docs/predictor.html

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<header>
<h1 class="title">Module <code>ktrain.predictor</code></h1>
</header>
<section id="section-intro">
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">from . import utils as U
from .imports import *


class Predictor(ABC):
    &#34;&#34;&#34;
    ```
    Abstract class to preprocess data
    ```
    &#34;&#34;&#34;

    @abstractmethod
    def predict(self, data):
        pass

    @abstractmethod
    def get_classes(self, filename):
        pass

    def explain(self, x):
        raise NotImplementedError(&#34;explain is not currently supported for this model&#34;)

    def _make_predictor_folder(self, fpath):
        if os.path.isfile(fpath):
            raise ValueError(
                f&#34;There is an existing file named {fpath}. &#34;
                + &#34;Please use dfferent value for fpath.&#34;
            )
        elif os.path.exists(fpath):
            # warnings.warn(&#39;predictor files are being saved to folder that already exists: %s&#39; % (fpath))
            pass
        elif not os.path.exists(fpath):
            os.makedirs(fpath)
        return

    def _save_preproc(self, fpath):
        with open(os.path.join(fpath, U.PREPROC_NAME), &#34;wb&#34;) as f:
            pickle.dump(self.preproc, f)
        return

    def _save_model(self, fpath):
        if U.is_crf(self.model):  # TODO: fix/refactor this
            from .text.ner.anago.layers import crf_loss

            self.model.compile(loss=crf_loss, optimizer=U.DEFAULT_OPT)
        model_path = os.path.join(fpath, U.MODEL_NAME)
        self.model.save(model_path, save_format=&#34;h5&#34;)
        return

    def save(self, fpath):
        &#34;&#34;&#34;
        ```
        saves both model and Preprocessor instance associated with Predictor
        Args:
          fpath(str): path to folder to store model and Preprocessor instance (.preproc file)
        Returns:
          None
        ```
        &#34;&#34;&#34;
        self._make_predictor_folder(fpath)
        self._save_model(fpath)
        self._save_preproc(fpath)
        return

    def export_model_to_tflite(self, fpath, verbose=1):
        &#34;&#34;&#34;
        ```
        Export model to TFLite
        Args:
          fpath(str): String representing full path to model file where TFLite model will be saved.
                      Example: &#39;/tmp/my_model.tflite&#39;
          verbose(bool): verbosity
        Returns:
          str: fpath is returned back
        ```
        &#34;&#34;&#34;
        if verbose:
            print(&#34;converting to TFLite format ... this may take a few moments...&#34;)
        if U.is_huggingface(model=self.model):
            tokenizer = self.preproc.get_tokenizer()
            maxlen = self.preproc.maxlen
            input_dict = tokenizer(
                &#34;Name&#34;, return_tensors=&#34;tf&#34;, padding=&#34;max_length&#34;, max_length=maxlen
            )

            if version.parse(tf.__version__) &lt; version.parse(&#34;2.2&#34;):
                raise Exception(&#34;export_model_to_tflite requires tensorflow&gt;=2.2&#34;)
                # self.model._set_inputs(input_spec, training=False) # for tf &lt; 2.2
            self.model._saved_model_inputs_spec = None  # for tf &gt; 2.2
            self.model._set_save_spec(input_dict)  # for tf &gt; 2.2
            self.model._get_save_spec()
            if verbose:
                print(&#34;----------------------------------------------&#34;)
                print(
                    &#39;NOTE: For Hugging Face models, please ensure you use return_tensors=&#34;tf&#34; and padding=&#34;max_length&#34; when encoding your inputs.&#39;
                )
                print(&#34;----------------------------------------------&#34;)

        converter = tf.lite.TFLiteConverter.from_keras_model(self.model)

        # normal conversion
        converter.target_spec.supported_ops = [tf.lite.OpsSet.SELECT_TF_OPS]
        tflite_model = converter.convert()
        open(fpath, &#34;wb&#34;).write(tflite_model)
        if verbose:
            print(&#34;done.&#34;)
        return fpath

