588 | 589 | return trainset, validset, testset, ord_trainset, ord_validset 590 | 591 | def get_clcifar20(dataset_name, data_aug=False, data_cleaning_rate=0): 592 | if data_aug == 'std': 593 | transform = transforms.Compose( 594 | [ 595 | transforms.RandomHorizontalFlip(), 596 | transforms.RandomCrop(32, padding=4), 597 | transforms.ToTensor(), 598 | transforms.Normalize( 599 | [0.5068, 0.4854, 0.4402], [0.2672, 0.2563, 0.2760] 600 | ), 601 | ] 602 | ) 603 | elif data_aug == 'autoaug': 604 | transform = transforms.Compose( 605 | [ 606 | transforms.ToPILImage(), 607 | transforms.AutoAugment(transforms.AutoAugmentPolicy.CIFAR10), 608 | transforms.ToTensor(), 609 | ] 610 | ) 611 | else: 612 | transform = transforms.Compose( 613 | [ 614 | transforms.ToTensor(), 615 | transforms.Normalize( 616 | [0.5068, 0.4854, 0.4402], [0.2672, 0.2563, 0.2760] 617 | ), 618 | ] 619 | ) 620 | if data_aug == 'autoaug': 621 | test_transform = transforms.Compose( 622 | [ 623 | transforms.ToTensor() 624 | ] 625 | ) 626 | else: 627 | test_transform = transforms.Compose( 628 | [ 629 | transforms.ToTensor(), 630 | transforms.Normalize( 631 | [0.4922, 0.4832, 0.4486], [0.2456, 0.2419, 0.2605] 632 | ), 633 | ] 634 | ) 635 | 636 | dataset = CustomDataset(root='./data', transform=transform, dataset_name=dataset_name, data_cleaning_rate=data_cleaning_rate) 637 | testset = torchvision.datasets.CIFAR100(root='./data', train=False, download=True, transform=test_transform) 638 | 639 | n_samples = len(dataset) 640 | validset_size = int(n_samples * 0.1) 641 | trainset_size = n_samples - validset_size 642 | 643 | 644 | def _cifar100_to_cifar20(target): 645 | _dict = {0: 4, 1: 1, 2: 14, 3: 8, 4: 0, 5: 6, 6: 7, 7: 7, 8: 18, 9: 3, 10: 3, 11: 14, 12: 9, 13: 18, 14: 7, 15: 11, 16: 3, 17: 9, 18: 7, 19: 11, 20: 6, 21: 11, 22: 5, 23: 10, 24: 7, 25: 6, 26: 13, 27: 15, 28: 3, 29: 15, 30: 0, 31: 11, 32: 1, 33: 10, 34: 12, 35: 14, 36: 16, 37: 9, 38: 11, 39: 5, 40: 5, 41: 19, 42: 8, 43: 8, 44: 15, 45: 13, 46: 14, 47: 17, 48: 18, 49: 10, 50: 16, 51: 4, 52: 17, 53: 4, 54: 2, 55: 0, 56: 17, 57: 4, 58: 18, 59: 17, 60: 10, 61: 3, 62: 2, 63: 12, 64: 12, 65: 16, 66: 12, 67: 1, 68: 9, 69: 19, 70: 2, 71: 10, 72: 0, 73: 1, 74: 16, 75: 12, 76: 9, 77: 13, 78: 15, 79: 13, 80: 16, 81: 18, 82: 2, 83: 4, 84: 6, 85: 19, 86: 5, 87: 5, 88: 8, 89: 19, 90: 18, 91: 1, 92: 2, 93: 15, 94: 6, 95: 0, 96: 17, 97: 8, 98: 14, 99: 13} 646 | return _dict[target] 647 | 648 | testset.targets = [_cifar100_to_cifar20(i) for i in testset.targets] 649 | ord_trainset, ord_validset = torch.utils.data.random_split(dataset, [trainset_size, validset_size]) 650 | 651 | trainset = deepcopy(ord_trainset) 652 | validset = deepcopy(ord_validset) 653 | 654 | ord_trainset.dataset.targets = ord_trainset.dataset.ord_labels 655 | ord_validset.dataset.targets = ord_validset.dataset.ord_labels 656 | 657 | return trainset, validset, testset, ord_trainset, ord_validset -------------------------------------------------------------------------------- /cll_experiment/models.py: -------------------------------------------------------------------------------- 1 | import torchvision 2 | import torch.nn as nn 3 | 4 | def get_resnet18(num_classes): 5 | resnet = torchvision.models.resnet18(weights=None) 6 | # resnet.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) 7 | # resnet.maxpool = nn.Identity() 8 | num_ftrs = resnet.fc.in_features 9 | resnet.fc = nn.Linear(num_ftrs, num_classes) 10 | return resnet 11 | 12 | def get_modified_resnet18(num_classes): 13 | resnet = torchvision.models.resnet18(weights=None) 14 | resnet.conv1 = nn.Conv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False) 15 | resnet.maxpool = nn.Identity() 16 | num_ftrs = resnet.fc.in_features 17 | resnet.fc = nn.Linear(num_ftrs, num_classes) 18 | return resnet -------------------------------------------------------------------------------- /cll_experiment/utils.py: -------------------------------------------------------------------------------- 1 | import argparse 2 | import numpy as np 3 | 4 | def get_args(): 5 | dataset_list = [ 6 | "uniform-cifar10", 7 | "uniform-cifar20", 8 | "clcifar10", 9 | "clcifar20", 10 | "clcifar10-noiseless", 11 | "clcifar20-noiseless", 12 | "noisy-uniform-cifar10", 13 | "noisy-uniform-cifar20", 14 | "clcifar10-n", 15 | "clcifar20-n", 16 | "b-clcifar10-n", 17 | "uniform-micro_imagenet10", 18 | "uniform-micro_imagenet20", 19 | "clmicro_imagenet10", 20 | "clmicro_imagenet20", 21 | "noisy-uniform-micro_imagenet10", 22 | "noisy-uniform-micro_imagenet20", 23 | ] 24 | 25 | algo_list = [ 26 | "scl-exp", 27 | "scl-nl", 28 | "ure-ga-u", 29 | "ure-ga-r", 30 | "fwd-u", 31 | "fwd-r", 32 | "l-w", 33 | "l-uw", 34 | "pc-sigmoid", 35 | "fwd-int", 36 | "rob-mae", 37 | "rob-cce", 38 | "rob-wmae", 39 | "rob-gce", 40 | "rob-sl" 41 | ] 42 | 43 | model_list = [ 44 | "resnet18", 45 | "m-resnet18" 46 | ] 47 | 48 | parser = argparse.ArgumentParser() 49 | 50 | parser.add_argument('--algo', type=str, choices=algo_list, help='Algorithm') 51 | parser.add_argument('--dataset_name', type=str, choices=dataset_list, help='Dataset name') 52 | 53 | parser.add_argument('--model', type=str, choices=model_list, help='Model name', default="resnet") 54 | parser.add_argument('--lr', type=float, help='Learning rate', default=1e-4) 55 | parser.add_argument('--seed', type=int, help='Random seed', default=0) 56 | parser.add_argument('--data_aug', type=str, default="std") 57 | parser.add_argument('--data_cleaning_rate', type=float, default=0) 58 | parser.add_argument('--eta', type=float, default=0) 59 | parser.add_argument('--alpha', type=float, default=0) 60 | parser.add_argument('--cutmix', type=str, default="false") 61 | 62 | args = parser.parse_args() 63 | return args 64 | 65 | def get_dataset_T(dataset, num_classes): 66 | dataset_T = np.zeros((num_classes,num_classes)) 67 | class_count = np.zeros(num_classes) 68 | for i in range(len(dataset)): 69 | dataset_T[dataset.dataset.ord_labels[i]][dataset.dataset.targets[i]] += 1 70 | class_count[dataset.dataset.ord_labels[i]] += 1 71 | for i in range(num_classes): 72 | dataset_T[i] /= class_count[i] 73 | return dataset_T 74 | -------------------------------------------------------------------------------- /cll_experiment/valid.py: -------------------------------------------------------------------------------- 1 | import torch 2 | import torch.nn.functional as F 3 | 4 | def validate(model, dataloader): 5 | total = 0 6 | correct = 0 7 | with torch.no_grad(): 8 | for inputs, labels in dataloader: 9 | inputs, labels = inputs.to(model.device), labels.to(model.device) 10 | outputs = model(inputs) 11 | predicted = torch.argmax(outputs.data, dim=1) 12 | total += labels.size(0) 13 | correct += (predicted == labels).sum().item() 14 | return correct/total 15 | 16 | def compute_ure(outputs, labels, dataset_T): 17 | with torch.no_grad(): 18 | outputs = -F.log_softmax(outputs, dim=1) 19 | if torch.det(dataset_T) != 0: 20 | T_inv = torch.inverse(dataset_T).to(labels.device) 21 | else: 22 | T_inv = torch.pinverse(dataset_T).to(labels.device) 23 | loss_mat = torch.mm(outputs, T_inv.transpose(1, 0)) 24 | ure = -F.nll_loss(loss_mat, labels) 25 | return ure 26 | 27 | def compute_scel(outputs, labels, algo, dataset_T): 28 | outputs = outputs.softmax(dim=1) 29 | if algo[:3] != "cpe": 30 | outputs = torch.mm(outputs, dataset_T) 31 | outputs = (outputs + 1e-6).log() 32 | return F.nll_loss(outputs, labels) -------------------------------------------------------------------------------- /clmicro_imagenet.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import Dataset 2 | import os 3 | import urllib.request 4 | from tqdm import tqdm 5 | import pickle 6 | from PIL import Image 7 | import gdown 8 | 9 | class CLMicro_ImageNet10(Dataset): 10 | def __init__(self, root="./data", train=True, transform=None): 11 | os.makedirs(os.path.join(root, 'clmicro_imagenet10'), exist_ok=True) 12 | if train: 13 | dataset_path = f"{root}/clmicro_imagenet10_train.pkl" 14 | gid = "1k02mwMpnBUM9de7TiJLBaCuS8myGuYFx" 15 | else: 16 | dataset_path = f"{root}/clmicro_imagenet10_test.pkl" 17 | gid = "1e8fZN8swbg9wc6BSOC0A5KHIqCY2C7me" 18 | if not os.path.exists(dataset_path): 19 | os.makedirs(root, exist_ok=True) 20 | gdown.download(id=gid, output=dataset_path) 21 | with open(dataset_path, "rb") as f: 22 | data = pickle.load(f) 23 | 24 | 25 | self.targets = [labels[0] for labels in data["cl_labels"]] 26 | self.data = data["images"] 27 | self.ord_labels = data["ord_labels"] 28 | if train: 29 | self.targets = [labels[0] for labels in data["cl_labels"]] 30 | else: 31 | self.targets = data["ord_labels"] 32 | self.transform = transform 33 | self.num_classes = 10 34 | self.input_dim = 64 * 64 * 3 35 | 36 | def __len__(self): 37 | return len(self.data) 38 | 39 | def __getitem__(self, index): 40 | image = self.data[index] 41 | image = Image.fromarray(image) 42 | if self.transform is not None: 43 | image = self.transform(image) 44 | return image, self.targets[index] 45 | 46 | class CLMicro_ImageNet20(Dataset): 47 | def __init__(self, root="./data", train=True, transform=None): 48 | os.makedirs(os.path.join(root, 'clmicro_imagenet20'), exist_ok=True) 49 | if train: 50 | dataset_path = f"{root}/clmicro_imagenet20_train.pkl" 51 | gid = "1Urdxs_QTxbb1gDBpmjP09Q35btckI3_d" 52 | else: 53 | dataset_path = f"{root}/clmicro_imagenet20_test.pkl" 54 | gid = "1EdBCrifSrIIUg1ioPWA-ZLEHO53P4NPl" 55 | if not os.path.exists(dataset_path): 56 | os.makedirs(root, exist_ok=True) 57 | gdown.download(id=gid, output=dataset_path) 58 | with open(dataset_path, "rb") as f: 59 | data = pickle.load(f) 60 | 61 | 62 | self.targets = [labels[0] for labels in data["cl_labels"]] 63 | self.data = data["images"] 64 | self.ord_labels = data["ord_labels"] 65 | if train: 66 | self.targets = [labels[0] for labels in data["cl_labels"]] 67 | else: 68 | self.targets = data["ord_labels"] 69 | self.transform = transform 70 | self.num_classes = 20 71 | self.input_dim = 64 * 64 * 3 72 | 73 | def __len__(self): 74 | return len(self.data) 75 | 76 | def __getitem__(self, index): 77 | image = self.data[index] 78 | image = Image.fromarray(image) 79 | if self.transform is not None: 80 | image = self.transform(image) 81 | return image, self.targets[index] -------------------------------------------------------------------------------- /design-layout-mturk.html: -------------------------------------------------------------------------------- 1 | 2 | 3 | 4 | 5 | 6 | 56 |
57 |
58 | 59 |
60 |
61 |
62 |

