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train_ddp.py
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train_ddp.py
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import argparse
import torch.optim as optim
import torch.optim.lr_scheduler as lr_scheduler
from torch.utils.tensorboard import SummaryWriter
import test # import test.py to get mAP after each epoch
from models import *
from utils.datasets import *
from utils.utils import *
from utils.prune_utils import *
import math
from torch.cuda import amp
from utils.torch_utils import ModelEMA, select_device # DDP import
import torch.distributed as dist
from torch.nn.parallel import DistributedDataParallel as DDP
wdir = 'weights' + os.sep # weights dir
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = 'results.txt'
# Hyperparameters
hyp = {'giou': 3.54, # giou loss gain
'cls': 37.4, # cls loss gain
'cls_pw': 1.0, # cls BCELoss positive_weight
'obj': 64.3, # obj loss gain (*=img_size/320 if img_size != 320)
'obj_pw': 1.0, # obj BCELoss positive_weight
'iou_t': 0.20, # iou training threshold
'lr0': 0.01, # initial learning rate (SGD=5E-3, Adam=5E-4)
'lrf': 0.0005, # final learning rate (with cos scheduler)
'momentum': 0.937, # SGD momentum
'weight_decay': 0.0005, # optimizer weight decay
'fl_gamma': 0.0, # focal loss gamma (efficientDet default is gamma=1.5)
'hsv_h': 0.0138, # image HSV-Hue augmentation (fraction)
'hsv_s': 0.678, # image HSV-Saturation augmentation (fraction)
'hsv_v': 0.36, # image HSV-Value augmentation (fraction)
'degrees': 1.98 * 0, # image rotation (+/- deg)
'translate': 0.05 * 0, # image translation (+/- fraction)
'scale': 0.05 * 0, # image scale (+/- gain)
'shear': 0.641 * 0} # image shear (+/- deg)
# Overwrite hyp with hyp*.txt (optional)
f = glob.glob('hyp*.txt')
if f:
print('Using %s' % f[0])
for k, v in zip(hyp.keys(), np.loadtxt(f[0])):
hyp[k] = v
# Print focal loss if gamma > 0
if hyp['fl_gamma']:
print('Using FocalLoss(gamma=%g)' % hyp['fl_gamma'])
def train(hyp):
cfg = opt.cfg
t_cfg = opt.t_cfg # teacher model cfg for knowledge distillation
data = opt.data
epochs = opt.epochs # 500200 batches at bs 64, 117263 images = 273 epochs
batch_size = opt.batch_size
accumulate = max(round(64 / batch_size), 1) # accumulate n times before optimizer update (bs 64)
weights = opt.weights # initial training weights
t_weights = opt.t_weights # teacher model weights
imgsz_min, imgsz_max, imgsz_test = opt.img_size # img sizes (min, max, test)
# Image Sizes
gs = 32 # (pixels) grid size
start_epoch = 0
assert math.fmod(imgsz_min, gs) == 0, '--img-size %g must be a %g-multiple' % (imgsz_min, gs)
opt.multi_scale |= imgsz_min != imgsz_max # multi if different (min, max)
if opt.multi_scale:
if imgsz_min == imgsz_max:
imgsz_min //= 1.5
imgsz_max //= 0.667
grid_min, grid_max = imgsz_min // gs, imgsz_max // gs
imgsz_min, imgsz_max = int(grid_min * gs), int(grid_max * gs)
img_size = imgsz_max # initialize with max size
# Configure run
init_seeds()
data_dict = parse_data_cfg(data)
train_path = data_dict['train']
test_path = data_dict['valid']
nc = 1 if opt.single_cls else int(data_dict['classes']) # number of classes
hyp['cls'] *= nc / 80 # update coco-tuned hyp['cls'] to current dataset
# Remove previous results
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
os.remove(f)
# DDP init
if opt.local_rank != -1:
if opt.local_rank == 0:
print("--------------using ddp---------------")
assert torch.cuda.device_count() > opt.local_rank
torch.cuda.set_device(opt.local_rank)
dist.init_process_group(backend='nccl', init_method='env://') # distributed backend
assert opt.batch_size % opt.world_size == 0, '--batch-size must be multiple of CUDA device count'
opt.batch_size = opt.batch_size // opt.world_size
else:
dist.init_process_group(backend='nccl', # 'distributed backend'
init_method='tcp://127.0.0.1:9999', # distributed training init method
world_size=1, # number of nodes for distributed training
rank=0) # distributed training node rank
# Initialize model
steps = math.ceil(len(open(train_path).readlines()) / batch_size) * epochs
model = Darknet(cfg, quantized=opt.quantized, a_bit=opt.a_bit, w_bit=opt.w_bit,
FPGA=opt.FPGA, steps=steps, is_gray_scale=opt.gray_scale, maxabsscaler=opt.maxabsscaler,
shortcut_way=opt.shortcut_way).to(device)
if t_cfg:
t_model = Darknet(t_cfg).to(device)
# print('<.....................using gridmask.......................>')
# gridmask = GridMask(d1=96, d2=224, rotate=360, ratio=0.6, mode=1, prob=0.8)
# Optimizer
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if '.bias' in k:
pg2 += [v] # biases
elif 'Conv2d.weight' in k:
pg1 += [v] # apply weight_decay
else:
pg0 += [v] # all else
if opt.adam:
# hyp['lr0'] *= 0.1 # reduce lr (i.e. SGD=5E-3, Adam=5E-4)
optimizer = optim.Adam(pg0, lr=hyp['lr0'])
# optimizer = AdaBound(pg0, lr=hyp['lr0'], final_lr=0.1)
else:
optimizer = optim.SGD(pg0, lr=hyp['lr0'], momentum=hyp['momentum'], nesterov=True)
optimizer.add_param_group({'params': pg1, 'weight_decay': hyp['weight_decay']}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
print('Optimizer groups: %g .bias, %g Conv2d.weight, %g other' % (len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
best_fitness = 0.0
if weights != 'None':
attempt_download(weights)
if weights.endswith('.pt'): # pytorch format
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
chkpt = torch.load(weights, map_location=device)
# load model
try:
chkpt['model'] = {k: v for k, v in chkpt['model'].items() if model.state_dict()[k].numel() == v.numel()}
model.load_state_dict(chkpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. Specify --weights '' or specify a --cfg compatible with %s. " \
"See https://github.com/ultralytics/yolov3/issues/657" % (opt.weights, opt.cfg, opt.weights)
raise KeyError(s) from e
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
if chkpt.get('best_fitness') is not None:
best_fitness = chkpt['best_fitness']
# load results
if chkpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(chkpt['training_results']) # write results.txt
if chkpt.get('epoch') is not None:
start_epoch = chkpt['epoch'] + 1
del chkpt
elif len(weights) > 0: # darknet format
# possible weights are '*.weights', 'yolov3-tiny.conv.15', 'darknet53.conv.74' etc.
load_darknet_weights(model, weights, pt=opt.pt, FPGA=opt.FPGA)
if t_cfg:
if t_weights.endswith('.pt'):
t_model.load_state_dict(torch.load(t_weights, map_location=device)['model'])
elif t_weights.endswith('.weights'):
load_darknet_weights(t_model, t_weights)
else:
raise Exception('pls provide proper teacher weights for knowledge distillation')
t_model.eval()
print('<.....................using knowledge distillation.......................>')
print('teacher model:', t_weights, '\n')
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: (((1 + math.cos(x * math.pi / epochs)) / 2) ** 1.0) * 0.95 + 0.05 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
scheduler.last_epoch = start_epoch - 1 # see link below
# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
# # 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.ylabel('LR')
# plt.tight_layout()
# plt.savefig('LR.png', dpi=300)
# Initialize distributed training
if opt.local_rank != -1:
model = DDP(model, device_ids=[opt.local_rank], output_device=opt.local_rank, find_unused_parameters=True)
