def run_ddp_parity_two_optim(rank, world_size, backend, temp_file_name):
url = "file://" + temp_file_name
dist.init_process_group(init_method=url,
backend=backend,
rank=rank,
world_size=world_size)
device = torch.device("cuda")
torch.cuda.set_device(rank)
torch.manual_seed(rank)
np.random.seed(rank) # Any model works. Add one different buffer per rank
model = Sequential(Linear(2, 3), Linear(3, 3), Linear(3, 3), Linear(3, 3),
Linear(3, 3), Linear(3, 3))
model.register_buffer("test_buffer", torch.ones((1)) * rank)
model.to(device)
n_half_params = len(list(model.parameters())) // 2
sharded_optimizer = OSS(params=list(model.parameters())[:n_half_params],
optim=torch.optim.SGD,
lr=1e-3,
momentum=0.99)
sharded_optimizer_2 = OSS(params=list(model.parameters())[n_half_params:],
optim=torch.optim.SGD,
lr=1e-3,
momentum=0.99)
sharded_ddp_model = ShardedDataParallel(
module=model,
sharded_optimizer=sharded_optimizer,
broadcast_buffers=True)
ddp_model_single = copy.deepcopy(model)
ddp_optimizer = torch.optim.SGD(list(
ddp_model_single.parameters())[:n_half_params],
lr=1e-3,
momentum=0.99)
ddp_optimizer_2 = torch.optim.SGD(list(
ddp_model_single.parameters())[n_half_params:],
lr=1e-3,
momentum=0.99)
ddp_model = DDP(ddp_model_single,
device_ids=[rank],
broadcast_buffers=True)
def check_same_model_params():
for pg, ddp_pg in zip(sharded_optimizer.param_groups,
ddp_optimizer.param_groups):
for p, ddp_p in zip(pg["params"], ddp_pg["params"]):
assert torch.allclose(
p, ddp_p, atol=1e-3
), f"Model parameters differ in between DDP and ShardedDDP {p} {ddp_p}"
for b, ddp_b in zip(sharded_ddp_model.buffers(), ddp_model.buffers()):
assert torch.allclose(
b, ddp_b, atol=1e-3
), "Model buffers differ in between DDP and ShardedDDP"
check_same_model_params(
) # The models should stay the same in between the ranks
for i in range(20):
input_tensor = torch.rand((64, 2)).to(device)
# Run DDP
ddp_optimizer.zero_grad()
ddp_optimizer_2.zero_grad()
ddp_loss = ddp_model(input_tensor).abs().sum()
ddp_loss.backward()
ddp_optimizer.step()
ddp_optimizer_2.step()
# Run Sharded
sharded_optimizer.zero_grad()
sharded_optimizer_2.zero_grad()
sharded_loss = sharded_ddp_model(input_tensor).abs().sum()
sharded_loss.backward()
sharded_optimizer.step()
sharded_optimizer_2.step()
check_same_model_params()
dist.destroy_process_group()
def run_training(rank, args, hp, port=None):
if args.n_gpus > 1:
init_distributed(rank, args.n_gpus, port)
torch.cuda.set_device(f'cuda:{rank}')
## NOTE: variable
model = TransformerWav2vec2(
hp,
pretrain_model='facebook/wav2vec2-large-lv60',
freeze_feature_extractor=hp.freeze_feature_extractor)
## TODO: change init_weight (maybe initialize all networks)
#model.apply(init_weight)
model.train()
if rank == 0:
print(model)
model = model.to(rank)
if args.n_gpus > 1:
model = DDP(torch.nn.SyncBatchNorm.convert_sync_batchnorm(model),
device_ids=[rank])
max_lr = hp.init_lr
if hp.optimizer_type == 'Noam':
## NOTE: scheduling?
## NOTE: learning rate?
optimizer = torch.optim.Adam(model.parameters(),
lr=max_lr,
betas=(0.9, 0.98),
eps=1e-9)
else:
optimizer = torch.optim.Adam(model.parameters(), lr=max_lr)
assert (hp.batch_size is None) != (hp.max_seqlen is None)
if args.n_gpus > 1:
dist.barrier()
# configure map_location properly
map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
if hp.loaded_epoch is not None:
start_epoch = hp.loaded_epoch
load_dir = hp.loaded_dir
print('epoch {} loaded'.format(hp.loaded_epoch))
loaded_dict = load_model("{}".format(
os.path.join(load_dir, 'network.epoch{}'.format(hp.loaded_epoch))),
map_location=map_location)
model.load_state_dict(loaded_dict)
if hp.is_flat_start:
step = 1
start_epoch = 0
print('flat_start')
else:
loaded_dict = torch.load("{}".format(
os.path.join(
load_dir,
'network.optimizer.epoch{}'.format(hp.loaded_epoch))),
map_location=map_location)
optimizer.load_state_dict(loaded_dict)
step = loaded_dict['state'][0]['step']
#lr = get_learning_rate(step//hp.accum_grad+1, hp)
lr = get_learning_rate_tristage(step // hp.accum_grad + 1)
for param_group in optimizer.param_groups:
param_group['lr'] = lr
del loaded_dict
torch.cuda.empty_cache()
else:
start_epoch = 0
step = 1
pytorch_total_params = sum(p.numel() for p in model.parameters())
print('params = {0:.2f}M'.format(pytorch_total_params / 1000 / 1000))
train_epoch(model,
optimizer,
args,
hp,
step=step,
start_epoch=start_epoch,
rank=rank)
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).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)
testset = LoadImagesAndLabels(test_path, imgsz_test, batch_size,
hyp=hyp,
rect=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls)
# 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,
hyp=hyp,
rect=True,
cache_images=opt.cache_images,
single_cls=opt.single_cls),
batch_size=batch_size,
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()
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
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)
imgs = imgs.to(device).float() / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# 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)
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,
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)
# Write
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
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
def run_ddp_parity_two_optim(rank, world_size, backend, temp_file_name,
reduce_buffer_size):
dist.init_process_group(init_method="file://" + temp_file_name,
backend=backend,
rank=rank,
world_size=world_size)
device = torch.device("cuda")
torch.cuda.set_device(rank)
torch.manual_seed(rank)
np.random.seed(rank) # Any model works. Add one different buffer per rank
BATCHS = 20
model = _get_mlp()
model.register_buffer("test_buffer", torch.ones((1)) * rank)
model.to(device)
n_half_params = len(list(model.parameters())) // 2
optim_settings = {"lr": 1e-3, "momentum": 0.99}
sharded_optimizer = OSS(params=list(model.parameters())[:n_half_params],
optim=torch.optim.SGD,
**optim_settings)
sharded_optimizer_2 = OSS(params=list(model.parameters())[n_half_params:],
optim=torch.optim.SGD,
**optim_settings)
sharded_ddp_model = ShardedDataParallel(
module=model,
sharded_optimizer=[sharded_optimizer, sharded_optimizer_2],
broadcast_buffers=True,
reduce_buffer_size=reduce_buffer_size,
)
ddp_model_single = copy.deepcopy(model)
ddp_optimizer = torch.optim.SGD(
list(ddp_model_single.parameters())[:n_half_params], **optim_settings)
ddp_optimizer_2 = torch.optim.SGD(
list(ddp_model_single.parameters())[n_half_params:], **optim_settings)
ddp_model = DDP(ddp_model_single,
device_ids=[rank],
broadcast_buffers=True)
check_same_model_params(
sharded_ddp_model,
ddp_model,
f"DDP parity two optim test failing. differing at startup, Buffers {reduce_buffer_size}",
)
for i in range(BATCHS):
input_tensor = torch.rand((64, 2)).to(device)
# Run DDP
ddp_optimizer.zero_grad()
ddp_optimizer_2.zero_grad()
ddp_loss = ddp_model(input_tensor).abs().sum()
ddp_loss.backward()
ddp_optimizer.step()
ddp_optimizer_2.step()
torch.cuda.synchronize(device)
# Run Sharded
sharded_optimizer.zero_grad()
sharded_optimizer_2.zero_grad()
sharded_loss = sharded_ddp_model(input_tensor).abs().sum()
sharded_loss.backward()
sharded_optimizer.step()
sharded_optimizer_2.step()
torch.cuda.synchronize(device)
check_same_model_params(
sharded_ddp_model,
ddp_model,
f"DDP parity two optim test failing, step {i}, buffers {reduce_buffer_size}",
)
dist.destroy_process_group()
def initialize(self, training=True, force_load_plans=False):
"""
this is a copy of nnUNetTrainerV2's initialize. We only add the regions to the data augmentation
:param training:
:param force_load_plans:
:return:
"""
if not self.was_initialized:
maybe_mkdir_p(self.output_folder)
if force_load_plans or (self.plans is None):
self.load_plans_file()
self.process_plans(self.plans)
self.setup_DA_params()
self.folder_with_preprocessed_data = join(self.dataset_directory, self.plans['data_identifier'] +
"_stage%d" % self.stage)
if training:
self.dl_tr, self.dl_val = self.get_basic_generators()
if self.unpack_data:
if self.local_rank == 0:
print("unpacking dataset")
unpack_dataset(self.folder_with_preprocessed_data)
print("done")
else:
# we need to wait until worker 0 has finished unpacking
npz_files = subfiles(self.folder_with_preprocessed_data, suffix=".npz", join=False)
case_ids = [i[:-4] for i in npz_files]
all_present = all(
[isfile(join(self.folder_with_preprocessed_data, i + ".npy")) for i in case_ids])
while not all_present:
print("worker", self.local_rank, "is waiting for unpacking")
sleep(3)
all_present = all(
[isfile(join(self.folder_with_preprocessed_data, i + ".npy")) for i in case_ids])
# there is some slight chance that there may arise some error because dataloader are loading a file
# that is still being written by worker 0. We ignore this for now an address it only if it becomes
# relevant
# (this can occur because while worker 0 writes the file is technically present so the other workers
# will proceed and eventually try to read it)
else:
print(
"INFO: Not unpacking data! Training may be slow due to that. Pray you are not using 2d or you "
"will wait all winter for your model to finish!")
# setting weights for deep supervision losses
net_numpool = len(self.net_num_pool_op_kernel_sizes)
# we give each output a weight which decreases exponentially (division by 2) as the resolution decreases
# this gives higher resolution outputs more weight in the loss
weights = np.array([1 / (2 ** i) for i in range(net_numpool)])
# we don't use the lowest 2 outputs. Normalize weights so that they sum to 1
mask = np.array([True if i < net_numpool - 1 else False for i in range(net_numpool)])
weights[~mask] = 0
weights = weights / weights.sum()
self.ds_loss_weights = weights
seeds_train = np.random.random_integers(0, 99999, self.data_aug_params.get('num_threads'))
seeds_val = np.random.random_integers(0, 99999, max(self.data_aug_params.get('num_threads') // 2, 1))
print("seeds train", seeds_train)
print("seeds_val", seeds_val)
self.tr_gen, self.val_gen = get_moreDA_augmentation(self.dl_tr, self.dl_val,
self.data_aug_params[
'patch_size_for_spatialtransform'],
self.data_aug_params,
deep_supervision_scales=self.deep_supervision_scales,
seeds_train=seeds_train,
seeds_val=seeds_val,
pin_memory=self.pin_memory,
regions=self.regions)
self.print_to_log_file("TRAINING KEYS:\n %s" % (str(self.dataset_tr.keys())),
also_print_to_console=False)
self.print_to_log_file("VALIDATION KEYS:\n %s" % (str(self.dataset_val.keys())),
also_print_to_console=False)
else:
pass
self.initialize_network()
self.initialize_optimizer_and_scheduler()
self._maybe_init_amp()
self.network = DDP(self.network, self.local_rank)
else:
self.print_to_log_file('self.was_initialized is True, not running self.initialize again')
self.was_initialized = True
def main():
parser = argparse.ArgumentParser(description='PyTorch CIFAR10 Example')
parser.add_argument('--bs',
'--batch_size',
type=int,
default=32,
metavar='N',
help='input batch size for training (default: 32)')
parser.add_argument('--epochs',
type=int,
default=10,
metavar='N',
help='number of epochs to train (default: 10)')
parser.add_argument('--lr',
'--learning_rate',
type=float,
default=1.0e-02,
metavar='LR',
help='learning rate (default: 1.0e-02)')
args = parser.parse_args()
master_addr = os.getenv("MASTER_ADDR", default="localhost")
master_port = os.getenv('MASTER_POST', default='8888')
method = "tcp://{}:{}".format(master_addr, master_port)
rank = int(os.getenv('OMPI_COMM_WORLD_RANK', '0'))
world_size = int(os.getenv('OMPI_COMM_WORLD_SIZE', '1'))
dist.init_process_group("nccl",
init_method=method,
rank=rank,
world_size=world_size)
ngpus = torch.cuda.device_count()
device = torch.device('cuda', rank % ngpus)
if rank == 0:
wandb.init()
wandb.config.update(args)
train_dataset = datasets.CIFAR10('./data',
train=True,
download=True,
transform=transforms.ToTensor())
val_dataset = datasets.CIFAR10('./data',
train=False,
transform=transforms.ToTensor())
train_sampler = torch.utils.data.distributed.DistributedSampler(
train_dataset,
num_replicas=dist.get_world_size(),
rank=dist.get_rank())
train_loader = torch.utils.data.DataLoader(dataset=train_dataset,
batch_size=args.bs,
sampler=train_sampler)
val_loader = torch.utils.data.DataLoader(dataset=val_dataset,
batch_size=args.bs,
shuffle=False)
# model = VGG('VGG19')
# model = ResNet18()
# model = PreActResNet18()
# model = GoogLeNet()
# model = DenseNet121()
# model = ResNeXt29_2x64d()
# model = MobileNet()
# model = MobileNetV2()
# model = DPN92()
# model = ShuffleNetG2()
# model = SENet18()
# model = ShuffleNetV2(1)
# model = EfficientNetB0()
# model = RegNetX_200MF()
model = VGG('VGG19').to(device)
if rank == 0:
wandb.config.update({
"model": model.__class__.__name__,
"dataset": "CIFAR10"
})
model = DDP(model, device_ids=[rank % ngpus])
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
for epoch in range(args.epochs):
model.train()
train_loss, train_acc = train(train_loader, model, criterion,
optimizer, epoch, device)
val_loss, val_acc = validate(val_loader, model, criterion, device)
if rank == 0:
wandb.log({
'train_loss': train_loss,
'train_acc': train_acc,
'val_loss': val_loss,
'val_acc': val_acc
})
dist.destroy_process_group()
def main(
cfg: AAEModelConfig,
encoder_gpu: int,
generator_gpu: int,
discriminator_gpu: int,
distributed: bool,
):
# Do some scaffolding for DDP
comm_rank = 0
comm_size = 1
comm = None
if distributed and dist.is_available():
import mpi4py
mpi4py.rc.initialize = False
from mpi4py import MPI # noqa: E402
MPI.Init_thread()
# get communicator: duplicate from comm world
comm = MPI.COMM_WORLD.Dup()
# now match ranks between the mpi comm and the nccl comm
os.environ["WORLD_SIZE"] = str(comm.Get_size())
os.environ["RANK"] = str(comm.Get_rank())
# init pytorch
dist.init_process_group(backend="nccl", init_method="env://")
comm_rank = dist.get_rank()
comm_size = dist.get_world_size()
model_hparams = AAE3dHyperparams(
num_features=cfg.num_features,
encoder_filters=cfg.encoder_filters,
encoder_kernel_sizes=cfg.encoder_kernel_sizes,
generator_filters=cfg.generator_filters,
discriminator_filters=cfg.discriminator_filters,
latent_dim=cfg.latent_dim,
encoder_relu_slope=cfg.encoder_relu_slope,
generator_relu_slope=cfg.generator_relu_slope,
discriminator_relu_slope=cfg.discriminator_relu_slope,
use_encoder_bias=cfg.use_encoder_bias,
use_generator_bias=cfg.use_generator_bias,
use_discriminator_bias=cfg.use_discriminator_bias,
noise_mu=cfg.noise_mu,
noise_std=cfg.noise_std,
lambda_rec=cfg.lambda_rec,
lambda_gp=cfg.lambda_gp,
)
# optimizers
optimizer_hparams = OptimizerHyperparams(name=cfg.optimizer_name,
hparams={"lr": cfg.optimizer_lr})
# Save hparams to disk and load initial weights and create virtual h5 file
if comm_rank == 0:
cfg.output_path.mkdir(exist_ok=True)
model_hparams.save(cfg.output_path.joinpath("model-hparams.json"))
optimizer_hparams.save(
cfg.output_path.joinpath("optimizer-hparams.json"))
init_weights = get_init_weights(cfg)
h5_file, h5_files = get_h5_training_file(cfg)
with open(cfg.output_path.joinpath("virtual-h5-metadata.json"),
"w") as f:
json.dump(h5_files, f)
else:
init_weights, h5_file = None, None
if comm_size > 1:
init_weights = comm.bcast(init_weights, 0)
h5_file = comm.bcast(h5_file, 0)
# construct model
aae = AAE3d(
cfg.num_points,
cfg.num_features,
cfg.batch_size,
model_hparams,
optimizer_hparams,
gpu=(encoder_gpu, generator_gpu, discriminator_gpu),
init_weights=init_weights,
)
enc_device = torch.device(f"cuda:{encoder_gpu}")
if comm_size > 1:
if (encoder_gpu == generator_gpu) and (encoder_gpu
== discriminator_gpu):
aae.model = DDP(aae.model,
device_ids=[enc_device],
output_device=enc_device)
else:
aae.model = DDP(aae.model, device_ids=None, output_device=None)
# set global default device
torch.cuda.set_device(enc_device.index)
if comm_rank == 0:
# Diplay model
print(aae)
assert isinstance(h5_file, Path)
# set up dataloaders
train_dataset = get_dataset(
cfg.dataset_location,
h5_file,
cfg.dataset_name,
cfg.rmsd_name,
cfg.fnc_name,
cfg.num_points,
cfg.num_features,
split="train",
shard_id=comm_rank,
num_shards=comm_size,
normalize="box",
cms_transform=False,
)
train_loader = DataLoader(
train_dataset,
batch_size=cfg.batch_size,
shuffle=True,
drop_last=True,
pin_memory=True,
num_workers=cfg.num_data_workers,
)
valid_dataset = get_dataset(
cfg.dataset_location,
h5_file,
cfg.dataset_name,
cfg.rmsd_name,
cfg.fnc_name,
cfg.num_points,
cfg.num_features,
split="valid",
shard_id=comm_rank,
num_shards=comm_size,
normalize="box",
cms_transform=False,
)
valid_loader = DataLoader(
valid_dataset,
batch_size=cfg.batch_size,
shuffle=True,
drop_last=True,
pin_memory=True,
num_workers=cfg.num_data_workers,
)
print(
f"Having {len(train_dataset)} training and {len(valid_dataset)} validation samples."
)
wandb_config = setup_wandb(cfg, aae.model, comm_rank)
# Optional callbacks
loss_callback = LossCallback(cfg.output_path.joinpath("loss.json"),
wandb_config=wandb_config,
mpi_comm=comm)
checkpoint_callback = CheckpointCallback(
out_dir=cfg.output_path.joinpath("checkpoint"), mpi_comm=comm)
save_callback = SaveEmbeddingsCallback(
out_dir=cfg.output_path.joinpath("embeddings"),
interval=cfg.embed_interval,
sample_interval=cfg.sample_interval,
mpi_comm=comm,
)
# TSNEPlotCallback requires SaveEmbeddingsCallback to run first
tsne_callback = TSNEPlotCallback(
out_dir=cfg.output_path.joinpath("embeddings"),
projection_type="3d",
target_perplexity=100,
interval=cfg.tsne_interval,
tsne_is_blocking=True,
wandb_config=wandb_config,
mpi_comm=comm,
)
# Train model with callbacks
callbacks = [
loss_callback,
checkpoint_callback,
save_callback,
tsne_callback,
]
# Optionaly train for a different number of
# epochs on the first DDMD iterations
if cfg.stage_idx == 0:
epochs = cfg.initial_epochs
else:
epochs = cfg.epochs
aae.train(train_loader, valid_loader, epochs, callbacks=callbacks)
# Save loss history to disk.
if comm_rank == 0:
loss_callback.save(cfg.output_path.joinpath("loss.json"))
# Save final model weights to disk
aae.save_weights(
cfg.output_path.joinpath("encoder-weights.pt"),
cfg.output_path.joinpath("generator-weights.pt"),
cfg.output_path.joinpath("discriminator-weights.pt"),
)
def __init__(self,
train_data,
model,
optimizer=None,
loss=None,
callbacks_all=None,
callbacks_master=None,
batch_size_per_gpu=8,
n_epochs=1,
num_workers=1,
drop_last=False,
dev_data=None,
metrics=None,
metric_key=None,
update_every=1,
print_every=10,
validate_every=-1,
save_every=-1,
save_path=None,
device='auto',
fp16='',
backend=None,
init_method=None):
assert device in [
'auto', 'cuda', 'cpu'
], "Please set correct device in [auto', 'cuda', 'cpu']"
if device == 'auto':
device = 'cuda' if torch.cuda.is_available() else 'cpu'
if backend is None:
backend = 'nccl' if device == 'cuda' else 'gloo'
# init distributed
if device == 'cuda':
torch.cuda.set_device(get_local_rank())
self.device = torch.device("cuda", get_local_rank())
else:
self.device = torch.device(device)
dist.init_process_group(backend=backend, init_method=init_method)
self.world_size = dist.get_world_size()
self.rank = dist.get_rank() # unique id for each process
self.model = model
self.train_data = train_data
self.batch_size_per_gpu = int(batch_size_per_gpu)
self.n_epochs = int(n_epochs)
self.num_data_workers = int(num_workers)
self.drop_last = drop_last
self.update_every = int(update_every)
self.print_every = int(print_every)
self.validate_every = int(validate_every)
self.save_every = int(save_every)
self.save_path = save_path
self.losser = _prepare_losser(loss)
self.fp16 = fp16
self.init_method = init_method
self.backend = backend
self.local_rank = get_local_rank()
self._forward_func = model.forward
self.callback_manager = DistCallbackManager(
env={"trainer": self},
callbacks_all=callbacks_all,
callbacks_master=callbacks_master)
self.metric_key = metric_key
model.to(self.device)
optimizer = self._get_optimizer(optimizer)
# init fp16, must before DataParallel init
if len(self.fp16):
assert isinstance(
self.fp16, str
), "Please set Apex AMP optimization level selected in ['O0', 'O1', 'O2', 'O3']"
try:
from apex import amp
except ImportError:
raise ImportError(
"Please install apex from https://www.github.com/nvidia/apex to use fp16 training."
)
assert torch.backends.cudnn.enabled, "Amp requires cudnn backend to be enabled."
assert device == 'cuda', "Amp requires cuda device"
model, optimizer = amp.initialize(model,
optimizer,
opt_level=self.fp16)
# init DataParallel
if parse_version(torch.__version__) >= parse_version('1.1'):
self.model = DDP(model,
device_ids=[self.local_rank],
output_device=self.local_rank,
find_unused_parameters=True)
else:
self.model = DDP(model,
device_ids=[self.local_rank],
output_device=self.local_rank)
self.optimizer = optimizer
self.sampler = DistributedSampler(self.train_data)
self.data_iterator = self._get_data_iter(self.train_data)
self.n_steps = self._get_n_steps()
# for evaluation, only run eval on master proc
if dev_data and metrics:
cb = TesterCallback(dev_data,
model,
metrics,
batch_size=batch_size_per_gpu,
num_workers=num_workers)
self.callback_manager.add_callback([cb], master=True)
# Setup logging
dist.barrier()
self.start_time = datetime.now().strftime('%m_%d_%Y-%H_%M')
if self.save_path:
self.cp_save_path = os.path.join(self.save_path, 'checkpoints',
self.start_time)
else:
self.cp_save_path = None
# use INFO in the master, WARN for others
logger.setLevel(logging.INFO if self.is_master else logging.WARNING)
self.logger = logger
self.logger.info("Setup Distributed Trainer")
self.logger.warning(
"Process pid: {}, rank: {}, local rank: {}, device: {}, fp16: {}".
format(os.getpid(), self.rank, self.local_rank, self.device,
self.fp16 if self.fp16 else False))
self.logger.info("Num of processes: {}".format(self.world_size))
self.logger.info("Use device: {}".format(device))
self.logger.info(
"Training with fp16: {}, optimization level: {}".format(
len(self.fp16) > 0, self.fp16 if self.fp16 else None))
def __init__(self,
model,
optimizer,
config,
data_loader,
valid_data_loader=None,
lr_scheduler=None,
max_len_step=None):
'''
:param model:
:param optimizer:
:param config:
:param data_loader:
:param valid_data_loader:
:param lr_scheduler:
:param max_len_step: controls number of batches(steps) in each epoch.
