Delete pipeline.py

pipeline.py

@@ -1,56 +0,0 @@
-import torch
-import torch.nn as nn
-from torchvision import transforms
-from PIL import Image
-from huggingface_hub import PyTorchModelHubMixin
-
-class Generator(nn.Module, PyTorchModelHubMixin):
-    def __init__(self):
-        super(Generator, self).__init__()
-        self.main = nn.Sequential(
-            nn.ConvTranspose2d(100, 64 * 8, 4, 1, 0, bias=False),
-            nn.BatchNorm2d(64 * 8),
-            nn.ReLU(True),
-
-            nn.ConvTranspose2d(64 * 8, 64 * 4, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(64 * 4),
-            nn.ReLU(True),
-
-            nn.ConvTranspose2d(64 * 4, 64 * 2, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(64 * 2),
-            nn.ReLU(True),
-
-            nn.ConvTranspose2d(64 * 2, 64, 4, 2, 1, bias=False),
-            nn.BatchNorm2d(64),
-            nn.ReLU(True),
-
-            nn.ConvTranspose2d(64, 3, 4, 2, 1, bias=False),
-            nn.Tanh()
-        )
-
-    def forward(self, input):
-        return self.main(input)
-
-class PreTrainedPipeline():
-    def __init__(self, path=""):
-        """
-        Initialize model
-        """
-        self.main = model = Generator.from_pretrained("miittnnss/testmodel")
-
-    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)
-        with torch.no_grad():
-            output = self.model(noise)
-
-        # Scale image
-        img = output[0]
-        img = (img + 1) /2
-        return transforms.ToPILImage()(img)