else:
model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# Dataset
dataset = LoadImagesAndLabels(train_path, img_size, batch_size,
augment=True,
hyp=hyp, # augmentation hyperparameters
rect=opt.rect, # rectangular training
cache_images=opt.cache_images,
single_cls=opt.single_cls,
rank=opt.local_rank,
is_gray_scale=True if opt.gray_scale else False)
testset = LoadImagesAndLabels(test_path, imgsz_test, batch_size // 4,
hyp=hyp,
rect=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls,
rank=opt.local_rank,
is_gray_scale=True if opt.gray_scale else False)
# 获得要剪枝的层
if hasattr(model, 'module'):
print('muti-gpus sparse')
if opt.prune == 0:
print('normal sparse training ')
_, _, prune_idx = parse_module_defs(model.module.module_defs)
elif opt.prune == 1:
print('shortcut sparse training')
_, _, prune_idx, _, _ = parse_module_defs2(model.module.module_defs)
elif opt.prune == 2:
print('layer sparse training')
_, _, prune_idx = parse_module_defs4(model.module.module_defs)
else:
print('single-gpu sparse')
if opt.prune == 0:
print('normal sparse training')
_, _, prune_idx = parse_module_defs(model.module_defs)
elif opt.prune == 1:
print('shortcut sparse training')
_, _, prune_idx, _, _ = parse_module_defs2(model.module_defs)
elif opt.prune == 2:
print('layer sparse training')
_, _, prune_idx = parse_module_defs4(model.module_defs)
train_sampler = torch.utils.data.distributed.DistributedSampler(dataset) # ddp sampler
test_sampler = torch.utils.data.distributed.DistributedSampler(testset)
# Dataloader
batch_size = min(batch_size, len(dataset))
nw = min([os.cpu_count(), batch_size if batch_size > 1 else 0, 8]) # number of workers
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=int(batch_size / opt.world_size),
num_workers=nw,
shuffle=False if (opt.local_rank != -1) else not opt.rect,
pin_memory=True,
collate_fn=dataset.collate_fn,
sampler=train_sampler if (opt.local_rank != -1) else None
)
# Testloader
testloader = torch.utils.data.DataLoader(LoadImagesAndLabels(test_path, imgsz_test, batch_size // 4,
hyp=hyp,
rect=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls,
rank=opt.local_rank,
is_gray_scale=True if opt.gray_scale else False),
batch_size=batch_size // 4,
num_workers=nw,
pin_memory=True,
collate_fn=dataset.collate_fn)
if opt.prune != -1:
for idx in prune_idx:
if hasattr(model, 'module'):
bn_weights = gather_bn_weights(model.module.module_list, [idx])
else:
bn_weights = gather_bn_weights(model.module_list, [idx])
tb_writer.add_histogram('before_train_perlayer_bn_weights/hist', bn_weights.numpy(), idx, bins='doane')
# Model parameters
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.gr = 1.0 # giou loss ratio (obj_loss = 1.0 or giou)
model.class_weights = labels_to_class_weights(dataset.labels, nc).to(device) # attach class weights
# Model EMA
if opt.ema:
ema = torch_utils.ModelEMA(model)
# Start training
nb = len(dataloader) # number of batches
n_burn = max(3 * nb, 500) # burn-in iterations, max(3 epochs, 500 iterations)
maps = np.zeros(nc) # mAP per class
# torch.autograd.set_detect_anomaly(True)
results = (0, 0, 0, 0, 0, 0, 0) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
t0 = time.time()
if opt.local_rank == -1 or opt.local_rank == 0:
print('Image sizes %g - %g train, %g test' % (imgsz_min, imgsz_max, imgsz_test))
print('Using %g dataloader workers' % nw)
print('Starting training for %g epochs...' % epochs)
if opt.mpt:
cuda = device.type != 'cpu'
scaler = amp.GradScaler(enabled=cuda)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
if opt.local_rank != -1:
dataloader.sampler.set_epoch(epoch) # DDP set seed
# gridmask.set_prob(epoch, max_epoch)
model.train()
# 稀疏化标志
if opt.prune == -1:
sr_flag = False
else:
sr_flag = True
# Update image weights (optional)
if dataset.image_weights:
w = model.class_weights.cpu().numpy() * (1 - maps) ** 2 # 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) # rand weighted idx
mloss = torch.zeros(4).to(device) # mean losses
if opt.local_rank == -1 or opt.local_rank == 0:
print(('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls', 'total', 'targets', 'img_size'))
pbar = tqdm(enumerate(dataloader), total=nb) # progress bar
for i, (imgs, targets, paths, _) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
if opt.maxabsscaler:
imgs = imgs.to(device).float() / 256.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
imgs = imgs * 2 - 1
else:
imgs = imgs.to(device).float() / 256.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
if opt.quantized != -1:
if opt.a_bit == 16:
img = img * (2 ** 14)
sign = torch.sign(img)
img = sign * torch.floor(torch.abs(img) + 0.5)
img = img / (2 ** 14)
# Burn-in
if ni <= n_burn:
xi = [0, n_burn] # x interp
model.gr = np.interp(ni, xi, [0.0, 1.0]) # giou loss ratio (obj_loss = 1.0 or giou)
accumulate = max(1, np.interp(ni, xi, [1, 64 / batch_size]).round())
for j, x in enumerate(optimizer.param_groups):
# bias lr falls from 0.1 to lr0, all other lrs rise from 0.0 to lr0
x['lr'] = np.interp(ni, xi, [0.1 if j == 2 else 0.0, x['initial_lr'] * lf(epoch)])
x['weight_decay'] = np.interp(ni, xi, [0.0, hyp['weight_decay'] if j == 1 else 0.0])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi, [0.9, hyp['momentum']])
# Multi-Scale
if opt.multi_scale:
if ni / accumulate % 1 == 0: # adjust img_size (67% - 150%) every 1 batch
img_size = random.randrange(grid_min, grid_max + 1) * gs
sf = img_size / max(imgs.shape[2:]) # scale factor
if sf != 1:
ns = [math.ceil(x * sf / gs) * gs for x in imgs.shape[2:]] # new shape (stretched to 32-multiple)
imgs = F.interpolate(imgs, size=ns, mode='bilinear', align_corners=False)
# Forward
if opt.mpt:
with amp.autocast(enabled=cuda):
targets = targets.to(device)
pred, feature_s = model(imgs)
# Loss
loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
soft_target = 0
if t_cfg:
_, output_t, feature_t = t_model(imgs)
if opt.KDstr == 1:
soft_target = compute_lost_KD(pred, output_t, model.nc, imgs.size(0))
elif opt.KDstr == 2:
soft_target, reg_ratio = compute_lost_KD2(model, targets, pred, output_t)
elif opt.KDstr == 3:
soft_target = compute_lost_KD3(model, targets, pred, output_t)
elif opt.KDstr == 4:
soft_target = compute_lost_KD4(model, targets, pred, output_t, feature_s, feature_t,
imgs.size(0))
elif opt.KDstr == 5:
soft_target = compute_lost_KD5(model, targets, pred, output_t, feature_s, feature_t,
imgs.size(0),
img_size)
else:
print("please select KD strategy!")
loss += soft_target
else:
targets = targets.to(device)
pred, feature_s = model(imgs)
# Loss
loss, loss_items = compute_loss(pred, targets, model)
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
soft_target = 0
if t_cfg:
_, output_t, feature_t = t_model(imgs)
if opt.KDstr == 1:
soft_target = compute_lost_KD(pred, output_t, model.nc, imgs.size(0))
elif opt.KDstr == 2:
soft_target, reg_ratio = compute_lost_KD2(model, targets, pred, output_t)
elif opt.KDstr == 3:
soft_target = compute_lost_KD3(model, targets, pred, output_t)
elif opt.KDstr == 4:
soft_target = compute_lost_KD4(model, targets, pred, output_t, feature_s, feature_t,
imgs.size(0))
elif opt.KDstr == 5:
soft_target = compute_lost_KD5(model, targets, pred, output_t, feature_s, feature_t,
imgs.size(0),
img_size)
else:
print("please select KD strategy!")
