[Fix] TF - add bf16 numpy dtype conversion

doctr/models/detection/differentiable_binarization/tensorflow.py

@@ -15,7 +15,7 @@
 from tensorflow.keras.applications import ResNet50
 
 from doctr.file_utils import CLASS_NAME
-from doctr.models.utils import IntermediateLayerGetter, conv_sequence, load_pretrained_params
+from doctr.models.utils import IntermediateLayerGetter, _bf16_to_numpy_dtype, conv_sequence, load_pretrained_params
 from doctr.utils.repr import NestedObject
 
 from ...classification import mobilenet_v3_large
@@ -241,7 +241,7 @@ def call(
             return out
 
         if return_model_output or target is None or return_preds:
-            prob_map = tf.math.sigmoid(logits)
+            prob_map = _bf16_to_numpy_dtype(tf.math.sigmoid(logits))
 
         if return_model_output:
             out["out_map"] = prob_map

doctr/models/detection/linknet/tensorflow.py

@@ -15,7 +15,7 @@
 
 from doctr.file_utils import CLASS_NAME
 from doctr.models.classification import resnet18, resnet34, resnet50
-from doctr.models.utils import IntermediateLayerGetter, conv_sequence, load_pretrained_params
+from doctr.models.utils import IntermediateLayerGetter, _bf16_to_numpy_dtype, conv_sequence, load_pretrained_params
 from doctr.utils.repr import NestedObject
 
 from .base import LinkNetPostProcessor, _LinkNet
@@ -229,7 +229,8 @@ def call(
             return out
 
         if return_model_output or target is None or return_preds:
-            prob_map = tf.math.sigmoid(logits)
+            prob_map = _bf16_to_numpy_dtype(tf.math.sigmoid(logits))
+
         if return_model_output:
             out["out_map"] = prob_map
 

doctr/models/modules/transformer/pytorch.py

@@ -37,7 +37,7 @@ def forward(self, x: torch.Tensor) -> torch.Tensor:
         Returns:
             positional embeddings (batch, max_len, d_model)
         """
-        x = x + self.pe[:, : x.size(1)]  # type: ignore
+        x = x + self.pe[:, : x.size(1)]
         return self.dropout(x)
 
 

doctr/models/recognition/crnn/pytorch.py

@@ -249,7 +249,7 @@ def _crnn(
     _cfg["input_shape"] = kwargs["input_shape"]
 
     # Build the model
-    model = CRNN(feat_extractor, cfg=_cfg, **kwargs)  # type: ignore[arg-type]
+    model = CRNN(feat_extractor, cfg=_cfg, **kwargs)
     # Load pretrained parameters
     if pretrained:
         # The number of classes is not the same as the number of classes in the pretrained model =>

doctr/models/recognition/crnn/tensorflow.py

@@ -13,7 +13,7 @@
 from doctr.datasets import VOCABS
 
 from ...classification import mobilenet_v3_large_r, mobilenet_v3_small_r, vgg16_bn_r
-from ...utils.tensorflow import load_pretrained_params
+from ...utils.tensorflow import _bf16_to_numpy_dtype, load_pretrained_params
 from ..core import RecognitionModel, RecognitionPostProcessor
 
 __all__ = ["CRNN", "crnn_vgg16_bn", "crnn_mobilenet_v3_small", "crnn_mobilenet_v3_large"]
@@ -199,7 +199,7 @@ def call(
         w, h, c = transposed_feat.get_shape().as_list()[1:]
         # B x W x H x C --> B x W x H * C
         features_seq = tf.reshape(transposed_feat, shape=(-1, w, h * c))
-        logits = self.decoder(features_seq, **kwargs)
+        logits = _bf16_to_numpy_dtype(self.decoder(features_seq, **kwargs))
 
         out: Dict[str, tf.Tensor] = {}
         if self.exportable:

doctr/models/recognition/master/tensorflow.py

@@ -13,7 +13,7 @@
 from doctr.models.classification import magc_resnet31
 from doctr.models.modules.transformer import Decoder, PositionalEncoding
 
-from ...utils.tensorflow import load_pretrained_params
+from ...utils.tensorflow import _bf16_to_numpy_dtype, load_pretrained_params
 from .base import _MASTER, _MASTERPostProcessor
 
 __all__ = ["MASTER", "master"]
@@ -183,6 +183,8 @@ def call(
         else:
             logits = self.decode(encoded, **kwargs)
 
+        logits = _bf16_to_numpy_dtype(logits)
+
         if self.exportable:
             out["logits"] = logits
             return out

doctr/models/recognition/parseq/tensorflow.py

@@ -16,7 +16,7 @@
 from doctr.models.modules.transformer import MultiHeadAttention, PositionwiseFeedForward
 
 from ...classification import vit_s
-from ...utils.tensorflow import load_pretrained_params
+from ...utils.tensorflow import _bf16_to_numpy_dtype, load_pretrained_params
 from .base import _PARSeq, _PARSeqPostProcessor
 
 __all__ = ["PARSeq", "parseq"]
@@ -390,6 +390,8 @@ def call(
         else:
             logits = self.decode_autoregressive(features, **kwargs)
 
