Merge branch 'fix-0.8-modelzoo-and-predictor-kba0709' into integrating_trocr_and_torch_ensembling_and_updating_characters_list-refactor

# Conflicts: # train/requirements.txt
2026-07-11 22:29:29 +02:00 · 2026-07-09 17:32:23 +02:00 · 2026-07-09 17:32:23 +02:00 · b2f3a8f2d8
commit b2f3a8f2d8
parent 89ce9de6fa b1f2f43051
15 changed files with 177 additions and 86 deletions
--- a/.gitignore
+++ b/.gitignore
@ -12,3 +12,5 @@ output.html
 *.sw?
 TAGS
 uv.lock
+/ignore
+*.log
--- a/6
+++ b/6
@ -15,7 +15,7 @@ LABEL \
    org.opencontainers.image.documentation="https://github.com/qurator-spk/eynollah/blob/${VCS_REF}/README.md" \
    org.opencontainers.image.revision=$VCS_REF \
    org.opencontainers.image.created=$BUILD_DATE \
-    org.opencontainers.image.base.name=ocrd/core-cuda-tf2
+    org.opencontainers.image.base.name=ocrd/core-cuda-onnx

 ENV DEBIAN_FRONTEND=noninteractive
 # set proper locales
@ -40,8 +40,8 @@ RUN ocrd ocrd-tool ocrd-tool.json dump-tools > $(dirname $(ocrd bashlib filename
 RUN ocrd ocrd-tool ocrd-tool.json dump-module-dirs > $(dirname $(ocrd bashlib filename))/ocrd-all-module-dir.json
 # install everything and reduce image size
 RUN make install EXTRAS=OCR && rm -rf /build/eynollah
-# fixup for broken cuDNN installation (Torch pulls in 8.5.0, which is incompatible with Tensorflow)
-RUN pip install nvidia-cudnn-cu11==8.6.0.163
+# fixup for broken cuDNN installation (Torch may pull in version which is incompatible with Tensorflow)
+RUN pip install "nvidia-cudnn-cu12<9.10.2.21"
 # smoke test
 RUN eynollah --help

--- a/2
+++ b/2
@ -2,7 +2,7 @@ PYTHON ?= python3
 PIP ?= pip3
 EXTRAS ?=

-DOCKER_BASE_IMAGE ?= docker.io/ocrd/core-cuda-tf2:v3.13.0
+DOCKER_BASE_IMAGE ?= docker.io/ocrd/core-cuda-onnx:v3.13.1
 DOCKER_TAG ?= ocrd/eynollah
 DOCKER ?= docker
 WGET = wget -O
--- a/README.md
+++ b/README.md
@ -25,9 +25,12 @@
 documents using a combination of multiple deep learning models and heuristics; therefore processing can be slow.

 ## Installation
-Python `3.8-3.11` with Tensorflow `<2.13` on Linux are currently supported.
-For (limited) GPU support the CUDA toolkit needs to be installed. 
-A working config is CUDA `11.8` with cuDNN `8.6`.
+
+Python `3.8-3.11` with ONNX Runtime on Linux are currently supported.
+
+For GPU support, NVidia drivers supporting CUDA 12 must be installed.
+The runtime dependencies will pull in ONNX, TensorRT and CUDA runtime
+libraries (including cuDNN) from PyPI.

 You can either install from PyPI

@ -52,6 +55,13 @@ pip install "eynollah[OCR]"
 make install EXTRAS=OCR
 ```

+> **Note**: Requirements for OCR are more involved, 
+> as they may need Tensorflow (with tf-keras) and/or
+> Torch (with transformers). Those two frameworks may
+> also have conflicting CUDA dependencies. An ONNX
+> conversion for these models may be achieved soon.
+> :construction:
+
 ### Docker

 Use
@ -66,6 +76,11 @@ When using Eynollah with Docker, see [`docker.md`](https://github.com/qurator-sp

 Pretrained models can be downloaded from [Zenodo](https://zenodo.org/records/17727267) or [Hugging Face](https://huggingface.co/SBB?search_models=eynollah).

+For fast runtime inference, download the ONNX models. 
