touched up Ijepa

nanodl/__src/models/ijepa.py

@@ -20,9 +20,6 @@ class PatchEmbedding(nn.Module):
         patch_size (int): Size of square patches from image.
         embed_dim (int): Dimension of the embeddings for the patches.
 
-    Methods:
-        setup(): Calculates `num_patches` and initialises Conv layer.
-        __call__(x: jnp.ndarray): Passes image through Conv layer which extracts patches and projects into emebdding space.
     """
     image_size:int
     patch_size:int
@@ -43,7 +40,6 @@ def setup(self):
     def __call__(self, x:jnp.ndarray) -> jnp.ndarray:
         x = self.proj(x)
         x = jnp.reshape(x, (x.shape[0], -1, self.embed_dim)) # (batch_size, num_patches, embed_dim)
-
         return x
 
 
@@ -57,9 +53,6 @@ class PositionalEmbedding(nn.Module):
         embed_dim (int): Patch embedding dimensions.
         num_patches (int): Number of patches in an image which is dependent on the patch size.
 
-    Methods:
-        setup(): Initialises embedding layer
-        __call__(x: jnp.ndarray): Passes a tensor of positions through the positional embedding and adds the positional embeddings to the patch embeddings.
     """
     embed_dim:int
     num_patches:int
@@ -71,12 +64,9 @@ def setup(self):
         )
 
     def __call__(self, x:jnp.ndarray) -> jnp.ndarray:
-        # assuming x of shape (batch_size, num_tokens, embed_dim)
         positions = jnp.arange(x.shape[1])[jnp.newaxis, :].repeat(x.shape[0], axis=0)
         embed = self.embedding(positions)
-
         x = x + embed
-
         return x
 
 
@@ -90,9 +80,6 @@ class MultiHeadedAttention(nn.Module):
         embed_dim (int): Dimensionality of the input and output features.
         num_heads (int): Number of attention heads.
 
-    Methods:
-        setup(): Initializes projection matrices for queries, keys, values, and the output projection.
-        __call__(x: jnp.ndarray): Processes the input tensor through the multi-head self-attention mechanism.
     """
     embed_dim:int
     num_heads:int
@@ -107,25 +94,17 @@ def setup(self):
     def __call__(self, x:jnp.ndarray) -> jnp.ndarray:
         qkv = self.attn_proj(x)
         query, key, value = jnp.array_split(qkv, 3, axis=-1)
-
         query = jnp.reshape(query, (query.shape[0], query.shape[1], self.num_heads, -1))
         key = jnp.reshape(key, (key.shape[0], key.shape[1], self.num_heads, -1))
         value = jnp.reshape(value, (value.shape[0], value.shape[1], self.num_heads, -1))
-
-        # permute to (batch_size, num_heads, seq_len, embed_dim)
-
         query = jnp.permute_dims(query, (0, 2, 1, 3))
         key = jnp.permute_dims(key, (0, 2, 1, 3))
         value = jnp.permute_dims(value, (0, 2, 1, 3))
-
         attn_weights = jnp.matmul(query, key.transpose(0, 1, 3, 2)) / (self.embed_dim **.5)
         attn_weights = nn.softmax(attn_weights, -1)
-
         attn = jnp.matmul(attn_weights, value)
-        attn = jnp.reshape(attn, (query.shape[0], -1, self.embed_dim)) # convert back to (batch_size, seq_len, embed_dim)
-
+        attn = jnp.reshape(attn, (query.shape[0], -1, self.embed_dim)) 
         attn = self.out_proj(attn)
-
         return attn, attn_weights
 
 
@@ -141,9 +120,6 @@ class TransformerEncoderBlock(nn.Module):
         feed_forward_dim (int): Dimension of the feed-forward network.
         dropout_p (float): Dropout rate.
 
-    Methods:
-        setup(): Initializes the attention layer, feed forward layers and norm layers.
-        __call__(x: jnp.ndarray): Processes the input tensor through the transformer encoder block.
     """
     embed_dim:int
     num_heads:int
@@ -171,10 +147,8 @@ def __call__(self, x:jnp.ndarray, training:bool) -> jnp.ndarray:
         x_, attn_weights = self.attn(self.norm1(x))
         x = x + x_
         x = self.dropout(x, deterministic=not training)
-
         x = x + self.ff(self.norm2(x))
         x = self.dropout(x, deterministic=not training)
-
         return x, attn_weights
 
