take care of the MAE portion from Kaiming He

dreamer4/dreamer4.py

@@ -362,7 +362,8 @@ class VideoTokenizer(Module):
             heads = 8,
         ),
         ff_kwargs: dict = dict(),
-        channels = 3
+        channels = 3,
+        per_image_patch_mask_prob = (0., 0.9), # probability of patch masking appears to be per image probabilities drawn uniformly between 0. and 0.9 - if you are a phd student and think i'm mistakened, please open an issue
     ):
         super().__init__()
 
@@ -371,6 +372,10 @@ class VideoTokenizer(Module):
         # special tokens
 
         self.latent_token = Parameter(torch.randn(dim) * 1e-2)
+
+        # mae masking - Kaiming He paper from long ago
+
+        self.per_image_patch_mask_prob = per_image_patch_mask_prob
         self.mask_token = Parameter(torch.randn(dim) * 1e-2)
 
         # patch and unpatch
@@ -425,7 +430,8 @@ class VideoTokenizer(Module):
     def forward(
         self,
         video, # (b c t h w) 
-        return_latents = False
+        return_latents = False,
+        mask_patches = None
     ):
         patch_size = self.patch_size
 
@@ -437,6 +443,24 @@ class VideoTokenizer(Module):
 
         tokens = self.patch_to_tokens(video)
 
+        # masking
+
+        mask_patches = default(mask_patches, self.training)
+
+        if mask_patches:
+            min_mask_prob, max_mask_prob = self.per_image_patch_mask_prob
+
+            uniform_prob = torch.rand(tokens.shape[:2], device = tokens.device) # (b t)
+
+            mask_prob = uniform_prob * (max_mask_prob - min_mask_prob) + min_mask_prob
+
+            mask_prob = repeat(mask_prob, 'b t -> b t vh vw', vh = tokens.shape[2], vw = tokens.shape[3])
+            mask_patch = torch.bernoulli(mask_prob) == 1.
+
+            tokens = einx.where('b t vh vw, d, b t vh vw d', mask_patch, self.mask_token, tokens)
+
+        # pack space
+
         tokens, inverse_pack_space = pack_one(tokens, 'b t * d')
 
         # add the latent