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@@ -12,6 +12,7 @@
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# See the License for the specific language governing permissions and
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# limitations under the License.
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+import numpy as np
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from ....utils import logging
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from ....utils.func_register import FuncRegister
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from ....modules.formula_recognition.model_list import MODELS
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@@ -38,6 +39,7 @@ from .result import FormulaRecResult
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class FormulaRecPredictor(BasicPredictor):
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+ """FormulaRecPredictor that inherits from BasicPredictor."""
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entities = MODELS
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@@ -45,7 +47,23 @@ class FormulaRecPredictor(BasicPredictor):
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register = FuncRegister(_FUNC_MAP)
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def __init__(self, *args, **kwargs):
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+ """Initializes FormulaRecPredictor.
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+ Args:
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+ *args: Arbitrary positional arguments passed to the superclass.
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+ **kwargs: Arbitrary keyword arguments passed to the superclass.
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+ """
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super().__init__(*args, **kwargs)
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+
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+ self.model_names_only_supports_batchsize_of_one = {
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+ "LaTeX_OCR_rec",
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+ }
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+ if self.model_name in self.model_names_only_supports_batchsize_of_one:
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+ logging.warning(
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+ f"Formula Recognition Models: \"{', '.join(list(self.model_names_only_supports_batchsize_of_one))}\" only supports prediction with a batch_size of one, "
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+ "if you set the predictor with a batch_size larger than one, no error will occur, however, it will actually inference with a batch_size of one, "
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+ f"which will lead to a slower inference speed. You are now using {self.config['Global']['model_name']}."
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+ )
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+
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self.pre_tfs, self.infer, self.post_op = self._build()
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def _build_batch_sampler(self):
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@@ -91,9 +109,25 @@ class FormulaRecPredictor(BasicPredictor):
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batch_imgs = self.pre_tfs["UniMERNetTestTransform"](imgs=batch_imgs)
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batch_imgs = self.pre_tfs["LatexImageFormat"](imgs=batch_imgs)
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- x = self.pre_tfs["ToBatch"](imgs=batch_imgs)
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- batch_preds = self.infer(x=x)
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- batch_preds = [p.reshape([-1]) for p in batch_preds[0]]
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+ if self.model_name in self.model_names_only_supports_batchsize_of_one:
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+ batch_preds = []
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+ max_length = 0
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+ for batch_img in batch_imgs:
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+ batch_pred_ = self.infer([batch_img])[0].reshape([-1])
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+ max_length = max(max_length, batch_pred_.shape[0])
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+ batch_preds.append(batch_pred_)
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+ for i in range(len(batch_preds)):
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+ batch_preds[i] = np.pad(
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+ batch_preds[i],
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+ (0, max_length - batch_preds[i].shape[0]),
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+ mode="constant",
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+ constant_values=0,
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+ )
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+ else:
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+ x = self.pre_tfs["ToBatch"](imgs=batch_imgs)
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+ batch_preds = self.infer(x=x)
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+ batch_preds = [p.reshape([-1]) for p in batch_preds[0]]
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+
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rec_formula = self.post_op(batch_preds)
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return {
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"input_path": batch_data.input_paths,
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liuhongen1234567