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https://github.com/qurator-spk/eynollah.git
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training.train: fix data batching for OCR in 27f43c17
This commit is contained in:
Robert Sachunsky
parent
86b009bc31
commit
92fc2bd815
2 changed files with 63 additions and 65 deletions
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@ -618,11 +618,6 @@ def run(_config,
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padding_token = len(characters) + 5
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# Mapping characters to integers.
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char_to_num = StringLookup(vocabulary=list(characters), mask_token=None)
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# Mapping integers back to original characters.
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##num_to_char = StringLookup(
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##vocabulary=char_to_num.get_vocabulary(), mask_token=None, invert=True
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##)
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n_classes = len(char_to_num.get_vocabulary()) + 2
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if continue_training:
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@ -649,21 +644,23 @@ def run(_config,
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char_to_num=char_to_num,
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padding_token=padding_token
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)
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train_ds = tf.data.Dataset.from_generator(gen)
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train_ds = train_ds.padded_batch(n_batch,
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padded_shapes=([input_height, input_width, 3], [None]),
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padding_values=(0, padding_token),
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drop_remainder=True,
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#num_parallel_calls=tf.data.AUTOTUNE,
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train_ds = (tf.data.Dataset.from_generator(gen, (tf.float32, tf.int64))
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.padded_batch(n_batch,
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padded_shapes=([input_height, input_width, 3], [None]),
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padding_values=(None, tf.constant(padding_token, dtype=tf.int64)),
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drop_remainder=True,
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#num_parallel_calls=tf.data.AUTOTUNE,
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)
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.map(lambda x, y: {"image": x, "label": y})
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.prefetch(tf.data.AUTOTUNE)
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)
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train_ds = train_ds.prefetch(tf.data.AUTOTUNE)
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#initial_learning_rate = 1e-4
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#decay_steps = int (n_epochs * ( len_dataset / n_batch ))
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#alpha = 0.01
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#lr_schedule = 1e-4
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#tf.keras.optimizers.schedules.CosineDecay(initial_learning_rate, decay_steps, alpha)
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opt = tf.keras.optimizers.Adam(learning_rate=learning_rate)
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opt = Adam(learning_rate=learning_rate)
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model.compile(optimizer=opt) # rs: loss seems to be (ctc_batch_cost) in last layer
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callbacks = [TensorBoard(os.path.join(dir_output, 'logs'), write_graph=False),
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@ -1073,58 +1073,59 @@ def preprocess_img(img,
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scaler=sc_ind)
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def preprocess_img_ocr(
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img,
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img_name,
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lab,
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char_to_num=None,
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padding_token=-1,
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max_len=500,
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n_batch=1,
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input_height=None,
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input_width=None,
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augmentation=False,
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color_padding_rotation=None,
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thetha_padd=None,
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padd_colors=None,
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rotation_not_90=None,
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thetha=None,
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padding_white=None,
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white_padds=None,
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degrading=False,
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bin_deg=None,
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degrade_scales=None,
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blur_aug=False,
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blur_k=None,
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brightening=False,
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brightness=None,
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binarization=False,
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image_inversion=False,
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channels_shuffling=False,
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shuffle_indexes=None,
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white_noise_strap=False,
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textline_skewing=False,
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textline_skewing_bin=False,
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skewing_amplitudes=None,
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textline_left_in_depth=False,
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textline_left_in_depth_bin=False,
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textline_right_in_depth=False,
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textline_right_in_depth_bin=False,
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textline_up_in_depth=False,
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textline_up_in_depth_bin=False,
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textline_down_in_depth=False,
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textline_down_in_depth_bin=False,
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pepper_aug=False,
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pepper_bin_aug=False,
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pepper_indexes=None,
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dir_img_bin=None,
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add_red_textlines=False,
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adding_rgb_background=False,
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dir_rgb_backgrounds=None,
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adding_rgb_foreground=False,
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dir_rgb_foregrounds=None,
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number_of_backgrounds_per_image=None,
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list_all_possible_background_images=None,
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list_all_possible_foreground_rgbs=None,
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img,
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img_name,
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lab,
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char_to_num=None,
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padding_token=-1,
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max_len=500,
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n_batch=1,
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input_height=None,
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input_width=None,
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augmentation=False,
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color_padding_rotation=None,
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thetha_padd=None,
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padd_colors=None,
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rotation_not_90=None,
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thetha=None,
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padding_white=None,
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white_padds=None,
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degrading=False,
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bin_deg=None,
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degrade_scales=None,
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blur_aug=False,
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blur_k=None,
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brightening=False,
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brightness=None,
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binarization=False,
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image_inversion=False,
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channels_shuffling=False,
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shuffle_indexes=None,
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white_noise_strap=False,
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textline_skewing=False,
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textline_skewing_bin=False,
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skewing_amplitudes=None,
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textline_left_in_depth=False,
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textline_left_in_depth_bin=False,
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textline_right_in_depth=False,
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textline_right_in_depth_bin=False,
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textline_up_in_depth=False,
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textline_up_in_depth_bin=False,
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textline_down_in_depth=False,
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textline_down_in_depth_bin=False,
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pepper_aug=False,
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pepper_bin_aug=False,
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pepper_indexes=None,
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dir_img_bin=None,
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add_red_textlines=False,
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adding_rgb_background=False,
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dir_rgb_backgrounds=None,
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adding_rgb_foreground=False,
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dir_rgb_foregrounds=None,
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number_of_backgrounds_per_image=None,
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list_all_possible_background_images=None,
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list_all_possible_foreground_rgbs=None,
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**kwargs
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):
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def scale_image(img):
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return scale_padd_image_for_ocr(img, input_height, input_width).astype(np.float32) / 255.
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