add stochastic depth according to code

vision_toolbox/backbones/convnext.py

@@ -55,7 +55,9 @@ def __init__(
         super().__init__()
         self.stem = nn.Sequential(nn.Conv2d(3, d_model, 4, 4), Permute(0, 2, 3, 1), norm(d_model))
 
+        stochastic_depth_rates = torch.linspace(0, stochastic_depth, sum(depths))
         self.stages = nn.Sequential()
+
         for stage_idx, depth in enumerate(depths):
             stage = nn.Sequential()
             if stage_idx > 0:
@@ -71,8 +73,9 @@ def __init__(
                 downsample = nn.Identity()
             stage.append(downsample)
 
-            for _ in range(depth):
-                block = ConvNeXtBlock(d_model, expansion_ratio, bias, layer_scale_init, stochastic_depth, norm, act)
+            for block_idx in range(depth):
+                rate = stochastic_depth_rates[sum(depths[:stage_idx]) + block_idx]
+                block = ConvNeXtBlock(d_model, expansion_ratio, bias, layer_scale_init, rate, norm, act)
                 stage.append(block)
 
             self.stages.append(stage)