    def export_model_to_onnx(self, fpath, quantize=False, target_opset=None, verbose=1):
        &#34;&#34;&#34;
        ```
        Export model to onnx
        Args:
          fpath(str): String representing full path to model file where ONNX model will be saved.
                      Example: &#39;/tmp/my_model.onnx&#39;
          quantize(str): If True, will create a total of three model files will be created using transformers.convert_graph_to_onnx:
                         1) ONNX model  (created directly using keras2onnx
                         2) an optimized ONNX model (created by transformers library)
                         3) a quantized version of optimized ONNX model (created by transformers library)
                         All files will be created in the parent folder of fpath:
                         Example:
                           If fpath=&#39;/tmp/model.onnx&#39;, then both /tmp/model-optimized.onnx and
                           /tmp/model-optimized-quantized.onnx will also be created.
          verbose(bool): verbosity
        Returns:
          str: string representing fpath.  If quantize=True, returned fpath will be different than supplied fpath
        ```
        &#34;&#34;&#34;
        try:
            import onnx
            import onnxruntime
        except ImportError:
            raise Exception(
                &#34;This method requires ONNX libraries to be installed: &#34;
                + &#34;pip install -q --upgrade onnxruntime==1.10.0 onnx sympy tf2onnx&#34;
            )
        from pathlib import Path

        if type(self.preproc).__name__ == &#34;BERTPreprocessor&#34;:
            raise Exception(
                &#39;currently_unsupported:  BERT models created with text_classifier(&#34;bert&#34;,...) are not supported (i.e., keras_bert models). &#39;
                + &#34;Only BERT models created with Transformer(...) are supported.&#34;
            )

        if verbose:
            print(
                &#34;converting to ONNX format by way of TFLite ... this may take a few moments...&#34;
            )
        if U.is_huggingface(model=self.model):
            tokenizer = self.preproc.get_tokenizer()
            maxlen = self.preproc.maxlen
            input_dict = tokenizer(
                &#34;Name&#34;, return_tensors=&#34;tf&#34;, padding=&#34;max_length&#34;, max_length=maxlen
            )

            if version.parse(tf.__version__) &lt; version.parse(&#34;2.2&#34;):
                raise Exception(&#34;export_model_to_tflite requires tensorflow&gt;=2.2&#34;)
                # self.model._set_inputs(input_spec, training=False) # for tf &lt; 2.2
            self.model._saved_model_inputs_spec = None  # for tf &gt; 2.2
            self.model._set_save_spec(input_dict)  # for tf &gt; 2.2
            self.model._get_save_spec()

        # onnx_model = keras2onnx.convert_keras(self.model, self.model.name, target_opset=target_opset)
        # keras2onnx.save_model(onnx_model, fpath)
        tflite_model_path = self.export_model_to_tflite(
            fpath + &#34;-TFLITE_TMP&#34;, verbose=verbose
        )

        import subprocess

        if verbose:
            print(&#34;converting to ONNX using tf2onnx...&#34;)
        proc = subprocess.run(
            f&#34;python -m tf2onnx.convert --tflite {tflite_model_path} --output {fpath}&#34;.split(),
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
        )
        if verbose:
            print(proc.returncode)
            print(proc.stdout.decode(&#34;ascii&#34;))
            print(proc.stderr.decode(&#34;ascii&#34;))
        return_fpath = fpath

        if quantize:
            from transformers.convert_graph_to_onnx import optimize, quantize

            # opt_path = optimize(Path(fpath))

            if U.is_huggingface(model=self.model) and type(self.model).__name__ in [
                &#34;TFDistilBertForSequenceClassification&#34;,
                &#34;TFBertForSequenceClassification&#34;,
            ]:
                try:
                    from onnxruntime.transformers import optimizer
                    from onnxruntime.transformers.onnx_model_bert import (
                        BertOptimizationOptions,
                    )

                    # disable embedding layer norm optimization for better model size reduction
                    opt_options = BertOptimizationOptions(&#34;bert&#34;)
                    opt_options.enable_embed_layer_norm = False
                    opt_model = optimizer.optimize_model(
                        fpath,
                        &#34;bert&#34;,  # bert_keras causes error with transformers
                        num_heads=12,
                        hidden_size=768,
                        optimization_options=opt_options,
                    )
                    opt_model.save_model_to_file(fpath)
                except:
                    warnings.warn(&#34;Could not run BERT-specific optimizations&#34;)
                    pass
            quantize_path = quantize(Path(fpath))
            return_fpath = quantize_path.as_posix()
        if verbose:
            print(&#34;done.&#34;)
        return return_fpath

    def create_onnx_session(self, onnx_model_path, provider=&#34;CPUExecutionProvider&#34;):
        &#34;&#34;&#34;
        ```
        Creates ONNX inference session from provided onnx_model_path
        ```
        &#34;&#34;&#34;

        from onnxruntime import (
            GraphOptimizationLevel,
            InferenceSession,
            SessionOptions,
            get_all_providers,
        )

        assert (
            provider in get_all_providers()
        ), f&#34;provider {provider} not found, {get_all_providers()}&#34;

        # Few properties that might have an impact on performances (provided by MS)
        options = SessionOptions()
        options.intra_op_num_threads = 0
        options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL

        # Load the model as a graph and prepare the CPU backend
        session = InferenceSession(onnx_model_path, options, providers=[provider])
        session.disable_fallback()

        # if &#39;OMP_NUM_THREADS&#39; not in os.environ or &#39;OMP_WAIT_POLICY&#39; not in os.environ:
        # warnings.warn(&#39;&#39;&#39;We recommend adding the following at top of script for CPU inference:

        # from psutil import cpu_count
        ##Constants from the performance optimization available in onnxruntime
        ##It needs to be done before importing onnxruntime
        # os.environ[&#34;OMP_NUM_THREADS&#34;] = str(cpu_count(logical=True))
        # os.environ[&#34;OMP_WAIT_POLICY&#34;] = &#39;ACTIVE&#39;
        #&#39;&#39;&#39;)
        return session</code></pre>
</details>
</section>
<section>
</section>
<section>
</section>
<section>
</section>
<section>
<h2 class="section-title" id="header-classes">Classes</h2>
<dl>
<dt id="ktrain.predictor.Predictor"><code class="flex name class">
<span>class <span class="ident">Predictor</span></span>
</code></dt>
<dd>
<div class="desc"><pre><code>Abstract class to preprocess data
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">class Predictor(ABC):
    &#34;&#34;&#34;
    ```
    Abstract class to preprocess data
    ```
    &#34;&#34;&#34;

    @abstractmethod
    def predict(self, data):
        pass

    @abstractmethod
    def get_classes(self, filename):
        pass

    def explain(self, x):
        raise NotImplementedError(&#34;explain is not currently supported for this model&#34;)

    def _make_predictor_folder(self, fpath):
        if os.path.isfile(fpath):
            raise ValueError(
                f&#34;There is an existing file named {fpath}. &#34;
                + &#34;Please use dfferent value for fpath.&#34;
            )
        elif os.path.exists(fpath):
            # warnings.warn(&#39;predictor files are being saved to folder that already exists: %s&#39; % (fpath))
            pass
        elif not os.path.exists(fpath):
            os.makedirs(fpath)
        return

    def _save_preproc(self, fpath):
        with open(os.path.join(fpath, U.PREPROC_NAME), &#34;wb&#34;) as f:
            pickle.dump(self.preproc, f)
        return

    def _save_model(self, fpath):
        if U.is_crf(self.model):  # TODO: fix/refactor this
            from .text.ner.anago.layers import crf_loss

            self.model.compile(loss=crf_loss, optimizer=U.DEFAULT_OPT)
        model_path = os.path.join(fpath, U.MODEL_NAME)
        self.model.save(model_path, save_format=&#34;h5&#34;)
        return

    def save(self, fpath):
        &#34;&#34;&#34;
        ```
        saves both model and Preprocessor instance associated with Predictor
        Args:
          fpath(str): path to folder to store model and Preprocessor instance (.preproc file)
        Returns:
          None
        ```
        &#34;&#34;&#34;
        self._make_predictor_folder(fpath)
        self._save_model(fpath)
        self._save_preproc(fpath)
        return

    def export_model_to_tflite(self, fpath, verbose=1):
        &#34;&#34;&#34;
        ```
        Export model to TFLite
        Args:
          fpath(str): String representing full path to model file where TFLite model will be saved.
                      Example: &#39;/tmp/my_model.tflite&#39;
          verbose(bool): verbosity
        Returns:
          str: fpath is returned back
        ```
        &#34;&#34;&#34;
        if verbose:
            print(&#34;converting to TFLite format ... this may take a few moments...&#34;)
        if U.is_huggingface(model=self.model):
            tokenizer = self.preproc.get_tokenizer()
            maxlen = self.preproc.maxlen
            input_dict = tokenizer(
                &#34;Name&#34;, return_tensors=&#34;tf&#34;, padding=&#34;max_length&#34;, max_length=maxlen
            )