+        logits = _bf16_to_numpy_dtype(logits)
+
         out: Dict[str, tf.Tensor] = {}
         if self.exportable:
             out["logits"] = logits

doctr/models/recognition/sar/tensorflow.py

@@ -13,7 +13,7 @@
 from doctr.utils.repr import NestedObject
 
 from ...classification import resnet31
-from ...utils.tensorflow import load_pretrained_params
+from ...utils.tensorflow import _bf16_to_numpy_dtype, load_pretrained_params
 from ..core import RecognitionModel, RecognitionPostProcessor
 
 __all__ = ["SAR", "sar_resnet31"]
@@ -316,7 +316,9 @@ def call(
         if kwargs.get("training", False) and target is None:
             raise ValueError("Need to provide labels during training for teacher forcing")
 
-        decoded_features = self.decoder(features, encoded, gt=None if target is None else gt, **kwargs)
+        decoded_features = _bf16_to_numpy_dtype(
+            self.decoder(features, encoded, gt=None if target is None else gt, **kwargs)
+        )
 
         out: Dict[str, tf.Tensor] = {}
         if self.exportable:

doctr/models/recognition/vitstr/tensorflow.py

@@ -12,7 +12,7 @@
 from doctr.datasets import VOCABS
 
 from ...classification import vit_b, vit_s
-from ...utils.tensorflow import load_pretrained_params
+from ...utils.tensorflow import _bf16_to_numpy_dtype, load_pretrained_params
 from .base import _ViTSTR, _ViTSTRPostProcessor
 
 __all__ = ["ViTSTR", "vitstr_small", "vitstr_base"]
@@ -131,7 +131,7 @@ def call(
         logits = tf.reshape(
             self.head(features, **kwargs), (B, N, len(self.vocab) + 1)
         )  # (batch_size, max_length, vocab + 1)
-        decoded_features = logits[:, 1:]  # remove cls_token
+        decoded_features = _bf16_to_numpy_dtype(logits[:, 1:])  # remove cls_token
 
         out: Dict[str, tf.Tensor] = {}
         if self.exportable:

doctr/models/utils/pytorch.py

@@ -25,6 +25,11 @@ def _copy_tensor(x: torch.Tensor) -> torch.Tensor:
     return x.clone().detach()
 
 
+def _bf16_to_numpy_dtype(x: torch.Tensor) -> torch.Tensor:
+    # bfloat16 is not supported in .numpy(): torch/csrc/utils/tensor_numpy.cpp:aten_to_numpy_dtype
+    return x.float() if x.dtype == torch.bfloat16 else x
+
+
 def load_pretrained_params(
     model: nn.Module,
     url: Optional[str] = None,
@@ -157,8 +162,3 @@ def export_model_to_onnx(model: nn.Module, model_name: str, dummy_input: torch.T
     )
     logging.info(f"Model exported to {model_name}.onnx")
     return f"{model_name}.onnx"
-
-
-def _bf16_to_numpy_dtype(x):
-    # bfloat16 is not supported in .numpy(): torch/csrc/utils/tensor_numpy.cpp:aten_to_numpy_dtype
-    return x.float() if x.dtype == torch.bfloat16 else x

doctr/models/utils/tensorflow.py

@@ -17,13 +17,25 @@
 logging.getLogger("tensorflow").setLevel(logging.DEBUG)
 
 
-__all__ = ["load_pretrained_params", "conv_sequence", "IntermediateLayerGetter", "export_model_to_onnx", "_copy_tensor"]
+__all__ = [
+    "load_pretrained_params",
+    "conv_sequence",
+    "IntermediateLayerGetter",
+    "export_model_to_onnx",
+    "_copy_tensor",
+    "_bf16_to_numpy_dtype",
+]
 
 
 def _copy_tensor(x: tf.Tensor) -> tf.Tensor:
     return tf.identity(x)
 
 
+def _bf16_to_numpy_dtype(x: tf.Tensor) -> tf.Tensor:
+    # Convert bfloat16 to float32 for numpy compatibility
+    return tf.cast(x, tf.float32) if x.dtype == tf.bfloat16 else x
+
+
 def load_pretrained_params(
     model: Model,
     url: Optional[str] = None,

tests/tensorflow/test_models_utils_tf.py

@@ -5,7 +5,13 @@
 from tensorflow.keras import Sequential, layers
 from tensorflow.keras.applications import ResNet50
 
-from doctr.models.utils import IntermediateLayerGetter, _copy_tensor, conv_sequence, load_pretrained_params
+from doctr.models.utils import (
+    IntermediateLayerGetter,
+    _bf16_to_numpy_dtype,
+    _copy_tensor,
+    conv_sequence,
+    load_pretrained_params,
+)
 
 
 def test_copy_tensor():
@@ -14,6 +20,12 @@ def test_copy_tensor():
     assert m.device == x.device and m.dtype == x.dtype and m.shape == x.shape and tf.reduce_all(tf.equal(m, x))
 
 
+def test_bf16_to_numpy_dtype():
+    x = tf.random.uniform(shape=[8], minval=0, maxval=1, dtype=tf.bfloat16)
+    m = _bf16_to_numpy_dtype(x)
+    assert x.dtype == tf.bfloat16 and m.dtype == tf.float32 and tf.reduce_all(tf.equal(m, tf.cast(x, tf.float32)))
+
+
 def test_load_pretrained_params(tmpdir_factory):
     model = Sequential([layers.Dense(8, activation="relu", input_shape=(4,)), layers.Dense(4)])
     # Retrieve this URL