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-rw-r--r--text_recognizer/networks/loss/label_smoothing_loss.py42
-rw-r--r--text_recognizer/networks/loss/loss.py39
-rw-r--r--text_recognizer/networks/transformer/layers.py42
3 files changed, 77 insertions, 46 deletions
diff --git a/text_recognizer/networks/loss/label_smoothing_loss.py b/text_recognizer/networks/loss/label_smoothing_loss.py
new file mode 100644
index 0000000..40a7609
--- /dev/null
+++ b/text_recognizer/networks/loss/label_smoothing_loss.py
@@ -0,0 +1,42 @@
+"""Implementations of custom loss functions."""
+import torch
+from torch import nn
+from torch import Tensor
+import torch.nn.functional as F
+
+
+class LabelSmoothingLoss(nn.Module):
+ """Label smoothing cross entropy loss."""
+
+ def __init__(
+ self, label_smoothing: float, vocab_size: int, ignore_index: int = -100
+ ) -> None:
+ assert 0.0 < label_smoothing <= 1.0
+ self.ignore_index = ignore_index
+ super().__init__()
+
+ smoothing_value = label_smoothing / (vocab_size - 2)
+ one_hot = torch.full((vocab_size,), smoothing_value)
+ one_hot[self.ignore_index] = 0
+ self.register_buffer("one_hot", one_hot.unsqueeze(0))
+
+ self.confidence = 1.0 - label_smoothing
+
+ def forward(self, output: Tensor, targets: Tensor) -> Tensor:
+ """Computes the loss.
+
+ Args:
+ output (Tensor): Predictions from the network.
+ targets (Tensor): Ground truth.
+
+ Shapes:
+ outpus: Batch size x num classes
+ targets: Batch size
+
+ Returns:
+ Tensor: Label smoothing loss.
+ """
+ model_prob = self.one_hot.repeat(targets.size(0), 1)
+ model_prob.scatter_(1, targets.unsqueeze(1), self.confidence)
+ model_prob.masked_fill_((targets == self.ignore_index).unsqueeze(1), 0)
+ return F.kl_div(output, model_prob, reduction="sum")
diff --git a/text_recognizer/networks/loss/loss.py b/text_recognizer/networks/loss/loss.py
deleted file mode 100644
index d12dc9c..0000000
--- a/text_recognizer/networks/loss/loss.py
+++ /dev/null
@@ -1,39 +0,0 @@
-"""Implementations of custom loss functions."""
-import torch
-from torch import nn
-from torch import Tensor
-
-__all__ = ["LabelSmoothingCrossEntropy"]
-
-
-class LabelSmoothingCrossEntropy(nn.Module):
- """Label smoothing loss function."""
-
- def __init__(
- self,
- classes: int,
- smoothing: float = 0.0,
- ignore_index: int = None,
- dim: int = -1,
- ) -> None:
- super().__init__()
- self.confidence = 1.0 - smoothing
- self.smoothing = smoothing
- self.ignore_index = ignore_index
- self.cls = classes
- self.dim = dim
-
- def forward(self, pred: Tensor, target: Tensor) -> Tensor:
- """Calculates the loss."""
- pred = pred.log_softmax(dim=self.dim)
- with torch.no_grad():
- # true_dist = pred.data.clone()
- true_dist = torch.zeros_like(pred)
- true_dist.fill_(self.smoothing / (self.cls - 1))
- true_dist.scatter_(1, target.data.unsqueeze(1), self.confidence)
- if self.ignore_index is not None:
- true_dist[:, self.ignore_index] = 0
- mask = torch.nonzero(target == self.ignore_index, as_tuple=False)
- if mask.dim() > 0:
- true_dist.index_fill_(0, mask.squeeze(), 0.0)
- return torch.mean(torch.sum(-true_dist * pred, dim=self.dim))
diff --git a/text_recognizer/networks/transformer/layers.py b/text_recognizer/networks/transformer/layers.py
index 1c951ae..a2fdb1a 100644
--- a/text_recognizer/networks/transformer/layers.py
+++ b/text_recognizer/networks/transformer/layers.py
@@ -10,6 +10,7 @@ from torch import nn, Tensor
from .attention import Attention
from .mlp import FeedForward
from .residual import Residual
+from .rotary_embedding import RotaryEmbedding
class AttentionLayers(nn.Module):
@@ -24,17 +25,23 @@ class AttentionLayers(nn.Module):
norm_fn: Type[nn.Module] = nn.LayerNorm,
ff_fn: Type[nn.Module] = FeedForward,
residual_fn: Type[nn.Module] = Residual,
+ rotary_emb: Optional[Type[nn.Module]] = None,
+ rotary_emb_dim: Optional[int] = None,
causal: bool = False,
cross_attend: bool = False,
+ pre_norm: bool = True,
) -> None:
super().__init__()
attn_fn = partial(attn_fn, dim=dim, num_heads=num_heads, **attn_kwargs)
norm_fn = partial(norm_fn, dim=dim)
ff_fn = partial(ff_fn, dim=dim, **ff_kwargs)
- layer_types = self._get_layer_types(cross_attend) * depth
+ self.layer_types = self._get_layer_types(cross_attend) * depth
self.layers = self._build_network(
- layer_types, causal, attn_fn, norm_fn, ff_fn, residual_fn
+ causal, attn_fn, norm_fn, ff_fn, residual_fn
)
+ rotary_emb_dim = max(rotary_emb_dim, 32) if rotary_emb_dim is not None else None
+ self.rotary_emb = RotaryEmbedding(rotary_emb_dim) if rotary_emb else None
+ self.pre_norm = pre_norm
@staticmethod
def _get_layer_types(cross_attend: bool) -> Tuple:
@@ -43,18 +50,17 @@ class AttentionLayers(nn.Module):
return "a", "c", "f"
return "a", "f"
- @staticmethod
def _build_network(
- layer_types: Tuple,
+ self,
causal: bool,
attn_fn: partial,
norm_fn: partial,
ff_fn: partial,
residual_fn: Type[nn.Module],
) -> nn.ModuleList:
- """Configures transformer layers."""
+ """Configures transformer network."""
layers = nn.ModuleList([])
- for layer_type in layer_types:
+ for layer_type in self.layer_types:
if layer_type == "a":
layer = attn_fn(causal=causal)
elif layer_type == "c":
@@ -74,4 +80,26 @@ class AttentionLayers(nn.Module):
mask: Optional[Tensor] = None,
context_mask: Optional[Tensor] = None,
) -> Tensor:
- pass
+ rotary_pos_emb = self.rotary_emb(x) if self.rotary_emb is not None else None
+
+ for i, (layer_type, (norm, block, residual_fn)) in enumerate(zip(self.layer_types, self.layers)):
+ is_last = i == len(self.layers) - 1
+
+ residual = x
+
+ if self.pre_norm:
+ x = norm(x)
+
+ if layer_type == "a":
+ out, _ = block(x=x, mask=mask, rotary_pos_emb=rotary_pos_emb)
+ elif layer_type == "c":
+ out, _ = block(x, context=context, mask=mask, context_mask=context_mask)
+ elif layer_type == "f":
+ out = block(x)
+
+ x = residual_fn(out, residual)
+
+ if not self.pre_norm and not is_last:
+ x = norm(x)
+
+ return x