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Add RTDETR Trainer (#2745)

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Ayush Chaurasia 2023-06-17 17:16:18 +05:30 • committed by GitHub
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35
ultralytics/yolo/utils/metrics.py
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@ -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.