'''
self.config = config
self.local_master = config['local_rank'] == 0
self.logger = config.get_logger(
'trainer',
config['trainer']['log_verbosity']) if self.local_master else None
# setup GPU device if available, move model into configured device
self.device, self.device_ids = self._prepare_device(
config['local_rank'], config['local_world_size'])
self.model = model.to(self.device)
self.optimizer = optimizer
cfg_trainer = config['trainer']
self.epochs = cfg_trainer['epochs']
self.save_period = cfg_trainer['save_period']
monitor_open = cfg_trainer['monitor_open']
if monitor_open:
self.monitor = cfg_trainer.get('monitor', 'off')
else:
self.monitor = 'off'
# configuration to monitor model performance and save best
if self.monitor == 'off':
self.monitor_mode = 'off'
self.monitor_best = 0
else:
self.monitor_mode, self.monitor_metric = self.monitor.split()
assert self.monitor_mode in ['min', 'max']
self.monitor_best = inf if self.monitor_mode == 'min' else -inf
self.early_stop = cfg_trainer.get('early_stop', inf)
self.early_stop = inf if self.early_stop == -1 else self.early_stop
self.start_epoch = 1
if self.local_master:
self.checkpoint_dir = config.save_dir
# setup visualization writer instance
self.writer = TensorboardWriter(config.log_dir, self.logger,
cfg_trainer['tensorboard'])
# load checkpoint for resume training
if config.resume is not None:
self._resume_checkpoint(config.resume)
# load checkpoint following load to multi-gpu, avoid 'module.' prefix
if self.config['trainer']['sync_batch_norm']:
self.model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(
self.model)
self.mode = DDP(self.model,
device_ids=self.device_ids,
output_device=self.device_ids[0],
find_unused_parameters=True)
self.data_loader = data_loader
if max_len_step is None: # max length of iteration step of every epoch
# epoch-based training
self.len_step = len(self.data_loader)
else:
# iteration-based training
self.data_loader = inf_loop(data_loader)
self.len_step = max_len_step
self.valid_data_loader = valid_data_loader
self.do_validation = self.valid_data_loader is not None
self.lr_scheduler = lr_scheduler
log_step = self.config['trainer']['log_step_interval']
self.log_step = log_step if log_step != -1 and 0 < log_step < self.len_step else int(
np.sqrt(data_loader.batch_size))
val_step_interval = self.config['trainer']['val_step_interval']
# self.val_step_interval = val_step_interval if val_step_interval!= -1 and 0 < val_step_interval < self.len_step\
# else int(np.sqrt(data_loader.batch_size))
self.val_step_interval = val_step_interval
self.gl_loss_lambda = self.config['trainer']['gl_loss_lambda']
self.train_loss_metrics = MetricTracker(
'loss',
'gl_loss',
'crf_loss',
writer=self.writer if self.local_master else None)
self.valid_f1_metrics = SpanBasedF1MetricTracker(iob_labels_vocab_cls)
def create_nerf(rank, args):
###### create network and wrap in ddp; each process should do this
# fix random seed just to make sure the network is initialized with same weights at different processes
torch.manual_seed(777)
# very important!!! otherwise it might introduce extra memory in rank=0 gpu
torch.cuda.set_device(rank)
models = OrderedDict()
models['cascade_level'] = args.cascade_level
models['cascade_samples'] = [
int(x.strip()) for x in args.cascade_samples.split(',')
]
for m in range(models['cascade_level']):
img_names = None
if args.optim_autoexpo:
# load training image names for autoexposure
f = os.path.join(args.basedir, args.expname, 'train_images.json')
with open(f) as file:
img_names = json.load(file)
net = NerfNetWithAutoExpo(args,
optim_autoexpo=args.optim_autoexpo,
img_names=img_names).to(rank)
net = DDP(net,
device_ids=[rank],
output_device=rank,
find_unused_parameters=True)
# net = DDP(net, device_ids=[rank], output_device=rank)
optim = torch.optim.Adam(net.parameters(), lr=args.lrate)
models['net_{}'.format(m)] = net
models['optim_{}'.format(m)] = optim
start = -1
###### load pretrained weights; each process should do this
if (args.ckpt_path is not None) and (os.path.isfile(args.ckpt_path)):
ckpts = [args.ckpt_path]
else:
ckpts = [
os.path.join(args.basedir, args.expname, f) for f in sorted(
os.listdir(os.path.join(args.basedir, args.expname)))
if f.endswith('.pth')
]
def path2iter(path):
tmp = os.path.basename(path)[:-4]
idx = tmp.rfind('_')
return int(tmp[idx + 1:])
ckpts = sorted(ckpts, key=path2iter)
logger.info('Found ckpts: {}'.format(ckpts))
if len(ckpts) > 0 and not args.no_reload:
fpath = ckpts[-1]
logger.info('Reloading from: {}'.format(fpath))
start = path2iter(fpath)
# configure map_location properly for different processes
map_location = {'cuda:%d' % 0: 'cuda:%d' % rank}
to_load = torch.load(fpath, map_location=map_location)
for m in range(models['cascade_level']):
for name in ['net_{}'.format(m), 'optim_{}'.format(m)]:
models[name].load_state_dict(to_load[name])
return start, models
def run(rank, n_gpus, hps):
global global_step
if rank == 0:
logger = utils.get_logger(hps.model_dir)
logger.info(hps)
utils.check_git_hash(hps.model_dir)
writer = SummaryWriter(log_dir=hps.model_dir)
writer_eval = SummaryWriter(
log_dir=os.path.join(hps.model_dir, "eval"))
dist.init_process_group(backend='nccl',
init_method='env://',
world_size=n_gpus,
rank=rank)
torch.manual_seed(hps.train.seed)
torch.cuda.set_device(rank)
train_dataset = TextAudioLoader(hps.data.training_files, hps.data)
train_sampler = DistributedBucketSampler(
train_dataset,
hps.train.batch_size, [32, 300, 400, 500, 600, 700, 800, 900, 1000],
num_replicas=n_gpus,
rank=rank,
shuffle=True)
collate_fn = TextAudioCollate()
train_loader = DataLoader(train_dataset,
num_workers=8,
shuffle=False,
pin_memory=True,
collate_fn=collate_fn,
batch_sampler=train_sampler)
if rank == 0:
eval_dataset = TextAudioLoader(hps.data.validation_files, hps.data)
eval_loader = DataLoader(eval_dataset,
num_workers=8,
shuffle=False,
batch_size=hps.train.batch_size,
pin_memory=True,
drop_last=False,
collate_fn=collate_fn)
net_g = SynthesizerTrn(len(symbols), hps.data.filter_length // 2 + 1,
hps.train.segment_size // hps.data.hop_length,
**hps.model).cuda(rank)
net_d = MultiPeriodDiscriminator(hps.model.use_spectral_norm).cuda(rank)
optim_g = torch.optim.AdamW(net_g.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps)
optim_d = torch.optim.AdamW(net_d.parameters(),
hps.train.learning_rate,
betas=hps.train.betas,
eps=hps.train.eps)
net_g = DDP(net_g, device_ids=[rank])
net_d = DDP(net_d, device_ids=[rank])
try:
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "G_*.pth"), net_g,
optim_g)
_, _, _, epoch_str = utils.load_checkpoint(
utils.latest_checkpoint_path(hps.model_dir, "D_*.pth"), net_d,
optim_d)
global_step = (epoch_str - 1) * len(train_loader)
except:
epoch_str = 1
global_step = 0
scheduler_g = torch.optim.lr_scheduler.ExponentialLR(
optim_g, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
scheduler_d = torch.optim.lr_scheduler.ExponentialLR(
optim_d, gamma=hps.train.lr_decay, last_epoch=epoch_str - 2)
scaler = GradScaler(enabled=hps.train.fp16_run)
for epoch in range(epoch_str, hps.train.epochs + 1):
if rank == 0:
train_and_evaluate(rank, epoch, hps, [net_g, net_d],
[optim_g, optim_d], [scheduler_g, scheduler_d],
scaler, [train_loader, eval_loader], logger,
[writer, writer_eval])
else:
train_and_evaluate(rank, epoch, hps, [net_g, net_d],
[optim_g, optim_d], [scheduler_g, scheduler_d],
scaler, [train_loader, None], None, None)
scheduler_g.step()
scheduler_d.step()
def train(opt, train_dict, device, tb_writer=None):
log_dir = Path(tb_writer.log_dir) if tb_writer else Path(
train_dict['logdir']) / 'logs'
wdir = str(log_dir / 'weights') + os.sep
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = 'results.txt'
with open(log_dir / 'hyp.yaml', 'w') as f:
yaml.dump(train_dict, f, sort_keys=False)
with open(log_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
cuda = device.type != 'cpu'
rank = opt.global_rank
init_seeds(2 + rank)
train_path = train_dict['train']
test_path = train_dict['val']
train_dict['weights'] = last if not train_dict['pretrain'] or (
train_dict['pretrain'] and
not os.path.exists(train_dict['weights'])) else train_dict['weights']
model = RetinaFace(train_dict, phase='Train')
pretrained = False
if os.path.exists(train_dict['weights']):
pretrained = True
logger('Loading resume network from ====>{}'.format(
train_dict['weights']))
state_dict = torch.load(train_dict['weights'], map_location=device)
# create new OrderedDict that does not contain `module.`
from collections import OrderedDict
new_state_dict = OrderedDict()
for k, v in state_dict['model'].items():
head = k[:7]
if head == 'module.':
name = k[7:] # remove `module.`
else:
name = k
new_state_dict[name] = v
model.load_state_dict(new_state_dict)
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
v.requires_grad = True
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
if train_dict['adam']:
optimizer = optim.Adam(pg0,
lr=train_dict['lr0'],
betas=(train_dict['momentum'],
0.999)) # adjust beta1 to momentum
else:
optimizer = optim.SGD(pg0,
lr=train_dict['lr0'],
momentum=train_dict['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': pg1,
'weight_decay': train_dict['weight_decay']
}) # add pg1 with weight_decay
optimizer.add_param_group({'params': pg2}) # add pg2 (biases)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
epochs = train_dict['epoch']
lf = lambda x: ((
(1 + math.cos(x * math.pi / epochs)) / 2)**1.0) * 0.8 + 0.2 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
plot_lr_scheduler(optimizer, scheduler, epochs)
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if state_dict['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = state_dict['best_fitness']
# Results
if state_dict.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(state_dict['training_results']) # write results.txt
# Epochs
start_epoch = state_dict['epoch'] + 1
if epochs < start_epoch:
logger.info(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, state_dict['epoch'], epochs))
epochs += state_dict['epoch'] # finetune additional epochs
del ckpt, state_dict
if train_dict['sync_bn'] and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Exponential moving average
ema = ModelEMA(model) if rank in [-1, 0] else None
# ddp
if cuda and rank != -1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=(opt.local_rank))
# Trainloader
batch_size = train_dict['batch_size']
image_size = train_dict['image_size']
# dataloader, dataset = create_dataloader(train_path,image_size, batch_size, opt, hyp=train_dict, augment=True,
# rect=opt.rect, rank=rank,
# world_size=opt.world_size, workers=train_dict['workers'])
rgb_mean = (104, 117, 123) # bgr order
dataset = WiderFaceDetection(train_path, preproc(image_size, rgb_mean))
sampler = torch.utils.data.distributed.DistributedSampler(dataset)
dataloader = torch.utils.data.DataLoader(dataset,
batch_size=batch_size,
num_workers=8,
sampler=sampler,
pin_memory=True,
collate_fn=detection_collate)
criterion = MultiBoxLoss(num_classes, 0.35, True, 0, True, 7, 0.35, False)
priorbox = PriorBox(train_dict, image_size=(image_size, image_size))
with torch.no_grad():
priors = priorbox.forward()
priors = priors.cuda()
for epoch in range(start_epoch, epochs):
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
if rank in [-1, 0]:
pbar = tqdm(pbar) # progress bar
optimizer.zero_grad()
for i, (
images, targets
) in pbar: # batch -------------------------------------------------------------
with amp.autocast(enabled=cuda):
images = images.cuda()
targets = [anno.cuda() for anno in targets]
out = model(images)
optimizer.zero_grad()
loss_l, loss_c, loss_landm = criterion(
out, priors, targets) * opt.world_size
loss = cfg['loc_weight'] * loss_l + loss_c + loss_landm
loss.backward()
optimizer.step()
load_t1 = time.time()
batch_time = load_t1 - load_t0
eta = int(batch_time * (max_iter - iteration))
if rank in [-1, 0]:
print(
'Epoch:{}/{} || Epochiter: {}/{} || Iter: {}/{} || Loc: {:.4f} Cla: {:.4f} Landm: {:.4f} || LR: {:.8f} || Batchtime: {:.4f} s || ETA: {}'
.format(epoch, max_epoch, (iteration % epoch_size) + 1,
epoch_size, iteration + 1, max_iter,
loss_l.item(), loss_c.item(),
loss_landm.item(), lr, batch_time,
str(datetime.timedelta(seconds=eta))))
torch.save(net.state_dict(),
wdir + os.sep + '{}_Final.pth'.format(i))
def train(hyp, opt, device, tb_writer=None):
logger.info(f'Hyperparameters {hyp}')
log_dir = Path(tb_writer.log_dir) if tb_writer else Path(
opt.logdir) / 'evolve' # logging directory
wdir = str(log_dir / 'weights') + os.sep # weights directory
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = str(log_dir / 'results.txt')
epochs, batch_size, total_batch_size, weights, rank = \
opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Save run settings
with open(log_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(log_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(
data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(opt.cfg or ckpt['model'].yaml, ch=3,
nc=nc).to(device) # create
exclude = ['anchor'] if opt.cfg else [] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info(
'Transferred %g/%g items from %s' %
(len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc).to(device) # create
# Freeze
freeze = [
'',
] # parameter names to freeze (full or partial)
if any(freeze):
for k, v in model.named_parameters():
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
v.requires_grad = True
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
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)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = lambda x: ((
(1 + math.cos(x * math.pi / epochs)) / 2)**1.0) * 0.8 + 0.2 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (
weights, epochs)
if epochs < start_epoch:
logger.info(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Exponential moving average
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=(opt.local_rank))
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Testloader
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(
test_path,
imgsz_test,
total_batch_size,
gs,
opt,
hyp=hyp,
augment=False,
cache=opt.cache_images,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers)[0] # only runs on process 0
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
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.names = names
# Class frequency
if rank in [-1, 0]:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1.
# model._initialize_biases(cf.to(device))
plot_labels(labels, save_dir=log_dir)
if tb_writer:
# tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384
tb_writer.add_histogram('classes', c, 0)
# Check anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
# Start training
t0 = time.time()
nw = max(3 * nb,
1e3) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test' % (imgsz, imgsz_test))
logger.info('Using %g dataloader workers' % dataloader.num_workers)
logger.info('Starting training for %g epochs...' % epochs)
# torch.autograd.set_detect_anomaly(True)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if dataset.image_weights:
# Generate indices
if rank in [-1, 0]:
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
# Broadcast if DDP
if rank != -1:
indices = torch.zeros([dataset.n], dtype=torch.int)
if rank == 0:
indices[:] = torch.from_tensor(dataset.indices,
dtype=torch.int)
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj', 'cls',
'total', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # 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, nbs / total_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)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi,
[0.9, hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / 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 gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Autocast
with amp.autocast(enabled=cuda):
# Forward
pred = model(imgs)
# Loss
loss, loss_items = compute_loss(pred, targets.to(device),
model) # scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# if not torch.isfinite(loss):
# logger.info('WARNING: non-finite loss, ending training ', loss_items)
# return results
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
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 +
'%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if ni < 3:
f = str(log_dir / ('train_batch%g.jpg' % ni)) # filename
result = plot_images(images=imgs,
targets=targets,
paths=paths,
fname=f)
if tb_writer and result is not None:
tb_writer.add_image(f,
result,
dataformats='HWC',
global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(
model,
include=['yaml', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(
opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
model=ema.ema.module
if hasattr(ema.ema, 'module') else ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=log_dir)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results +
'\n') # P, R, mAP, F1, test_losses=(GIoU, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' %
(results_file, opt.bucket, opt.name))
# Tensorboard
if tb_writer:
tags = [
'train/giou_loss',
'train/obj_loss',
'train/cls_loss', # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95',
'val/giou_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
tb_writer.add_scalar(tag, x, epoch)
# 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 save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
ema.ema.module if hasattr(ema, 'module') else ema.ema,
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
n = ('_'
if len(opt.name) and not opt.name.isnumeric() else '') + opt.name
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
# Finish
if not opt.evolve:
plot_results(save_dir=log_dir) # save as results.png
logger.info('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if rank not in [-1, 0] else None
torch.cuda.empty_cache()
return results
def get_network(name,
n_classes,
in_channels=3,
feature_scale=4,
tensor_dim='2D',
nonlocal_mode='embedded_gaussian',
attention_dsample=(2, 2, 2),
aggregation_mode='concat',
dataDims=[4, 1, 96, 192, 112],
rank=0):
# NOTE: batchSize is only used for the STN based network. For the other networks, it is irrelevant
model = _get_model_instance(name, tensor_dim)
if name in ['unet', 'unet_ct_dsv']:
model = model(n_classes=n_classes,
is_batchnorm=True,
in_channels=in_channels,
feature_scale=feature_scale,
is_deconv=False)
elif name in ['unet_nonlocal']:
model = model(n_classes=n_classes,
is_batchnorm=True,
in_channels=in_channels,
is_deconv=False,
nonlocal_mode=nonlocal_mode,
feature_scale=feature_scale)
elif name in [
'unet_grid_gating', 'unet_ct_single_att_dsv',
'unet_ct_multi_att_dsv', 'unet_ct_dense_multi_att_dsv'
]:
model = model(n_classes=n_classes,
is_batchnorm=True,
in_channels=in_channels,
nonlocal_mode=nonlocal_mode,
feature_scale=feature_scale,
attention_dsample=attention_dsample,
is_deconv=False)
elif name in [
'unet_ct_multi_att_dsv_stn', 'unet_ct_multi_att_dsv_stn_v2',
'unet_ct_multi_att_dsv_stn_unreg_v2',
'unet_ct_multi_att_dsv_deform',
'unet_ct_multi_att_dsv_deform_small',
'unet_CT_multi_att_dsv_deform_ax_cor'
]:
model = model(n_classes=n_classes,
is_batchnorm=True,
in_channels=in_channels,
nonlocal_mode=nonlocal_mode,
feature_scale=feature_scale,
attention_dsample=attention_dsample,
is_deconv=False,
dataDims=dataDims).to(rank)
elif name in ['unet_t2_ax_cor']:
model = model(n_classes=n_classes,
is_batchnorm=True,
in_channels=in_channels,
nonlocal_mode=nonlocal_mode,
feature_scale=feature_scale,
attention_dsample=attention_dsample,
is_deconv=False,
dataDims=dataDims).to(rank)
# ToDo: Uncomment the last line for DDP processing - need to do mor eefficiently
# Currently, we do this locally only for this model
#model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
model = DDP(model, device_ids=[rank],
find_unused_parameters=True) # Uncomment this for DDP
elif name in ['transformer_registration']:
# TODO: Still needs work!
model = model(vol_size=dataDims[2:],
enc_nf=[16, 32, 32, 32],
dec_nf=[32, 32, 32, 32, 32, 16, 16])
else:
raise 'Model {} not available'.format(name)
# Utilize multiple GPUs in parallel
#model = nn.DataParallel(model)
return model
def __init__(self,
args,
train_loader=None,
val_loader=None,
logger=None,
num_answers=0,
train=True):
self.args = args
self.max_text_length = args.max_text_length
self.train_loader = train_loader
self.val_loader = val_loader
self.num_answers = num_answers
self.logger = logger
# Model
self.model = VQAModel.from_pretrained("bert-base-uncased",
args=args,
num_answers=self.num_answers)
self.verbose = True
if self.args.distributed:
if self.args.gpu != 0:
self.verbose = False
# Load Checkpoint
self.start_epoch = None
if args.load is not None:
path = args.load + '.pth'
self.load(path, verbose=self.verbose)
elif args.load_lxmert_qa is not None:
path = args.load_lxmert_qa + '_LXRT.pth'
load_lxmert_qa(
args,
path,
self.model,
label2ans=self.train_loader.dataset.raw_dataset.label2ans,
verbose=self.verbose)
# GPU Options
print(f'Model Launching at GPU {self.args.gpu}')
from time import time
start = time()
self.model.cuda(args.gpu)
# Optimizer
if train:
self.optim, self.lr_scheduler = self.create_optimizer_and_scheduler(
)
self.bce_loss = nn.BCEWithLogitsLoss()
if args.multiGPU:
assert args.distributed
self.model = DDP(self.model,
device_ids=[args.gpu],
find_unused_parameters=True)
if args.gpu == 0:
print(f'It took {time() - start:.1f}s')
# Output Directory
self.output = args.output
os.makedirs(self.output, exist_ok=True)
USE_GPU = False
model = BatchProgramCC(EMBEDDING_DIM, HIDDEN_DIM, MAX_TOKENS + 1,
ENCODE_DIM, LABELS, BATCH_SIZE, USE_GPU, embeddings)
if USE_GPU:
model.cuda()
# setup devices for this process, rank 1 uses GPUs [0, 1, 2, 3] and
# rank 2 uses GPUs [4, 5, 6, 7].
n = torch.cuda.device_count() // world_size
device_ids = list(range(2 * n, (2 + 1) * n))
# create model and move it to device_ids[0]
model = model.to(device_ids[0])
# output_device defaults to device_ids[0]
ddp_model = DDP(model, device_ids=device_ids)
parameters = ddp_model.parameters()
optimizer = torch.optim.Adamax(parameters)
loss_function = torch.nn.BCELoss()
#print(train_data)
precision, recall, f1 = 0, 0, 0
print('Start training...')
for t in range(1, categories + 1):
if lang == 'java':
train_data_t = train_data[train_data['label'].isin([t, 0])]
train_data_t.loc[train_data_t['label'] > 0, 'label'] = 1
test_data_t = test_data[test_data['label'].isin([t, 0])]
test_data_t.loc[test_data_t['label'] > 0, 'label'] = 1
def train(hyp, opt, device, tb_writer=None, wandb=None):
logger.info(f'Hyperparameters {hyp}')
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # data dict
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(
data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Darknet(opt.cfg).to(device) # create
state_dict = {
k: v
for k, v in ckpt['model'].items()
if model.state_dict()[k].numel() == v.numel()
}
model.load_state_dict(state_dict, strict=False)
print('Transferred %g/%g items from %s' %
(len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Darknet(opt.cfg).to(device) # create
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in dict(model.named_parameters()).items():
if '.bias' in k:
pg2.append(v) # biases
elif 'Conv2d.weight' in k:
pg1.append(v) # apply weight_decay
elif 'm.weight' in k:
pg1.append(v) # apply weight_decay
elif 'w.weight' in k:
pg1.append(v) # apply weight_decay
else:
pg0.append(v) # all else
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
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)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
lf = lambda x: ((1 + math.cos(x * math.pi / epochs)) / 2) * (1 - hyp[
'lrf']) + hyp['lrf'] # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# Logging
if wandb and wandb.run is None:
opt.hyp = hyp # add hyperparameters
wandb_run = wandb.init(
config=opt,
resume="allow",
project='YOLOR'
if opt.project == 'runs/train' else Path(opt.project).stem,
name=save_dir.stem,
id=ckpt.get('wandb_id') if 'ckpt' in locals() else None)
# Resume
start_epoch, best_fitness = 0, 0.0
best_fitness_p, best_fitness_r, best_fitness_ap50, best_fitness_ap, best_fitness_f = 0.0, 0.0, 0.0, 0.0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
best_fitness_p = ckpt['best_fitness_p']
best_fitness_r = ckpt['best_fitness_r']
best_fitness_ap50 = ckpt['best_fitness_ap50']
best_fitness_ap = ckpt['best_fitness_ap']
best_fitness_f = ckpt['best_fitness_f']
# Results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (
weights, epochs)
if epochs < start_epoch:
logger.info(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = 64 #int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# DDP mode
if cuda and rank != -1:
model = DDP(model,
device_ids=[opt.local_rank],
output_device=opt.local_rank)
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
ema.updates = start_epoch * nb // accumulate # set EMA updates
testloader = create_dataloader(test_path,
imgsz_test,
batch_size * 2,
gs,
opt,
hyp=hyp,
cache=opt.cache_images
and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers)[0] # testloader
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, save_dir=save_dir)
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
if wandb:
wandb.log({
"Labels": [
wandb.Image(str(x), caption=x.name)
for x in save_dir.glob('*labels*.png')
]
})
# Anchors
# if not opt.noautoanchor:
# check_anchors(dataset, model=model, thr=hyp['anchor_t'], imgsz=imgsz)
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
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.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
logger.info('Image sizes %g train, %g test\n'
'Using %g dataloader workers\nLogging results to %s\n'
'Starting training for %g epochs...' %
(imgsz, imgsz_test, dataloader.num_workers, save_dir, epochs))
torch.save(model, wdir / 'init.pt')
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
cw = model.class_weights.cpu().numpy() * (
1 - maps)**2 # 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
if rank != -1:
indices = (torch.tensor(dataset.indices)
if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls',
'total', 'targets', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_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, [
hyp['warmup_bias_lr'] if j == 2 else 0.0,
x['initial_lr'] * lf(epoch)
])
if 'momentum' in x:
x['momentum'] = np.interp(
ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / 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 gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device),
model) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
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 +
'%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f'train_batch{ni}.jpg' # filename
plot_images(images=imgs,
targets=targets,
paths=paths,
fname=f)
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
elif plots and ni == 3 and wandb:
wandb.log({
"Mosaics": [
wandb.Image(str(x), caption=x.name)
for x in save_dir.glob('train*.jpg')
]
})
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
if ema:
ema.update_attr(model)
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
if epoch >= 3:
results, maps, times = test.test(
opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema.module
if hasattr(ema.ema, 'module') else ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
plots=plots and final_epoch,
log_imgs=opt.log_imgs if wandb else 0)
# Write
with open(results_file, 'a') as f:
f.write(
s + '%10.4g' * 7 % results +
'\n') # P, R, [email protected], [email protected], val_loss(box, obj, cls)
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' %
(results_file, opt.bucket, opt.name))
# Log
tags = [
'train/box_loss',
'train/obj_loss',
'train/cls_loss', # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95',
'val/box_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb:
wandb.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
fi_p = fitness_p(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
fi_r = fitness_r(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
fi_ap50 = fitness_ap50(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
fi_ap = fitness_ap(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if (fi_p > 0.0) or (fi_r > 0.0):
fi_f = fitness_f(np.array(results).reshape(
1,
-1)) # weighted combination of [P, R, [email protected], [email protected]]
else:
fi_f = 0.0
if fi > best_fitness:
best_fitness = fi
if fi_p > best_fitness_p:
best_fitness_p = fi_p
if fi_r > best_fitness_r:
best_fitness_r = fi_r
if fi_ap50 > best_fitness_ap50:
best_fitness_ap50 = fi_ap50
if fi_ap > best_fitness_ap:
best_fitness_ap = fi_ap
if fi_f > best_fitness_f:
best_fitness_f = fi_f
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'best_fitness_p':
best_fitness_p,
'best_fitness_r':
best_fitness_r,
'best_fitness_ap50':
best_fitness_ap50,
'best_fitness_ap':
best_fitness_ap,
'best_fitness_f':
best_fitness_f,
'training_results':
f.read(),
'model':
ema.ema.module.state_dict()
if hasattr(ema, 'module') else ema.ema.state_dict(),
'optimizer':
None if final_epoch else optimizer.state_dict(),
'wandb_id':
wandb_run.id if wandb else None
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if (best_fitness == fi) and (epoch >= 200):
torch.save(ckpt, wdir / 'best_{:03d}.pt'.format(epoch))
if best_fitness == fi:
torch.save(ckpt, wdir / 'best_overall.pt')
if best_fitness_p == fi_p:
torch.save(ckpt, wdir / 'best_p.pt')
if best_fitness_r == fi_r:
torch.save(ckpt, wdir / 'best_r.pt')
if best_fitness_ap50 == fi_ap50:
torch.save(ckpt, wdir / 'best_ap50.pt')
if best_fitness_ap == fi_ap:
torch.save(ckpt, wdir / 'best_ap.pt')
if best_fitness_f == fi_f:
torch.save(ckpt, wdir / 'best_f.pt')
if epoch == 0:
torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
if ((epoch + 1) % 25) == 0:
torch.save(ckpt, wdir / 'epoch_{:03d}.pt'.format(epoch))
if epoch >= (epochs - 5):
torch.save(ckpt, wdir / 'last_{:03d}.pt'.format(epoch))
elif epoch >= 420:
torch.save(ckpt, wdir / 'last_{:03d}.pt'.format(epoch))
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Strip optimizers
n = opt.name if opt.name.isnumeric() else ''
fresults, flast, fbest = save_dir / f'results{n}.txt', wdir / f'last{n}.pt', wdir / f'best{n}.pt'
for f1, f2 in zip([wdir / 'last.pt', wdir / 'best.pt', results_file],
[flast, fbest, fresults]):
if f1.exists():
os.rename(f1, f2) # rename
if str(f2).endswith('.pt'): # is *.pt
strip_optimizer(f2) # strip optimizer
os.system(
'gsutil cp %s gs://%s/weights' %
(f2, opt.bucket)) if opt.bucket else None # upload
# Finish
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb:
wandb.log({
"Results": [
wandb.Image(str(save_dir / x), caption=x)
for x in ['results.png', 'precision-recall_curve.png']
]
})
logger.info('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
else:
dist.destroy_process_group()
wandb.run.finish() if wandb and wandb.run else None
torch.cuda.empty_cache()
return results
def train(
hyp, # path/to/hyp.yaml or hyp dictionary
opt,
device,
callbacks):
save_dir, epochs, batch_size, weights, single_cls, evolve, data, cfg, resume, noval, nosave, workers, freeze = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.weights, opt.single_cls, opt.evolve, opt.data, opt.cfg, \
opt.resume, opt.noval, opt.nosave, opt.workers, opt.freeze
# Directories
w = save_dir / 'weights' # weights dir
(w.parent if evolve else w).mkdir(parents=True, exist_ok=True) # make dir
last, best = w / 'last.pt', w / 'best.pt'
# Hyperparameters
if isinstance(hyp, str):
with open(hyp, errors='ignore') as f:
hyp = yaml.safe_load(f) # load hyps dict
LOGGER.info(
colorstr('hyperparameters: ') + ', '.join(f'{k}={v}'
for k, v in hyp.items()))
# Save run settings
if not evolve:
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.safe_dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.safe_dump(vars(opt), f, sort_keys=False)
# Loggers
data_dict = None
if RANK in [-1, 0]:
loggers = Loggers(save_dir, weights, opt, hyp,
LOGGER) # loggers instance
if loggers.wandb:
data_dict = loggers.wandb.data_dict
if resume:
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp
# Register actions
for k in methods(loggers):
callbacks.register_action(k, callback=getattr(loggers, k))
# Config
plots = not evolve # create plots
cuda = device.type != 'cpu'
init_seeds(1 + RANK)
with torch_distributed_zero_first(LOCAL_RANK):
data_dict = data_dict or check_dataset(data) # check if None
train_path, val_path = data_dict['train'], data_dict['val']
nc = 1 if single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if single_cls and len(
data_dict['names']) != 1 else data_dict['names'] # class names
assert len(
names
) == nc, f'{len(names)} names found for nc={nc} dataset in {data}' # check
is_coco = isinstance(val_path, str) and val_path.endswith(
'coco/val2017.txt') # COCO dataset
# Model
check_suffix(weights, '.pt') # check weights
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(LOCAL_RANK):
weights = attempt_download(
weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(cfg or ckpt['model'].yaml,
ch=3,
nc=nc,
anchors=hyp.get('anchors')).to(device) # create
exclude = [
'anchor'
] if (cfg or hyp.get('anchors')) and not resume else [] # exclude keys
csd = ckpt['model'].float().state_dict(
) # checkpoint state_dict as FP32
csd = intersect_dicts(csd, model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(csd, strict=False) # load
LOGGER.info(
f'Transferred {len(csd)}/{len(model.state_dict())} items from {weights}'
) # report
else:
model = Model(cfg, ch=3, nc=nc,
anchors=hyp.get('anchors')).to(device) # create
# Freeze
freeze = [
f'model.{x}.'
for x in (freeze if len(freeze) > 1 else range(freeze[0]))
] # layers to freeze
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
LOGGER.info(f'freezing {k}')
v.requires_grad = False
# Image size
gs = max(int(model.stride.max()), 32) # grid size (max stride)
imgsz = check_img_size(opt.imgsz, gs,
floor=gs * 2) # verify imgsz is gs-multiple
# Batch size
if RANK == -1 and batch_size == -1: # single-GPU only, estimate best batch size
batch_size = check_train_batch_size(model, imgsz)
loggers.on_params_update({"batch_size": batch_size})
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= batch_size * accumulate / nbs # scale weight_decay
LOGGER.info(f"Scaled weight_decay = {hyp['weight_decay']}")
g0, g1, g2 = [], [], [] # optimizer parameter groups
for v in model.modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter): # bias
g2.append(v.bias)
if isinstance(v, nn.BatchNorm2d): # weight (no decay)
g0.append(v.weight)
elif hasattr(v, 'weight') and isinstance(
v.weight, nn.Parameter): # weight (with decay)
g1.append(v.weight)
if opt.optimizer == 'Adam':
optimizer = Adam(g0, lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
elif opt.optimizer == 'AdamW':
optimizer = AdamW(g0, lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
else:
optimizer = SGD(g0,
lr=hyp['lr0'],
momentum=hyp['momentum'],
nesterov=True)
optimizer.add_param_group({
'params': g1,
'weight_decay': hyp['weight_decay']
}) # add g1 with weight_decay
optimizer.add_param_group({'params': g2}) # add g2 (biases)
LOGGER.info(
f"{colorstr('optimizer:')} {type(optimizer).__name__} with parameter groups "
f"{len(g0)} weight, {len(g1)} weight (no decay), {len(g2)} bias")
del g0, g1, g2
# Scheduler
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp[
'lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(
optimizer,
lr_lambda=lf) # plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
ema = ModelEMA(model) if RANK in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# EMA
if ema and ckpt.get('ema'):
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
ema.updates = ckpt['updates']
# Epochs
start_epoch = ckpt['epoch'] + 1
if resume:
assert start_epoch > 0, f'{weights} training to {epochs} epochs is finished, nothing to resume.'
if epochs < start_epoch:
LOGGER.info(
f"{weights} has been trained for {ckpt['epoch']} epochs. Fine-tuning for {epochs} more epochs."