Problem 1

63 | 64 |

65 | Choose any one "incorrect" label for this image: 66 |

67 |
68 | 74 | 80 | 86 | 92 |
93 |
94 | 95 |
96 |
97 |
98 |

Problem 2

99 | 100 |

101 | Choose any one "incorrect" label for this image: 102 |

103 |
104 | 110 | 116 | 122 | 128 |
129 |
130 | 131 |
132 |
133 |
134 |

Problem 3

135 | 136 |

137 | Choose any one "incorrect" label for this image: 138 |

139 |
140 | 146 | 152 | 158 | 164 |
165 |
166 | 167 |
168 |
169 |
170 |

Problem 4

171 | 172 |

173 | Choose any one "incorrect" label for this image: 174 |

175 |
176 | 182 | 188 | 194 | 200 |
201 |
202 | 203 |
204 |
205 |
206 |

Problem 5

207 | 208 |

209 | Choose any one "incorrect" label for this image: 210 |

211 |
212 | 218 | 224 | 230 | 236 |
237 |
238 | 239 |
240 |
241 |
242 |

Problem 6

243 | 244 |

245 | Choose any one "incorrect" label for this image: 246 |

247 |
248 | 254 | 260 | 266 | 272 |
273 |
274 | 275 |
276 |
277 |
278 |

Problem 7

279 | 280 |

281 | Choose any one "incorrect" label for this image: 282 |

283 |
284 | 290 | 296 | 302 | 308 |
309 |
310 | 311 |
312 |
313 |
314 |

Problem 8

315 | 316 |

317 | Choose any one "incorrect" label for this image: 318 |

319 |
320 | 326 | 332 | 338 | 344 |
345 |
346 | 347 |
348 |
349 |
350 |

Problem 9

351 | 352 |

353 | Choose any one "incorrect" label for this image: 354 |

355 |
356 | 362 | 368 | 374 | 380 |
381 |
382 | 383 |
384 |
385 |
386 |

Problem 10

387 | 388 |

389 | Choose any one "incorrect" label for this image: 390 |

391 |
392 | 398 | 404 | 410 | 416 |
417 |
418 |
419 |
420 | Links to all the problems:
421 | problem 1
422 | problem 2
423 | problem 3
424 | problem 4
425 | problem 5
426 | problem 6
427 | problem 7
428 | problem 8
429 | problem 9
430 | problem 10 431 |
432 |
433 |
434 | 435 | 436 | 437 | -------------------------------------------------------------------------------- /requirement.txt: -------------------------------------------------------------------------------- 1 | torch 2 | numpy 3 | tqdm 4 | torchvision 5 | wandb -------------------------------------------------------------------------------- /run.sh: -------------------------------------------------------------------------------- 1 | alg=$1 2 | dataset=$2 3 | lr=$3 4 | seed=$4 5 | 6 | python train.py \ 7 | --algo=${alg} \ 8 | --dataset_name=${dataset} \ 9 | --model=m-resnet18 \ 10 | --lr=${lr} \ 11 | --seed=${seed} \ 12 | --data_aug=autoaug -------------------------------------------------------------------------------- /run_all_experiments.sh: -------------------------------------------------------------------------------- 1 | strategy=$1 2 | strategies=($strategy) 3 | datasets=("uniform-micro_imagenet10" "uniform-micro_imagenet20" "clmicro_imagenet10" "clmicro_imagenet20") 4 | lrs=("5e-4") 5 | seeds=("197" "101" "1126" "3333") 6 | 7 | for strategy in ${strategies[@]}; do 8 | for dataset in ${datasets[@]}; do 9 | for lr in ${lrs[@]}; do 10 | for seed in ${seeds[@]}; do 11 | echo "./run.sh ${strategy} ${dataset} ${lr} ${seed}" 12 | ./run.sh ${strategy} ${dataset} ${lr} ${seed} 13 | done 14 | done 15 | done 16 | done -------------------------------------------------------------------------------- /train.py: -------------------------------------------------------------------------------- 1 | from torch.utils.data import DataLoader 2 | import torch 3 | import numpy as np 4 | import torch.optim as optim 5 | from tqdm import tqdm 6 | import torch.nn.functional as F 7 | import wandb 8 | from torchvision.transforms import v2 9 | import os 10 | import json 11 | 12 | from cll_experiment.datasets import get_dataset 13 | from cll_experiment.models import get_resnet18, get_modified_resnet18 14 | from cll_experiment.algo import ga_loss, robust_ga_loss 15 | from cll_experiment.valid import compute_ure, compute_scel, validate 16 | from cll_experiment.utils import get_args, get_dataset_T 17 | 18 | num_classes = 10 19 | eval_n_epoch = 5 20 | epochs = 300 21 | batch_size = 512 22 | num_workers = 4 23 | device = "cuda" 24 | 25 | def train(args): 26 | algo = args.algo 27 | model = args.model 28 | lr = args.lr 29 | seed = args.seed 30 | dataset_name = args.dataset_name 31 | os.makedirs("logs/", exist_ok=True) 32 | # data_aug = True if args.data_aug.lower()=="true" else False 33 | 34 | np.random.seed(seed) 35 | torch.manual_seed(seed) 36 | torch.cuda.manual_seed_all(seed) 37 | 38 | trainset, validset, testset, ord_trainset, ord_validset, num_classes = get_dataset(args) 39 | 40 | # Print the complementary label distribution T 41 | dataset_T = get_dataset_T(trainset, num_classes) 42 | dataset_T = torch.tensor(dataset_T, dtype=torch.float).to(device) 43 | 44 | # Set Q for forward algorithm 45 | if algo in ["fwd-u", "ure-ga-u"]: 46 | Q = torch.full([num_classes, num_classes], 1/(num_classes-1), device=device) 47 | for i in range(num_classes): 48 | Q[i][i] = 0 49 | elif algo in ["fwd-r", "ure-ga-r"] or algo[:3] == "rob": 50 | Q = dataset_T 51 | elif algo == "fwd-int": 52 | U = np.full([num_classes, num_classes], 1/(num_classes-1)) 53 | for i in range(num_classes): 54 | U[i][i] = 0 55 | dataset_T = get_dataset_T(trainset, num_classes) 56 | Q = torch.tensor(args.alpha * U + (1-args.alpha) * dataset_T).to(device).float() 57 | dataset_T = torch.tensor(dataset_T, dtype=torch.float).to(device) 58 | 59 | count_cls_wrong_label = np.zeros(num_classes) 60 | count_wrong_label = 0 61 | for i in range(len(trainset)): 62 | if trainset.dataset.targets[i] == trainset.dataset.ord_labels[i]: 63 | count_cls_wrong_label[trainset.dataset.targets[i]] += 1 64 | count_wrong_label += 1 65 | 66 | trainloader = DataLoader(trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers) 67 | validloader = DataLoader(validset, batch_size=batch_size, shuffle=True, num_workers=num_workers) 68 | ord_trainloader = DataLoader(ord_trainset, batch_size=batch_size, shuffle=True, num_workers=num_workers) 69 | ord_validloader = DataLoader(ord_validset, batch_size=batch_size, shuffle=True, num_workers=num_workers) 70 | testloader = DataLoader(testset, batch_size=batch_size, shuffle=False, num_workers=num_workers) 71 | 72 | print(f'use augment: {args.data_aug}') 73 | # if args.cutmix: 74 | # print('use cutmix') 75 | # cutmix = v2.CutMix(num_classes=num_classes) 76 | 77 | train_labels = torch.tensor(np.array(trainset.dataset.targets), dtype=torch.int).squeeze() 78 | class_prior = train_labels.bincount().float() / train_labels.shape[0] 79 | 80 | if args.model == "resnet18": 81 | model = get_resnet18(num_classes).to(device) 82 | elif args.model == "m-resnet18": 83 | model = get_modified_resnet18(num_classes).to(device) 84 | else: 85 | raise NotImplementedError 86 | model.device = device 87 | optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=1e-4) 88 | 89 | validation_obj = ["valid_acc", "ure", "scel", "last"] 90 | best_epoch = {obj: None for obj in validation_obj} 91 | wandb.login() 92 | wandb.init(project=args.dataset_name, name=f"{algo}-{dataset_name}-{lr}-{seed}", config={"lr": lr, "seed": seed}, tags=[algo]) 93 | 94 | with tqdm(range(epochs), unit="epoch") as tepoch: 95 | for epoch in tepoch: 96 | training_loss = 0.0 97 | model.train() 98 | 99 | for inputs, labels in trainloader: 100 | 101 | # if args.cutmix: 102 | # inputs, labels = cutmix(inputs, labels) 103 | inputs, labels = inputs.to(device), labels.to(device) 104 | 105 | optimizer.zero_grad() 106 | outputs = model(inputs) 107 | 108 | if algo == "scl-exp": 109 | outputs = F.softmax(outputs, dim=1) 110 | loss = -F.nll_loss(outputs.exp(), labels) 111 | loss.backward() 112 | 113 | elif algo[:6] == "ure-ga": 114 | loss = ga_loss(outputs, labels, class_prior, Q, num_classes) 115 | if torch.min(loss) > 0: 116 | loss = loss.sum() 117 | loss.backward() 118 | else: 119 | beta_vec = torch.zeros(num_classes, requires_grad=True).to(device) 120 | loss = torch.minimum(beta_vec, loss).sum() * -1 121 | loss.backward() 122 | 123 | elif algo[:3] == "fwd": 124 | q = torch.mm(F.softmax(outputs, dim=1), Q) + 1e-6 125 | loss = F.nll_loss(q.log(), labels.squeeze()) 126 | loss.backward() 127 | 128 | elif algo == "l-w": 129 | outputs1 = 1 - F.softmax(outputs, dim=1) 130 | loss = F.cross_entropy(outputs1, labels.squeeze(), reduction='none') 131 | w = (1-F.softmax(outputs, dim=1)) / (num_classes-1) 132 | w = 1-F.nll_loss(w, labels.squeeze(), reduction='none') 133 | loss = (loss * w).mean() 134 | loss.backward() 135 | 136 | elif algo == "l-uw": 137 | outputs = 1 - F.softmax(outputs, dim=1) 138 | loss = F.cross_entropy(outputs, labels.squeeze()) 139 | loss.backward() 140 | 141 | elif algo == "scl-nl": 142 | p = (1 - F.softmax(outputs, dim=1) + 1e-6).log() 143 | loss = F.nll_loss(p, labels) 144 | loss.backward() 145 | 146 | elif algo == "pc-sigmoid": 147 | outputs = outputs + F.nll_loss(outputs, labels, reduction='none').view(-1, 1) 148 | loss = torch.sigmoid(-1 * outputs).sum(dim=1).mean() - 0.5 149 | loss.backward() 150 | 151 | elif algo == "fwd-int": 152 | q = torch.mm(F.softmax(outputs, dim=1), Q) + 1e-6 153 | loss = F.nll_loss(q.log(), labels.squeeze()) 154 | loss.backward() 155 | 156 | elif algo[:3] == "rob": 157 | loss = robust_ga_loss(outputs, labels, class_prior, Q, num_classes, algo) 158 | if torch.min(loss) > 0: 159 | loss = loss.sum() 160 | loss.backward() 161 | else: 162 | beta_vec = torch.zeros(num_classes, requires_grad=True).to(device) 163 | loss = torch.minimum(beta_vec, loss).sum() * -1 164 | loss.backward() 165 | 166 | else: 167 | raise NotImplementedError 168 | 169 | optimizer.step() 170 | training_loss += loss.item() 171 | tepoch.set_postfix(loss=loss.item()) 172 | 173 | training_loss /= len(trainloader) 174 | wandb.log({"training_loss": training_loss}) 175 | 176 | if (epoch+1) % eval_n_epoch == 0: 177 | model.eval() 178 | with torch.no_grad(): 179 | ure = 0 180 | scel = 0 181 | for inputs, labels in validloader: 182 | inputs, labels = inputs.to(device), labels.to(device) 183 | outputs = model(inputs) 184 | ure += compute_ure(outputs, labels, dataset_T) 185 | scel += compute_scel(outputs, labels, algo, dataset_T) 186 | ure = ure.item() 187 | scel = scel.item() 188 | ure /= len(validloader) 189 | scel /= len(validloader) 190 | train_acc, valid_acc = validate(model, ord_trainloader), validate(model, ord_validloader) 191 | test_acc = validate(model, testloader) 192 | epoch_info = { 193 | "epoch": epoch, 194 | "train_acc": train_acc, 195 | "valid_acc": valid_acc, 196 | "test_acc": test_acc, 197 | "ure": ure, 198 | "scel": scel, 199 | "training_loss": training_loss 200 | } 201 | print(train_acc, valid_acc, test_acc) 202 | print(ure, scel, valid_acc) 203 | wandb.log({"ure": ure, "scel": scel, "train_acc": train_acc, "valid_acc": valid_acc, "test_acc": test_acc}) 204 | if best_epoch["valid_acc"] is None or valid_acc > best_epoch["valid_acc"]["valid_acc"]: 205 | best_epoch["valid_acc"] = epoch_info 206 | if best_epoch["ure"] is None or ure < best_epoch["ure"]["ure"]: 207 | best_epoch["ure"] = epoch_info 208 | if best_epoch["scel"] is None or scel < best_epoch["scel"]["scel"]: 209 | best_epoch["scel"] = epoch_info 210 | best_epoch["last"] = epoch_info 211 | print(best_epoch) 212 | with open(f"logs/{algo}-{dataset_name}-{lr}-{seed}.json", "w") as f: 213 | json.dump(best_epoch, f) 214 | wandb.summary["best_epoch-valid_acc"] = best_epoch["valid_acc"] 215 | wandb.summary["best_epoch-ure"] = best_epoch["ure"] 216 | wandb.summary["best_epoch-scel"] = best_epoch["scel"] 217 | wandb.finish() 218 | 219 | if __name__ == "__main__": 220 | args = get_args() 221 | train(args) 222 | -------------------------------------------------------------------------------- /worker_ids/clcifar10_workerids.txt: -------------------------------------------------------------------------------- 1 | No,WorkerId 2 | 1,fced47d9b290b444627ff9e8b5f319b813c427b3 3 | 2,d71540e55b68d6d8568a201eadc54690c91c5fc3 4 | 3,62b23938d5b5a856e074857ac4c3a1a67f2cfca8 5 | 4,7ef756b4d275c8e1749126862e213773628824e2 6 | 5,a45887a4a77cf28ac904eaf655994b69f8ba997d 7 | 6,ac3b1078753882f89a3e9dbec0debff0a56b0306 8 | 7,c8ef344707242dbd133bfa9450b401de4dbad6ba 9 | 8,8a570f57aeafb233357073ae335475606ef85dfd 10 | 9,812e37b346b68cebb98ac9f418c0266551f0bbf4 11 | 10,9476e964122c4b613e4e89816eeb8e9304e5d870 12 | 11,86f8ff1640ad2e75738835f11e2c010eb0d6a760 13 | 12,4ccb616f4518263ad2e40b9576b1538d880092c9 14 | 13,0dd1f664aca17fba378b6a2fff77a326658e0e68 15 | 14,6faa871e067249d43c26052be3c3035331627fc5 16 | 15,4d687d411a5d04fa75a68202e12b71d48022f0b9 17 | 16,ce13efd8b4256ed9599a8634f215810d123e834a 18 | 17,2b485521a8ccc88f044d04d38d4f89cda92b2d7c 19 | 18,31556b686fcee4a0a9cba2d44f93c7cec104e043 20 | 19,c65e79c5a877b5655bd0fda1ffacd193143e7222 21 | 20,c24ebcfb95572bbe442316e0bc4c47aa1ae3f8f7 22 | 21,804c791aa69259e02abc7331f4aad11dc49daec5 23 | 22,62b36c142003aedb2b6d19b05e10cf3189366412 24 | 23,6218f0251db60ebddd51cc73146d2788df6e95a3 25 | 24,8a2dc37eb278702d42ec70a7d99f5413109662f0 26 | 25,b8f0703c535ef8bc504f442104a2f9f90e04ed99 27 | 26,c7db672bbf4893cbe5890c4380bfd23cc6a269f9 28 | 27,e4fe4eef34e41608fd131cdef500a0e0ec11b1b1 29 | 28,9206a0bfa84cb8bdc047b1d501c2290077106c0c 30 | 29,5b581bcbf632cc86faea986299af12d4e5316dbe 31 | 30,5a3f460d68bf33427ba8aa998acd3b01e97c51bd 32 | 31,c04dacb033c0f264699a2378ff69e0a51045b75f 33 | 32,b7575592d4f704340aa731b89be7c98aa52c55d2 34 | 33,a531c6d8d8a60142f7506979a02f1182dab8d3a3 35 | 34,2154c5ca8484eb7239fb817db407a90f594c5850 36 | 35,1a588c27faa2c35ae4a42172d48bb932918a2508 37 | 36,a4877000f852e739d95a1e8ab15c12b9204a3a72 38 | 37,5bc0b3b5c5fbc7f9fa23d4ba9a988c9c05d049c9 39 | 38,2f337439be10589f293c9634735c77e8d610df66 40 | 39,b62628f45a14fd942660ab0096892d4f082de221 41 | 40,cf3c325571bcd8515faf0ba4a250a3119ba098d9 42 | 41,0cf52608e931747586b951bf7b5bedf2b2b97d04 43 | 42,db53c1151bf9087c3c555e3e10994a486f458b1d 44 | 43,4e9f37caf563fefc94a65f098d4cadcbc35bad0e 45 | 44,0e42d661d9985be06efda9888684ae2b0fe23e35 46 | 45,2957ab8c958fbe45f70d7641096651283da6b83c 47 | 46,16fd2497cc659de82b238ca0a5db1fb4dfe42d81 48 | 47,bbf1e6495b8fc33252b2e91e35cf106af9ab0d74 49 | 48,9329ce80a4e8d86fb2303cd00c4c41c82a2a48a9 50 | 49,a80af9ac56b235ad420c8a6fd78dbec59a7ee2c6 51 | 50,5033bcd120a2d854b5c541ed5ada7d932381d3d4 52 | 51,5b1589fade56f2c83b770acfc4ecb6aec82d42e2 53 | 52,c7ab2736dc8d769c04267b07b8875fdc4e24dc51 54 | 53,1ff656871f0cfc6f654506a9ef4a0c87a92a00bb 55 | 54,51687c606053207fbd878c19af533d61eb5deadf 56 | 55,bc937d78c00c147da371ff77463874dd33a8ab64 57 | 56,93788efae967d10f894dce445e2b5169c8dec60e 58 | 57,a09cd1c86e828e21955b89da44c11243b05e84df 59 | 58,a697bc05772ff8f0c2bbf21e9bc2146601effdae 60 | 59,f082f7e319673e5191881c1e23fb2fa22aa2440a 61 | 60,68c538f020b8d4130d917d8c982115ce1c9d93ef 62 | 61,fd59b78d7b88697fa4e14ddea86a71df8617c253 63 | 62,fe0dc8ef6704229c7249f46e755bb4ab7750dc37 64 | 63,e22e55c35612f329a054a22bbf7b92f65fe14eb6 65 | 64,6f2e48919b8f4b6404294c1a573ad48b426c2982 66 | 65,aa5a891163929ec2f8fd4c33be7e5daa28d66757 67 | 66,d5bd1f9bce94c0b61fa84e9209967db930b6232b 68 | 67,43b1670d86065ded85ab3e53042369ef2f2a13d4 69 | 68,b22c230f048b0c591cd3ac5933c67c4d615cf7f7 70 | 69,78c54f9054458f2d4503445a65d4af378c18b309 71 | 70,31c05490e88cc989854348c6f06862b1a9db5ead 72 | 71,8ab180c156c6aa5fb8785d26ff901f5b3f91a758 73 | 72,d5964b70a9045391152082c1187b56e7d03cb1c3 74 | 73,e72a50758cea65eabf58ee41b3044387b8934431 75 | 74,0caee5b5ef28f2ab850d4918394f10cca259934c 76 | 75,9abd9cb38026001eef532de330c3173f34659b98 77 | 76,4f1d85428452f7700a515b524c9d8c32fab2f6b1 78 | 77,9e884d92ab2995b1fa5e6f4bfefa6cea4eb6f823 79 | 78,6dde3c9a16137ffa954d2b5163836d375065000a 80 | 79,b0627340faf1d27333bb0204c5bb6c1a2a690a7c 81 | 80,477df7ae38381304480c354e74250672c8745b80 82 | 81,341ab3c53ce6e1a1ac054a31b67775843aafb174 83 | 82,8af49304b323199ab672f1f50351b5a8fafab551 84 | 83,582577ab47f21f0f2b0b9103b59471d522338091 85 | 84,48ac10d6099f310e2953329cc865c5db2bd27eca 86 | 85,9a4124f0ffffd62803601dfc6a0b05d69d39cf44 87 | 86,c4c77b412c8cd2dc09f9afc49d367e4391ff9898 88 | 87,ca7894d61d797ab702fc563e2dac42d83b3878a1 89 | 88,178b499774a3e3626781cee8bc3f812244660a18 90 | 89,a4aa04a1b1814137273134566e3955b5a2f7b872 91 | 90,9093dd7982c178b2483a7da3b1e117d6558d1573 92 | 91,4b5927cb927f19b68c0f9256c07c98c53b7026ac 93 | 92,cf265f1e314dc7ee75cab171ac2ef59db9099dd2 94 | 93,85a528543b05c42f96def5e709979f98ead398e5 95 | 94,eaf8a723c02c583406bb79269d944016d273fa46 96 | 95,a45cd7991b03681c8631831b726f4f8a7b384398 97 | 96,25e2831c01f8e31ad06a68d63e32b02290aa801d 98 | 97,405b59a076d0bdf72c9b4b306f268a9ac6417366 99 | 98,8a195e35c25ed330d761c66da1946637481f756c 100 | 99,b05dea95b8478af12e096abb1c225375332d27cb 101 | 100,dd24a05bbcd98d2a9c1051b9594520fbd4db84d1 102 | 101,67444043871417cc406185f2d9933a31c53eb7a3 103 | 102,6c97aae8421a078072461c3910a8621bc024c8d6 104 | 103,34befc474f2780d7fc8db1e3a0b41c4db83b4e4d 105 | 104,8cbc3232e800942ae84ab1bb5735078e496a64a7 106 | 105,0d97f8ef2ca62518cb3308d763aa4974f2cc13cb 107 | 106,66c7b0e87f2c67d81199d07dfc49e16adf393e5a 108 | 107,81f2b7d4d2cf8f878a1a90ac7b430886c8776508 109 | 108,a8edd480957e3517f66725b699b092bf08fbb3e3 110 | 109,d769e06ca3fcb3e455c4f0c7fd984f3f529e9e5d 111 | 110,8ebc1874a6b1477ceceab806cf3403042ad86080 112 | 111,d761cae8517ce2480205c53cf91c0f52365a799d 113 | 112,7498443699f7d8bc7ab6adff54cfca08c8b83407 114 | 113,d57a1a3b1c9ea22bf230ef4e2e24eb4b8dab2a42 115 | 114,aa0323d9c23073ad8979d82f96d530151543058e 116 | 115,ada8256dcc964a83b45c33f693d0ebef42c914ed 117 | 116,b078409264e682c561fe67a4aa680cbbfb0c6fb5 118 | 117,e4d3d49c721795faee3bc3c316606ca46e2a96dc 119 | 118,e2b8cca625b0f94753a282e12a4680466b57fda9 120 | 119,6d6016abe4014c55b542236e136b1d72057664b4 121 | 120,47cab84d22340df71cf1af8dc7d3f5b91ba2b066 122 | 121,33ad5eab7968f5b4130f19ca784465d9d6dc9d26 123 | 122,4e885628b31292d7055d71bd0c3690465e0ccc7a 124 | 123,df9e9bf2e2fa20122da0f689c4bf879deb88fe5b 125 | 124,a2005e1ece82c15378f4917eb15d437499646843 126 | 125,72f23469e33c224cd2c25d17f49a1d351c1288b2 127 | 126,901f94e8deefd5febcd48dd9cc709bca805c33b8 128 | 127,6534373e2277a58efacc27989add87392526195d 129 | 128,acea264c3abb842316adde11ecd18155606da0aa 130 | 129,d0b546836f45bd7ea8ea56dab577a5c5dd0cb56d 131 | 130,92564402c6e6491a98c7843aa4c0aebf2c634ec6 132 | 131,0150c9640893a51b802375e28b3d8b2f9627fe8e 133 | 132,6ece7cd074452ef8e9dd5a7631fedaa932f501f1 134 | 133,ef1a43524d9ac1a635ba5056c1012f06228812e8 135 | 134,1856476a924d123c1d31e0c9351e426f84104395 136 | 135,c67675ed3c5a8a995e383f841ea3ca0cd803716a 137 | 136,c1d4f1bc7a4aca446f72800297d8f3ac756cd8b1 138 | 137,1df66030efeae951e29467e245daa7e095dd3492 139 | 138,718f7f16b9a162f27e36c9909c7bc4994344d61d 140 | 139,11660c55cb30580f743b47f7591c1a6cd8184dcb 141 | 140,34794b97ed16238054eca42b7f771a1855646c6a 142 | 141,b94e4b1701c29b36349f5a7c2224d6a723312732 143 | 142,12e0ed7618eafd28174889d0f3d769111df8ba16 144 | 143,f4f64dd28f2a5c8cc5d28054fc16ba96d009ec3d 145 | 144,914d2f881df6cbf61fa102273768778b19d1773e 146 | 145,2ed90bcb0e3b2652b60bbd0be2c2f034951cb986 147 | 146,360ca7661c6eb1485b2395c874ef220c3c74e2a1 148 | 147,141ced2b835094364193665ba0b54313b31abb89 149 | 148,5b2614e0abc79571d6ba9e4ad2d48f074b5fb2a5 150 | 149,35697b6017d41ee7c85111f6301b88c68a1e43b1 151 | 150,8861d911a9bce2e83d6868ad05747a905196afaf 152 | 151,ef8486e4e14d9bf1329a4f63841a44a724a71879 153 | 152,83d3aa0de1a111e25c6e824354d3ce8e92de760d 154 | 153,4711082f1a3cb433eb321a1346831453f0da2f95 155 | 154,2586a7f359055be55c00ca1f075e9b68b2eb0267 156 | 155,673e2bdf9675d75c6c8f1db0d021e867cfaf4f1a 157 | 156,29192528c7a29165450453f053683fbc40b472c3 158 | 157,a02add4bc18b05dc44896503fbd1a6263fdfadf5 159 | 158,3e09f6cd599560512ec45c20bc0d33549ee9399a 160 | 159,854a2464110d478fd5d8e8b2d95b3e80437934a9 161 | 160,c69f4bffd949d77dada5d3604346d818583ac205 162 | 161,d2cbe1f265552d6832edfd8522e09a565c105f47 163 | 162,fe31d42014d984a64b9e1e4d240e18189db9d465 164 | 163,67cec738d64bcf1e92291e1c3e276e010e5f24da 165 | 164,d81eddbe25a9dcf7787a94613a8b7caaaa692232 166 | 165,fef4b6d1a1ae95e7b661f9b8b5405d6cf33fb209 167 | 166,378300d1a51cdf1d9ac102cd37037c932ee34574 168 | 167,addf3436e3ef293c514ded85265239616fea6fbb 169 | 168,21d49591d313d662413ffb82b457e6878a758671 170 | 169,936fcb5d84c073c4151876260a6f48c8d3ae8963 171 | 170,c857e499c7a40ce5bd03126ec1c8c70d91c1f104 172 | 171,d57dc6f3eb81c4063fe67c0b4b1084548a0a3c20 173 | 172,b805d6fc109dc98015627b67094390d86d5c71ff 174 | 173,85e091b674ca01d686de0bec0b4a0b39effbd0ec 175 | 174,0be77fb3d22432c6e87a61bf7997d343dfb8569b 176 | 175,44994cfec96699bebaed1811e219b58e14475af6 177 | 176,6d619a444c17a63bb6467e8b93fd0f7e950f97e4 178 | 177,5f3125902af3b4f409238ae02243c414cf17306e 179 | 178,96f6e9d485c3cc54de0070fd75156a63c2279422 180 | 179,ff97105714fdd09e70398a04ac6772e734b70c32 181 | 180,0bfae48b2cd108f7bbe26979cde729c9f1af029e 182 | 181,9a941e2c1e1efd4f999cbb353ee9dc25b097dee7 183 | 182,4b44cc331117efd1d3ddb257f0be4dad41cd2f61 184 | 183,2560553fb3a23d98150706eb9c75e2f852190902 185 | 184,dc16fc023d47d1b014b4436ac8beafe7b0fabab1 186 | 185,13444132b87276192c4b8973c851891e3ac064c5 187 | 186,ea9c34fc7d9dbf3f6a8d9266030e28c1e91c2fc8 188 | 187,38a8195d6d2bbcec21c3797f9c1d2e4b0134450b 189 | 188,f5d1d7cd7b97aa4c6ce02f5a80a4c08a584c1b2e 190 | 189,7ea9533ea09bb92597e91a19c618a3484e75c960 191 | 190,cba1f6b9ebce5f320e14a4576b95b4446588621e 192 | 191,0f6e8c9812c4294247907c1db9427d0f3b45cbfd 193 | 192,559c968f4d616d6e073096531b6b4b7231691025 194 | 193,9e47b3297596cbcd6c6c164d046ca86ca8f33e4d 195 | 194,f6f5defcb9a66bef3451a53d73a533106a1fe7ce 196 | 195,ce7f4f8f95697d7dd33b6a04c5e1183bfd011092 197 | 196,eeeae6e9b565c13d26a8cc7b7f40ee8990901e41 198 | 197,107cef463c683ed0dc3aa16d951ed072a26bf642 199 | 198,631fbc7ae9b75bf41da444f3544172477c8bfe1f 200 | 199,8d3f709de47a081bffd0ee44fd68403bd990d325 201 | 200,131b2c9e1e1321eb45926087c1669a7fcdbde276 202 | 201,a3bb30278be74244788a4a1325d915f878256bfc 203 | 202,06652ffe610d30715eda5fd757461cb4b1a8ba98 204 | 203,673a69c7865220907e907cee1091710673539dfc 205 | 204,da5bc41ac13fd59d3d4c9c0e7e6fbfa7565c429b 206 | 205,2f5e0596e1e37eb569e09f9ad0ac117979010911 207 | 206,aeecf30d360a6d6047e00d5ec14393a0c4e0ccf4 208 | 207,f1ba8780a6fe8b602288afd9c8652fde3cf2e89f 209 | 208,ee993dead692f9c7cd18348b9b0779b563e8d039 210 | 209,0a495d2d93920cd49bf7cef95011d39848815ac0 211 | 210,886ee1b17966ed68679543097dffb6ad093486e5 212 | 211,4ff9162c5efb8335c7256b3464df40638e21c23a 213 | 212,423648cef1152c2ccecd6692ebb2b6e4c03ef0fa 214 | 213,0bfde8e68dbe10ac502849b84d065e5ab97b9158 215 | 214,940dd6a9eb65ec1741705b8e2756c22b245ab9b9 216 | 215,b285bbae7f6032ab91b9a74ef16212c880f91a5d 217 | 216,ab810a6bb716d9bd79a36e564e8f50dab53813de 218 | 217,6e4d6354b8e106933239d8fcef3889aa919fcb6d 219 | 218,7267399bf1ddd64b27c3446ae01310bf21acb268 220 | 219,8c9bf4e8810278a2bf1f0e6574dbdf51851eff02 221 | 220,b5ca782de4591a038eec99d743025883f1c5035b 222 | 221,35a1bbad47ba2ce5a6b30585957f6dfb5332c96c 223 | 222,e14ab3e7814737317a1d252eb012bca7a6542b06 224 | 223,7e80c94451eeebf072f2532bb73f1b4f891bd310 225 | 224,f9de74e4aa03f59683913a2dcfa37bd3ed9d0a2d 226 | 225,f556488d1ceea52a0f7062f539607c4f65133f54 227 | 226,cfa74f2a8c0f82b4ebf63fcadc7929889ba52eae 228 | 227,bf7ec3e41f23e13478fd64fb7388436a8929d5b6 229 | 228,8b8d6ae6baa0d52523a9679a30cdb3476efea0f5 230 | 229,896d7a9f4227e769fadec6f2e7dade92d5db3e0f 231 | 230,7f9c1a462077ae739afe2509526797d07a412582 232 | 231,f8c952987e30dc6388169d22de7d1441ec2d8db8 233 | 232,8ae85d6d3414174c58627d96f9a4567b6b1cbed9 234 | 233,c708995999a581663f49f6e941cf0210235b7d7a 235 | 234,96b60160b4a1b4a8c82a46c9bdff8a237acea8bd 236 | 235,52dd8fe0b19608403f023762725a38aaec100e09 237 | 236,b7467aa7c73990414f818471a62f3980c1b85cf6 238 | 237,3a36d56d7f11d30d265f96fc23b2f3cb0f8d9fc7 239 | 238,d8e4ae0289bcce139d862c85e3f615d54a5ab1fa 240 | 239,623902fb6262e8876ea4f0cf4278d2179c8adc9d 241 | 240,9ac7e4568331c9eea7b6e32f88b920863ac30a0e 242 | 241,ebcd76891a09caa84d3b36b6df9a6ae758aa3531 243 | 242,1fa32ae5b06f2b32f0fe4ce3b8812c481b89dd0d 244 | 243,6e71febf7f6da6ca4a7b737548c9677efbd71450 245 | 244,fa3613bcdc82f1f01c4ddd3348303e520c3ee9d3 246 | 245,0e93b3668199f4bf177d56ec15cd70be99bf1ca9 247 | 246,e96dc4f758df1f39eabfc6addb2cdf0525813938 248 | 247,a5b5befa0c665edd4a1ecee4fe1c8975d6b5da38 249 | 248,7b1522fadef01e04202e20ccbac9564043a840e3 250 | 249,da7218601c16989640301257b2ed4050d8743f1c 251 | 250,5bc4746833332902e0258b2c71571e47e5de0630 252 | 251,e4175c7c5d9c67ed85a913363ed8ced70b373889 253 | 252,db721d9e5143c7d4cf46382207bfc563765d6933 254 | 253,8b52c50536d4ce43fc5679a2fb6d2962c0f3236e 255 | 254,8541493966645b07e344db1e8018627c6aeaeced 256 | 255,8c7119f722a13c1511d59449555eeefb722d0709 257 | 256,55fc03a4fbad3fade91886a4f3ae6dc8b4b74a55 258 | 257,b7f56ce4ef24f526be7fb7d2a544a9990e18390a 259 | 258,4d4e2bf9a4740f412576d71587481eb5d20098d1 260 | 259,3c08f35b16ccd344500d6047ce56f845351e788a 261 | 260,b8d83ff3c7d745f59834e91696a805566b27446c 262 | 261,4ae5359db7ba70e7a544bae853ce7eec2e7387a8 263 | 262,1ac2c2426841f79f45ff45612dee9607a7808bdc 264 | 263,f1830a2912cf7a8c0bc08ace36553269b1189510 265 | 264,56186d651cbad4ebfaa5df3dfc0fd9a6fa243ab8 266 | 265,744d28008ca857232623a9ff890911aea83243ac 267 | 266,508ab41120f0e3c6441de6cdcca462baded0c0f8 268 | 267,387cc18f9e00d3e2828c85b8afe800393cd20343 269 | 268,b777c542ec5aba36e0efc784414dfbc7a40fd406 270 | 269,71064f339c2ef86613c7e85f850946f6cf384099 271 | 270,a0d175f7f96325b6afce3edaab5eef25b1c189fb 272 | 271,f9d658088649118acd51eaedd2ebed75fb4d528f 273 | 272,1f6fdbd8c6b0064dbcc38e74a5be31664510188e 274 | 273,fd295dfcb3e830363ea8b834b5530e052d592f1f 275 | 274,8c1809f3664eb627c960b0d7efdeda5542a1902d 276 | 275,7df42161c0acf7cd385e618caafcf3bf7c2168e2 277 | 276,1a470a36b2352dcae60f41275acd1e70cd298201 278 | 277,e115949f9e977c84efdd6989a8f24094acd74628 279 | 278,d0301fad7aed71c90167a14415e354f1352a5d2c 280 | 279,faf8033fba73dd2ab1b9b43dd7bccdd2610bd5a1 281 | 280,181ce8ca493ab7368b25d62d9824fbaf3cfb4805 282 | 281,3900482f70bdb13970e577ea4cbabb16eff09149 283 | 282,417818d3feac26ecb564abe559e63ddf04bd124d 284 | 283,4f40f56a45498a2ad6da6301a7031c82f3b83be0 285 | 284,2d327cbb03df7581a36f75391122104eca9ce26c 286 | 285,e3de6fd2bb353306df357a697d577c74d59d9d73 287 | 286,5da1ac214344a70f2ff3716ef5c8d7803cc096ab 288 | 287,f87103b631470fe1ec36f1050e802fc85600bec2 289 | 288,b362b424eb13b7d7dd39b04476defd6d39150b2d 290 | 289,59cd879e2aa63052d3c75103d388df2b9fe44702 291 | 290,66b5785162dd0e6aeba4ab7e17f392c706032a9f 292 | 291,bf2c0f75200a23cc76356ecd9c260dea02d64cf0 293 | 292,8d96e8ea88b30ff3e4b959b341799e6067b30a64 294 | 293,621ad7f4460dc9d681e1ae18364fe14ab1d69f65 295 | 294,a248b256d3f5f07779dfed368c768212f799f007 296 | 295,5e66e032b89c96f415fcd772243b65b94a99e45d 297 | 296,31e70a4b1e6a43f5bf29b354aa03d873eaf3c740 298 | 297,98a18f79b5d74840499d676b50b4f2f97f5fd9d1 299 | 298,a0596486f4fc3eb642eee523d624e97ab5fefa31 300 | 299,0fd50d97411000cc61cc7218074bcbd00f26e9de 301 | 300,187645ee5194d0560ca395fa8b7d2c6f71946589 302 | 301,6e9d09e0ebfe8adcbf0d2245a4d5a8eaca67fb4b 303 | 302,4732f9b70440a47c706dcf77e94b91cea5c6cea5 304 | 303,c39b1f1c3f9deb4c456b34b99d6744ce3f1f6d79 305 | 304,c1ceb78d82c77d093686bb75969c545862b93450 306 | 305,c9a4dbca87fa57aa3597411bc76cebbb3ffa3629 307 | 306,49225085fc9bf552d86ef016c0d1ef1edf7ee199 308 | 307,14bc76e665536f042ae880d8cfef40c9177254c4 309 | 308,dee9ef52ea9b2fed994d03c0ad097a0b41f90d94 310 | 309,0925f9898bc6fb52daadfd8dafb18425fe7d0603 311 | 310,949814dbbf90188b7717913816076b37d8e89b33 312 | 311,a69572a5061c69b4ceb6aa524c5f3e233518fb35 313 | 312,192e59743c58172ca3cab2dcbf4c72913e7fde42 314 | 313,6b084fed24367a60f4e3b092312082f0e0abc3fa 315 | 314,46e62c7ced13685caabb45c5648bcd724403c2ee 316 | 315,984ee6aa9bc3b7150812c5fd3bf747df1c653cb0 317 | 316,5edba264746b8912c81b464c26c6bb14db140895 318 | 317,0af5ea0f59afccc5e9bb72b24d9fc4c1f8802d7a 319 | 318,44a92f4cd3c905190dda58440ed069d6010064d7 320 | 319,0ac4172e3af94a4078263dcc7b6ff8ca054322f9 321 | 320,61fe5496b4b89329c6a917a40282c89be24e6d47 322 | 321,e385a911b41aeab6fc4fbe13bb689bae65d71d00 323 | 322,74c1b73394d000b5aac2a1da150a06a051f56681 324 | 323,a9ac892a64a449763d2def33080c1da957d9eb43 325 | 324,66be7eaf65fd5402c621b0abb6dcedb5646784f4 326 | 325,b011fc4618cd0c5714dcf932dc4bb0f96698b02e 327 | 326,8f095c001e6b18446bec51b386de9d9cad92b218 328 | 327,ccf21de8ec3567bbbfb6497ca2c7b31a3371c896 329 | 328,321574f26c47be2a6f07714283cef81d28dd73d5 330 | 329,3d70396507b1ecf2160d1faedd590bb5a4fcc31f 331 | 330,032917c46ad7fd1cff6e34460aeb1b7fb5b6a9e0 332 | 331,c34c09fa8243a9b5d841b22e3937f8a3709b1e96 333 | 332,6fafbdfa2ff2169856b0ca0df3a36d7662b85df0 334 | 333,da4f7f64c36d2c48fcf6d560b525a98f8178cfc2 335 | 334,76466fed9807bfc610c3b15ae95bae5088187146 336 | 335,9829b2ba01356491b13c4c7d228848301071db8c 337 | 336,2cbdba81b1a3a6f73d0c8f14b7821428f5594212 338 | 337,3f0b4245059ceb6776e7b8cab9bed6b938370200 339 | 338,d1ab5c67f4bd4ff3a7732366759934904e4ea3a8 340 | 339,d79c685ad2bb85f2ef8f3d6d8bd9908390cde0f5 341 | 340,0e715e1b43a6514f99d77ff6cdd5e7a7819a5eb9 342 | 341,d6b6e51cf1e3ab20c4927c08478d0fec8694cf0a 343 | 342,12b628566f7f70df2a07dbe0c54a8771f6dc8df8 344 | 343,9158c9f192bf334c4b6f98be6472da186e26f23a 345 | 344,f759be6c953aaa89fd24d277873f3abe3f864fff 346 | 345,861fbcac494aa721c56f50e1451f8141b05169cb 347 | 346,8906a77c5d2e4aa852d5af52819022732d8d280a 348 | 347,49cff6a4b4c29e1e725fc1ee63075397effa44ef 349 | 348,4cb088a8adc989bce33350c8a0a983012c43f4e0 350 | 349,73d43ad6d11394a4be7273ced851759280264e25 351 | 350,42d4b48b8fab7c713a0bc4a8807121f1197a711b 352 | 351,a9c988acc40e48ce5c153347230841c0e99c32f9 353 | 352,5dd1024fa91ee77c302931e20b5bc11efd20cbef 354 | 353,4d1a5d864c3fc7bd8be4a7c60ebe3b731f63f3ea 355 | 354,9ed8e6c1b435fcae10844e7341713d9d2ac63933 356 | 355,f13a68699ea3c1153b8f9ca05c8bb2cc6c4dd815 357 | 356,651c80d11bdbf8f297d03a80971f6a0c4f2776f3 358 | 357,fc400e7c09bf0107456b0e7e906448daeef9168a 359 | 358,2f3e8aaf847d5885e2141a057c2f075178a06118 360 | 359,20b5cc2c5c16344a87b130445019a0d2e433fe88 361 | 360,6b9bfcf42ee86c693bdcc75ae29f72ad42f8fc74 362 | 361,b1cfffe99bb83bcff4be415777f7daea2db9532a 363 | 362,2223256f38deb144f07216a8357ff6ce84f9ffeb 364 | 363,ed6e0991020a0f9078a8074deea3c0f962ded482 365 | 364,f99c89381bbd1e2cd27b5ed4c78c0ca4b405c5ba 366 | 365,4c6414c73612e2813837c0bb857e65cfc5211e00 367 | 366,7defccbdb6c599e03d732de204825061d6255074 368 | 367,0d64c2e3be410ba2f7058ab0fb660bdc19ead62a 369 | 368,55bf05eab20f97f4c5380a1a572df811349a7a4f 370 | 369,92a28aaaa6f05ae4453fc7308a5938082a23d46e 371 | 370,8f23fe2fbfcb4202420fcb0b256697e8532d0afa 372 | 371,69579d6b65fa3ddd522ce3a32629338b8a041092 373 | 372,9d16661dd899145c1e5450fd2a7b1b26443d58d6 374 | 373,a97c6fe23dcdf3621a5454fadc4e2f9cfbfcaa8f 375 | 374,599b4ba3085e483d433715ef766b6147168e4c08 376 | 375,e354855ee8e1ee19fa350d9f5be9302d4c238f71 377 | 376,53881c667d622b160ef850816200a9a283481480 378 | 377,f9200f123299d2f38c2d43d905612ae919521582 379 | 378,173608dec7064396f5c26f3ea03cd126671896a8 380 | 379,5f8ae38cce393dfcead961172e8e0b3afe83ce59 381 | 380,2eac8242ba42392fca6932cfd37e7c818557145a 382 | 381,501ccc4a50d76216a343ec409ffd7c4628142542 383 | 382,236f6c3aa80f63a110f5c104940d0fa527b614aa 384 | 383,15ca397512cc608f26f4c5ac25113dc3385c0f00 385 | 384,4630556428279ca526aff682c1379db2d526654a 386 | 385,deb2a1025ef8283cdc4d295b3763a0a3576b0a89 387 | 386,a1c619a66a771bc367a0090fabe4d9bdf0413502 388 | 387,989d2fdb4e46dbf394195e4892bc332e1c162041 389 | 388,0b575b4e12f4f55cf1f6458164d8034dc46a8c2f 390 | 389,33e2b451832bb928beb2519aef54ff0d630f0344 391 | 390,8016568085b56f024993c1ef9bf5c5103769386b 392 | 391,4befa456bae5819af3c7688b5c9b8a83352deac4 393 | 392,38aaf77023f30eac9d23cd40e42b8fe709893501 394 | 393,a0e0ea831df977fbd92c17c9f8abec38d84b5f4b 395 | 394,6134482ee0f7196ae472537e91ab0ca9c2cf9170 396 | 395,7c33c3a3f2ec3c646d3694bda6f26beea20b3ecb 397 | 396,9bced75eddc50e15706ee6e820a43c509173cf42 398 | 397,2f0b6e45edd36fac72e9d19f1b680be604483488 399 | 398,2344a17345bb05bbd18a4761619fac7a7814dd4d 400 | 399,114e420519fb9251411320a10125501031604baa 401 | 400,169bf8da87ad83dbf230d056adb36c766a72e5be 402 | 401,1085b841866bfce4321073bec2beb3d3b033c5e8 403 | 402,37188d4d42bfdf5088b74b33946aa2e8dc7e892c 404 | 403,c8a004acbc6a423f3bc644f1db6b80c9318ac192 405 | 404,70a60e61ccc3e93de946a35bad2f5448db1519dd 406 | 405,48e501a0e061cf1bfc75ef36351f949b1fa825fd 407 | 406,1b862a1882b4e7facf9b9b23a5bdde3e3bf07613 408 | 407,0f268de64babac7dc07c09d104f718c2f12a4454 409 | 408,7b8169b601c6dc52a1ad22a5c0c4b5901f6fef2a 410 | 409,ee24b9da90b4335e2b5e0dfac87de8639f8904e6 411 | 410,a9714e593b39f0eaa57a7b31c7d262c35066c94e 412 | 411,4c8552704db705491a75f3305633faa030d2bb4f 413 | 412,f548d3d30c40016ac7981af86e08571c7b6900ee 414 | 413,ab40caaa691b5d30264f146016c2b613b5001448 415 | 414,a972139b751b4c243fca27e9575c078944ae14d3 416 | 415,6016217dbfd495725f9e6f2e507470a4adcb3a47 417 | 416,11789613ed9db52af131af1ffccc05fd9bd4a46a 418 | 417,4a005004c716d558f18fc7180c8598c75affc87c 419 | 418,1e08f35b2af9eb517a733a74b7056b4bf81aaf2c 420 | 419,6290b599855ac3b0b8ce375a4e255a56ed8ccb7c 421 | 420,acb9990e771063a427088a36b47cdbe9b94f5092 422 | 421,e5bc825adc4188db1481e79bc8ec06d8a19eeec7 423 | 422,2b093a1dc6acd4e1c9510d8e99180957e5b05f05 424 | 423,5a99247206a46af8f517e4417dfb7080b1672588 425 | 424,5822914e089cce5b54a7c621229ccb6f10672683 426 | 425,fca10d6c20bcf81d415ca2a530f22e72e27ce3f6 427 | 426,52dbc5c7af860d15bb347f9830c7251d98ff5376 428 | 427,6219455a8480cb66ed4b5e56eb1e14891e097720 429 | 428,906612603ea2faa8a498b16a1600b47b21efe08f 430 | 429,81d64b510f7753f388583904cae000c93f718c47 431 | 430,3e9e01b019e5dc45b6452b8a4af1b6b4af4728cd 432 | 431,d496eb4dfe2ae7ea29fb40d820b56edb93d1f0d1 433 | 432,ba88167c391f6b32ef670d0099783f43960dbd20 434 | 433,2b6ead823f313753660ac2f7a4f4fc9ba9740380 435 | 434,b85b6da910fd84a9ebba27106d641e8fad8317ba 436 | 435,990af9f77a81a54cca0dd943a1aaba21a8af0e9d 437 | 436,e8a5440742b465e5a33247f5ef840109598e92ff 438 | 437,bf304cf25c50d445e0fbcd035c083a9895845e61 439 | 438,8b14d70bed026cd2d74c5d7adae1f96f7ee3706b 440 | 439,5a909e6e9fbb06403ee406d3fa211eef966a5f77 441 | 440,6a2cda67dc8c401164f42a0abcc6ff59ef8b3f5d 442 | 441,f02711c22fbe45bfc5c9b7e62b5d036c454d5c76 443 | 442,b5b21266213656cb5e5e2eaf037682ae1d1e6620 444 | 443,cd414e2f22921e5849654f707ebb7b077449341e 445 | 444,0f493c0c1a52c0198a330bdbe81f61d36fc6e2b7 446 | 445,cf265d06551de9c81e6031f7795d920638d2c4c6 447 | 446,acea018eb91eb53ad41965b806d010d1ac1cdff3 448 | 447,f106d1f3da62cdf815b123a8da12c4e26022c993 449 | 448,8be20325f0e5cf3418406dec5db381aad04715e3 450 | 449,58ff7e17eeac597dbb8a9836b088555262ba70dc 451 | 450,b7d06b6aae7ebf5a7f8062dae861438ea65a47ad 452 | 451,c98edb370754e6b25a80385f98d71ea1eed26e9f 453 | 452,6ae91db28d4b0387143aef681f81d0a2ea9187d9 454 | 453,3dd3381692751b16ad11e8779aae70630dc4e362 455 | 454,e9815ed154df97ba0fadd3284b061d3e628e79a7 456 | 455,d8154b76c4849ee6038d10919c650f17f780225e 457 | 456,6e163a847cac45eff76803881613e7c1de3d0ee3 458 | 457,79546a3b0a5d6055609c3998347c6ee772a8dcd0 459 | 458,2069616d382c3621d5891ed9d6cd1817208e3c48 460 | 459,66d77f139e4db9bd50e1d05afabf9a155aad46be 461 | 460,9ef8eab541c420d609a37c84f8a50280195f5cf4 462 | 461,8dedf730dadded21153de841dc1411582a8fb37f 463 | 462,c234dd515fce8a3be0d08ee65df57fd94201a517 464 | 463,a5c8d7a537a908d8563f42e6f791a00cbb6d9979 465 | 464,ea36fe9e99acd3598b6fd2f3edb3a019d9da450f 466 | 465,f4c2e991c2b0bd96396a48729d58c02f60408308 467 | 466,bab3a45bf76426168377fe6f9a720d28e8a120d6 468 | 467,b32ebae0555dffd8ebc196dc08d208f8aadfae95 469 | 468,966af7409f47d8b5becd11f76643a20ec4c8ad4a 470 | 469,ea7b5b6fb3a2a443f8f0a75828911223c66d80a1 471 | 470,071b2044cb39cd22dba8f47cbe3a0ea146a493b8 472 | 471,b3b42052ca552e946eeb943252c61ad3e5bd0756 473 | 472,211b316cb474874471a28482652dc6e1755b0816 474 | 473,f480458921d2ede0a641858c6b6bf08eba892746 475 | 474,af66ec51bb8154a910aefadb18c0cc16f1c1acf5 476 | 475,ccd201da442e24a2adecec21bf4e9a922a0d3016 477 | 476,bb6c574e3c5e220e28a2b06778fd21b4c9b461dd 478 | 477,3f5315f8ee4a5a439267829e78e86b0ffc0e232c 479 | 478,5d7d665ac1ac3bbbaecab3b9efa1cdd4dbb2cf88 480 | 479,d373b07c9e6cd8245af25be4deab072656fc2b36 481 | 480,01f1fd714a0cfc6dd8dd34f142dc14a1936bfd79 482 | 481,4fc04ebbcaec14a5677b4c223210c8903ae9c81d 483 | 482,fe5e90e1eec3388b313dd2afa251ec81d2257d26 484 | 483,8b5bcd5c743159d67124365e42861cb7349d10f3 485 | 484,b212190ab4c50f2643e4ed613f8ca51e279e543c 486 | 485,6afc75b3d810d4ec65e78a93f886f574dce118da 487 | 486,ff45f43898757719e01fbec893e5d803ed1786c8 488 | 487,8c0cd4324722a82d16846893fd30dcc8f1303646 489 | 488,d08d229142aedb5ba5f78399f6cdda30290edb46 490 | 489,64421da010faa7a7965ed771698f12d125c62fc2 491 | 490,e85c2a83fae2275ffebe048f883fb449034114f7 492 | 491,d6c9db1af035c895a702e107ba9c99551637248b 493 | 492,31e5be15c6ad9cb3e675f278246574fbe253be9d 494 | 493,c0c6bfa88bea5af499bb2208aee02246039a0970 495 | 494,89799c53c2ee4103cb834a0bf454141b15958f14 496 | 495,5b1c1e7b16f719bf59259a82018e5ea7851a38e4 497 | 496,f414c4711ecf8ec800a5c19875bcae701e30c0af 498 | 497,481b547fee56c7e8bba69a3918aab07bce425298 499 | 498,7d4c3c87d6de1099fd722ef3b00bb3c15fd2817f 500 | 499,8a9df7ed42f74f8b12645141f25cc438b25a5e41 501 | 500,d91b0996f7d8ae015d06cb32e8e02521b95b40a1 502 | 501,7c9f3e13ff9a11b8332a96a0e673a05983307e6e 503 | 502,ed6c72ee85abde7bb855ec63830ede3673264f6e 504 | 503,22f5265958f04fb27cf46ebe23c2c2ac8278cde4 505 | 504,e8f094af7b25aa5cf19fa53a0a0b57e774d5ee75 506 | 505,ef061c3906c68dd0725377b014badff94c9ff253 507 | 506,17f791fc79ced75451f25e46219ad56b99fd5ad4 508 | 507,d7aec722c451e85bcc1615150246b03b0ad7fb7c 509 | 508,6136d7c2749313e76b9600090479f37c2f26d29a 510 | 509,0ca86c987bd7fc657ecfcf3c5417e0186de284aa 511 | 510,aa2e68eec38e0494c4cdf4df31c6d1bd87e8a29f 512 | 511,c37cef83fe4b2006421d70ac4e597597cb44d34a 513 | 512,b80f4c2894d0e0f8f36397a3358f62f44d6abf98 514 | 513,1b29de5857fc5ee3766b0ed823d75982234b0c6f 515 | 514,c99fc73f3ebdb460b121f19c492ee22948679077 516 | 515,3c305c95c9135b466dfdb9516ea1788349d85188 517 | 516,78d4598f33fc0a632595f8800357b859b41dc8f2 518 | 517,8941d441f54238c958bed5987a548956e50edf9e 519 | 518,c4119c934f004481b26a462c2f9f11825adf92e3 520 | 519,a6969879871b3ca47d19d0e2e42cc25596d60877 521 | 520,d825d4c6352fa54f322abb702b6359834957b5b0 522 | 521,75e9f8f8ecc15ce215839915bcf94591271a1ae7 523 | 522,5daacced56f0cdbb59803fcbee98c3d7ded20228 524 | 523,46fe267357f0903e2bd225ebd35f24aec0dd9368 525 | 524,f0018f1e85c950887634367943af8d1ec304cc34 526 | 525,7dc98437091be41fe4f4107c05fcc2a7a3243d4f 527 | 526,f4e4de30a937a81fb96c80406f3a7e601c8c9b52 528 | 527,41365709d6b04f0fac40aed31ff8985cd9d1a7c8 529 | 528,a29a8e0a2f49309e0091eb29a08881652ec9d2c3 530 | 529,dc9efa2f5ddb7ef821a11521747db40544e1038f 531 | 530,11b408c16bb79e843d02dbd04f11ffadfa5e1469 532 | 531,e2eef9a75fa04fb345437bf2440a15113bdba622 533 | 532,9d80c6ebb7783288316a0c0dd2596856a48acd71 534 | 533,2da7267bf04658d464f4b79c02e2e3fbb2f11cc4 535 | 534,1c62e405038c3daa13845add324c064f57604779 536 | 535,83a4d8b232315e1b17ede2c4cdedbb04bbec2c65 537 | 536,3a87d5e221569e3314dd1d20320878e1da8a58f3 538 | 537,6e23ce25ff089df70ba9e881bb2057a08303af6c 539 | 538,9b2c54068bbd1e45507853a6a73d6e44101348ca 540 | 539,b84a02336aecab51538ef05ed537435211868383 541 | 540,f6326d5ff15f8d2d59538968a21f424229e11415 542 | 541,16120d40c52936c959c50f780101168fbb852529 543 | 542,e28c0e83ffe8b740a82fd5e1098fef33ba0839e0 544 | 543,b39cd2685be6721fba31f22948e395006b3b9eb5 545 | 544,ef186d8674edf71ab491ac3195468e2a6cd1cd96 546 | 545,912389b5bf3f6d4de157cdbd9dfcf5be6212fdd2 547 | 546,d509ec7342f89c6424cea5b6f9fcc6499c741746 548 | 547,e0696740e9873bac3dd7b794f40b4856d9d963e3 549 | 548,0ddb4bc57c3e84848ca279990e07f09c11067d4a 550 | 549,dae83b3268204f699f46878df6920ffa0bc010b0 551 | 550,fe4664deb2b1611cc6991fad441b15150d9a61a0 552 | 551,d082a3d71a14cb98b4ac56e7d3df04e3aaaeb067 553 | 552,29d69e298e503058ca9a66ecd7201df0d0c0f648 554 | 553,778ceba0c11c613ed7680888bfc62d837aa52b16 555 | 554,ba33f4bb3b4fcebc1496040886a4b205129ea2c6 556 | 555,d766512a67af5eb743cdf0a1397ac598ed1db0ca 557 | 556,726e327e183a4aac31ea74ddf570dfa22c5d625e 558 | 557,f18742d6bc5c6a834bffd20dc4a9e66092d9c61a 559 | 558,766ef47e6b7aaabb24c3875eb692cf8eeeba6742 560 | 559,d8a24a9a0148b5ecfae0188118a41107ec01d826 561 | 560,449513b726246a0b318cbba0d1bc28de4e5d6cd4 562 | 561,3a4efdd03bf43305937b7c42699389da010990b8 563 | 562,d85479e3794ec89439f0311a4e4c92d1b608111d 564 | 563,4476ba9eec63dfe6ff52b8a182c8233fddb9db5a 565 | 564,ecdc231aa3205e35339c3ca2c224a64c4ae7f7ea 566 | 565,3a2950b8cd810d16565a35211fd62fb7747f33cd 567 | 566,accefffaf3b5c4b856ed8f77d21eb1bfcafc1979 568 | 567,b2f1581a9c8146c57f7d4e692bf01a3b2643e1eb 569 | 568,94f5619ed7f91a2c0c3c099a4d07ac79784dbf19 570 | 569,0ba8577c14492bc5e295c22219b6accb30021258 571 | 570,275708fc3d575aef61fac5bc986ccad25aa6181d 572 | 571,8be1a1fa1d193d4f9df4ac7eb6189fa591d39c83 573 | 572,7d625423a9112a78ad0901899f80d351ab082e6e 574 | 573,74c317dc572a8d8f6cbc462f5db673a173e96d5f 575 | 574,f774788b6c2f7d008364c469f1d950011756eab7 576 | 575,f10a10059e707fd455315271afbee2f4e8675c4b 577 | 576,cc91678cf1a3277bee4706e1e5568b45f016f731 578 | 577,75fdfbc8290ef69a2538b4bc0307b9e3e1876d7f 579 | 578,2384dba468a7a77575629aa166e9bfea18c0fbe6 580 | 579,851be5c8dc6160b63eb5e28b72cbf18814afe4c2 581 | 580,06ebe41be43ee03d887cefb38544084974213dff 582 | 581,7d53f75e6845aaf7f98331eb49dcafe468510e8d 583 | 582,1d5eb1053e66dd440668d39b3345ca9328485e5d 584 | 583,17d57aee1a85e104d8b1f6696eaa1021ea7aa8af 585 | 584,c13a507727722a16a66dc53cadc172642e0a0da5 586 | 585,16430882b8b78fba385983f29ae0aab4afd02195 587 | 586,85bd02d14770649b2793b31d53fd57d3f32ec262 588 | 587,7c7c03e3966973ad32d1f0ee78e4667f083a2194 589 | 588,7aa3fc43b885e1b477d25461bf2cecf1f95da71e 590 | 589,c191df1fe0ba268d064e65664a062cf76feec17a 591 | 590,0be67070463c4c24d95f53634b21d9bea6bf8319 592 | 591,0eb2abac9fcd8e25e1c2747340eafe8e5fe29141 593 | 592,49921c1b64834e00ed3b11317453376d4c71f315 