Removed dice_similarity_coefficient_with_logits_clip and updated references. (#217) (#218)

Author: ATATC

mipcandy/__init__.py

@@ -8,9 +8,9 @@
 from mipcandy.layer import batch_int_multiply, batch_int_divide, LayerT, HasDevice, auto_device, WithPaddingModule, \
     WithNetwork
 from mipcandy.metrics import do_reduction, dice_similarity_coefficient_binary, \
-    dice_similarity_coefficient_multiclass, dice_similarity_coefficient_with_logits, \
-    dice_similarity_coefficient_with_logits_clip, soft_dice_coefficient, accuracy_binary, accuracy_multiclass, \
-    precision_binary, precision_multiclass, recall_binary, recall_multiclass, iou_binary, iou_multiclass
+    dice_similarity_coefficient_multiclass, dice_similarity_coefficient_with_logits, soft_dice_coefficient, \
+    accuracy_binary, accuracy_multiclass, precision_binary, precision_multiclass, recall_binary, recall_multiclass, \
+    iou_binary, iou_multiclass
 from mipcandy.presets import *
 from mipcandy.profiler import ProfilerFrame, Profiler
 from mipcandy.run import config

mipcandy/common/optim/loss.py

@@ -5,7 +5,7 @@
 
 from mipcandy.data import convert_ids_to_logits, convert_logits_to_ids
 from mipcandy.metrics import do_reduction, soft_dice_coefficient, dice_similarity_coefficient_binary, \
-    dice_similarity_coefficient_with_logits_clip
+    dice_similarity_coefficient_with_logits
 
 
 class FocalBCEWithLogits(nn.Module):
@@ -56,7 +56,7 @@ def forward(self, outputs: torch.Tensor, labels: torch.Tensor) -> tuple[torch.Te
                 class_dice = dice_similarity_coefficient_binary(outputs == i, labels == i).item()
                 dice += class_dice
                 metrics[f"dice {i}"] = class_dice
-            metrics["dice"] = dice_similarity_coefficient_with_logits_clip(
+            metrics["dice"] = dice_similarity_coefficient_with_logits(
                 self.logitfy_no_grad(outputs), self.logitfy_no_grad(labels)
             ).item()
             return c, metrics

mipcandy/metrics.py

@@ -85,13 +85,6 @@ def dice_similarity_coefficient_with_logits(outputs: torch.Tensor, labels: torch
     return dice.mean()
 
 
-def dice_similarity_coefficient_with_logits_clip(outputs: torch.Tensor, labels: torch.Tensor, *,
-                                                 clip_min: float = 1e-8) -> torch.Tensor:
-    tp, volume_sum = _dice_with_logits(outputs, labels)
-    dice = 2 * tp / torch.clip(volume_sum, clip_min)
-    return dice.mean()
-
-
 def soft_dice_coefficient(outputs: torch.Tensor, labels: torch.Tensor, *, smooth: float = 1, clip_min: float = 1e-8,
                           batch_dice: bool = True) -> torch.Tensor:
     _args_check(outputs, labels)