Fix typos

CONTRIBUTING.md

@@ -10,7 +10,7 @@ Code linting and auto-format (black) are not currently in place but open to cons
 
 A few specific differences from Google style (or black)
 1. Line length is 120 char. Going over is okay in some cases (e.g. I prefer not to break URL across lines).
-2. Hanging indents are always prefered, please avoid aligning arguments with closing brackets or braces.
+2. Hanging indents are always preferred, please avoid aligning arguments with closing brackets or braces.
 
 Example, from Google guide, but this is a NO here:
 ```

README.md

@@ -238,7 +238,7 @@ Add a set of new very well trained ResNet & ResNet-V2 18/34 (basic block) weight
 ### May 14, 2024
 * Support loading PaliGemma jax weights into SigLIP ViT models with average pooling.
 * Add Hiera models from Meta (https://github.com/facebookresearch/hiera).
-* Add `normalize=` flag for transorms, return non-normalized torch.Tensor with original dytpe (for `chug`)
+* Add `normalize=` flag for transforms, return non-normalized torch.Tensor with original dytpe (for `chug`)
 * Version 1.0.3 release
 
 ### May 11, 2024

hfdocs/source/changes.mdx

@@ -93,7 +93,7 @@
 ### May 14, 2024
 * Support loading PaliGemma jax weights into SigLIP ViT models with average pooling.
 * Add Hiera models from Meta (https://github.com/facebookresearch/hiera).
-* Add `normalize=` flag for transorms, return non-normalized torch.Tensor with original dytpe (for `chug`)
+* Add `normalize=` flag for transforms, return non-normalized torch.Tensor with original dytpe (for `chug`)
 * Version 1.0.3 release
 
 ### May 11, 2024
@@ -125,7 +125,7 @@
 ### April 11, 2024
 * Prepping for a long overdue 1.0 release, things have been stable for a while now.
 * Significant feature that's been missing for a while, `features_only=True` support for ViT models with flat hidden states or non-std module layouts (so far covering  `'vit_*', 'twins_*', 'deit*', 'beit*', 'mvitv2*', 'eva*', 'samvit_*', 'flexivit*'`)
-* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or direclty.
+* Above feature support achieved through a new `forward_intermediates()` API that can be used with a feature wrapping module or directly.
 ```python
 model = timm.create_model('vit_base_patch16_224')
 final_feat, intermediates = model.forward_intermediates(input)
@@ -360,7 +360,7 @@ Datasets & transform refactoring
 * 0.8.15dev0
 
 ### Feb 20, 2023
-* Add 320x320 `convnext_large_mlp.clip_laion2b_ft_320` and `convnext_lage_mlp.clip_laion2b_ft_soup_320` CLIP image tower weights for features & fine-tune
+* Add 320x320 `convnext_large_mlp.clip_laion2b_ft_320` and `convnext_large_mlp.clip_laion2b_ft_soup_320` CLIP image tower weights for features & fine-tune
 * 0.8.13dev0 pypi release for latest changes w/ move to huggingface org
 
 ### Feb 16, 2023
@@ -745,7 +745,7 @@ More models, more fixes
 * Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler
 * Gradient checkpointing support added to many models
 * `forward_head(x, pre_logits=False)` fn added to all models to allow separate calls of `forward_features` + `forward_head`
-* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `foward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head`
+* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `forward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head`
 
 ### Feb 2, 2022
 * [Chris Hughes](https://github.com/Chris-hughes10) posted an exhaustive run through of `timm` on his blog yesterday. Well worth a read. [Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055)
@@ -1058,7 +1058,7 @@ More models, more fixes
 * Add 'group matching' API to all models to allow grouping model parameters for application of 'layer-wise' LR decay, lr scale added to LR scheduler
 * Gradient checkpointing support added to many models
 * `forward_head(x, pre_logits=False)` fn added to all models to allow separate calls of `forward_features` + `forward_head`
-* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `foward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head`
+* All vision transformer and vision MLP models update to return non-pooled / non-token selected features from `forward_features`, for consistency with CNN models, token selection or pooling now applied in `forward_head`
 
 ### Feb 2, 2022
 * [Chris Hughes](https://github.com/Chris-hughes10) posted an exhaustive run through of `timm` on his blog yesterday. Well worth a read. [Getting Started with PyTorch Image Models (timm): A Practitioner’s Guide](https://towardsdatascience.com/getting-started-with-pytorch-image-models-timm-a-practitioners-guide-4e77b4bf9055)

hfdocs/source/models/adversarial-inception-v3.mdx

@@ -1,6 +1,6 @@
 # Adversarial Inception v3
 
-**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
+**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 
 This particular model was trained for study of adversarial examples (adversarial training).
 

hfdocs/source/models/gloun-inception-v3.mdx

@@ -1,6 +1,6 @@
 # (Gluon) Inception v3
 
-**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
+**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 
 The weights from this model were ported from [Gluon](https://cv.gluon.ai/model_zoo/classification.html).
 

hfdocs/source/models/inception-v3.mdx

@@ -1,6 +1,6 @@
 # Inception v3
 
-**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
+**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 
 ## How do I use this model on an image?
 

hfdocs/source/models/tf-inception-v3.mdx

@@ -1,6 +1,6 @@
 # (Tensorflow) Inception v3
 
-**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifer](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
+**Inception v3** is a convolutional neural network architecture from the Inception family that makes several improvements including using [Label Smoothing](https://paperswithcode.com/method/label-smoothing), Factorized 7 x 7 convolutions, and the use of an [auxiliary classifier](https://paperswithcode.com/method/auxiliary-classifier) to propagate label information lower down the network (along with the use of batch normalization for layers in the sidehead). The key building block is an [Inception Module](https://paperswithcode.com/method/inception-v3-module).
 
 The weights from this model were ported from [Tensorflow/Models](https://github.com/tensorflow/models).
 

timm/data/auto_augment.py

@@ -954,7 +954,7 @@ def augment_and_mix_transform(config_str: str, hparams: Optional[Dict] = None):
     Args:
         config_str (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
+            The remaining sections, not order specific determine
                 'm' - integer magnitude (severity) of augmentation mix (default: 3)
                 'w' - integer width of augmentation chain (default: 3)
                 'd' - integer depth of augmentation chain (-1 is random [1, 3], default: -1)

timm/data/imagenet_info.py

@@ -52,7 +52,7 @@ class ImageNetInfo(DatasetInfo):
         subset = re.sub(r'[-_\s]', '', subset.lower())
         assert subset in _SUBSETS, f'Unknown imagenet subset {subset}.'
 
-        # WordNet synsets (part-of-speach + offset) are the unique class label names for ImageNet classifiers
+        # WordNet synsets (part-of-speech + offset) are the unique class label names for ImageNet classifiers
         synset_file = _SUBSETS[subset]
         synset_data = pkgutil.get_data(__name__, os.path.join('_info', synset_file))
         self._synsets = synset_data.decode('utf-8').splitlines()

timm/data/readers/reader_hfids.py

@@ -80,7 +80,7 @@ class ReaderHfids(Reader):
             self.num_samples = split_info.num_examples
         else:
             raise ValueError(
-                "Dataset length is unknown, please pass `num_samples` explicitely. "
+                "Dataset length is unknown, please pass `num_samples` explicitly. "
                 "The number of steps needs to be known in advance for the learning rate scheduler."
             )
 

timm/data/readers/reader_image_folder.py

@@ -25,7 +25,7 @@ def find_images_and_targets(
     """ Walk folder recursively to discover images and map them to classes by folder names.
 