594 | 593,0bbd211f6e4b87efe0b72d7e112a47985ee4ca2a 595 | 594,97c171dfc3450ceb8db6e4d00149b7b0cf022ddc 596 | 595,e6bf9469f85bd451561242da6b3e56e675be6143 597 | 596,c920561a034a089f6641f6f2960dc9977dc6774a 598 | 597,9b49c904f5f7a3d36063d7c5e90b01394e9e21cb 599 | 598,ed228633165173694c565e67b2fc361037e58f76 600 | 599,753459490a6bb05ed9e9723bbaf66d85edf66cc1 601 | 600,9b0d7343d5bdb00831662ff56072b079a8cf85fd 602 | 601,3074939be580679df299aa434c7fbd8e69075c02 603 | 602,0626abe5ec7bb163e5747d558b7c3c730186f4ea 604 | 603,a423a7d2d0eec71ea45517a2e52ce86ee5befe09 605 | 604,7d2c166a940dcf24cd6daee9520f550ae0dd627b 606 | 605,4f19e2543ddfcbb94dffc290f9f04c2835499b44 607 | 606,04caafe13cb43623727b217751b6e74170ece9a6 608 | 607,d120050f23d303176c919be90957e73315861a8e 609 | 608,0806b577e3c7f500375fe9b31d8544a83d9e637c 610 | 609,cd16e4d8b70020e8093370e4af76c52695bbcb54 611 | 610,d43da1ef10a8edb23915180689b457e3029ccb79 612 | 611,d89e052998226fd2afb9580ca87999f9902ed736 613 | 612,aba7fccaa34223cddbd644a8de07c75992538ee7 614 | 613,07e9354122c5e774a70b0d39b7eea3e1097825d9 615 | 614,c713f7fe25e1275ac30396cbe3d4e2cf1496d511 616 | 615,81646a9ffc75165836ac008894ce45c0d98a50b3 617 | 616,c9bb3512a9762ed0aaa04eff0d1d4f413f623a34 618 | 617,960fe77381d7ed14b8795508567ede6b0a6cdb20 619 | 618,19b2fbead1cf6c40f290a7abe483704c423ac1b5 620 | 619,bc31cee9256f1339db6fcdb6957b6d0d31305247 621 | 620,786d8c05d4ed09ead98b36e37088b2a23035b7cc 622 | 621,6e1a8bebbe1092c1ef0499f01082ae029e2072f0 623 | 622,9b8e77f2d91142ca72ca6155311585692e312847 624 | 623,447f5d60c2253d8e5c43d1ad0e3cec7d4bd3b8a5 625 | 624,a65126e21be4c5f9866c8c7621925c408459a725 626 | 625,893e45b13bdde708a58f436496876e5769e76173 627 | 626,346d0bb189958ee1036aeed1809615b7affcf9b8 628 | 627,b243dc6a8eff14e4ee8ba42107c827d2969cc369 629 | 628,67680fe478635f8df7d12dec5a866981aecb3d7c 630 | 629,5a0b6e0bf1eccd18c040cb99dec9d1cddc71995e 631 | 630,1dc5d538193c16627ba5ddee32f02612c3ba57e1 632 | 631,4e3d3a5552428c2fde557cd45869e607a754113c 633 | 632,d49e49320291e336ce66d9e780da5792b41a8673 634 | 633,7b93321e1d014631ea758b5731270c84c6744102 635 | 634,54097e438d9eb0011111cc54c3a670ecdbb9a864 636 | 635,6d5ba68dae00510142cd4902ba7544783a5a208f 637 | 636,19f791303452f5b5d9ab5b7f6aed40524727b525 638 | 637,57005f30dea94cd0b49b256948e0aa7a7975fdc2 639 | 638,b47f91970a600c996b3ebd2493e000b321b3505b 640 | 639,6821427dd87b59bd13ff99325b07b79f75035e30 641 | 640,7bd89121356db2d96ab31886245e558b9f3f845f 642 | 641,6b1c1d9ab5f06da36df873525f28ee1459e1b8d7 643 | 642,c7b0202638721b66071ae3e7a414af606c98fcb4 644 | 643,4762d70b2a52f68a493ed4905258af5cf9f41149 645 | 644,4dddbe0836e085b5d43f425255d240dad520d388 646 | 645,5e57567a3941f86966cccb242e3d8e7387603de6 647 | 646,84d11222358c2f3df03379c8cd9619d4ccb76483 648 | 647,f15623d915cd107746761c966ea9956d298f85a8 649 | 648,70542d21b6507cf495f6e82c8ab964d1c2a917e8 650 | 649,0c36e885a47d98949443cb7cecfb15923a6fcd79 651 | 650,a55fea4dd4c63da61587c3a28081017459054140 652 | 651,66c03386705c561b7b56637eb03eeff3bf6c52f3 653 | 652,9ed85dd5a986a701bc933fdd64ea8ac4e13fc37c 654 | 653,e96e8f4cdf6bf876586a4f0f6e05634ba4f949d0 655 | 654,358ce70152bf0a79a40aa1ec370e103ecb867c9d 656 | 655,500fba08448b43f7b5ee9535e013a58af7f33100 657 | 656,ad089cba30a77067065310c1b416cb7012c0e22c 658 | 657,654d37bdb42cd2b5cc39310ff9d0ecaff186efe6 659 | 658,fcaa72283e765a0814e902410937f07c14829f03 660 | 659,444426ab0d573ffb81c22fde4228c8c4fb1ebc88 661 | 660,324942dfd79d68ca362af90efd78c8e0fba4e2b0 662 | 661,0ba813580453f86d8456af320685d2d939a6e2b0 663 | 662,4f9e90df923b68412a462cacd996c2876d7acf1b 664 | 663,d2ba4f4985129833e298380ddb4f86d3e5355a38 665 | 664,4513a9a6d9190d9fdb5ab1d2b22a3fac27f666b6 666 | 665,f0ee80ccf190684a38cee9399647735abcb3ed58 667 | 666,4465667530d631cf4ab0b1b0f0108f659dd95181 668 | 667,053e6539a57813f020637c753a4b2fe55a305e03 669 | 668,2cce4b4be7d9fb4a54913f30c7c71670225a2599 670 | 669,dd226510bdd878119e3c7e0ced1b1800155a95db 671 | 670,cc919160c3d292335a0db683ac5510d496a1444f 672 | 671,c3f2f0d3a88fb1320ab7235d29fd3a4f9a0437a6 673 | 672,008cf2959c7a46ae2197d2244cc0bccdc618a5ad 674 | 673,afcb5aa894795fabb42a1bb292142e6624510ee0 675 | 674,8a5231cc32968805014267c9216a4015057089c1 676 | 675,5f5e3570b761930ed9bbc4c4008853eafbc89b89 677 | 676,add0278dda0cb3061b2168e751364d074a533bd4 678 | 677,ea00382b34ff00bfa147f705982441222b7ef654 679 | 678,5616534a625939f6f7eb8104a26e618a5204c9d6 680 | 679,7f2383a08004066d417c554b7bce1774d7c94fdf 681 | 680,61a101b4016cef0134dac1c1b22c2a07b4ec7905 682 | 681,afeacc98d2d58b1e72d6975a5803d40b11d3b7db 683 | 682,7399447316ed952e5e45adac8426e8f7164667fb 684 | 683,dba25175bdb632b5947c3779d402ec61cb7faa47 685 | 684,defa6c3f0864ece1265d022c3d1a492dc196ba2d 686 | 685,eddaaa218f31c24d65e739200d11184fdfebda6f 687 | 686,c12bad70ca11c72a38b5059012e80b39ad3d3ecd 688 | 687,11eeccdba3254f0db53d6bf5b652396eed0fa10e 689 | 688,a662f48fba2d5d1fda0e1980cb3afdfe7ae2819e 690 | 689,66d4718d2355f61518f910d38437ec1d6671cf92 691 | 690,3cc5b732f069b19515f81a8ff25cfdfdd051fb6d 692 | 691,9fca3194bc81c6cbfd91cba24d9f2f04fa86f937 693 | 692,94761186bcd6a4b4cfb3505f72cdf3b8a9abbafa 694 | 693,ab840087dbc30c894006ae8f51256a8e18a5e2b5 695 | 694,7ff5f460f778f4a27d0e7b6b80a12432d3fd087d 696 | 695,57f149e101bdd07542d72b1340026d55f716d514 697 | 696,5d7c16f0739c51b19b73a90aebcf2cfbce1c8e94 698 | 697,19cda9b9035410422e77bba88b3ae3c38130c5b9 699 | 698,598a546cdbf9457c881f97d8532e1197fade1211 700 | 699,e740f952d7e32580fc28ad637adb152650ff3ace 701 | 700,e257ef1f9821a9254454d65aa410ff094bc03f33 702 | 701,00b8985c624bd6a0c168077db12836ee7ff518c1 703 | 702,bbfc193d0b5e03528fe869fa5001873a778d52ba 704 | 703,92ecfd763faf4d690a3f68b81eb51cb17d21c973 705 | 704,8cd7d4a83e242d8e26fe729003c0bcdb7f13f8bd 706 | 705,1495a0b8ffa6495c165e02efc474650b5e4d14c8 707 | 706,1f838bb66feab838606d28ce36c6ea671afa9834 708 | 707,9d6ee26394595174dfeddf925f1da74105fb180a 709 | 708,770a06221d8df0f219a649b4a0bbcff17b71343b 710 | 709,73d8a6f01e5a8f2177b72f4e1bf2beb1583e09af 711 | 710,9e5a72d7e17aea9eb24648f41211ccfc925f7e5c 712 | 711,6a8c9231f0dc0e0816212979fe0b7ff216ee0994 713 | 712,f85263779af42aec190c58ef2047033decc02ca7 714 | 713,bf5ed6449b37cfcd8271960f77ff5d488f8bea4d 715 | 714,cc1e4a22300939ea211005f4888e2c44997ec836 716 | 715,a03cbd7081e830c4bb073cca32e140f88d3f9cbb 717 | 716,1a74e8e5a5e029c06b374f7147fea0cd0bed0544 718 | 717,0015ba319103456c2f9d250deb656efa72e2d6e3 719 | 718,44bc02ec18b000b380b9e51dffc2404446bf55db 720 | 719,309256fddcf2463a7e2d425dc643d2633a094b9c 721 | 720,12d7c12f6ed26b753109e0815b516d973a80a549 722 | 721,a3e4985526d63067671780683cb40638bffb97af 723 | 722,f14a3253ea3a6ea664194c44eedc08aa18d7378f 724 | 723,865713255bada4c2cfb9e755656a105bb607bb30 725 | 724,38c619e24980a86fcb39adfe6958118875ec5a33 726 | 725,9c93c5a828320eaf67538f502c85ce7b8257c79f 727 | 726,c09ea9ff50d5ae57fc6992523d126ab98828f6d6 728 | 727,1c3a5954e0c3cae20abb38d6a6c3123402807f8d 729 | 728,24401f717a534a927a34c2fae3240cd7571d5652 730 | 729,33757d6900ee35da9c72db6cd9307c3b923a0952 731 | 730,73398d1d1e8adcead6a5f6454c822fcfa0403e1c 732 | 731,d76701ee6c1936bd238b68ecbd7d94ef789ea60e 733 | 732,46b3f5c8c29c6318d9409026de26d2a394e4581c 734 | 733,0172fefc4f93bfc7514c2ef276b661d21b7f4ff9 735 | 734,74e4370fbb6313d5899c8eab8355e3d37a9a515d 736 | 735,a2ea7acf3dc2e8da9695c58e81572b046cad3e32 737 | 736,876eef4507b950812b398977735d76de9a09c018 738 | 737,c10a9787c66d3efc941dd91a1adc55fbf54575f4 739 | 738,5e7be7b3a18a51dba8d97ec431943c32292017ab 740 | 739,ed62b4dd690a5f7121f0507f9cb746c1bde7e162 741 | 740,e3c6d29b8aa4ac49894c64a8dee026fb67cd7240 742 | 741,215a6f6d9993fdc9d001630f4df0600cdbfc4d78 743 | 742,088d8f3ef06d006d7b7a9dac01b31d73c1496367 744 | 743,2bc4d8f5d4b643a34bf8624f375bbf8377089f29 745 | 744,12aec1921ac4f99dbd4054fd42d0c819d9e6cf00 746 | 745,abbf7e74cbba480d0b7d7fe8e900687ebc3f18b7 747 | 746,8f720c517a8c55a0d82dafda1c98a0597f5e6673 748 | 747,25e8092208708fbc75c1a96bd3f147a30eb88752 749 | 748,aa94f8c7d95319e250ef5d27d1c56fe36e406ece 750 | 749,9068a63d4627a90bbbc70b43b4c6dd365fe2523f 751 | 750,54c1cded7ac957a2e271c8dc9860e46c2f3dc24a 752 | 751,f35c230b5614c8f0bb8a95654318593f75dc18fb 753 | 752,f9b0db20ddb5016918202c1daf6418e37e530101 754 | 753,53fa74323cfc483729cc210580381fa9066ce9d0 755 | 754,f4657cff552b12aa24e6b00e38b82044724ede1d 756 | 755,31735e20d761b94064cbf5466b1ca2cba0b7e94f 757 | 756,fd7183b3e2469b38b69c5ecbb3e646de9a82d01c 758 | 757,a8729a2948e09b48bce6199eeb92ae98f6348ebf 759 | 758,3885e1e55d9195593821d455f811c181bdaa6cd5 760 | 759,2eafb409931398f721038ff78b31bd9db5cb720e 761 | 760,78a8f6bbc2bcfe5905370e4494cd834d73f0d3b5 762 | 761,dfb601faee6775712be4a2a7759b89263ffc70d1 763 | 762,6e3c7b6d1902467f2b8bbba570c59f2536eca1c1 764 | 763,11cb1758e2b9ef6dcd8ace03663a13296923774d 765 | 764,51bf98580bd536937fa0b480ab03b52b16130ec4 766 | 765,cc1b0180b9f63d288e6b85f4b3ca533fd7581c86 767 | 766,a03963645782ad07d54011dab3f339405392e0df 768 | 767,9a85ae1e9c1d9f3c88dc8f8b087ce47ef267cb92 769 | 768,7179239eba784ae702f1265ee3bd095ef56dbde7 770 | 769,cc68cfd600bab98803c7742485f26ad1eedf1661 771 | 770,16dc7e13fa95a23e02dde816b2ebc10a94023f00 772 | 771,74c899238cbfbc6632906eb0fc9401c8e28a2d74 773 | 772,1e7825c21845a66dd188c6c2458c720d2dc085ca 774 | 773,6af6d58eb1b80b4a3f310a534b0df963f93960b6 775 | 774,d18a56a9081c376bb795ff4651ae37cb3ce78de4 776 | 775,b7b618d72d62f0087c81daddda478ff763b21acf 777 | 776,8a2db636043017164e458513a650dba2cac4a60d 778 | 777,b6294601b4e11a420e04bb7eca5bc2b68fffdb6a 779 | 778,a8f9a43b05f65b5495fad8a39f86c854ad17fe45 780 | 779,3423580cfa59f5315639b9beb3012455b38e531e 781 | 780,7b0093f9257b6e83e9ae48b479b40068a094c1c8 782 | 781,f7ff4bb9afa1257114a0feb263df89e1df5549f9 783 | 782,422904ffd63e854f3c2d67c3cf0e115461158ece 784 | 783,ffb8bed78056c6210f78852b3937da0163c9d741 785 | 784,93ce718972f1e68f4267bf0e5fece76fc3088c48 786 | 785,22e91d1e59bd2adf2a5f8aa072fa33cf63bb412e 787 | 786,7f223b6d784009c4e4977d0d6f45234284675183 788 | 787,9812543c25a0972f41359ca3402707aff3dfe2b7 789 | 788,36356638c44df75cb9fcb16c8fc50024034ed1b1 790 | 789,5498cc063e73f5fa45291277495bd37ebb1831d5 791 | 790,464fffa47ae514fecb838a6923e2e1b452605ce1 792 | 791,ab358ef64aa975c1c2c24ca33d3bae1e0d3fb723 793 | 792,c2d05b846119d68fadbb8c2ed0f51d26550155a3 794 | 793,dc77cc7937917e2c093a5506d7984e77346fccb8 795 | 794,f58215b6929b799835a5cb157bfbee27983db300 796 | 795,e45b6e8e57035c9b8000c2dffada763b3f037923 797 | 796,ff8bdb317818cf3d61be04a5aefc4e1ec4861e2b 798 | 797,382e68ac0a5c1a5224df306cf20c062758be6667 799 | 798,b8f9ac64dc5164434b6b0e64982189dc2bdb6594 800 | 799,38c4ab27282f5e372e002ddd012d61cecdd7c7df 801 | 800,d608ccaa65e11293fceff317f90dd8c3bc49435e 802 | 801,5ecab7b1bd5fb5ed2496c734fa1f17c13c16c951 803 | 802,c80b3f6ef407367dba144a02e86ba23e6683868c 804 | 803,7359f610ce8c52a08a26678d940d3ca03f21b7be 805 | 804,53cb14be51e6f8a77e1521438160ea1e1315a2ae 806 | 805,946e6a623620c2cc4a15b24453fb42a7d9bfe730 807 | 806,825edaf764053358a43e8b4526e62dd9bf3db980 808 | 807,09af61b927794777ffe05ab86d262de0404d1e4c 809 | 808,b39db4e0c1fdaa6bfc338612e9688bee8b645af3 810 | 809,f921f86daf6bcb7ab8f1702b0976efd9bb5b4e10 811 | 810,816ca14a40df556bf833c3735010b4716138fcea 812 | 811,d8a39435353d9a4000e2c7040da624c26b79fe14 813 | 812,9b4a524edb5db41309d16a854f2d74c0ae2a0cae 814 | 813,85313db925a658d1fb32639627b1bd1357da9ece 815 | 814,fe693cb60168e21200a041faa2c896d824c07151 816 | 815,74473243398d365ee0b50ff971f65e440c575a74 817 | 816,0eb3887c770e963d0c512b840d0dfeda16477453 818 | 817,9d3ddbbdd7f8648fe73da8b5c73366498a082356 819 | 818,a071cc3ae5e9d511cf642f4d8d2b9afd544acf50 820 | 819,dd8745ffb73e7be6ab627aa834ae55177ff33054 821 | 820,475cde48b098333060c77e922045a0fe93802048 822 | 821,0f02ba622acec085b7b8088a44d87dfcedeaca1e 823 | 822,86824a05243bfbcb790780f7b24ee121b8186b17 824 | 823,c975a297b0d110c6f3a0b11b667ebb8a1ef4784b 825 | 824,b74279a4cdc0b6dec9309fc036071fdb772b54b1 826 | 825,299cb2b7537034aae1b834ee4ad549d4691cba06 827 | 826,993b07c7691788f041f68b2b20452f2b1c00c786 828 | 827,3567ed1d621ee0b2ff78214a611fcc842fe9920d 829 | 828,cba08e746cd7fd98c2c438750aa23743f1f9eed2 830 | 829,072fdaddcc1242304e15d1deae56316deb4b8d2b 831 | 830,22e77d3289229705ac5e02187ef4316001f9945e 832 | 831,a7ef9657b4f7266fc77c4bb81630854a0f6432c3 833 | 832,cb9088941061eda09a9b0e869c7ff3a100af02e1 834 | 833,b698f9eef9fa2ae5e3156126b4e49592ba07f621 835 | 834,69bc9adef58a23b062d616ab8e9b010ea91824ad 836 | 835,a80a6e5db47051d4f5666d122018b8e764c42920 837 | 836,e4587a4a2b2cd806a3ca41269da1252aa32d9d50 838 | 837,e6baf9f7333dbb82555a5d06e7a5b8358f2297f1 839 | 838,f564c54e027e71e806f08d73450250c3ade5bf70 840 | 839,589f5f83426ad4bf86c128c3f9d5b27481ca6014 841 | 840,c306599c2d61debb89b620d873b65381b6f048a9 842 | 841,9cdfeb0ca1d5d7acb3dd36ef6975fd619238e2a7 843 | 842,77d3bda6c4b3f564dc4d360a0744b8128e4d6217 844 | 843,1f3589e00a58293a2fa0ac9b345927ae5c623619 845 | 844,e5c450e71898dd7f3edf9c3c669436f6da36eacc 846 | 845,f7405ecebbb34d5adf69faec04bf3990ccd91a0e 847 | 846,84a379ef4a6de7176a78488f9bd48cd9eb157c08 848 | 847,7baf191fae52a2cdf94a80ab82ba4d866013c648 849 | 848,a0ea9c8b96991501ad7d30405e92cbb7288e4a69 850 | 849,e95fb4383b52ece5c0b82aebfc8c22cd93113eea 851 | 850,9ea38a661d0044ed2c7a9ed0ede43487c09d8985 852 | 851,b8d935624b8cc1404e162e208bcfbe072dc6cbde 853 | 852,a2ae4f837f49d0c833d8ad974c1832b7b5fd1205 854 | 853,e4edde7f89f72ebbaf3d0d86d7e4dfa9c5096f10 855 | 854,88a69441ad548dccb9df7d21eb10505604a1038b 856 | 855,5a53a6b84f5f0856549e5b4f5367e9337a4a6104 857 | 856,b5515f1ff7b7363a2b3c14880b4b3923419a7a59 858 | 857,1d3b91faad2e83a2bc5776a2bff6c04bec54c145 859 | 858,1e1a7fb85a22979b5b93e3f25a0bf3f4c1db5ed1 860 | 859,db62096402c05a36fde019bda5f11282e5abec56 861 | 860,130d8347c54b2987b39cdbb791b0df1c359dbd0b 862 | 861,e2e6791cdab09214c171b7fde3c8889d483b0b93 863 | 862,242cb836380c3219b8bb755c1ebcec15ccf2ab90 864 | 863,0458c7d6fae443623845d1e05af02f811b118909 865 | 864,3016cd8fd1896052c68a49a58c670524c931af33 866 | 865,6798ff1f94906e9e540f37f0023b5dcb1fed65bf 867 | 866,dd18cbdff101e93616ba8ec1d850884ff869b390 868 | 867,ad70d391a85ed03d50cdd45a34cf415a67211dea 869 | 868,8ffb1ee2f769d0844ea0d9ac679246ed2f110c0e 870 | 869,2e07c5ab052ea7d6df635b81cf6d6103e8bce10d 871 | 870,f529242ad2ac3b9707623f611a12974d18d1eb0a 872 | 871,43f8d2dfe69dbefa63770ac3da4cd43b643e4c4c 873 | 872,79766397623b293bc235795ca00d9bb1445c9bb0 874 | 873,ddfd7e0a22202d52d3b8650350f7c1dbf596dc90 875 | 874,f426163024e42d763260c8c4f74ac92e060272ae 876 | 875,bf93d917de7894aef803ae109f6c2cb616abcf94 877 | 876,ecb3d4d1dc23a02f8a9d85953b74a70e4660d984 878 | 877,cb2c329e39fb0567c60d44b4ac525fc008200a09 879 | 878,470d3c3d2de3f7ea36c8db504cc5e065cdbe7a2c 880 | 879,e54d12794dce3744c28b012ef23f914a2a0ed630 881 | 880,d7d5546887a4cee53ed78d6e7fb1e81248843546 882 | 881,0cf753362f89d838290bdb174885d5a6735580fd 883 | 882,3b9c83babc356092314683f0fa2e53e8b50554b1 884 | 883,728d4e8e43dec91a5889145cc487e54d605904d7 885 | 884,5a5f0a810ba363d83ce08beec85884b1696018b9 886 | 885,898c07bf934bd954e8536a1f1a6326202fed559c 887 | 886,979ea95ef7a839a5047eb10637ba295dc6d5e001 888 | 887,62819b2241c4c16a8ef9153590f39a3f2d97f968 889 | 888,ece5a112e3e1228130c8357a29fe93128326f6c8 890 | 889,8e6f8fea3a56618dd7609dabcbb42d0e00ce1ba9 891 | 890,7068564fdfaf3a67b4d4bdb13653c8b841b1f5de 892 | 891,42a956ae484284cadd00f21b44517dc34e4adbb6 893 | 892,6c87303de3a910d8531e0d4d176c7f91cadb70f7 894 | 893,45c53e641e699785eca5a105f1c7deb72d122f24 895 | 894,ac0d9c73380c282b5cdca03748d1c78764e74adc 896 | 895,ee94ad1a99dad5b5c7ffbfa024a43d9c5af36580 897 | 896,6decad0c7a30683fcd4c6833c709da90fa503137 898 | 897,c1dd828c0e80880f0b29e56091536428477d7997 899 | 898,25f6e4ffbfae316193d9df7f504e79c57f50550c 900 | 899,41d40fc983facb5c45b955e436ce712184a7325d 901 | 900,54da8b0b77b77fbce1cd8a8dbd44bba00bd39ffc 902 | 901,70317cb0f66ae826109bef3dd4d63be4506c1361 903 | 902,ec2ca11f066ec40bbcaea48c43612533f9d41c4b 904 | 903,708f00bbacb54eb8ea6288755c7b51ed9f5dcb49 905 | 904,8e4841df4dbfadf7170c40f982c9f45e95d9fd86 906 | 905,4d2aa5acef363de2d5529d33886a78b61183aabe 907 | 906,f22d06e2aa5ab290e560f30ea36e6e1b93a13a5d 908 | 907,ad9c58236fd6d1a0628503d11c8ea069941c8901 909 | 908,846f9f75f15dd33ecf62d60fcb150476045ac4c6 910 | 909,83edc377f6dd08cfa9b48ce606f95c0a0e45077a 911 | 910,c1555b8b2faf2ae6955faf3bdfe1135b1338925b 912 | 911,26c2eb747cb43a06999c269eeecdb16dbdc41939 913 | 912,5f79af27ec1072ea61b9423f6b10b67b456ec0fa 914 | 913,e58dd14dfa6c50e3900f78cbd2d2c298f542b386 915 | 914,055bb9bcf00ab3e906218927c3d4e9bf2151c4d8 916 | 915,f9e70fba3e3e35998c80352cb8c2f902e5fd0b1b 917 | 916,1fcd0681b7906440194eec1ccf068a090de90cd7 918 | 917,1f102f62455ce7ff092d2606190094347b3f1356 919 | 918,3f1de4ca4a3c13b4c0549293e3302a9fb686dc25 920 | 919,5f3d40c1af2529fde5b513734bb9e8c0eccf608e 921 | 920,2be94c6abf2aaa6bbdb1206b3a78dbe12420c318 922 | 921,fbf5c9a4ba2a0391b2f305f4284cd1c7408ba97d 923 | 922,c293a127683885f0c6ffacb77ed5d089514293cf 924 | 923,482dbefad0db9238cf8f14abd9b3f13d4c2d6f8c 925 | 924,94eed7656f82f29ebb0c8178fd14c2df58133e57 926 | 925,47d19915ae02217a2823c598c7f5e91b37892de0 927 | 926,7ab7e22cb632877ae67f8d5803f3fa834cfd2a9c 928 | 927,e7735feb23baa71fd6ef0886f93aa1797627d272 929 | 928,8d068a9f6c0b38c5c35340f4b64b462023190e07 930 | 929,a22231963e082a59f1daa66eddbf38fcfe905335 931 | 930,d49a5dafa24524f66001c811cc96699f894d36ed 932 | 931,ab0828c250b84a16135a317b887d49247a21a2ba 933 | 932,f5207cf8ae425e8648c72f7e7ae61d390e0bad56 934 | 933,6df5a75b311566eeb96f0dab872dda90cfec13d3 935 | 934,861f3b60f14b3aa187f6651d141f7a236d2acb29 936 | 935,580765a2d1191cc7f3cbafc1eaf913208e807e9e 937 | 936,a9e788d883c1424fb988db87c21a9b0dd365a462 938 | 937,365296492d11b39e5b45ad7a99241a1ced3b231e 939 | 938,8aea4269642df7da35d8fa49147228690cefd64c 940 | 939,70f642121436f8e0091b7c3bceacac8c95def25f 941 | 940,f8f7c1acc98cfebecd737cbeb69d0f312755cd31 942 | 941,9685c5168daeb291e31c97cd9a66e1091d7b67a3 943 | 942,11989ddb4886d97d1b21efebb1f480f16e4ce670 944 | 943,4f4cf799a2d86793f5677b8a0a92f42dd20b68de 945 | 944,aecfb0ae5dc83023cfbf3a491dada1deebd81979 946 | 945,7e3d43e4998ff9fa45fe7b7d89d4c25f6dc053d4 947 | 946,991fc045d454cc6d41447f32778de3a4aa69d302 948 | 947,c5bfb1f73419f90ec05ee1b43ea4a4d8083229da 949 | 948,70918b224f61f44795d8bcdb6ae61f7225a5ea87 950 | 949,f828685b2dc7534e6ca3b0131d2972822873a697 951 | 950,b3c4dc6f1bd44be2c1fba5afca823a47e55ce2d9 952 | 951,da0e88fb44c8837a7245c7f07d93854c1a0a2b24 953 | 952,81f6ae682610e5e1db434c4ff865364b43a4a8d2 954 | 953,767713f142390b9cb89a3ffa475c0bd1d85b542c 955 | 954,91131dc84ecceb75f843d2716a2e291cd34c16c9 956 | 955,53b199e190802e17487243b52e705fb70ca8dd01 957 | 956,1b34b29f2391da0b808e3df64c9e9883026a9377 958 | 957,4d7555b80d1f8bb98d68fb2bd10de2e5e96908d6 959 | 958,ff1241102a99996aaf93cb8d0ef47e581ac5ace8 960 | 959,9235b2be943d1f73f104452eb3a704891e961b79 961 | 960,490aabe72e2ac791f5b67dc5e18bcd232d80d931 962 | 961,d56aa32b8c0ed589d3d16e91e945a4acb41b8e2b 963 | 962,92c5b80ccdec099ef233fd09f2ec56bcdde0de76 964 | 963,b56572ba94548823d75c8aa729c866c7d66ca522 965 | 964,37156fa12f84e9d3d267004603191bc8ba5cbe99 966 | 965,8087ae1db3c475baa2251d661796a1bada807f2e 967 | 966,fb38b9692f72322f5bf182687f4f958f0a283c4d 968 | 967,4415dc6ac5d46d976b89c0faa54c0ddf9e150bec 969 | 968,fdcaa6d5c5add624b63dac69b0da2c12a0509e0e 970 | 969,acdd6cc5d9603b9394d3bd4fd74d981104a256a3 971 | 970,63d678645e3e25a5c4e831d261514ae9f47b2b38 972 | 971,6071c023765e93d39d56052b60a96d99880febc1 973 | 972,ea8cc438a0f2999bca7352b8b362e7e41f643d5f 974 | 973,0908808ff87868e6947cbdb01c4100f6f7a18c53 975 | 974,9b70bb2eaebee2ceffd6cbaaf7d89c18ddf22ff1 976 | 975,796930a226fcb03d2dd560283b14192102dd2523 977 | 976,4b490e82a39b5da67a71fb8b66ed2ccc871a256e 978 | 977,6268a6092b425431b2d7707f05192b7702d817da 979 | 978,3d3baf9ae4d8c5276c91e7009d68e8e27dda3962 980 | 979,2bf3780fc22e89ccc2a7b5d52b95c3fa375caa8f 981 | 980,5cf99a3058cad2dd19a2e4bcacfcd5d1d178e762 982 | 981,072c54d98a69cf4a464bf4b09ddbce3e0a3db639 983 | 982,b0e1f196458f45befa3ab45114c40e02e0cf5691 984 | 983,a650ff3bf4dab8067cf25deb5c50c4cb74c249cf 985 | 984,c15bdc4389e32b5d6dbd7189fc0726b1f55a1aba 986 | 985,b9cacea2c7f601fd70454e02f6cf889f5107d3a3 987 | 986,6f600d9da883e22e66a145b8e3b19e37f14821b9 988 | 987,f764b3e36cdecf3b7264d0a86b0c5cec63e1db30 989 | 988,3ed652b7136d0bd3838c97b19b5d71b562b3125d 990 | 989,f67a7b264960fa3a796c035e0f1a4173ca87ba6b 991 | 990,1d43cf6e9cda41873fe828703d6c7b6e86036323 992 | 991,9c5a7251540449af05553887eef17db90534d647 993 | 992,a8f0bc633a1ca86663b727e4f05705dbfb57f9ec 994 | 993,c8755ddf3eb1d5496f9fb39b8d672792a94f224e 995 | 994,129fa44c1597b7fd9e7f7c6587631dac02653fef 996 | 995,906b0e50fd4da2edaa13aed7e2fe41195c5279b0 997 | 996,b6e77333aee17eb90203ebc15dd122dee8bfe6fd 998 | 997,6eefd3578de391fbdd505c7cb3842724b4fe39ca 999 | 998,a299fc2d4d85656a6ea2309bfdcd7bf1095995ac 1000 | 999,90e8324a1d8bbfcfa9fe266f5fe40e5f8a32e535 1001 | 1000,a35e718f799942e1af1f6644f2795bd96fb6314f 1002 | 