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Fedavg and YOLOv11 training

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2025-10-02 16:26:27 +08:00
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"""
Utility functions for yolo.
"""
import copy
import random
from time import time
import math
import numpy
import torch
import torchvision
from torch.nn.functional import cross_entropy
def setup_seed():
"""
Setup random seed.
"""
random.seed(0)
numpy.random.seed(0)
torch.manual_seed(0)
torch.backends.cudnn.benchmark = False
torch.backends.cudnn.deterministic = True
def setup_multi_processes():
"""
Setup multi-processing environment variables.
"""
import cv2
from os import environ
from platform import system
# set multiprocess start method as `fork` to speed up the training
if system() != "Windows":
torch.multiprocessing.set_start_method("fork", force=True)
# disable opencv multithreading to avoid system being overloaded
cv2.setNumThreads(0)
# setup OMP threads
if "OMP_NUM_THREADS" not in environ:
environ["OMP_NUM_THREADS"] = "1"
# setup MKL threads
if "MKL_NUM_THREADS" not in environ:
environ["MKL_NUM_THREADS"] = "1"
def export_onnx(args):
import onnx # noqa
inputs = ["images"]
outputs = ["outputs"]
dynamic = {"outputs": {0: "batch", 1: "anchors"}}
m = torch.load("./weights/best.pt", weights_only=False)["model"].float()
x = torch.zeros((1, 3, args.input_size, args.input_size))
torch.onnx.export(
m.cpu(),
(x.cpu(),),
f="./weights/best.onnx",
verbose=False,
opset_version=12,
# WARNING: DNN inference with torch>=1.12 may require do_constant_folding=False
do_constant_folding=True,
input_names=inputs,
output_names=outputs,
dynamic_axes=dynamic or None,
)
# Checks
model_onnx = onnx.load("./weights/best.onnx") # load onnx model
onnx.checker.check_model(model_onnx) # check onnx model
onnx.save(model_onnx, "./weights/best.onnx")
# Inference example
# https://github.com/ultralytics/ultralytics/blob/main/ultralytics/nn/autobackend.py
def wh2xy(x):
y = x.clone() if isinstance(x, torch.Tensor) else numpy.copy(x)
y[:, 0] = x[:, 0] - x[:, 2] / 2 # top left x
y[:, 1] = x[:, 1] - x[:, 3] / 2 # top left y
y[:, 2] = x[:, 0] + x[:, 2] / 2 # bottom right x
y[:, 3] = x[:, 1] + x[:, 3] / 2 # bottom right y
return y
def make_anchors(x, strides, offset=0.5):
assert x is not None
anchor_tensor, stride_tensor = [], []
dtype, device = x[0].dtype, x[0].device
for i, stride in enumerate(strides):
_, _, h, w = x[i].shape
sx = torch.arange(end=w, device=device, dtype=dtype) + offset # shift x
sy = torch.arange(end=h, device=device, dtype=dtype) + offset # shift y
sy, sx = torch.meshgrid(sy, sx, indexing="ij")
anchor_tensor.append(torch.stack((sx, sy), -1).view(-1, 2))
stride_tensor.append(torch.full((h * w, 1), stride, dtype=dtype, device=device))
return torch.cat(anchor_tensor), torch.cat(stride_tensor)
def compute_metric(output, target, iou_v):
# intersection(N,M) = (rb(N,M,2) - lt(N,M,2)).clamp(0).prod(2)
(a1, a2) = target[:, 1:].unsqueeze(1).chunk(2, 2)
(b1, b2) = output[:, :4].unsqueeze(0).chunk(2, 2)
intersection = (torch.min(a2, b2) - torch.max(a1, b1)).clamp(0).prod(2)
# IoU = intersection / (area1 + area2 - intersection)
iou = intersection / ((a2 - a1).prod(2) + (b2 - b1).prod(2) - intersection + 1e-7)
correct = numpy.zeros((output.shape[0], iou_v.shape[0]))
correct = correct.astype(bool)
for i in range(len(iou_v)):
# IoU > threshold and classes match
x = torch.where((iou >= iou_v[i]) & (target[:, 0:1] == output[:, 5]))
if x[0].shape[0]:
matches = torch.cat((torch.stack(x, 1), iou[x[0], x[1]][:, None]), 1).cpu().numpy() # [label, detect, iou]
if x[0].shape[0] > 1:
matches = matches[matches[:, 2].argsort()[::-1]]
