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debuging input shape + enable finetuning a model

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vahidrezanezhad 2025-12-15 11:36:09 +01:00
parent 4fc3ff33cb
commit 4651000191
2 changed files with 18 additions and 5 deletions
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12
src/eynollah/training/train.py
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@ -428,8 +428,11 @@ def run(_config, n_classes, n_epochs, input_height,
n_classes = len(char_to_num.get_vocabulary()) + 2
model = cnn_rnn_ocr_model(image_height=input_height, image_width=input_width, n_classes=n_classes, max_seq=max_len)
if continue_training:
model = load_model(dir_of_start_model)
else:
index_start = 0
model = cnn_rnn_ocr_model(image_height=input_height, image_width=input_width, n_classes=n_classes, max_seq=max_len)
print(model.summary())
@ -459,8 +462,7 @@ def run(_config, n_classes, n_epochs, input_height,
if save_interval:
save_weights_callback = SaveWeightsAfterSteps(save_interval, dir_output, _config)
indexer_start = 0
for i in range(n_epochs):
for i in tqdm(range(index_start, n_epochs + index_start)):
if save_interval:
model.fit(
train_ds,
@ -476,7 +478,7 @@ def run(_config, n_classes, n_epochs, input_height,
)
if i >=0:
model.save( os.path.join(dir_output,'model_'+str(i+indexer_start) ))
model.save( os.path.join(dir_output,'model_'+str(i) ))
with open(os.path.join(os.path.join(dir_output,'model_'+str(i)),"config.json"), "w") as fp:
json.dump(_config, fp) # encode dict into JSON

11
src/eynollah/training/utils.py
View file

@ -1390,6 +1390,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1599,6 +1600,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1619,6 +1621,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1639,6 +1642,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1659,6 +1663,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1679,6 +1684,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1699,6 +1705,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1719,6 +1726,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1739,6 +1747,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1759,6 +1768,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)
@ -1779,6 +1789,7 @@ def data_gen_ocr(padding_token, n_batch, input_height, input_width, max_len, dir
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
except:
img_out = np.copy(img_bin_corr)
img_out = scale_padd_image_for_ocr(img_out, input_height, input_width)
ret_x[batchcount, :,:,:] = img_out[:,:,:]
ret_y[batchcount, :] = vectorize_label(txt_inp, char_to_num, padding_token, max_len)