Add RTDETR Trainer (#2745)

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Glenn Jocher <glenn.jocher@ultralytics.com>
Co-authored-by: Laughing-q <1185102784@qq.com>
Co-authored-by: Kayzwer <68285002+Kayzwer@users.noreply.github.com>
Co-authored-by: Laughing <61612323+Laughing-q@users.noreply.github.com>

ultralytics/yolo/utils/loss.py

@@ -24,10 +24,34 @@ class VarifocalLoss(nn.Module):
         weight = alpha * pred_score.sigmoid().pow(gamma) * (1 - label) + gt_score * label
         with torch.cuda.amp.autocast(enabled=False):
             loss = (F.binary_cross_entropy_with_logits(pred_score.float(), gt_score.float(), reduction='none') *
-                    weight).sum()
+                    weight).mean(1).sum()
         return loss
 
 
+# Losses
+class FocalLoss(nn.Module):
+    """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
+
+    def __init__(self, ):
+        super().__init__()
+
+    def forward(self, pred, label, gamma=1.5, alpha=0.25):
+        """Calculates and updates confusion matrix for object detection/classification tasks."""
+        loss = F.binary_cross_entropy_with_logits(pred, label, reduction='none')
+        # p_t = torch.exp(-loss)
+        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
+
+        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
+        pred_prob = pred.sigmoid()  # prob from logits
+        p_t = label * pred_prob + (1 - label) * (1 - pred_prob)
+        modulating_factor = (1.0 - p_t) ** gamma
+        loss *= modulating_factor
+        if alpha > 0:
+            alpha_factor = label * alpha + (1 - label) * (1 - alpha)
+            loss *= alpha_factor
+        return loss.mean(1).sum()
+
+
 class BboxLoss(nn.Module):
 
     def __init__(self, reg_max, use_dfl=False):

ultralytics/yolo/utils/metrics.py

@@ -9,7 +9,6 @@ from pathlib import Path
 import matplotlib.pyplot as plt
 import numpy as np
 import torch
-import torch.nn as nn
 
 from ultralytics.yolo.utils import LOGGER, SimpleClass, TryExcept, plt_settings
 
@@ -175,40 +174,6 @@ def smooth_BCE(eps=0.1):  # https://github.com/ultralytics/yolov3/issues/238#iss
     return 1.0 - 0.5 * eps, 0.5 * eps
 
 
-# Losses
-class FocalLoss(nn.Module):
-    """Wraps focal loss around existing loss_fcn(), i.e. criteria = FocalLoss(nn.BCEWithLogitsLoss(), gamma=1.5)."""
-
-    def __init__(self, loss_fcn, gamma=1.5, alpha=0.25):
-        """Initialize FocalLoss object with given loss function and hyperparameters."""
-        super().__init__()
-        self.loss_fcn = loss_fcn  # must be nn.BCEWithLogitsLoss()
-        self.gamma = gamma
-        self.alpha = alpha
-        self.reduction = loss_fcn.reduction
-        self.loss_fcn.reduction = 'none'  # required to apply FL to each element
-
-    def forward(self, pred, true):
-        """Calculates and updates confusion matrix for object detection/classification tasks."""
-        loss = self.loss_fcn(pred, true)
-        # p_t = torch.exp(-loss)
-        # loss *= self.alpha * (1.000001 - p_t) ** self.gamma  # non-zero power for gradient stability
-
-        # TF implementation https://github.com/tensorflow/addons/blob/v0.7.1/tensorflow_addons/losses/focal_loss.py
-        pred_prob = torch.sigmoid(pred)  # prob from logits
-        p_t = true * pred_prob + (1 - true) * (1 - pred_prob)
-        alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
-        modulating_factor = (1.0 - p_t) ** self.gamma
-        loss *= alpha_factor * modulating_factor
-
-        if self.reduction == 'mean':
-            return loss.mean()
-        elif self.reduction == 'sum':
-            return loss.sum()
-        else:  # 'None'
-            return loss
-
-
 class ConfusionMatrix:
     """
     A class for calculating and updating a confusion matrix for object detection and classification tasks.

ultralytics/yolo/utils/torch_utils.py

@@ -327,6 +327,9 @@ def init_seeds(seed=0, deterministic=False):
             os.environ['PYTHONHASHSEED'] = str(seed)
         else:
             LOGGER.warning('WARNING ⚠️ Upgrade to torch>=2.0.0 for deterministic training.')
+    else:
+        torch.use_deterministic_algorithms(False)
+        torch.backends.cudnn.deterministic = False
 
 
 class ModelEMA: