Update loss criteria constructor (#1711)

train.py

@@ -1,5 +1,6 @@
 import argparse
 import logging
+import math
 import os
 import random
 import time
@@ -7,7 +8,6 @@ from pathlib import Path
 from threading import Thread
 from warnings import warn
 
-import math
 import numpy as np
 import torch.distributed as dist
 import torch.nn as nn
@@ -217,7 +217,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
     model.nc = nc  # attach number of classes to model
     model.hyp = hyp  # attach hyperparameters to model
     model.gr = 1.0  # iou loss ratio (obj_loss = 1.0 or iou)
-    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device)  # attach class weights
+    model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) * nc  # attach class weights
     model.names = names
 
     # Start training
@@ -238,7 +238,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         if opt.image_weights:
             # Generate indices
             if rank in [-1, 0]:
-                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2  # class weights
+                cw = model.class_weights.cpu().numpy() * (1 - maps) ** 2 / nc  # class weights
                 iw = labels_to_image_weights(dataset.labels, nc=nc, class_weights=cw)  # image weights
                 dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n)  # rand weighted idx
             # Broadcast if DDP
@@ -330,7 +330,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
         if rank in [-1, 0]:
             # mAP
             if ema:
-                ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
+                ema.update_attr(model, include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride', 'class_weights'])
             final_epoch = epoch + 1 == epochs
             if not opt.notest or final_epoch:  # Calculate mAP
                 results, maps, times = test.test(opt.data,

tutorial.ipynb

@@ -1199,7 +1199,7 @@
         "\n",
         "m1 = lambda x: x * torch.sigmoid(x)\n",
         "m2 = torch.nn.SiLU()\n",
-        "profile(x=torch.randn(16, 3, 640, 640), [m1, m2], n=100)"
+        "profile(x=torch.randn(16, 3, 640, 640), ops=[m1, m2], n=100)"
       ],
       "execution_count": null,
       "outputs": []

utils/loss.py

@@ -57,8 +57,8 @@ class FocalLoss(nn.Module):
             return loss.sum()
         else:  # 'none'
             return loss
-        
-        
+
+
 class QFocalLoss(nn.Module):
     # Wraps Quality 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):
@@ -71,7 +71,7 @@ class QFocalLoss(nn.Module):
 
     def forward(self, pred, true):
         loss = self.loss_fcn(pred, true)
-        
+
         pred_prob = torch.sigmoid(pred)  # prob from logits
         alpha_factor = true * self.alpha + (1 - true) * (1 - self.alpha)
         modulating_factor = torch.abs(true - pred_prob) ** self.gamma
@@ -92,8 +92,8 @@ def compute_loss(p, targets, model):  # predictions, targets, model
     h = model.hyp  # hyperparameters
 
     # Define criteria
-    BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_pw']])).to(device)
-    BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['obj_pw']])).to(device)
+    BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['cls_pw']], device=device))  # weight=model.class_weights)
+    BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
 
     # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
     cp, cn = smooth_BCE(eps=0.0)
@@ -119,7 +119,7 @@ def compute_loss(p, targets, model):  # predictions, targets, model
             # Regression
             pxy = ps[:, :2].sigmoid() * 2. - 0.5
             pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i]
-            pbox = torch.cat((pxy, pwh), 1).to(device)  # predicted box
+            pbox = torch.cat((pxy, pwh), 1)  # predicted box
             iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True)  # iou(prediction, target)
             lbox += (1.0 - iou).mean()  # iou loss
 

utils/torch_utils.py

@@ -81,8 +81,8 @@ def profile(x, ops, n=100, device=None):
     #     m1 = lambda x: x * torch.sigmoid(x)
     #     m2 = nn.SiLU()
     #     profile(x, [m1, m2], n=100)  # profile speed over 100 iterations
-    
-    device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')    
+
+    device = device or torch.device('cuda:0' if torch.cuda.is_available() else 'cpu')
     x = x.to(device)
     x.requires_grad = True
     print(torch.__version__, device.type, torch.cuda.get_device_properties(0) if device.type == 'cuda' else '')
@@ -99,8 +99,11 @@ def profile(x, ops, n=100, device=None):
             t[0] = time_synchronized()
             y = m(x)
             t[1] = time_synchronized()
-            _ = y.sum().backward()
-            t[2] = time_synchronized()
+            try:
+                _ = y.sum().backward()
+                t[2] = time_synchronized()
+            except:  # no backward method
+                t[2] = float('nan')
             dtf += (t[1] - t[0]) * 1000 / n  # ms per op forward
             dtb += (t[2] - t[1]) * 1000 / n  # ms per op backward