1001,55f9327af3b0c4579ffae8581ac14a982ba7fb8b 1003 | 1002,d0c65eaa468f04fca811f710b63ad435131f2225 1004 | 1003,eb8afc292322e43096c3ae679ae0486feed5c2ad 1005 | 1004,5d5b2c6a4a6ca1d8b4e86eed8d174ba9359d2561 1006 | 1005,9c7a386ff5df609eff15497c90f6f6999f8f94ff 1007 | 1006,b47176980330ddfd2fb5aa8971a174a52432438b 1008 | 1007,471e16f72de18490e6265f7ddd2fa620ac64a621 1009 | 1008,ca91b8a429007067ba2bd85a7773117791e75a53 1010 | 1009,4f35b6b368b628951759bb0ba3efef295ba2ebd2 1011 | 1010,200dca9e4129a1e3981c684b40f7546fe540f24f 1012 | 1011,9a1f69ef533de90825bfeaa502fd19cf968e9b19 1013 | 1012,721bb4e2cf79f84abea572ac24067666fb09f767 1014 | 1013,f1471017e600a5e995a5641b5cd98f5275cf663b 1015 | 1014,6eeb0b58b5b2d7bb88a198a501576db3531094b8 1016 | 1015,705aef6142554c92243b7c19340b2aa6ac841a09 1017 | 1016,e25648153f71058f72f0fcdcd685db5786c05a2c 1018 | 1017,defa2ab994e4e270c29e91313cf56e611930b9dd 1019 | 1018,3325e50dcf7024c02bb0c0927c17605d5935b11d 1020 | 1019,ec373ceafffedb32dafe72021cb2291c02272ebd 1021 | 1020,eabdfe42cf80969d983d07a523456d97d9434068 1022 | 1021,19a43f4065a977229ac15c69b94c2384c457e49d 1023 | 1022,ab18f42b77dde4b17f700ef4c3f5eff31df04076 1024 | 1023,29763b3155aa9809335eb5302c5ac89fd1288fd5 1025 | 1024,a8690914e77a707401da4de42440ce9ed8ac4214 1026 | 1025,6e267eeead616e42a98ee6da16fa01bba8b462ba 1027 | 1026,720c298ac9c3eea56f7a49469122bf1791e22660 1028 | 1027,77cd8205114304509b19319dd035fc499d146ca9 1029 | 1028,5efb5ec959279bb5b688db173ef29d2b25f9503b 1030 | 1029,a43dd4a38ff57e82db0100c6dc0e40718208e9b5 1031 | 1030,d4226956ccba198e1af2229c537ff2015c880390 1032 | 1031,cc00a092236da025ac0f8b0a51aff3e398c96f3a 1033 | 1032,7646e22f5472ddce0af0d8adc2391e497ca9d18f 1034 | 1033,54ec571febe071e71d8ccf224c8f7ef0f5959696 1035 | 1034,402c9591f9b044ec4bee971dd319b526c1331200 1036 | 1035,a2636248c7f023cd69ac42aa961601295e18befa 1037 | 1036,23fab6ddbc3c473d411234eecb68d3a5901cafcb 1038 | 1037,23ef9f8f6e503699c7cd7793ade8371e687b21df 1039 | 1038,9d3bebb5876037a9a40181f8181ed242f4f955f8 1040 | 1039,2fc3983334238e490df5bea50e5572c0fc354282 1041 | 1040,56bb881778f726f242d4cabf7e47c60111ed108f 1042 | 1041,b05486633a8d7d9440d6bb4fed7a1cd7451d6187 1043 | 1042,ffcebdfb9c914d4b88f8a4a86d2a287cdb593318 1044 | 1043,8feb214cb7d72eef29be7332c981e0cd0b21f999 1045 | 1044,e9e938551aa498995ea3ec42dac089b53d19ad4e 1046 | 1045,71f12f9836d18555d9ac30ff12303145dee54c93 1047 | 1046,802482e796bd57f6987ca68081bd8f917d0b5bcd 1048 | 1047,eaab48869dd7fbdd4cd12dc32db4e423bf6dff09 1049 | 1048,c5ab7b9378b97a2fbb176ca8c2410ccbf47ae2da 1050 | 1049,a915bb14e53535c70527693172c751449a4f3966 1051 | 1050,e9e37b8c63d7e28912a13d6f62be4954cb4d63e9 1052 | 1051,4d22ef6940c69a128922c8b4522dc7ce60841c8a 1053 | 1052,3fe09fc7e2930617300a750cba8250ddb5f624ca 1054 | 1053,9d5154ff3de1c5df45ac38b550135f451c157d11 1055 | 1054,69df8c83e775ca5c3b15f86815b58af3ec5f3cda 1056 | 1055,abe96ed79d1babb41113a4f222ff718f9d1e313f 1057 | 1056,8e980f1201a14120ce3c6ea0eacc2435b72dc40a 1058 | 1057,5cc7a791fa29e0549f9b1421daa8dc04d28846be 1059 | 1058,abde4596e66023f256605485d3e53493fe650beb 1060 | 1059,9c785a276167989d9862628e2fa261e1506262b5 1061 | 1060,5e152bc0d978268e2d76d2a0a1e2b48e188c7389 1062 | 1061,58d7f4f917bcbfaed72828c7e095831db2c7a0bf 1063 | 1062,07200f9022bee66c736537fd5df056e36b18d0e9 1064 | 1063,4442e3ad57cd561a3e6f1d345ab795f3167bcad0 1065 | 1064,d3b206a95fff2eeb4b9a1faabdc4300194fe1cb0 1066 | 1065,29a1540cb2af95c8ca6a59c086ff63b4d70fa1e1 1067 | 1066,711d2fcc521de5c36e0fdd649b68fdc48335355a 1068 | 1067,53794e58308271b4afad3de1290c8bee746df5a8 1069 | 1068,c92964dd10ceef74fd6f60480718f06383099750 1070 | 1069,f814e96c10e9347598d3b4249b166cc3210f8404 1071 | 1070,48ed554d3cbc1a9aa9f64526c503540a307d7744 1072 | 1071,723b1b2c68e6966ae86aaaabb5ff23dffc045e1b 1073 | 1072,3c8061b59e4387a001096994aff5c875448d304d 1074 | 1073,039f36b085b1808842ddbb0c4617876c3286ca75 1075 | 1074,d283cf3615122f6f1a751ceea48c3f5b9f001074 1076 | 1075,91ab9b6404aeaad0b23b819e4b387de50de94416 1077 | 1076,f614c8401d78ac899ea0c90387122eb334b81c5e 1078 | 1077,ccd7eab16c3b0b4fef1e0061f5df75a528a3c965 1079 | 1078,436de0d9dfbc87ed16c7a2a231a8f2a822e2af94 1080 | 1079,b2f7e3e5b805286574ef5a31b14401d4d878cc39 1081 | 1080,f2ecc46fb9c8ee54fff3bae2f43a03862c927f22 1082 | 1081,7e9d01a0d72a6377a352158ec52a59269e36e1bd 1083 | 1082,357c3c4c91027f7f0b1998aa10ac00e610423965 1084 | -------------------------------------------------------------------------------- /worker_ids/clcifar20_workerids.txt: -------------------------------------------------------------------------------- 1 | No,WorkerId 2 | 1,49eedab26cdc367981a9445bf09a6af16ad12a33 3 | 2,0c36e885a47d98949443cb7cecfb15923a6fcd79 4 | 3,42d4b48b8fab7c713a0bc4a8807121f1197a711b 5 | 4,77d3bda6c4b3f564dc4d360a0744b8128e4d6217 6 | 5,da7218601c16989640301257b2ed4050d8743f1c 7 | 6,9b0d7343d5bdb00831662ff56072b079a8cf85fd 8 | 7,5033bcd120a2d854b5c541ed5ada7d932381d3d4 9 | 8,69ec1ca0298c6ed369ebc154543f6565c7c9a7cf 10 | 9,b80716659d27d410aa587f23096dabcef61bf5ef 11 | 10,893e45b13bdde708a58f436496876e5769e76173 12 | 11,ddfd7e0a22202d52d3b8650350f7c1dbf596dc90 13 | 12,34f94532e05f235a51af2275c717e26ae912b8dc 14 | 13,2a95a47c1e058f8238d03ed73802c6e4f288d8a0 15 | 14,d6c9db1af035c895a702e107ba9c99551637248b 16 | 15,b864f6b83fb287cb763ca644328c56b6dd1c046c 17 | 16,5efb5ec959279bb5b688db173ef29d2b25f9503b 18 | 17,ea38693ae25d567646292f4af03fa5448223cd90 19 | 18,17a52c60bcb36c2f8311ef8f54d0c410e4b1d90c 20 | 19,c77ce7ce4e94143f8e0f1c6825eaeeec37c12582 21 | 20,7eec33e8e386da2bbc52260caf15e488953060bf 22 | 21,d4faee492bdb14e176c16e64c792ff16a4d393d2 23 | 22,0eb3887c770e963d0c512b840d0dfeda16477453 24 | 23,f35c230b5614c8f0bb8a95654318593f75dc18fb 25 | 24,dcf6707f1362c4f8b12a1d28af01148e44089c1e 26 | 25,f02ef69aa9c962ec879b523fa6b9cd21dcc6939f 27 | 26,b92ef19769c4eb4ac412090a677ce05e317983a7 28 | 27,efd2c948d64944de9bf0220dbd6405d9e492f8ee 29 | 28,1cc53f27255ce22f04a5d92020e08f39460a6f82 30 | 29,4fe03b933bcc3f833a4e0bec6212a413f3ec6d3d 31 | 30,3bbab7b9b72c243f6d5a6ce5c64c8d0e2f90606e 32 | 31,becb6b0b2c79f30eb1ab86087e9d5df2d65322e1 33 | 32,f0b840e348f79b5752698fc49f0ae9e640379d91 34 | 33,fae7e9cec4c3ace2c3a5a1f80db4c70875c1a120 35 | 34,05ddcae3cb62a3c8c493f0ab275a3a0b28d379d1 36 | 35,a697bc05772ff8f0c2bbf21e9bc2146601effdae 37 | 36,abefc622bca5926d66eb8c5bfa6d3dbef7d0de69 38 | 37,aba7fccaa34223cddbd644a8de07c75992538ee7 39 | 38,fc0e3faadd1ae1e1ba0ccb7a4744434c8f4cc315 40 | 39,6af6d58eb1b80b4a3f310a534b0df963f93960b6 41 | 40,d03afe8e683718eaf84c8bd5a5c2c78dd2fd52cc 42 | 41,09f11d9e9d8a739f22cd7280f5049c99cb10774c 43 | 42,aa737559420719430a902463002a450e9efee214 44 | 43,23189ce791869d73685beb634eb13b4694604b0d 45 | 44,0993cef7a0f70b8472b4ac824d7010eaf7055ff8 46 | 45,0f479b96cabf258fbab29a7deec8d03258c21322 47 | 46,b865c31cc86454dc9bb1d4cf6a0926ceb730f656 48 | 47,8fad7ef5cb15e159573fe05abbc026a1a43ba9a1 49 | 48,7320b6d3ea492e651ace16acf80161b55f011390 50 | 49,d082a3d71a14cb98b4ac56e7d3df04e3aaaeb067 51 | 50,f4f64dd28f2a5c8cc5d28054fc16ba96d009ec3d 52 | 51,a46907bfaf65926dc65e0fab539cbd1a53927fbf 53 | 52,b6c6fbbcd157b89e872ee5d177ba1b7133ed05d5 54 | 53,54097e438d9eb0011111cc54c3a670ecdbb9a864 55 | 54,d79c685ad2bb85f2ef8f3d6d8bd9908390cde0f5 56 | 55,2fcfc075ab721fc3ad93fa48bb06c158af04c71f 57 | 56,1991f420f260994acc2e7a47156a0202b92338b3 58 | 57,d2b803f39a4120612d7d75435a196dcc5c24cc5b 59 | 58,0af5ea0f59afccc5e9bb72b24d9fc4c1f8802d7a 60 | 59,b644b421b70a31159f7e96b96223f6878176f2fd 61 | 60,6b9bfcf42ee86c693bdcc75ae29f72ad42f8fc74 62 | 61,ba5bf53f54747569ff4a71427060ddc30e3326e9 63 | 62,a02add4bc18b05dc44896503fbd1a6263fdfadf5 64 | 63,861fbcac494aa721c56f50e1451f8141b05169cb 65 | 64,906fb7c4e1afcda72db59eb1ca101913d46a4d43 66 | 65,f9e70fba3e3e35998c80352cb8c2f902e5fd0b1b 67 | 66,71064f339c2ef86613c7e85f850946f6cf384099 68 | 67,0806b577e3c7f500375fe9b31d8544a83d9e637c 69 | 68,29d69e298e503058ca9a66ecd7201df0d0c0f648 70 | 69,3f5315f8ee4a5a439267829e78e86b0ffc0e232c 71 | 70,e5c6851644deb3998e9013d26d3f6a8f875cc583 72 | 71,dab533af9902c13151ea1d7a341a3a482761ca7e 73 | 72,54055fb8e0ebc101e76ae134bf6a605c22c35868 74 | 73,0a8f23a63349d41816ba4943ce4fa491357861d5 75 | 74,a248b256d3f5f07779dfed368c768212f799f007 76 | 75,78a8f6bbc2bcfe5905370e4494cd834d73f0d3b5 77 | 76,a7ef7e2126ee768ce2670e6cb891e3128d6b4857 78 | 77,a2fb375dbeec145df349ce078ce03dc0b4c18065 79 | 78,b0627340faf1d27333bb0204c5bb6c1a2a690a7c 80 | 79,242cb836380c3219b8bb755c1ebcec15ccf2ab90 81 | 80,ca67d9249a8fecadddf6bfff09d9af3eebd5cc16 82 | 81,90fab1060267ea7df0fac4b04e150a7cb54de4a4 83 | 82,92ecfd763faf4d690a3f68b81eb51cb17d21c973 84 | 83,500fba08448b43f7b5ee9535e013a58af7f33100 85 | 84,f7ff4bb9afa1257114a0feb263df89e1df5549f9 86 | 85,86f8ff1640ad2e75738835f11e2c010eb0d6a760 87 | 86,3307ddb60c1ef12cc5ca11ce7348373a59c94031 88 | 87,d17ccb1aebf5003a498d3f8bf1413ef220c5aeaa 89 | 88,d761cae8517ce2480205c53cf91c0f52365a799d 90 | 89,cbb80b1080e4da7b3159e253f0fc0f01415b27d9 91 | 90,0fe4930ae93a750634f9b04e9032da1c646bec93 92 | 91,38aaf77023f30eac9d23cd40e42b8fe709893501 93 | 92,67bc96c1159b5042f364d3c37d31f0ab91be2f11 94 | 93,c291da9e7db4203bcab4fdc22c3e3670e14ef1b8 95 | 94,46fd7cab67b65f812f3254a26845e6a312e31826 96 | 95,0fb10bedfab67a13ff9e35c03a8413fc286f0261 97 | 96,aac516f4d1ccb6c432fbb5fbf9f27c9c63841cfe 98 | 97,3a2c30147ec2cd76c7d53258be5f2a3e52aa0800 99 | 98,c5ab7b9378b97a2fbb176ca8c2410ccbf47ae2da 100 | 99,e415d5c897469d4f5aa4d9090cdbc19d2525f0b4 101 | 100,7c33c3a3f2ec3c646d3694bda6f26beea20b3ecb 102 | 101,ac3b1078753882f89a3e9dbec0debff0a56b0306 103 | 102,81d64b510f7753f388583904cae000c93f718c47 104 | 103,263601ce92c51526f77030a4dc9408001a033891 105 | 104,d297a0f3f969e7ad83e84e85388427603ed7ad06 106 | 105,c5bfb1f73419f90ec05ee1b43ea4a4d8083229da 107 | 106,4465667530d631cf4ab0b1b0f0108f659dd95181 108 | 107,26c2eb747cb43a06999c269eeecdb16dbdc41939 109 | 108,a5b5befa0c665edd4a1ecee4fe1c8975d6b5da38 110 | 109,dbd867f0680483ea04881311393c7be8add471b5 111 | 110,6e1a8bebbe1092c1ef0499f01082ae029e2072f0 112 | 111,ba88167c391f6b32ef670d0099783f43960dbd20 113 | 112,8209f2bfae74b346fcd5d080a09aabdcd26059d4 114 | 113,06652ffe610d30715eda5fd757461cb4b1a8ba98 115 | 114,bcb3662c92305a8c28f578926a75e0ddb1008c12 116 | 115,072fdaddcc1242304e15d1deae56316deb4b8d2b 117 | 116,21dad8eb04541506a17a087a15ab3d2e185f3eab 118 | 117,ebcd76891a09caa84d3b36b6df9a6ae758aa3531 119 | 118,ef1f7690c33d11977e8dfeb6058abc401e6e2275 120 | 119,6a2cda67dc8c401164f42a0abcc6ff59ef8b3f5d 121 | 120,4dcd2f841d9b4b511237b918f3f05a0b010d9c42 122 | 121,bb28eb2171f6d376ce8db199d8ae8c0f56bd3e18 123 | 122,d4f56bfc8f51c09d4ceb2e51738f4c502c6079e7 124 | 123,5910d8084394f149a41d3ec404f9e69dd81e4bb0 125 | 124,8906a77c5d2e4aa852d5af52819022732d8d280a 126 | 125,fc3acd197dd32347b5478dda8a8351232821b532 127 | 126,c34c09fa8243a9b5d841b22e3937f8a3709b1e96 128 | 127,46fe267357f0903e2bd225ebd35f24aec0dd9368 129 | 128,723b1b2c68e6966ae86aaaabb5ff23dffc045e1b 130 | 129,defa6c3f0864ece1265d022c3d1a492dc196ba2d 131 | 130,66b5785162dd0e6aeba4ab7e17f392c706032a9f 132 | 131,047b5ce23a86b215cf12512644c638c72f42d377 133 | 132,53fa74323cfc483729cc210580381fa9066ce9d0 134 | 133,3c946bb71f5e8471e6bf6968e11b48f21a27604b 135 | 134,98f68e4c61e7966f2ed9b4e52cad20ce586970c6 136 | 135,f940a15f6b5f04bcb07d8107be9b105d8735e47b 137 | 136,654d37bdb42cd2b5cc39310ff9d0ecaff186efe6 138 | 137,b3ed83c5eb7aa5e4b0e494695159f9fbd7ca215a 139 | 138,a7f94bb5caa180a3f2cb4e354e2dc225df368ce2 140 | 139,a9e788d883c1424fb988db87c21a9b0dd365a462 141 | 140,31735e20d761b94064cbf5466b1ca2cba0b7e94f 142 | 141,ab40caaa691b5d30264f146016c2b613b5001448 143 | 142,11f243aa60e773cb2e41d1ccbe67aafc016a2870 144 | 143,bc31cee9256f1339db6fcdb6957b6d0d31305247 145 | 144,0a110fe6e7466200e276a0cdb3f812956a62251d 146 | 145,60952000f9ea78a1c57b00cf79072f8577b5f060 147 | 146,ea36fe9e99acd3598b6fd2f3edb3a019d9da450f 148 | 147,b56572ba94548823d75c8aa729c866c7d66ca522 149 | 148,e8643ab7db74c66436b02e681e97b55f849d0029 150 | 149,caec3aa50cfecf3cb1e3b565a850b60c259087b1 151 | 150,8177b4a010780a4b18f6032f37080a67378d06db 152 | 151,ab6b5d9cfa7d4addcdbb4fb934703462e51a3ff7 153 | 152,8cbc3232e800942ae84ab1bb5735078e496a64a7 154 | 153,83d3aa0de1a111e25c6e824354d3ce8e92de760d 155 | 154,cb31275a4a588e6b2a9e82e38f7eecbf7d279f02 156 | 155,3e21f43e847658a6a32daa2175433391665f987c 157 | 156,2e4057f0c082b0b2839950c8cdfe077ce4c58dd0 158 | 157,12238e77613db06cd70c085bb8274ce39dca3467 159 | 158,55e7114e86d71ea8c56c4960677b49975ffb7846 160 | 159,69e95f2624617cd96f4beb6bc4b23f1740aedcc5 161 | 160,cb0d27bc04a45e64b7352502b97daf5ffc9092c6 162 | 161,f529242ad2ac3b9707623f611a12974d18d1eb0a 163 | 162,bfb3c4bff5d860d4eaf8f8bfa7dad17a9a3d56d2 164 | 163,3ae85aa8fcf0b7aed1c73c26a3db9c54375652bd 165 | 164,3525523aa4121ee520a032d2f8a76d39a8858838 166 | 165,faf8033fba73dd2ab1b9b43dd7bccdd2610bd5a1 167 | 166,e9815ed154df97ba0fadd3284b061d3e628e79a7 168 | 167,cee3a8f1e0e92945ae401b8d04e230d8a74338de 169 | 168,8178e4c5a96f01f0f691f1640c2270b42879d10f 170 | 169,ffb8bed78056c6210f78852b3937da0163c9d741 171 | 170,318c95eb2bfc041952faec42a2b8950eaab6de7e 172 | 171,a03cbd7081e830c4bb073cca32e140f88d3f9cbb 173 | 172,7ef756b4d275c8e1749126862e213773628824e2 174 | 173,6134482ee0f7196ae472537e91ab0ca9c2cf9170 175 | 174,b631793d1e823a2c7833518077b9dc48b4176c3e 176 | 175,200a236a1c19fa8704bdbe3ef8ebf5b09803c5be 177 | 176,e814bec3020d476a907d32a62a36f2d709f02d6b 178 | 177,0605c44986eba9f899f790c79b0e21cfaaf058a0 179 | 178,13444132b87276192c4b8973c851891e3ac064c5 180 | 179,01611e4b7ea070ff7792ebfd661ee8094a030498 181 | 180,7aa3fc43b885e1b477d25461bf2cecf1f95da71e 182 | 181,18331e1c87f49f6ce5724852b2df0756358e75e5 183 | 182,7445159de1f74b6c86d25efeebc2071107af9a71 184 | 183,015b800b4d557efca5ff4bffcf41aedb9b8b7c74 185 | 184,0be67070463c4c24d95f53634b21d9bea6bf8319 186 | 185,a8dfe8f5a32e54886358d1b014ca9aa910f6dcf1 187 | 186,7b03390b0652652b8cf374248c1c215c68e287ba 188 | 187,72da6d6a0ea75a820e4b59f6295d7e5d32a130a9 189 | 188,a85fd4e4dae2824aebf8a5896d2b2a1eceb2959e 190 | 189,af1ece7f23f904757a737917141cf02d1e813160 191 | 190,47d19915ae02217a2823c598c7f5e91b37892de0 192 | 191,1dab0092945da83877f4693e95e65b6dd455967a 193 | 192,da0e88fb44c8837a7245c7f07d93854c1a0a2b24 194 | 193,f1830a2912cf7a8c0bc08ace36553269b1189510 195 | 194,55436fc958b93fcf0157004ca317f2d0307b132f 196 | 195,6b8d248f193a8ab6e869f8cfc585921e8de54bda 197 | 196,5eecc7466073fff9df1177d5ad6d18524fcf6f65 198 | 197,9206a0bfa84cb8bdc047b1d501c2290077106c0c 199 | 198,f0ee80ccf190684a38cee9399647735abcb3ed58 200 | 199,d57dc6f3eb81c4063fe67c0b4b1084548a0a3c20 201 | 200,e36864f6586ffa36352b9b399d2490efaa71d174 202 | 201,0a89357a2b763c3d9b44d0a0070003775e5bb590 203 | 202,913bd3669e578f476eb6e1d94323a5f543032553 204 | 203,b85b6da910fd84a9ebba27106d641e8fad8317ba 205 | 204,fdf27b3c3c59eccfd02d2df6105139505cca9a08 206 | 205,5da68cdea6f677a9749921ab754ca6955a010356 207 | 206,cc27844400d421f2904c5507f625738b5f44b4b6 208 | 207,3792d3ea8ecbf36bebf4ab4b3e806679ac3804b6 209 | 208,8b8d6ae6baa0d52523a9679a30cdb3476efea0f5 210 | 209,7045774c745810640b18f9c29950e045a58fff9b 211 | 210,482dbefad0db9238cf8f14abd9b3f13d4c2d6f8c 212 | 211,79952294872d89786390a70253f268ef1a3ba8fc 213 | 212,8fbd4a5d9021626bb057773f0fa5ab48213ffb70 214 | 213,d4b91a298afd5d8ca0cb0a63934b78c2001f3e39 215 | 214,88a84dae1883cc84716d7570338ae2bf05a0eff2 216 | 215,c9539dc0ac52b925bca416da9ae1a65716283faf 217 | 216,53fd4e34dc412d233b181b3297f60a1fc78dd90d 218 | 217,37188d4d42bfdf5088b74b33946aa2e8dc7e892c 219 | 218,51bf98580bd536937fa0b480ab03b52b16130ec4 220 | 219,d512962b5cf3d148b8bcd24fe9037974560581bb 221 | 220,53930cfdd598b65c32df3d950778d5e35189e145 222 | 221,f564c54e027e71e806f08d73450250c3ade5bf70 223 | 222,4e2c116ec4f47c4583be55ba17a140579f71d409 224 | 223,387cc18f9e00d3e2828c85b8afe800393cd20343 225 | 224,6b0fc787b370eda6550f409144b58cae343856f1 226 | 225,9ff40d51b0d7575e86d3dc146fb81a58a5e26f91 227 | 226,71dc9f7bf3bc3e193827d49954a567590697844e 228 | 227,21da843ce0d40515788366c1d8eb15d3c315e1f9 229 | 228,a2ea7acf3dc2e8da9695c58e81572b046cad3e32 230 | 229,f9f1c17a9b78ba5674b0064070c389ff6a96dda3 231 | 230,e8500ae45e1ec2e349e72a1e68f23fba76545bdd 232 | 231,299cb2b7537034aae1b834ee4ad549d4691cba06 233 | 232,56b83b904e638ca5ab82cb0654dbcf6161aa38b1 234 | 233,ecb3d4d1dc23a02f8a9d85953b74a70e4660d984 235 | 234,c1ceb78d82c77d093686bb75969c545862b93450 236 | 235,c19d054b50ff726a7fb551443bf4503530de52e2 237 | 236,f9aa83984a9b245034c3bdcc35ebd789a7b37362 238 | 237,4c6414c73612e2813837c0bb857e65cfc5211e00 239 | 238,5047a09b3b5768e94d65e7b3c05790e4bb979d49 240 | 239,681df9000c840c4a292513982869429c62bdf667 241 | 240,dd9d7c969ba2c89ee81690984cf4380551d4d88a 242 | 241,a662f48fba2d5d1fda0e1980cb3afdfe7ae2819e 243 | 242,119cd736fec669c6f2c3a1f9e951b3ef5d03e315 244 | 243,07322eb4fe42fe034c593044955a63c46c5f650d 245 | 244,ad212f1cefa114c340f77b394f378d361d5fedf2 246 | 245,5a3c7fb6f7ffeb9b798eb467007161216b4a8f76 247 | 246,5ad9e697914a5aa06c2f0680c8961bb27219cff7 248 | 247,ff45f43898757719e01fbec893e5d803ed1786c8 249 | 248,792104cbbfcf4c77541de928b7674a1c232d594e 250 | 249,cccb3339dc4ac6ad5768a8c5555b17fd5b9ec29a 251 | 250,a97c6fe23dcdf3621a5454fadc4e2f9cfbfcaa8f 252 | 251,b62628f45a14fd942660ab0096892d4f082de221 253 | 252,e83f650a7404a1674add8172f832f0ae6a9df0be 254 | 253,93ce718972f1e68f4267bf0e5fece76fc3088c48 255 | 254,1eea4e81dfc1c317867e1566308ee2ff0b4d47d9 256 | 255,5c3188a7ef9763d4f08b04a2a665828b530d46b8 257 | 256,9685c5168daeb291e31c97cd9a66e1091d7b67a3 258 | 257,909a75918e6c964e61b8b0fac14e7badec7608a9 259 | 258,e2c51542c4415d182a26cf51a300d487f9a13bce 260 | 259,79e537b8928efc4c4d1d189d08aba7d78e3bde96 261 | 260,b8d212626319e8ecca8393d8d07416fa073b74db 262 | 261,fe0dc8ef6704229c7249f46e755bb4ab7750dc37 263 | 262,4a43b64dc8dda53424e7f257b7f8622bbec43589 264 | 263,627c2ee12aef15b3f6a20a8a0a3523264ffbd2d5 265 | 264,516d6989a878a97eb9e4f01603c046b9d03c2cbf 266 | 265,09ea5a2c5c93199b363a71e0fdbd5e895a1dd136 267 | 266,3061bfb90b4eb1b999fc24fa2e506c79045ebea3 268 | 267,a5369e988157bb71c73e8abf4c9e83e6e05ad1e7 269 | 268,a7b5918b13935358d2f31dde5a6c47bbd997e60a 270 | 269,ee963b96ad6b558c6cee5a369ef8917a4bc75b83 271 | 270,c67675ed3c5a8a995e383f841ea3ca0cd803716a 272 | 271,9c93c5a828320eaf67538f502c85ce7b8257c79f 273 | 272,20d09d2e5eea3d0f301eb685fd760ca77fb6ffdb 274 | 273,d99f378f2513fa93e54645dea50043ffe37acafa 275 | 274,ceb79eea4630a74b2d7a8411a21734179c6d6455 276 | 275,71c4eafbcef31ce60827c1e3e7b19ca09f33fb3a 277 | 276,6b084fed24367a60f4e3b092312082f0e0abc3fa 278 | 277,88fb8ca895082e604d157e6836f03fa645328521 279 | 278,944ad09ee0381b21e93ffb626ce5a924e724e87c 280 | 279,70417db64b5bf5ac9fbfbf0321d98762a752d658 281 | 280,a585711b73c1ff627ca8ca5d44c6326edf566324 282 | 281,27072d4d67a8b7b5fc221dba034d2b8acde302f0 283 | 282,cea9707a9f3d24fc47642ecb8d54f8a8fef9b1f2 284 | 283,cfec26431c1a94375e30814b651600c6adaeca93 285 | 284,f67a7b264960fa3a796c035e0f1a4173ca87ba6b 286 | 285,43022783c7227d5a3cbfb9144f3087c85978b376 287 | 286,1495a0b8ffa6495c165e02efc474650b5e4d14c8 288 | 287,53d98468ffbcecaeb74577df82811927854c00f5 289 | 288,700fda6a9c57864d38aafa517d2a4efa620b44ce 290 | 289,0d4e1d83d9b6c3e0d6f15b4b2577e6acead93690 291 | 290,623902fb6262e8876ea4f0cf4278d2179c8adc9d 292 | 291,de5a9f2133fe246769a1d0aa528edd1c51544370 293 | 292,d8579ffe3cffd47cc273dfb40aa573ab41309040 294 | 293,0a70737601735b36dcf0186d25067b8ecf6233e8 295 | 294,c64d53912dbebda81c6244468a46c06b6bc43ab6 296 | 295,d71fe7d6ab7755bc19c35243426071592ff64a8a 297 | 296,cc0a118bb6e6e061c534a865fdaf33781dceff15 298 | 297,03d2245a0abc821c47ffecaca9a13db08d97577c 299 | 298,cddd2d4d4a31ac213db27908625cd7d758fd768b 300 | 299,439a6ed9caee677560bbe90c1f7a93be3ab566c0 301 | 300,25062758af4898f722d8faa1d3574e05dfee57ee 302 | 301,df229cb754734777b918d781a6cc3b51ff277014 303 | 302,2154c5ca8484eb7239fb817db407a90f594c5850 304 | 303,52dbc5c7af860d15bb347f9830c7251d98ff5376 305 | 304,d19af24d9ca089dae4f23699122bb1c60f426650 306 | 305,7d4c3c87d6de1099fd722ef3b00bb3c15fd2817f 307 | 306,2069616d382c3621d5891ed9d6cd1817208e3c48 308 | 307,89e2d949cf5c5822cce85e6f926d7ebeefb4634a 309 | 308,9a653c210bb57e338e2a50ba1488b46aa89225d5 310 | 309,29763b3155aa9809335eb5302c5ac89fd1288fd5 311 | 310,af67d5f059337a0eb9006a13aed50fb0442873f6 312 | 311,b96a0f922a5352ee32ddb09f73923f37cbd001a9 313 | 312,e36cf02cbd67a3937ef06ddfa2e7777585f9deaa 314 | 313,3a5bd77aa865b4c89db36b0970e5d518c39ca4c4 315 | 314,d37fb5fdb514302152f7365fb71bc234fa9a977b 316 | 315,0189bee9e083dded8a2c5c30346978f135bb3ab1 317 | 316,37fb22a416084bd6b41704d1dad3e57bd8e3d7a6 318 | 317,91e40c94e8d1f7f7aac180cac13477a34395ae7e 319 | 318,349072b64a28b7ca14e12e46d969084176d30b1b 320 | 319,d309ba4fca94c511b33e8cdd025e18178019c6b5 321 | 320,9a551c151f671f755003a2bb5d45fe6e77a89ac7 322 | 321,02b620b137891fbda126c4945b1574fa77f96de4 323 | 322,495679824558509ea64018fbbac5a6943829e59e 324 | 323,ef41ce2f2f2d9c318137619c1924cf55141c4bb6 325 | 324,decb51e6479bc212cba83777647e24cd17b4fa1b 