Add conditional model (#2)

denoising_diffusion_pytorch/denoising_diffusion_pytorch.py

@@ -279,6 +279,7 @@ def __init__(
         dim_mults = (1, 2, 4, 8),
         channels = 3,
         self_condition = False,
+        masked_observations = False,
         resnet_block_groups = 8,
         learned_variance = False,
         learned_sinusoidal_cond = False,
@@ -296,7 +297,10 @@ def __init__(
 
         self.channels = channels
         self.self_condition = self_condition
-        input_channels = channels * (2 if self_condition else 1)
+        self.masked_observations = masked_observations
+        #  If we accept masked observations, add `channels` + 1 mask channel to the input
+        input_channels = channels + ((channels + 1) if self.masked_observations else 0)
+        input_channels = input_channels * (2 if self_condition else 1)
 
         init_dim = default(init_dim, dim)
         self.init_conv = nn.Conv2d(input_channels, init_dim, 7, padding = 3)
@@ -385,13 +389,19 @@ def __init__(
     def downsample_factor(self):
         return 2 ** (len(self.downs) - 1)
 
-    def forward(self, x, time, x_self_cond = None):
+    def forward(self, x, time, x_self_cond = None, x_obs=None, x_obs_mask=None):
         assert all([divisible_by(d, self.downsample_factor) for d in x.shape[-2:]]), f'your input dimensions {x.shape[-2:]} need to be divisible by {self.downsample_factor}, given the unet'
 
         if self.self_condition:
             x_self_cond = default(x_self_cond, lambda: torch.zeros_like(x))
             x = torch.cat((x_self_cond, x), dim = 1)
 
+        if self.masked_observations:
+            batch, channel, height, width = x.shape
+            x_obs = default(x_obs, lambda: torch.zeros_like(x, device=x.device))
+            x_obs_mask = default(x_obs_mask, lambda: torch.zeros((batch, 1, height, width), device=x.device))
+            x = torch.cat((x, x_obs, x_obs_mask), dim=1)
+
         x = self.init_conv(x)
         r = x.clone()
 
@@ -496,6 +506,7 @@ def __init__(
 
         self.channels = self.model.channels
         self.self_condition = self.model.self_condition
+        self.masked_observations = self.model.masked_observations
 
         # make image size a tuple of (height, width)
         if isinstance(image_size, int):
@@ -630,8 +641,8 @@ def q_posterior(self, x_start, x_t, t):
         posterior_log_variance_clipped = extract(self.posterior_log_variance_clipped, t, x_t.shape)
         return posterior_mean, posterior_variance, posterior_log_variance_clipped
 
-    def model_predictions(self, x, t, x_self_cond = None, clip_x_start = False, rederive_pred_noise = False):
-        model_output = self.model(x, t, x_self_cond)
+    def model_predictions(self, x, t, x_self_cond = None, clip_x_start = False, rederive_pred_noise = False, x_obs=None, x_obs_mask=None):
+        model_output = self.model(x, t, x_self_cond, x_obs, x_obs_mask)
         maybe_clip = partial(torch.clamp, min = -1., max = 1.) if clip_x_start else identity
 
         if self.objective == 'pred_noise':
@@ -655,8 +666,8 @@ def model_predictions(self, x, t, x_self_cond = None, clip_x_start = False, rede
 
         return ModelPrediction(pred_noise, x_start)
 
-    def p_mean_variance(self, x, t, x_self_cond = None, clip_denoised = True):
-        preds = self.model_predictions(x, t, x_self_cond)
+    def p_mean_variance(self, x, t, x_self_cond = None, clip_denoised = True, x_obs=None, x_obs_mask=None):
+        preds = self.model_predictions(x, t, x_self_cond, x_obs=x_obs, x_obs_mask=x_obs_mask)
         x_start = preds.pred_x_start
 
         if clip_denoised:
@@ -666,16 +677,16 @@ def p_mean_variance(self, x, t, x_self_cond = None, clip_denoised = True):
         return model_mean, posterior_variance, posterior_log_variance, x_start
 
     @torch.inference_mode()
-    def p_sample(self, x, t: int, x_self_cond = None):
+    def p_sample(self, x, t: int, x_self_cond = None, x_obs=None, x_obs_mask=None):
         b, *_, device = *x.shape, self.device
         batched_times = torch.full((b,), t, device = device, dtype = torch.long)
-        model_mean, _, model_log_variance, x_start = self.p_mean_variance(x = x, t = batched_times, x_self_cond = x_self_cond, clip_denoised = True)
+        model_mean, _, model_log_variance, x_start = self.p_mean_variance(x = x, t = batched_times, x_self_cond = x_self_cond, clip_denoised = True, x_obs=x_obs, x_obs_mask=x_obs_mask)
         noise = torch.randn_like(x) if t > 0 else 0. # no noise if t == 0
         pred_img = model_mean + (0.5 * model_log_variance).exp() * noise
         return pred_img, x_start
 