loss += soft_target
# Backward
loss *= batch_size / 64 # scale loss
if opt.mpt:
scaler.scale(loss).backward()
else:
loss.backward()
# 对要剪枝层的γ参数稀疏化
if hasattr(model, 'module'):
if opt.prune != -1:
BNOptimizer.updateBN(sr_flag, model.module.module_list, opt.s, prune_idx)
else:
if opt.prune != -1:
BNOptimizer.updateBN(sr_flag, model.module_list, opt.s, prune_idx)
# Optimize
if ni % accumulate == 0:
if opt.mpt:
scaler.step(optimizer) # optimizer.step
scaler.update()
else:
optimizer.step()
optimizer.zero_grad()
if opt.ema:
ema.update(model)
# Print
mloss = (mloss * i + loss_items) / (i + 1) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
pbar.set_description(s)
# Plot
if i == 0:
if not os.path.isdir('train_sample/'):
os.makedirs('train_sample/')
f = 'train_sample/train_batch%g.jpg' % epoch # filename
res = plot_images(images=imgs, targets=targets, paths=paths, fname=f, is_gray_scale=opt.gray_scale)
if tb_writer:
tb_writer.add_image(f, res, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Update scheduler
scheduler.step()
# Process epoch results
if opt.ema:
ema.update_attr(model)
if hasattr(model, 'module'):
module_defs, module_list = ema.eam.module.module_defs, ema.eam.module.module_list
else:
module_defs, module_list = ema.eam.module_defs, ema.eam.module_list
for i, (mdef, module) in enumerate(zip(module_defs, module_list)):
if mdef['type'] == 'yolo':
yolo_layer = module
yolo_layer.nx, yolo_layer.ny = 0, 0
if hasattr(model, 'module'):
module_defs, module_list = model.module.module_defs, model.module.module_list
else:
module_defs, module_list = model.module_defs, model.module_list
for i, (mdef, module) in enumerate(zip(module_defs, module_list)):
if mdef['type'] == 'yolo':
yolo_layer = module
yolo_layer.nx, yolo_layer.ny = 0, 0
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
is_coco = any([x in data for x in ['coco.data', 'coco2014.data', 'coco2017.data']]) and model.nc == 80
results, maps = test.test(cfg,
data,
batch_size=batch_size // 4,
imgsz=imgsz_test,
model=ema.ema if opt.ema else model,
save_json=final_epoch and is_coco,
single_cls=opt.single_cls,
dataloader=testloader,
multi_label=ni > n_burn,
quantized=opt.quantized,
a_bit=opt.a_bit,
w_bit=opt.w_bit,
FPGA=opt.FPGA,
rank=opt.local_rank,
plot=True,
maxabsscaler=opt.maxabsscaler,
shortcut_way=opt.shortcut_way)
torch.cuda.empty_cache()
# Write
if opt.local_rank in [-1, 0]:
with open(results_file, 'a') as f:
f.write(s + '%10.3g' * 7 % results + '\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp results.txt gs://%s/results/results%s.txt' % (opt.bucket, opt.name))
# Tensorboard
if tb_writer:
tags = ['train/giou_loss', 'train/obj_loss', 'train/cls_loss',
'metrics/precision', 'metrics/recall', 'metrics/mAP_0.5', 'metrics/F1',
'val/giou_loss', 'val/obj_loss', 'val/cls_loss']
for x, tag in zip(list(mloss[:-1]) + list(results), tags):
tb_writer.add_scalar(tag, x, epoch)
if opt.prune != -1:
if hasattr(model, 'module'):
bn_weights = gather_bn_weights(model.module.module_list, [idx])
else:
bn_weights = gather_bn_weights(model.module_list, [idx])
tb_writer.add_histogram('bn_weights/hist', bn_weights.numpy(), epoch, bins='doane')
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness:
best_fitness = fi
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if opt.ema:
if hasattr(model, 'module'):
model_temp = ema.ema.module.state_dict()
else:
model_temp = ema.ema.state_dict()
else:
if hasattr(model, 'module'):
model_temp = model.module.state_dict()
else:
model_temp = model.state_dict()
if save and dist.get_rank() == 0: # DDP save model only once
with open(results_file, 'r') as f: # create checkpoint
chkpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': model_temp,
'optimizer': None if final_epoch else optimizer.state_dict()}
# Save last, best and delete
torch.save(chkpt, last)
if (best_fitness == fi) and not final_epoch:
torch.save(chkpt, best)
del chkpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
n = opt.name
if len(n):
n = '_' + n if not n.isnumeric() else n
fresults, flast, fbest = 'results%s.txt' % n, wdir + 'last%s.pt' % n, wdir + 'best%s.pt' % n
for f1, f2 in zip([wdir + 'last.pt', wdir + 'best.pt', 'results.txt'], [flast, fbest, fresults]):
if os.path.exists(f1):
os.rename(f1, f2) # rename
