__call__ -> forward

labml_nn/sketch_rnn/__init__.py

@@ -211,7 +211,7 @@ class EncoderRNN(Module):
         # Head to get $\hat{\sigma}$
         self.sigma_head = nn.Linear(2 * enc_hidden_size, d_z)
 
-    def __call__(self, inputs: torch.Tensor, state=None):
+    def forward(self, inputs: torch.Tensor, state=None):
         # The hidden state of the bidirectional LSTM is the concatenation of the
         # output of the last token in the forward direction and
         # first token in the reverse direction, which is what we want.
@@ -269,7 +269,7 @@ class DecoderRNN(Module):
         self.n_distributions = n_distributions
         self.dec_hidden_size = dec_hidden_size
 
-    def __call__(self, x: torch.Tensor, z: torch.Tensor, state: Optional[Tuple[torch.Tensor, torch.Tensor]]):
+    def forward(self, x: torch.Tensor, z: torch.Tensor, state: Optional[Tuple[torch.Tensor, torch.Tensor]]):
         # Calculate the initial state
         if state is None:
             # $[h_0; c_0] = \tanh(W_{z}z + b_z)$
@@ -314,7 +314,7 @@ class ReconstructionLoss(Module):
     ## Reconstruction Loss
     """
 
-    def __call__(self, mask: torch.Tensor, target: torch.Tensor,
+    def forward(self, mask: torch.Tensor, target: torch.Tensor,
                  dist: 'BivariateGaussianMixture', q_logits: torch.Tensor):
         # Get $\Pi$ and $\mathcal{N}(\mu_{x}, \mu_{y}, \sigma_{x}, \sigma_{y}, \rho_{xy})$
         pi, mix = dist.get_distribution()
@@ -355,7 +355,7 @@ class KLDivLoss(Module):
     This calculates the KL divergence between a given normal distribution and $\mathcal{N}(0, 1)$
     """
 
-    def __call__(self, sigma_hat: torch.Tensor, mu: torch.Tensor):
+    def forward(self, sigma_hat: torch.Tensor, mu: torch.Tensor):
         # $$L_{KL} = - \frac{1}{2 N_z} \bigg( 1 + \hat{\sigma} - \mu^2 - \exp(\hat{\sigma}) \bigg)$$
         return -0.5 * torch.mean(1 + sigma_hat - mu ** 2 - torch.exp(sigma_hat))
 

labml_nn/transformers/fast_weights/__init__.py

@@ -145,7 +145,7 @@ class DPFP(Module):
         self.relu = nn.ReLU()
         self.eps = eps
 
-    def __call__(self, k: torch.Tensor):
+    def forward(self, k: torch.Tensor):
         # Get $\color{lightgreen}{\phi(k)}$
         k = self.dpfp(k)
         # Normalize by $\sum^{d_{dot}}_{j=1} \color{lightgreen}{\phi(k)_j}$
@@ -228,7 +228,7 @@ class FastWeightsAttention(Module):
         # Dropout
         self.dropout = nn.Dropout(dropout_prob)
 
-    def __call__(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor):
         # Get the number of steps $L$
         seq_len = x.shape[0]
         # $\color{lightgreen}{\phi'(q^{(i)})}$ for all steps and heads
@@ -291,7 +291,7 @@ class FastWeightsAttentionTransformerLayer(Module):
         self.norm_self_attn = nn.LayerNorm([d_model])
         self.norm_ff = nn.LayerNorm([d_model])
 
-    def __call__(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor):
         # Calculate fast weights self attention
         attn = self.attn(x)
         # Add the self attention results
@@ -319,7 +319,7 @@ class FastWeightsAttentionTransformer(Module):
         # Final normalization layer
         self.norm = nn.LayerNorm([layer.size])
 
-    def __call__(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor):
         for i, layer in enumerate(self.layers):
             # Get layer output
             x = layer(x)

labml_nn/transformers/fast_weights/token_wise.py

@@ -43,7 +43,7 @@ class FastWeightsAttention(Module):
         # Dropout
         self.dropout = nn.Dropout(dropout_prob)
 
-    def __call__(self, x: torch.Tensor, weights: Optional[torch.Tensor]):
+    def forward(self, x: torch.Tensor, weights: Optional[torch.Tensor]):
         query = self.phi(self.query(x))
         key = self.phi(self.key(x))
         value = self.value(x)
@@ -84,7 +84,7 @@ class FastWeightsAttentionTransformerLayer(Module):
         self.norm_self_attn = nn.LayerNorm([d_model])
         self.norm_ff = nn.LayerNorm([d_model])
 
-    def __call__(self, x: torch.Tensor, weights: Optional[torch.Tensor]):
+    def forward(self, x: torch.Tensor, weights: Optional[torch.Tensor]):
         attn, weights = self.attn(x, weights)
         # Add the self attention results
         x = x + self.dropout(attn)
@@ -108,7 +108,7 @@ class FastWeightsAttentionTransformer(Module):
         # Final normalization layer
         self.norm = nn.LayerNorm([layer.size])
 
-    def __call__(self, x_seq: torch.Tensor):
+    def forward(self, x_seq: torch.Tensor):
         # Split the input to a list along the sequence axis
         x_seq = torch.unbind(x_seq, dim=0)
         # List to store the outputs

labml_nn/transformers/vit/__init__.py

@@ -75,7 +75,7 @@ class PatchEmbeddings(Module):
         # transformation on each patch.
         self.conv = nn.Conv2d(in_channels, d_model, patch_size, stride=patch_size)
 
-    def __call__(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor):
         """
         * `x` is the input image of shape `[batch_size, channels, height, width]`
         """
@@ -109,7 +109,7 @@ class LearnedPositionalEmbeddings(Module):
         # Positional embeddings for each location
         self.positional_encodings = nn.Parameter(torch.zeros(max_len, 1, d_model), requires_grad=True)
 
-    def __call__(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor):
         """
         * `x` is the patch embeddings of shape `[patches, batch_size, d_model]`
         """
@@ -141,7 +141,7 @@ class ClassificationHead(Module):
         # Second layer
         self.linear2 = nn.Linear(n_hidden, n_classes)
 
-    def __call__(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor):
         """
         * `x` is the transformer encoding for `[CLS]` token
         """
@@ -187,7 +187,7 @@ class VisionTransformer(Module):
         # Final normalization layer
         self.ln = nn.LayerNorm([transformer_layer.size])
 
-    def __call__(self, x: torch.Tensor):
+    def forward(self, x: torch.Tensor):
         """
         * `x` is the input image of shape `[batch_size, channels, height, width]`
         """