Add dimensionality of heads argument to SABlock

monai/networks/blocks/selfattention.py

@@ -32,6 +32,7 @@ def __init__(
  dropout_rate: float = 0.0,
  qkv_bias: bool = False,
  save_attn: bool = False,
+ dim_head: int | None = None,
  ) -> None:
  """
  Args:
@@ -40,6 +41,7 @@ def __init__(
  dropout_rate (float, optional): fraction of the input units to drop. Defaults to 0.0.
  qkv_bias (bool, optional): bias term for the qkv linear layer. Defaults to False.
  save_attn (bool, optional): to make accessible the attention matrix. Defaults to False.
+ dim_head (int, optional): dimension of each head. Defaults to hidden_size // num_heads.
 
  """
 
@@ -52,8 +54,11 @@ def __init__(
  raise ValueError("hidden size should be divisible by num_heads.")
 
  self.num_heads = num_heads
- self.out_proj = nn.Linear(hidden_size, hidden_size)
- self.qkv = nn.Linear(hidden_size, hidden_size * 3, bias=qkv_bias)
+ self.dim_head = hidden_size // num_heads if dim_head is None else dim_head
+ self.inner_dim = self.dim_head * num_heads
+
+ self.out_proj = nn.Linear(self.inner_dim, hidden_size)
+ self.qkv = nn.Linear(hidden_size, self.inner_dim * 3, bias=qkv_bias)
  self.input_rearrange = Rearrange("b h (qkv l d) -> qkv b l h d", qkv=3, l=num_heads)
  self.out_rearrange = Rearrange("b h l d -> b l (h d)")
  self.drop_output = nn.Dropout(dropout_rate)