Add requirements and inference

pipeline.py

@@ -0,0 +1,38 @@
+import torch
+import nltk
+
+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`. The raw image representation as PIL.
+        """
+        class_vector = one_hot_from_names([inputs], batch_size=1)
+        if class_vector == None:
+            raise ValueError("Input is not in ImageNet")
+
+
+        noise_vector = truncated_noise_sample(truncation=truncation, batch_size=1)
+
+        noise_vector = torch.from_numpy(noise_vector)
+        class_vector = torch.from_numpy(class_vector)
+
+        with torch.no_grad():
+            output = model(noise_vector, class_vector, truncation)
+
+        return transforms.ToPILImage()(output[0])

requirements.txt

@@ -0,0 +1,2 @@
+pytorch-pretrained-biggan
+nltk