            if version.parse(tf.__version__) &lt; version.parse(&#34;2.2&#34;):
                raise Exception(&#34;export_model_to_tflite requires tensorflow&gt;=2.2&#34;)
                # self.model._set_inputs(input_spec, training=False) # for tf &lt; 2.2
            self.model._saved_model_inputs_spec = None  # for tf &gt; 2.2
            self.model._set_save_spec(input_dict)  # for tf &gt; 2.2
            self.model._get_save_spec()
            if verbose:
                print(&#34;----------------------------------------------&#34;)
                print(
                    &#39;NOTE: For Hugging Face models, please ensure you use return_tensors=&#34;tf&#34; and padding=&#34;max_length&#34; when encoding your inputs.&#39;
                )
                print(&#34;----------------------------------------------&#34;)

        converter = tf.lite.TFLiteConverter.from_keras_model(self.model)

        # normal conversion
        converter.target_spec.supported_ops = [tf.lite.OpsSet.SELECT_TF_OPS]
        tflite_model = converter.convert()
        open(fpath, &#34;wb&#34;).write(tflite_model)
        if verbose:
            print(&#34;done.&#34;)
        return fpath

    def export_model_to_onnx(self, fpath, quantize=False, target_opset=None, verbose=1):
        &#34;&#34;&#34;
        ```
        Export model to onnx
        Args:
          fpath(str): String representing full path to model file where ONNX model will be saved.
                      Example: &#39;/tmp/my_model.onnx&#39;
          quantize(str): If True, will create a total of three model files will be created using transformers.convert_graph_to_onnx:
                         1) ONNX model  (created directly using keras2onnx
                         2) an optimized ONNX model (created by transformers library)
                         3) a quantized version of optimized ONNX model (created by transformers library)
                         All files will be created in the parent folder of fpath:
                         Example:
                           If fpath=&#39;/tmp/model.onnx&#39;, then both /tmp/model-optimized.onnx and
                           /tmp/model-optimized-quantized.onnx will also be created.
          verbose(bool): verbosity
        Returns:
          str: string representing fpath.  If quantize=True, returned fpath will be different than supplied fpath
        ```
        &#34;&#34;&#34;
        try:
            import onnx
            import onnxruntime
        except ImportError:
            raise Exception(
                &#34;This method requires ONNX libraries to be installed: &#34;
                + &#34;pip install -q --upgrade onnxruntime==1.10.0 onnx sympy tf2onnx&#34;
            )
        from pathlib import Path

        if type(self.preproc).__name__ == &#34;BERTPreprocessor&#34;:
            raise Exception(
                &#39;currently_unsupported:  BERT models created with text_classifier(&#34;bert&#34;,...) are not supported (i.e., keras_bert models). &#39;
                + &#34;Only BERT models created with Transformer(...) are supported.&#34;
            )

        if verbose:
            print(
                &#34;converting to ONNX format by way of TFLite ... this may take a few moments...&#34;
            )
        if U.is_huggingface(model=self.model):
            tokenizer = self.preproc.get_tokenizer()
            maxlen = self.preproc.maxlen
            input_dict = tokenizer(
                &#34;Name&#34;, return_tensors=&#34;tf&#34;, padding=&#34;max_length&#34;, max_length=maxlen
            )

            if version.parse(tf.__version__) &lt; version.parse(&#34;2.2&#34;):
                raise Exception(&#34;export_model_to_tflite requires tensorflow&gt;=2.2&#34;)
                # self.model._set_inputs(input_spec, training=False) # for tf &lt; 2.2
            self.model._saved_model_inputs_spec = None  # for tf &gt; 2.2
            self.model._set_save_spec(input_dict)  # for tf &gt; 2.2
            self.model._get_save_spec()

        # onnx_model = keras2onnx.convert_keras(self.model, self.model.name, target_opset=target_opset)
        # keras2onnx.save_model(onnx_model, fpath)
        tflite_model_path = self.export_model_to_tflite(
            fpath + &#34;-TFLITE_TMP&#34;, verbose=verbose
        )

        import subprocess

        if verbose:
            print(&#34;converting to ONNX using tf2onnx...&#34;)
        proc = subprocess.run(
            f&#34;python -m tf2onnx.convert --tflite {tflite_model_path} --output {fpath}&#34;.split(),
            stdout=subprocess.PIPE,
            stderr=subprocess.PIPE,
        )
        if verbose:
            print(proc.returncode)
            print(proc.stdout.decode(&#34;ascii&#34;))
            print(proc.stderr.decode(&#34;ascii&#34;))
        return_fpath = fpath

        if quantize:
            from transformers.convert_graph_to_onnx import optimize, quantize

            # opt_path = optimize(Path(fpath))

            if U.is_huggingface(model=self.model) and type(self.model).__name__ in [
                &#34;TFDistilBertForSequenceClassification&#34;,
                &#34;TFBertForSequenceClassification&#34;,
            ]:
                try:
                    from onnxruntime.transformers import optimizer
                    from onnxruntime.transformers.onnx_model_bert import (
                        BertOptimizationOptions,
                    )

                    # disable embedding layer norm optimization for better model size reduction
                    opt_options = BertOptimizationOptions(&#34;bert&#34;)
                    opt_options.enable_embed_layer_norm = False
                    opt_model = optimizer.optimize_model(
                        fpath,
                        &#34;bert&#34;,  # bert_keras causes error with transformers
                        num_heads=12,
                        hidden_size=768,
                        optimization_options=opt_options,
                    )
                    opt_model.save_model_to_file(fpath)
                except:
                    warnings.warn(&#34;Could not run BERT-specific optimizations&#34;)
                    pass
            quantize_path = quantize(Path(fpath))
            return_fpath = quantize_path.as_posix()
        if verbose:
            print(&#34;done.&#34;)
        return return_fpath

    def create_onnx_session(self, onnx_model_path, provider=&#34;CPUExecutionProvider&#34;):
        &#34;&#34;&#34;
        ```
        Creates ONNX inference session from provided onnx_model_path
        ```
        &#34;&#34;&#34;

        from onnxruntime import (
            GraphOptimizationLevel,
            InferenceSession,
            SessionOptions,
            get_all_providers,
        )

        assert (
            provider in get_all_providers()
        ), f&#34;provider {provider} not found, {get_all_providers()}&#34;

        # Few properties that might have an impact on performances (provided by MS)
        options = SessionOptions()
        options.intra_op_num_threads = 0
        options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL

        # Load the model as a graph and prepare the CPU backend
        session = InferenceSession(onnx_model_path, options, providers=[provider])
        session.disable_fallback()

        # if &#39;OMP_NUM_THREADS&#39; not in os.environ or &#39;OMP_WAIT_POLICY&#39; not in os.environ:
        # warnings.warn(&#39;&#39;&#39;We recommend adding the following at top of script for CPU inference:

        # from psutil import cpu_count
        ##Constants from the performance optimization available in onnxruntime
        ##It needs to be done before importing onnxruntime
        # os.environ[&#34;OMP_NUM_THREADS&#34;] = str(cpu_count(logical=True))
        # os.environ[&#34;OMP_WAIT_POLICY&#34;] = &#39;ACTIVE&#39;
        #&#39;&#39;&#39;)
        return session</code></pre>
</details>
<h3>Ancestors</h3>
<ul class="hlist">
<li>abc.ABC</li>
</ul>
<h3>Subclasses</h3>
<ul class="hlist">
<li><a title="ktrain.graph.predictor.LinkPredictor" href="graph/predictor.html#ktrain.graph.predictor.LinkPredictor">LinkPredictor</a></li>
<li><a title="ktrain.graph.predictor.NodePredictor" href="graph/predictor.html#ktrain.graph.predictor.NodePredictor">NodePredictor</a></li>
<li><a title="ktrain.tabular.predictor.TabularPredictor" href="tabular/predictor.html#ktrain.tabular.predictor.TabularPredictor">TabularPredictor</a></li>
<li><a title="ktrain.text.ner.predictor.NERPredictor" href="text/ner/predictor.html#ktrain.text.ner.predictor.NERPredictor">NERPredictor</a></li>
<li><a title="ktrain.text.predictor.TextPredictor" href="text/predictor.html#ktrain.text.predictor.TextPredictor">TextPredictor</a></li>
<li><a title="ktrain.vision.predictor.ImagePredictor" href="vision/predictor.html#ktrain.vision.predictor.ImagePredictor">ImagePredictor</a></li>
</ul>
<h3>Methods</h3>
<dl>
<dt id="ktrain.predictor.Predictor.create_onnx_session"><code class="name flex">
<span>def <span class="ident">create_onnx_session</span></span>(<span>self, onnx_model_path, provider='CPUExecutionProvider')</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Creates ONNX inference session from provided onnx_model_path
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def create_onnx_session(self, onnx_model_path, provider=&#34;CPUExecutionProvider&#34;):
    &#34;&#34;&#34;
    ```
    Creates ONNX inference session from provided onnx_model_path
    ```
    &#34;&#34;&#34;

