full codespell coverage

Signed-off-by: Vladimir Mandic <mandic00@live.com>

modules/control/proc/depth_anything/util/transform.py

@@ -131,7 +131,7 @@ class Resize(object):
                     # fit height
                     scale_width = scale_height
             elif self.__resize_method == "minimal":
-                # scale as least as possbile
+                # scale as least as possible
                 if abs(1 - scale_width) < abs(1 - scale_height):
                     # fit width
                     scale_height = scale_width
@@ -209,7 +209,7 @@ class Resize(object):
 
 
 class NormalizeImage(object):
-    """Normlize image by given mean and std.
+    """Normalize image by given mean and std.
     """
 
     def __init__(self, mean, std):

modules/control/proc/leres/leres/depthmap.py

@@ -49,7 +49,7 @@ def estimateleres(img, model, w, h):
     return prediction
 
 def generatemask(size):
-    # Generates a Guassian mask
+    # Generates a Gaussian mask
     mask = np.zeros(size, dtype=np.float32)
     sigma = int(size[0]/16)
     k_size = int(2 * np.ceil(2 * int(size[0]/16)) + 1)
@@ -395,7 +395,7 @@ def estimateboost(img, model, model_type, pix2pixmodel, max_res=512, depthmap_sc
     gc.collect()
     torch_gc()
 
-    # Generate mask used to smoothly blend the local pathc estimations to the base estimate.
+    # Generate mask used to smoothly blend the local patch estimations to the base estimate.
     # It is arbitrarily large to avoid artifacts during rescaling for each crop.
     mask_org = generatemask((3000, 3000))
     mask = mask_org.copy()

modules/control/proc/leres/pix2pix/models/base_model.py

@@ -136,7 +136,7 @@ class BaseModel(ABC):
         return visual_ret
 
     def get_current_losses(self):
-        """Return traning losses / errors. train.py will print out these errors on console, and save them to a file"""
+        """Return training losses / errors. train.py will print out these errors on console, and save them to a file"""
         errors_ret = OrderedDict()
         for name in self.loss_names:
             if isinstance(name, str):
@@ -229,7 +229,7 @@ class BaseModel(ABC):
         print('-----------------------------------------------')
 
     def set_requires_grad(self, nets, requires_grad=False):
-        """Set requies_grad=Fasle for all the networks to avoid unnecessary computations
+        """Set requies_grad=False for all the networks to avoid unnecessary computations
         Parameters:
             nets (network list)   -- a list of networks
             requires_grad (bool)  -- whether the networks require gradients or not

modules/control/proc/leres/pix2pix/models/networks.py

@@ -255,7 +255,7 @@ class GANLoss(nn.Module):
         """Create label tensors with the same size as the input.
 
         Parameters:
-            prediction (tensor) - - tpyically the prediction from a discriminator
+            prediction (tensor) - - typically the prediction from a discriminator
             target_is_real (bool) - - if the ground truth label is for real images or fake images
 
         Returns:
@@ -272,7 +272,7 @@ class GANLoss(nn.Module):
         """Calculate loss given Discriminator's output and grount truth labels.
 
         Parameters:
-            prediction (tensor) - - tpyically the prediction output from a discriminator
+            prediction (tensor) - - typically the prediction output from a discriminator
             target_is_real (bool) - - if the ground truth label is for real images or fake images
 
         Returns:

modules/control/proc/leres/pix2pix/models/pix2pix4depth_model.py

@@ -9,7 +9,7 @@ class Pix2Pix4DepthModel(BaseModel):
     The model training requires '--dataset_mode aligned' dataset.
     By default, it uses a '--netG unet256' U-Net generator,
     a '--netD basic' discriminator (PatchGAN),
-    and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the orignal GAN paper).
+    and a '--gan_mode' vanilla GAN loss (the cross-entropy objective used in the original GAN paper).
 
     pix2pix paper: https://arxiv.org/pdf/1611.07004.pdf
     """
@@ -152,4 +152,4 @@ class Pix2Pix4DepthModel(BaseModel):
         self.set_requires_grad(self.netD, False)  # D requires no gradients when optimizing G
         self.optimizer_G.zero_grad()        # set G's gradients to zero
         self.backward_G()                   # calculate graidents for G
-        self.optimizer_G.step()             # udpate G's weights
+        self.optimizer_G.step()             # update G's weights

modules/control/proc/leres/pix2pix/options/base_options.py

@@ -14,7 +14,7 @@ class BaseOptions():
     """
 
     def __init__(self):
-        """Reset the class; indicates the class hasn't been initailized"""
+        """Reset the class; indicates the class hasn't been initialized"""
         self.initialized = False
 
     def initialize(self, parser):

modules/control/proc/midas/midas/transforms.py

@@ -125,7 +125,7 @@ class Resize(object):
                     # fit height
                     scale_width = scale_height
             elif self.__resize_method == "minimal":
-                # scale as least as possbile
+                # scale as least as possible
                 if abs(1 - scale_width) < abs(1 - scale_height):
                     # fit width
                     scale_height = scale_width
@@ -195,7 +195,7 @@ class Resize(object):
 
 
 class NormalizeImage(object):
-    """Normlize image by given mean and std.
+    """Normalize image by given mean and std.
     """
 
     def __init__(self, mean, std):

modules/control/proc/openpose/body.py

@@ -109,7 +109,7 @@ class Body(object):
         limbSeq = [[2, 3], [2, 6], [3, 4], [4, 5], [6, 7], [7, 8], [2, 9], [9, 10], \
                    [10, 11], [2, 12], [12, 13], [13, 14], [2, 1], [1, 15], [15, 17], \
                    [1, 16], [16, 18], [3, 17], [6, 18]]
-        # the middle joints heatmap correpondence
+        # the middle joints heatmap correspondence
         mapIdx = [[31, 32], [39, 40], [33, 34], [35, 36], [41, 42], [43, 44], [19, 20], [21, 22], \
                   [23, 24], [25, 26], [27, 28], [29, 30], [47, 48], [49, 50], [53, 54], [51, 52], \
                   [55, 56], [37, 38], [45, 46]]

modules/control/proc/segment_anything/modeling/tiny_vit_sam.py

@@ -285,7 +285,7 @@ class TinyViTBlock(nn.Module):
 
     Args:
         dim (int): Number of input channels.
-        input_resolution (tuple[int, int]): Input resulotion.
+        input_resolution (tuple[int, int]): Input resolution.
         num_heads (int): Number of attention heads.
         window_size (int): Window size.
         mlp_ratio (float): Ratio of mlp hidden dim to embedding dim.

modules/control/proc/zoe/zoedepth/models/base_models/midas.py

@@ -134,7 +134,7 @@ class Resize(object):
                     # fit height
                     scale_width = scale_height
             elif self.__resize_method == "minimal":
-                # scale as least as possbile
+                # scale as least as possible
                 if abs(1 - scale_width) < abs(1 - scale_height):
                     # fit width
                     scale_height = scale_width

modules/control/proc/zoe/zoedepth/models/base_models/midas_repo/midas/transforms.py

@@ -125,7 +125,7 @@ class Resize(object):
                     # fit height
                     scale_width = scale_height
             elif self.__resize_method == "minimal":
-                # scale as least as possbile
+                # scale as least as possible
                 if abs(1 - scale_width) < abs(1 - scale_height):
                     # fit width
                     scale_height = scale_width
@@ -195,7 +195,7 @@ class Resize(object):
 
 
 class NormalizeImage(object):
-    """Normlize image by given mean and std.
+    """Normalize image by given mean and std.
     """
 
     def __init__(self, mean, std):

modules/control/proc/zoe/zoedepth/models/layers/localbins_layers.py

@@ -153,7 +153,7 @@ class LinearSplitter(nn.Module):
         b_prev = nn.functional.interpolate(b_prev, (h,w), mode='bilinear', align_corners=True)
 
 
-        b_prev = b_prev / b_prev.sum(dim=1, keepdim=True)  # renormalize for gurantees
+        b_prev = b_prev / b_prev.sum(dim=1, keepdim=True)  # renormalize for guarantees
         # print(b_prev.shape, S_normed.shape)
         # if is_for_query:(1).expand(-1, b_prev.size(0)//n, -1, -1, -1, -1).flatten(0,1)
         b = b_prev.unsqueeze(2) * S_normed

modules/control/proc/zoe/zoedepth/models/zoedepth_nk/zoedepth_nk_v1.py

@@ -63,7 +63,7 @@ class ZoeDepthNK(DepthModel):
             min_temp (int, optional): Lower bound for temperature of output probability distribution. Defaults to 5.
             max_temp (int, optional): Upper bound for temperature of output probability distribution. Defaults to 50.
 
-            memory_efficient (bool, optional): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended incase of multiple metric heads in order save GPU memory. Defaults to False.
+            memory_efficient (bool, optional): Whether to use memory efficient version of attractor layers. Memory efficient version is slower but is recommended in case of multiple metric heads in order save GPU memory. Defaults to False.
 
             train_midas (bool, optional): Whether to train "core", the base midas model. Defaults to True.
             is_midas_pretrained (bool, optional): Is "core" pretrained? Defaults to True.