From 4c8abfe19cfba385bc358ee4f2c31456d0ed7bb3 Mon Sep 17 00:00:00 2001 From: kba Date: Wed, 22 Oct 2025 10:40:49 +0200 Subject: [PATCH] eynollah_ocr: actually replace the model calls --- src/eynollah/eynollah_ocr.py | 20 ++++++++++---------- 1 file changed, 10 insertions(+), 10 deletions(-) diff --git a/src/eynollah/eynollah_ocr.py b/src/eynollah/eynollah_ocr.py index 69dd6b7..b021e92 100644 --- a/src/eynollah/eynollah_ocr.py +++ b/src/eynollah/eynollah_ocr.py @@ -199,7 +199,7 @@ class Eynollah_ocr: indexer_b_s = 0 pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_ocr.generate( + generated_ids_merged = self.model_zoo.get('ocr').generate( pixel_values_merged.to(self.device)) generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_ids_merged, skip_special_tokens=True) @@ -222,7 +222,7 @@ class Eynollah_ocr: indexer_b_s = 0 pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_ocr.generate( + generated_ids_merged = self.model_zoo.get('ocr').generate( pixel_values_merged.to(self.device)) generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_ids_merged, skip_special_tokens=True) @@ -242,7 +242,7 @@ class Eynollah_ocr: indexer_b_s = 0 pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_ocr.generate( + generated_ids_merged = self.model_zoo.get('ocr').generate( pixel_values_merged.to(self.device)) generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_ids_merged, skip_special_tokens=True) @@ -260,7 +260,7 @@ class Eynollah_ocr: indexer_b_s = 0 pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_ocr.generate( + generated_ids_merged = self.model_zoo.get('ocr').generate( pixel_values_merged.to(self.device)) generated_text_merged = self.model_zoo.get('processor').batch_decode( generated_ids_merged, skip_special_tokens=True) @@ -277,7 +277,7 @@ class Eynollah_ocr: indexer_b_s = 0 pixel_values_merged = self.model_zoo.get('processor')(imgs, return_tensors="pt").pixel_values - generated_ids_merged = self.model_ocr.generate(pixel_values_merged.to(self.device)) + generated_ids_merged = self.model_zoo.get('ocr').generate(pixel_values_merged.to(self.device)) generated_text_merged = self.model_zoo.get('processor').batch_decode(generated_ids_merged, skip_special_tokens=True) extracted_texts = extracted_texts + generated_text_merged @@ -753,10 +753,10 @@ class Eynollah_ocr: self.logger.debug("processing next %d lines", len(imgs)) - preds = self.prediction_model.predict(imgs, verbose=0) + preds = self.model_zoo.get('ocr').predict(imgs, verbose=0) if len(indices_ver)>0: - preds_flipped = self.prediction_model.predict(imgs_ver_flipped, verbose=0) + preds_flipped = self.model_zoo.get('ocr').predict(imgs_ver_flipped, verbose=0) preds_max_fliped = np.max(preds_flipped, axis=2 ) preds_max_args_flipped = np.argmax(preds_flipped, axis=2 ) pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character @@ -786,10 +786,10 @@ class Eynollah_ocr: preds[indices_to_be_replaced,:,:] = \ preds_flipped[indices_where_flipped_conf_value_is_higher, :, :] if dir_in_bin is not None: - preds_bin = self.prediction_model.predict(imgs_bin, verbose=0) + preds_bin = self.model_zoo.get('ocr').predict(imgs_bin, verbose=0) if len(indices_ver)>0: - preds_flipped = self.prediction_model.predict(imgs_bin_ver_flipped, verbose=0) + preds_flipped = self.model_zoo.get('ocr').predict(imgs_bin_ver_flipped, verbose=0) preds_max_fliped = np.max(preds_flipped, axis=2 ) preds_max_args_flipped = np.argmax(preds_flipped, axis=2 ) pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character @@ -821,7 +821,7 @@ class Eynollah_ocr: preds = (preds + preds_bin) / 2. - pred_texts = decode_batch_predictions(preds, self.num_to_char) + pred_texts = decode_batch_predictions(preds, self.model_zoo.get('num_to_char')) preds_max = np.max(preds, axis=2 ) preds_max_args = np.argmax(preds, axis=2 )