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import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from openTSNE import TSNE
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import SpectralEmbedding
from scipy.sparse import save_npz, load_npz
from functools import partial
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from umap.umap_ import fuzzy_simplicial_set, find_ab_params
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import SpectralEmbedding
from scipy.sparse import save_npz, load_npz
import random
import torch
import torch.optim as optim
import torch.nn.functional as F
from openTSNE import TSNE
from umap.umap_ import fuzzy_simplicial_set, find_ab_params
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import SpectralEmbedding
from scipy.sparse import save_npz, load_npz
import random
import torch
import torch.optim as optim
import torch.nn as nn
import torch.nn.functional as F
from openTSNE import TSNE
import numpy as np
import matplotlib.pyplot as plt
from sklearn.manifold import SpectralEmbedding
from scipy.sparse import save_npz, load_npz
import random
{
"lkunrate": [
{
"startDate": "2002-03-11",
"endDate": "2002-08-26",
"light": 1,
},
{
"startDate": "2002-09-02",
"endDate": "2002-12-02",
# draw a bunch of samples
def random_walk(png, Gs, cx, cy, cw, ch, step, seeds):
print(png)
latents = np.stack(np.random.RandomState(seed).randn(Gs.input_shape[1]) for seed in seeds)
# images = Gs.run(latents, None, **synthesis_kwargs) # [seed, y, x, rgb]
idx = 0
for idxx, (l1, l2) in enumerate(zip(latents, latents[1:])):
print(idxx, '/', len(latents))
latent = np.stack([l1 * (1-alpha) + l2 * alpha for alpha in np.linspace(0, 1, step)])
import torch
import torch.nn as nn
from tqdm import tqdm
device = torch.device('cuda')
# 5629MiB / 11178MiB
# NVIDIA-SMI 410.78 Driver Version: 410.78 CUDA Version: 10.0
torch.backends.cudnn.benchmark = True

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