Create pipeline.py

pipeline.py

@@ -0,0 +1,55 @@
+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