to numpy

Author: kashif

pts/modules/gaussian_diffusion.py

@@ -28,17 +28,17 @@ def noise_like(shape, device, repeat=False):
     return repeat_noise() if repeat else noise()
 
 
-def cosine_beta_schedule(timesteps, s = 0.008):
+def cosine_beta_schedule(timesteps, s=0.008):
     """
     cosine schedule
     as proposed in https://openreview.net/forum?id=-NEXDKk8gZ
     """
     steps = timesteps + 1
-    x = torch.linspace(0, timesteps, steps)
-    alphas_cumprod = torch.cos(((x / timesteps) + s) / (1 + s) * torch.pi * 0.5) ** 2
+    x = np.linspace(0, timesteps, steps)
+    alphas_cumprod = np.cos(((x / timesteps) + s) / (1 + s) * np.pi * 0.5) ** 2
     alphas_cumprod = alphas_cumprod / alphas_cumprod[0]
     betas = 1 - (alphas_cumprod[1:] / alphas_cumprod[:-1])
-    return torch.clip(betas, 0, 0.999)
+    return np.clip(betas, 0, 0.999)
 
 
 class GaussianDiffusion(nn.Module):