training and synthetic data generation code

manga_ocr_dev/training/metrics.py

@@ -0,0 +1,32 @@
+import numpy as np
+from datasets import load_metric
+
+
+class Metrics:
+    def __init__(self, processor):
+        self.cer_metric = load_metric("cer")
+        self.processor = processor
+
+    def compute_metrics(self, pred):
+        label_ids = pred.label_ids
+        pred_ids = pred.predictions
+        print(label_ids.shape, pred_ids.shape)
+
+        pred_str = self.processor.batch_decode(pred_ids, skip_special_tokens=True)
+        label_ids[label_ids == -100] = self.processor.tokenizer.pad_token_id
+        label_str = self.processor.batch_decode(label_ids, skip_special_tokens=True)
+
+        pred_str = np.array([''.join(text.split()) for text in pred_str])
+        label_str = np.array([''.join(text.split()) for text in label_str])
+
+        results = {}
+        try:
+            results['cer'] = self.cer_metric.compute(predictions=pred_str, references=label_str)
+        except Exception as e:
+            print(e)
+            print(pred_str)
+            print(label_str)
+            results['cer'] = 0
+        results['accuracy'] = (pred_str == label_str).mean()
+
+        return results