matches = matches[numpy.unique(matches[:, 1], return_index=True)[1]]
matches = matches[numpy.unique(matches[:, 0], return_index=True)[1]]
correct[matches[:, 1].astype(int), i] = True
return torch.tensor(correct, dtype=torch.bool, device=output.device)
def non_max_suppression(outputs, confidence_threshold=0.001, iou_threshold=0.65):
max_wh = 7680
max_det = 300
max_nms = 30000
bs = outputs.shape[0] # batch size
nc = outputs.shape[1] - 4 # number of classes
xc = outputs[:, 4 : 4 + nc].amax(1) > confidence_threshold # candidates
# Settings
start = time()
limit = 0.5 + 0.05 * bs # seconds to quit after
output = [torch.zeros((0, 6), device=outputs.device)] * bs
for index, x in enumerate(outputs): # image index, image inference
x = x.transpose(0, -1)[xc[index]] # confidence
# If none remain process next image
if not x.shape[0]:
continue
# matrix nx6 (box, confidence, cls)
box, cls = x.split((4, nc), 1)
box = wh2xy(box) # (cx, cy, w, h) to (x1, y1, x2, y2)
if nc > 1:
i, j = (cls > confidence_threshold).nonzero(as_tuple=False).T
x = torch.cat((box[i], x[i, 4 + j, None], j[:, None].float()), dim=1)
else: # best class only
conf, j = cls.max(1, keepdim=True)
x = torch.cat((box, conf, j.float()), 1)[conf.view(-1) > confidence_threshold]
# Check shape
n = x.shape[0] # number of boxes
if not n: # no boxes
continue
x = x[x[:, 4].argsort(descending=True)[:max_nms]] # sort by confidence and remove excess boxes
# Batched NMS
c = x[:, 5:6] * max_wh # classes
boxes, scores = x[:, :4] + c, x[:, 4] # boxes, scores
indices = torchvision.ops.nms(boxes, scores, iou_threshold) # NMS
indices = indices[:max_det] # limit detections
output[index] = x[indices]
if (time() - start) > limit:
break # time limit exceeded
return output
def smooth(y, f=0.1):
# Box filter of fraction f
nf = round(len(y) * f * 2) // 2 + 1 # number of filter elements (must be odd)
p = numpy.ones(nf // 2) # ones padding
yp = numpy.concatenate((p * y[0], y, p * y[-1]), 0) # y padded
return numpy.convolve(yp, numpy.ones(nf) / nf, mode="valid") # y-smoothed
def plot_pr_curve(px, py, ap, names, save_dir):
from matplotlib import pyplot
fig, ax = pyplot.subplots(1, 1, figsize=(9, 6), tight_layout=True)
py = numpy.stack(py, axis=1)
if 0 < len(names) < 21: # display per-class legend if < 21 classes
for i, y in enumerate(py.T):
ax.plot(px, y, linewidth=1, label=f"{names[i]} {ap[i, 0]:.3f}") # plot(recall, precision)
else:
ax.plot(px, py, linewidth=1, color="grey") # plot(recall, precision)
ax.plot(
px,
py.mean(1),
linewidth=3,
color="blue",
label="all classes %.3f mAP@0.5" % ap[:, 0].mean(),
)
ax.set_xlabel("Recall")
ax.set_ylabel("Precision")
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.set_title("Precision-Recall Curve")
fig.savefig(save_dir, dpi=250)
pyplot.close(fig)
def plot_curve(px, py, names, save_dir, x_label="Confidence", y_label="Metric"):
from matplotlib import pyplot
figure, ax = pyplot.subplots(1, 1, figsize=(9, 6), tight_layout=True)
if 0 < len(names) < 21: # display per-class legend if < 21 classes
for i, y in enumerate(py):
ax.plot(px, y, linewidth=1, label=f"{names[i]}") # plot(confidence, metric)
else:
ax.plot(px, py.T, linewidth=1, color="grey") # plot(confidence, metric)
y = smooth(py.mean(0), f=0.05)
ax.plot(
px,
y,
linewidth=3,
color="blue",
label=f"all classes {y.max():.3f} at {px[y.argmax()]:.3f}",
)
ax.set_xlabel(x_label)
ax.set_ylabel(y_label)
ax.set_xlim(0, 1)
ax.set_ylim(0, 1)
ax.legend(bbox_to_anchor=(1.04, 1), loc="upper left")
ax.set_title(f"{y_label}-Confidence Curve")
figure.savefig(save_dir, dpi=250)
pyplot.close(figure)
def compute_ap(tp, conf, output, target, plot=False, names=(), eps=1e-16):
"""
Compute the average precision, given the recall and precision curves.