326 | 325,9dfc36129abc2d640c9e21baaaff493b240495c8 327 | 326,c19c236a94680103fffffaacf66320b81243ac58 328 | 327,fc1a2b5154e6052fd83f15f3fa3a42da039b8c54 329 | 328,4ec7c323bd1bf723bc1fd228da9978185988e890 330 | 329,acc6c3bd85a411f5c6044e9dcf32511e1fd27251 331 | 330,d6501a8a3a67e39cde21ebb9731e5c46e568c8d4 332 | 331,8a2db636043017164e458513a650dba2cac4a60d 333 | 332,019d4ae7a406e33475f01c4677df82903195bb8e 334 | 333,b6b1aa4b5cedbabd937a099c8247bdcc0ad3bcbf 335 | 334,7675cfc3606a3ca8ce236c4a4fee4c18c6c45db0 336 | 335,9d16661dd899145c1e5450fd2a7b1b26443d58d6 337 | 336,4ff5da0be1aee04897feabb85793d10e015b0954 338 | 337,2879cfda8ac29d06d6a2675127744ea32e4f4804 339 | 338,bf304cf25c50d445e0fbcd035c083a9895845e61 340 | 339,4b94b7f8f339bf722862a9cedfb482d51f7afd61 341 | 340,e38f00e532f350d18bddb64a680d5c7357893782 342 | 341,c8ef344707242dbd133bfa9450b401de4dbad6ba 343 | 342,989d2fdb4e46dbf394195e4892bc332e1c162041 344 | 343,a0596486f4fc3eb642eee523d624e97ab5fefa31 345 | 344,8107a2ad0fc902e9f97d9bc1cecdb04aa66656b1 346 | 345,e011c312ac543d3893b102437d278a0716f68af3 347 | 346,bfdbcc7fc65b3b9da91cbd832e6d81673429a246 348 | 347,7ce01d940841731478115a5d0c8ca86f664d47a0 349 | 348,5d404ec3c08334dfc45eb3462a335aa0e3c0ec2e 350 | 349,8c6585ee0b337478a4e40d6c2ec4f6c4411e4bc2 351 | 350,09a585134ea7739d826f1fbca07e9c50066c44e1 352 | 351,0bfde8e68dbe10ac502849b84d065e5ab97b9158 353 | 352,de85a76411490c82f0711ab26c6862eb6dbd41ae 354 | 353,a7ef9657b4f7266fc77c4bb81630854a0f6432c3 355 | 354,f535344a83893193d34b11504e88fb29dfe0672c 356 | 355,d7c17b66b8c8d401b76de567189db33042f9a8c8 357 | 356,fad87a198c1f93b11ca830c26b4882ccd31401f4 358 | 357,e153894ecc16596accfb14065eccc0cfb9a6055f 359 | 358,04e14caabc88d907d661e120ddf3b8f1f8f38630 360 | 359,36e20e6e703eedf9e199c3296170771ec5cddba2 361 | 360,8c9bf4e8810278a2bf1f0e6574dbdf51851eff02 362 | 361,8067727c48086937bb99cab0052c0c2fa74bd14b 363 | 362,04d632c5ce8f87abdd6a60b40975218c4fcf9a8f 364 | 363,44748b6c42c6f73022f2249d8e06beff0492bbd5 365 | 364,ea90887e6c337cc65a570dbc3892ed3d8b0d81d5 366 | 365,b285bbae7f6032ab91b9a74ef16212c880f91a5d 367 | 366,133a3de56b5b3c92fb82df5519c9d062b10e3748 368 | 367,59e4d6b4cf75698be69a9472ea18eeeb21e2ae69 369 | 368,9eed96e5a9b66ced966d094e287126f32811b7a8 370 | 369,c2c4fc19d9c136c5b3998d976cddb0b44c62dca4 371 | 370,f759be6c953aaa89fd24d277873f3abe3f864fff 372 | 371,f1471017e600a5e995a5641b5cd98f5275cf663b 373 | 372,f32e97963f2379aed32e8a59b994d333d90f4ef9 374 | 373,d9e7f94df5658c42425efdd9fedd1b0b4412718a 375 | 374,960f51762603e1f658d3baf1445284e9d9ec0f87 376 | 375,0d2893a4494dbb598a62305b6640e318bb973564 377 | 376,c722a60cfa5e86e1f8a5f2057122ab1bcb0b4f1f 378 | 377,783cda012520abecbfb0ccb96043f186ab1843f8 379 | 378,b1c3dd078c401ddfe9561266c5fbd3fcbafc4e81 380 | 379,744d28008ca857232623a9ff890911aea83243ac 381 | 380,36a78503c1baf41d4ad1d0d46ee474168db36b51 382 | 381,4d4e2bf9a4740f412576d71587481eb5d20098d1 383 | 382,2f95da2ce5fbee6abbb68f0a959f799f62280e28 384 | 383,d863e5e1473917f5d85bef59b9abbf697c5fc217 385 | 384,ab3c87c8df7dccdc2dc1a83ebec53b3fcd8f391f 386 | 385,10fde858a771b35950c3991b56087e3c8f64bb5c 387 | 386,dd4c05b9ffaf7aeba64a4fb9e4cc54f87d3a0b35 388 | 387,144a0c6e8b7c220d417bb4296e962fdcba56fd12 389 | 388,686d4b3170f3ac89690cda52aaadaac9ff231729 390 | 389,6e3c7b6d1902467f2b8bbba570c59f2536eca1c1 391 | 390,9ea38a661d0044ed2c7a9ed0ede43487c09d8985 392 | 391,31567599037459d20b8f099accbdbb7fd2fdb950 393 | 392,6a1a4944e13d89da2124ba408cd9a4d36b01d16f 394 | 393,e0ba45bc983328eb7a95763a473fec8501b41be1 395 | 394,fc87a37255e11b6d551bce4f4c5995210d39e895 396 | 395,2d5849c65be9123b303b4e16adffef3ad19b5d67 397 | 396,0ea4df886fa66b9cffd025b2c6e30179988d018f 398 | 397,6aaf44f9fb29cfe0ef8333052ae0fe70cf9f6f2e 399 | 398,dbe01a4b88d6dad1441aed7c23ee0159e53c5ec2 400 | 399,14bc76e665536f042ae880d8cfef40c9177254c4 401 | 400,dc41c292af56b85faeaee9f02c514e73a73ded61 402 | 401,db0af0e7d1bb94af227b2c1f9cad19099bf10537 403 | 402,071b2044cb39cd22dba8f47cbe3a0ea146a493b8 404 | 403,0acbc39e5726ff335f34cba7a95f4232556e23fb 405 | 404,7e6cc9c04ae22c183320484df6239605cf5daa60 406 | 405,cb0eccdfba4ccc55cae2dcf75bbe6a062e7ad009 407 | 406,61a101b4016cef0134dac1c1b22c2a07b4ec7905 408 | 407,3ed652b7136d0bd3838c97b19b5d71b562b3125d 409 | 408,00eff7922398b7ec696087ebedfc616f514d397c 410 | 409,6e163a847cac45eff76803881613e7c1de3d0ee3 411 | 410,4554ac06d1a1d148f4ef5161a5fd490a07be5a5a 412 | 411,5894839be0b5b325b4aacbeeb373f5e31d1364e7 413 | 412,9341aef5676f60cdf4a6c741c841d9090ecd3dff 414 | 413,f139b3533ee7f3bdde60908925ec4b716cc7d3a0 415 | 414,12e653c9876803f7b51b1e830f6825faf71d4191 416 | 415,e1fb602bff7e0409ebfe926b4732de2ee039662c 417 | 416,001256afb8669701a18c009df888cb3f4222d6aa 418 | 417,3c6766d02ee42bbe01240cef18194a099a2253a6 419 | 418,f00bd9a8b1f98a3aedc11051dd742529b058acce 420 | 419,93a8a647d6bcfe2c8b46d997bc78618150fa0e8d 421 | 420,4dcd8a0c0d714d01e1be009ab3bebe3ed995627d 422 | 421,43f246c3e8fc5e1c2ccc65d07e3e475136180700 423 | 422,015923a9e8d7b763df4cfb09c06c285404188a37 424 | 423,100b625f97d6e0e0eed9b506a4fd7688b11ab5c1 425 | 424,ff0cab3eccd41ef56dfa7370c713ca684adac61c 426 | 425,c191df1fe0ba268d064e65664a062cf76feec17a 427 | 426,211b316cb474874471a28482652dc6e1755b0816 428 | 427,9ef04e4f5b8638874f2da18876e717207d9e48ba 429 | 428,fc8d8b9c9e52120d84df82e149c7e6390dd5c57c 430 | 429,c3b7d8378db6c16b9732e6253cbae2a6d8eff17e 431 | 430,f692da3900c0cc7f1d296312103d647ee874bc36 432 | 431,c8b72a94d8708723082c1a4b454747cf4632bc8b 433 | 432,c4737896f6456f44aa115ba38f2fb96e6dd77885 434 | 433,dd1f5244f6ef985d72e71b28f94071ff3c485dc6 435 | 434,1c62e405038c3daa13845add324c064f57604779 436 | 435,1516c478dfc5202a3b26ffb1f39a4644ac131c5d 437 | 436,a614e360cfee6de0a6163cc954e577be98e53307 438 | 437,bd701ad7c1dd11d98259ffa1cdd6923cb69ae6bc 439 | 438,75e9f8f8ecc15ce215839915bcf94591271a1ae7 440 | 439,64ae18818c572a1ea6abd7ab9a7124bca0deffc2 441 | 440,6b0a6029bd072fe3d73e4237fb2a27c46472fcbe 442 | 441,f83eb72f26bab0c02dd48573967855c8afceaffa 443 | 442,e460e0852fd0860f4fd83bbc2b794244bdab29f2 444 | 443,9491190df1f61f3fae997b6f079183869bf1ab4f 445 | 444,321574f26c47be2a6f07714283cef81d28dd73d5 446 | 445,5143f313cfb3110b40ad152339587bd13250abda 447 | 446,1e08f35b2af9eb517a733a74b7056b4bf81aaf2c 448 | 447,dcccd02cf2a0aa160e6286dde178ecdea863c954 449 | 448,bf52a360d83801208df3f921be085ceac2acf5de 450 | 449,765b2bb78f4495749622917bddc7e0252e04d277 451 | 450,8c63ba7208b58c05df4c2ed724860810d060c125 452 | 451,df9f9450271c8dd04b1470a9bd3acb8e07bb25d2 453 | 452,ec373ceafffedb32dafe72021cb2291c02272ebd 454 | 453,04e8108d7304715b38725cf948345f562997cd27 455 | 454,11462f426ea0d5473b2a62be089e9a4744524186 456 | 455,8366b914c15c756ad264de66b87d3538b9f3b59e 457 | 456,5f88891ebb29e2336face30b317c04d4decf4a94 458 | 457,da2ecaa75d9dea3968f05831a4b0aa0efd1e6ecd 459 | 458,f08ac41f3ccc043a187c4a4ebb2243539815e7d7 460 | 459,13d62b487362d55e47dcdd7cb8843446bacfa8c5 461 | 460,8d18d62fdadc8f47e6ed407724f82991d55a0c7b 462 | 461,a861eb0a140d126bde2ea0848f4efead5da638ea 463 | 462,64fc2c06ff3a34873cb03977b1ee781ebcae3c5a 464 | 463,897780a38bb0b6d6c464cabac5759c10a2d77442 465 | 464,a82acc5bc7646b885356a5ec722235657c2e39f3 466 | 465,7a901a000e2a398f5c60e71cebc82ff1288c9193 467 | 466,53b199e190802e17487243b52e705fb70ca8dd01 468 | 467,5379a4421c657d838d6f2ef24b025516acbcc08e 469 | 468,0c314d893ba3e482170a7d178ea6d05884853722 470 | 469,2f3e8aaf847d5885e2141a057c2f075178a06118 471 | 470,f424b7f0def83f9cf77cc367f2b67979e26e687f 472 | 471,da4f7f64c36d2c48fcf6d560b525a98f8178cfc2 473 | 472,22063eb34cba306464f7b4f4eda21573a0fef605 474 | 473,7e3d43e4998ff9fa45fe7b7d89d4c25f6dc053d4 475 | 474,5d7d665ac1ac3bbbaecab3b9efa1cdd4dbb2cf88 476 | 475,9d5de73409ff43f47fb5e51043c2be57e69e3696 477 | 476,830c04af2dd7465e0fca572986715d7c0c387df6 478 | 477,dbc79fd4826f7b067cc476843c156d957669a842 479 | 478,2db8e3f33ea6fd0cc63ae44cea5d5820c4007b66 480 | 479,2b50a42530341637362c94abdf11581fa9285ae3 481 | 480,7954da2e3a85faea5a1c83f7749831f9a30b5d50 482 | 481,fcaa72283e765a0814e902410937f07c14829f03 483 | 482,be44aa161f4b64489fafcd9e0b0700a3e3ab1c4d 484 | 483,135b8bd8ed3ffdd1285b769ebcb53ec89b8eb869 485 | 484,1bfc7a04413ce81c2342860b386e4342d3138d6b 486 | 485,5dc8eae0f2addc2943231c92db9f3569e61011dc 487 | 486,346d0bb189958ee1036aeed1809615b7affcf9b8 488 | 487,fbe96e6b96b5a6b87d24596388843a4a11d4059d 489 | 488,e1084e65cbe6a1f0326e95b99334385614e1f691 490 | 489,7abec0f8949817b3d26f056ae76555179593c84d 491 | 490,a02e40a9d7c59303f7f7191e53a2803d56ccfcaf 492 | 491,c0f51c3ab956949c1d47d0c5072ef13394bd5421 493 | 492,3423580cfa59f5315639b9beb3012455b38e531e 494 | 493,7c7c03e3966973ad32d1f0ee78e4667f083a2194 495 | 494,b386c6a03202cbb186c16c51ec41644f2e960205 496 | 495,003320fdb38883e3600b8cd9345549224b6f2bfd 497 | 496,a27ea397a55ce3e6cb3350f0559d311709ce600b 498 | 497,4ad5f3e2116bff5f0f404f6ab9ccbfc45eac8d66 499 | 498,9da532eae53357919c0c5b8c5d62e9b8e10edffd 500 | 499,19617ae72c41a8ad07df114d83cbc1f1019139e2 501 | 500,5080733bf29bb7a95e03b5b5e3d60be5fba5cc85 502 | 501,63982610e4cba8d6b466da6dc3c9e21ddec15ef8 503 | 502,ed9add902bfd5b50d8ba8c1628ec9dca28752d9f 504 | 503,b2248927b1122ab6180ea6f206f65d0d19fcd6e0 505 | 504,59cd879e2aa63052d3c75103d388df2b9fe44702 506 | 505,e28e7813a4f9861c2954c7399d166e046c3b8870 507 | 506,62f2c014f50a72da54a0c5ebfcf4811789d26ea8 508 | 507,8f187c6135f1de85cb06a282fef1e351af74d42c 509 | 508,aee3d39aba8799a4cba183e68f2cd9af80b16893 510 | 509,3f9ccf524012a2060ef7bb240e5c5c7fb9e779e6 511 | 510,a68f7f2a5f922c1071b0b2ea42f94947cd9b9e63 512 | 511,ffd8fd55bfca0eed15864e2cc780bc3245bf813c 513 | 512,f7d44d9e28d6d224b8720ba74cdeec55b842f057 514 | 513,50f10663b41a697b26e2a53f969aaaa264f4c8b1 515 | 514,2d2619d36787ff0e38034281e067e351270a79c9 516 | 515,b132bad5d99b69497875a74c22587401674a1674 517 | 516,acea018eb91eb53ad41965b806d010d1ac1cdff3 518 | 517,b47f91970a600c996b3ebd2493e000b321b3505b 519 | 518,8a2dc37eb278702d42ec70a7d99f5413109662f0 520 | 519,556d726f7a3eeaf220015afc63327dc447b3152b 521 | 520,dad67764f677e34bbd18ab7345f97759920f1f30 522 | 521,6a91471c28fb8530a59f844de2418859d22f9406 523 | 522,70f642121436f8e0091b7c3bceacac8c95def25f 524 | 523,02fbb509ab83ab4ba666b6021ba6138ac5676a4f 525 | 524,ee0941caaf7a869e431bf578deffdca9a1326849 526 | 525,1e476be739ea4ea5bbec4a5d60c10c38d44210dc 527 | 526,e421252930dedf1b019bd5564462766ac687685d 528 | 527,6b0b5549685e8e9c728e382f3cf91b950ef15df0 529 | 528,c3db597901e44b206081d29bdfe41ff230b67ce4 530 | 529,7ac32e75121b0c1527f739e4bc87cb5243906773 531 | 530,5f79af27ec1072ea61b9423f6b10b67b456ec0fa 532 | 531,79546a3b0a5d6055609c3998347c6ee772a8dcd0 533 | 532,5bd7b00979a226fad0dc3b49aebe5f42441d3ad4 534 | 533,e9acaba6d4f65ef13b69cfc8805c860451e0bf2c 535 | 534,5dd1024fa91ee77c302931e20b5bc11efd20cbef 536 | 535,c40a8ae215e0e24daf7f34eff44e10b05af4179d 537 | 536,1141621026b085ba3a4df4be0cec095cd76a89b3 538 | 537,6a8c9231f0dc0e0816212979fe0b7ff216ee0994 539 | 538,b8f0703c535ef8bc504f442104a2f9f90e04ed99 540 | 539,a5c8d7a537a908d8563f42e6f791a00cbb6d9979 541 | 540,bb6f607e7d5d3dd837f9c137022601c040b86ab8 542 | 541,b10afe14b81cf87398be8410151f2dd9c4573119 543 | 542,dbe85fb24a84e31a7588b9b48b237fef3f950ebd 544 | 543,021dbd912082737a38c619b0890acfa5d30cb54c 545 | 544,8b3fd9cd59a54aa926d1c10f87efee5c5db4cdca 546 | 545,aeecf30d360a6d6047e00d5ec14393a0c4e0ccf4 547 | 546,5e82389123eb11f4938891d2d8498461806b4aa1 548 | 547,1ca4dd7d506445c91f30579ba9e38b49736774a1 549 | 548,37156fa12f84e9d3d267004603191bc8ba5cbe99 550 | 549,998f23d1c2bdc1c13a486f75d756b3891ea16fd8 551 | 550,949814dbbf90188b7717913816076b37d8e89b33 552 | 551,9e0305fc6a4a486ba127ac9e5762b925838e0c17 553 | 552,2ac69c84dfcd77efcb702c9eb79be83d416362e0 554 | 553,57005f30dea94cd0b49b256948e0aa7a7975fdc2 555 | 554,ef186d8674edf71ab491ac3195468e2a6cd1cd96 556 | 555,51d2739eec375949c4a5dfb2fa5fe86eafefb8d7 557 | 556,143e588cc752e08c7901b232a1f124d97d4a35bb 558 | 557,847155986d9d7aaaeea3e22e71676a0efaf8167b 559 | 558,d5b634069a04ed2578368cb3d366c1085c161188 560 | 559,ece5a112e3e1228130c8357a29fe93128326f6c8 561 | 560,7fd10190963f9ebcc242e1ebda0637aaa8e6530a 562 | 561,eb56330468a8b86c98089ba7b605e1a157cec8a8 563 | 562,50d15104e27e99fd37af4bf265f7a2918e459aa6 564 | 563,f921f86daf6bcb7ab8f1702b0976efd9bb5b4e10 565 | 564,b68fd036f194b43dba519565d140fa17adb157a6 566 | 565,10a75fddcfe34f06dfcb6fc0f01f2b465fd7c3a3 567 | 566,e8a5440742b465e5a33247f5ef840109598e92ff 568 | 567,6b3c851823a64c08caa45eb396aa0d883e47ddc2 569 | 568,c9d2300112c8b0d4c2da2a43e2df6c68ebc7b56c 570 | 569,33f230755b655bb2fad1089e64fe37b086bc16e4 571 | 570,b9cd7a7d6004b477cfca01ae000d71d3ffcb1e02 572 | 571,54fca0331d3637a52a9e5054f04b510a6e0532f2 573 | 572,d120050f23d303176c919be90957e73315861a8e 574 | 573,1f6fdbd8c6b0064dbcc38e74a5be31664510188e 575 | 574,adf0ab2a771350d971e90d6f42d61f6a36e2444e 576 | 575,53794e58308271b4afad3de1290c8bee746df5a8 577 | 576,f4d1114d95c6cb9510d4c4cdf20c4d81ed819818 578 | 577,126fabd66ef8acfe55ebab546067e5c602e8e635 579 | 578,f58215b6929b799835a5cb157bfbee27983db300 580 | 579,c90438f1ace1bf8bfd0f894b9b8176391158fc15 581 | 580,acf458912a5634938f32583d15b8128e8ebcb3a4 582 | 581,0e91ffb553561d7cc93b12b482b171c7d6b78736 583 | 582,105b9f0430028437301d4a252511bb2aadb66f69 584 | 583,f5b2b7e66563d4267aa5efb9c6b965f9b8bd1ead 585 | 584,c3b15fc23e38b4a19b8b4151195a652299fa261a 586 | 585,1f838bb66feab838606d28ce36c6ea671afa9834 587 | 586,b36db84dfc9f4143b3e6c59d945e9f089518cc64 588 | 587,e6baf9f7333dbb82555a5d06e7a5b8358f2297f1 589 | 588,cd414e2f22921e5849654f707ebb7b077449341e 590 | 589,2448b48f8b1b37e51b13d9d39cf62a4fcdd8afe5 591 | 590,8be20325f0e5cf3418406dec5db381aad04715e3 592 | 591,a0d175f7f96325b6afce3edaab5eef25b1c189fb 593 | 592,21fc930653ed84440850ac8e48693a934dfc845c 594 | 593,7695660721cf5f04d142a01e4b91fd61421db052 595 | 594,f569df4554f52163b8036fec9d4d5e99f2ec8ee1 596 | 595,748f32ab1905f5795bcf38158561f547483c9710 597 | 596,3cc5b732f069b19515f81a8ff25cfdfdd051fb6d 598 | 597,ae4415a38575ddc84b37a29ebb3e82f845f11471 599 | 598,9065b4b81cf8a7828559645abb8ce2b3d9e6ae42 600 | 599,df36a36dde8df13764fcaea810edb6dd50f0d537 601 | 600,d619c550293d52971281c003cce89a1af739f7e1 602 | 601,8ae418c8ec3404ac9538596e69b5ba7ffc96af6f 603 | 602,d0301fad7aed71c90167a14415e354f1352a5d2c 604 | 603,c38d9b2f9365232748fe4376d4b413dc0de65239 605 | 604,e22e55c35612f329a054a22bbf7b92f65fe14eb6 606 | 605,b0c34fdceed6a84356bf610f71ad6a52be03b3fa 607 | 606,8be35fb11c707757698b0d5d31ea6cd767d75c3d 608 | 607,c2f5c925a7c3ae9f6c88e72766d4a5ef18f5f906 609 | 608,d5964b70a9045391152082c1187b56e7d03cb1c3 610 | 609,178885fb1af3b2befddb961edbad232967603d04 611 | 610,8327a817d5bcc230f35e9bdcdefa503ca9d1ee1b 612 | 611,81646a9ffc75165836ac008894ce45c0d98a50b3 613 | 612,b45967ef2a62e95d27758b63443e708243ac743e 614 | 613,2a8a31e0993d29b034f39368ce17f4abafcdcf1b 615 | 614,23c7b7d43b6b2c336a1df075ed32ab3525265da5 616 | 615,143f73f242cc89b7d6d489cd3af66e441b8b68d3 617 | 616,b8d83ff3c7d745f59834e91696a805566b27446c 618 | 617,7426d1cec9c15535d175045a1b9c28a005029c79 619 | 618,a7854da1912b0369bcbdc5bdca4dbb6b91299654 620 | 619,d323e6c6519ea23302e1daa8bdd28a88d462f7d1 621 | 620,e58e44746acd4de474df76dc5338c49d2dfeba65 622 | 621,5a0b6e0bf1eccd18c040cb99dec9d1cddc71995e 623 | 622,1abbd37998122a4ddd965c7c0fc83e7aca5eacdf 624 | 623,a8690914e77a707401da4de42440ce9ed8ac4214 625 | 624,477df7ae38381304480c354e74250672c8745b80 626 | 625,8bfdbb9331cdd0e299f365bc094e2fe0f49717e6 627 | 626,c9586e96bd8f2211ac4e27c34eab5bb70608a982 628 | 627,fe9b5e68418d4fa7ef2ae3419f1b3dc7cdb92b7a 629 | 628,ee3190d0422a6f61985ff9790c7d65b0148714a5 630 | 629,24401f717a534a927a34c2fae3240cd7571d5652 631 | 630,a1b7a49605bf64be08fb0d16737fc1df04a38610 632 | 631,fced47d9b290b444627ff9e8b5f319b813c427b3 633 | 632,c10a9787c66d3efc941dd91a1adc55fbf54575f4 634 | 633,d81eddbe25a9dcf7787a94613a8b7caaaa692232 635 | 634,54c1cded7ac957a2e271c8dc9860e46c2f3dc24a 636 | 635,ee32e335ee46c7794adafb07c5b18e82aa06847a 637 | 636,af18d18b33f37764daf7315a877525b91e7b18d2 638 | 637,afa5f14a7d16a13bd561eb204c612670e6ef2e18 639 | 638,1d3b91faad2e83a2bc5776a2bff6c04bec54c145 640 | 639,0bfae48b2cd108f7bbe26979cde729c9f1af029e 641 | 640,9a1e5883d5694f73a187a2c2f48092b840d11dde 642 | 641,23bda3bd14b9d16ea55e6592936385aa93c998b7 643 | 642,fbbdec3156ea47eb0634a683a98b5c0090bdf36b 644 | 643,1846c63b7b413c5339117531d75853c465da89b5 645 | 644,21d49591d313d662413ffb82b457e6878a758671 646 | 645,dfc187f2a0cbabe5607a65598a9e565b81101c07 647 | 646,6d619a444c17a63bb6467e8b93fd0f7e950f97e4 648 | 647,778ceba0c11c613ed7680888bfc62d837aa52b16 649 | 648,a84b8403a8ecb11616245867d81dc0332ab43b89 650 | 649,9bb09641471fe6544ce1aa9294eac9b103f9bb6e 651 | 650,4cb79ca3c3beba65c96596dfb44e657054aae352 652 | 651,1dc5d538193c16627ba5ddee32f02612c3ba57e1 653 | 652,d1ab5c67f4bd4ff3a7732366759934904e4ea3a8 654 | 653,30631cfe1aabbdae92d883c6dd063a364ec9b145 655 | 654,a8729a2948e09b48bce6199eeb92ae98f6348ebf 656 | 655,770a06221d8df0f219a649b4a0bbcff17b71343b 657 | 656,a4aa04a1b1814137273134566e3955b5a2f7b872 658 | 657,1e1a7fb85a22979b5b93e3f25a0bf3f4c1db5ed1 659 | 658,064e3bace7f7d5209a8b0d039a6e3b555d3b9131 660 | 659,41365709d6b04f0fac40aed31ff8985cd9d1a7c8 661 | 660,b2f1581a9c8146c57f7d4e692bf01a3b2643e1eb 662 | 661,62ec0a26d3b825986ac3a55929553fa9f9fda3bf 663 | 662,9c7e6794de327c461426ffe94cc7f82e699410f6 664 | 663,51687c606053207fbd878c19af533d61eb5deadf 665 | 664,ef8486e4e14d9bf1329a4f63841a44a724a71879 666 | 665,a4877000f852e739d95a1e8ab15c12b9204a3a72 667 | 666,008cf2959c7a46ae2197d2244cc0bccdc618a5ad 668 | 667,e0a5643c94cb0467ec600c56b00727e4ab0f3c42 669 | 668,192e59743c58172ca3cab2dcbf4c72913e7fde42 670 | 669,215a6f6d9993fdc9d001630f4df0600cdbfc4d78 671 | 670,bf1b1649c6b4730f1321258c4ae68e5cbe925bef 672 | 671,b11fb5b0912f886571e42eb8104fb83bbc08120b 673 | 672,f106d1f3da62cdf815b123a8da12c4e26022c993 674 | 673,6c8221b34b515b43d9bb4f09ba71763c698a1467 675 | 674,346b933050041cf442f1b794d0f9ede29c65abd9 676 | 675,16fe686d950bdaeb3e496419522735997dc58d64 677 | 676,9f5848dd851fa0072c327bbf958b7156537e7269 678 | 677,0ddb4bc57c3e84848ca279990e07f09c11067d4a 679 | 678,7b0093f9257b6e83e9ae48b479b40068a094c1c8 680 | 679,dcca8e5fcf001cc167e4c98ec5d8f406d1ead53e 681 | 680,0e8de28367830f758aa6693bdcd57b5bb20c7c51 682 | 681,dd226510bdd878119e3c7e0ced1b1800155a95db 683 | 682,7c6ecec6cf80e4cd5a827c2d50d8e68ec91e548a 684 | 683,bfb8fa540ae56067911cd7c3ab12987c77a9209a 685 | 684,f4657cff552b12aa24e6b00e38b82044724ede1d 686 | 685,acb9990e771063a427088a36b47cdbe9b94f5092 687 | 686,8e6f8fea3a56618dd7609dabcbb42d0e00ce1ba9 688 | 687,c66a90b34842592f10d21bab941886edcdf0b0f5 689 | 688,16e0955cbafb683a3df1ed4eec9c82832f535b87 690 | 689,000bb26828bba1aaf3933a2156263e5b82a32dd9 691 | 690,072c54d98a69cf4a464bf4b09ddbce3e0a3db639 692 | 691,92a28aaaa6f05ae4453fc7308a5938082a23d46e 693 | 692,e4099a5a1bf95db49251d037c7334f5f869e72c8 694 | 693,047893fda2efcb71213553fee951c1cac1f92d40 695 | 694,8c2634f9c9b72c9da8c6e82e938f879660199c13 696 | 695,f6a01d7f6c7056da7d5156a73412722db9348441 697 | 696,8e980f1201a14120ce3c6ea0eacc2435b72dc40a 698 | 697,d49e49320291e336ce66d9e780da5792b41a8673 699 | 698,7cc3a7bbea503bfe243b1be9b510b6d93bb91c42 700 | 699,6f0e9c91c2b67a1e77a26fa95ee1a7a7334706a9 701 | 700,0ee922a92c62c5f1d1061a2da04df5b5c209e308 702 | 701,afeacc98d2d58b1e72d6975a5803d40b11d3b7db 703 | 702,c0782fc84fed7fe82bbcce93e30a2c44630e17b5 704 | 703,44728793f0ff4e46da50368146fd6fcd6e3a3bc3 705 | 704,efd3fba08a614c08053dbe381cc1c784c8d0b00d 706 | 705,aa0323d9c23073ad8979d82f96d530151543058e 707 | 706,6a389214d0352c0a2254f5cd5072ec2acad1348d 708 | 707,031cd46b3a2544a4485dd3ed31de3d4ea27d1dcb 709 | 708,da182d69d78575b3660f310d1265c9b0db739d01 710 | 709,b76946e3ebb32c99037706fe905a37feac47299c 711 | 710,fe5e90e1eec3388b313dd2afa251ec81d2257d26 712 | 711,4f9e90df923b68412a462cacd996c2876d7acf1b 713 | 712,4d2f4cf33a779fb8440f6122a26a08435cd554c1 714 | 713,bd0cb97ee9b5d9d473e6a3e1c692f4c4e9a6a0e2 715 | 714,4a005004c716d558f18fc7180c8598c75affc87c 716 | 715,3fbcdf5833b3df891632d50e218bbaacb73154d6 717 | 716,d5c9797de31574d894c30749fe532f3eb42e2892 718 | 717,0b74a58d45202d37830a86855e6f9ba62213d4d2 719 | 718,64cf2425f499298b260b4fd6489d38aea9479625 720 | 719,5fc1005efc38c26835150b6d8f0058cdfad84af8 721 | 720,bde9d8e6bf7365e3f509de7364250773cf6c3ba8 722 | 721,0b0b367866072fdef2eaeabdc8dcc56207b379b8 723 | 722,292fa54824359055fdc25207ddc640b2eb72a83a 724 | 723,b3ae6bdf178e4eb32abe7641ad7858501a22c532 725 | 724,7328ef9f76b61fc864e5e63c907c1423fd98b936 726 | 725,fe4664deb2b1611cc6991fad441b15150d9a61a0 727 | 726,f02024a4272606911671fda10f25b844e004c099 728 | 727,7231665fafd82c843685bf962077fd3d61be9910 729 | 728,7bd89121356db2d96ab31886245e558b9f3f845f 730 | 729,c5d294fcb057f9432f6e3a4e82623b07083bddaf 731 | 730,9d70c46b0cc2bfb2244053198fb15fcd0ccc79b5 732 | 731,74473243398d365ee0b50ff971f65e440c575a74 733 | 732,66be7eaf65fd5402c621b0abb6dcedb5646784f4 734 | 