Update perceiver

1 files changed, 76 insertions, 0 deletions

diff --git a/text_recognizer/models/perceiver.py b/text_recognizer/models/perceiver.py
new file mode 100644
index 0000000..c482235
--- /dev/null
+++ b/text_recognizer/models/perceiver.py
@@ -0,0 +1,76 @@
+"""Lightning model for base Perceiver."""
+from typing import Optional, Tuple, Type
+
+from omegaconf import DictConfig
+import torch
+from torch import nn, Tensor
+
+from text_recognizer.data.mappings import EmnistMapping
+from text_recognizer.models.base import LitBase
+from text_recognizer.models.metrics import CharacterErrorRate
+
+
+class LitPerceiver(LitBase):
+    """A PyTorch Lightning model for transformer networks."""
+
+    def __init__(
+        self,
+        network: Type[nn.Module],
+        loss_fn: Type[nn.Module],
+        optimizer_config: DictConfig,
+        lr_scheduler_config: Optional[DictConfig],
+        mapping: EmnistMapping,
+        max_output_len: int = 682,
+        start_token: str = "<s>",
+        end_token: str = "<e>",
+        pad_token: str = "<p>",
+    ) -> None:
+        super().__init__(
+            network, loss_fn, optimizer_config, lr_scheduler_config, mapping
+        )
+        self.max_output_len = max_output_len
+        self.start_token = start_token
+        self.end_token = end_token
+        self.pad_token = pad_token
+        self.start_index = int(self.mapping.get_index(self.start_token))
+        self.end_index = int(self.mapping.get_index(self.end_token))
+        self.pad_index = int(self.mapping.get_index(self.pad_token))
+        self.ignore_indices = set([self.start_index, self.end_index, self.pad_index])
+        self.val_cer = CharacterErrorRate(self.ignore_indices)
+        self.test_cer = CharacterErrorRate(self.ignore_indices)
+
+    def forward(self, data: Tensor) -> Tensor:
+        """Forward pass with the transformer network."""
+        return self.predict(data)
+
+    def training_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> Tensor:
+        """Training step."""
+        data, targets = batch
+        logits = self.network(data)
+        loss = self.loss_fn(logits, targets)
+        self.log("train/loss", loss)
+        return loss
+
+    def validation_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+        """Validation step."""
+        data, targets = batch
+        preds = self.predict(data)
+        self.val_acc(preds, targets)
+        self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
+        self.val_cer(preds, targets)
+        self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
+
+    def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
+        """Test step."""
+        data, targets = batch
+
+        # Compute the text prediction.
+        pred = self(data)
+        self.test_cer(pred, targets)
+        self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
+        self.test_acc(pred, targets)
+        self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
+
+    @torch.no_grad()
+    def predict(self, x: Tensor) -> Tensor:
+        return self.network(x).argmax(dim=1)