Source: https://github.com/rafaelpadilla/Object-Detection-Metrics.
# Arguments
tp: True positives (nparray, nx1 or nx10).
conf: Object-ness value from 0-1 (nparray).
output: Predicted object classes (nparray).
target: True object classes (nparray).
# Returns
The average precision
"""
# Sort by object-ness
i = numpy.argsort(-conf)
tp, conf, output = tp[i], conf[i], output[i]
# Find unique classes
unique_classes, nt = numpy.unique(target, return_counts=True)
nc = unique_classes.shape[0] # number of classes, number of detections
# Create Precision-Recall curve and compute AP for each class
p = numpy.zeros((nc, 1000))
r = numpy.zeros((nc, 1000))
ap = numpy.zeros((nc, tp.shape[1]))
px, py = numpy.linspace(start=0, stop=1, num=1000), [] # for plotting
for ci, c in enumerate(unique_classes):
i = output == c
nl = nt[ci] # number of labels
no = i.sum() # number of outputs
if no == 0 or nl == 0:
continue
# Accumulate FPs and TPs
fpc = (1 - tp[i]).cumsum(0)
tpc = tp[i].cumsum(0)
# Recall
recall = tpc / (nl + eps) # recall curve
# negative x, xp because xp decreases
r[ci] = numpy.interp(-px, -conf[i], recall[:, 0], left=0)
# Precision
precision = tpc / (tpc + fpc) # precision curve
p[ci] = numpy.interp(-px, -conf[i], precision[:, 0], left=1) # p at pr_score
# AP from recall-precision curve
for j in range(tp.shape[1]):
m_rec = numpy.concatenate(([0.0], recall[:, j], [1.0]))
m_pre = numpy.concatenate(([1.0], precision[:, j], [0.0]))
# Compute the precision envelope
m_pre = numpy.flip(numpy.maximum.accumulate(numpy.flip(m_pre)))
# Integrate area under curve
x = numpy.linspace(start=0, stop=1, num=101) # 101-point interp (COCO)
ap[ci, j] = numpy.trapz(numpy.interp(x, m_rec, m_pre), x) # integrate
if plot and j == 0:
py.append(numpy.interp(px, m_rec, m_pre)) # precision at mAP@0.5
# Compute F1 (harmonic mean of precision and recall)
f1 = 2 * p * r / (p + r + eps)
if plot:
names = dict(enumerate(names)) # to dict
names = [v for k, v in names.items() if k in unique_classes] # list: only classes that have data
plot_pr_curve(px, py, ap, names, save_dir="./weights/PR_curve.png")
plot_curve(px, f1, names, save_dir="./weights/F1_curve.png", y_label="F1")
plot_curve(px, p, names, save_dir="./weights/P_curve.png", y_label="Precision")
plot_curve(px, r, names, save_dir="./weights/R_curve.png", y_label="Recall")
i = smooth(f1.mean(0), 0.1).argmax() # max F1 index
p, r, f1 = p[:, i], r[:, i], f1[:, i]
tp = (r * nt).round() # true positives
fp = (tp / (p + eps) - tp).round() # false positives
ap50, ap = ap[:, 0], ap.mean(1) # AP@0.5, AP@0.5:0.95
m_pre, m_rec = p.mean(), r.mean()
map50, mean_ap = ap50.mean(), ap.mean()
return tp, fp, m_pre, m_rec, map50, mean_ap
def compute_iou(box1, box2, eps=1e-7):
# Returns Intersection over Union (IoU) of box1(1,4) to box2(n,4)
# Get the coordinates of bounding boxes
b1_x1, b1_y1, b1_x2, b1_y2 = box1.chunk(4, -1)
b2_x1, b2_y1, b2_x2, b2_y2 = box2.chunk(4, -1)
w1, h1 = b1_x2 - b1_x1, b1_y2 - b1_y1 + eps
