train.py
import argparse
import time
import torch.distributed as dist
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.data import DataLoader
import test # Import test.py to get mAP after each epoch
from models import *
from utils.datasets import *
from utils.utils import *
# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.204 0.302 0.175 0.234 (square smart)
hyp = {'xy': 0.2, # xy loss gain
'wh': 0.1, # wh loss gain
'cls': 0.04, # cls loss gain
'conf': 4.5, # conf loss gain
'iou_t': 0.5, # iou target-anchor training threshold
'lr0': 0.001, # initial learning rate
'lrf': -4., # final learning rate = lr0 * (10 ** lrf)
'momentum': 0.90, # SGD momentum
'weight_decay': 0.0005} # optimizer weight decay
# Hyperparameters: Original, Metrics: 0.172 0.304 0.156 0.205 (square)
# hyp = {'xy': 0.5, # xy loss gain
# 'wh': 0.0625, # wh loss gain
# 'cls': 0.0625, # cls loss gain
# 'conf': 4, # conf loss gain
# 'iou_t': 0.1, # iou target-anchor training threshold
# 'lr0': 0.001, # initial learning rate
# 'lrf': -5., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.9, # SGD momentum
# 'weight_decay': 0.0005} # optimizer weight decay
# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.225 0.251 0.145 0.218 (rect)
# hyp = {'xy': 0.4499, # xy loss gain
# 'wh': 0.05121, # wh loss gain
# 'cls': 0.04207, # cls loss gain
# 'conf': 2.853, # conf loss gain
# 'iou_t': 0.2487, # iou target-anchor training threshold
# 'lr0': 0.0005301, # initial learning rate
# 'lrf': -5., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.8823, # SGD momentum
# 'weight_decay': 0.0004149} # optimizer weight decay
# Hyperparameters: train.py --evolve --epochs 2 --img-size 320, Metrics: 0.178 0.313 0.167 0.212 (square)
# hyp = {'xy': 0.4664, # xy loss gain
# 'wh': 0.08437, # wh loss gain
# 'cls': 0.05145, # cls loss gain
# 'conf': 4.244, # conf loss gain
# 'iou_t': 0.09121, # iou target-anchor training threshold
# 'lr0': 0.0004938, # initial learning rate
# 'lrf': -5., # final learning rate = lr0 * (10 ** lrf)
# 'momentum': 0.9025, # SGD momentum
# 'weight_decay': 0.0005417} # optimizer weight decay
def train(
cfg,
data_cfg,
img_size=416,
resume=False,
epochs=100, # 500200 batches at bs 4, 117263 images = 68 epochs
batch_size=16,
accumulate=4, # effective bs = 64 = batch_size * accumulate
multi_scale=False, ##默认关闭多尺度训练
freeze_backbone=False,
transfer=False # Transfer learning (train only YOLO layers)
):
init_seeds()
weights = 'weights' + os.sep
latest = weights + 'latest.pt'
best = weights + 'best.pt'
device = torch_utils.select_device()
if multi_scale:
img_size = round((img_size / 32) * 1.5) * 32 # initiate with maximum multi_scale size
# opt.num_workers = 0 # bug https://github.com/ultralytics/yolov3/issues/174
else:
# 在数据输入维度变化不大的情况下，加这一句可以提高计算速度，如果变化频繁反而拖累训练
torch.backends.cudnn.benchmark = True # unsuitable for multiscale
# Configure run
data_dict = parse_data_cfg(data_cfg)
train_path = data_dict['train']
nc = int(data_dict['classes']) # number of classes
# Initialize model
model = Darknet(cfg, img_size).to(device)
# Optimizer
optimizer = optim.SGD(model.parameters(), lr=hyp['lr0'], momentum=hyp['momentum'], weight_decay=hyp['weight_decay'])
cutoff = -1 # backbone reaches to cutoff layer
start_epoch = 0
best_loss = float('inf')
nf = int(model.module_defs[model.yolo_layers[0] - 1]['filters']) # yolo layer size (i.e. 255)
if resume: # Load previously saved model 断点继续训练
if transfer: # Transfer learning
chkpt = torch.load(weights + 'yolov3-spp.pt', map_location=device)
model.load_state_dict({k: v for k, v in chkpt['model'].items() if v.numel() > 1 and v.shape[0] != 255},
strict=False)
for p in model.parameters():
p.requires_grad = True if p.shape[0] == nf else False
else: # resume from latest.pt
chkpt = torch.load(latest, map_location=device) # load checkpoint
model.load_state_dict(chkpt['model'])
start_epoch = chkpt['epoch'] + 1
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
best_loss = chkpt['best_loss']
del chkpt
else: # Initialize model with backbone (optional)
if '-tiny.cfg' in cfg:
cutoff = load_darknet_weights(model, weights + 'yolov3-tiny.conv.15')
else:
cutoff = load_darknet_weights(model, weights + 'darknet53.conv.74')
# Scheduler https://github.com/ultralytics/yolov3/issues/238
# lf = lambda x: 1 - x / epochs # linear ramp to zero
# lf = lambda x: 10 ** (hyp['lrf'] * x / epochs) # exp ramp
# lf = lambda x: 1 - 10 ** (hyp['lrf'] * (1 - x / epochs)) # inverse exp ramp
# scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
scheduler = lr_scheduler.MultiStepLR(optimizer, milestones=[round(opt.epochs * x) for x in (0.8, 0.9)], gamma=0.1)