    from onnxruntime import (
        GraphOptimizationLevel,
        InferenceSession,
        SessionOptions,
        get_all_providers,
    )

    assert (
        provider in get_all_providers()
    ), f&#34;provider {provider} not found, {get_all_providers()}&#34;

    # Few properties that might have an impact on performances (provided by MS)
    options = SessionOptions()
    options.intra_op_num_threads = 0
    options.graph_optimization_level = GraphOptimizationLevel.ORT_ENABLE_ALL

    # Load the model as a graph and prepare the CPU backend
    session = InferenceSession(onnx_model_path, options, providers=[provider])
    session.disable_fallback()

    # if &#39;OMP_NUM_THREADS&#39; not in os.environ or &#39;OMP_WAIT_POLICY&#39; not in os.environ:
    # warnings.warn(&#39;&#39;&#39;We recommend adding the following at top of script for CPU inference:

    # from psutil import cpu_count
    ##Constants from the performance optimization available in onnxruntime
    ##It needs to be done before importing onnxruntime
    # os.environ[&#34;OMP_NUM_THREADS&#34;] = str(cpu_count(logical=True))
    # os.environ[&#34;OMP_WAIT_POLICY&#34;] = &#39;ACTIVE&#39;
    #&#39;&#39;&#39;)
    return session</code></pre>
</details>
</dd>
<dt id="ktrain.predictor.Predictor.explain"><code class="name flex">
<span>def <span class="ident">explain</span></span>(<span>self, x)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def explain(self, x):
    raise NotImplementedError(&#34;explain is not currently supported for this model&#34;)</code></pre>
</details>
</dd>
<dt id="ktrain.predictor.Predictor.export_model_to_onnx"><code class="name flex">
<span>def <span class="ident">export_model_to_onnx</span></span>(<span>self, fpath, quantize=False, target_opset=None, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Export model to onnx
Args:
  fpath(str): String representing full path to model file where ONNX model will be saved.
              Example: '/tmp/my_model.onnx'
  quantize(str): If True, will create a total of three model files will be created using transformers.convert_graph_to_onnx:
                 1) ONNX model  (created directly using keras2onnx
                 2) an optimized ONNX model (created by transformers library)
                 3) a quantized version of optimized ONNX model (created by transformers library)
                 All files will be created in the parent folder of fpath:
                 Example:
                   If fpath='/tmp/model.onnx', then both /tmp/model-optimized.onnx and
                   /tmp/model-optimized-quantized.onnx will also be created.
  verbose(bool): verbosity
Returns:
  str: string representing fpath.  If quantize=True, returned fpath will be different than supplied fpath
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def export_model_to_onnx(self, fpath, quantize=False, target_opset=None, verbose=1):
    &#34;&#34;&#34;
    ```
    Export model to onnx
    Args:
      fpath(str): String representing full path to model file where ONNX model will be saved.
                  Example: &#39;/tmp/my_model.onnx&#39;
      quantize(str): If True, will create a total of three model files will be created using transformers.convert_graph_to_onnx:
                     1) ONNX model  (created directly using keras2onnx
                     2) an optimized ONNX model (created by transformers library)
                     3) a quantized version of optimized ONNX model (created by transformers library)
                     All files will be created in the parent folder of fpath:
                     Example:
                       If fpath=&#39;/tmp/model.onnx&#39;, then both /tmp/model-optimized.onnx and
                       /tmp/model-optimized-quantized.onnx will also be created.
      verbose(bool): verbosity
    Returns:
      str: string representing fpath.  If quantize=True, returned fpath will be different than supplied fpath
    ```
    &#34;&#34;&#34;
    try:
        import onnx
        import onnxruntime
    except ImportError:
        raise Exception(
            &#34;This method requires ONNX libraries to be installed: &#34;
            + &#34;pip install -q --upgrade onnxruntime==1.10.0 onnx sympy tf2onnx&#34;
        )
    from pathlib import Path