w2, h2 = b2_x2 - b2_x1, b2_y2 - b2_y1 + eps
# Intersection area
inter = (b1_x2.minimum(b2_x2) - b1_x1.maximum(b2_x1)).clamp(0) * (
b1_y2.minimum(b2_y2) - b1_y1.maximum(b2_y1)
).clamp(0)
# Union Area
union = w1 * h1 + w2 * h2 - inter + eps
# IoU
iou = inter / union
cw = b1_x2.maximum(b2_x2) - b1_x1.minimum(b2_x1) # convex (smallest enclosing box) width
ch = b1_y2.maximum(b2_y2) - b1_y1.minimum(b2_y1) # convex height
c2 = cw**2 + ch**2 + eps # convex diagonal squared
rho2 = ((b2_x1 + b2_x2 - b1_x1 - b1_x2) ** 2 + (b2_y1 + b2_y2 - b1_y1 - b1_y2) ** 2) / 4 # center dist ** 2
# https://github.com/Zzh-tju/DIoU-SSD-pytorch/blob/master/utils/box/box_utils.py#L47
v = (4 / math.pi**2) * (torch.atan(w2 / h2) - torch.atan(w1 / h1)).pow(2)
with torch.no_grad():
alpha = v / (v - iou + (1 + eps))
return iou - (rho2 / c2 + v * alpha) # CIoU
def strip_optimizer(filename):
x = torch.load(filename, map_location="cpu", weights_only=False)
x["model"].half() # to FP16
for p in x["model"].parameters():
p.requires_grad = False
torch.save(x, f=filename)
def clip_gradients(model, max_norm=10.0):
parameters = model.parameters()
torch.nn.utils.clip_grad_norm_(parameters, max_norm=max_norm)
def load_weight(model, ckpt):
dst = model.state_dict()
src = torch.load(ckpt, weights_only=False)["model"].float().cpu()
ckpt = {}
for k, v in src.state_dict().items():
if k in dst and v.shape == dst[k].shape:
ckpt[k] = v
model.load_state_dict(state_dict=ckpt, strict=False)
return model
def set_params(model, decay):
p1 = []
p2 = []
norm = tuple(v for k, v in torch.nn.__dict__.items() if "Norm" in k)
for m in model.modules():
for n, p in m.named_parameters(recurse=0):
if not p.requires_grad:
continue
if n == "bias": # bias (no decay)
p1.append(p)
elif n == "weight" and isinstance(m, norm): # norm-weight (no decay)
p1.append(p)
else:
p2.append(p) # weight (with decay)
return [{"params": p1, "weight_decay": 0.00}, {"params": p2, "weight_decay": decay}]
def plot_lr(args, optimizer, scheduler, num_steps):
from matplotlib import pyplot
optimizer = copy.copy(optimizer)
scheduler = copy.copy(scheduler)
y = []
for epoch in range(args.epochs):
for i in range(num_steps):
step = i + num_steps * epoch
scheduler.step(step, optimizer)
y.append(optimizer.param_groups[0]["lr"])
pyplot.plot(y, ".-", label="LR")
pyplot.xlabel("step")
pyplot.ylabel("LR")
pyplot.grid()
pyplot.xlim(0, args.epochs * num_steps)
pyplot.ylim(0)
pyplot.savefig("./weights/lr.png", dpi=200)
pyplot.close()
class CosineLR:
def __init__(self, args, params, num_steps):
max_lr = params["max_lr"]
min_lr = params["min_lr"]
warmup_steps = int(max(params["warmup_epochs"] * num_steps, 100))
decay_steps = int(args.epochs * num_steps - warmup_steps)
warmup_lr = numpy.linspace(min_lr, max_lr, int(warmup_steps))
decay_lr = []
for step in range(1, decay_steps + 1):
alpha = math.cos(math.pi * step / decay_steps)
decay_lr.append(min_lr + 0.5 * (max_lr - min_lr) * (1 + alpha))
self.total_lr = numpy.concatenate((warmup_lr, decay_lr))
