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test.py
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48 lines (34 loc) · 1.13 KB
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import torch
from torchvision import datasets, transforms
from torchvision.utils import save_image
from torch.autograd import Variable
from modules import ae_module
import os
###
batch_size = 16
cuda = torch.cuda.is_available()
dtype = torch.cuda.FloatTensor if cuda else torch.FloatTensor
###
data_dir = '/media/peter/HDD 1/datasets_peter/CelebA/Img'
prep_transform = transforms.Compose([
transforms.Resize(64),
transforms.CenterCrop(64),
transforms.ToTensor()
])
dataset = datasets.ImageFolder(root=data_dir, transform=prep_transform)
train_loader = torch.utils.data.DataLoader(dataset, batch_size=batch_size, shuffle=False)
###
auto_encoder = ae_module.Auto_Encoder(input_channels=3, bn_momentum=0.9)
auto_encoder.load_state_dict(torch.load('./models/ae_params_epoch4.pt'))
if cuda:
auto_encoder.cuda()
###
auto_encoder.eval()
for i, (images, _) in enumerate(train_loader):
images = Variable(images.type(dtype))
reconstruced, _, _ = auto_encoder(images)
break
###
os.makedirs('./results', exist_ok=1)
save_image(images.data, './results/input.png')
save_image(reconstruced.data, './results/reconstructed.png')