     Args:
-        folder: root of folder to recrusively search
+        folder: root of folder to recursively search
         types: types (file extensions) to search for in path
         class_to_idx: specify mapping for class (folder name) to class index if set
         leaf_name_only: use only leaf-name of folder walk for class names

timm/data/readers/reader_wds.py

@@ -124,7 +124,7 @@ def _parse_split_info(split: str, info: Dict):
 
 
 def log_and_continue(exn):
-    """Call in an exception handler to ignore exceptions, isssue a warning, and continue."""
+    """Call in an exception handler to ignore exceptions, issue a warning, and continue."""
     _logger.warning(f'Handling webdataset error ({repr(exn)}). Ignoring.')
     # NOTE: try force an exit on errors that are clearly code / config and not transient
     if isinstance(exn, TypeError):
@@ -277,7 +277,7 @@ class ReaderWds(Reader):
             target_img_mode: str = '',
             filename_key: str = 'filename',
             sample_shuffle_size: Optional[int] = None,
-            smaple_initial_size: Optional[int] = None,
+            sample_initial_size: Optional[int] = None,
     ):
         super().__init__()
         if wds is None:
@@ -290,7 +290,7 @@ class ReaderWds(Reader):
         self.common_seed = seed  # a seed that's fixed across all worker / distributed instances
         self.shard_shuffle_size = 500
         self.sample_shuffle_size = sample_shuffle_size or SAMPLE_SHUFFLE_SIZE
-        self.sample_initial_size = smaple_initial_size or SAMPLE_INITIAL_SIZE
+        self.sample_initial_size = sample_initial_size or SAMPLE_INITIAL_SIZE
 
         self.input_key = input_key
         self.input_img_mode = input_img_mode

timm/layers/activations.py

@@ -47,7 +47,7 @@ def sigmoid(x, inplace: bool = False):
     return x.sigmoid_() if inplace else x.sigmoid()
 
 
-# PyTorch has this, but not with a consistent inplace argmument interface
+# PyTorch has this, but not with a consistent inplace argument interface
 class Sigmoid(nn.Module):
     def __init__(self, inplace: bool = False):
         super(Sigmoid, self).__init__()
@@ -61,7 +61,7 @@ def tanh(x, inplace: bool = False):
     return x.tanh_() if inplace else x.tanh()
 
 
-# PyTorch has this, but not with a consistent inplace argmument interface
+# PyTorch has this, but not with a consistent inplace argument interface
 class Tanh(nn.Module):
     def __init__(self, inplace: bool = False):
         super(Tanh, self).__init__()

timm/layers/attention2d.py

@@ -16,7 +16,7 @@ class MultiQueryAttentionV2(nn.Module):
     Fast Transformer Decoding: One Write-Head is All You Need
     https://arxiv.org/pdf/1911.02150.pdf
 
-    This is an acceletor optimized version - removing multiple unneccessary
+    This is an acceletor optimized version - removing multiple unnecessary
     tensor transpose by re-arranging indices according to the following rules: 1)
     contracted indices are at the end, 2) other indices have the same order in the
     input and output tensores.
@@ -87,7 +87,7 @@ class MultiQueryAttention2d(nn.Module):
      2. query_strides: horizontal & vertical strides on Query only.
 
     This is an optimized version.
-    1. Projections in Attention is explict written out as 1x1 Conv2D.
+    1. Projections in Attention is explicit written out as 1x1 Conv2D.
     2. Additional reshapes are introduced to bring a up to 3x speed up.
     """
     fused_attn: torch.jit.Final[bool]

timm/layers/create_norm_act.py

@@ -1,7 +1,7 @@
-""" NormAct (Normalizaiton + Activation Layer) Factory
+""" NormAct (Normalization + Activation Layer) Factory
 
 Create norm + act combo modules that attempt to be backwards compatible with separate norm + act
-isntances in models. Where these are used it will be possible to swap separate BN + act layers with
+instances in models. Where these are used it will be possible to swap separate BN + act layers with
 combined modules like IABN or EvoNorms.
 