    if type(self.preproc).__name__ == &#34;BERTPreprocessor&#34;:
        raise Exception(
            &#39;currently_unsupported:  BERT models created with text_classifier(&#34;bert&#34;,...) are not supported (i.e., keras_bert models). &#39;
            + &#34;Only BERT models created with Transformer(...) are supported.&#34;
        )

    if verbose:
        print(
            &#34;converting to ONNX format by way of TFLite ... this may take a few moments...&#34;
        )
    if U.is_huggingface(model=self.model):
        tokenizer = self.preproc.get_tokenizer()
        maxlen = self.preproc.maxlen
        input_dict = tokenizer(
            &#34;Name&#34;, return_tensors=&#34;tf&#34;, padding=&#34;max_length&#34;, max_length=maxlen
        )

        if version.parse(tf.__version__) &lt; version.parse(&#34;2.2&#34;):
            raise Exception(&#34;export_model_to_tflite requires tensorflow&gt;=2.2&#34;)
            # self.model._set_inputs(input_spec, training=False) # for tf &lt; 2.2
        self.model._saved_model_inputs_spec = None  # for tf &gt; 2.2
        self.model._set_save_spec(input_dict)  # for tf &gt; 2.2
        self.model._get_save_spec()

    # onnx_model = keras2onnx.convert_keras(self.model, self.model.name, target_opset=target_opset)
    # keras2onnx.save_model(onnx_model, fpath)
    tflite_model_path = self.export_model_to_tflite(
        fpath + &#34;-TFLITE_TMP&#34;, verbose=verbose
    )

    import subprocess

    if verbose:
        print(&#34;converting to ONNX using tf2onnx...&#34;)
    proc = subprocess.run(
        f&#34;python -m tf2onnx.convert --tflite {tflite_model_path} --output {fpath}&#34;.split(),
        stdout=subprocess.PIPE,
        stderr=subprocess.PIPE,
    )
    if verbose:
        print(proc.returncode)
        print(proc.stdout.decode(&#34;ascii&#34;))
        print(proc.stderr.decode(&#34;ascii&#34;))
    return_fpath = fpath

    if quantize:
        from transformers.convert_graph_to_onnx import optimize, quantize

        # opt_path = optimize(Path(fpath))

        if U.is_huggingface(model=self.model) and type(self.model).__name__ in [
            &#34;TFDistilBertForSequenceClassification&#34;,
            &#34;TFBertForSequenceClassification&#34;,
        ]:
            try:
                from onnxruntime.transformers import optimizer
                from onnxruntime.transformers.onnx_model_bert import (
                    BertOptimizationOptions,
                )

                # disable embedding layer norm optimization for better model size reduction
                opt_options = BertOptimizationOptions(&#34;bert&#34;)
                opt_options.enable_embed_layer_norm = False
                opt_model = optimizer.optimize_model(
                    fpath,
                    &#34;bert&#34;,  # bert_keras causes error with transformers
                    num_heads=12,
                    hidden_size=768,
                    optimization_options=opt_options,
                )
                opt_model.save_model_to_file(fpath)
            except:
                warnings.warn(&#34;Could not run BERT-specific optimizations&#34;)
                pass
        quantize_path = quantize(Path(fpath))
        return_fpath = quantize_path.as_posix()
    if verbose:
        print(&#34;done.&#34;)
    return return_fpath</code></pre>
</details>
</dd>
<dt id="ktrain.predictor.Predictor.export_model_to_tflite"><code class="name flex">
<span>def <span class="ident">export_model_to_tflite</span></span>(<span>self, fpath, verbose=1)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>Export model to TFLite
Args:
  fpath(str): String representing full path to model file where TFLite model will be saved.
              Example: '/tmp/my_model.tflite'
  verbose(bool): verbosity
Returns:
  str: fpath is returned back
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def export_model_to_tflite(self, fpath, verbose=1):
    &#34;&#34;&#34;
    ```
    Export model to TFLite
    Args:
      fpath(str): String representing full path to model file where TFLite model will be saved.
                  Example: &#39;/tmp/my_model.tflite&#39;
      verbose(bool): verbosity
    Returns:
      str: fpath is returned back
    ```
    &#34;&#34;&#34;
    if verbose:
        print(&#34;converting to TFLite format ... this may take a few moments...&#34;)
    if U.is_huggingface(model=self.model):
        tokenizer = self.preproc.get_tokenizer()
        maxlen = self.preproc.maxlen
        input_dict = tokenizer(
            &#34;Name&#34;, return_tensors=&#34;tf&#34;, padding=&#34;max_length&#34;, max_length=maxlen
        )

        if version.parse(tf.__version__) &lt; version.parse(&#34;2.2&#34;):
            raise Exception(&#34;export_model_to_tflite requires tensorflow&gt;=2.2&#34;)
            # self.model._set_inputs(input_spec, training=False) # for tf &lt; 2.2
        self.model._saved_model_inputs_spec = None  # for tf &gt; 2.2
        self.model._set_save_spec(input_dict)  # for tf &gt; 2.2
        self.model._get_save_spec()
        if verbose:
            print(&#34;----------------------------------------------&#34;)
            print(
                &#39;NOTE: For Hugging Face models, please ensure you use return_tensors=&#34;tf&#34; and padding=&#34;max_length&#34; when encoding your inputs.&#39;
            )
            print(&#34;----------------------------------------------&#34;)

    converter = tf.lite.TFLiteConverter.from_keras_model(self.model)

    # normal conversion
    converter.target_spec.supported_ops = [tf.lite.OpsSet.SELECT_TF_OPS]
    tflite_model = converter.convert()
    open(fpath, &#34;wb&#34;).write(tflite_model)
    if verbose:
        print(&#34;done.&#34;)
    return fpath</code></pre>
</details>
</dd>
<dt id="ktrain.predictor.Predictor.get_classes"><code class="name flex">
<span>def <span class="ident">get_classes</span></span>(<span>self, filename)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def get_classes(self, filename):
    pass</code></pre>
</details>
</dd>
<dt id="ktrain.predictor.Predictor.predict"><code class="name flex">
<span>def <span class="ident">predict</span></span>(<span>self, data)</span>
</code></dt>
<dd>
<div class="desc"></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">@abstractmethod
def predict(self, data):
    pass</code></pre>
</details>
</dd>
<dt id="ktrain.predictor.Predictor.save"><code class="name flex">
<span>def <span class="ident">save</span></span>(<span>self, fpath)</span>
</code></dt>
<dd>
<div class="desc"><pre><code>saves both model and Preprocessor instance associated with Predictor
Args:
  fpath(str): path to folder to store model and Preprocessor instance (.preproc file)
Returns:
  None
</code></pre></div>
<details class="source">
<summary>
<span>Expand source code</span>
</summary>
<pre><code class="python">def save(self, fpath):
    &#34;&#34;&#34;
    ```
    saves both model and Preprocessor instance associated with Predictor
    Args:
      fpath(str): path to folder to store model and Preprocessor instance (.preproc file)
    Returns:
      None
    ```
    &#34;&#34;&#34;
    self._make_predictor_folder(fpath)
    self._save_model(fpath)
    self._save_preproc(fpath)
    return</code></pre>
</details>
</dd>
</dl>
</dd>
</dl>
</section>
</article>
<nav id="sidebar">
<h1>Index</h1>
<div class="toc">
<ul></ul>
</div>
<ul id="index">
<li><h3>Super-module</h3>
<ul>
<li><code><a title="ktrain" href="index.html">ktrain</a></code></li>
</ul>
</li>
<li><h3><a href="#header-classes">Classes</a></h3>
<ul>
<li>
<h4><code><a title="ktrain.predictor.Predictor" href="#ktrain.predictor.Predictor">Predictor</a></code></h4>
<ul class="">
<li><code><a title="ktrain.predictor.Predictor.create_onnx_session" href="#ktrain.predictor.Predictor.create_onnx_session">create_onnx_session</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.explain" href="#ktrain.predictor.Predictor.explain">explain</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_onnx" href="#ktrain.predictor.Predictor.export_model_to_onnx">export_model_to_onnx</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.export_model_to_tflite" href="#ktrain.predictor.Predictor.export_model_to_tflite">export_model_to_tflite</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.get_classes" href="#ktrain.predictor.Predictor.get_classes">get_classes</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.predict" href="#ktrain.predictor.Predictor.predict">predict</a></code></li>
<li><code><a title="ktrain.predictor.Predictor.save" href="#ktrain.predictor.Predictor.save">save</a></code></li>
</ul>
</li>
</ul>
</li>
</ul>
</nav>
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