     @torch.inference_mode()
-    def p_sample_loop(self, shape, return_all_timesteps = False):
+    def p_sample_loop(self, shape, return_all_timesteps = False, x_obs=None, x_obs_mask=None):
         batch, device = shape[0], self.device
 
         img = torch.randn(shape, device = device)
@@ -685,7 +696,7 @@ def p_sample_loop(self, shape, return_all_timesteps = False):
 
         for t in tqdm(reversed(range(0, self.num_timesteps)), desc = 'sampling loop time step', total = self.num_timesteps):
             self_cond = x_start if self.self_condition else None
-            img, x_start = self.p_sample(img, t, self_cond)
+            img, x_start = self.p_sample(img, t, self_cond, x_obs=x_obs, x_obs_mask=x_obs_mask)
             imgs.append(img)
 
         ret = img if not return_all_timesteps else torch.stack(imgs, dim = 1)
@@ -694,7 +705,7 @@ def p_sample_loop(self, shape, return_all_timesteps = False):
         return ret
 
     @torch.inference_mode()
-    def ddim_sample(self, shape, return_all_timesteps = False):
+    def ddim_sample(self, shape, return_all_timesteps = False, x_obs=None, x_obs_mask=None):
         batch, device, total_timesteps, sampling_timesteps, eta, objective = shape[0], self.device, self.num_timesteps, self.sampling_timesteps, self.ddim_sampling_eta, self.objective
 
         times = torch.linspace(-1, total_timesteps - 1, steps = sampling_timesteps + 1)   # [-1, 0, 1, 2, ..., T-1] when sampling_timesteps == total_timesteps
@@ -709,7 +720,7 @@ def ddim_sample(self, shape, return_all_timesteps = False):
         for time, time_next in tqdm(time_pairs, desc = 'sampling loop time step'):
             time_cond = torch.full((batch,), time, device = device, dtype = torch.long)
             self_cond = x_start if self.self_condition else None
-            pred_noise, x_start, *_ = self.model_predictions(img, time_cond, self_cond, clip_x_start = True, rederive_pred_noise = True)
+            pred_noise, x_start, *_ = self.model_predictions(img, time_cond, self_cond, clip_x_start = True, rederive_pred_noise = True, x_obs=x_obs, x_obs_mask=x_obs_mask)
 
             if time_next < 0:
                 img = x_start
@@ -736,13 +747,16 @@ def ddim_sample(self, shape, return_all_timesteps = False):
         return ret
 
     @torch.inference_mode()
-    def sample(self, batch_size = 16, return_all_timesteps = False):
+    def sample(self, batch_size = 16, return_all_timesteps = False, x_obs=None, x_obs_mask=None):
         image_size, channels = self.image_size, self.channels
         sample_fn = self.p_sample_loop if not self.is_ddim_sampling else self.ddim_sample
-        return sample_fn((batch_size, channels, image_size[0], image_size[1]), return_all_timesteps = return_all_timesteps)
+        return sample_fn((batch_size, channels, image_size[0], image_size[1]), return_all_timesteps = return_all_timesteps, x_obs=x_obs, x_obs_mask=x_obs_mask)
 
     @torch.inference_mode()
     def interpolate(self, x1, x2, t = None, lam = 0.5):
+        if self.masked_observations:
+            # The call to p_sample needs an x_obs and x_obs_mask argument
+            raise NotImplementedError()
         b, *_, device = *x1.shape, x1.device
         t = default(t, self.num_timesteps - 1)
 
@@ -770,7 +784,7 @@ def q_sample(self, x_start, t, noise = None):
             extract(self.sqrt_one_minus_alphas_cumprod, t, x_start.shape) * noise
         )
 
-    def p_losses(self, x_start, t, noise = None, offset_noise_strength = None):
+    def p_losses(self, x_start, t, noise = None, offset_noise_strength = None, x_obs=None, x_obs_mask=None):
         b, c, h, w = x_start.shape
 
         noise = default(noise, lambda: torch.randn_like(x_start))
@@ -794,12 +808,12 @@ def p_losses(self, x_start, t, noise = None, offset_noise_strength = None):
         x_self_cond = None
         if self.self_condition and random() < 0.5:
             with torch.no_grad():
-                x_self_cond = self.model_predictions(x, t).pred_x_start
+                x_self_cond = self.model_predictions(x, t, x_obs=x_obs, x_obs_mask=x_obs_mask).pred_x_start
                 x_self_cond.detach_()
 
         # predict and take gradient step
 
-        model_out = self.model(x, t, x_self_cond)
+        model_out = self.model(x, t, x_self_cond, x_obs, x_obs_mask)
 
         if self.objective == 'pred_noise':
             target = noise
@@ -817,16 +831,24 @@ def p_losses(self, x_start, t, noise = None, offset_noise_strength = None):
         loss = loss * extract(self.loss_weight, t, loss.shape)
         return loss.mean()
 
