whoops

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -439,6 +439,8 @@ class GaussianDiffusion(nn.Module):
 
         for i in tqdm(reversed(range(0, self.num_timesteps)), desc='sampling loop time step', total=self.num_timesteps):
             img = self.p_sample(img, torch.full((b,), i, device=device, dtype=torch.long))
+
+        img = unnormalize_to_zero_to_one(img)
         return img
 
     @torch.no_grad()
@@ -497,11 +499,13 @@ class GaussianDiffusion(nn.Module):
         loss = self.loss_fn(model_out, target)
         return loss
 
-    def forward(self, x, *args, **kwargs):
-        b, c, h, w, device, img_size, = *x.shape, x.device, self.image_size
+    def forward(self, img, *args, **kwargs):
+        b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size
         assert h == img_size and w == img_size, f'height and width of image must be {img_size}'
         t = torch.randint(0, self.num_timesteps, (b,), device=device).long()
-        return self.p_losses(x, t, *args, **kwargs)
+
+        img = normalize_to_neg_one_to_one(img)
+        return self.p_losses(img, t, *args, **kwargs)
 
 # dataset classes
 
@@ -516,8 +520,7 @@ class Dataset(data.Dataset):
             transforms.Resize(image_size),
             transforms.RandomHorizontalFlip(),
             transforms.CenterCrop(image_size),
-            transforms.ToTensor(),
-            transforms.Lambda(normalize_to_neg_one_to_one)
+            transforms.ToTensor()
         ])
 
     def __len__(self):
@@ -603,34 +606,36 @@ class Trainer(object):
         self.scaler.load_state_dict(data['scaler'])
 
     def train(self):
-        while self.step < self.train_num_steps:
-            for i in range(self.gradient_accumulate_every):
-                data = next(self.dl).cuda()
+        with tqdm(initial = self.step, total = self.train_num_steps) as pbar:
 
-                with autocast(enabled = self.amp):
-                    loss = self.model(data)
-                    self.scaler.scale(loss / self.gradient_accumulate_every).backward()
+            while self.step < self.train_num_steps:
+                for i in range(self.gradient_accumulate_every):
+                    data = next(self.dl).cuda()
 
-                print(f'{self.step}: {loss.item()}')
+                    with autocast(enabled = self.amp):
+                        loss = self.model(data)
+                        self.scaler.scale(loss / self.gradient_accumulate_every).backward()
 
-            self.scaler.step(self.opt)
-            self.scaler.update()
-            self.opt.zero_grad()
+                    pbar.set_description(f'loss: {loss.item():.4f}')
 
-            if self.step % self.update_ema_every == 0:
-                self.step_ema()
+                self.scaler.step(self.opt)
+                self.scaler.update()
+                self.opt.zero_grad()
 
-            if self.step != 0 and self.step % self.save_and_sample_every == 0:
-                self.ema_model.eval()
+                if self.step % self.update_ema_every == 0:
+                    self.step_ema()
 
-                milestone = self.step // self.save_and_sample_every
-                batches = num_to_groups(36, self.batch_size)
-                all_images_list = list(map(lambda n: self.ema_model.sample(batch_size=n), batches))
-                all_images = torch.cat(all_images_list, dim=0)
-                all_images = unnormalize_to_zero_to_one(all_images)
-                utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
-                self.save(milestone)
+                if self.step != 0 and self.step % self.save_and_sample_every == 0:
+                    self.ema_model.eval()
 
-            self.step += 1
+                    milestone = self.step // self.save_and_sample_every
+                    batches = num_to_groups(36, self.batch_size)
+                    all_images_list = list(map(lambda n: self.ema_model.sample(batch_size=n), batches))
+                    all_images = torch.cat(all_images_list, dim=0)
+                    utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = 6)
+                    self.save(milestone)
 
-        print('training completed')
+                self.step += 1
+                pbar.update(1)
+
+        print('training complete')

setup.py

@@ -3,7 +3,7 @@ from setuptools import setup, find_packages
 setup(
   name = 'denoising-diffusion-pytorch',
   packages = find_packages(),
-  version = '0.15.2',
+  version = '0.15.6',
   license='MIT',
   description = 'Denoising Diffusion Probabilistic Models - Pytorch',
   author = 'Phil Wang',