[misc] rename helper function for bf16 to float32 casting (#1347)

doctr/models/detection/differentiable_binarization/pytorch.py

@@ -16,7 +16,7 @@
 from doctr.file_utils import CLASS_NAME
 
 from ...classification import mobilenet_v3_large
-from ...utils import _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils import _bf16_to_float32, load_pretrained_params
 from .base import DBPostProcessor, _DBNet
 
 __all__ = ["DBNet", "db_resnet50", "db_resnet34", "db_mobilenet_v3_large", "db_resnet50_rotation"]
@@ -203,7 +203,7 @@ def forward(
             return out
 
         if return_model_output or target is None or return_preds:
-            prob_map = _bf16_to_numpy_dtype(torch.sigmoid(logits))
+            prob_map = _bf16_to_float32(torch.sigmoid(logits))
 
         if return_model_output:
             out["out_map"] = prob_map

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, _bf16_to_numpy_dtype, conv_sequence, load_pretrained_params
+from doctr.models.utils import IntermediateLayerGetter, _bf16_to_float32, 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 = _bf16_to_numpy_dtype(tf.math.sigmoid(logits))
+            prob_map = _bf16_to_float32(tf.math.sigmoid(logits))
 
         if return_model_output:
             out["out_map"] = prob_map

doctr/models/detection/linknet/pytorch.py

@@ -14,7 +14,7 @@
 from doctr.file_utils import CLASS_NAME
 from doctr.models.classification import resnet18, resnet34, resnet50
 
-from ...utils import _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils import _bf16_to_float32, load_pretrained_params
 from .base import LinkNetPostProcessor, _LinkNet
 
 __all__ = ["LinkNet", "linknet_resnet18", "linknet_resnet34", "linknet_resnet50"]
@@ -175,7 +175,7 @@ def forward(
             return out
 
         if return_model_output or target is None or return_preds:
-            prob_map = _bf16_to_numpy_dtype(torch.sigmoid(logits))
+            prob_map = _bf16_to_float32(torch.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, _bf16_to_numpy_dtype, conv_sequence, load_pretrained_params
+from doctr.models.utils import IntermediateLayerGetter, _bf16_to_float32, conv_sequence, load_pretrained_params
 from doctr.utils.repr import NestedObject
 
 from .base import LinkNetPostProcessor, _LinkNet
@@ -229,7 +229,7 @@ def call(
             return out
 
         if return_model_output or target is None or return_preds:
-            prob_map = _bf16_to_numpy_dtype(tf.math.sigmoid(logits))
+            prob_map = _bf16_to_float32(tf.math.sigmoid(logits))
 
         if return_model_output:
             out["out_map"] = prob_map

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 _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.tensorflow import _bf16_to_float32, 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 = _bf16_to_numpy_dtype(self.decoder(features_seq, **kwargs))
+        logits = _bf16_to_float32(self.decoder(features_seq, **kwargs))
 
         out: Dict[str, tf.Tensor] = {}
         if self.exportable:

doctr/models/recognition/master/pytorch.py

@@ -15,7 +15,7 @@
 from doctr.models.classification import magc_resnet31
 from doctr.models.modules.transformer import Decoder, PositionalEncoding
 
-from ...utils.pytorch import _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.pytorch import _bf16_to_float32, load_pretrained_params
 from .base import _MASTER, _MASTERPostProcessor
 
 __all__ = ["MASTER", "master"]
@@ -195,7 +195,7 @@ def forward(
         else:
             logits = self.decode(encoded)
 
-        logits = _bf16_to_numpy_dtype(logits)
+        logits = _bf16_to_float32(logits)
 
         if self.exportable:
             out["logits"] = logits

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 _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.tensorflow import _bf16_to_float32, load_pretrained_params
 from .base import _MASTER, _MASTERPostProcessor
 
 __all__ = ["MASTER", "master"]
@@ -183,7 +183,7 @@ def call(
         else:
             logits = self.decode(encoded, **kwargs)
 
-        logits = _bf16_to_numpy_dtype(logits)
+        logits = _bf16_to_float32(logits)
 
         if self.exportable:
             out["logits"] = logits

doctr/models/recognition/parseq/pytorch.py

@@ -18,7 +18,7 @@
 from doctr.models.modules.transformer import MultiHeadAttention, PositionwiseFeedForward
 
 from ...classification import vit_s
-from ...utils.pytorch import _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.pytorch import _bf16_to_float32, load_pretrained_params
 from .base import _PARSeq, _PARSeqPostProcessor
 
 __all__ = ["PARSeq", "parseq"]
@@ -362,7 +362,7 @@ def forward(
         else:
             logits = self.decode_autoregressive(features)
 
-        logits = _bf16_to_numpy_dtype(logits)
+        logits = _bf16_to_float32(logits)
 
         out: Dict[str, Any] = {}
         if self.exportable:

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 _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.tensorflow import _bf16_to_float32, load_pretrained_params
 from .base import _PARSeq, _PARSeqPostProcessor
 
 __all__ = ["PARSeq", "parseq"]
@@ -390,7 +390,7 @@ def call(
         else:
             logits = self.decode_autoregressive(features, **kwargs)
 
-        logits = _bf16_to_numpy_dtype(logits)
+        logits = _bf16_to_float32(logits)
 
         out: Dict[str, tf.Tensor] = {}
         if self.exportable:

doctr/models/recognition/sar/pytorch.py

@@ -14,7 +14,7 @@
 from doctr.datasets import VOCABS
 
 from ...classification import resnet31
-from ...utils.pytorch import _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.pytorch import _bf16_to_float32, load_pretrained_params
 from ..core import RecognitionModel, RecognitionPostProcessor
 