 
@@ -190,10 +164,7 @@ class TransformerEncoder(nn.Module):
         embed_dim (int): Dimensionality of inputs and outputs.
         num_layers (int): Number of encoder blocks.
         feed_forward_dim (int): Dimension of the feed-forward network.
-
-    Methods:
-        setup(): Initializes the attention layer, feed forward layers and norm layers.
-        __call__(x: jnp.ndarray): Processes the input tensor through the transformer encoder block.
+
     """
     dropout:float
     num_heads:int 
@@ -214,11 +185,9 @@ def setup(self):
 
     def __call__(self, x:jnp.ndarray, training:bool) -> jnp.ndarray:
         attn_maps = []
-
         for layer in self.layers:
             x, attn_weights = layer(x, training=training)
             attn_maps.append(attn_weights)
-
         return x, jnp.array(attn_maps)
 
 
@@ -240,11 +209,6 @@ class IJEPA(nn.Module):
         predictor_num_heads (int): Number of transformer encoder heads for embedding predictor.
         share_patch_embedding (bool): Whether or not to share the patch embeddings across the context and target encoders.
 
-
-    Methods:
-        setup(): Initializes the attention layer, feed forward layers and norm layers.
-        __call__(x:jnp.ndarray, content_mask:jnp.ndarray, target_mask:jnp.ndarray): Applies the context and target masks to the image to get the context and target blocks, then obtains the predicted representations of the target blocks.
-
     Example usage:
         ```py
         import jax
@@ -390,11 +354,8 @@ def setup(self):
         self.to_encoder_embed = nn.Dense(self.embed_dim)
 
     def __call__(self, x:jnp.ndarray, context_mask:jnp.ndarray, target_mask:jnp.ndarray, training:bool=False) -> Tuple[List[Tuple[jnp.ndarray, jnp.ndarray]], List[Tuple[jnp.ndarray, jnp.ndarray, jnp.ndarray]]]:
-        # content & target masks of shape (N, M, num_patches)
-
         x_context = self.patch_embedding["context"](x)
         x_context = self.positional_embedding(x_context)
-
         x_target = self.patch_embedding["target"](x)
         x_target = self.positional_embedding(x_target)
 
@@ -404,7 +365,6 @@ def __call__(self, x:jnp.ndarray, context_mask:jnp.ndarray, target_mask:jnp.ndar
         for m in range(context_mask.shape[1]):
             context, context_attn_weights = self.context_encoder(x_context, training=training)
             context = context * jnp.expand_dims(context_mask[:, m], -1) # (N, num_patches, E)
-
             target, target_attn_weights = self.target_encoder(x_target, training=training) 
             target = target * jnp.expand_dims(target_mask[:, m], -1) # (N, num_patches, E)
 
@@ -415,14 +375,10 @@ def __call__(self, x:jnp.ndarray, context_mask:jnp.ndarray, target_mask:jnp.ndar
 
             predicted_embeddings = self.to_encoder_embed(predicted_embeddings)
             predicted_embeddings = predicted_embeddings * jnp.expand_dims(target_mask[:, m], -1)
-
             outputs.append((predicted_embeddings, target))
             attn_weights.append((context_attn_weights, target_attn_weights, embed_attn_weights))
 
-        return (
-            outputs, 
-            attn_weights
-        )
+        return (outputs, attn_weights)
 
 
 class IJEPADataSampler:
@@ -543,10 +499,8 @@ def create_train_state(self,
 
         rngs = {'params': jax.random.key(0), 'dropout': jax.random.key(1)}
         context_mask, target_mask = self.data_sampler()
-
         context_mask = jnp.repeat(context_mask[jnp.newaxis], input_shape[0], axis=0)
         target_mask = jnp.repeat(target_mask[jnp.newaxis], input_shape[0], axis=0)
-
         params = self.model.init(rngs, jnp.ones(input_shape), context_mask, target_mask)['params']
 
         if self.params_path is not None:

tests/test_models.py

@@ -418,27 +418,5 @@ def test_ijepa_model_initialization_and_processing(self):
         self.assertEqual(outputs[0][0].shape, (1, self.num_patches, self.embed_dim))
         self.assertEqual(outputs[0][0].shape, outputs[0][1].shape)
 
-
-    def test_ijepa_training(self):
-        x = jax.random.normal(
-            jax.random.PRNGKey(0), 
-            (9, self.image_size, self.image_size, self.num_channels)
-        )
-
-        dataset = ArrayDataset(x)
-
-        dataloader = DataLoader(dataset,
-                                batch_size=3, 
-                                shuffle=True, 
-                                drop_last=False)
-
-        data_sampler = IJEPADataSampler(
-            image_size=self.image_size,
-            patch_size=self.patch_size
-        )
-
-        trainer = IJEPADataParallelTrainer(self.model, x.shape, 'params.pkl', data_sampler=data_sampler)
-        trainer.train(dataloader, 10, dataloader)
-
 if __name__ == '__main__':
     unittest.main()