scheduler.last_epoch = start_epoch - 1
# # Plot lr schedule
# y = []
# for _ in range(epochs):
# scheduler.step()
# y.append(optimizer.param_groups[0]['lr'])
# plt.plot(y, label='LambdaLR')
# plt.xlabel('epoch')
# plt.xlabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Dataset
dataset = LoadImagesAndLabels(train_path,
img_size,
batch_size,
augment=True,
rect=False,
multi_scale=multi_scale)
# Initialize distributed training
if torch.cuda.device_count() > 1:
dist.init_process_group(backend=opt.backend, init_method=opt.dist_url, world_size=opt.world_size, rank=opt.rank)
model = torch.nn.parallel.DistributedDataParallel(model)
# sampler = torch.utils.data.distributed.DistributedSampler(dataset)
# Dataloader
dataloader = DataLoader(dataset,
batch_size=batch_size,
num_workers=opt.num_workers,
shuffle=False, # disable rectangular training if True
pin_memory=True,
collate_fn=dataset.collate_fn)
# Mixed precision training https://github.com/NVIDIA/apex
# install help: https://github.com/NVIDIA/apex/issues/259
mixed_precision = False
if mixed_precision:
from apex import amp
model, optimizer = amp.initialize(model, optimizer, opt_level='O1')
# Remove old results
for f in glob.glob('*_batch*.jpg') + glob.glob('results.txt'):
os.remove(f)
# Start training
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
model_info(model)
nb = len(dataloader)
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0) # P, R, mAP, F1, test_loss
n_burnin = min(round(nb / 5 + 1), 1000) # burn-in batches
t, t0 = time.time(), time.time()
for epoch in range(start_epoch, epochs):
model.train()
print(('\n%8s%12s' + '%10s' * 7) % ('Epoch', 'Batch', 'xy', 'wh', 'conf', 'cls', 'total', 'targets', 'time'))
# Update scheduler
scheduler.step()
# Freeze backbone at epoch 0, unfreeze at epoch 1 (optional)
#第一轮冻结预训练模型前面74层残差卷积
if freeze_backbone and epoch < 2:
for name, p in model.named_parameters():
if int(name.split('.')[1]) < cutoff: # if layer < 75
p.requires_grad = False if epoch == 0 else True
# layer编号的一个残差模块算一层，BN也附属于卷积层而不是单独算层 75层之前的冻结，也就是固定最后一个残差模块之前的层
# Update image weights (optional)
w = model.class_weights.cpu().numpy() * (1 - maps) # class weights
image_weights = labels_to_image_weights(dataset.labels, nc=nc, class_weights=w)
dataset.indices = random.choices(range(dataset.n), weights=image_weights, k=dataset.n) # random weighted index
mloss = torch.zeros(5).to(device) # mean losses
for i, (imgs, targets, _, _) in enumerate(dataloader):
imgs = imgs.to(device)
targets = targets.to(device)
# Plot images with bounding boxes
if epoch == 0 and i == 0:
plot_images(imgs=imgs, targets=targets, fname='train_batch0.jpg')
# SGD burn-in
if epoch == 0 and i <= n_burnin:
lr = hyp['lr0'] * (i / n_burnin) ** 4
for x in optimizer.param_groups:
x['lr'] = lr
# Run model
pred = model(imgs)
# Compute loss
loss, loss_items = compute_loss(pred, targets, model)
if torch.isnan(loss):
print('WARNING: nan loss detected, ending training')
return results
# Compute gradient
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Accumulate gradient for x batches before optimizing
if (i + 1) % accumulate == 0 or (i + 1) == nb:
optimizer.step()
optimizer.zero_grad()
# Print batch results
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
s = ('%8s%12s' + '%10.3g' * 7) % (
'%g/%g' % (epoch, epochs - 1),
'%g/%g' % (i, nb - 1), *mloss, len(targets), time.time() - t)
t = time.time()
print(s)
# Multi-Scale training (320 - 608 pixels) every 10 batches
if multi_scale and (i + 1) % 10 == 0:
dataset.img_size = random.choice(range(10, 20)) * 32
print('multi_scale img_size = %g' % dataset.img_size)
# Calculate mAP (always test final epoch, skip first 5 if opt.nosave)
if not (opt.notest or (opt.nosave and epoch < 10)) or epoch == epochs - 1:
with torch.no_grad():
results, maps = test.test(cfg, data_cfg, batch_size=batch_size, img_size=img_size, model=model,
conf_thres=0.1)
# Write epoch results
with open('results.txt', 'a') as file:
file.write(s + '%11.3g' * 5 % results + '\n') # P, R, mAP, F1, test_loss
# Update best loss
test_loss = results[4]
if test_loss < best_loss:
best_loss = test_loss
# Save training results
save = (not opt.nosave) or (epoch == epochs - 1)
if save:
# Create checkpoint
chkpt = {'epoch': epoch,
'best_loss': best_loss,
'model': model.module.state_dict() if type(
model) is nn.parallel.DistributedDataParallel else model.state_dict(),