)
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, csd
# DP mode
if cuda and RANK == -1 and torch.cuda.device_count() > 1:
LOGGER.warning(
'WARNING: DP not recommended, use torch.distributed.run for best DDP Multi-GPU results.\n'
'See Multi-GPU Tutorial at https://github.com/ultralytics/yolov5/issues/475 to get started.'
)
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and RANK != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
LOGGER.info('Using SyncBatchNorm()')
# Trainloader
train_loader, dataset = create_dataloader(train_path,
imgsz,
batch_size // WORLD_SIZE,
gs,
single_cls,
hyp=hyp,
augment=True,
cache=opt.cache,
rect=opt.rect,
rank=LOCAL_RANK,
workers=workers,
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr('train: '),
shuffle=True)
mlc = int(np.concatenate(dataset.labels, 0)[:, 0].max()) # max label class
nb = len(train_loader) # number of batches
assert mlc < nc, f'Label class {mlc} exceeds nc={nc} in {data}. Possible class labels are 0-{nc - 1}'
# Process 0
if RANK in [-1, 0]:
val_loader = create_dataloader(val_path,
imgsz,
batch_size // WORLD_SIZE * 2,
gs,
single_cls,
hyp=hyp,
cache=None if noval else opt.cache,
rect=True,
rank=-1,
workers=workers,
pad=0.5,
prefix=colorstr('val: '))[0]
if not resume:
labels = np.concatenate(dataset.labels, 0)
# c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
callbacks.run('on_pretrain_routine_end')
# DDP mode
if cuda and RANK != -1:
model = DDP(model, device_ids=[LOCAL_RANK], output_device=LOCAL_RANK)
# Model attributes
nl = de_parallel(
model).model[-1].nl # number of detection layers (to scale hyps)
hyp['box'] *= 3 / nl # scale to layers
hyp['cls'] *= nc / 80 * 3 / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640)**2 * 3 / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing
model.nc = nc # attach number of classes to model
model.hyp = hyp # attach hyperparameters to model
model.class_weights = labels_to_class_weights(
dataset.labels, nc).to(device) * nc # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
last_opt_step = -1
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
stopper = EarlyStopping(patience=opt.patience)
compute_loss = ComputeLoss(model) # init loss class
LOGGER.info(
f'Image sizes {imgsz} train, {imgsz} val\n'
f'Using {train_loader.num_workers * WORLD_SIZE} dataloader workers\n'
f"Logging results to {colorstr('bold', save_dir)}\n"
f'Starting training for {epochs} epochs...')
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional, single-GPU only)
if opt.image_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
# Update mosaic border (optional)
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(3, device=device) # mean losses
if RANK != -1:
train_loader.sampler.set_epoch(epoch)
pbar = enumerate(train_loader)
LOGGER.info(
('\n' + '%10s' * 7) %
('Epoch', 'gpu_mem', 'box', 'obj', 'cls', 'labels', 'img_size'))
if RANK in [-1, 0]:
pbar = tqdm(
pbar, total=nb,
bar_format='{l_bar}{bar:10}{r_bar}{bar:-10b}') # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# compute_loss.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / 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, [
hyp['warmup_bias_lr'] if j == 2 else 0.0,
x['initial_lr'] * lf(epoch)
])
if 'momentum' in x:
x['momentum'] = np.interp(
ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / 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 gs-multiple)
imgs = nn.functional.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device)) # loss scaled by batch_size
if RANK != -1:
loss *= WORLD_SIZE # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# Backward
scaler.scale(loss).backward()
# Optimize
if ni - last_opt_step >= accumulate:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
last_opt_step = ni
# Log
if RANK in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = f'{torch.cuda.memory_reserved() / 1E9 if torch.cuda.is_available() else 0:.3g}G' # (GB)
pbar.set_description(('%10s' * 2 + '%10.4g' * 5) %
(f'{epoch}/{epochs - 1}', mem, *mloss,
targets.shape[0], imgs.shape[-1]))
callbacks.run('on_train_batch_end', ni, model, imgs, targets,
paths, plots, opt.sync_bn)
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for loggers
scheduler.step()
if RANK in [-1, 0]:
# mAP
callbacks.run('on_train_epoch_end', epoch=epoch)
ema.update_attr(model,
include=[
'yaml', 'nc', 'hyp', 'names', 'stride',
'class_weights'
])
final_epoch = (epoch + 1 == epochs) or stopper.possible_stop
if not noval or final_epoch: # Calculate mAP
results, maps, _ = val.run(data_dict,
batch_size=batch_size //
WORLD_SIZE * 2,
imgsz=imgsz,
model=ema.ema,
single_cls=single_cls,
dataloader=val_loader,
save_dir=save_dir,
plots=False,
callbacks=callbacks,
compute_loss=compute_loss)
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
log_vals = list(mloss) + list(results) + lr
callbacks.run('on_fit_epoch_end', log_vals, epoch, best_fitness,
fi)
# Save model
if (not nosave) or (final_epoch and not evolve): # if save
ckpt = {
'epoch': epoch,
'best_fitness': best_fitness,
'model': deepcopy(de_parallel(model)).half(),
'ema': deepcopy(ema.ema).half(),
'updates': ema.updates,
'optimizer': optimizer.state_dict(),
'wandb_id':
loggers.wandb.wandb_run.id if loggers.wandb else None,
'date': datetime.now().isoformat()
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if (epoch > 0) and (opt.save_period >
0) and (epoch % opt.save_period == 0):
torch.save(ckpt, w / f'epoch{epoch}.pt')
del ckpt
callbacks.run('on_model_save', last, epoch, final_epoch,
best_fitness, fi)
# Stop Single-GPU
if RANK == -1 and stopper(epoch=epoch, fitness=fi):
break
# Stop DDP TODO: known issues shttps://github.com/ultralytics/yolov5/pull/4576
# stop = stopper(epoch=epoch, fitness=fi)
# if RANK == 0:
# dist.broadcast_object_list([stop], 0) # broadcast 'stop' to all ranks
# Stop DPP
# with torch_distributed_zero_first(RANK):
# if stop:
# break # must break all DDP ranks
# end epoch ----------------------------------------------------------------------------------------------------
# end training -----------------------------------------------------------------------------------------------------
if RANK in [-1, 0]:
LOGGER.info(
f'\n{epoch - start_epoch + 1} epochs completed in {(time.time() - t0) / 3600:.3f} hours.'
)
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if f is best:
LOGGER.info(f'\nValidating {f}...')
results, _, _ = val.run(
data_dict,
batch_size=batch_size // WORLD_SIZE * 2,
imgsz=imgsz,
model=attempt_load(f, device).half(),
iou_thres=0.65 if is_coco else
0.60, # best pycocotools results at 0.65
single_cls=single_cls,
dataloader=val_loader,
save_dir=save_dir,
save_json=is_coco,
verbose=True,
plots=True,
callbacks=callbacks,
compute_loss=compute_loss) # val best model with plots
if is_coco:
callbacks.run('on_fit_epoch_end',
list(mloss) + list(results) + lr, epoch,
best_fitness, fi)
callbacks.run('on_train_end', last, best, plots, epoch, results)
LOGGER.info(f"Results saved to {colorstr('bold', save_dir)}")
torch.cuda.empty_cache()
return results
def run(self):
## init distributed
self.cfg = init_distributed(self.cfg)
cfg = self.cfg
# cfg.print()
## parser_dict
self.dictionary = self._parser_dict()
## parser_datasets
datasets, dataloaders, data_samplers, dataset_sizes = self._parser_datasets(
)
## parser_model
model_ft = self._parser_model()
# Scale learning rate based on global batch size
if cfg.SCALE_LR.ENABLED:
cfg.INIT_LR = cfg.INIT_LR * float(
self.batch_size) / cfg.SCALE_LR.VAL
scaler = amp.GradScaler(enabled=True)
if cfg.WARMUP.NAME is not None:
logger.info('Start warm-up ... ')
self.warm_up(scaler, model_ft, dataloaders['train'], cfg)
logger.info('finish warm-up!')
## parser_optimizer
optimizer_ft = build_optimizer(cfg, model_ft)
## parser_lr_scheduler
lr_scheduler_ft = build_lr_scheduler(cfg, optimizer_ft)
if cfg.distributed:
model_ft = DDP(model_ft,
device_ids=[cfg.local_rank],
output_device=(cfg.local_rank))
# Freeze
freeze_models(model_ft)
if self.cfg.PRETRAIN_MODEL is not None:
if self.cfg.RESUME:
self.start_epoch = self.ckpts.resume_checkpoint(
model_ft, optimizer_ft)
else:
self.start_epoch = self.ckpts.load_checkpoint(
self.cfg.PRETRAIN_MODEL, model_ft, optimizer_ft)
## vis network graph
if self.cfg.TENSORBOARD_MODEL and False:
self.tb_writer.add_graph(model_ft, (model_ft.dummy_input.cuda(), ))
self.steps_per_epoch = int(dataset_sizes['train'] // self.batch_size)
best_acc = 0.0
best_perf_rst = None
for epoch in range(self.start_epoch + 1, self.cfg.N_MAX_EPOCHS):
if cfg.distributed:
dataloaders['train'].sampler.set_epoch(epoch)
self.train_epoch(scaler, epoch, model_ft, datasets['train'],
dataloaders['train'], optimizer_ft)
lr_scheduler_ft.step()
if self.cfg.DATASET.VAL and (
not epoch % cfg.EVALUATOR.EVAL_INTERVALS
or epoch == self.cfg.N_MAX_EPOCHS - 1):
acc, perf_rst = self.val_epoch(epoch, model_ft,
datasets['val'],
dataloaders['val'])
if cfg.local_rank == 0:
# start to save best performance model after learning rate decay to 1e-6
if best_acc < acc:
self.ckpts.autosave_checkpoint(model_ft, epoch, 'best',
optimizer_ft)
best_acc = acc
best_perf_rst = perf_rst
# continue
if not epoch % cfg.N_EPOCHS_TO_SAVE_MODEL:
if cfg.local_rank == 0:
self.ckpts.autosave_checkpoint(model_ft, epoch, 'last',
optimizer_ft)
if best_perf_rst is not None:
logger.info(best_perf_rst.replace("(val)", "(best)"))
if cfg.local_rank == 0:
self.tb_writer.close()
dist.destroy_process_group() if cfg.local_rank != 0 else None
torch.cuda.empty_cache()
def run(rank, size, outputfile):
normalize = transforms.Normalize(
mean=[x / 255.0 for x in [125.3, 123.0, 113.9]],
std=[x / 255.0 for x in [63.0, 62.1, 66.7]])
"""set randomseed"""
torch.manual_seed(randomseed)
print('manual_seed=', randomseed)
"""set up data"""
train_set, bsz = partition_dataset(normalize)
transform_test = transforms.Compose([transforms.ToTensor(), normalize])
test_set = datasets.CIFAR10(root="./data",
train=False,
download=True,
transform=transform_test)
test_loader = torch.utils.data.DataLoader(test_set,
num_workers=2,
batch_size=batch_size,
shuffle=False,
pin_memory=True)
criterion = torch.nn.CrossEntropyLoss().to(device)
"""set up model"""
model = mdl.VGG11()
model.to(device)
model = DDP(model)
optimizer = optim.SGD(model.parameters(),
lr=0.1,
momentum=0.9,
weight_decay=0.0001)
num_batches = math.ceil(len(train_set.dataset) / float(bsz))
"""write output to file"""
if os.path.exists(outputfile):
os.remove(outputfile)
fp = open(
outputfile + "_r" + str(dist.get_rank()) + "_size" +
str(dist.get_world_size()), "a")
"""start training"""
total_epoch = 1
for epoch in range(total_epoch):
running_loss = 0.0
# remember to exit the train loop at end of the epoch
for batch_idx, (data, target) in enumerate(train_set):
if batch_idx < 10:
start = timeit.default_timer()
# zero the parameter gradients
optimizer.zero_grad()
# forward + backward + optimize
outputs = model(data)
loss = criterion(outputs, target)
running_loss += loss.item()
loss.backward()
optimizer.step()
running_loss += loss.item()
if batch_idx % 20 == 19: # print every 20 mini-batches
print('[%d, %5d] loss: %.3f' %
(epoch + 1, batch_idx + 1, running_loss / 20))
fp.write('[%d, %5d] loss: %.3f\n' %
(epoch + 1, batch_idx + 1, running_loss / 20))
running_loss = 0.0
if batch_idx == 0:
fp.write("Batch\trunning time\n")
if batch_idx < 10:
end = timeit.default_timer() - start
print("Batch " + str(batch_idx) + " running time:" + str(end))
fp.write('%d\t%.5f\n' % (batch_idx, end))
# # training stop
fp.close()
test_model(model, test_loader, criterion, outputfile)
def train(lm_dataloader, model, criterion, optimizer, vocab_size, args):
model.train()
from functools import reduce
import operator
num_params = reduce(operator.add, (reduce(operator.mul, x.size())
for x in model.parameters()))
if model.group:
total = torch.Tensor([num_params])
if torch.cuda.is_available():
total = total.cuda()
torch.distributed.all_reduce(total, group=model.group)
logging.info(
f"training model, #prams = {num_params}, group: {model.group.rank()}, grank:"
f" {torch.distributed.get_rank()}, sizes {model.group.size()}")
torch.distributed.barrier()
if model.group.rank() == 0:
logging.info(f"total #prams = {total.item()}")
else:
logging.info(f"training model, #prams = {num_params}")
vocab_size = 10000 # FIXME
total_loss = 0.0
start_time = time.time()
word_counter = 0
optimizer = optimizer(model)
def get_first_device(model):
if isinstance(model, DDP):
model = model.module
if not torch.cuda.is_available():
return torch.device("cpu")
if model.devices:
return model.devices[0]
else:
return torch.cuda.current_device()
def get_last_device(model):
if isinstance(model, DDP):
model = model.module
if not torch.cuda.is_available():
return torch.device("cpu")
if model.devices:
return model.devices[-1]
else:
return torch.cuda.current_device()
pipe_group = model.group
if args.ddp_zero:
model = DDP(
model,
device_ids=[torch.cuda.current_device()],
process_group=get_data_parallel_group(),
find_unused_parameters=False,
)
if pipe_group and pipe_group.rank() != 0 and pipe_group.rank() != (
pipe_group.size() - 1):
thing = {"input": torch.zeros(args.batch_size)}
class FakeDataset:
def __getitem__(self, index):
return thing
def __len__(self):
return len(lm_dataloader)
lm_dataloader = FakeDataset()
for i, batch in enumerate(lm_dataloader):
bi = batch["input"]
if args.max_batch and i > args.max_batch:
break
optimizer.zero_grad()
try:
if (pipe_group is None
or pipe_group.rank() == 0) and not args.ddp_zero:
tmp = batch["input"].to(get_first_device(model))
output = model(tmp)
else:
output = model(batch["input"])
except Exception as e:
raise RuntimeError(
f"training failed on {torch.distributed.get_rank()}") from e
if pipe_group is None or pipe_group.rank() == pipe_group.size() - 1:
target = batch["target"].to(get_last_device(model))
output = output.to(target.device)
loss = criterion(output.view(-1, vocab_size), target.view(-1))
if args.ddp_zero:
ddp_group = get_data_parallel_group()
torch.distributed.all_reduce(loss,
op=torch.distributed.ReduceOp.SUM,
group=ddp_group)
loss /= ddp_group.size()
loss.backward()
del target
else:
if args.ddp_zero:
model.module.back_helper(output)
else:
model.back_helper(output)
del output
torch.nn.utils.clip_grad_value_(model.parameters(), 0.05)
optimizer.step()
if pipe_group is None or pipe_group.rank() == pipe_group.size() - 1:
total_loss += loss.item()
log_interval = 1
word_counter += batch["ntokens"]
if i % log_interval == 0 and i > 0:
cur_loss = total_loss / log_interval
elapsed = time.time() - start_time
print(
"| batch {:5d} | wps {:5.2f} | loss {:5.2f} | ppl {:8.2f}".