 Hacked together by / Copyright 2020 Ross Wightman

timm/layers/weight_init.py

@@ -78,7 +78,7 @@ def trunc_normal_tf_(tensor, mean=0., std=1., a=-2., b=2.):
 
     NOTE: this 'tf' variant behaves closer to Tensorflow / JAX impl where the
     bounds [a, b] are applied when sampling the normal distribution with mean=0, std=1.0
-    and the result is subsquently scaled and shifted by the mean and std args.
+    and the result is subsequently scaled and shifted by the mean and std args.
 
     Args:
         tensor: an n-dimensional `torch.Tensor`

timm/models/_efficientnet_blocks.py

@@ -490,7 +490,7 @@ class MobileAttention(nn.Module):
         # https://arxiv.org/abs/2102.10882
         # 1. Rather than adding one CPE before the attention blocks, we add a CPE
         #    into every attention block.
-        # 2. We replace the expensive Conv2D by a Seperable DW Conv.
+        # 2. We replace the expensive Conv2D by a Separable DW Conv.
         if use_cpe:
             self.conv_cpe_dw = create_conv2d(
                 in_chs, in_chs,

timm/models/_features.py

@@ -32,7 +32,7 @@ def feature_take_indices(
 ) -> Tuple[List[int], int]:
     """ Determine the absolute feature indices to 'take' from.
 
-    Note: This function can be called in forwar() so must be torchscript compatible,
+    Note: This function can be called in forward() so must be torchscript compatible,
     which requires some incomplete typing and workaround hacks.
 
     Args:

timm/models/byobnet.py

@@ -611,7 +611,7 @@ class RepVggBlock(nn.Module):
         return kernel_final, bias_final
 
     def _fuse_bn_tensor(self, branch) -> Tuple[torch.Tensor, torch.Tensor]:
-        """ Method to fuse batchnorm layer with preceeding conv layer.
+        """ Method to fuse batchnorm layer with preceding conv layer.
         Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
         """
         if isinstance(branch, ConvNormAct):
@@ -800,7 +800,7 @@ class MobileOneBlock(nn.Module):
         return kernel_final, bias_final
 
     def _fuse_bn_tensor(self, branch) -> Tuple[torch.Tensor, torch.Tensor]:
-        """ Method to fuse batchnorm layer with preceeding conv layer.
+        """ Method to fuse batchnorm layer with preceding conv layer.
         Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
         """
         if isinstance(branch, ConvNormAct):

timm/models/crossvit.py

@@ -21,7 +21,7 @@ Modifications and additions for timm hacked together by / Copyright 2021, Ross W
 
 
 """
-Modifed from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
+Modified from Timm. https://github.com/rwightman/pytorch-image-models/blob/master/timm/models/vision_transformer.py
 
 """
 from functools import partial

timm/models/efficientvit_msra.py

@@ -246,7 +246,7 @@ class LocalWindowAttention(torch.nn.Module):
     def forward(self, x):
         H = W = self.resolution
         B, C, H_, W_ = x.shape
-        # Only check this for classifcation models
+        # Only check this for classification models
         _assert(H == H_, f'input feature has wrong size, expect {(H, W)}, got {(H_, W_)}')
         _assert(W == W_, f'input feature has wrong size, expect {(H, W)}, got {(H_, W_)}')
         if H <= self.window_resolution and W <= self.window_resolution:

timm/models/fastvit.py

@@ -231,7 +231,7 @@ class MobileOneBlock(nn.Module):
     def _fuse_bn_tensor(
         self, branch: Union[nn.Sequential, nn.BatchNorm2d]
     ) -> Tuple[torch.Tensor, torch.Tensor]:
-        """Method to fuse batchnorm layer with preceeding conv layer.
+        """Method to fuse batchnorm layer with preceding conv layer.
         Reference: https://github.com/DingXiaoH/RepVGG/blob/main/repvgg.py#L95
 
