testing autocast to fp16

hfapp.py

@@ -19,10 +19,11 @@ def run_inference(model, img):
     model = model.to('cuda')
     print("model on cuda:", next(model.scorenet.net.parameters()).is_cuda)
     print("img on cuda:", img.is_cuda)
-    img = torch.nn.functional.interpolate(img, size=64, mode="bilinear")
-    score_norms = model.scorenet(img)
-    score_norms = score_norms.square().sum(dim=(2, 3, 4)) ** 0.5
-    img_likelihood = model(img).cpu().numpy()
+    with torch.autocast(device_type='cuda', dtype=torch.float16, enabled=True):
+        img = torch.nn.functional.interpolate(img, size=64, mode="bilinear")
+        score_norms = model.scorenet(img)
+        score_norms = score_norms.square().sum(dim=(2, 3, 4)) ** 0.5
+        img_likelihood = model(img).cpu().numpy()
     score_norms = score_norms.cpu().numpy()
     return img_likelihood, score_norms