format(i, word_counter / elapsed, cur_loss,
math.exp(cur_loss)))
word_counter = 0
total_loss = 0
start_time = time.time()
def run_main(args, rank=None):
# Set the random seed manually for reproducibility.
torch.manual_seed(args.seed)
if args.parallel == 'DDP':
n = torch.cuda.device_count() // args.world_size
device = list(range(rank * n, (rank + 1) * n))
else:
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
vocab = torch.load(args.save_vocab)
cls_id = vocab.stoi['<cls>']
pad_id = vocab.stoi['<pad>']
sep_id = vocab.stoi['<sep>']
if args.dataset == 'WikiText103':
from torchtext.experimental.datasets import WikiText103
train_dataset, valid_dataset, test_dataset = WikiText103(vocab=vocab,
single_line=False)
elif args.dataset == 'BookCorpus':
from data import BookCorpus
train_dataset, test_dataset, valid_dataset = BookCorpus(vocab, min_sentence_len=60)
if rank is not None:
chunk_len = len(train_dataset.data) // args.world_size
train_dataset.data = train_dataset.data[(rank * chunk_len):((rank + 1) * chunk_len)]
if args.checkpoint != 'None':
model = torch.load(args.checkpoint)
else:
pretrained_bert = BertModel(len(vocab), args.emsize, args.nhead, args.nhid, args.nlayers, args.dropout)
pretrained_bert.load_state_dict(torch.load(args.bert_model))
model = NextSentenceTask(pretrained_bert)
if args.parallel == 'DDP':
model = model.to(device[0])
model = DDP(model, device_ids=device)
else:
model = model.to(device)
criterion = nn.CrossEntropyLoss()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr)
scheduler = torch.optim.lr_scheduler.StepLR(optimizer, 1.0, gamma=0.1)
adaptdl.torch.init_process_group("nccl" if torch.cuda.is_available() else "gloo") # Changed
model = adaptdl.torch.AdaptiveDataParallel(model, optimizer, scheduler) # Changed
best_val_loss = None
train_loss_log, val_loss_log = [], []
tensorboard_dir = os.path.join(os.getenv("ADAPTDL_TENSORBOARD_LOGDIR", "/tmp") if adaptdl.env.replica_rank() == 0 else "/tmp", adaptdl.env.job_id())
writer = SummaryWriter(tensorboard_dir)# Added for tensorboard
# for epoch in range(1, args.epochs + 1): # original
for epoch in adaptdl.torch.remaining_epochs_until(args.epochs): # Changed
epoch_start_time = time.time()
train(process_raw_data(train_dataset, args), model, train_loss_log, device, optimizer,
criterion, epoch, scheduler, cls_id, sep_id, pad_id, args, rank, writer)
val_loss = evaluate(process_raw_data(valid_dataset, args), model, device, criterion,
cls_id, sep_id, pad_id, args, test = False, epoch = epoch, writer = writer)
val_loss_log.append(val_loss)
if (rank is None) or (rank == 0):
print('-' * 89)
print('| end of epoch {:3d} | time: {:5.2f}s '
'| valid loss {:8.5f} | '.format(epoch,
(time.time() - epoch_start_time),
val_loss))
print('-' * 89)
if not best_val_loss or val_loss < best_val_loss:
if rank is None:
with open(args.save, 'wb') as f:
torch.save(model.module, f)
elif rank == 0:
with open(args.save, 'wb') as f:
torch.save(model.module.state_dict(), f)
best_val_loss = val_loss
else:
scheduler.step()
if args.parallel == 'DDP':
rank0_devices = [x - rank * len(device) for x in device]
device_pairs = zip(rank0_devices, device)
map_location = {'cuda:%d' % x: 'cuda:%d' % y for x, y in device_pairs}
model.load_state_dict(torch.load(args.save, map_location=map_location))
model = adaptdl.torch.AdaptiveDataParallel(model, optimizer, scheduler) # Changed
test_loss = evaluate(process_raw_data(test_dataset, args), model, device, criterion,
cls_id, sep_id, pad_id, args)
if rank == 0:
wrap_up(train_loss_log, val_loss_log, test_loss, args, model.module, 'ns_loss.txt', 'ns_model.pt')
else:
with open(args.save, 'rb') as f:
model = torch.load(f)
test_loss = evaluate(process_raw_data(test_dataset, args), model, device, criterion,
cls_id, sep_id, pad_id, args)
wrap_up(train_loss_log, val_loss_log, test_loss, args, model, 'ns_loss.txt', 'ns_model.pt')
def check_parity(amp: bool, manual_reduction: bool):
# The API should be the exact same in between the sharded and non-sharded variants, generic closure
def closure(model,
scaler,
input_tensor,
should_accumulate,
_manual_reduction=False):
accumulate_steps = 3 if should_accumulate else 1
model.zero_grad()
def step():
if scaler is not None:
with torch.cuda.amp.autocast():
loss = model(input_tensor).abs().sum()
scaler.scale(loss).backward()
else:
loss = model(input_tensor).abs().sum()
loss.backward()
with model.no_sync() if should_accumulate else suppress():
for _ in range(accumulate_steps - 1):
step()
if not _manual_reduction:
step()
else:
with model.no_sync():
step()
model.reduce()
# Any model works. Add one different buffer per rank
model = _get_mlp()
model.register_buffer("test_buffer", torch.ones((1)) * rank)
model.to(device)
# Make sure that the model starts with non-trainable, so that we check for the buckets to be
# properly reassigned when/if this changes
next(model.parameters()).requires_grad = False
sharded_optimizer = OSS(params=model.parameters(),
optim=torch.optim.SGD,
lr=1e-4,
momentum=0.99)
sharded_ddp_model = ShardedDataParallel(
module=model,
sharded_optimizer=sharded_optimizer,
broadcast_buffers=True,
reduce_buffer_size=reduce_buffer_size,
reduce_fp16=fp16_reduction,
)
ddp_model_single = copy.deepcopy(model)
ddp_optimizer = torch.optim.SGD(ddp_model_single.parameters(),
lr=1e-4,
momentum=0.99)
ddp_model = DDP(ddp_model_single,
device_ids=[rank],
broadcast_buffers=True,
find_unused_parameters=True)
if fp16_reduction:
from dist.algorithms.ddp_com_hooks.default_hooks import fp16_compress_hook
ddp_model.register_comm_hook(
state=None, hook=fp16_compress_hook) # type: ignore
ddp_scaler = TorchGradScaler() if amp else None
sharded_ddp_scaler = ShardedGradScaler() if amp else None
# The model should be synchronized in between the ranks at construction time, check that
check_same_model_params(sharded_ddp_model, ddp_model)
# Typical training loop, check that we get the exact same results as DDP
for i in range(NUMBER_BATCHS):
input_tensor = torch.rand((BATCH_SIZE, 2)).to(device)
def closure_ddp(input_tensor=input_tensor):
return closure(ddp_model, ddp_scaler, input_tensor,
grad_accumulation)
def closure_sharded(input_tensor=input_tensor):
return closure(
sharded_ddp_model,
sharded_ddp_scaler,
input_tensor,
grad_accumulation,
_manual_reduction=manual_reduction,
)
# Step/scale both
if ddp_scaler is not None:
_ = closure_ddp(input_tensor)
ddp_scaler.step(ddp_optimizer)
ddp_scaler.update()
else:
ddp_optimizer.step(closure=closure_ddp)
if sharded_ddp_scaler is not None:
_ = closure_sharded(input_tensor)
sharded_ddp_scaler.step(sharded_optimizer)
sharded_ddp_scaler.update()
else:
sharded_optimizer.step(closure=closure_sharded)
check_same_model_params(sharded_ddp_model, ddp_model,
f"Rank: {rank} - Step {i} broke")
# Flip the trainability of the first parameter back and forth
if i == 0 and change_train_graph:
next(sharded_ddp_model.parameters()).requires_grad = not next(
sharded_ddp_model.parameters()).requires_grad
next(ddp_model.parameters()).requires_grad = not next(
ddp_model.parameters()).requires_grad
check_same_model_params(
sharded_ddp_model, ddp_model,
f"Rank: {rank} - Trainability refresh {i} broke")
def check_optimizer_equivalence(optimizer: Type[torch.optim.Optimizer]):
# Any model works. Add one different buffer per rank
model = torch.nn.Sequential(
torch.nn.Linear(2, 3),
torch.nn.Linear(3, 3),
torch.nn.Linear(3, 3),
)
model.register_buffer("test_buffer", torch.ones((1)) * self.rank)
model.to(self.device)
sharded_optimizer = ZeroRedundancyOptimizer(
params=model.parameters(), optimizer_class=optimizer, lr=1e-3
)
sharded_ddp_model = DDP(
module=model, device_ids=[self.rank], broadcast_buffers=True, find_unused_parameters=True
)
ddp_model_single = copy.deepcopy(model)
ddp_model_single.to(self.device)
ddp_optimizer = optimizer(ddp_model_single.parameters(), lr=1e-3)
ddp_model = DDP(
ddp_model_single, device_ids=[self.rank], broadcast_buffers=True, find_unused_parameters=True
)
# The model should be synchronized in between the ranks at construction time, check that
check_same_model_params(sharded_ddp_model, ddp_model, "Models differ from the start")
def check_step():
input_tensor = torch.rand((64, 2))
def closure_ddp(input_tensor=input_tensor):
ddp_optimizer.zero_grad()
ddp_loss = ddp_model(input_tensor).abs().sum()
ddp_loss.backward()
return ddp_loss
def closure_sharded(input_tensor=input_tensor):
sharded_optimizer.zero_grad()
sharded_loss = sharded_ddp_model(input_tensor).abs().sum()
sharded_loss.backward()
return sharded_loss
loss_ddp = cast(torch.Tensor, ddp_optimizer.step(closure=closure_ddp))
loss_sharded_optim = cast(torch.Tensor, sharded_optimizer.step(closure=closure_sharded))
assert torch.allclose(
loss_ddp, loss_sharded_optim
), "Losses differ in between Pytorch optim and ZeroRedundancyOptimizer"
check_same_model_params(sharded_ddp_model, ddp_model, "Models differ after a step")
# The models should stay the same in between the ranks
for i in range(BATCHS):
check_step()
# Change the models trainability, check that parity is maintained
# only check after a couple of constant batchs to go through both regimes
if i > BATCHS // 2:
next(ddp_model.parameters()).requires_grad = bool(i % 2)
next(sharded_ddp_model.parameters()).requires_grad = bool(i % 2)
# Check that the checkpoints are compatible
reference_rank = 0
# - get states
ddp_state_dict = ddp_optimizer.state_dict()
sharded_optimizer.consolidate_state_dict(to=reference_rank)
sharded_optim_state_dict = [sharded_optimizer.state_dict() if self.rank == reference_rank else {}]
dist.broadcast_object_list(sharded_optim_state_dict, src=reference_rank, group=dist.group.WORLD)
sharded_optim_state_dict = sharded_optim_state_dict[0]
# - cross load the states
# run one step and check that the models are still the same
ddp_state_dict_ref = copy.deepcopy(ddp_state_dict) # OSS will remove some states
ddp_optimizer.load_state_dict(sharded_optim_state_dict) # mixup on purpose !