+
+For finetuning training, download the original (Tensorflow / Torch) models
+(and install the `[training]` extra).
+
 For model documentation and model cards, see [`models.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/models.md).

 ## Training
@ -83,18 +98,26 @@ Eynollah supports five use cases:

 Some example outputs can be found in [`examples.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/examples.md).

+The **generic options** shared by all subcommands are:
+```sh
+  -m <directory containing model files>
+  -mv <model category> <model variant> <model path>
+  -D <device specifier>
+  -l <log level>
+```
+
 ### Layout Analysis

-The layout analysis module is responsible for detecting layout elements, identifying text lines, and determining reading 
-order using heuristic methods or a [pretrained model](https://github.com/qurator-spk/eynollah#machine-based-reading-order).
+Detects layout elements, i.e. regions of various types and text lines,
+and determines their reading order using either heuristic methods or a
+[pretrained model](https://github.com/qurator-spk/eynollah#machine-based-reading-order).

 The command-line interface for layout analysis can be called like this:

 ```sh
-eynollah layout \
+eynollah [GENERIC_OPTIONS] layout \
  -i <single image file> | -di <directory containing image files> \
  -o <output directory> \
-  -m <directory containing model files> \
     [OPTIONS]
 ```

@ -106,7 +129,7 @@ The following options can be used to further configure the processing:
 | `-tab`            | apply table detection                                                                       |
 | `-ae`             | apply enhancement (the resulting image is saved to the output directory)                    |
 | `-as`             | apply scaling                                                                               |
-| `-cl`             | apply contour detection for curved text lines instead of bounding boxes                     |
+| `-cl`             | apply contour detection for curved text lines, deskewing all regions independently          |
 | `-ib`             | apply binarization (the resulting image is saved to the output directory)                   |
 | `-ep`             | enable plotting (MUST always be used with `-sl`, `-sd`, `-sa`, `-si` or `-ae`)              |
 | `-ho`             | ignore headers for reading order dectection                                                 |
@ -115,79 +138,93 @@ The following options can be used to further configure the processing:
 | `-sl <directory>` | save layout prediction as plot to this directory                                            |
 | `-sp <directory>` | save cropped page image to this directory                                                   |
 | `-sa <directory>` | save all (plot, enhanced/binary image, layout) to this directory                            |
-| `-thart`          | threshold of artifical class in the case of textline detection. The default value is 0.1    |
-| `-tharl`          | threshold of artifical class in the case of layout detection. The default value is 0.1      |
+| `-thart`          | confidence threshold of artifical boundary class during textline detection                  |
+| `-tharl`          | confidence threshold of artifical boundary class during region detection                    |
 | `-ncu`            | upper limit of columns in document image                                                    |
 | `-ncl`            | lower limit of columns in document image                                                    |
 | `-slro`           | skip layout detection and reading order                                                     |
 | `-romb`           | apply machine based reading order detection                                                 |
 | `-ipe`            | ignore page extraction                                                                      |
+| `-j`              | number of CPU jobs to run parallel (useful with -di)                                        |
+| `-H`              | when to halt when some jobs fail                                                            |

+The default is to only perform layout detection of main regions
+(background, text, images, separators and marginals).

-If no further option is set, the tool performs layout detection of main regions (background, text, images, separators 
-and marginals).
-The best output quality is achieved when RGB images are used as input rather than greyscale or binarized images.
+The best output quality is achieved when RGB images are used as input
+rather than greyscale or binarized images.

-Additional documentation can be found in [`usage.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/usage.md).
+Additional documentation can be found in
+[`usage.md`](https://github.com/qurator-spk/eynollah/tree/main/docs/usage.md).

 ### Binarization

-The binarization module performs document image binarization using pretrained pixelwise segmentation models. 
+Performs document image binarization (thresholding)
+using pretrained pixelwise segmentation models. 

 The command-line interface for binarization can be called like this:

 ```sh
-eynollah binarization \
+eynollah [GENERIC_OPTIONS] binarization \
  -i <single image file> | -di <directory containing image files> \
  -o <output directory> \
-  -m <directory containing model files> 
+  [OPTIONS]
 ```

 ### Image Enhancement
-TODO
+
+This enlarges and enhances images. Useful in case the scan quality is low.
