Refactor with attr, working on cnn+transformer network

4 files changed, 16 insertions, 49 deletions

diff --git a/text_recognizer/networks/cnn_tranformer.py b/text_recognizer/networks/cnn_tranformer.py
new file mode 100644
index 0000000..da69311
--- /dev/null
+++ b/text_recognizer/networks/cnn_tranformer.py
@@ -0,0 +1,14 @@
+"""Vision transformer for character recognition."""
+from typing import Type
+
+import attr
+from torch import nn, Tensor
+
+
+@attr.s
+class CnnTransformer(nn.Module):
+    def __attrs_pre_init__(self) -> None:
+        super().__init__()
+
+    backbone: Type[nn.Module] = attr.ib()
+    head = Type[nn.Module] = attr.ib()
diff --git a/text_recognizer/networks/loss/__init__.py b/text_recognizer/networks/loss/__init__.py
deleted file mode 100644
index cb83608..0000000
--- a/text_recognizer/networks/loss/__init__.py
+++ /dev/null
@@ -1,2 +0,0 @@
-"""Loss module."""
-from .loss import LabelSmoothingCrossEntropy
diff --git a/text_recognizer/networks/loss/label_smoothing_loss.py b/text_recognizer/networks/loss/label_smoothing_loss.py
deleted file mode 100644
index 40a7609..0000000
--- a/text_recognizer/networks/loss/label_smoothing_loss.py
+++ /dev/null
@@ -1,42 +0,0 @@
-"""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/util.py b/text_recognizer/networks/util.py
index 05b10a8..109bf4d 100644
--- a/text_recognizer/networks/util.py
+++ b/text_recognizer/networks/util.py
@@ -1,10 +1,6 @@
 """Miscellaneous neural network functionality."""
-import importlib
-from pathlib import Path
-from typing import Dict, NamedTuple, Union, Type
+from typing import Type
 
-from loguru import logger
-import torch
 from torch import nn
 
 
@@ -19,6 +15,7 @@ def activation_function(activation: str) -> Type[nn.Module]:
             ["none", nn.Identity()],
             ["relu", nn.ReLU(inplace=True)],
             ["selu", nn.SELU(inplace=True)],
+            ["mish", nn.Mish(inplace=True)],
         ]
     )
     return activation_fns[activation.lower()]