sharded_optimizer.load_state_dict(ddp_state_dict)
check_step()
# - self load, rewind, check no problem
# run one step and check that the models are still the same
ddp_optimizer.load_state_dict(ddp_state_dict_ref)
sharded_optimizer.load_state_dict(sharded_optim_state_dict)
check_step()
def train(local_rank, args):
torch.backends.cudnn.benchmark = True
import os
# torch.multiprocessing.set_sharing_strategy('file_system')
# too many barriers / one node data parallel and multiple node DDP
os.environ['MASTER_ADDR'] = args["master_addr"]
os.environ['MASTER_PORT'] = args["master_port"]
os.environ["NCCL_DEBUG"] = "WARN"
# os.environ["CUDA_VISIBLE_DEVICES"] = str(local_rank)
# gpu_device = 0
gpu_device = local_rank
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
if args["wandb_dryrun"]:
os.environ["WANDB_MODE"] = "dryrun"
os.environ["WANDB_SILENT"] = "true"
os.environ['TOKENIZERS_PARALLELISM'] = "true"
torch.backends.cudnn.benchmark = True
rank = args["nr"] if args["cpu"] else (args["nr"] * args["gpus_per_node"] + local_rank)
nr = args["nr"]
if args["cpu"]:
assert local_rank == 0
device = torch.device("cpu")
args["dist_backend"] = "gloo"
# init_method = "tcp://%s:%s" % ("127.0.0.1", "9999")
else:
device = torch.device(f'cuda:{gpu_device}') # Unique only on individual node.
torch.cuda.set_device(device)
if args["init_method"] == "tcp":
if args["nr"] == 0:
args["master_addr"] = "0.0.0.0"
init_method="tcp://%s:%s" % (args["master_addr"], args["master_port"])
elif args["init_method"] == "file":
init_method = 'file://%s/%s' % (args["master_addr"], args["master_port"])
else:
raise ValueError
rnd = torch.tensor(0.0, device="cpu")
if args["world_size"] > 1:
dist.init_process_group(args["dist_backend"], rank=rank, world_size=args["world_size"], init_method=init_method)
rnd = torch.tensor(int(time.time())).to(device)
dist.broadcast(rnd, 0)
barrier = get_barrier(args["world_size"] > 1)
format = "%Y-%m-%d %H-%M %Z"
# + timedelta(hours=5, minutes=30)
time_string = (datetime.fromtimestamp(time.mktime(time.gmtime(rnd.cpu().item())))).astimezone(timezone('Asia/Kolkata')).strftime(format)
ds_name = list(filter(lambda x: len(x.strip()) > 0, args["dataset"].split("/")))[-1].replace("train_fastformer_resampled_", "")
set_seeds(args["seed"])
batch_size = 8
optimizer_config.lr = args["lr"]
optimizer_config.weight_decay = args["weight_decay"]
optimizer_config.gradient_clipping = args["gradient_clipping"]
optimizer_config.beta_1 = args["beta_1"]
optimizer_config.beta_2 = args["beta_2"]
eps = 1e-4
if args["no_autocast"]:
optimizer_config.eps = 1e-7
eps = 1e-7
reinit = args["pretrained_model"] is None or "pretrained_model" not in args or args["pretrained_model"] == ""
backbone, tokenizer = get_mtt_backbone(args["model_config"], args["cls_tokens"], args["enable_layer_normalizers"], args["sampling_alpha"], reinit,
args["enable_layer_normalizers"], args["enable_layer_normalizers_statistics"], dropout_prob=0.01)
teacher_backbone, _ = get_mtt_backbone(args["model_config"], args["cls_tokens"], args["enable_layer_normalizers"], None, reinit, args["enable_layer_normalizers"],
args["enable_layer_normalizers_statistics"], dropout_prob=0.0)
batch_size = args["batch_size"] if "batch_size" in args and isinstance(args["batch_size"], int) else batch_size
generator_w = args["generator_w"] if "generator_w" in args else 0.0
discriminator_w = args["discriminator_w"] if "discriminator_w" in args else 0.0
dino_w = args["dino_w"] if "dino_w" in args else 0.0
sentence_order_prediction_w = args["sentence_order_prediction_w"] if "sentence_order_prediction_w" in args else 0.0
attention_penalty_w = args["attention_penalty_w"] if "attention_penalty_w" in args else 0.0
student = MTTModel(backbone, tokenizer, args["cls_tokens"],
generator_w=generator_w, discriminator_w=discriminator_w,
dino_w=dino_w, sentence_order_prediction_w=sentence_order_prediction_w, attention_penalty_w=attention_penalty_w,
lm_layers=args["lm_layers"], electra_layers=args["electra_layers"],
lm_layers_total=args["lm_layers_total"], electra_layers_total=args["electra_layers_total"],
drop_unused_layers=args["drop_unused_layers"], approximate_unused_layers=args["consecutive_layers"],
exclude_layers=args["exclude_layers"], keep_last_layer=args["keep_last_layer"],
lm_temperature=args["lm_temperature"])
teacher = MTTModel(teacher_backbone, tokenizer, args["cls_tokens"],
generator_w=generator_w, discriminator_w=discriminator_w,
dino_w=1.0, sentence_order_prediction_w=sentence_order_prediction_w, attention_penalty_w=0.0,
lm_layers=None, electra_layers=None,
lm_layers_total=args["lm_layers_total"], electra_layers_total=args["electra_layers_total"],
lm_temperature=args["lm_temperature"])
teacher = teacher.eval()
model = MultiTaskHighwayCLSPretraining(student, teacher, eps, device if args["move_unused_to_cpu"] else None).to(device)
trainable_model = student
if dino_w == 0:
model.teacher = None
teacher = None
clean_memory()
del teacher
if local_rank == 0 and rank == 0:
print("[Train]: Time = %s, Trainable Params = %s" % (get_time_string(), numel(trainable_model) / 1_000_000))
if args["pretrained_model"] is not None and os.path.exists(args["pretrained_model"]):
state_dict = torch.load(args["pretrained_model"], map_location='cpu' if args['cpu'] else 'cuda:%d' % gpu_device)
try:
trainable_model.load_state_dict(state_dict, strict=True)
load_type = "strict"
except:
try:
try:
trainable_model.load_state_dict(state_dict, strict=False)
load_type = "not_strict"
except:
state_dict = {k: v for k, v in state_dict.items() if k.startswith("backbone.")}
trainable_model.load_state_dict(state_dict, strict=False)
load_type = "not_strict_no_ffn"
except:
try:
try:
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
trainable_model.load_state_dict(state_dict, strict=True)
load_type = "strict-from-ddp"
except:
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
state_dict = {k: v for k, v in state_dict.items() if not k.startswith("backbone.")}
trainable_model.load_state_dict(state_dict, strict=True)
load_type = "strict-from-ddp-no-ffn"
except:
try:
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
trainable_model.load_state_dict(state_dict, strict=False)
load_type = "not_strict-from-ddp"
except:
state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
state_dict = {k: v for k, v in state_dict.items() if not k.startswith("backbone.")}
trainable_model.load_state_dict(state_dict, strict=False)
load_type = "not_strict-from-ddp-no-ffn"
if dino_w > 0:
student_teacher_param_update(model.student, model.teacher, 0.001, device if args["move_unused_to_cpu"] else None)
print("[Train]: Time = %s, Loaded Pretrained model with Load type = %s, Torch Version = %s" % (get_time_string(), load_type, torch.__version__))
del state_dict
model = model.train()
# print("[Train]: Time = %s, Trainable Params = %s" % (get_time_string(), {k for k, v in model.named_parameters() if v.requires_grad}))
if args["world_size"] > 1:
# model = FSDP(model, **fsdp_params) # find_unused_parameters=True
trainable_model = DDP(trainable_model, device_ids=None if args["cpu"] else [gpu_device], find_unused_parameters=True, bucket_cap_mb=50) # find_unused_parameters=True
model.student = trainable_model
if dino_w > 0:
model.teacher = model.teacher.eval()
student_teacher_param_update(model.student, model.teacher, 0.01, device if args["move_unused_to_cpu"] else None)
try:
from torch.distributed.algorithms.ddp_comm_hooks.default_hooks import fp16_compress_hook
trainable_model.register_comm_hook(state=None, hook=fp16_compress_hook)
except:
print("[Train]: Time = %s, No fp16_compress_hook present, Torch Version = %s" % (get_time_string(), torch.__version__))
del backbone
del teacher_backbone
del student
clean_memory()
barrier()
optc = optimizer_config.to_dict()
trainable_params = list(filter(lambda p: p.requires_grad, trainable_model.parameters()))
if args["optimizer"] == "adamw":
optimizer = torch.optim.AdamW(trainable_params, lr=optc["lr"], eps=optc["eps"], weight_decay=optc["weight_decay"],
betas=(optc["beta_1"], optc["beta_2"]))
elif args["optimizer"] == "sgd":
optimizer = torch.optim.SGD(trainable_params, lr=optc["lr"], momentum=0.9, weight_decay=optc["weight_decay"], nesterov=True)
elif args["optimizer"] == "novograd":
optimizer = Novograd(trainable_params, lr=optc["lr"], eps=optc["eps"], betas=(optc["beta_1"], optc["beta_2"]), weight_decay=optc["weight_decay"],)
elif args["optimizer"] == "rangerlars":
optimizer = RangerLars(trainable_params, lr=optc["lr"], eps=optc["eps"], betas=(optc["beta_1"], optc["beta_2"]), weight_decay=optc["weight_decay"],)
else:
raise ValueError
# print("[Train]: Time = %s, Trainable Params = %s" % (get_time_string(), {k for k, v in trainable_model.named_parameters() if v.requires_grad}))
del trainable_params
optimizer.zero_grad(set_to_none=True)
model_save_dir = args["model_save_dir"]
model_save_name = args["model_save_name"]
set_seeds(args["seed"] + rank)
if local_rank == 0:
if not os.path.exists(model_save_dir):
os.makedirs(model_save_dir)
assert os.path.exists(model_save_dir)
try:
dataloader = build_dataloader(os.path.join(args["dataset"], "all_512_only"), args["shuffle_dataset"], batch_size,
tokenizer, args["cls_tokens"],
world_size=args["world_size"], num_workers=args["num_workers"], max_length=512)
dataloader128 = build_dataloader(os.path.join(args["dataset"], "all_128_only"), args["shuffle_dataset"], batch_size * 6,
tokenizer, args["cls_tokens"],
world_size=args["world_size"], num_workers=args["num_workers"], max_length=128)
dataloader256 = build_dataloader(os.path.join(args["dataset"], "all_256_only"), args["shuffle_dataset"], batch_size * 3,
tokenizer, args["cls_tokens"],
world_size=args["world_size"], num_workers=args["num_workers"], max_length=256)
except:
print("[WARN] [Train]: Time = %s, All dataloaders and datasets are same = %s" % (get_time_string(), args["dataset"]))
dataloader = build_dataloader(args["dataset"], args["shuffle_dataset"], batch_size,
tokenizer, args["cls_tokens"],
world_size=args["world_size"], num_workers=args["num_workers"], max_length=512)
dataloader128 = build_dataloader(args["dataset"], args["shuffle_dataset"], batch_size * 4,
tokenizer, args["cls_tokens"],
world_size=args["world_size"], num_workers=args["num_workers"], max_length=128)
dataloader256 = build_dataloader(args["dataset"], args["shuffle_dataset"], batch_size * 2,
tokenizer, args["cls_tokens"],
world_size=args["world_size"], num_workers=args["num_workers"], max_length=256)
iter_size = max(args["accumulation_steps"], 1)
no_sync = iter_size > 1
steps_per_epoch = int(np.ceil(len(dataloader.sampler) / (batch_size * iter_size)) if dataloader.sampler is not None else (len(dataloader) / iter_size))
if local_rank == 0:
print("[Train]: Time = %s, Optimizer and Scheduler Initialised, max lr = %.5f, steps_per_epoch = %s, batch size = %s, dataloader length = %s, Sampler Present = %s, Sampler Length = %s" %
(get_time_string(), optc["lr"], steps_per_epoch, batch_size, len(dataloader), dataloader.sampler is not None, len(dataloader.sampler) if dataloader.sampler is not None else -1))
dataloader = get_next(dataloader)
dataloader128 = get_next(dataloader128)
dataloader256 = get_next(dataloader256)
log_every_steps = args["log_every_steps"] * iter_size
save_every_steps = args["save_every_steps"]
# scheduler = optimization.get_constant_schedule_with_warmup(optimizer, optc["warmup_steps"])
# scheduler = optimization.get_linear_schedule_with_warmup(optimizer, optc["warmup_steps"], args["epochs"] * len(dataloader))
div_factor = optc["lr"]/1e-6
scheduler = torch.optim.lr_scheduler.OneCycleLR(optimizer, optc["lr"], total_steps=args["total_steps"],
div_factor=div_factor, three_phase=False, pct_start=0.06, anneal_strategy="linear", cycle_momentum=False)
# scheduler1 = optimization.get_constant_schedule_with_warmup(optimizer, optc["warmup_steps"])
# scheduler2 = torch.optim.lr_scheduler.StepLR(optimizer, step_size=(steps_per_epoch * args["epochs"]) // args["lr_steps"], gamma=0.5)
# scheduler = [scheduler1, scheduler2]
barrier()
gradient_clipping = optc["gradient_clipping"]
group = "%s-%s-%s-%sN-%s" % (args["wandb_name"], ds_name, args["model_config"], args["nodes"], time_string)
wandb_init_args = dict(project="de_lm", name="%s-%s-%s-%s" % (group, args["nr"], rank, local_rank), group=group, id=f"{group}-worker-{nr}-{rank}-{local_rank}",
config={"args":args, "optimizer_config": optc},
settings=wandb.Settings(start_method="fork"))
time.sleep(random.random())
wandb.init(**wandb_init_args)
full_times = []
batch_times = []
model_times = []
model.zero_grad(set_to_none=True)
samples_processed = 0
samples_processed_this_log_iter = 0
if args["detect_anomaly"]:
torch.autograd.set_detect_anomaly(True)
def hook(grad):
is_nan_inf = torch.logical_not(torch.isfinite(grad))
if is_nan_inf.any():
# print("[GRAD-HOOK]: Time = %s, Param Name = %s, Detected Nan/Inf" % (get_time_string(), name_of_param))
grad[is_nan_inf] = 0.0
return grad
return None
if not args["no_autocast"] and args["backward_hook"]:
for name, param in model.named_parameters():
param.register_hook(hook)
dino_center = None
discriminator_dino_center = None
total_steps = args["total_steps"]
steps_done = 0
step = 0
start_time = time.time()
while steps_done < total_steps:
random.seed(step)
len_proba = random.random()
if len_proba < 0.5:
