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import os | |
import matplotlib.pyplot as plt | |
import numpy as np | |
import torch | |
import torch.optim as optim | |
import torchvision | |
from scipy import integrate | |
from torch.utils.data import DataLoader | |
from torch.utils.tensorboard import SummaryWriter |
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import matplotlib.pyplot as plt | |
import numpy as np | |
import torch | |
import torch.optim as optim | |
import torchvision | |
from scipy import integrate | |
from torch.utils.data import DataLoader | |
from torchvision import datasets, transforms | |
from unet import Unet |
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import math | |
from functools import partial | |
import torch | |
from einops import rearrange | |
from einops.layers.torch import Rearrange | |
from torch import einsum, nn | |
class SinusoidalPositionEmbeddings(nn.Module): |
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import matplotlib.pyplot as plt | |
import numpy as np | |
import torch | |
import torch.optim as optim | |
import torchvision | |
from scipy import integrate | |
from torch.utils.data import DataLoader | |
from torchvision import datasets, transforms | |
from unet import Unet |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
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import math | |
from functools import partial | |
import torch | |
from einops import rearrange | |
from einops.layers.torch import Rearrange | |
from torch import einsum, nn | |
class SinusoidalPositionEmbeddings(nn.Module): |
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from typing import NamedTuple | |
import gymnasium as gym | |
import numpy as np | |
import torch | |
from torch import nn | |
import torch.nn.functional as F | |
import torchvision.transforms as transforms | |
from torch.utils.tensorboard import SummaryWriter | |
writer = SummaryWriter() |
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import argparse | |
import glob | |
import os | |
from tqdm import tqdm | |
from concurrent.futures import ThreadPoolExecutor, wait | |
from threading import Lock | |
import queue | |
from cshogi import * | |
from cshogi import CSA | |
from cshogi.usi.Engine import Engine |
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import os | |
import torch | |
import torch.nn as nn | |
import torch.optim as optim | |
import torchvision | |
from torchvision import transforms | |
from torch.ao.quantization import get_default_qat_qconfig_mapping | |
from torch.ao.quantization.quantize_fx import prepare_qat_fx, convert_fx |
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