Revert "much stricter ruff linting"

This reverts commit 310dbf1574.

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

@@ -66,7 +66,7 @@ def attention_forward(self, x, resolution, shared_rel_pos_bias: Optional[torch.T
     """
     Modification of timm.models.beit.py: Attention.forward to support arbitrary window sizes.
     """
-    B, N, _C = x.shape
+    B, N, C = x.shape
 
     qkv_bias = torch.cat((self.q_bias, self.k_bias, self.v_bias)) if self.q_bias is not None else None
     qkv = F.linear(input=x, weight=self.qkv.weight, bias=qkv_bias)

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

@@ -81,7 +81,7 @@ def forward_default(pretrained, x, function_name="forward_features"):
 
 
 def forward_adapted_unflatten(pretrained, x, function_name="forward_features"):
-    _b, _c, h, w = x.shape
+    b, c, h, w = x.shape
 
     exec(f"glob = pretrained.model.{function_name}(x)")
 

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

@@ -31,7 +31,7 @@ def _resize_pos_embed(self, posemb, gs_h, gs_w):
 
 
 def forward_flex(self, x):
-    _b, _c, h, w = x.shape
+    b, c, h, w = x.shape
 
     pos_embed = self._resize_pos_embed(
         self.pos_embed, h // self.patch_size[1], w // self.patch_size[0]

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

@@ -5,6 +5,10 @@ from .backbones.beit import (
     _make_pretrained_beitl16_512,
     _make_pretrained_beitl16_384,
     _make_pretrained_beitb16_384,
+    forward_beit,
+)
+from .backbones.swin_common import (
+    forward_swin,
 )
 from .backbones.swin2 import (
     _make_pretrained_swin2l24_384,
@@ -16,11 +20,13 @@ from .backbones.swin import (
 )
 from .backbones.levit import (
     _make_pretrained_levit_384,
+    forward_levit,
 )
 from .backbones.vit import (
     _make_pretrained_vitb_rn50_384,
     _make_pretrained_vitl16_384,
     _make_pretrained_vitb16_384,
+    forward_vit,
 )
 
 def _make_encoder(backbone, features, use_pretrained, groups=1, expand=False, exportable=True, hooks=None,

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

@@ -6,7 +6,7 @@ import torch
 import torch.nn as nn
 
 from .base_model import BaseModel
-from .blocks import FeatureFusionBlock_custom, Interpolate, _make_encoder
+from .blocks import FeatureFusionBlock, FeatureFusionBlock_custom, Interpolate, _make_encoder
 
 
 class MidasNet_small(BaseModel):

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

@@ -100,7 +100,7 @@ class AttractorLayer(nn.Module):
         A = self._net(x)
         eps = 1e-3
         A = A + eps
-        n, _c, h, w = A.shape
+        n, c, h, w = A.shape
         A = A.view(n, self.n_attractors, 2, h, w)
         A_normed = A / A.sum(dim=2, keepdim=True)  # n, a, 2, h, w
         A_normed = A[:, :, 0, ...]  # n, na, h, w
@@ -177,7 +177,7 @@ class AttractorLayerUnnormed(nn.Module):
             x = x + prev_b_embedding
 
         A = self._net(x)
-        _n, _c, h, w = A.shape
+        n, c, h, w = A.shape
 
         b_prev = nn.functional.interpolate(
             b_prev, (h, w), mode='bilinear', align_corners=True)

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

@@ -146,7 +146,7 @@ class LinearSplitter(nn.Module):
         S = self._net(x)
         eps = 1e-3
         S = S + eps
-        n, _c, h, w = S.shape
+        n, c, h, w = S.shape
         S = S.view(n, self.prev_nbins, self.split_factor, h, w)
         S_normed = S / S.sum(dim=2, keepdim=True)  # fractional splits
 

modules/control/proc/zoe/zoedepth/models/zoedepth/zoedepth_v1.py

@@ -26,13 +26,13 @@ import itertools
 
 import torch
 import torch.nn as nn
-from ..depth_model import DepthModel  # noqa: TID252
-from ..base_models.midas import MidasCore  # noqa: TID252
-from ..layers.attractor import AttractorLayer, AttractorLayerUnnormed  # noqa: TID252
-from ..layers.dist_layers import ConditionalLogBinomial  # noqa: TID252
-from ..layers.localbins_layers import (Projector, SeedBinRegressor,  # noqa: TID252
+from ..depth_model import DepthModel
+from ..base_models.midas import MidasCore
+from ..layers.attractor import AttractorLayer, AttractorLayerUnnormed
+from ..layers.dist_layers import ConditionalLogBinomial
+from ..layers.localbins_layers import (Projector, SeedBinRegressor,
                                             SeedBinRegressorUnnormed)
-from ..model_io import load_state_from_resource  # noqa: TID252
+from ..model_io import load_state_from_resource
 
 
 class ZoeDepth(DepthModel):
@@ -139,7 +139,7 @@ class ZoeDepth(DepthModel):
                 - probs (torch.Tensor): Output probability distribution of shape (B, n_bins, H, W). Present only if return_probs is True
 
         """
-        b, _c, h, w = x.shape
+        b, c, h, w = x.shape
         # print("input shape ", x.shape)
         self.orig_input_width = w
         self.orig_input_height = h

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

@@ -27,14 +27,14 @@ import itertools
 import torch
 import torch.nn as nn
 
-from ..depth_model import DepthModel  # noqa: TID252
-from ..base_models.midas import MidasCore  # noqa: TID252
-from ..layers.attractor import AttractorLayer, AttractorLayerUnnormed  # noqa: TID252
-from ..layers.dist_layers import ConditionalLogBinomial  # noqa: TID252
-from ..layers.localbins_layers import (Projector, SeedBinRegressor,  # noqa: TID252
+from ..depth_model import DepthModel
+from ..base_models.midas import MidasCore
+from ..layers.attractor import AttractorLayer, AttractorLayerUnnormed
+from ..layers.dist_layers import ConditionalLogBinomial
+from ..layers.localbins_layers import (Projector, SeedBinRegressor,
                                             SeedBinRegressorUnnormed)
-from ..layers.patch_transformer import PatchTransformerEncoder  # noqa: TID252
-from ..model_io import load_state_from_resource  # noqa: TID252
+from ..layers.patch_transformer import PatchTransformerEncoder
+from ..model_io import load_state_from_resource
 
 class ZoeDepthNK(DepthModel):
     def __init__(self, core,  bin_conf, bin_centers_type="softplus", bin_embedding_dim=128,
@@ -173,10 +173,10 @@ class ZoeDepthNK(DepthModel):
                 - "bin_centers": Bin centers of shape (B, N, H, W). Present only if return_final_centers is True
                 - "probs": Bin probabilities of shape (B, N, H, W). Present only if return_probs is True
         """
-        b, _c, h, w = x.shape
+        b, c, h, w = x.shape
         self.orig_input_width = w
         self.orig_input_height = h
-        _rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True)
+        rel_depth, out = self.core(x, denorm=denorm, return_rel_depth=True)
 
         outconv_activation = out[0]
         btlnck = out[1]