Create pipeline.py

pipeline.py

@@ -0,0 +1,59 @@
+import torch
+import torch.nn as nn
+from huggingface_hub import PyTorchModelHubMixin
+
+class Generator(nn.Module, PyTorchModelHubMixin):
+    def __init__(self, num_channels=3, latent_dim=100, hidden_size=64):
+        super(Generator, self).__init__()
+        self.model = nn.Sequential(
+            # input is Z, going into a convolution
+            nn.ConvTranspose2d(latent_dim, hidden_size * 8, 4, 1, 0, bias=False),
+            nn.BatchNorm2d(hidden_size * 8),
+            nn.ReLU(True),
+            # state size. (hidden_size*8) x 4 x 4
+            nn.ConvTranspose2d(hidden_size * 8, hidden_size * 4, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 4),
+            nn.ReLU(True),
+            # state size. (hidden_size*4) x 8 x 8
+            nn.ConvTranspose2d(hidden_size * 4, hidden_size * 2, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size * 2),
+            nn.ReLU(True),
+            # state size. (hidden_size*2) x 16 x 16
+            nn.ConvTranspose2d(hidden_size * 2, hidden_size, 4, 2, 1, bias=False),
+            nn.BatchNorm2d(hidden_size),
+            nn.ReLU(True),
+            # state size. (hidden_size) x 32 x 32
+            nn.ConvTranspose2d(hidden_size, num_channels, 4, 2, 1, bias=False),
+            nn.Tanh()
+            # state size. (num_channels) x 64 x 64
+        )
+
+    def forward(self, noise):
+        pixel_values = self.model(noise)
+
+        return pixel_values
+
+
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        """
+        Initialize model
+        """
+        self.model = model = Generator.from_pretrained("huggan/dcgan-mnist")
+
+    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, 100, 1, 1, device=device)
+        with torch.no_grad():
+            output = self.model(noise)
+
+        # Scale image
+        img = transforms.ToPILImage()(output)
+        return img