def step(self, step, optimizer):
for param_group in optimizer.param_groups:
param_group["lr"] = self.total_lr[step]
class LinearLR:
def __init__(self, args, params, num_steps):
max_lr = params["max_lr"]
min_lr = params["min_lr"]
warmup_steps = int(max(params["warmup_epochs"] * num_steps, 100))
decay_steps = max(1, int(args.epochs * num_steps - warmup_steps))
warmup_lr = numpy.linspace(min_lr, max_lr, int(warmup_steps), endpoint=False)
decay_lr = numpy.linspace(max_lr, min_lr, decay_steps)
self.total_lr = numpy.concatenate((warmup_lr, decay_lr))
def step(self, step, optimizer):
for param_group in optimizer.param_groups:
param_group["lr"] = self.total_lr[step]
class EMA:
"""
Updated Exponential Moving Average (EMA) from https://github.com/rwightman/pytorch-image-models
Keeps a moving average of everything in the model state_dict (parameters and buffers)
For EMA details see https://www.tensorflow.org/api_docs/python/tf/train/ExponentialMovingAverage
"""
def __init__(self, model, decay=0.9999, tau=2000, updates=0):
# Create EMA
self.ema = copy.deepcopy(model).eval() # FP32 EMA
self.updates = updates # number of EMA updates
# decay exponential ramp (to help early epochs)
self.decay = lambda x: decay * (1 - math.exp(-x / tau))
for p in self.ema.parameters():
p.requires_grad_(False)
def update(self, model):
if hasattr(model, "module"):
model = model.module
# Update EMA parameters
with torch.no_grad():
self.updates += 1
d = self.decay(self.updates)
msd = model.state_dict() # model state_dict
for k, v in self.ema.state_dict().items():
if v.dtype.is_floating_point:
v *= d
v += (1 - d) * msd[k].detach()
class AverageMeter:
def __init__(self):
self.num = 0
self.sum = 0
self.avg = 0
def update(self, v, n):
if not math.isnan(float(v)):
self.num = self.num + n
self.sum = self.sum + v * n
self.avg = self.sum / self.num
class Assigner(torch.nn.Module):
def __init__(self, nc=80, top_k=13, alpha=1.0, beta=6.0, eps=1e-9):
super().__init__()
self.top_k = top_k
self.nc = nc
self.alpha = alpha
self.beta = beta
self.eps = eps
@torch.no_grad()
def forward(self, pd_scores, pd_bboxes, anc_points, gt_labels, gt_bboxes, mask_gt):
batch_size = pd_scores.size(0)
num_max_boxes = gt_bboxes.size(1)
if num_max_boxes == 0:
device = gt_bboxes.device
return (
torch.zeros_like(pd_bboxes).to(device),
torch.zeros_like(pd_scores).to(device),
torch.zeros_like(pd_scores[..., 0]).to(device),
)
num_anchors = anc_points.shape[0]
shape = gt_bboxes.shape
lt, rb = gt_bboxes.view(-1, 1, 4).chunk(2, 2)
mask_in_gts = torch.cat((anc_points[None] - lt, rb - anc_points[None]), dim=2)
mask_in_gts = mask_in_gts.view(shape[0], shape[1], num_anchors, -1).amin(3).gt_(self.eps)
na = pd_bboxes.shape[-2]
gt_mask = (mask_in_gts * mask_gt).bool() # b, max_num_obj, h*w
overlaps = torch.zeros(
[batch_size, num_max_boxes, na],
dtype=pd_bboxes.dtype,
device=pd_bboxes.device,
)
bbox_scores = torch.zeros(
[batch_size, num_max_boxes, na],
dtype=pd_scores.dtype,
device=pd_scores.device,
