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import numpy as np
import torch.nn as nn
import torch
class Generator(nn.Module):
def __init__(
self,
image_shape: (int, int, int),
latent_space_dimension: int,
use_cuda: bool = False,
saved_model: str or None = None
):
super(Generator, self).__init__()
self.image_shape = image_shape
def block(in_feat, out_feat, normalize=True):
layers = [nn.Linear(in_feat, out_feat)]
if normalize:
layers.append(nn.BatchNorm1d(out_feat, 0.8))
layers.append(nn.LeakyReLU(0.2, inplace=True))
return layers
self.model = nn.Sequential(
*block(latent_space_dimension, 128, normalize=False),
*block(128, 256),
*block(256, 512),
*block(512, 1024),
nn.Linear(1024, int(np.prod(image_shape))),
nn.Tanh()
)
if saved_model is not None:
self.model.load_state_dict(
torch.load(
saved_model,
map_location=torch.device('cuda' if use_cuda else 'cpu')
)
)
def forward(self, z):
img = self.model(z)
img = img.view(img.shape[0], *self.image_shape)
return img
def save(self, to):
torch.save(self.model.state_dict(), to)
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