Fix warnings and errors introduced with new versions of tensorflow an…

…d skimage
mikegerber · Oct 23, 2023 · 9c8fb00 · 9c8fb00
1 parent 13b544b
commit 9c8fb00
Show file tree

Hide file tree

Showing 6 changed files with 8 additions and 5 deletions.
diff --git a/calamari_ocr/ocr/backends/tensorflow_backend/callbacks/earlystopping.py b/calamari_ocr/ocr/backends/tensorflow_backend/callbacks/earlystopping.py
@@ -152,6 +152,6 @@ def _compute_current_cer_on_validation_set(self, count):
         def generate_cer():
             it = iter(self.data_gen)
             for _ in range(count):
-                yield np.mean(self.predict_func(next(it))[0])
+                yield np.mean(self.predict_func(next(it)[0])[0])
 
         return np.mean([cer for cer in tqdm_wrapper(generate_cer(), total=count, progress_bar=self.progress_bar, desc="Early stopping") if np.isfinite(cer)])
diff --git a/calamari_ocr/ocr/backends/tensorflow_backend/callbacks/visualize.py b/calamari_ocr/ocr/backends/tensorflow_backend/callbacks/visualize.py
@@ -64,5 +64,5 @@ def on_epoch_end(self, epoch, logs):
 
     def _generate(self, count):
         it = iter(self.data_gen)
-        cer, target, decoded = zip(*[self.predict_func(next(it)) for _ in range(count)])
+        cer, target, decoded = zip(*[self.predict_func(next(it)[0]) for _ in range(count)])
         return np.mean(cer), sum(map(sparse_to_lists, target), []), sum(map(sparse_to_lists, decoded), [])
diff --git a/calamari_ocr/ocr/backends/tensorflow_backend/tensorflow_model.py b/calamari_ocr/ocr/backends/tensorflow_backend/tensorflow_model.py
@@ -317,7 +317,7 @@ def train(self, dataset, validation_dataset, checkpoint_params, text_post_proc,
         else:
             val_dataset_gen = None
 
-        predict_func = K.function({t.op.name: t for t in [self.input_data, self.input_length, self.input_params, self.targets, self.targets_length]}, [self.cer, self.sparse_targets, self.sparse_decoded])
+        predict_func = K.function({t.name: t for t in [self.input_data, self.input_length, self.input_params, self.targets, self.targets_length]}, [self.cer, self.sparse_targets, self.sparse_decoded])
         steps_per_epoch = max(1, int(dataset.epoch_size() / checkpoint_params.batch_size))
         v_cb = VisCallback(training_callback, self.codec, dataset_gen, predict_func, checkpoint_params, steps_per_epoch, text_post_proc)
         es_cb = EarlyStoppingCallback(training_callback, self.codec, val_dataset_gen, predict_func, checkpoint_params,

diff --git a/calamari_ocr/ocr/data_processing/center_normalizer.py b/calamari_ocr/ocr/data_processing/center_normalizer.py
@@ -1,5 +1,5 @@
 import numpy as np
-from scipy.ndimage import filters
+import scipy.ndimage as filters
 from calamari_ocr.ocr.data_processing.data_preprocessor import DataPreprocessor
 from calamari_ocr.ocr.data_processing.scale_to_height_processor import ScaleToHeightProcessor
 

diff --git a/calamari_ocr/ocr/data_processing/scale_to_height_processor.py b/calamari_ocr/ocr/data_processing/scale_to_height_processor.py
@@ -1,7 +1,7 @@
 import numpy as np
 from calamari_ocr.ocr.data_processing.data_preprocessor import DataPreprocessor
 from calamari_ocr.proto import DataPreprocessorParams
-from scipy.ndimage import interpolation
+import scipy.ndimage as interpolation
 
 
 class ScaleToHeightProcessor(DataPreprocessor):

diff --git a/calamari_ocr/test/test_prediction.py b/calamari_ocr/test/test_prediction.py
@@ -18,6 +18,9 @@ def __init__(self):
         # self.checkpoint = sorted(glob_all([os.path.join(this_dir, "..", "..", "..", "calamari_models/antiqua_modern/*.ckpt.json")]))
         self.processes = 1
         self.batch_size = 1
+        self.beam_width = 25
+        self.dictionary = False
+        self.dataset_pad = 0
         self.verbose = True
         self.voter = "confidence_voter_default_ctc"
         self.output_dir = None