starting docs

docs/source/using_doctr/using_model_export.rst

@@ -3,69 +3,81 @@ Preparing your model for inference
 
 A well-trained model is a good achievement but you might want to tune a few things to make it production-ready!
 
-.. currentmodule:: doctr.models.export
+.. currentmodule:: doctr.models.utils
 
 
-Model compression
------------------
+Model optimization
+------------------
 
-This section is meant to help you perform inference with compressed versions of your model.
+This section is meant to help you perform inference with optimized versions of your model.
 
 
-TensorFlow Lite
-^^^^^^^^^^^^^^^
+Export to ONNX
+^^^^^^^^^^^^^^
 
-TensorFlow provides utilities packaged as TensorFlow Lite to take resource constraints into account. You can easily convert any Keras model into a serialized TFLite version as follows:
+ONNX (Open Neural Network Exchange) is an open and interoperable format for representing and exchanging machine learning models.
+ONNX defines a common format for representing models, including the network structure, layer types, parameters, and metadata.
+ONNX Runtime is an inference engine that enables efficient execution of ONNX models across different platforms and hardware accelerators.
+It provides optimized performance and supports a wide range of hardware configurations.
 
-    >>> import tensorflow as tf
-    >>> from tensorflow.keras import Sequential
-    >>> from doctr.models import conv_sequence
-    >>> model = Sequential(conv_sequence(32, 'relu', True, kernel_size=3, input_shape=(224, 224, 3)))
-    >>> converter = tf.lite.TFLiteConverter.from_keras_model(tf_model)
-    >>> serialized_model = converter.convert()
+.. tabs::
 
-Half-precision
-^^^^^^^^^^^^^^
+    .. tab:: TensorFlow
+
+        .. code:: python3
+
+            import tensorflow as tf
+            from doctr.models import vitstr_small
+            from doctr.models.utils import export_model_to_onnx
+
+            batch_size = 16
+            input_shape = (3, 32, 128)
+            model = vitstr_small(pretrained=True, exportable=True)
+            dummy_input = [tf.TensorSpec([batch_size, input_shape], tf.float32, name="input")]
+            model_path, output = export_model_to_onnx(model, model_name="vitstr.onnx", dummy_input=dummy_input)
 
-If you want to convert it to half-precision using your TFLite converter
 
-    >>> converter.optimizations = [tf.lite.Optimize.DEFAULT]
-    >>> converter.target_spec.supported_types = [tf.float16]
-    >>> serialized_model = converter.convert()
+    .. tab:: PyTorch
 
+        .. code:: python3
+
+            import torch
+            from doctr.models import vitstr_small
+            from doctr.models.utils import export_model_to_onnx
+
+            batch_size = 16
+            input_shape = (32, 128, 3)
+            model = vitstr_small(pretrained=True, exportable=True)
+            dummy_input = torch.rand((batch_size, input_shape), dtype=torch.float32)
+            model_path = export_model_to_onnx(model, model_name="vitstr.onnx, dummy_input=dummy_input)
+
+
+Half-precision
+^^^^^^^^^^^^^^
 
-Post-training quantization
-^^^^^^^^^^^^^^^^^^^^^^^^^^
+Half-precision (or FP16) is a binary floating-point format that occupies 16 bits in computer memory.
 
-Finally if you wish to quantize the model with your TFLite converter
+.. tabs::
 
-    >>> converter.optimizations = [tf.lite.Optimize.DEFAULT]
-    >>> # Float fallback for operators that do not have an integer implementation
-    >>> def representative_dataset():
-    >>>     for _ in range(100): yield [np.random.rand(1, *input_shape).astype(np.float32)]
-    >>> converter.representative_dataset = representative_dataset
-    >>> converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
-    >>> converter.inference_input_type = tf.int8
-    >>> converter.inference_output_type = tf.int8
-    >>> serialized_model = converter.convert()
+    .. tab:: TensorFlow
 
+        .. code:: python3
 
-Using SavedModel
-----------------
+            import tensorflow as tf
+            from tensorflow.keras import mixed_precision
+            mixed_precision.set_global_policy('mixed_float16')
+            predictor = ocr_predictor(reco_arch="crnn_mobilenet_v3_small", det_arch="linknet_resnet34", pretrained=True)
 
-Additionally, models in docTR inherit TensorFlow 2 model properties and can be exported to
-`SavedModel <https://www.tensorflow.org/guide/saved_model>`_ format as follows:
+    .. tab:: PyTorch
 
+        .. code:: python3
 
-    >>> import tensorflow as tf
-    >>> from doctr.models import db_resnet50
-    >>> model = db_resnet50(pretrained=True)
-    >>> input_t = tf.random.uniform(shape=[1, 1024, 1024, 3], maxval=1, dtype=tf.float32)
-    >>> _ = model(input_t, training=False)
-    >>> tf.saved_model.save(model, 'path/to/your/folder/db_resnet50/')
+            import torch
+            predictor = ocr_predictor(reco_arch="crnn_mobilenet_v3_small", det_arch="linknet_resnet34", pretrained=True).cuda().half()
+            res = predictor(doc)
 
-And loaded just as easily:
 
+Using your ONNX model inside DocTR
+----------------------------------
 
-    >>> import tensorflow as tf
-    >>> model = tf.saved_model.load('path/to/your/folder/db_resnet50/')
+** Coming soon **