Fix typos

doctr/models/_utils.py

@@ -184,7 +184,7 @@ def invert_data_structure(
         dictionary of list when x is a list of dictionaries or a list of dictionaries when x is dictionary of lists
     """
     if isinstance(x, dict):
-        assert len({len(v) for v in x.values()}) == 1, "All the lists in the dictionnary should have the same length."
+        assert len({len(v) for v in x.values()}) == 1, "All the lists in the dictionary should have the same length."
         return [dict(zip(x, t)) for t in zip(*x.values())]
     elif isinstance(x, list):
         return {k: [dic[k] for dic in x] for k in x[0]}

doctr/models/recognition/crnn/pytorch.py

@@ -82,7 +82,7 @@ def ctc_best_path(
 
     def __call__(self, logits: torch.Tensor) -> list[tuple[str, float]]:
         """Performs decoding of raw output with CTC and decoding of CTC predictions
-        with label_to_idx mapping dictionnary
+        with label_to_idx mapping dictionary
 
         Args:
             logits: raw output of the model, shape (N, C + 1, seq_len)

doctr/models/recognition/crnn/tensorflow.py

@@ -59,7 +59,7 @@ def __call__(
         top_paths: int = 1,
     ) -> list[tuple[str, float]] | list[tuple[list[str] | list[float]]]:
         """Performs decoding of raw output with CTC and decoding of CTC predictions
-        with label_to_idx mapping dictionnary
+        with label_to_idx mapping dictionary
 
         Args:
             logits: raw output of the model, shape BATCH_SIZE X SEQ_LEN X NUM_CLASSES + 1

doctr/models/recognition/master/pytorch.py

@@ -176,7 +176,7 @@ def forward(
             return_preds: if True, decode logits
 
         Returns:
-            A dictionnary containing eventually loss, logits and predictions.
+            A dictionary containing eventually loss, logits and predictions.
         """
         # Encode
         features = self.feat_extractor(x)["features"]

doctr/models/recognition/master/tensorflow.py

@@ -165,7 +165,7 @@ def call(
             **kwargs: keyword arguments passed to the decoder
 
         Returns:
-            A dictionnary containing eventually loss, logits and predictions.
+            A dictionary containing eventually loss, logits and predictions.
         """
         # Encode
         feature = self.feat_extractor(x, **kwargs)

doctr/models/recognition/viptr/pytorch.py

@@ -70,7 +70,7 @@ def ctc_best_path(
 
     def __call__(self, logits: torch.Tensor) -> list[tuple[str, float]]:
         """Performs decoding of raw output with CTC and decoding of CTC predictions
-        with label_to_idx mapping dictionnary
+        with label_to_idx mapping dictionary
 
         Args:
             logits: raw output of the model, shape (N, C + 1, seq_len)

references/detection/README.md

@@ -29,7 +29,7 @@ python references/detection/train_pytorch.py db_resnet50 --train_path path/to/yo
 
 We now use the built-in [`torchrun`](https://pytorch.org/docs/stable/elastic/run.html) launcher to spawn your DDP workers. `torchrun` will set all the necessary environment variables (`LOCAL_RANK`, `RANK`, etc.) for you. Arguments are the same than the ones from single GPU, except:
 
-- `--backend`: you can specify another `backend` for `DistribuedDataParallel` if the default one is not available on
+- `--backend`: you can specify another `backend` for `DistributedDataParallel` if the default one is not available on
 your operating system. Fastest one is `nccl` according to [PyTorch Documentation](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html).
 
 #### Key `torchrun` parameters:

references/recognition/README.md

@@ -29,7 +29,7 @@ python references/recognition/train_pytorch.py crnn_vgg16_bn --train_path path/t
 
 We now use the built-in [`torchrun`](https://pytorch.org/docs/stable/elastic/run.html) launcher to spawn your DDP workers. `torchrun` will set all the necessary environment variables (`LOCAL_RANK`, `RANK`, etc.) for you. Arguments are the same than the ones from single GPU, except:
 
-- `--backend`: you can specify another `backend` for `DistribuedDataParallel` if the default one is not available on
+- `--backend`: you can specify another `backend` for `DistributedDataParallel` if the default one is not available on
 your operating system. Fastest one is `nccl` according to [PyTorch Documentation](https://pytorch.org/docs/stable/generated/torch.nn.parallel.DistributedDataParallel.html).
 
 #### Key `torchrun` parameters: