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Update loss criteria constructor (#1711)

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Glenn Jocher 2020-12-16 08:39:35 -08:00 committed by GitHub
parent 799724108f
commit 8bc0027afc
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4 changed files with 18 additions and 15 deletions
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8
train.py
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@ -1,5 +1,6 @@
import argparse import argparse
import logging import logging
import math
import os import os
import random import random
import time import time
@ -7,7 +8,6 @@ from pathlib import Path
from threading import Thread from threading import Thread
from warnings import warn from warnings import warn
import math
import numpy as np import numpy as np
import torch.distributed as dist import torch.distributed as dist
import torch.nn as nn 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.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # iou loss ratio (obj_loss = 1.0 or iou) 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 model.names = names
# Start training # Start training
@ -238,7 +238,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
if opt.image_weights: if opt.image_weights:
# Generate indices # Generate indices
if rank in [-1, 0]: 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 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 dataset.indices = random.choices(range(dataset.n), weights=iw, k=dataset.n) # rand weighted idx
# Broadcast if DDP # Broadcast if DDP
@ -330,7 +330,7 @@ def train(hyp, opt, device, tb_writer=None, wandb=None):
if rank in [-1, 0]: if rank in [-1, 0]:
# mAP # mAP
if ema: 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 final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(opt.data, results, maps, times = test.test(opt.data,

2
tutorial.ipynb vendored
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@ -1199,7 +1199,7 @@
"\n", "\n",
"m1 = lambda x: x * torch.sigmoid(x)\n", "m1 = lambda x: x * torch.sigmoid(x)\n",
"m2 = torch.nn.SiLU()\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, "execution_count": null,
"outputs": [] "outputs": []

6
utils/loss.py
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@ -92,8 +92,8 @@ def compute_loss(p, targets, model): # predictions, targets, model
h = model.hyp # hyperparameters h = model.hyp # hyperparameters
# Define criteria # Define criteria
BCEcls = nn.BCEWithLogitsLoss(pos_weight=torch.Tensor([h['cls_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']])).to(device) BCEobj = nn.BCEWithLogitsLoss(pos_weight=torch.tensor([h['obj_pw']], device=device))
# Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3 # Class label smoothing https://arxiv.org/pdf/1902.04103.pdf eqn 3
cp, cn = smooth_BCE(eps=0.0) cp, cn = smooth_BCE(eps=0.0)
@ -119,7 +119,7 @@ def compute_loss(p, targets, model): # predictions, targets, model
# Regression # Regression
pxy = ps[:, :2].sigmoid() * 2. - 0.5 pxy = ps[:, :2].sigmoid() * 2. - 0.5
pwh = (ps[:, 2:4].sigmoid() * 2) ** 2 * anchors[i] 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) iou = bbox_iou(pbox.T, tbox[i], x1y1x2y2=False, CIoU=True) # iou(prediction, target)
lbox += (1.0 - iou).mean() # iou loss lbox += (1.0 - iou).mean() # iou loss

7
utils/torch_utils.py
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@ -99,8 +99,11 @@ def profile(x, ops, n=100, device=None):
t[0] = time_synchronized() t[0] = time_synchronized()
y = m(x) y = m(x)
t[1] = time_synchronized() t[1] = time_synchronized()
_ = y.sum().backward() try:
t[2] = time_synchronized() _ = 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 dtf += (t[1] - t[0]) * 1000 / n # ms per op forward
dtb += (t[2] - t[1]) * 1000 / n # ms per op backward dtb += (t[2] - t[1]) * 1000 / n # ms per op backward