From 2d19b3ce1dca606a27cd8d0e978ebe8710f7995c Mon Sep 17 00:00:00 2001
From: Francois Fleuret <francois@fleuret.org>
Date: Sun, 14 Aug 2022 15:29:45 +0200
Subject: [PATCH] Update.

---
 minidiffusion.py | 25 +++++++++++--------------
 1 file changed, 11 insertions(+), 14 deletions(-)

diff --git a/minidiffusion.py b/minidiffusion.py
index 879b796..e1f6abd 100755
--- a/minidiffusion.py
+++ b/minidiffusion.py
@@ -105,7 +105,7 @@ parser.add_argument('--learning_rate',
 
 parser.add_argument('--ema_decay',
                     type = float, default = 0.9999,
-                    help = 'EMA decay, < 0 is no EMA')
+                    help = 'EMA decay, <= 0 is no EMA')
 
 data_list = ', '.join( [ str(k) for k in samplers ])
 
@@ -129,23 +129,20 @@ class EMA:
     def __init__(self, model, decay):
         self.model = model
         self.decay = decay
-        if self.decay < 0: return
-        self.ema = { }
+        self.mem = { }
         with torch.no_grad():
             for p in model.parameters():
-                self.ema[p] = p.clone()
+                self.mem[p] = p.clone()
 
     def step(self):
-        if self.decay < 0: return
         with torch.no_grad():
             for p in self.model.parameters():
-                self.ema[p].copy_(self.decay * self.ema[p] + (1 - self.decay) * p)
+                self.mem[p].copy_(self.decay * self.mem[p] + (1 - self.decay) * p)
 
-    def copy(self):
-        if self.decay < 0: return
+    def copy_to_model(self):
         with torch.no_grad():
             for p in self.model.parameters():
-                p.copy_(self.ema[p])
+                p.copy_(self.mem[p])
 
 ######################################################################
 
@@ -234,7 +231,7 @@ alpha = 1 - beta
 alpha_bar = alpha.log().cumsum(0).exp()
 sigma = beta.sqrt()
 
-ema = EMA(model, decay = args.ema_decay)
+ema = EMA(model, decay = args.ema_decay) if args.ema_decay > 0 else None
 
 for k in range(args.nb_epochs):
 
@@ -245,8 +242,8 @@ for k in range(args.nb_epochs):
         x0 = (x0 - train_mean) / train_std
         t = torch.randint(T, (x0.size(0),) + (1,) * (x0.dim() - 1), device = x0.device)
         eps = torch.randn_like(x0)
-        input = torch.sqrt(alpha_bar[t]) * x0 + torch.sqrt(1 - alpha_bar[t]) * eps
-        input = torch.cat((input, t.expand_as(x0[:,:1]) / (T - 1) - 0.5), 1)
+        xt = torch.sqrt(alpha_bar[t]) * x0 + torch.sqrt(1 - alpha_bar[t]) * eps
+        input = torch.cat((xt, t.expand_as(x0[:,:1]) / (T - 1) - 0.5), 1)
         loss = (eps - model(input)).pow(2).mean()
         acc_loss += loss.item() * x0.size(0)
 
@@ -254,11 +251,11 @@ for k in range(args.nb_epochs):
         loss.backward()
         optimizer.step()
 
-        ema.step()
+        if ema is not None: ema.step()
 
     print(f'{k} {acc_loss / train_input.size(0)}')
 
-ema.copy()
+if ema is not None: ema.copy_to_model()
 
 ######################################################################
 # Plot
-- 
2.20.1