def VAE_interpolating_experiment(device):
batch_size = 2
latent_dim = 100
z = Variable(torch.randn(batch_size, latent_dim)).to(device)
model = VAE(100).to(device)
model.load_state_dict(torch.load(vae_path, map_location=device), strict=False)
x = Variable(model.generate(z)).to(device)
x_0 = x[0]
x_1 = x[1]
a_list = np.arange(0, 1.1, 0.1)
z_list = []
x_list = []
for a in a_list:
z_list.append(a*z[0] + (1-a)*z[1])
x_list.append(a*x_0 + (1-a)*x_1)
z_list = torch.cat(z_list, dim=0).view(len(a_list),-1)
x_list = torch.cat(x_list, dim=0).view(-1,3,32,32)
zh_y = Variable(model.generate(z_list)).to(device)
path = 'vae/results/interpolated/VAE_interpolated_zs.png'
save_images(zh_y, path, nrow=len(zh_y))
path = 'vae/results/interpolated/VAE_interpolated_xs.png'
save_images(x_list, path, nrow= len(x_list))
path = 'vae/results/interpolated/VAE_interpolated_xs_zs.png'
save_images(torch.cat((x_list, zh_y), dim=0), path, nrow=11)
results = torch.cat((x_list, zh_y), dim=0)
difference = x_list - zh_y
results = torch.cat((results, difference), dim=0)
save_images(results, path, nrow=11)
def VAE_disentangled_representation_experiment(device):
batch_size = 1
latent_dim=100
noise = Variable(torch.randn(batch_size, latent_dim)).to(device)
model = VAE(100).to(device)
model.load_state_dict(torch.load(vae_path, map_location=device), strict=False)
dims = range(0,100)
outputs = []
z_y = Variable(model.generate(noise)).to(device)
for d in dims:
zh = make_interpolation(noise, dim=d).view(batch_size, latent_dim)
output = Variable(model.generate(zh)).to(device)
outputs.append(output)
outputs = torch.cat(outputs, dim=0)
difference = outputs - z_y
difference = torch.abs(difference).view(100,-1)
sum_dif = torch.sum(difference, dim=1).detach().cpu().numpy()
top_sum_diff_indcs = np.unravel_index(np.argsort(sum_dif, axis=None), sum_dif.shape)[0]
top_sum_diff_indcs = top_sum_diff_indcs[-10:]
top_k_images = Variable(outputs[top_sum_diff_indcs]).to(device).view(len(top_sum_diff_indcs), -1)
top_k_images = torch.cat((z_y.view(1, -1), top_k_images))
path = 'vae/results/interpolated/VAE_top_disentangleds.png'
save_images(top_k_images, path, nrow=len(top_k_images))
difference = top_k_images - z_y.view(1, -1)
top_k_images = torch.cat((top_k_images, difference), dim=0)
save_images(top_k_images, path, nrow=11)
path = 'vae/results/interpolated/VAE_disentangleds_all.png'
save_images(outputs, path, nrow=10)
sindex = 999
a = train_x[sindex]
b = train_cond[sindex]
print(a.shape)
print(b.shape)
a = np.reshape(a, (-1, 32, 130))
b = np.reshape(b, (-1, 32, 12))
print(a.shape)
print(b.shape)
#initialize model
model = VAE(130, 2048, 3, 12, 128, 128, 32)
model.eval()
dic = torch.load("vae/tr_chord.pt")
for name in list(dic.keys()):
dic[name.replace('module.', '')] = dic.pop(name)
model.load_state_dict(dic)
if torch.cuda.is_available():
model = model.cuda()
print(model)
a = torch.from_numpy(a).float()
b = torch.from_numpy(b).float()
res = model.encoder(a, b)
z1 = res[0].loc.detach().numpy()
z2 = res[1].loc.detach().numpy()
print(z1)
print(z1.shape)
print(z2)
print(z2.shape)
z1 = torch.from_numpy(z1).float()
import torchvision
from torchvision import transforms
from torchvision.utils import save_image
import numpy as np
from tqdm import trange
from sa.model import MnistClassifier
from vae.model import VAE
img_size = 28*28*1
torch.no_grad() # since nothing is trained here
### Prep (e.g. Load Models) ###
vae = VAE(img_size = 28*28, h_dim = 1600, z_dim = 400)
vae.load_state_dict(torch.load('./vae/models/MNIST_EnD.pth'))
vae.cuda()
classifier = MnistClassifier(img_size = img_size)
classifier.load_state_dict(torch.load('./sa/models/MNIST_conv_classifier.pth'))
classifier.eval()
classifier.cuda()
print("models loaded...")
test_dataset = torchvision.datasets.MNIST(root='./data', train=False, transform=transforms.ToTensor(), download=False)
test_data_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=1, shuffle=True)
print("Data loader ready...")
### GA Params ###
gen_num = 500
pop_size = 50