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import argparse |
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import json |
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import matplotlib.pyplot as plt |
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import math |
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import numpy as np |
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import os |
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from scipy import io |
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from scipy import stats |
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from tqdm import tqdm |
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import math |
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import PIL.Image as Img |
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import uuid |
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def show_img(image): |
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image = np.multiply(image, 255) |
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image = image.reshape(int(math.sqrt(len(image))), int(math.sqrt(len(image)))) |
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image = Img.fromarray(image) |
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image = image.convert('L') |
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image.save('outfile.png') |
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def gen_data_dict(data_dict, raw_data, group_name): |
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data_dict[group_name] = [] |
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for _, value in raw_data.items(): |
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for img_data, img_label in zip(value['x'], value['y']): |
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data_dict[group_name].append({ |
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'image': np.multiply(img_data, 255), |
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'label': img_label |
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}) |
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def construct_data_dirs_clientwise(raw_data, group_name, data_dir, data_type='train'): |
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data_group_dir = os.path.join(data_dir, 'huggingface', 'clientwise', group_name, data_type) |
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if not os.path.exists(data_group_dir): |
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os.makedirs(data_group_dir) |
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for _, value in raw_data.items(): |
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for img_data, img_label in zip(value['x'], value['y']): |
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sample_dir = os.path.join(data_group_dir, str(img_label)) |
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if not os.path.exists(sample_dir): |
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os.makedirs(sample_dir) |
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sampel_path = os.path.join(sample_dir, str(uuid.uuid1())[:8]+'.png') |
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image = np.multiply(img_data, 255) |
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image = image.reshape(int(math.sqrt(len(image))), int(math.sqrt(len(image)))) |
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image = Img.fromarray(image) |
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image = image.convert('L') |
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image.save(sampel_path) |
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def construct_data_dirs(raw_data, data_dir, data_type='train'): |
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data_group_dir = os.path.join(data_dir, 'huggingface', 'centralized', data_type) |
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if not os.path.exists(data_group_dir): |
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os.makedirs(data_group_dir) |
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for _, value in raw_data.items(): |
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for img_data, img_label in zip(value['x'], value['y']): |
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sample_dir = os.path.join(data_group_dir, str(img_label)) |
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if not os.path.exists(sample_dir): |
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os.makedirs(sample_dir) |
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sampel_path = os.path.join(sample_dir, str(uuid.uuid1())[:8]+'.png') |
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image = np.multiply(img_data, 255) |
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image = image.reshape(int(math.sqrt(len(image))), int(math.sqrt(len(image)))) |
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image = Img.fromarray(image) |
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image = image.convert('L') |
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image.save(sampel_path) |
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def load_data(name): |
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train_users = [] |
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train_num_samples = [] |
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train_data = {} |
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test_users = [] |
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test_num_samples = [] |
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test_data = {} |
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parent_path = os.path.dirname(os.path.realpath(__file__)) |
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data_dir = os.path.join(parent_path, 'dataset', name) |
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train_subdir = os.path.join(data_dir, 'train') |
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test_subdir = os.path.join(data_dir, 'test') |
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train_files = os.listdir(train_subdir) |
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train_files = [f for f in train_files if f.endswith('.json')] |
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for index, f in tqdm(enumerate(train_files), desc='Training Data Generating', total=len(train_files)): |
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group_name = 'client_' + str(index) |
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file_dir = os.path.join(train_subdir, f) |
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with open(file_dir) as inf: |
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data = json.load(inf) |
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train_num_samples.extend([sum(data['num_samples'])]) |
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gen_data_dict(train_data, data['user_data'], group_name) |
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construct_data_dirs_clientwise(data['user_data'], group_name, data_dir, data_type='train') |
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construct_data_dirs(data['user_data'], data_dir, data_type='train') |
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test_files = os.listdir(test_subdir) |
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test_files = [f for f in test_files if f.endswith('.json')] |
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for index, f in tqdm(enumerate(test_files), desc='Testing Data Generating', total=len(test_files)): |
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group_name = 'client_' + str(index) |
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file_dir = os.path.join(test_subdir, f) |
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with open(file_dir) as inf: |
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data = json.load(inf) |
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test_users.extend(data['users']) |
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test_num_samples.extend([sum(data['num_samples'])]) |
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gen_data_dict(test_data, data['user_data'], group_name) |
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construct_data_dirs_clientwise(data['user_data'], group_name, data_dir, data_type='test') |
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construct_data_dirs(data['user_data'], data_dir, data_type='test') |
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return train_num_samples, train_data, test_num_samples, test_data |
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if __name__ == '__main__': |
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name = 'femnist-small' |
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train_num_samples, train_data, \ |
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test_num_samples, test_data = load_data(name) |
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print('####################################') |
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print('DATASET: %s' % name) |
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print('%d train samples (total)' % np.sum(train_num_samples)) |
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print('%d test samples (total)' % np.sum(test_num_samples)) |
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print('%.2f train samples per user (mean)' % np.mean(train_num_samples)) |
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print('%.2f test samples per user (mean)' % np.mean(test_num_samples)) |
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