-    def forward(self, img, *args, **kwargs):
+    def forward(self, img, *args, x_obs=None, x_obs_mask=None, **kwargs):
         b, c, h, w, device, img_size, = *img.shape, img.device, self.image_size
         assert h == img_size[0] and w == img_size[1], f'height and width of image must be {img_size}'
         t = torch.randint(0, self.num_timesteps, (b,), device=device).long()
 
         img = self.normalize(img)
-        return self.p_losses(img, t, *args, **kwargs)
+        return self.p_losses(img, t, *args, x_obs=x_obs, x_obs_mask=x_obs_mask, **kwargs)
 
 # dataset classes
 
+def generate_masks(mask_generator):
+    def __inner__(input_tensor):
+        out = mask_generator(input_tensor)
+        for x in ['img', 'obs', 'mask']:
+            assert hasattr(out, x)
+        return out
+    return __inner__
+
 class Dataset(Dataset):
     def __init__(
         self,
@@ -835,7 +857,8 @@ def __init__(
         exts = ['jpg', 'jpeg', 'png', 'tiff'],
         interpolation = 'bilinear',
         augment_horizontal_flip = False,
-        convert_image_to = None
+        convert_image_to = None,
+        mask_generator = None,
     ):
         super().__init__()
         self.folder = folder
@@ -850,7 +873,8 @@ def __init__(
             T.Resize(image_size, interpolation=interpolation),
             T.RandomHorizontalFlip() if augment_horizontal_flip else nn.Identity(),
             T.CenterCrop(image_size),
-            T.ToTensor()
+            T.ToTensor(),
+            T.Lambda(generate_masks(mask_generator)) if mask_generator else nn.Identity(),
         ])
 
     def __len__(self):
@@ -889,7 +913,8 @@ def __init__(
         inception_block_idx = 2048,
         max_grad_norm = 1.,
         num_fid_samples = 50000,
-        save_best_and_latest_only = False
+        save_best_and_latest_only = False,
+        mask_generator = None,
     ):
         super().__init__()
 
@@ -934,6 +959,7 @@ def __init__(
             interpolation = interpolation,
             augment_horizontal_flip = augment_horizontal_flip,
             convert_image_to = convert_image_to,
+            mask_generator= mask_generator,
         )
 
         assert len(self.ds) >= 100, 'you should have at least 100 images in your folder. at least 10k images recommended'
@@ -1045,7 +1071,11 @@ def train(self):
                     data = next(self.dl).to(device)
 
                     with self.accelerator.autocast():
-                        loss = self.model(data)
+                        if self.model.masked_observation:
+                            img, x_obs, x_obs_mask = data
+                            loss = self.model(img, x_obs=x_obs, x_obs_mask=x_obs_mask)
+                        else:
+                            loss = self.model(data)
                         loss = loss / self.gradient_accumulate_every
                         total_loss += loss.item()
 
@@ -1068,17 +1098,36 @@ def train(self):
                     if self.step != 0 and divisible_by(self.step, self.save_and_sample_every):
                         self.ema.ema_model.eval()
 
+                        is_masked_observations = self.ema.ema_model.masked_observations
                         with torch.inference_mode():
                             milestone = self.step // self.save_and_sample_every
                             batches = num_to_groups(self.num_samples, self.batch_size)
-                            all_images_list = list(map(lambda n: self.ema.ema_model.sample(batch_size=n), batches))
+                            additional_args = {}
+                            if is_masked_observations:
+                                num_masks = int(math.sqrt(self.num_samples))
+                                data = next(self.dl).to(device)
 
-                        all_images = torch.cat(all_images_list, dim = 0)
+                                imgs = data.img[:num_masks]
+                                obs = data.obs[:num_masks]
+                                masks = data.mask[:num_masks]
+                                split_obs = torch.split(obs.repeat(num_masks, 1, 1, 1), batches)
+                                split_masks = torch.split(masks.repeat(num_masks, 1, 1, 1), batches)
 
-                        utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = int(math.sqrt(self.num_samples)))
+                                additional_args_list = [{
+                                        'x_obs': o,
+                                        'x_obs_mask': m,
+                                } for o, m in zip(split_obs, split_masks)]
 
-                        # whether to calculate fid
+                            all_images_list = list(map(lambda args: self.ema.ema_model.sample(batch_size=args[0], **args[1]), zip(batches, additional_args_list)))
+
+                        all_images = torch.cat(
+                                ([imgs, obs] if is_masked_observations else []) +
+                                all_images_list, dim = 0)
 
+                        num_rows = int(math.sqrt(self.num_samples))
+                        utils.save_image(all_images, str(self.results_folder / f'sample-{milestone}.png'), nrow = num_rows)
+
+                        # whether to calculate fid
                         if self.calculate_fid:
                             fid_score = self.fid_scorer.fid_score()
                             accelerator.print(f'fid_score: {fid_score}')