Merge pull request #17 from didier-durand/fix-typos

fixing typos in various texts

Author: ballasnicolas

README.md

@@ -310,7 +310,7 @@ python -m notebooks.vjepa2_demo
 
 ### Probe-based evaluation
 
-Probe-based evaluation consist in training an attentive probe on top of frozen V-JEPA 2 features. We provide training scripts for training your own probes, and checkpoints to run inference directly.
+Probe-based evaluation consists in training an attentive probe on top of frozen V-JEPA 2 features. We provide training scripts for training your own probes, and checkpoints to run inference directly.
 
 #### Training probes
 

app/vjepa_droid/droid.py

@@ -127,7 +127,7 @@ def __getitem__(self, index):
                 buffer, actions, states, extrinsics, indices = self.loadvideo_decord(path)
                 loaded_video = True
             except Exception as e:
-                logger.info(f"Ecountered exception when loading video {path=} {e=}")
+                logger.info(f"Encountered exception when loading video {path=} {e=}")
                 loaded_video = False
                 index = np.random.randint(self.__len__())
                 path = self.samples[index]

evals/action_anticipation_frozen/epickitchens.py

@@ -55,7 +55,7 @@ def get_dataset_size(shards_list):
 
 
 def log_and_continue(exn):
-    """Call in an exception handler to ignore any exception, isssue a warning, and continue."""
+    """Call in an exception handler to ignore any exception, issue a warning, and continue."""
     logging.warning(f"Handling webdataset error ({repr(exn)}). Ignoring.")
     return True
 

evals/image_classification_frozen/eval.py

@@ -367,7 +367,7 @@ def make_dataloader(
         normalization = DEFAULT_NORMALIZATION
 
     if training:
-        logger.info("implementing auto-agument strategy")
+        logger.info("implementing auto-augment strategy")
         transform = timm_make_transforms(
             input_size=img_size,
             is_training=training,

notebooks/utils/mpc_utils.py

@@ -48,7 +48,7 @@ def cem(
     :param context_frame: [B=1, T=1, HW, D]
     :param goal_frame: [B=1, T=1, HW, D]
     :param world_model: f(context_frame, action) -> next_frame [B, 1, HW, D]
-    :return: [B=1, rollout, 7] an action trjectory over rollout horizon
+    :return: [B=1, rollout, 7] an action trajectory over rollout horizon
 
     Cross-Entropy Method
     -----------------------

src/datasets/utils/dataloader.py

@@ -213,7 +213,7 @@ def _next_data(self):
                     continue
 
             if idx != self._rcvd_idx:
-                # Tombstone to recieve later
+                # Tombstone to receive later
                 self._task_info[idx] = None
                 if isinstance(data, ExceptionWrapper):
                     data.reraise()

src/datasets/utils/video/randaugment.py

@@ -494,10 +494,10 @@ def rand_augment_transform(config_str, hparams):
     Create a RandAugment transform
     :param config_str: String defining configuration of random augmentation. Consists of multiple sections separated by
     dashes ('-'). The first section defines the specific variant of rand augment (currently only 'rand'). The remaining
-    sections, not order sepecific determine
+    sections, not order specific determine
         'm' - integer magnitude of rand augment
         'n' - integer num layers (number of transform ops selected per image)
-        'w' - integer probabiliy weight index (index of a set of weights to influence choice of op)
+        'w' - integer probability weight index (index of a set of weights to influence choice of op)
         'mstd' -  float std deviation of magnitude noise applied
         'inc' - integer (bool), use augmentations that increase in severity with magnitude (default: 0)
     Ex 'rand-m9-n3-mstd0.5' results in RandAugment with magnitude 9, num_layers 3, magnitude_std 0.5

src/models/ac_predictor.py

@@ -137,7 +137,7 @@ def forward(self, x, actions, states, extrinsics=None):
         """
         :param x: context tokens
         """
-        # Map tokens to pedictor dimensions
+        # Map tokens to predictor dimensions
         x = self.predictor_embed(x)
         B, N_ctxt, D = x.size()
         T = N_ctxt // (self.grid_height * self.grid_width)