+
+```sh
+eynollah [GENERIC_OPTIONS] enhancement \
+  -i <single image file> | -di <directory containing image files> \
+  -o <output directory> \
+  [OPTIONS]
+```
+
+| option            | description                                                                                 |
+|-------------------|:--------------------------------------------------------------------------------------------|
+| `-sos`            | save the enhanced image in original image size                                              |
+| `-ncu`            | upper limit of columns in document image                                                    |
+| `-ncl`            | lower limit of columns in document image                                                    |

 ### OCR

-The OCR module performs text recognition using either a CNN-RNN model or a Transformer model.
+Performs text recognition using either a CNN-RNN model or a Transformer model.
+Needs a PAGE-XML input file.

 The command-line interface for OCR can be called like this:

 ```sh
-eynollah ocr \
+eynollah [GENERIC_OPTIONS] ocr \
  -i <single image file> | -di <directory containing image files> \
  -dx <directory of xmls> \
  -o <output directory> \
-  -m <directory containing model files> | --model_name <path to specific model>
 ```

 The following options can be used to further configure the ocr processing:

 | option            | description                                                                                |
 |-------------------|:-------------------------------------------------------------------------------------------|
+| `-trocr`          | use transformer OCR model instead of CNN-RNN model                                         |
 | `-dib`            | directory of binarized images (file type must be '.png'), prediction with both RGB and bin |
 | `-doit`           | directory for output images rendered with the predicted text                               |
-| `--model_name`    | file path to use specific model for OCR                                                    |
-| `-trocr`          | use transformer ocr model (otherwise cnn_rnn model is used)                                |
-| `-etit`           | export textline images and text in xml to output dir (OCR training data)                   |
 | `-nmtc`           | cropped textline images will not be masked with textline contour                           |
 | `-bs`             | ocr inference batch size. Default batch size is 2 for trocr and 8 for cnn_rnn models       |
-| `-ds_pref`        | add an abbrevation of dataset name to generated training data                              |
 | `-min_conf`       | minimum OCR confidence value. OCR with textline conf lower than this will be ignored       |


 ### Reading Order Detection
-Reading order detection can be performed either as part of layout analysis based on image input, or, currently under 
-development, based on pre-existing layout analysis data in PAGE-XML format as input.

-The reading order detection module employs a pretrained model to identify the reading order from layouts represented in PAGE-XML files.
+Reading order can be detected either during layout analysis,
+or as a separate module, which requires a PAGE-XML input file.

 The command-line interface for machine based reading order can be called like this:

 ```sh
-eynollah machine-based-reading-order \
+eynollah [GENERIC_OPTIONS] machine-based-reading-order \
  -i <single image file> | -di <directory containing image files> \
  -xml <xml file name> | -dx <directory containing xml files> \
-  -m <path to directory containing model files> \
  -o <output directory> 
 ```

--- a/requirements.txt
+++ b/requirements.txt
@ -1,9 +1,7 @@
 # ocrd includes opencv, numpy, shapely, click
 ocrd >= 3.3.0
-numpy < 2.0
+onnxruntime-gpu[cuda,cudnn] # w/ .onnx models
+tensorrt_cu12 < 11 # 11 incompatible with CUDA libs from onnxruntime-gpu[cuda,cudnn]
 scikit-learn >= 0.23.2
-tensorflow
-tf-keras # avoid keras 3 (also needs TF_USE_LEGACY_KERAS=1)
-numba <= 0.58.1
 scikit-image
 tabulate
--- a/src/eynollah/eynollah.py
+++ b/src/eynollah/eynollah.py
@ -901,7 +901,7 @@ class Eynollah:
        if N > 1:
            mean_y_diff = np.median(diff_cy)
            mean_x_diff = np.median(diff_cx)
-            count_hor = np.count_nonzero(np.diff(w_h_textline) > 0)
+            count_hor = np.count_nonzero(np.diff(w_h_textline, axis=0) > 0)
            count_ver = N - count_hor
        else:
            mean_y_diff = 0
--- a/src/eynollah/eynollah_ocr.py
+++ b/src/eynollah/eynollah_ocr.py
@ -296,13 +296,11 @@ class Eynollah_ocr(Eynollah):
                    probs[ver_index > 0][flipped_ver_is_better] = probs_ver[flipped_ver_is_better]

            def nooov(x):
-                return x != b'[UNK]'
+                if x == b'[UNK]':
+                    return b''
+                return x
            for pred, prob in zip(preds, probs):
-                text = b''.join(
-                    filter(nooov,
-                           map(bytes,
-                               (filter(None, char)
-                                for char in pred.tolist())))).decode('utf-8')
+                text = b''.join(map(nooov, pred.tolist())).decode('utf-8')
                extracted_texts.append(text)
                extracted_confs.append(prob)
        del cropped_lines_rgb
--- a/src/eynollah/model_zoo/model_zoo.py
+++ b/src/eynollah/model_zoo/model_zoo.py
@ -19,7 +19,7 @@ MODEL_VRAM_LIMITS = {
    "enhancement": 980, # due to bs 3
    "col_classifier": 210,
    "page": 618,
-    "textline": 1680, # 954 for bs 1
+    "textline": 1880, # 954 for bs 1
    "region_1_2": 1580,
    "region_fl_np": 1756,
    "table": 1818,
@ -306,6 +306,9 @@ class EynollahModelZoo:
    def _load_onnx_model(self, model_category, model_path, device=''):
        import onnxruntime as ort
        import numpy as np
+        from ocrd_utils import config
+
+        ort.set_default_logger_severity(3)

        providers = ort.get_available_providers()
        if device:
@ -336,6 +339,14 @@ class EynollahModelZoo:
                    # 'cudnn_conv_algo_search': 'EXHAUSTIVE',
                    #'cudnn_conv_use_max_workspace': 0,
                    # 'do_copy_in_default_stream': True,
+                    # enable_cuda_graph
+                    # cudnn_conv1d_pad_to_nc1d
+                    # prefer_nhwc
+                    # tunable_op_enable
+                    # tunable_op_tuning_enable
+                    # tunable_op_max_tuning_duration_ms
+                    # use_ep_level_unified_stream
+                    # enable_skip_layer_norm_strict_mode
                    # ...
                })] + providers
        if 'TensorrtExecutionProvider' in providers:
@ -345,14 +356,38 @@ class EynollahModelZoo:
                    'device_id': gpu,
                    'trt_max_workspace_size': MODEL_VRAM_LIMITS[model_category] * 1024 * 1024,
                    # 'trt_fp16_enable': True,
-                    # 'trt_engine_cache_enable': True,
-                    # 'trt_timing_cache_enable': True,
+                    # trt_bf16_enable
+                    'trt_engine_cache_enable': True,
+                    'trt_timing_cache_enable': True,
+                    'trt_engine_cache_path': config.XDG_CONFIG_HOME,
+                    'trt_timing_cache_path': config.XDG_CONFIG_HOME,
                    # ...
+                    # trt_engine_hw_compatible
+                    # trt_engine_cache_prefix
+                    # trt_onnx_model_folder_path
+                    # trt_ep_context_file_path
+                    # trt_cuda_graph_enable
+                    # trt_profile_opt_shapes
+                    # trt_profile_min_shapes
+                    # trt_profile_max_shapes
+                    # trt_builder_optimization_level
+                    # trt_build_heuristics_enable
+                    # trt_sparsity_enable
+                    # trt_weight_stripped_engine_enable
+                    # trt_dla_core
+                    # trt_dla_enable
+                    # trt_min_subgraph_size
+                    # trt_ep_context_embed_mode
                })] + providers
+        provider0 = providers[0]
+        if isinstance(provider0, tuple):
+            provider0 = provider0[0]
+        self.logger.info("using %s with ONNX provider %s for model %s",
+                         "GPU %d" % gpu if gpu >= 0 else "CPU",
+                         provider0[:-17], model_category)
        model = ort.InferenceSession(
            model_path,
            providers=providers)
-        # FIXME: notify about selected provider/device
        model_inputs = [model_input.name
                        for model_input in model.get_inputs()]
        model_outputs = [model_output.name
--- a/src/eynollah/patch_encoder.py
+++ b/src/eynollah/patch_encoder.py
@ -29,7 +29,13 @@ class Patches(layers.Layer):
        self.patch_size_y = patch_size_y

    def call(self, images):
-        batch_size = tf.shape(images)[0]
+        #batch_size = tf.shape(images)[0]
+        return tf.map_fn(self.call_single, images)
+
+    def call_single(self, image):
+        # avoid batched extract_patches: too much memory,
+        # and variable batch dim not supported by ONNX implementation
+        images = tf.expand_dims(image, axis=0)
        patches = tf.image.extract_patches(
            images=images,
            sizes=[1, self.patch_size_y, self.patch_size_x, 1],
@ -37,8 +43,9 @@ class Patches(layers.Layer):
            rates=[1, 1, 1, 1],
            padding="VALID",
        )
-        patch_dims = patches.shape[-1]
-        return tf.reshape(patches, [batch_size, -1, patch_dims])
+        _, n_rows, n_cols, patch_dims = patches.shape
+        n_tiles = patches.shape[1] * patches.shape[2] #-1
+        return tf.reshape(patches, [1, n_tiles, patch_dims])

    def get_config(self):
        return dict(patch_size_x=self.patch_size_x,
--- a/src/eynollah/predictor.py
+++ b/src/eynollah/predictor.py
@ -191,13 +191,23 @@ class Predictor(mp.context.SpawnProcess):
                        #result = self.model.predict(data, verbose=0)
                        # faster, less VRAM
                        result = self.model.predict_on_batch(data)
-                    if isinstance(result, tuple):
+                    def make_shareable(x):
+                        # convert tf.string/np.object to fixed-length bytes
+                        # (because object segfaults in shm)
+                        if x.dtype is np.dtype(object):
+                            # ONNX conversion for some reason decodes bytes into str already
+                            # so here we undo this, too
+                            if x[0].dtype is np.dtype(object) and isinstance(x[0, 0], str):
+                                x = np.char.encode(x.astype(str), 'utf-8')
+                            return x.astype(bytes)
+                        return x
+                    if isinstance(result, (list, tuple)):
                        multi_output = True
-                        results = zip(*(np.split(result0, len(jobs))
+                        results = zip(*(np.split(make_shareable(result0), len(jobs))
                                        for result0 in result))
                    else:
                        multi_output = False
-                        results = np.split(result, len(jobs))
+                        results = np.split(make_shareable(result), len(jobs))
                    #self.logger.debug("sharing result array for '%d'", jobid)
                    with ExitStack() as stack:
                        for jobid, result in zip(jobs, results):
@ -214,7 +224,7 @@ class Predictor(mp.context.SpawnProcess):
                            self.resultq.put((jobid, result))
                            #self.logger.debug("sent result for '%d': %s", jobid, result)
            except Exception as e:
-                self.logger.error("prediction for %s failed: %s", self.name, e.__class__.__name__)
+                self.logger.exception("prediction for %s failed: %s", self.name, e.__class__.__name__)
                result = e
                self.resultq.put((jobid, result))
        close_all()
--- a/src/eynollah/training/convert.py
+++ b/src/eynollah/training/convert.py
@ -68,12 +68,13 @@ def convert_cli(rebuild, format_, in_, out):
        ex.add_config(str(config_path))
        # some models deviate between training and inference
        ex.add_config(inference=True)
-        # make sure the local vocab file gets re-used
+        # OCR models: make sure the local vocab file gets re-used, if available
        characters_txt_file = model_path / "characters_org.txt"
-        with open(characters_txt_file, "r") as voc_file:
-            voc = json.load(voc_file)
-        ex.add_config(characters_txt_file=characters_txt_file)
-        ex.add_config(n_classes=len(voc) + 3)
+        if characters_txt_file.exists():
+            with open(characters_txt_file, "r") as voc_file:
+                voc = json.load(voc_file)
+            ex.add_config(characters_txt_file=characters_txt_file)
+            ex.add_config(n_classes=len(voc) + 3)
        # just retrieve final config (via pseudo-run)
        ex.main(lambda: 0)
        config = ex.run(options={'--loglevel': 'ERROR'}).config
@ -98,7 +99,12 @@ def convert_cli(rebuild, format_, in_, out):
        model.export(out)
    elif format_ == "onnx":
        import tf2onnx
+        import onnx
        tf2onnx.convert.from_keras(model, opset=18, output_path=out)
+        model = onnx.load(out)
+        model = onnx.shape_inference.infer_shapes(model, strict_mode=True)
+        onnx.checker.check_model(model, full_check=True)
+        onnx.save(model, out)
    else:
        raise ValueError("unknown output format '%s'" % format_)

--- a/src/eynollah/training/metrics.py
+++ b/src/eynollah/training/metrics.py
@ -5,7 +5,10 @@ import tensorflow as tf
 from tensorflow.keras import backend as K
 from tensorflow.keras.metrics import Metric, MeanMetricWrapper, get
 from tensorflow.keras.initializers import Zeros
-from tensorflow_addons.image import connected_components
+try:
+    from tensorflow_addons.image import connected_components
+except ModuleNotFoundError:
+    pass # n/a beyond TF 2.15 (and only needed for training) ...
 import numpy as np


--- a/src/eynollah/training/models.py
+++ b/src/eynollah/training/models.py
@ -21,6 +21,7 @@ from tensorflow.keras.layers import (
    LSTM,
    MaxPooling2D,
    MultiHeadAttention,
+    Permute,
    Reshape,
    UpSampling2D,
    ZeroPadding2D,
@ -70,7 +71,7 @@ class CTCDecoder(Layer):
        ## but Keras greedy sometimes removes arbitrary letters
        # outputs, logits = tf.keras.backend.ctc_decode(inputs,
        #                                               lengths,
-        #                                               beam_width=20
+        #                                               beam_width=20,
        #                                               greedy=False, # True,
        #                                               # backend does not allow these kwargs
        #                                               #merge_repeated=False,
@ -81,18 +82,17 @@ class CTCDecoder(Layer):
        inputs = tf.math.log(
            tf.transpose(inputs, perm=[1, 0, 2]) + tf.keras.backend.epsilon()
        )
-        # tf.nn.ctc_greedy_decoder() is not as precise
        # tf.compat.v1.nn.ctc_beam_search_decoder() also needs merge_repeated=False
-        decoded, logits = tf.nn.ctc_beam_search_decoder(
+        # tf.nn.ctc_beam_search_decoder() is not supported by ONNX, yet
+        # tf.nn.ctc_greedy_decoder() is not as precise, though:
+        decoded, logits = tf.nn.ctc_greedy_decoder(
            inputs,
            lengths,
-            beam_width=10,
-            top_paths=2,
        )
        # get top path for all sequences in batch
        decoded = decoded[0]
-        logits = logits[:, 0] - logits[:, 1]
-        probs = tf.exp(-logits)
+        logits = logits[:, 0]
+        probs = tf.exp(-logits / n_steps)
        # convert to dense
        outputs = tf.SparseTensor(decoded.indices, decoded.values,
                                  (n_samples, n_steps))
@ -530,10 +530,12 @@ def cnn_rnn_ocr_model(input_height=None, input_width=None, n_classes=None, max_l

    addition_rnn = Bidirectional(LSTM(input_width, return_sequences=True, dropout=0.25))(addition)

-    out = Conv1D(max_len, 1, data_format="channels_first")(addition_rnn)
+    #out = Conv1D(max_len, 1, data_format="channels_first")(addition_rnn)
+    out = Permute((2, 1))(addition_rnn)
+    out = Conv1D(max_len, 1, data_format="channels_last")(out)
+    out = Permute((2, 1))(out)
    out = BatchNormalization(name="bn9")(out)
    out = Activation("relu", name="relu9")(out)
-    #out = Conv1D(n_classes, 1, activation='relu', data_format="channels_last")(out)

    out = Dense(n_classes, activation="softmax", name="dense2")(out)

@ -552,8 +554,6 @@ def cnn_rnn_ocr_model(input_height=None, input_width=None, n_classes=None, max_l
        voc = char2num.get_vocabulary()
        num2char = StringLookup(vocabulary=voc, invert=True)
        output = num2char(out)