ispt = f2.endswith('.pt') # is *.pt
strip_optimizer(f2) if ispt else None # strip optimizer
os.system('gsutil cp %s gs://%s/weights' % (f2, opt.bucket)) if opt.bucket and ispt else None # upload
if not opt.evolve:
plot_results() # save as results.png
if opt.local_rank in [-1, 0]:
print('%g epochs completed in %.3f hours.\n' % (epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if torch.cuda.device_count() > 1 else None
torch.cuda.empty_cache()
return results
if __name__ == '__main__':
parser = argparse.ArgumentParser()
parser.add_argument('--epochs', type=int, default=300) # 500200 batches at bs 16, 117263 COCO images = 273 epochs
parser.add_argument('--batch-size', type=int, default=16) # effective bs = batch_size * accumulate = 16 * 4 = 64
parser.add_argument('--cfg', type=str, default='cfg/yolov3-spp.cfg', help='*.cfg path')
parser.add_argument('--t_cfg', type=str, default='', help='teacher model cfg file path for knowledge distillation')
parser.add_argument('--data', type=str, default='data/coco2017.data', help='*.data path')
parser.add_argument('--multi-scale', action='store_true', help='adjust (67%% - 150%%) img_size every 10 batches')
parser.add_argument('--img-size', nargs='+', type=int, default=[320, 640], help='[min_train, max-train, test]')
parser.add_argument('--rect', action='store_true', help='rectangular training')
parser.add_argument('--resume', action='store_true', help='resume training from last.pt')
parser.add_argument('--nosave', action='store_true', help='only save final checkpoint')
parser.add_argument('--notest', action='store_true', help='only test final epoch')
parser.add_argument('--evolve', action='store_true', help='evolve hyperparameters')
parser.add_argument('--bucket', type=str, default='', help='gsutil bucket')
parser.add_argument('--cache-images', action='store_true', help='cache images for faster training')
parser.add_argument('--weights', type=str, default='weights/yolov3-spp-ultralytics.pt', help='initial weights path')
parser.add_argument('--t_weights', type=str, default='', help='teacher model weights')
parser.add_argument('--KDstr', type=int, default=-1, help='KD strategy')
parser.add_argument('--name', default='', help='renames results.txt to results_name.txt if supplied')
parser.add_argument('--device', default='', help='device id (i.e. 0 or 0,1 or cpu)')
parser.add_argument('--adam', action='store_true', help='use adam optimizer')
parser.add_argument('--ema', action='store_true', help='use ema')
parser.add_argument('--single-cls', action='store_true', help='train as single-class dataset')
parser.add_argument('--pretrain', '-pt', dest='pt', action='store_true',
help='use pretrain model')
parser.add_argument('--mixedprecision', '-mpt', dest='mpt', action='store_true',
help='use mixed precision training')
parser.add_argument('--s', type=float, default=0.001, help='scale sparse rate')
parser.add_argument('--prune', type=int, default=-1,
help='0:nomal prune or regular prune 1:shortcut prune 2:layer prune')
parser.add_argument('--quantized', type=int, default=-1, help='quantization way')
parser.add_argument('--shortcut_way', type=int, default=1, help='--shortcut quantization way')
parser.add_argument('--a-bit', type=int, default=8, help='a-bit')
parser.add_argument('--w-bit', type=int, default=8, help='w-bit')
parser.add_argument('--FPGA', '-FPGA', dest='FPGA', action='store_true', help='FPGA')
parser.add_argument('--gray-scale', action='store_true', help='gray scale trainning')
parser.add_argument('--maxabsscaler', '-mas', action='store_true', help='Standarize input to (-1,1)')
# DDP get local-rank
parser.add_argument('--rank', default=0, help='rank of current process')
parser.add_argument('--local_rank', type=int, default=-1, help='DDP parameter, do not modify')
opt = parser.parse_args()
opt.weights = last if opt.resume else opt.weights
opt.cfg = list(glob.iglob('./**/' + opt.cfg, recursive=True))[0] # find file
# opt.data = list(glob.iglob(' ./**/' + opt.data, recursive=True))[0] # find file
if opt.local_rank in [-1, 0]:
print(opt)
opt.img_size.extend([opt.img_size[-1]] * (3 - len(opt.img_size))) # extend to 3 sizes (min, max, test)
# DDP set variables
opt.world_size = int(os.environ['WORLD_SIZE']) if 'WORLD_SIZE' in os.environ else 1
