Create pipeline.py

pipeline.py

@@ -0,0 +1,40 @@
+import torch
+import nltk
+import io
+import base64
+import shutil
+from torchvision import transforms
+
+from pytorch_pretrained_biggan import BigGAN, one_hot_from_names, truncated_noise_sample
+
+class PreTrainedPipeline():
+    def __init__(self, path=""):
+        """
+        Initialize model
+        """
+        nltk.download('wordnet')
+        self.model = BigGAN.from_pretrained(path)
+        self.truncation = 0.1
+
+    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.
+        """
+        class_vector = one_hot_from_names([inputs], batch_size=1)
+        if type(class_vector) == type(None):
+            raise ValueError("Input is not in ImageNet")
+        noise_vector = truncated_noise_sample(truncation=self.truncation, batch_size=1)
+        noise_vector = torch.from_numpy(noise_vector)
+        class_vector = torch.from_numpy(class_vector)
+        with torch.no_grad():
+            output = self.model(noise_vector, class_vector, self.truncation)
+
+        # Scale image
+        img = output[0]
+        img = (img + 1) / 2.0
+        img = transforms.ToPILImage()(img)
+        return img