         Args:

timm/models/focalnet.py

@@ -78,7 +78,7 @@ class FocalModulation(nn.Module):
         x = self.f(x)
         q, ctx, gates = torch.split(x, self.input_split, 1)
 
-        # context aggreation
+        # context aggregation
         ctx_all = 0
         for l, focal_layer in enumerate(self.focal_layers):
             ctx = focal_layer(ctx)
@@ -353,7 +353,7 @@ class FocalNet(nn.Module):
             focal_levels: How many focal levels at all stages. Note that this excludes the finest-grain level.
             focal_windows: The focal window size at all stages.
             use_overlap_down: Whether to use convolutional embedding.
-            use_post_norm: Whether to use layernorm after modulation (it helps stablize training of large models)
+            use_post_norm: Whether to use layernorm after modulation (it helps stabilize training of large models)
             layerscale_value: Value for layer scale.
             drop_rate: Dropout rate.
             drop_path_rate: Stochastic depth rate.

timm/models/metaformer.py

@@ -618,7 +618,7 @@ class MetaFormer(nn.Module):
         return x
 
 
-# this works but it's long and breaks backwards compatability with weights from the poolformer-only impl
+# this works but it's long and breaks backwards compatibility with weights from the poolformer-only impl
 def checkpoint_filter_fn(state_dict, model):
     if 'stem.conv.weight' in state_dict:
         return state_dict

timm/models/regnet.py

@@ -175,7 +175,7 @@ def create_shortcut(
 class Bottleneck(nn.Module):
     """ RegNet Bottleneck
 
-    This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
+    This is almost exactly the same as a ResNet Bottleneck. The main difference is the SE block is moved from
     after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
     """
 
@@ -250,7 +250,7 @@ class Bottleneck(nn.Module):
 class PreBottleneck(nn.Module):
     """ RegNet Bottleneck
 
-    This is almost exactly the same as a ResNet Bottlneck. The main difference is the SE block is moved from
+    This is almost exactly the same as a ResNet Bottleneck. The main difference is the SE block is moved from
     after conv3 to after conv2. Otherwise, it's just redefining the arguments for groups/bottleneck channels.
     """
 

timm/models/resnetv2.py

@@ -4,7 +4,7 @@ A PyTorch implementation of ResNetV2 adapted from the Google Big-Transfer (BiT)
 at https://github.com/google-research/big_transfer to match timm interfaces. The BiT weights have
 been included here as pretrained models from their original .NPZ checkpoints.
 
-Additionally, supports non pre-activation bottleneck for use as a backbone for Vision Transfomers (ViT) and
+Additionally, supports non pre-activation bottleneck for use as a backbone for Vision Transformers (ViT) and
 extra padding support to allow porting of official Hybrid ResNet pretrained weights from
 https://github.com/google-research/vision_transformer
 
@@ -436,7 +436,7 @@ class ResNetV2(nn.Module):
             stem_chs (int): stem width (default: 64)
             stem_type (str): stem type (default: '' == 7x7)
             avg_down (bool): average pooling in residual downsampling (default: False)
-            preact (bool): pre-activiation (default: True)
+            preact (bool): pre-activation (default: True)
             act_layer (Union[str, nn.Module]): activation layer
             norm_layer (Union[str, nn.Module]): normalization layer
             conv_layer (nn.Module): convolution module

timm/models/twins.py

@@ -280,7 +280,7 @@ class PatchEmbed(nn.Module):
 
 
 class Twins(nn.Module):
-    """ Twins Vision Transfomer (Revisiting Spatial Attention)
+    """ Twins Vision Transformer (Revisiting Spatial Attention)
 
     Adapted from PVT (PyramidVisionTransformer) class at https://github.com/whai362/PVT.git
     """

timm/models/vision_transformer_sam.py

@@ -364,7 +364,7 @@ class VisionTransformerSAM(nn.Module):
             img_size: Input image size.
             patch_size: Patch size.
             in_chans: Number of image input channels.
-            num_classes: Mumber of classes for classification head.
+            num_classes: Number of classes for classification head.
             global_pool: Type of global pooling for final sequence (default: 'token').
             embed_dim: Transformer embedding dimension.
             depth: Depth of transformer.
@@ -667,7 +667,7 @@ def _cfg(url='', **kwargs):
 
 default_cfgs = generate_default_cfgs({
 
-    # Segment-Anyhing Model (SAM) pretrained - https://github.com/facebookresearch/segment-anything (no classifier head, for fine-tune/features only)
+    # Segment-Anything Model (SAM) pretrained - https://github.com/facebookresearch/segment-anything (no classifier head, for fine-tune/features only)
     'samvit_base_patch16.sa1b': _cfg(
         url='https://dl.fbaipublicfiles.com/segment_anything/sam_vit_b_01ec64.pth',
         hf_hub_id='timm/',

timm/models/volo.py

@@ -507,7 +507,7 @@ class VOLO(nn.Module):
         )
         r = patch_size
 
-        # inital positional encoding, we add positional encoding after outlooker blocks
+        # initial positional encoding, we add positional encoding after outlooker blocks
         patch_grid = (img_size[0] // patch_size // pooling_scale, img_size[1] // patch_size // pooling_scale)
         self.pos_embed = nn.Parameter(torch.zeros(1, patch_grid[0], patch_grid[1], embed_dims[-1]))
         self.pos_drop = nn.Dropout(p=pos_drop_rate)

timm/optim/adabelief.py

@@ -35,7 +35,7 @@ class AdaBelief(Optimizer):
 
     For a complete table of recommended hyperparameters, see https://github.com/juntang-zhuang/Adabelief-Optimizer'
     For example train/args for EfficientNet see these gists
-      - link to train_scipt: https://gist.github.com/juntang-zhuang/0a501dd51c02278d952cf159bc233037
+      - link to train_script: https://gist.github.com/juntang-zhuang/0a501dd51c02278d952cf159bc233037
       - link to args.yaml: https://gist.github.com/juntang-zhuang/517ce3c27022b908bb93f78e4f786dc3
     """
 

timm/optim/adahessian.py

@@ -80,7 +80,7 @@ class Adahessian(torch.optim.Optimizer):
 
     def zero_hessian(self):
         """
-        Zeros out the accumalated hessian traces.
+        Zeros out the accumulated hessian traces.
         """
 
         for p in self.get_params():

timm/utils/jit.py

@@ -10,7 +10,7 @@ import torch
 def set_jit_legacy():
     """ Set JIT executor to legacy w/ support for op fusion
     This is hopefully a temporary need in 1.5/1.5.1/1.6 to restore performance due to changes
-    in the JIT exectutor. These API are not supported so could change.
+    in the JIT executor. These API are not supported so could change.
     """
     #
     assert hasattr(torch._C, '_jit_set_profiling_executor'), "Old JIT behavior doesn't exist!"

timm/utils/model.py

@@ -62,7 +62,7 @@ class ActivationStatsHook:
     Inspiration from https://docs.fast.ai/callback.hook.html.
 
     Refer to https://gist.github.com/amaarora/6e56942fcb46e67ba203f3009b30d950 for an example 
-    on how to plot Signal Propogation Plots using `ActivationStatsHook`.
+    on how to plot Signal Propagation Plots using `ActivationStatsHook`.
     """
 
     def __init__(self, model, hook_fn_locs, hook_fns):
@@ -96,7 +96,7 @@ def extract_spp_stats(
         hook_fns,
         input_shape=[8, 3, 224, 224]):
     """Extract average square channel mean and variance of activations during 
-        forward pass to plot Signal Propogation Plots (SPP).
+        forward pass to plot Signal Propagation Plots (SPP).
     
     Paper: https://arxiv.org/abs/2101.08692