733,396b3c560066a54dc08befe93bcfa5268c4be174 735 | 734,47be4dbfa9ce29d049b242c0a8422b0eec455364 736 | 735,c0ed1db227d1f5104c06078f076e7601c83dbb3b 737 | 736,8448857321de1d8784370b8d889b3c6f3ace36e1 738 | 737,51a281a0c19f5e7978743d76734029c8e9718ec0 739 | 738,6b127070fcc53204ad49e693b84254893fecaac5 740 | 739,21590aab3f8299ec596c7330328a160cae232427 741 | 740,991fdf0f0f5a900305f5e2e0d2273b6b221480a2 742 | 741,d0a0e99bd88e3c2fcf55cca8b56bde51c54ebabf 743 | 742,558590f4daeb5b7f622e2c3602780a5d0765e47d 744 | 743,09af61b927794777ffe05ab86d262de0404d1e4c 745 | 744,9a9842e5fcbd08b55da55d1549b0ae22a572dd51 746 | 745,846f9f75f15dd33ecf62d60fcb150476045ac4c6 747 | 746,eb1e67d2dc762e7502c347d355586782d16a64c8 748 | 747,3b648c9d5d2710dfb345efac57ef15c8d90db4a4 749 | 748,ee94ad1a99dad5b5c7ffbfa024a43d9c5af36580 750 | 749,2fe6a19d5aa528ef19f84da48ee14fb381975735 751 | 750,38a5472e3b2cda0d3753af19d49cb3c6769a8d2c 752 | 751,da9666018a897d11cafddc9c2fd42ada25d51480 753 | 752,613c083bbaaa7faa8f684f31bf04abf451e385fb 754 | 753,eb4001bb9e2756e98bb0f0649aba80274ae9620a 755 | 754,73c859d4f24eb0c5aa3e0b0de74591bfe7386455 756 | 755,0494e4df26bfe5cda18d6cf3f8549475ae2d4f5d 757 | 756,7bef263c782c84779d2a7b1cd6a5431bbaf89646 758 | 757,3a528cd10d08a7eed0544ef6c698aa36daea1ac5 759 | 758,114c9c59396909c42240e66a7b37dd42ebee863c 760 | 759,2bc4d8f5d4b643a34bf8624f375bbf8377089f29 761 | 760,90e9334f848d7528cfe239e549778c1287211002 762 | 761,0de47a4f39cc5adfcf8e457382a92f38c861fbe5 763 | 762,7179239eba784ae702f1265ee3bd095ef56dbde7 764 | 763,16cd580e3bbb1ce614a0d98415446fc227227aa6 765 | 764,712df6d4039532ad3d1b00920b5e33af88ed3eab 766 | 765,f833815bc06b759b2f12b4aa658893ed6d5000ca 767 | 766,5ab06d59bffe9e8d194b2e0483d96c52b30650b3 768 | 767,ee200da4e21efcefc18d111322a6355857f78582 769 | 768,f18742d6bc5c6a834bffd20dc4a9e66092d9c61a 770 | 769,b0cafef03e9633cfffc963808256fb17941a0306 771 | 770,f530f4737905c573d337d4109cbf37f5186dec00 772 | 771,16dc7e13fa95a23e02dde816b2ebc10a94023f00 773 | 772,721b4496a1468f42a814dfcda45e100323b125c0 774 | 773,349545f8ddd16028cbd04213f61296c685a11a0a 775 | 774,19cda9b9035410422e77bba88b3ae3c38130c5b9 776 | 775,61cc40533a728561bde4006ad707c2475bb62157 777 | 776,db69764a8aa98474b5ba8f39d6b588b2381dc3f4 778 | 777,c09133e1f9e6c71fc4c3abee47eac1d460638081 779 | 778,e9e938551aa498995ea3ec42dac089b53d19ad4e 780 | 779,b74b7cbe5fe662e811d817badf3051f32fd6be97 781 | 780,a214ad6de6fe2849c6ace1d1abefcb19e238019b 782 | 781,2248cf9f47e3711f5ea3ef54e5e117200a35c3eb 783 | 782,38c4ab27282f5e372e002ddd012d61cecdd7c7df 784 | 783,932de50153f0106c76fa82284358b21496b2bdf5 785 | 784,b26296e477d2f3368c5f182df8fcf45e399ef241 786 | 785,5f3c9c57c99b79824cfda6ec8d14d6917b657b8b 787 | 786,c1d4f1bc7a4aca446f72800297d8f3ac756cd8b1 788 | 787,49cff6a4b4c29e1e725fc1ee63075397effa44ef 789 | 788,771055f1d528682a7da7169c05b210f82e538274 790 | 789,b79a1b8689191718d831854f956ee4ccedc3d350 791 | 790,a80af9ac56b235ad420c8a6fd78dbec59a7ee2c6 792 | 791,01f990cb0ce2f58cd53f17dfc533fe43814df936 793 | 792,c506ed929bab0a53c91ee33347db6e4e7f8e1abc 794 | 793,382d10ac7eff3d58722807d3f97be30b86aec379 795 | 794,1f102f62455ce7ff092d2606190094347b3f1356 796 | 795,1a7d089b4bb9bb6eaa4967e5cc3811d861d659b9 797 | 796,a35bfc5a4872f6d5e5c47f422436798978174da7 798 | 797,936bcb2c5c29c05769ca50e53820394ab3b8020f 799 | 798,f56e76766dbd2f0255f377e39359658ad2fd606b 800 | 799,91131dc84ecceb75f843d2716a2e291cd34c16c9 801 | 800,56dc24d2eefbf5060cee1ccb8f302b834ce0dcfe 802 | 801,c32a24e9f21d75cfb2df607219658c6c38fc3392 803 | 802,ddb35adefc96c01797b5f29436fb79c01ba588d8 804 | 803,dedb8a27e31b69eb62b3a96d089727b39a36f88b 805 | 804,e949d67495a155595baf492e76fafa0874a0b24f 806 | 805,3016cd8fd1896052c68a49a58c670524c931af33 807 | 806,2523af65fc7c003cc806f002c1ced4bedf7d5d81 808 | 807,186f545099e61b59e7d9578a099168e721478a70 809 | 808,8785d096605d2b737620e7f71a8c38f269e95f38 810 | 809,c43ae6d62ec2a4dab4b9a155c22c8cebb407914b 811 | 810,93a467f17cf6ca169f8577d53863ad60ca37d931 812 | 811,3c9470f8fbee658a852445f44daa1748c85b83d1 813 | 812,5cdad26fa54e26bd01f8be077dd8aae219035831 814 | 813,c1a320a1ecbdebc2d83561f43a0f4acd4830bbd9 815 | 814,9592ee8b6064b9bf1868a8bd4edad999d9658903 816 | 815,46316d91f536d96f72a63815a1a731a7c72c6a88 817 | 816,270d85925b981f8dcf0815ea1480f2c9e774947a 818 | 817,11789613ed9db52af131af1ffccc05fd9bd4a46a 819 | 818,bc3689fd6207d57e7c6009258c8c043abc8c035d 820 | 819,4dad660772225ff6122c42bb0d680889720d8d9a 821 | 820,e6273bc54fd6a24ea486227ff62810d4db841c78 822 | 821,8de2f3ae8de0a1976745947badb3e326c6e5c54c 823 | 822,3e6b1ffdb5af4f4e73ef266b746df707d6a9ac8e 824 | 823,990af9f77a81a54cca0dd943a1aaba21a8af0e9d 825 | 824,b777c542ec5aba36e0efc784414dfbc7a40fd406 826 | 825,49f55a30c321c2d13474def502ad41cb88aec06e 827 | 826,751a25c95e47e780377d31456866b48473239e11 828 | 827,2fff90870bf10ac5f6a17ca1def97cc60206ac79 829 | 828,e257ef1f9821a9254454d65aa410ff094bc03f33 830 | 829,0113f03f65b41521fc1bb1278aad2d3a48487c9a 831 | 830,bac6a1273e2b38e249fcf8aaa9e25eb23e53424d 832 | 831,c796af8a65f201a8f878df33a9de8c2f6c68d29c 833 | 832,e63f035f80581519057f82d6ab2106effbe8267e 834 | 833,ce2d96f4b4a426ed3811b320934f55a673767799 835 | 834,0086b03129c86a7c70d237a07cad444cfa6ff63a 836 | 835,a569b23f7f28749e5635aaa7e52465eb0fff6a3c 837 | 836,b4cdf19f4465a487a62cb89570d0c46c35653eaa 838 | 837,c69a59438015bbbc5adc598a0b30ec5171009cb3 839 | 838,382e68ac0a5c1a5224df306cf20c062758be6667 840 | 839,791a66fd02723258daf0bf78f76c36543bd8d207 841 | 840,aa33305495dd15bc7a9c22df58880a2a559415cf 842 | 841,1a74e8e5a5e029c06b374f7147fea0cd0bed0544 843 | 842,4617ad4003bbd3211790eeee4ef170bf4c5cc8cc 844 | 843,c3be7c85e827377fd0f621196cf39d74c3f42fb2 845 | 844,0151338b8fd73d1446cac032846cd70836f22c86 846 | 845,37a54118b4f32a0c6415dace8cd58ab862f7c056 847 | 846,9d6ee26394595174dfeddf925f1da74105fb180a 848 | 847,d99650f8bb00538e44d2608d5c97d69cd19459d9 849 | 848,cc68cfd600bab98803c7742485f26ad1eedf1661 850 | 849,257442b09fb96717db323b9a784640756243551f 851 | 850,7024692d5b7f90402709f609893ddddbdb257cb1 852 | 851,b5b21266213656cb5e5e2eaf037682ae1d1e6620 853 | 852,83edc377f6dd08cfa9b48ce606f95c0a0e45077a 854 | 853,72ccf6a4499e19dade6583b34cbd2c501f7a78cd 855 | 854,ee6cee4bd9dbdba89c93bcc225ef1da5e5a5f659 856 | 855,c0b3428f9b1f3b1edc914ac78defa3df0b8b219a 857 | 856,9b8580b46451f542cd6f2602301cf6153193ea22 858 | 857,e85c2a83fae2275ffebe048f883fb449034114f7 859 | 858,f96f778d1ae1b1167cf9b32e188dc29e58d031e6 860 | 859,8228c1c7b57fb5152e34ada6fde5754a755a4e4f 861 | 860,529bda1c7ae9ed6a3a7ed165a4d8988afabc3c2c 862 | 861,25b0593f02204d3faac67e8209e2f4ee92933d48 863 | 862,a87e4426c5fdf0be7f4ca2129861180b4d4176a8 864 | 863,02aa9124c70b00be1344e17bcb96615ed8a6225d 865 | 864,7512786906bac124a75065f082a855fb41bc56a1 866 | 865,74d0568c2f5d6ba72b8a2cca08e40110de3703fd 867 | 866,6c673c88aba7dcfb5c3e6b6209a615a43532c259 868 | 867,3c08f35b16ccd344500d6047ce56f845351e788a 869 | 868,f75f1843ad72e526f13567b3915dd35d41175d16 870 | 869,1a27fc49370c70db64662831b1404a66bb3be0a1 871 | 870,a89e882f3dcbc433f66de3024b86f769023cd9d1 872 | 871,c75c7a68e618a16a94c60948cddee775f593edd9 873 | 872,66d77f139e4db9bd50e1d05afabf9a155aad46be 874 | 873,5035c87d7e5f2965eb33e7bf9fe2bdaafbb73e7b 875 | 874,63a924baf6b03060ab3116c30aef83432518a4ba 876 | 875,c9b24aef6e575ecff49626bafc5cfdb54cd6862b 877 | 876,6eeb0b58b5b2d7bb88a198a501576db3531094b8 878 | 877,4ed28b574fde5d6ef0b1abbd395f2eae8b0f7206 879 | 878,c0f97e26093a25eebd9e61741e0b8f12a91a342a 880 | 879,bf88cec5cb8f29d0228590d157af2b8186bbf1d5 881 | 880,16120d40c52936c959c50f780101168fbb852529 882 | 881,c29e3539c88a58df9fa0607bce76f24706d2279b 883 | 882,2eaa0eef405b310f4f69ba0e9597b10c2a6d75c9 884 | 883,e2fde702732ead1f913e4c695396db876f7ac378 885 | 884,2e5dba9ff896968b1a7e9a333ebcbedd5976e2f2 886 | 885,1cfe451abfca02f337a12046d21b2a7825e0805b 887 | 886,01b2a080f3071255a4712c07bb370d913084ac75 888 | 887,027509088eb9fed4f21623f17f7f144f12e4e274 889 | 888,bf2c0f75200a23cc76356ecd9c260dea02d64cf0 890 | 889,85313db925a658d1fb32639627b1bd1357da9ece 891 | 890,030853136729fbd5e1c04d589613c8ab98e15ea8 892 | 891,2415ef77cb40585f71bb70eeb075b313a72ca8bc 893 | 892,bcaf4ce1bdee5361600161e2cd2f5b4c73b25068 894 | 893,b84a091aafeb5294b9ca8561ca47c9cafc06f33d 895 | 894,7be0cc2afe4124cd53fd81be63bfc282fe3afc14 896 | 895,3cb275fae75b32e9ebdbd07d2dbfc141263d923f 897 | 896,4d92faa3a5874e64f32ff5991e4306439a2c3c92 898 | 897,56186d651cbad4ebfaa5df3dfc0fd9a6fa243ab8 899 | 898,b39cd2685be6721fba31f22948e395006b3b9eb5 900 | 899,74c41827a74e6068d886dd330766ac4f83ba244d 901 | 900,62b23938d5b5a856e074857ac4c3a1a67f2cfca8 902 | 901,ed22417fb1296875f3deef880e4b67bff9dfdb3f 903 | 902,1af8f388049aca6484f4f7e29debd7033de594be 904 | 903,c78a40e68acfd4f1210aabca150d3a8f51e258dc 905 | 904,07cd47e0b69cf198c0de30dad00b8525101a8579 906 | 905,1e011aaae51cda4350a2bb1bd583992791045953 907 | 906,0bec553cd4e7359d894692dd72f9525f09fab620 908 | 907,fe0a7029b984cedcb0b5a33f098d47e1abbf0320 909 | 908,cdadd12bdc0ca9572fe46032d06264272285c811 910 | 909,a6fe8fba540962f89d7b8e7ee7c37c4ee0f1c5a2 911 | 910,e45b6e8e57035c9b8000c2dffada763b3f037923 912 | 911,7ff5f460f778f4a27d0e7b6b80a12432d3fd087d 913 | 912,2daed02b289177915283750c40a268ad2356b668 914 | 913,6078efd10d63d208f57c072cf6b6c6d813a9f057 915 | 914,e6662997d4e57c8deec0e6f08c6a01dab2853281 916 | 915,9fca3194bc81c6cbfd91cba24d9f2f04fa86f937 917 | 916,9bf65f6fee772486db11a293fdb99da42b8c4c31 918 | 917,7a8b303b6bb7e7e5f92e4d95d53e28eca80e0b07 919 | 918,a9762101e9e36783f0f23989a52b9b7749fc5109 920 | 919,984ee6aa9bc3b7150812c5fd3bf747df1c653cb0 921 | 920,214c2b40d66fdfd64e4805b3ae66465cc80a1e8b 922 | 921,36ad6a5dde01ea89b9e48ba87238cc851ab45b40 923 | 922,991ea3f1a4f40c625dcc13fd71d7d34f86578990 924 | 923,2415cddd357a8381036904cb52422708781d3747 925 | 924,173608dec7064396f5c26f3ea03cd126671896a8 926 | 925,4034eb339848ca52a95c11193926e0956618e4eb 927 | 926,f9cf7ae78960810eae97f0036dff48b418dced6b 928 | 927,6e4758ee17a7c8b903ecc0f687feec126b8d631b 929 | 928,d48c8ec65d13d28c9783df603b36afc81c510b63 930 | 929,e6516b3f9246347bbcaaf204147313f878966bcf 931 | 930,4cbb10d6dcf3f8ff9f70840a45b4d57264eafc19 932 | 931,cf451b67bdc061e86d83053e85b8fdc7df96ee9d 933 | 932,d283b33e4e2d8362b267d940e9023efb35246b72 934 | 933,0e93b3668199f4bf177d56ec15cd70be99bf1ca9 935 | 934,b658f06fd2ac66ca86f31876d4b01cfc485d4927 936 | 935,014d5f734471c0ac2c193e6c2b4ff8218b20b87f 937 | 936,25ff96ef0f7e0f552de8fa0d40ab6d4efa443580 938 | 937,9158c9f192bf334c4b6f98be6472da186e26f23a 939 | 938,b2aca96fd0155da14e415cba58a4b5716876cd05 940 | 939,775a8ad083a38703b4990b42fb0a35724ab09c2d 941 | 940,01238931dc07a913e1bcfa538414932aa1d952ac 942 | 941,853ff12df08ec896dd8162f1316919ca4d35345a 943 | 942,508ab41120f0e3c6441de6cdcca462baded0c0f8 944 | 943,171f9bed3343b030b8f39a81ba5c52e653301a71 945 | 944,a69572a5061c69b4ceb6aa524c5f3e233518fb35 946 | 945,7892bd3cd736097a4be07ea0b81c7f8650f672b4 947 | 946,2fd85038275e20586c0fec99cad44daac28ee644 948 | 947,8feb214cb7d72eef29be7332c981e0cd0b21f999 949 | 948,cdd22f3262a15793ab906e9111918992652970ce 950 | 949,981aa63fc05eeb2cbe679fa100ea5fbf608ad335 951 | 950,ef100e0094cafbceaa8f0a7fce0f768a6920f85b 952 | 951,83ef2952dbcdf3a6274ace82c92837ffacde2cc3 953 | 952,fc2192c808e8fdacf2d5394f0c647f696e84351b 954 | 953,a6969879871b3ca47d19d0e2e42cc25596d60877 955 | 954,9847866346b948477407617c10ee4b5568de37c1 956 | 955,9614906057b977c540ffd6008aba048bfb902b6f 957 | 956,c57b806144d9fcbdcd49bbc999fccc7f58d7deeb 958 | 957,3525059274374464096c7715e863cf8cc0aa3118 959 | 958,bf83f01d0a4702559ae36f8ce7d45fcaf51898f3 960 | 959,a9e75137ff5b25480fcb59bd235d10eecd779827 961 | -------------------------------------------------------------------------------- /worker_ids/clmin10_workerids.txt: -------------------------------------------------------------------------------- 1 | No,WorkerId 2 | 1,5175bbb0c623c6452b04b8f89a19fd3b9c22f8ab 3 | 2,9c7ac7ffd2d34470439071be5f1fea4d97f44b41 4 | 3,d59a7a4ed15a534956d30b6e1522cb35a8767b70 5 | 4,9c8a707ef73bfbdb07208eb423db9cf0205af837 6 | 5,e879fae43a9cd5dddb408a4756ebb5e1fa26da85 7 | 6,620379c9ae9481f4af039ed5f2f203c2afde9b2b 8 | 7,d0105275624d4480a82db0eeb2bc3eda8092c94e 9 | 8,6865fb22a0e885117fcddea96e0ef35c630cefef 10 | 9,66cd3acafa71bf3488e7d5bd57587b7146dd634b 11 | 10,618d737b15c407334c342ca7352d2cfc7d5d62ed 12 | 11,b9f7c6bf5bb275eb4dfce593b1c43c9fd25c4124 13 | 12,b4977945bc74868885624fa011d069f098f7a8be 14 | 13,1c1ac3ffd30e620d65a8289d7b0a0e0b99485d9c 15 | 14,e7f5ed9a261433f020678cb4f0c5108cce604c9a 16 | 15,1b4565ae213fc50829133b8d126cf2de0af09213 17 | 16,2a8a4898f1f45ea85fce487fb3b58372b9ed3b35 18 | 17,263b4902d4bd7a6cd38c61a77932c56b0391bab9 19 | 18,65a2b36ea6567144560b08ed4fd7bfb8295fa501 20 | 19,3e2272313af223e42bfee1d738efb10f62bf6ba9 21 | 20,b68dc572a05c461054abcaf7ec25393637f9417c 22 | 21,efbb3669bbc2b29bd29dd7c8c6125db6b3240cde 23 | 22,535d46c5236320405c7e5b81c817881b4eea9f5c 24 | 23,b4e816eb4cc6c22e9e83006c00dde597a3f7b6e9 25 | 24,677fb2984ed664c040fe3a137dbe70c1b7ed5446 26 | 25,66aab639b5c504aa8024e2b381ca9edf161c5b58 27 | 26,a64fe0ded2dc887a1346d926c4021b6be3e29734 28 | 27,59edf4127c4dd3ae704aca825eb84fc160aa2d39 29 | 28,53b9f3a50980468a1364b8a678590cdc987b41ac 30 | 29,448f60985fef17a03a71f69fac04168986d69f42 31 | 30,874a330e6b532e4a719eacb35270a03f9ab30cbd 32 | 31,a6c4255f9a928c3f6475ae68a204e3d0b9a88f08 33 | 32,6797c2edc45cbd0e305326c22e7a40a546920510 34 | 33,4f5e043e00217a762be7360314309ee616abdda8 35 | 34,d9d64a57fc3e9594cc8737def28058074c8175a6 36 | 35,b24edc39fd718dac389711a628bb29897e63aa30 37 | 36,29c82d632d83fc740a333f24b128953d226d045e 38 | 37,cc764cd594cca41913c9866eb80fed1c75b2ec84 39 | 38,18c89a885b3d4482a0a439cbe80c95048ff3be1d 40 | 39,db09d51a4ba54cd5842546523a191530c6bbdc72 41 | 40,f360538807bb173ae6f3a3cadf220c1b5f1b0e0f 42 | 41,8327a817d5bcc230f35e9bdcdefa503ca9d1ee1b 43 | 42,21e4db9106b2436154ee0b057e4863bbe80faacd 44 | 43,ca875c991fc5c631e841638c34ea0f403f3c33a3 45 | 44,d899b087c7e703623ad9d671b32a76379cb6ab12 46 | 45,3a4efdd03bf43305937b7c42699389da010990b8 47 | 46,9158c9f192bf334c4b6f98be6472da186e26f23a 48 | 47,66f393ec4986b8692dcbfe3173b46cde398fb69a 49 | 48,bc9f239d21d3f5a4e090c5d5f24b58d2d72a1c70 50 | 49,accefffaf3b5c4b856ed8f77d21eb1bfcafc1979 51 | 50,fbbf2951e9346652e9d5b800fa40a484efa271ed 52 | 51,f8600ec0c5613a73295c036f065c11debfb30452 53 | 52,354ebfeeb7ca71ffd94952fcdbf6ab0cf28e2353 54 | 53,814e10188f6c0d7270274f8a42ed79f1c42a9eec 55 | 54,c426ffe41389a40ba68a3ad0416131a1d09ff0a7 56 | 55,a3eeff0545267217120b7be52754038ae588fa18 57 | 56,631589ec27ef9a2fd528185f48e22b150bd25f5d 58 | 57,0e3f86af2fbe7a806e49490f27d8a8429039cc9c 59 | 58,3219dc522e01475e1d91f9ac816cc296ad5ca931 60 | 59,625af0c5b1caccb5409a88bbac29bed3bc8443f6 61 | 60,b7eef865618348faea25d6918a78520a1548fc1d 62 | 61,c20b9a16dc76249d3ede03a217da69c9d89fb770 63 | 62,a4f82a3c9af902c4b984215839ae93ab4cd524a6 64 | 63,eb1de028edefa9ec6dac999b0a1c76fbb4dd5bbd 65 | 64,8d629d47ebf09be4acbb7ffea383968bb2783709 66 | 65,5622df01652698567665659cc3012d21aaa77a63 67 | 66,47f8c3357d82bbbb79343d0f2f7346973b021911 68 | 67,e1f356a322a1207b884f493ded8a503896e64a6e 69 | 68,42e30be7432ca6f15a2b62a3470500a419665611 70 | 69,9c9861adcbd3f6ab4e147dbd6a8b874fc99c123d 71 | 70,cd4614bd4c6472766959988bf698b676941619b2 72 | 71,9e5120a743900929313522543ff9bc9bae30ee31 73 | 72,b307cd4db486e2eb4e0b143421481b4645ccfebd 74 | 73,d17064d376e2f3a9596d6541cdf8eddff709ebad 75 | 74,767ec0163dde0a7b0ecf7251acd5c6090874e35d 76 | 75,7c464072fa52cc07f0fe20852292a2a98b5e4a82 77 | 76,e8c4e0ffd1c9e98203bc145b06d8c0f5118fd80c 78 | 77,f0af0adbef740252601e0f64cdf671dbbc776806 79 | 78,6b027cd7f6c5869d56d33d45219dcbb0fefe4346 80 | 79,fa328c568c43b51be2faa9e6417a994baf4161c8 81 | 80,d66c46dbd633d1becfb5754e8622719c463cdc23 82 | 81,d578127fbe29cd8df6508ce5b084ba9a2b835133 83 | 82,b21f3325b23eeec6bf33965f0ab4078ef3d6f82a 84 | 83,636e0e5409cf88e26cd33627077762a959272f9c 85 | 84,b1cfffe99bb83bcff4be415777f7daea2db9532a 86 | 85,496feea80606e77774f4b6740b86f6d472ee0303 87 | 86,5b181fe1ad7e739c469bd2bfa929fcfa2d4ce7e8 88 | 87,2dfa7c6111648f5c85b37f2129814352aeafdeb4 89 | 88,bbcf94076464c3b626d949dcdb41df79446d7c84 90 | 89,21d49591d313d662413ffb82b457e6878a758671 91 | 90,6367cf60bb33ddb349d9833a65baa5fc98c270bb 92 | 91,de3a51d111856a72371cc15c0738df676ab3b716 93 | 92,34860999df756993caaac47879efbb4f0bff130c 94 | 93,9b4b4e88c72ab8842bbb51f3748b3674f9ec4f27 95 | 94,c67675ed3c5a8a995e383f841ea3ca0cd803716a 96 | 95,464b3e0d3f985087c43894054d844855b7b82747 97 | 96,5c5cf95b246400cffd9257ae59d9f1693dbfe304 98 | 97,dbec6f8a7951745b479d58e629c313330cde2709 99 | 98,b702945d8c48e3710224ee1758eebf88511fa99a 100 | 99,f4d371d5d2b1b5b0352c3b8993d12d43f0fb776f 101 | 100,7a9a266de212021918c00bfc207e477fc373558c 102 | 101,49355f049649eed0981822ff26205f1b89bf8ab7 103 | 102,06f0e033d3826e841eee953aab668bb37a620aa4 104 | 103,89b4c59f995b3cce18a17a6015438679e6bd196f 105 | 104,86c0053c402d611656176b52354bad0bfa5139a1 106 | 105,bb261e83de110ba0d09766803020a00a31d8cf2c 107 | 106,a0a7184365935f86f311957eccd9edf81defdf4e 108 | 107,9bb79ac0efa68d6af08b582b1f6066b3e8c431f1 109 | 108,ec07949179d197c8d77549366a5669868dcbcc7f 110 | 109,da16990fe0bfed1d130256b68aecc16780003833 111 | 110,5e66e032b89c96f415fcd772243b65b94a99e45d 112 | 111,53fc7574e98b8e08a48d4f613f8d04d94e4d46af 113 | 112,a7e037841753957d9484cd440ca669829c14b391 114 | 113,68b767f3b29c37253282037c0120280e2601bf5f 115 | 114,c51c8696740285ce8366255aaa4a8dd7993ad61c 116 | 115,37c740ea075964f9b7b9e02a176af5cdab3bc371 117 | 116,5cdad26fa54e26bd01f8be077dd8aae219035831 118 | 117,905dbcb0383015f5ad596e996bea8d9c80aabfd3 119 | 118,b29a105aebb6b72d5f90c5e550fb7f2454e1dbac 120 | 119,b5b21266213656cb5e5e2eaf037682ae1d1e6620 121 | 120,1c6315f7e21289d1abe2adb0b729d9cd33e78645 122 | 121,6ab73a5ac450a6c7dd6c764dd8867cd51c848169 123 | 122,d99889fe71d6b9e52a200c4cc5395c6d9ce0a5b3 124 | 123,95bd2bd4e45851e557ed71dbab8c6e2aa0e7522b 125 | 124,7d6657b463f23642dbb15eca1c6e8e1e5809afc1 126 | 125,4fae5654bd662299a86fc783b6a365bac040479d 127 | 126,bd3aafc799afb620f88d1037ea78ea7cc8a417e9 128 | 127,def7fcd1fae077f5608fb0c3adbc57e23b256c59 129 | 128,7383c1a48c90f95c150ab679c0f4ffe9cb2276a3 130 | 129,d5c4904da727265c403bf2a401419b940d0d82e8 131 | 130,1dfd7a22065e74640b85d5d46bb79c6dc86cf2fb 132 | 131,db51c51e5f63802aea09f945fc1b6cc1bb918868 133 | 132,3c8031841d560474d34212639d8bac63184b9fb1 134 | 133,434c11c996510aa1c9d78518d9d3475ae5d135f4 135 | 134,cce0f0c2bb99f4065684a4a83ec03221da8cefb7 136 | 135,59a7e3c308f930a51fe717f2db1f0968277a7ed3 137 | 136,ad0a56414f6a7af4cafb1b5d26bfc94a1ac93a08 138 | 137,34456040598781d6bedf51d06ade6695e2a47783 139 | 138,cfb78b8cefa9c28c2602de7c0e8101f7eb208c72 140 | 139,9f7856957572211fd7e42a5409c9452b1db082a9 141 | 140,f64d26cb4afdc970256ea97c93ce0e944f62a704 142 | 141,dd4f44a901146c668ec5a786d19ae20f5e3b7304 143 | 142,4e428de6920ecc43308bf7ec0776f4a89bfe8f07 144 | 143,31cf5e7e11eb9f09016556a56afa1859abe7e69e 145 | 144,37156fa12f84e9d3d267004603191bc8ba5cbe99 146 | 145,7acbcab62de7dff3a89f8edf1cc8d6f653c3cdab 147 | 146,6b72d109c417d6494eea13a86c50c6853e613d50 148 | 147,a11fc231d1b6cb1134031cf8e35d9a8db5c12ce9 149 | 148,d6f55adecd963c2f1a5ea6fc3d4d410f16ea3a4f 150 | 149,4998e2dc9d39bd64f2fad633550516ae2292113a 151 | 150,cff58667749d30253b56a9e7354e7f45f80deae0 152 | 151,b11bc9b71d4e7315f9dd2dd87adb7293059356fe 153 | 152,935d98f02d86f326721b6c923b96197941dc5e75 154 | 153,dee9ef52ea9b2fed994d03c0ad097a0b41f90d94 155 | 154,da2923f1deb036e3b9c2ec2b1417d339474233e1 156 | 155,15efde3be2176c63831b51f4da516211b3ca2104 157 | 156,f3d214c79705a3288d3987f54d3e2654c821f880 158 | 157,3779af73d57116a408683be073ef18b87fe6c94c 159 | 158,19a0e89464cc877639a4aa68c067b6b4d3d2bdce 160 | 159,8296b0e83826a337d0501360cc04e1ad29efd27d 161 | 160,7cd176f244fa7950ec77e0a2453e084c48091f16 162 | 161,fa1b087840ec6ea2d490c3499fc815432dc9859f 163 | 162,367eac6cbd1e2cfaa9b5e210f6dc14268205459f 164 | 163,8d46c1461b7dd162f5d19294febb84d81920affc 165 | 164,d67103aad58092c1c561f1b2c11d83b6e4ef95c6 166 | 165,1ace2ba6a4b9f6c4e09fefa7b2f77f2c49537efc 167 | 166,b9451b43506f952172f7bacdc9e2163eb14ffcf4 168 | 167,661325128f42a0d80cf08d4ca16c9228827ede20 169 | 168,5dcd434ed339665b792e5fab057839d3f3c7db7f 170 | 169,d67a294dddd69075a17ec7ea58560a5f12fe66ad 171 | 170,259ad22993f9c4da65251ead0a424e7a819e7873 172 | 171,9d16661dd899145c1e5450fd2a7b1b26443d58d6 173 | 172,266eaf17dab35b6f72dfd22389efa1db6395c868 174 | 173,2f3916d505ac51c7329cbc371610c0f28635391f 175 | 174,9592ee8b6064b9bf1868a8bd4edad999d9658903 176 | 175,f8e572ca564aa15111d1b56407b819c47d8bef28 177 | 176,5d4166031ead60ed7677de8e6f874260aaf86d26 178 | 177,c625fe905e83bee1e5acd5b535501d531b0aed9f 179 | 178,845044f9ebe2a3972844a2527c13d569a8b6dc24 180 | 179,83a16e47ab2d31b246857223ff99bb5a0ebbefad 181 | 180,4117f7105d61564fbff5256092d8b515e6a2a0a5 182 | 181,476303d14329789ea3a8c45c8ec582ce28869ba7 183 | 182,3b1a79902496e1150995875634003272c6de7d7d 184 | 183,0ac4a2f6bad024445cee45e3347f7e6c2761fe3d 185 | 184,291145267837100546ad507222b93216f357ba9e 186 | 185,5321a02893c8ad958653cd192e2cbaad2dbcc82e 187 | 186,370c0ff97121ed59e9b81b6e8b3b5806ee7a942a 188 | 187,0c0e8a3835164241b800325c747da3eb11a2abf1 189 | 188,5c3e1e2258de3c68f5a9e581759dba88bdd0a4ff 190 | 189,aa6d08e866135539d2586d3c58e87d5c1b25f073 191 | 190,877d3f3b40688a6cf292a42c90dc0e7cd84bc59c 192 | 191,f0c238b75da4b0d6c36a158a462a7828c8b7f75d 193 | 192,af4909e12a8042922eb26fd20fd9f65ad9c39d89 194 | 193,7aefb0aa0ca3167ba10e1a573231092a3fc40cfa 195 | 194,4119a5ee96d778dfc42169abe88e856fb43045cc 196 | 195,59e221d65924eab9bbfb778249203a5d767c3135 197 | 196,fcf9190c5ede637159952acae37b07f367dd9c2f 198 | 197,708126b41dca569b4ae4cf02743e08f1de976d33 199 | 198,f83ad6b1b7e5d4c8324ce7d32ac426165391a9ce 200 | 199,5a3cd16e90f1e4a3f947e04cca3ef5b8c15ebebd 201 | 200,e0c98cfd332149cf03e541680ed7459eb52d98d1 202 | 201,a931ad1c4eb6d774cfa0632059cfcf35d7addb74 203 | 202,00c562f37c168ef84285302d98c83a4f8d216384 204 | 203,4c80a3241734732c43a3ce6570d6a927d4f6e124 205 | 204,6907ea263c9af62ad925c9da65183d481a166ddf 206 | 205,50c35c38e5016e56e9516c941283486c222f881a 207 | 206,18a942cf816d1a63fff2d11f5dd778772aa8914d 208 | 207,5da50be35ff25227ebbbb596a53aa8caa759cd70 209 | 208,9c242631cd66642facd78f2aa435deeeb5d3915e 210 | 209,caed54fd5bfdf54117ae8cd40a9ce1fdc1397da5 211 | 210,165c1bc7fbbb639549c1e74086d91103e17b58c5 212 | 211,d22340b30239bf9492a7c9ce2e3649200f4ccacc 213 | 212,726a649884bcc67a4f374e4534d7ebdfa3734040 214 | 213,e1ef4856d4a5b9e64b698a2a9ddf04525e42f225 215 | 214,682aa25b3646aeb83cc6b5daeefbf1b6833b7dab 216 | 215,973a673eb5af3db071f47ff9577521d8765d2675 217 | 216,abd9821d02128dee2c95d193263a0dcccf5ab8f7 218 | 217,bdf006624e851095805f741c6730e8f0219ca242 219 | 218,ce01814eb404610b473e76c95769bcd15a727ab5 220 | 219,9b3eeb18a6b93d11057ccca5b501471d3b96cd5d 221 | 220,8b47caf20104e3d448c0398fff1aab8e92bd04b7 222 | 221,1f93574d051d8bab169e6971278d12698a6592be 223 | 222,9e8cfaa3ee823592df8eab38ea5be3d99431331f 224 | 223,ff8bcabcdc9a0be67b85c361a68ee90688d2ffea 225 | 224,e9bc5acd6db7db5c8006f4a6f55bf1092694856c 226 | 225,29c35a84f82baff1bb386c04ce6a6c994ecdcb67 227 | 226,dda94cb832ded9feebe2ffae2beb6901407836b3 228 | 227,a21c37b5e462cec53a8932ea59500275a9b3530a 229 | 228,8b3fd9cd59a54aa926d1c10f87efee5c5db4cdca 230 | 229,a347c769dcc9caa3cb2414b53991f931d768b2d4 231 | 230,1174778876504d0bb3f477651ac6e9001bc16b57 232 | 231,29b96b5051a2c4698a8ecbfbb05f3fba211916b5 233 | 232,444426ab0d573ffb81c22fde4228c8c4fb1ebc88 234 | 233,ae8f5d2d4a2f2400440d5e7370c4b6c47a96bd42 235 | -------------------------------------------------------------------------------- /worker_ids/clmin20_workerids.txt: -------------------------------------------------------------------------------- 1 | No,WorkerId 2 | 1,e5aaa6becb5bc0c8bdafbf9f57d54b82045d8295 3 | 2,5c6f491ed1f694edcdc51649b6a7ee761dd165f7 4 | 3,003cda6c2ada0c36d1b1a419c0329b80886677a7 5 | 4,d89887aeac3d0a77836ecd83bf7e22183dd6b8d4 6 | 5,59d08e8976a0e1fb35d9ed171d39bb25c3554d39 7 | 6,0cc8d6f2464abab29d5a8813d7c14613256f7645 8 | 7,a0e9f47b93f55b1f72b0091262c5459798320963 9 | 8,dd12c9a582ec6e96ceadab603e5edc7d97dbb9b2 10 | 9,387769c58103212af71c1b76f9645c1513cb3ef5 11 | 10,8ad6dc833bbcf358c638ab7b321860aa7292a63d 12 | 11,9c49fdcfcf3e9cd9848b961cda085d64f8ea50f4 13 | 12,41bb316141ce725c049d61c4ddd4d98cb68ef05c 14 | 13,b702945d8c48e3710224ee1758eebf88511fa99a 15 | 14,5ddb4a09e34a710b12fbee0ad7b38eb1a912a220 16 | 15,00d110a0966fd9cce34201fdff1748ca7d176be8 17 | 16,39f0ee23297e568d6470fc73e4ae20c49504894f 18 | 17,18c89a885b3d4482a0a439cbe80c95048ff3be1d 19 | 18,65c1f64dfb4f86f77c244f59968564c2b401013c 20 | 19,b26de394d7bfeca5a760ca9ce09494b2bd617735 21 | 20,2a8a4898f1f45ea85fce487fb3b58372b9ed3b35 22 | 21,3377d571a98933dafc70e793b380b6fa8b6563f7 23 | 22,49d8f3a14ddfc1044f954d674fa4c2ef0a35e5e3 24 | 23,66fd5679b6414175d454a6255526f87b42aacd23 25 | 24,d14976727168c3416662b4025eac7e81b6a44ff1 26 | 25,239424e15dcb36f0269658843d77b3b19a299f25 27 | 26,d1f724a7dccaaa5f7fcafb784a05dfbad6cc2219 28 | 27,08642a6ade709564cfbb57dac6ebc9301e91574a 29 | 28,8327a817d5bcc230f35e9bdcdefa503ca9d1ee1b 30 | 29,915a2b6f5f6f4420d9126d736a3245387fb64ae7 31 | 30,2ea5534c12fdabbf8e42b4b554ab282f7530ab58 32 | 31,d5c4904da727265c403bf2a401419b940d0d82e8 33 | 32,8aa6c9e9604ee55a7e3a25598195d17bb4ea08ad 34 | 33,da7218601c16989640301257b2ed4050d8743f1c 35 | 34,6100a4d74a1d0c233c322431720892aebc6e7b5b 36 | 35,aa6d08e866135539d2586d3c58e87d5c1b25f073 37 | 36,03e0a9a20573097c222d2a9db113bc076e20fd23 38 | 37,f3101b2f4a076e61702f77918c920a7559d6ba7f 39 | 38,57e386ba6838acb8b894d9359e68ac3ef248edda 40 | 39,b1cfffe99bb83bcff4be415777f7daea2db9532a 41 | 40,339c5946c9d29cf646a291e42651c647c3f71cbb 42 | 41,6ab73a5ac450a6c7dd6c764dd8867cd51c848169 43 | 42,63615fced9f79803379c04be1984dbfebd032b83 44 | 43,c621ff946a870b7c01aff1f3b1cd4a90007e5b3d 45 | 44,814e10188f6c0d7270274f8a42ed79f1c42a9eec 46 | 45,a30041e6ad25440212944a778f3e61aa334650cc 47 | 46,dbfb90fdfa7385f1df92c4ab3898dceccbe1a634 48 | 47,8248c55c044543ef069f4589736b9dd6ccec39ef 49 | 48,900f1f69e4135107ff673fa104419fd2dd4131e0 50 | 49,aaf175a44a169ab2a245be13dd847fdb127282d3 51 | 50,4cfbf43bf4ca6c983f42df1dc44e7eb69173a485 52 | 51,80f6079231b18c3cd3af02d6fbba82feb437fde8 53 | 52,0c06ff4e99f456bebbaaacd7d7edf2f79a7644ed 54 | 53,a347c769dcc9caa3cb2414b53991f931d768b2d4 55 | 54,3ff2b4593c41d33907d1adfeecb73973c4fc767b 56 | 55,d4af012db34ad6b6e62fc59b6bfac6fbba3a473a 57 | 56,2b2a7705ea45cdd7f9a9a0d21c147a30b1f5e3a3 58 | 57,1f9c97214e78514bc8df5dd199c61f177928a958 59 | 58,0bebbe6495a78061a5aa199380e79fdb4f22517e 60 | 59,dafe73c71c77c5ad5b2d71fbb69eb9f030c88a37 61 | 60,d70006821e80f64b2236ced3eaeade1d285ac3dd 62 | 61,78d8ead5c27af361ac30438801335176f3677404 63 | 62,4007242174711546b5a089cfb69e28a353b0ce4f 64 | 63,aaa40393bcc1a54aba3747c15bb6c484475ed449 65 | 64,8a8e5c7ef57defaa00bd93dc10a69200e6b7063c 66 | 65,296dd3dadb45343644f8dc12c888dd1b4c8acca8 67 | 66,1fe273a674b899b68147d5ec4848992da070fa54 68 | 67,54ee6ecbde32cf3cb0be98e7bc55ef5db9265767 69 | 68,7829b05b0c9162bdfa66bc7806de13220f2a7f43 70 | 69,3c33cf5bf97cf2e39914dbbba9a62c9a64106be2 71 | 70,44236e3d9992b1364ded317a9fbc05d3632abb8f 72 | 71,f56cb543765e0339a80000a0dfcb31037ccbf7af 73 | 72,2379cd512e45a357e3be20edad84a8a94042f8b3 74 | 73,df7bf209956c2cf3d7cea1edcaea5c2bc411e6b1 75 | 74,47bf6372b8559460738f1aab7f9bb0ab54f9d86e 76 | 75,f48036490f89666e3e87db44569c9171821c1e59 77 | 76,cd4614bd4c6472766959988bf698b676941619b2 78 | 77,4328815d641cfd289bb06366a66d142368ce747a 79 | 78,808fd10e8d3b1f5fbccfedf684cfe30696a09a3e 80 | 79,14438b915d3bf08a8fc5ad72e9c7b9c5d19d0d3c 81 | 80,723b1b2c68e6966ae86aaaabb5ff23dffc045e1b 82 | 81,5c6699f6240871195bdb9b1f38c1125196ea0b87 83 | 82,b15f98a00d82abe4742f9c10850e25818e5b8586 84 | 83,86f8ff1640ad2e75738835f11e2c010eb0d6a760 85 | 84,f7e19cc290882955c5ef09ae40c2dd248b630ba1 86 | 85,e18ac301641df832afaa171aec33cafe9a9beb66 87 | 86,8d72551e39fc88b9e893e8b62cbf31093defca85 88 | 87,ef71f458641b3f3caed8faa99dee73305e3f5783 89 | 88,b88742a94e752e92775b3ff925d93431e770627c 90 | 89,58494481bac725292d2818a89910a0fa164af18e 91 | 90,50e927155bef5276acc02a05a741394c24cd7898 92 | 91,54810a2800ba3a106178581235c894475befe344 93 | 92,27cadd770300a7eff38bff0093b3ccc23501ce34 94 | 93,21011b1c1a4ecf96b6df7fcc2cbb9d5d937f40ab 95 | 94,9c42fae95a5d905151989f7ef8830c49ed846bf9 96 | 95,31d9b17872b3e6bcfe0904c627422d9a2871c04a 97 | 96,e199d9b6cefadc61dd1e2f7ae14bb10861e431c9 98 | 97,bc8ecb1c32c1fc5feb143eac777698a43a96788a 99 | 98,a8729a2948e09b48bce6199eeb92ae98f6348ebf 100 | 99,8541493966645b07e344db1e8018627c6aeaeced 101 | 100,88d85e5c6d9a995baf097ef043b6207cc2842b1f 102 | 101,c2c4fc19d9c136c5b3998d976cddb0b44c62dca4 103 | 102,9bb79ac0efa68d6af08b582b1f6066b3e8c431f1 104 | 103,066468a2a92ad84c6cd8be981f5889f7c1bbbd9f 105 | 104,57b507ba1db2b7b36f27c4c5cd1ac2697f2a29e1 106 | 105,1478f6aa5994eaa559ab7e7b1811ee139a0aed51 107 | 106,8dfd0b4b640aaee7c008f9b8f853a34c7079280a 108 | 107,a43d117e34bc37f2e6370ddade686ac7eac68917 109 | 108,c1ffea16dd5c3407372e1c90b452529ff65042ac 110 | 109,d3cc604a0996114659557046c70bc921827c3d59 111 | 110,656e3351fcb304f6171b67260c8951ad3b75bff2 112 | 111,ca30353277d6bd8f1190eb479f5e2ee363eca51f 113 | 112,56c6a83d826dbce851148e5ebe05b2dd456bf05d 114 | 113,c625fe905e83bee1e5acd5b535501d531b0aed9f 115 | 114,142f409a66707627cb2ec236c6a1adec3b3c201b 116 | 115,1433c7ab59aa851c68bb5ef7b71321f28182e557 117 | 116,b9f7c6bf5bb275eb4dfce593b1c43c9fd25c4124 118 | 117,260cb02a4f401064b01f25cb70035f2f3c01feef 119 | 118,c8028f8107dc2307b39d64b4dc30ff58d08cc184 120 | 119,ed56d85f107283880232227a8e14b746d0d6978a 121 | 120,29192528c7a29165450453f053683fbc40b472c3 122 | 121,6ef5b83278bb71c1ab130be3337ed0d27a434197 123 | 122,3219dc522e01475e1d91f9ac816cc296ad5ca931 124 | 123,c25ea4375e66bb518457fc0e24160e57580b59b7 125 | 124,adc321a958d6ee04f1605bf9ec28608fec0556f7 126 | 125,fbc9f831bd02e880b7828ee28f2adc126f391095 127 | 126,269eef4758961c758bcc3fee3a37b16e33fbf7b0 128 | 127,811e797e197c688844b078f4fa87f1cddf67a62c 129 | 128,07c2afa222ed7a79298e2a6379f50b0ae9922ad0 130 | 129,c1fff62a0c5e057e730853479ef8c7f6663a3bf3 131 | 130,76634ec9f2222961892bddcb3077b9840f3a04ba 132 | 131,42e30be7432ca6f15a2b62a3470500a419665611 133 | 132,ddefb444784c282075e8c39513d0088567ac8f57 134 | 133,af6a45d015284a773c828ce907370f32a591521b 135 | 134,9c8a707ef73bfbdb07208eb423db9cf0205af837 136 | 135,63a924baf6b03060ab3116c30aef83432518a4ba 137 | 136,afccfba5b43d08240578d1c79a0d673958f7ccbb 138 | 137,535a9f6dcb33ba540e540552a9715deb703cc065 139 | 138,a09a0122d7869441080e9cde96b388a9085ac847 140 | 139,5d4166031ead60ed7677de8e6f874260aaf86d26 141 | 140,59e221d65924eab9bbfb778249203a5d767c3135 142 | 141,670dc8aa8e03ec8ad4b65c0c898e54d4c806660a 143 | 142,15108081aaabe3b3f2a4f02d0deacaaaf9b7d28b 144 | 143,ec07949179d197c8d77549366a5669868dcbcc7f 145 | 144,5d9b32ffac757dcfed13504bab7b163fb79adc29 146 | 145,0babf3861c7cc80399c0eba1719dc8be9abb5047 147 | 146,fc185d613074513ad2daf5cfacd97c353a86927a 148 | 147,ef186d8674edf71ab491ac3195468e2a6cd1cd96 149 | 148,ec9f35c3bd0f446162731d8cf1c4dcc377ffe354 150 | 149,8d2fb087195e398d243868e039c2fd445aa0fe80 151 | 150,2d0c72ad0f79ff4ee536e8dc9808a9ca0c7001a9 152 | 151,8b3fd9cd59a54aa926d1c10f87efee5c5db4cdca 153 | 152,d17ffac818526ee5cee07127ff8df1563d1d1867 154 | 153,ea30c4f4b44344a088ef0c336764e551ff2724c4 155 | 154,3b1d9ed5f53ed371a417d681873be52942f3b529 156 | 155,e348f369d9ef702b343ff552f69474d2d35558e8 157 | 156,25942d10cdb9ca8f2fe63291d680934231b03e37 158 | 157,1500e899a8407c7ae3fe66dd90e0412b1125e926 159 | 158,993b07c7691788f041f68b2b20452f2b1c00c786 160 | 159,caed54fd5bfdf54117ae8cd40a9ce1fdc1397da5 161 | 160,23d14fa067b6e6f06d277658c190a12228a29cf0 162 | 161,4e5c4c2aaa74c864e8d97b74af00b220ae46f307 163 | 162,5f0e4ddec7200b7decda1f8d687e8af739fd6704 164 | 163,f9ddf2dc3d81fb165b8e1553abc8f5b8548657d6 165 | 164,231af12d3bad8cbc87494ebb090060184c817219 166 | 165,b39bcc1b3d4c7ab81054991e99b2b4fd4d6cb4da 167 | 166,b81fb71e736fd899c59017c097171d7424e225db 168 | 167,4403ebcd8603cab8d24ed4799566e63b7b134078 169 | 168,33777a6c5fe2097a3940360d39f0830eede467f8 170 | 169,520863eb8029f8bfd72c2eca04447624ee199037 171 | 170,ab3c87c8df7dccdc2dc1a83ebec53b3fcd8f391f 172 | 171,270f8c1aa93f586feb1babd3a0a6660215f60ad4 173 | 172,32b6d2717125b5bb20a34e497a94af034f607603 174 | 173,c4cad00c78b3edab74677c99a98b40d2a98593a6 175 | 174,89b4c59f995b3cce18a17a6015438679e6bd196f 176 | 175,846b172bbb75ab05fc0efefab23544e01cd43a12 177 | 176,9f293cd51b190ecb8bd0bcece98ab3a0390d643e 178 | 177,037f6b71e18456c53cb149d5f8c811c2967e8d0a 179 | 178,ceaad3509be910c249a84ae3120f6a26d6d53113 180 | 179,601294fb535aa62d348d4b098fd9718871a30f99 181 | 180,421f21885127f4cee561543a236bcb8fc96f6ef4 182 | 181,fc3acd197dd32347b5478dda8a8351232821b532 183 | 182,21e4db9106b2436154ee0b057e4863bbe80faacd 184 | 183,3b648c9d5d2710dfb345efac57ef15c8d90db4a4 185 | 184,e835e9408f5c8997bdca887f0a5fcd7fb2de9598 186 | 185,e7ac23dea3a3f3dd815552ce67073c6e2ec36c7c 187 | 186,ae268d0f9746e5ca467171814d954d68eed26b3c 188 | 187,7d4c3c87d6de1099fd722ef3b00bb3c15fd2817f 189 | 188,9b938683bbb5c8ef8c6b03278091d6d5524e8ca6 190 | 189,3671fa8fa13f9f6652e2af3b24a714c837fa2fb3 191 | 190,6e7cbd9064bc0db5f9ed64efb79be43d12845037 192 | 191,9b70bb2eaebee2ceffd6cbaaf7d89c18ddf22ff1 193 | 192,96f9f5bd383698af5b183ef820e947dcf600c566 194 | 193,cd0e697df0d7ccdfe95c5bdd273fd7407b8efb0c 195 | 194,4c84670c788ddd9a969ceb0c1a9f5ae2b19178c8 196 | 195,93ef6691698928e2dd279c50835bf78add77bab6 197 | 196,e38f00e532f350d18bddb64a680d5c7357893782 198 | 197,a64fe0ded2dc887a1346d926c4021b6be3e29734 199 | 198,486331b736e40a2a2a3dd305b4852eb398e2a0a9 200 | 199,db09d51a4ba54cd5842546523a191530c6bbdc72 201 | 200,42061f250c06236b05f27b632b8b413a258b3a02 202 | 201,2846e49355761c7707bf9a11e443030f4e604634 203 | 202,8906a77c5d2e4aa852d5af52819022732d8d280a 204 | 203,79a8812120e2fb7b7c880cfc49b82b06ba1349f4 205 | 204,76b5824a898697cc234502bf9fee2af6ba4f9690 206 | 205,f08b7a9523977842cd6c9e1891027422c6758d66 207 | 206,3779af73d57116a408683be073ef18b87fe6c94c 208 | 207,5b01bb17e9028ad57cc0a47d1640459ce6b793cc 209 | 208,52b77e4781f7bf6f47084df669c057786c1fa571 210 | 209,a6619a3c173abad32aa59962e7afd87e6007da70 211 | 210,0d259d0cb1e108c2675eb198f01fe1f67bccf97a 212 | 211,c4aca45c86348cac1193eb4522c06a430f81fb60 213 | 212,76fbe19e7a25bd4244f6b3bb636bfce15e1668aa 214 | 213,8dedf730dadded21153de841dc1411582a8fb37f 215 | 214,afa011483e5032c8abb746c182cb49afcb25f788 216 | 215,c6e880636f53eb02f4fb36ad5505d5ec1172b1f9 217 | 216,c2b5f275c6fbc7ba6b1320df094caf8dd08438b9 218 | 217,4dd1825a15f50b7f8ae3286be1820536e2a43543 219 | 218,f7344c25d755ddc524eefea9ad06e34d1e874596 220 | 219,e9ceb394cf1fdb04e5afd9e4445021eac12f3146 221 | 220,1c62e405038c3daa13845add324c064f57604779 222 | 221,0aaacd85f072cedab6d919db7b6ab55a3f7f443d 223 | 222,65a2b36ea6567144560b08ed4fd7bfb8295fa501 224 | 223,30b51203382437e40f8eb755224ea01f1620569d 225 | 224,d124ac73466b04f835712adedabda798f64c7a27 226 | 225,000f0a6d90b4cad21266c98f235ca244e9971747 227 | 226,d03f079cc0b6d36556d9fc4f1e1b2fc452d3a7b4 228 | 227,50408901a0dd1d27eda7c73c04f9c0dbed1784e8 229 | 228,496feea80606e77774f4b6740b86f6d472ee0303 230 | 229,4a6b11b338e82c5eb248d4037bc8dcaeead79e57 231 | 230,105b9f0430028437301d4a252511bb2aadb66f69 232 | 231,7a5cb23dc307efd07dd501fa1baac968302fed95 233 | 232,403ce973276039ff40ee1eb62dc6359139def04c 234 | 233,91b9ddc07daf2955328d943173fd1c774cd34643 235 | 234,a26d029c6794cf7baa66afdb899f708bfc57aac0 236 | 235,a2716cfae1f0057d2662a5e62cce81c2850f162e 237 | 236,7aefb0aa0ca3167ba10e1a573231092a3fc40cfa 238 | 237,eb39b347af975621347d9127d4364d33c398565b 239 | 238,46316d91f536d96f72a63815a1a731a7c72c6a88 240 | 239,20240a819d385aedfa281086a0bf153e6ed92441 241 | 240,d079614e053d51f67344dfaf2ba2713e879ce32e 242 | 241,cff1396f25f2cce989f2fad150fb80635218217c 243 | 242,b24edc39fd718dac389711a628bb29897e63aa30 244 | 243,fc0089796aa4a86810fff6f93442d19e9b7488f7 245 | 244,4332dc23d8861ac00d997f2caa7491fedb5666bb 246 | 245,28f99ebf422199ba10e72020689fef363dd62562 247 | 246,bf1546bc4f2167cc4abcd47548c41716d7e20ce6 248 | 247,724ca62ead2ba17419562b44a3687a11f94ce454 249 | 248,6ada26c1621e8b666832018e08d50309654d9168 250 | 249,0e594d45ae06324fe33d2e0ac5de9a945a28a723 251 | 250,473a9cc9ef644b683386e8a364e207b1769edf3d 252 | 251,d943c98ff7a145328248f247c15410042439c6b9 253 | 252,448435fb22f37b60a2f6c81d2bbae5f16d317faf 254 | 253,7d88e77c7e2736bb9fcbc510a35ccf232cc361b5 255 | 254,8c50a1c7f34f38ce8e958d13623b93517afb981e 256 | 255,7a1b13a439377b11cc9bc71afb2e1b66c3d7513c 257 | 256,bd0cb97ee9b5d9d473e6a3e1c692f4c4e9a6a0e2 258 | 257,998f420b3a0aedf07dc5e5228efa964e508066fc 259 | 258,34860999df756993caaac47879efbb4f0bff130c 260 | 259,d9a71c4a66e319215551f34df76a89683640dc69 261 | 260,bcbcf1779a330409a66184f23ec6cf0db9f8ca16 262 | 261,4df8ef2b3045f5c002de684665f36152667e0e41 263 | 262,09cfe7fb5cfeaf982b4bd90ec98f8218f7876fa4 264 | 263,42b4b271c24323ac391143ec0f6d9e1aaee38bd6 265 | 264,bb759069d521de4b12679798f1d1ef20da983690 266 | 265,79d95aa35855afddec47a984da0d13fba82351c3 267 | 266,6134482ee0f7196ae472537e91ab0ca9c2cf9170 268 | 267,578a3333fdfd7f9a239e55a6f3843af417954cf3 269 | 268,9614906057b977c540ffd6008aba048bfb902b6f 270 | 269,064637478bf93a3117d3104ee6559ccaacb15030 271 | 270,cf24695cd3a4df8d75a7c84cccb9cb4c03650c2b 272 | 271,107cef463c683ed0dc3aa16d951ed072a26bf642 273 | 272,d0105275624d4480a82db0eeb2bc3eda8092c94e 274 | 273,47c25b394de69edacaf8774d01372e27092e5613 275 | 274,26e9c80174468280b0fac0fc8be94b750fd15125 276 | 275,b76946e3ebb32c99037706fe905a37feac47299c 277 | 276,3b4b469de70def30ee56eb168cafe1b94d797b22 278 | 277,eeb7c7b99f0148b2b707c817772b0d579dd5fd2d 279 | 278,14d1c779186103ff3475e8ee98c9937837012197 280 | 279,601e96b9fdebb4038d80d6a70bfd9ae8e90b4fa4 281 | 280,370c0ff97121ed59e9b81b6e8b3b5806ee7a942a 282 | 281,080081b2a52ac162bfd8e145bc4fe034c69d3b57 283 | 282,222692ea244e53af3162ebd2f14a75fc1613f1c8 284 | 283,49af56153166d3200f68cb8228fa1fad67dd23a3 285 | 284,91131dc84ecceb75f843d2716a2e291cd34c16c9 286 | 285,d3cd41007d14541c8da460ef198ffef37ddca116 287 | 286,b0432705f6d26abef8c71f852961272ec64b4110 288 | 287,d254e865cfe44fe41f1d992ad93e86aa2c8a032b 289 | 288,8168e479d990f438a0292a6e3f4f457480ab930b 290 | 289,cb83a99a153254e5eb0f71c30e67e87e165e92a2 291 | 290,59bdeb55acea5346f3d7ba277ca8758b78aac9d2 292 | 291,8a165381fd1c61b7c19c65756e562ba05e74f23d 293 | 292,be6c20e1690f9aa158370397c629718fff01bcb5 294 | 293,16f08caa56e87159ce9235f947336f3eb6bc7e5f 295 | 294,ec22911166c3809f712a9bb9755382042334e524 296 | 295,d1c80205634f9abd1447d89472f679754efef55b 297 | 296,b9451b43506f952172f7bacdc9e2163eb14ffcf4 298 | 297,f3cd13572e404c5fe370c4ccd8fd4c0043f3cf1b 299 | 298,7344d293acb8babf3f390005e31a8e7131a02ded 300 | 299,a02d4b2c50c35a1e7f06d42af325517d11d66661 301 | 300,1d5ac8bb451a384d34703b497c6501827e9e4379 302 | 301,192e59743c58172ca3cab2dcbf4c72913e7fde42 303 | 302,19b6615c03d67efbcf909a24114559c90f6d1878 304 | 303,0ffd44093a11a745530ff2d68060a8b2737c1fbf 305 | 304,6f6b25184b7b9fcf9d0db51ba4692b6b82a837ee 306 | 305,99c9db6019a91e160fd6fa63e50f28d69351299b 307 | 306,2846dd182c66c2ef2d69e2e8170d341374c9d2aa 308 | 307,ae57d928553d011e882c13b485a9a73556e310f3 309 | 308,63f028d0a8a00f11cf48ac3f5963d61b95b40458 310 | 309,e483f91b1c8eddabb7d5b258382ce7520235dcf6 311 | 310,ee5f2dcd5094d67094b5068f69bebd90efc132e8 312 | 311,b7573c585aeac151df29ca34ddea6a479b3d147a 313 | 312,890399c5bf656a06c2fafed7e73c23adddddb7ae 314 | 313,5710bfbff629d4edc5f3f2811e1400c3b92aa080 315 | 314,5a997d7e1d68e4a8b7fca3ac10a46fc875628050 316 | 315,1339d2888c3bb76aefe0fe3d98902280c1e95992 317 | 316,eb45cb4f3d3b8cb8a70b40f39a0f217ded0159bc 318 | 317,620379c9ae9481f4af039ed5f2f203c2afde9b2b 319 | 318,53477488e2d7581ce81fbfb182edff4e326f0279 320 | 319,84769864aa731ab79447aae6407bffce48809d34 321 | 320,896f6da2d8207c680ba774544e00ee8864cc80df 322 | 321,615c8b9f13bbd119e7ee784f3a4d647e4d3d87e0 323 | 322,1aa7066b3e519078a0cd85240833403fdcd67155 324 | 323,39d8d23e0ae33181d6ba54737be93087cf055be9 325 | 324,3793071462515bc8b9949f035d58431a5bc99ca0 326 | 325,2ac2e5bdd6df714305898969cf3fea88eacab86c 327 | 326,d233976cd2c527983f6e882488506dafbb73cb77 328 | 327,d67103aad58092c1c561f1b2c11d83b6e4ef95c6 329 | 328,19ae2a1a70a393a7eb1b20eb54527b433f4aeb6b 330 | 329,4f19e2543ddfcbb94dffc290f9f04c2835499b44 331 | 330,f7750e8db3f6caaf81274b8e290c6ebb38b5c45e 332 | 331,9bce0171075f510e88091b7328072be1d2cddf34 333 | 332,730fc1a5573f746eca7c000060cc780aa0f67c67 334 | 333,e8232b1dcdf44c695ddb4d75ffc73dbe684238ba 335 | 334,e879fae43a9cd5dddb408a4756ebb5e1fa26da85 336 | 335,06c9e2c0d4178a24ce27dce415c94466fe7e3ba7 337 | 336,5deaaec301e5ce9a3bad79966b9f3c9aa1dfacf3 338 | 337,8c60e4c606dad77df3b4947235278ef32e2d9fcf 339 | 338,c5cb77fc10a9bf97778135ac945534bd8136afb1 340 | 339,4a3c268352f76b90ad4e667f060f158623638208 341 | 340,8076da0f016ef76f1404946f4389923b3e1ca111 342 | 341,e5734740022745f9cc2e731b2ec1657033033792 343 | 342,f7bc8e3df4534602807ae6cd1e76edce1995ec42 344 | 343,295247349473d1f2b5b5226ebe720d5249ae943b 345 | 344,e19d800c907db15ece595ad6b3334570b890b5d2 346 | 345,ef57affb15cdba2fe0db11fa43c56d43a336de55 347 | 346,cbe2a3a4eaefe5da4b655c9c039dd3908bc592f9 348 | 347,282fd212e659dcc8ef8ede7dabb35360b7533e7e 349 | 348,d17064d376e2f3a9596d6541cdf8eddff709ebad 350 | 349,186bf8617e37a8c88cd8ea379f214d5cc6977e4b 351 | 350,db42fe931819645f9335a532d0a60bd3764b4fb5 352 | 351,3984e364cc07bb76ac5706f9c0d23f3960c6c3ef 353 | 352,9b86e898b44406fe3de844d86f22eca535bf91cb 354 | 353,16af363744dcc9ed100f7c0633cc457d4c81321d 355 | 354,161e0c1cdc3c87651041a0128f4fe98342944c0f 356 | 355,3f3875d0c689356bb566f9e7d3117c6d9cca30b0 357 | 356,29027b2ce6c3412d361fcf0f22f87e0bc224a95b 358 | --------------------------------------------------------------------------------