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import torch
import torch.nn as nn
from torchvision import transforms
class Generator(nn.Module):
def __init__(self, nz=128, ngf=64, nc=3):
super(Generator, self).__init__()
self.main = nn.Sequential(
nn.ConvTranspose2d(nz, ngf * 8, 4, 1, 0, bias=False),
nn.BatchNorm2d(ngf * 8),
nn.LeakyReLU(0.2, inplace=True),
nn.Dropout(0.2),
nn.ConvTranspose2d(ngf * 8, ngf * 4, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 4),
nn.LeakyReLU(0.2, inplace=True),
nn.ConvTranspose2d(ngf * 4, ngf * 2, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf * 2),
nn.LeakyReLU(0.2, inplace=True),
nn.ConvTranspose2d(ngf * 2, ngf, 4, 2, 1, bias=False),
nn.BatchNorm2d(ngf),
nn.LeakyReLU(0.2, inplace=True),
nn.ConvTranspose2d(ngf, nc, 4, 2, 1, bias=False),
nn.Tanh()
)
def forward(self, input):
output = self.main(input)
return output
class PreTrainedPipeline():
def __init__(self, path=""):
self.device = "cuda" if torch.cuda.is_available() else "cpu"
self.model = Generator().to(self.device)
self.model.load_state_dict(torch.load("pytorch_model.bin", map_location=device))
def __call__(self, inputs: str):
"""
Args:
inputs (:obj:`str`):
a string containing some text
Return:
A :obj:`PIL.Image` with the raw image representation as PIL.
"""
noise = torch.randn(1, 128, 1, 1, device=self.device)
with torch.no_grad():
output = self.model(noise).cpu()
img = output[0]
img = (img + 1) / 2
img = transforms.ToPILImage()(img)
return img |