gd perplexity

# Conflicts:
#	codes/trainer/eval/music_diffusion_fid.py

codes/models/diffusion/gaussian_diffusion.py

@@ -510,7 +510,7 @@ class GaussianDiffusion:
                 cond_fn, out, x, t, model_kwargs=model_kwargs
             )
         sample = out["mean"] + nonzero_mask * th.exp(0.5 * out["log_variance"]) * noise
-        return {"sample": sample, "pred_xstart": out["pred_xstart"]}
+        return {"sample": sample, "pred_xstart": out["pred_xstart"], "mean": out["mean"], "log_variance": out["log_variance"]}
 
     def p_sample_loop(
         self,
@@ -654,6 +654,46 @@ class GaussianDiffusion:
                 img = model_driven_out + guidance_driven_out
         return img
 
+    def p_sample_loop_for_perplexity(
+        self,
+        model,
+        truth,
+        noise=None,
+        clip_denoised=True,
+        denoised_fn=None,
+        cond_fn=None,
+        model_kwargs=None,
+        device=None,
+    ):
+        if device is None:
+            device = next(model.parameters()).device
+        shape = truth.shape
+        if noise is None:
+            noise = th.randn(*shape, device=device)
+        indices = list(range(self.num_timesteps))[::-1]
+
+        img = noise
+        perp = 1
+        for i in tqdm(indices):
+            t = th.tensor([i] * shape[0], device=device)
+            with th.no_grad():
+                out = self.p_sample(
+                    model,
+                    img,
+                    t,
+                    clip_denoised=clip_denoised,
+                    denoised_fn=denoised_fn,
+                    cond_fn=cond_fn,
+                    model_kwargs=model_kwargs,
+                )
+                mean = out["mean"]
+                std = out["log_variance"].exp().sqrt()
+                q = self.q_sample(truth, t, noise=noise)
+                err = out - q
+                prob = (err - mean) / std
+                perp = prob * perp
+        return perp
+
     def ddim_sample(
         self,
         model,