feat: DDPM add noise & get time sequence

ddpm.py

@@ -0,0 +1,59 @@
+import torch
+import torch.nn as nn
+from unet import Unet
+import matplotlib.pyplot as plt
+
+class DDPM(nn.Module):
+    '''
+        Denoising Diffussion Probabilistic Model
+        
+        Inputs:
+
+        Args:
+            batch_size (int): batch_size, for generate time_seq, etc.
+            iteration (int): max time_seq
+            beta_min, beta_max (float): for beta scheduling
+            time_emb_dim (int): for Unet's PositionEncode layer
+            device (nn.Device)
+    '''
+    def __init__(self, batch_size, iteration, beta_min, beta_max, time_emb_dim, device):
+        super(DDPM, self).__init__()
+        self.batch_size = batch_size
+        self.iteration = iteration
+        self.device = device
+        self.unet = Unet(time_emb_dim, device)
+        self.time_emb_dim = time_emb_dim
+
+        self.beta = torch.linspace(beta_min, beta_max, steps=iteration)             # (iteration)
+        self.alpha = 1 - self.beta                                                  # (iteration)
+        self.overline_alpha = torch.cumprod(self.alpha, dim=0)
+
+    def get_time_seq(self):
+        '''
+            Get random time sequence for each picture in the batch
+
+            Inputs:
+                None
+            Outputs:
+                time_seq: rand int from 0 to ITERATION
+        '''
+        return torch.randint(0, self.iteration, (self.batch_size,) )
+    
+    def get_x_t(self, x_0, time_seq):
+        '''
+            Input pictures then return noised pictures
+
+            Inputs:
+                x_0: pictures (b, c, w, h)
+                time_seq: times apply on each pictures (b, )
+            Outputs:
+                x_t: noised pictures (b, c, w, h)
+        '''
+        b, c, w, h = x_0.shape
+        mu = torch.sqrt(self.overline_alpha[time_seq])[:, None, None, None].repeat(1, c, w, h)
+        mu = mu * x_0
+
+        sigma = torch.sqrt(1-self.overline_alpha[time_seq])[:, None, None, None].repeat(1, c, w, h)
+        epsilon = torch.randn_like(x_0)
+
+        return mu + sigma * epsilon