Rename mask to input_mask

Rename mask to input_mask

Rename mask to input_mask

3 files changed, 16 insertions, 10 deletions

diff --git a/text_recognizer/networks/conv_transformer.py b/text_recognizer/networks/conv_transformer.py
index 0c838d8..59ce814 100644
--- a/text_recognizer/networks/conv_transformer.py
+++ b/text_recognizer/networks/conv_transformer.py
@@ -118,7 +118,7 @@ class ConvTransformer(nn.Module):
             if self.token_pos_embedding is not None
             else trg
         )
-        out = self.decoder(x=trg, context=src, mask=trg_mask)
+        out = self.decoder(x=trg, context=src, input_mask=trg_mask)
         logits = self.head(out)  # [B, Sy, T]
         logits = logits.permute(0, 2, 1)  # [B, T, Sy]
         return logits
diff --git a/text_recognizer/networks/transformer/attention.py b/text_recognizer/networks/transformer/attention.py
index 54ef5e2..b86636e 100644
--- a/text_recognizer/networks/transformer/attention.py
+++ b/text_recognizer/networks/transformer/attention.py
@@ -51,7 +51,7 @@ class Attention(nn.Module):
         self,
         x: Tensor,
         context: Optional[Tensor] = None,
-        mask: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
         context_mask: Optional[Tensor] = None,
     ) -> Tensor:
         """Computes the attention."""
@@ -71,10 +71,10 @@ class Attention(nn.Module):
         energy = einsum("b h i d, b h j d -> b h i j", q, k) * self.scale
         mask_value = -torch.finfo(energy.dtype).max
         energy = apply_input_mask(
-            b, n, k, energy, mask, context, context_mask, mask_value, device
+            b, n, k, energy, input_mask, context, context_mask, mask_value, device
         )
         if self.causal:
-            energy = apply_causal_mask(energy, mask, mask_value, device)
+            energy = apply_causal_mask(energy, input_mask, mask_value, device)
 
         attn = F.softmax(energy, dim=-1)
         attn = self.dropout(attn)
@@ -98,15 +98,19 @@ def apply_input_mask(
     n: int,
     k: Tensor,
     energy: Tensor,
-    mask: Optional[Tensor],
+    input_mask: Optional[Tensor],
     context: Optional[Tensor],
     context_mask: Optional[Tensor],
     mask_value: Tensor,
     device: str,
 ) -> Tensor:
     """Applies an input mask."""
-    if any(x is not None for x in (mask, context_mask)):
-        q_mask = mask if mask is not None else torch.ones((b, n), device=device).bool()
+    if any(x is not None for x in (input_mask, context_mask)):
+        q_mask = (
+            input_mask
+            if input_mask is not None
+            else torch.ones((b, n), device=device).bool()
+        )
         k_mask = q_mask if context is None else context_mask
         k_mask = (
             torch.ones((b, k.shape[-2]), device=device).bool()
diff --git a/text_recognizer/networks/transformer/layers.py b/text_recognizer/networks/transformer/layers.py
index f740244..8387fa4 100644
--- a/text_recognizer/networks/transformer/layers.py
+++ b/text_recognizer/networks/transformer/layers.py
@@ -77,7 +77,7 @@ class AttentionLayers(nn.Module):
         self,
         x: Tensor,
         context: Optional[Tensor] = None,
-        mask: Optional[Tensor] = None,
+        input_mask: Optional[Tensor] = None,
         context_mask: Optional[Tensor] = None,
     ) -> Tensor:
         """Forward pass."""
@@ -91,9 +91,11 @@ class AttentionLayers(nn.Module):
                 x = norm(x)
 
             if layer_type == "a":
-                out = block(x=x, mask=mask)
+                out = block(x=x, input_mask=input_mask)
             elif layer_type == "c":
-                out = block(x, context=context, mask=mask, context_mask=context_mask)
+                out = block(
+                    x, context=context, input_mask=input_mask, context_mask=context_mask
+                )
             elif layer_type == "f":
                 out = block(x)