)
ind = torch.zeros([2, batch_size, num_max_boxes], dtype=torch.long) # 2, b, max_num_obj
ind[0] = torch.arange(end=batch_size).view(-1, 1).expand(-1, num_max_boxes) # b, max_num_obj
ind[1] = gt_labels.squeeze(-1) # b, max_num_obj
bbox_scores[gt_mask] = pd_scores[ind[0], :, ind[1]][gt_mask] # b, max_num_obj, h*w
pd_boxes = pd_bboxes.unsqueeze(1).expand(-1, num_max_boxes, -1, -1)[gt_mask]
gt_boxes = gt_bboxes.unsqueeze(2).expand(-1, -1, na, -1)[gt_mask]
overlaps[gt_mask] = compute_iou(gt_boxes, pd_boxes).squeeze(-1).clamp_(0)
align_metric = bbox_scores.pow(self.alpha) * overlaps.pow(self.beta)
top_k_mask = mask_gt.expand(-1, -1, self.top_k).bool()
top_k_metrics, top_k_indices = torch.topk(align_metric, self.top_k, dim=-1, largest=True)
if top_k_mask is None:
top_k_mask = (top_k_metrics.max(-1, keepdim=True)[0] > self.eps).expand_as(top_k_indices)
top_k_indices.masked_fill_(~top_k_mask, 0)
mask_top_k = torch.zeros(align_metric.shape, dtype=torch.int8, device=top_k_indices.device)
ones = torch.ones_like(top_k_indices[:, :, :1], dtype=torch.int8, device=top_k_indices.device)
for k in range(self.top_k):
mask_top_k.scatter_add_(-1, top_k_indices[:, :, k : k + 1], ones)
mask_top_k.masked_fill_(mask_top_k > 1, 0)
mask_top_k = mask_top_k.to(align_metric.dtype)
mask_pos = mask_top_k * mask_in_gts * mask_gt
fg_mask = mask_pos.sum(-2)
if fg_mask.max() > 1:
mask_multi_gts = (fg_mask.unsqueeze(1) > 1).expand(-1, num_max_boxes, -1)
max_overlaps_idx = overlaps.argmax(1)
is_max_overlaps = torch.zeros(mask_pos.shape, dtype=mask_pos.dtype, device=mask_pos.device)
is_max_overlaps.scatter_(1, max_overlaps_idx.unsqueeze(1), 1)
mask_pos = torch.where(mask_multi_gts, is_max_overlaps, mask_pos).float()
fg_mask = mask_pos.sum(-2)
target_gt_idx = mask_pos.argmax(-2)
# Assigned target
index = torch.arange(end=batch_size, dtype=torch.int64, device=gt_labels.device)[..., None]
target_index = target_gt_idx + index * num_max_boxes
target_labels = gt_labels.long().flatten()[target_index]
target_bboxes = gt_bboxes.view(-1, gt_bboxes.shape[-1])[target_index]
# Assigned target scores
target_labels.clamp_(0)
target_scores = torch.zeros(
(target_labels.shape[0], target_labels.shape[1], self.nc),
dtype=torch.int64,
device=target_labels.device,
)
target_scores.scatter_(2, target_labels.unsqueeze(-1), 1)
fg_scores_mask = fg_mask[:, :, None].repeat(1, 1, self.nc)
target_scores = torch.where(fg_scores_mask > 0, target_scores, 0)
# Normalize
align_metric *= mask_pos
pos_align_metrics = align_metric.amax(dim=-1, keepdim=True)
pos_overlaps = (overlaps * mask_pos).amax(dim=-1, keepdim=True)
norm_align_metric = (align_metric * pos_overlaps / (pos_align_metrics + self.eps)).amax(-2).unsqueeze(-1)
target_scores = target_scores * norm_align_metric
return target_bboxes, target_scores, fg_mask.bool()
class QFL(torch.nn.Module):
def __init__(self, beta=2.0):
super().__init__()
self.beta = beta
self.bce_loss = torch.nn.BCEWithLogitsLoss(reduction="none")
def forward(self, outputs, targets):
bce_loss = self.bce_loss(outputs, targets)
return torch.pow(torch.abs(targets - outputs.sigmoid()), self.beta) * bce_loss
class VFL(torch.nn.Module):
def __init__(self, alpha=0.75, gamma=2.00, iou_weighted=True):
super().__init__()
assert alpha >= 0.0
self.alpha = alpha
self.gamma = gamma
self.iou_weighted = iou_weighted
self.bce_loss = torch.nn.BCEWithLogitsLoss(reduction="none")
def forward(self, outputs, targets):
assert outputs.size() == targets.size()
targets = targets.type_as(outputs)
if self.iou_weighted:
focal_weight = (
targets * (targets > 0.0).float()
+ self.alpha * (outputs.sigmoid() - targets).abs().pow(self.gamma) * (targets <= 0.0).float()
)
else:
focal_weight = (targets > 0.0).float() + self.alpha * (outputs.sigmoid() - targets).abs().pow(
self.gamma
) * (targets <= 0.0).float()
return self.bce_loss(outputs, targets) * focal_weight
class FocalLoss(torch.nn.Module):
def __init__(self, alpha=0.25, gamma=1.5):
super().__init__()
self.alpha = alpha
self.gamma = gamma
self.bce_loss = torch.nn.BCEWithLogitsLoss(reduction="none")
def forward(self, outputs, targets):
loss = self.bce_loss(outputs, targets)
if self.alpha > 0:
alpha_factor = targets * self.alpha + (1 - targets) * (1 - self.alpha)
loss *= alpha_factor
if self.gamma > 0:
outputs_sigmoid = outputs.sigmoid()
p_t = targets * outputs_sigmoid + (1 - targets) * (1 - outputs_sigmoid)
gamma_factor = (1.0 - p_t) ** self.gamma
loss *= gamma_factor
return loss
class BoxLoss(torch.nn.Module):
def __init__(self, dfl_ch):
super().__init__()
self.dfl_ch = dfl_ch
def forward(
self,
pred_dist,
pred_bboxes,
anchor_points,
target_bboxes,
target_scores,
target_scores_sum,
fg_mask,
):
# IoU loss
weight = torch.masked_select(target_scores.sum(-1), fg_mask).unsqueeze(-1)
iou = compute_iou(pred_bboxes[fg_mask], target_bboxes[fg_mask])
loss_box = ((1.0 - iou) * weight).sum() / target_scores_sum
# DFL loss
a, b = target_bboxes.chunk(2, -1)
target = torch.cat((anchor_points - a, b - anchor_points), -1)
target = target.clamp(0, self.dfl_ch - 0.01)
loss_dfl = self.df_loss(pred_dist[fg_mask].view(-1, self.dfl_ch + 1), target[fg_mask])
loss_dfl = (loss_dfl * weight).sum() / target_scores_sum
return loss_box, loss_dfl
@staticmethod
def df_loss(pred_dist, target):
# Distribution Focal Loss (DFL)
# https://ieeexplore.ieee.org/document/9792391
tl = target.long() # target left
tr = tl + 1 # target right
wl = tr - target # weight left
wr = 1 - wl # weight right
left_loss = cross_entropy(pred_dist, tl.view(-1), reduction="none").view(tl.shape)
right_loss = cross_entropy(pred_dist, tr.view(-1), reduction="none").view(tl.shape)
return (left_loss * wl + right_loss * wr).mean(-1, keepdim=True)
class ComputeLoss:
def __init__(self, model, params):
if hasattr(model, "module"):
model = model.module
device = next(model.parameters()).device
m = model.head # Head() module
self.params = params
self.stride = m.stride
self.nc = m.nc
self.no = m.no
self.reg_max = m.ch
self.device = device
self.box_loss = BoxLoss(m.ch - 1).to(device)
self.cls_loss = torch.nn.BCEWithLogitsLoss(reduction="none")
self.assigner = Assigner(nc=self.nc, top_k=10, alpha=0.5, beta=6.0)
self.project = torch.arange(m.ch, dtype=torch.float, device=device)
def box_decode(self, anchor_points, pred_dist):
b, a, c = pred_dist.shape
pred_dist = pred_dist.view(b, a, 4, c // 4)
pred_dist = pred_dist.softmax(3)
pred_dist = pred_dist.matmul(self.project.type(pred_dist.dtype))
lt, rb = pred_dist.chunk(2, -1)
x1y1 = anchor_points - lt
x2y2 = anchor_points + rb
return torch.cat(tensors=(x1y1, x2y2), dim=-1)
def __call__(self, outputs, targets):
x = torch.cat([i.view(outputs[0].shape[0], self.no, -1) for i in outputs], dim=2)
pred_distri, pred_scores = x.split(split_size=(self.reg_max * 4, self.nc), dim=1)
pred_scores = pred_scores.permute(0, 2, 1).contiguous()
pred_distri = pred_distri.permute(0, 2, 1).contiguous()
data_type = pred_scores.dtype
batch_size = pred_scores.shape[0]
input_size = torch.tensor(outputs[0].shape[2:], device=self.device, dtype=data_type) * self.stride[0]
anchor_points, stride_tensor = make_anchors(outputs, self.stride, offset=0.5)
idx = targets["idx"].view(-1, 1)
cls = targets["cls"].view(-1, 1)
box = targets["box"]
targets = torch.cat((idx, cls, box), dim=1).to(self.device)
if targets.shape[0] == 0:
gt = torch.zeros(batch_size, 0, 5, device=self.device)
else:
i = targets[:, 0]
_, counts = i.unique(return_counts=True)
counts = counts.to(dtype=torch.int32)
gt = torch.zeros(batch_size, counts.max(), 5, device=self.device)
for j in range(batch_size):
matches = i == j
n = matches.sum()
if n:
gt[j, :n] = targets[matches, 1:]
x = gt[..., 1:5].mul_(input_size[[1, 0, 1, 0]])
y = torch.empty_like(x)
dw = x[..., 2] / 2 # half-width
dh = x[..., 3] / 2 # half-height
y[..., 0] = x[..., 0] - dw # top left x
y[..., 1] = x[..., 1] - dh # top left y
y[..., 2] = x[..., 0] + dw # bottom right x
y[..., 3] = x[..., 1] + dh # bottom right y
gt[..., 1:5] = y
gt_labels, gt_bboxes = gt.split((1, 4), 2)
mask_gt = gt_bboxes.sum(2, keepdim=True).gt_(0)
pred_bboxes = self.box_decode(anchor_points, pred_distri)
assigned_targets = self.assigner(
pred_scores.detach().sigmoid(),
(pred_bboxes.detach() * stride_tensor).type(gt_bboxes.dtype),
anchor_points * stride_tensor,
gt_labels,
gt_bboxes,
mask_gt,
)
target_bboxes, target_scores, fg_mask = assigned_targets
target_scores_sum = max(target_scores.sum(), 1)
loss_cls = self.cls_loss(pred_scores, target_scores.to(data_type)).sum() / target_scores_sum # BCE
# Box loss
loss_box = torch.zeros(1, device=self.device)
loss_dfl = torch.zeros(1, device=self.device)
if fg_mask.sum():
target_bboxes /= stride_tensor
loss_box, loss_dfl = self.box_loss(
pred_distri,
pred_bboxes,
anchor_points,
target_bboxes,
target_scores,
target_scores_sum,
fg_mask,
)
loss_box *= self.params["box"] # box gain
loss_cls *= self.params["cls"] # cls gain
loss_dfl *= self.params["dfl"] # dfl gain
return loss_box, loss_cls, loss_dfl