opt.global_rank = int(os.environ['RANK']) if 'RANK' in os.environ else -1
# scale hyp['obj'] by img_size (evolved at 320)
# hyp['obj'] *= opt.img_size[0] / 320.
# DDP set device
if opt.local_rank != -1:
if opt.local_rank == 0:
device = select_device(opt.device, batch_size=opt.batch_size)
device = torch.device('cuda', opt.local_rank)
else:
device = torch_utils.select_device(opt.device, batch_size=opt.batch_size)
tb_writer = None
if not opt.evolve: # Train normally
if opt.local_rank in [-1, 0]:
print('Start Tensorboard with "tensorboard --logdir=runs", view at http://localhost:6006/')
tb_writer = SummaryWriter(comment=opt.name)
train(hyp) # train normally
else: # Evolve hyperparameters (optional)
opt.notest, opt.nosave = True, True # only test/save final epoch
if opt.bucket:
os.system('gsutil cp gs://%s/evolve.txt .' % opt.bucket) # download evolve.txt if exists
for _ in range(1): # generations to evolve
if os.path.exists('evolve.txt'): # if evolve.txt exists: select best hyps and mutate
# Select parent(s)
parent = 'single' # parent selection method: 'single' or 'weighted'
x = np.loadtxt('evolve.txt', ndmin=2)
n = min(5, len(x)) # number of previous results to consider
x = x[np.argsort(-fitness(x))][:n] # top n mutations
w = fitness(x) - fitness(x).min() # weights
if parent == 'single' or len(x) == 1:
# x = x[random.randint(0, n - 1)] # random selection
x = x[random.choices(range(n), weights=w)[0]] # weighted selection
elif parent == 'weighted':
x = (x * w.reshape(n, 1)).sum(0) / w.sum() # weighted combination
# Mutate
method, mp, s = 3, 0.9, 0.2 # method, mutation probability, sigma
npr = np.random
npr.seed(int(time.time()))
g = np.array([1, 1, 1, 1, 1, 1, 1, 0, .1, 1, 0, 1, 1, 1, 1, 1, 1, 1]) # gains
ng = len(g)
if method == 1:
v = (npr.randn(ng) * npr.random() * g * s + 1) ** 2.0
elif method == 2:
v = (npr.randn(ng) * npr.random(ng) * g * s + 1) ** 2.0
elif method == 3:
v = np.ones(ng)
while all(v == 1): # mutate until a change occurs (prevent duplicates)
# v = (g * (npr.random(ng) < mp) * npr.randn(ng) * s + 1) ** 2.0
v = (g * (npr.random(ng) < mp) * npr.randn(ng) * npr.random() * s + 1).clip(0.3, 3.0)
for i, k in enumerate(hyp.keys()): # plt.hist(v.ravel(), 300)
hyp[k] = x[i + 7] * v[i] # mutate
# Clip to limits
keys = ['lr0', 'iou_t', 'momentum', 'weight_decay', 'hsv_s', 'hsv_v', 'translate', 'scale', 'fl_gamma']
limits = [(1e-5, 1e-2), (0.00, 0.70), (0.60, 0.98), (0, 0.001), (0, .9), (0, .9), (0, .9), (0, .9), (0, 3)]
for k, v in zip(keys, limits):
hyp[k] = np.clip(hyp[k], v[0], v[1])
# Train mutation
results = train(hyp.copy())
# Write mutation results
print_mutation(hyp, results, opt.bucket)
# Plot results
# plot_evolution_results(hyp)