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| import os | |
| import zipfile | |
| import numpy as np | |
| import cv2 | |
| def images_to_zip(images: list, out_filename: str): | |
| if os.path.exists(out_filename): | |
| print('File', out_filename, 'already exists. Abort.') | |
| return |
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| def seed_everything(seed: int): | |
| import random, os | |
| import numpy as np | |
| import torch | |
| random.seed(seed) | |
| os.environ['PYTHONHASHSEED'] = str(seed) | |
| np.random.seed(seed) | |
| torch.manual_seed(seed) | |
| torch.cuda.manual_seed(seed) |
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| from torch import nn | |
| class NN(nn.Module): | |
| def device(self): | |
| return next(self.parameters()).device |
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| import functools | |
| import operator | |
| from torch import nn | |
| def get_n_params(model: nn.Module) -> int: | |
| return sum((functools.reduce(operator.mul, p.size()) for p in model.parameters())) |
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| import tensorflow as tf | |
| from keras.backend.tensorflow_backend | |
| config = tf.ConfigProto(device_count={'CPU': 1}) | |
| session = tf.Session(config=config) | |
| tensorflow_backend.set_session(session) |
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| package main | |
| import ( | |
| "encoding/hex" | |
| "log" | |
| "net" | |
| "time" | |
| ) | |
| const ( |