-        # avoid output tf.dtype=string → np.dtype=object (which cannot be shm-ed)
-        output = tf.io.decode_raw(output, tf.uint8, fixed_length=max(map(len, voc)))

        return Model((inputs, inputs_bin), (output, prob))

@ -582,8 +582,6 @@ def cnn_rnn_ocr_model4inference(model, model_path):
        voc = char2num.get_vocabulary()
        num2char = StringLookup(vocabulary=voc, invert=True)
        output = num2char(output)
-        # avoid output tf.dtype=string → np.dtype=object (which cannot be shm-ed)
-        output = tf.io.decode_raw(output, tf.uint8, fixed_length=max(map(len, voc)))
        inputs = (inputs, inputs_bin)
        outputs = (output, prob)
        return Model(inputs, outputs)
--- a/src/eynollah/training/reload-models-v0.8.mk
+++ b/src/eynollah/training/reload-models-v0.8.mk
@ -38,7 +38,7 @@ $(MODELS_DST)/%: $(MODELS_SRC)/%
 		--in $< \
 		--format $(FORMAT) \
 		--out $@ \
-	2>&1 | tee $(notdir $<).$(FORMAT).log
+	> $(notdir $<).$(FORMAT).log 2>&1 || { cat $(notdir $<).$(FORMAT).log; false; }

 $(MODELS_DST)/%.keras: $(MODELS_SRC)/%
 	eynollah-training convert \
@ -46,7 +46,7 @@ $(MODELS_DST)/%.keras: $(MODELS_SRC)/%
 		--in $< \
 		--format keras \
 		--out $@ \
-	2>&1 | tee $(notdir $<).keras.log
+	> $(notdir $<).keras.log 2>&1 || { cat $(notdir $<).keras.log; false; }

 $(MODELS_DST)/%.h5: $(MODELS_SRC)/%
 	eynollah-training convert \
@ -54,20 +54,15 @@ $(MODELS_DST)/%.h5: $(MODELS_SRC)/%
 		--in $< \
 		--format hdf5 \
 		--out $@ \
-	2>&1 | tee $(notdir $<).hdf5.log
+	> $(notdir $<).hdf5.log 2>&1 || { cat $(notdir $<).hdf5.log; false; }

 $(MODELS_DST)/%.onnx: $(MODELS_SRC)/%
-	if jq -e '.task == "segmentation" and .backbone_type == "transformer"' $</config.json &>/dev/null; then \
-	echo skipping $@: vision transformer architecture currently does not work with ONNX; \
-	elif jq -e '.task == "cnn-rnn-ocr"' $</config.json &>/dev/null || test x$(findstring _ocr,$@) = x_ocr; then \
-	echo skipping $@: OCR CTC decoder does not work with ONNX; \
-	else \
 	eynollah-training convert \
 		$(and $(wildcard $</config.json),--rebuild) \
 		--in $< \
 		--format onnx \
 		--out $@ \
-	2>&1 | tee $(notdir $<).onnx.log; fi
+	> $(notdir $<).onnx.log 2>&1 || { cat $(notdir $<).onnx.log; false; }

 compare: 
 	for i in `find $(MODELS_DST) -mindepth 2`;do \
--- a/train/requirements.txt
+++ b/train/requirements.txt
@ -6,8 +6,10 @@ imutils
 scipy
 tensorflow-addons # for connected_components, depublished and only compatible with tensorflow < 2.16
 tensorflow < 2.16 # for tensorflow-addons, so only needed in training
-tf_data < 2.16 # for tensorflow-addons, so only needed in training
+tf-keras < 2.16 # avoid keras 3 (also needs TF_USE_LEGACY_KERAS=1)
 protobuf < 5 # for tensorflow-addons, so only needed in training
 torch
 transformers <= 4.30.2 ; python_version < '3.10'
 transformers >= 5 ; python_version >= '3.10'
+eynollah-fork-tf2onnx == 1.17.0.post2
+ml_dtypes >= 0.5