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update model names

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This commit is contained in:
vahidrezanezhad 2025-07-21 10:54:20 +02:00
parent e0f4a007e4
commit 920705c3b1
1 changed files with 9 additions and 9 deletions
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18
src/eynollah/eynollah.py
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@ -5129,7 +5129,7 @@ class Eynollah_ocr:
self.b_s = int(batch_size)
else:
self.model_ocr_dir = dir_models + "/model_eynollah_ocr_cnnrnn_20250716"
self.model_ocr_dir = dir_models + "/model_eynollah_ocr_cnnrnn_20250716"#"/model_ens_ocrcnn_new6"#"/model_ens_ocrcnn_new2"#
model_ocr = load_model(self.model_ocr_dir , compile=False)
self.prediction_model = tf.keras.models.Model(
@ -5143,7 +5143,6 @@ class Eynollah_ocr:
with open(os.path.join(self.model_ocr_dir, "characters_org.txt"),"r") as config_file:
characters = json.load(config_file)
AUTOTUNE = tf.data.AUTOTUNE
@ -5154,6 +5153,7 @@ class Eynollah_ocr:
self.num_to_char = StringLookup(
vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
)
self.end_character = len(characters) + 2
def run(self, overwrite : bool = False):
if self.dir_in:
@ -5340,8 +5340,8 @@ class Eynollah_ocr:
tree1.write(out_file_ocr,xml_declaration=True,method='xml',encoding="utf8",default_namespace=None)
#print("Job done in %.1fs", time.time() - t0)
else:
max_len = 512#280#512
padding_token = 299#1500#299
###max_len = 280#512#280#512
###padding_token = 1500#299#1500#299
image_width = 512#max_len * 4
image_height = 32
@ -5656,13 +5656,13 @@ class Eynollah_ocr:
preds_flipped = self.prediction_model.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[:,:]!=256
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
masked_means_flipped = np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 )
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=256
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
masked_means = np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / np.sum(pred_max_not_unk_mask_bool, axis=1)
masked_means[np.isnan(masked_means)] = 0
@ -5683,13 +5683,13 @@ class Eynollah_ocr:
preds_flipped = self.prediction_model.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[:,:]!=256
pred_max_not_unk_mask_bool_flipped = preds_max_args_flipped[:,:]!=self.end_character
masked_means_flipped = np.sum(preds_max_fliped * pred_max_not_unk_mask_bool_flipped, axis=1) / np.sum(pred_max_not_unk_mask_bool_flipped, axis=1)
masked_means_flipped[np.isnan(masked_means_flipped)] = 0
preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 )
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=256
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
masked_means = np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / np.sum(pred_max_not_unk_mask_bool, axis=1)
masked_means[np.isnan(masked_means)] = 0
@ -5711,7 +5711,7 @@ class Eynollah_ocr:
preds_max = np.max(preds, axis=2 )
preds_max_args = np.argmax(preds, axis=2 )
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=256
pred_max_not_unk_mask_bool = preds_max_args[:,:]!=self.end_character
masked_means = np.sum(preds_max * pred_max_not_unk_mask_bool, axis=1) / np.sum(pred_max_not_unk_mask_bool, axis=1)
for ib in range(imgs.shape[0]):