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authorGustaf Rydholm <gustaf.rydholm@gmail.com>2022-09-28 23:43:56 +0200
committerGustaf Rydholm <gustaf.rydholm@gmail.com>2022-09-28 23:43:56 +0200
commit923e18913a6518d6278d1fc1843a01bacf955c60 (patch)
tree849c50af1ecf33e648732804e9a00d5eb146df29 /text_recognizer
parent2f611f8c13e4084a50f97b5c94ca82cf71891654 (diff)
Update metrics
Diffstat (limited to 'text_recognizer')
-rw-r--r--text_recognizer/models/transformer.py25
1 files changed, 12 insertions, 13 deletions
diff --git a/text_recognizer/models/transformer.py b/text_recognizer/models/transformer.py
index ff4d08d..b2e5d5f 100644
--- a/text_recognizer/models/transformer.py
+++ b/text_recognizer/models/transformer.py
@@ -52,12 +52,12 @@ class LitTransformer(LitBase):
"""Validation step."""
data, targets = batch
preds = self.predict(data)
- pred_text, target_text = self.get_text(preds, targets)
- self.val_acc(pred_text, target_text)
- self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
+ pred_text, target_text = self._get_text(preds), self._get_text(targets)
+ self.val_acc(preds, targets)
self.val_cer(pred_text, target_text)
- self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
self.val_wer(pred_text, target_text)
+ self.log("val/acc", self.val_acc, on_step=False, on_epoch=True)
+ self.log("val/cer", self.val_cer, on_step=False, on_epoch=True, prog_bar=True)
self.log("val/wer", self.val_wer, on_step=False, on_epoch=True, prog_bar=True)
def test_step(self, batch: Tuple[Tensor, Tensor], batch_idx: int) -> None:
@@ -66,20 +66,19 @@ class LitTransformer(LitBase):
# Compute the text prediction.
preds = self(data)
- pred_text, target_text = self.get_text(preds, targets)
- self.test_acc(pred_text, target_text)
- self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
+ pred_text, target_text = self._get_text(preds), self._get_text(targets)
+ self.test_acc(preds, targets)
self.test_cer(pred_text, target_text)
- self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
self.test_wer(pred_text, target_text)
+ self.log("test/acc", self.test_acc, on_step=False, on_epoch=True)
+ self.log("test/cer", self.test_cer, on_step=False, on_epoch=True, prog_bar=True)
self.log("test/wer", self.test_wer, on_step=False, on_epoch=True, prog_bar=True)
- def get_text(
- self, preds: Tensor, targets: Tensor
+ def _get_text(
+ self,
+ xs: Tensor,
) -> Tuple[Sequence[str], Sequence[str]]:
- pred_text = [self.tokenizer.decode(p) for p in preds]
- target_text = [self.tokenizer.decode(t) for t in targets]
- return pred_text, target_text
+ return [self.tokenizer.decode(x) for x in xs]
@torch.no_grad()
def predict(self, x: Tensor) -> Tensor: