style: formatting

luxonis_train/models/predefined_models/ddrnet_segmentation_model.py

@@ -1,5 +1,4 @@
 from dataclasses import dataclass, field
-from typing import Literal
 
 from luxonis_train.utils.config import (
     LossModuleConfig,

luxonis_train/nodes/backbones/ddrnet/__init__.py

@@ -1,3 +1,3 @@
 from .ddrnet import DDRNet
 
-__all__ = ["DDRNet"]
+__all__ = ["DDRNet"]

luxonis_train/nodes/backbones/ddrnet/blocks.py

@@ -5,14 +5,12 @@
 Paper: U{https://arxiv.org/pdf/2101.06085.pdf}
 @license: U{https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md}
 """
-from abc import ABC
-from typing import Dict, Type
+from typing import Type
 
 import torch
 from torch import Tensor, nn
 from torch.nn import functional as F
 
-from luxonis_train.nodes.base_node import BaseNode
 from luxonis_train.nodes.blocks import ConvModule
 
 
@@ -371,6 +369,7 @@ def forward(self, x: Tensor, output_height: int, output_width: int) -> Tensor:
         """
         return F.interpolate(x, size=[output_height, output_width], mode=self.mode)
 
+
 class BasicDDRBackBone(nn.Module):
     def __init__(
         self,
@@ -505,6 +504,7 @@ def get_backbone_output_number_of_channels(self) -> dict[str, int]:
 
         return output_shapes
 
+
 def _make_layer(
     block: Type[nn.Module],
     in_planes: int,
@@ -567,10 +567,8 @@ def drop_path(x: Tensor, drop_prob: float = 0.0, scale_by_keep: bool = True) ->
     @return: Tensor with dropped paths based on the provided drop probability.
     """
     keep_prob = 1 - drop_prob
-    shape = (x.shape[0],) + (1,) * (
-        x.ndim - 1
-    )
+    shape = (x.shape[0],) + (1,) * (x.ndim - 1)
     random_tensor = x.new_empty(shape).bernoulli_(keep_prob)
     if keep_prob > 0.0 and scale_by_keep:
         random_tensor.div_(keep_prob)
-    return x * random_tensor
+    return x * random_tensor

luxonis_train/nodes/backbones/ddrnet/ddrnet.py

@@ -5,18 +5,21 @@
 Paper: U{https://arxiv.org/pdf/2101.06085.pdf}
 @license: U{https://github.com/Deci-AI/super-gradients/blob/master/LICENSE.md}
 """
-from abc import ABC
 from typing import Dict, Type
 
-import torch
 from torch import Tensor, nn
-from torch.nn import functional as F
 
 from luxonis_train.nodes.base_node import BaseNode
 from luxonis_train.nodes.blocks import ConvModule
-from luxonis_train.nodes.heads import DDRNetSegmentationHead
 
-from .blocks import BasicResNetBlock, Bottleneck, UpscaleOnline, BasicDDRBackBone, DAPPM, _make_layer
+from .blocks import (
+    DAPPM,
+    BasicDDRBackBone,
+    BasicResNetBlock,
+    Bottleneck,
+    UpscaleOnline,
+    _make_layer,
+)
 
 
 class DDRNet(BaseNode[Tensor, list[Tensor]]):
@@ -113,7 +116,7 @@ def __init__(
         self.backbone_layers, self.additional_layers = self.layers[:4], self.layers[4:]
         self.input_channels = input_channels
 
-        self._backbone: DDRBackBoneBase = BasicDDRBackBone(
+        self._backbone = BasicDDRBackBone(
             block=self.block,
             width=self.planes,
             layers=self.backbone_layers,