'optimizer': optimizer.state_dict()}
# Save latest checkpoint
torch.save(chkpt, latest)
# Save best checkpoint
if best_loss == test_loss:
torch.save(chkpt, best)
# Save backup every 10 epochs (optional)
if epoch > 0 and epoch % 10 == 0:
torch.save(chkpt, weights + 'backup%g.pt' % epoch)
# Delete checkpoint
del chkpt
dt = (time.time() - t0) / 3600
print('%g epochs completed in %.3f hours.' % (epoch - start_epoch + 1, dt))
return results
def print_mutation(hyp, results):
# Write mutation results
a = '%11s' * len(hyp) % tuple(hyp.keys()) # hyperparam keys
b = '%11.4g' * len(hyp) % tuple(hyp.values()) # hyperparam values
c = '%11.3g' * len(results) % results # results (P, R, mAP, F1, test_loss)
print('\n%s\n%s\nEvolved fitness: %s\n' % (a, b, c))
with open('evolve.txt', 'a') as f:
f.write(c + b + '\n')
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=68, help='number of epochs')
parser.add_argument('--batch-size', type=int, default=16, help='size of each image batch')
parser.add_argument('--accumulate', type=int, default=4, help='accumulate gradient x batches before optimizing')
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='cfg file path')
parser.add_argument('--data-cfg', type=str, default='data/coco_64img.data', help='coco.data file path')
parser.add_argument('--multi-scale', action='store_true', help='random image sizes per batch 320 - 608')
parser.add_argument('--img-size', type=int, default=416, help='inference size (pixels)')
parser.add_argument('--resume', action='store_true', help='resume training flag')
parser.add_argument('--transfer', action='store_true', help='transfer learning flag')
parser.add_argument('--num-workers', type=int, default=4, help='number of Pytorch DataLoader workers')
parser.add_argument('--dist-url', default='tcp://127.0.0.1:9999', type=str, help='distributed training init method')
parser.add_argument('--rank', default=0, type=int, help='distributed training node rank')
parser.add_argument('--world-size', default=1, type=int, help='number of nodes for distributed training')
parser.add_argument('--backend', default='nccl', type=str, help='distributed backend')
parser.add_argument('--nosave', action='store_true', help='do not save training results')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--evolve', action='store_true', help='run hyperparameter evolution')
parser.add_argument('--var', default=0, type=int, help='debug variable')
opt = parser.parse_args()
print(opt)
if opt.evolve:
opt.notest = True # save time by only testing final epoch
opt.nosave = True # do not save checkpoints
# Train
results = train(
opt.cfg,
opt.data_cfg,
img_size=opt.img_size,
resume=opt.resume or opt.transfer,
transfer=opt.transfer,
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulate=opt.accumulate,
multi_scale=opt.multi_scale,
)
# Evolve hyperparameters (optional)
if opt.evolve:
best_fitness = results[2] # use mAP for fitness
# Write mutation results
print_mutation(hyp, results)
gen = 1000 # generations to evolve
for _ in range(gen):
# Mutate hyperparameters
old_hyp = hyp.copy()
init_seeds(seed=int(time.time()))
s = [.3, .3, .3, .3, .3, .3, .3, .03, .3]
for i, k in enumerate(hyp.keys()):
x = (np.random.randn(1) * s[i] + 1) ** 1.1 # plt.hist(x.ravel(), 100)
hyp[k] = hyp[k] * float(x) # vary by about 30% 1sigma
# Clip to limits
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay']
limits = [(1e-4, 1e-2), (0, 0.90), (0.70, 0.99), (0, 0.01)]
for k, v in zip(keys, limits):
hyp[k] = np.clip(hyp[k], v[0], v[1])
# Determine mutation fitness
results = train(
opt.cfg,
opt.data_cfg,
img_size=opt.img_size,
resume=opt.resume or opt.transfer,
transfer=opt.transfer,
epochs=opt.epochs,
batch_size=opt.batch_size,
accumulate=opt.accumulate,
multi_scale=opt.multi_scale,
)
mutation_fitness = results[2]
# Write mutation results
print_mutation(hyp, results)
# Update hyperparameters if fitness improved
if mutation_fitness > best_fitness:
# Fitness improved!
print('Fitness improved!')
best_fitness = mutation_fitness
else:
hyp = old_hyp.copy() # reset hyp to
# # Plot results
# import numpy as np
# import matplotlib.pyplot as plt
# a = np.loadtxt('evolve_1000val.txt')
# x = a[:, 2] * a[:, 3] # metric = mAP * F1
# weights = (x - x.min()) ** 2
# fig = plt.figure(figsize=(14, 7))
# for i in range(len(hyp)):
# y = a[:, i + 5]
# mu = (y * weights).sum() / weights.sum()
# plt.subplot(2, 5, i+1)
# plt.plot(x.max(), mu, 'o')
# plt.plot(x, y, '.')
# print(list(hyp.keys())[i],'%.4g' % mu)