 __all__ = ["SAR", "sar_resnet31"]
@@ -249,7 +249,7 @@ def forward(
         if self.training and target is None:
             raise ValueError("Need to provide labels during training for teacher forcing")
 
-        decoded_features = _bf16_to_numpy_dtype(self.decoder(features, encoded, gt=None if target is None else gt))
+        decoded_features = _bf16_to_float32(self.decoder(features, encoded, gt=None if target is None else gt))
 
         out: Dict[str, Any] = {}
         if self.exportable:

doctr/models/recognition/sar/tensorflow.py

@@ -13,7 +13,7 @@
 from doctr.utils.repr import NestedObject
 
 from ...classification import resnet31
-from ...utils.tensorflow import _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.tensorflow import _bf16_to_float32, load_pretrained_params
 from ..core import RecognitionModel, RecognitionPostProcessor
 
 __all__ = ["SAR", "sar_resnet31"]
@@ -316,7 +316,7 @@ def call(
         if kwargs.get("training", False) and target is None:
             raise ValueError("Need to provide labels during training for teacher forcing")
 
-        decoded_features = _bf16_to_numpy_dtype(
+        decoded_features = _bf16_to_float32(
             self.decoder(features, encoded, gt=None if target is None else gt, **kwargs)
         )
 

doctr/models/recognition/vitstr/pytorch.py

@@ -14,7 +14,7 @@
 from doctr.datasets import VOCABS
 
 from ...classification import vit_b, vit_s
-from ...utils.pytorch import _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.pytorch import _bf16_to_float32, load_pretrained_params
 from .base import _ViTSTR, _ViTSTRPostProcessor
 
 __all__ = ["ViTSTR", "vitstr_small", "vitstr_base"]
@@ -95,7 +95,7 @@ def forward(
         B, N, E = features.size()
         features = features.reshape(B * N, E)
         logits = self.head(features).view(B, N, len(self.vocab) + 1)  # (batch_size, max_length, vocab + 1)
-        decoded_features = _bf16_to_numpy_dtype(logits[:, 1:])  # remove cls_token
+        decoded_features = _bf16_to_float32(logits[:, 1:])  # remove cls_token
 
         out: Dict[str, Any] = {}
         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 _bf16_to_numpy_dtype, load_pretrained_params
+from ...utils.tensorflow import _bf16_to_float32, 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 = _bf16_to_numpy_dtype(logits[:, 1:])  # remove cls_token
+        decoded_features = _bf16_to_float32(logits[:, 1:])  # remove cls_token
 
         out: Dict[str, tf.Tensor] = {}
         if self.exportable:

doctr/models/utils/pytorch.py

@@ -17,15 +17,15 @@
     "set_device_and_dtype",
     "export_model_to_onnx",
     "_copy_tensor",
-    "_bf16_to_numpy_dtype",
+    "_bf16_to_float32",
 ]
 
 
 def _copy_tensor(x: torch.Tensor) -> torch.Tensor:
     return x.clone().detach()
 
 
-def _bf16_to_numpy_dtype(x: torch.Tensor) -> torch.Tensor:
+def _bf16_to_float32(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
 

doctr/models/utils/tensorflow.py

@@ -23,15 +23,15 @@
     "IntermediateLayerGetter",
     "export_model_to_onnx",
     "_copy_tensor",
-    "_bf16_to_numpy_dtype",
+    "_bf16_to_float32",
 ]
 
 
 def _copy_tensor(x: tf.Tensor) -> tf.Tensor:
     return tf.identity(x)
 
 
-def _bf16_to_numpy_dtype(x: tf.Tensor) -> tf.Tensor:
+def _bf16_to_float32(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
 

tests/pytorch/test_models_utils_pt.py

@@ -5,7 +5,7 @@
 from torch import nn
 
 from doctr.models.utils import (
-    _bf16_to_numpy_dtype,
+    _bf16_to_float32,
     _copy_tensor,
     conv_sequence_pt,
     load_pretrained_params,
@@ -19,9 +19,9 @@ def test_copy_tensor():
     assert m.device == x.device and m.dtype == x.dtype and m.shape == x.shape and torch.allclose(m, x)
 
 
-def test_bf16_to_numpy_dtype():
+def test_bf16_to_float32():
     x = torch.randn([2, 2], dtype=torch.bfloat16)
-    converted_x = _bf16_to_numpy_dtype(x)
+    converted_x = _bf16_to_float32(x)
     assert x.dtype == torch.bfloat16 and converted_x.dtype == torch.float32 and torch.equal(converted_x, x.float())
 
 

tests/tensorflow/test_models_utils_tf.py

@@ -7,7 +7,7 @@
 
 from doctr.models.utils import (
     IntermediateLayerGetter,
-    _bf16_to_numpy_dtype,
+    _bf16_to_float32,
     _copy_tensor,
     conv_sequence,
     load_pretrained_params,
@@ -20,9 +20,9 @@ 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():
+def test_bf16_to_float32():
     x = tf.random.uniform(shape=[8], minval=0, maxval=1, dtype=tf.bfloat16)
-    m = _bf16_to_numpy_dtype(x)
+    m = _bf16_to_float32(x)
     assert x.dtype == tf.bfloat16 and m.dtype == tf.float32 and tf.reduce_all(tf.equal(m, tf.cast(x, tf.float32)))