batch = dataloader128()
elif len_proba < 0.6:
batch = dataloader256()
else:
batch = dataloader()
epoch_128 = dataloader128.epoch
epoch_256 = dataloader256.epoch
epoch_512 = dataloader.epoch
# batch = None
# if len_proba < 0.9:
# batches = [dataloader128() for _ in range(4)]
# elif len_proba < 0.97:
# batches = [dataloader256() for _ in range(2)]
# else:
# batch = dataloader()
#
# if batch is None:
# keys = batches[0].keys()
# batch = dict()
# for k in keys:
# elems = [b[k] for b in batches]
# if isinstance(elems[0], (list, tuple)):
# new_elems = [i for e in elems for i in e]
# elif isinstance(elems[0], torch.Tensor):
# new_elems = torch.cat(elems, 0)
# else:
# raise TypeError("Expected List or Tensor")
# batch[k] = new_elems
key = list(batch.keys())[0]
bs_size = list(batch[key].size())
batch = {k: v.to(device, non_blocking=True) if hasattr(v, "to") else v for k, v in batch.items()}
gen_batch_time = time.time() - start_time
teacher_update_w = np.interp(steps_done, [0, args["teacher_warmup_steps"]], [0.95, 0.999])
inner_model = getattr(trainable_model, "module", trainable_model)
if hasattr(inner_model, "start_from_proba"):
start_from_proba = np.interp(steps_done, [0, args["warmup_steps"], args["warmup_steps"] * 2], [0.0, 0.0, args["start_from_proba"]])
inner_model.start_from_proba = start_from_proba
if hasattr(inner_model.backbone.encoder, "sampling_alpha") and args["sampling_alpha"] is not None and args["sampling_alpha"] != 1.0:
sampling_alpha = np.interp(steps_done, [0, args["warmup_steps"], args["warmup_steps"] * 2], [1.0, 1.0, args["sampling_alpha"]])
inner_model.backbone.encoder.sampling_alpha = max(sampling_alpha, 0.01)
inner_model.sampling_alpha = max(sampling_alpha, 0.01)
if args["dino_w"] > 0:
dino_w = np.interp(steps_done, [0, args["teacher_warmup_steps"], args["teacher_warmup_steps"] * 2], [0.0, 0.0, args["dino_w"]])
inner_model.dino_w = dino_w
lm_temperature = np.interp(steps_done, [0, args["warmup_steps"], args["warmup_steps"] * 2],
[args["lm_temperature"], args["lm_temperature"], args["lm_temperature"] + 1.0])
inner_model.lm_temperature = lm_temperature
batch_times.append(gen_batch_time)
if (steps_done + 1) % save_every_steps == 0 or (args["total_steps"] is not None and (steps_done + 1) >= args["total_steps"]):
state_dict = trainable_model.state_dict() if not isinstance(trainable_model, DDP) else trainable_model.module.state_dict()
if local_rank == 0:
torch.save(state_dict, os.path.join(model_save_dir, model_save_name))
del state_dict
clean_memory()
barrier()
if args["total_steps"] is not None and (steps_done + 1) >= args["total_steps"]:
return
samples_processed += int(batch[key].size(0))
samples_processed_this_log_iter += int(batch[key].size(0))
inner_args = dict(no_autocast=args["no_autocast"], cpu=args["cpu"])
validation_iter = (step + 1) % log_every_steps == 0 or step == 0
model_start = time.time()
if no_sync and (step + 1) % iter_size != 0 and hasattr(trainable_model, "no_sync"):
with trainable_model.no_sync():
output = train_inner_loop(inner_args, model, batch, optimizer,
scheduler, gradient_clipping, iter_size=iter_size,
no_sync=True, validation_iter=validation_iter,
dino_center=dino_center,
discriminator_dino_center=discriminator_dino_center,
freeze_last_layer=steps_done < args["freeze_last_layer"], step=step + 1)
model_times.append(time.time() - model_start)
else:
output = train_inner_loop(inner_args, model, batch, optimizer,
scheduler, gradient_clipping, iter_size=iter_size,
no_sync=False, validation_iter=validation_iter,
dino_center=dino_center,
discriminator_dino_center=discriminator_dino_center,
freeze_last_layer=steps_done < args["freeze_last_layer"], step=step + 1)
optimizer.zero_grad(set_to_none=True)
steps_done += 1
model_times.append(time.time() - model_start)
step += 1
del batch
if dino_w > 0 and (step + 1) % iter_size:
student_teacher_param_update(model.student, model.teacher, teacher_update_w, device if args["move_unused_to_cpu"] else None)
dino_center = output.pop("dino_center", None)
discriminator_dino_center = output.pop("discriminator_dino_center", None)
# if dino_w > 0 and (step + 1) % (1 * iter_size) == 0 and args["world_size"] > 1:
# if dino_center is not None:
# dtype = dino_center.dtype
# dino_center = dino_center.type(torch.float64) / args["world_size"]
# torch.distributed.all_reduce(dino_center, torch.distributed.ReduceOp.SUM)
# dino_center = dino_center.type(dtype)
# if discriminator_dino_center is not None:
# dtype = discriminator_dino_center.dtype
# discriminator_dino_center = discriminator_dino_center.type(torch.float64) / args["world_size"]
# torch.distributed.all_reduce(discriminator_dino_center, torch.distributed.ReduceOp.SUM)
# discriminator_dino_center = discriminator_dino_center.type(dtype)
if (step + 1) % (4 * iter_size) == 0 and hasattr(getattr(trainable_model, "module", trainable_model).backbone, "layer_normalizers") and args["world_size"] > 1:
layer_normalizers = getattr(trainable_model, "module", trainable_model).backbone.layer_normalizers
if layer_normalizers is not None:
dtype = layer_normalizers.dtype
layer_normalizers = layer_normalizers.type(torch.float64)
torch.distributed.all_reduce(layer_normalizers, torch.distributed.ReduceOp.SUM)
layer_normalizers = layer_normalizers / args["world_size"]
getattr(trainable_model, "module", trainable_model).backbone.layer_normalizers = layer_normalizers.type(dtype)
if (step + 1) % (4 * iter_size) == 0 and hasattr(getattr(trainable_model, "module", trainable_model).backbone, "layer_normalizers_small") and args["world_size"] > 1:
layer_normalizers_small = getattr(trainable_model, "module", trainable_model).backbone.layer_normalizers_small
if layer_normalizers_small is not None:
dtype = layer_normalizers_small.dtype
layer_normalizers_small = layer_normalizers_small.type(torch.float64)
torch.distributed.all_reduce(layer_normalizers_small, torch.distributed.ReduceOp.SUM)
layer_normalizers_small = layer_normalizers_small / args["world_size"]
getattr(trainable_model, "module", trainable_model).backbone.layer_normalizers_small = layer_normalizers_small.type(dtype)
full_time = time.time() - start_time
full_times.append(full_time)
if step == 0 and local_rank == 0:
print("[Train]: Time = %s, First Batch Training for Rank = %s" % (get_time_string(), rank))
if validation_iter:
steps_remaining = total_steps - steps_done
# print({k for k, v in output.items() if isinstance(v, torch.Tensor)})
output = {k: float(v) if v else v for k, v in output.items()}
samples_per_second = samples_processed_this_log_iter / np.sum(full_times)
wandb_log = dict(lr=optimizer.param_groups[0]['lr'], step=step, samples_processed=samples_processed, samples_per_second=samples_per_second,
batch_times=np.mean(batch_times), full_times=np.mean(full_times), model_times=np.mean(model_times),
steps_remaining=steps_remaining, pct_complete=(100 * steps_done / total_steps),
epoch_128=epoch_128, epoch_256=epoch_256, epoch_512=epoch_512,
**{k: v for k, v in output.items() if v is not None})
wandb.log(wandb_log)
if local_rank == 0:
print("[Train]: Time = %s, Rank = %s, steps = %s, samples_processed=%s, batch_size = %s, Details = %s, LR = %s" %
(get_time_string(), rank, step, samples_processed, bs_size, output, optimizer.param_groups[0]['lr']))
print("[Train-Timings]: Time = %s, Batch time = %.4f, Full Time = %.4f, Model Time = %.4f, samples_per_second = %s, steps_remaining = %s, pct_complete = %.4f" % (
get_time_string(), np.mean(batch_times), np.mean(full_times), np.mean(model_times), samples_per_second, steps_remaining, (100 * steps_done / total_steps),))
# print("Step = %s, Steps Done = %s, log_every_steps = %s, total_steps = %s, steps_remaining = %s, validation_iter = %s, %s" % (step, steps_done, log_every_steps, total_steps, steps_remaining, validation_iter, (step + 1) % log_every_steps == 0))
batch_times = []
full_times = []
model_times = []
samples_processed_this_log_iter = 0
if args["enable_layer_normalizers_statistics"] and local_rank == 0:
backbone = getattr(model.student, "module", model.student).backbone
stats = backbone.layer_normalizers
inp_stats=backbone.encoder.layer_normalizers_statistics
norms = stats[:, 2, 0].tolist()
inp_norms = inp_stats[:, 2, 0].tolist()
centers = stats[:, 0, 0:8].tolist()
inp_centers = inp_stats[:, 0, 0:8].tolist()
stds = stats[:, 1, 0:8].tolist()
inp_stds = inp_stats[:, 1, 0:8].tolist()
dist_stats = backbone.encoder.distance_statistics.tolist()
print("Branch Norms = \n", tabulate(pd.DataFrame(norms), tablefmt="psql"))
print("Skip Norms = \n", tabulate(pd.DataFrame({"norm": inp_norms, "dist": dist_stats}), tablefmt="psql"))
print("Branch centers = \n", tabulate(pd.DataFrame(centers), tablefmt="psql"))
print("Skip centers = \n", tabulate(pd.DataFrame(inp_centers), tablefmt="psql"))
print("Branch stds = \n", tabulate(pd.DataFrame(stds), tablefmt="psql"))
print("Skip stds = \n", tabulate(pd.DataFrame(inp_stds), tablefmt="psql"))
# clean_memory()
# barrier()
del output
del bs_size
start_time = time.time()
print("Time = %s, Finished Training for Rank = %s" % (get_time_string(), rank))
state_dict = trainable_model.state_dict() if not isinstance(trainable_model, DDP) else trainable_model.module.state_dict()
if local_rank == 0:
torch.save(state_dict, os.path.join(model_save_dir, model_save_name))
del model
def _test_zero_join(self, device):
r"""
Check that the ZeRO join hook allows training with uneven inputs when using the given device.
Arguments:
device (torch.device): device used to store parameters and perform
collective communications.
"""
NUM_INPUTS = 3
NUM_EPOCHS = 2
torch.manual_seed(0)
torch.cuda.manual_seed(0)
rank = self.rank
world_size = self.world_size
is_gpu = device.type == "cuda"
backend = dist.Backend.NCCL if is_gpu else dist.Backend.GLOO
self.dist_init(rank, world_size, backend)
if BACKEND == dist.Backend.NCCL and is_gpu:
torch.cuda.set_device(self.device)
model = torch.nn.Sequential(
torch.nn.Linear(2, 3),
torch.nn.Linear(3, 3),
torch.nn.Linear(3, 3),
)
model.to(device)
# DDP ensures correct gradients in data parallel training, so DDP with
# local optimizers on uneven inputs should be equivalent to ZeRO on
# uneven inputs with gradients being manually set
ddp_model = DDP(model, device_ids=[rank]) if is_gpu else DDP(model)
local_optim = torch.optim.Adam(ddp_model.parameters(), lr=0.01)
zero_model = copy.deepcopy(model)
zero_model.to(device)
zero_optim = ZeroRedundancyOptimizer(zero_model.parameters(), torch.optim.Adam, lr=0.01)
loss_fn = torch.nn.MSELoss()
# Use uneven inputs: rank i has i extra inputs
inputs = [torch.randn(20, 2).to(device) for _ in range(NUM_INPUTS + rank)]
labels = torch.randn(20, 3).to(device)
# Save the gradients and parameters from DDP as the ground truth; do
# so on the last-joining rank (in this case, the largest rank)
grads_at_each_iter = []
params_at_each_iter = []
with ddp_model.join():
for _ in range(NUM_EPOCHS):
for input in inputs:
output = ddp_model(input)
loss_fn(output, labels).backward()
if rank == world_size - 1:
grads = []
for p in ddp_model.parameters():
grads.append(p.grad.detach().clone().to(device))
local_optim.step()
if rank == world_size - 1:
params = []
for p in ddp_model.parameters():
params.append(p.detach().clone().to(device))
grads_at_each_iter.append(grads)
params_at_each_iter.append(params)
# Broadcast the saved gradients and parameters to all of the other
# ranks (which joined early)
grads_and_params = [grads_at_each_iter, params_at_each_iter]
grads_and_params = _broadcast_object(grads_and_params, src_rank=world_size - 1, group=dist.group.WORLD, device=device)
grads_at_each_iter = grads_and_params[0]
params_at_each_iter = grads_and_params[1]
# TODO: Replace this `_broadcast_object` with `broadcast_object_list`
# once the latter supports loading to the destination device instead
# of the source device
# A process must still set the remaining gradients after joining, so we
# define a join hook to do this before the ZeRO join hook
class _JoinGradInfo():
def __init__(self, grads):
self.grads = grads # remaining gradients to set (in order)
self.index = 0
class _SetGradsJoinHook(_JoinHook):
def __init__(self, zero_optim, grads):
zero_optim._join_grad_info = _JoinGradInfo(grads)
self.zero = zero_optim
super().__init__()
def main_hook(self):
grads = self.zero._join_grad_info.grads[self.zero._join_grad_info.index]
self.zero._join_grad_info.index += 1
for p, grad in zip(self.zero._all_params, grads):
p.grad = grad.detach().clone().to(device)
class _GradientSetter(_Joinable):
def __init__(self):
super().__init__()
def _join_hook(self, **kwargs):
assert "zero_optim" in kwargs
assert "grads" in kwargs
zero_optim = kwargs["zero_optim"]
grads = kwargs["grads"]
return _SetGradsJoinHook(zero_optim, grads)
@property
def _join_device(self):
return device
@property
def _join_process_group(self):
return dist.group.WORLD
num_grads_after_joining = NUM_EPOCHS * (world_size - rank - 1)
grads = grads_at_each_iter[-num_grads_after_joining:]
gradient_setter = _GradientSetter()
iter = 0
with _Join([gradient_setter, zero_optim], zero_optim=zero_optim, grads=grads):
for _ in range(NUM_EPOCHS):
for input in inputs:
# Notify join context that this process has not joined
_Join.notify_join_context(gradient_setter)
# Set gradients manually
for p, grad in zip(zero_model.parameters(), grads_at_each_iter[iter]):
p.grad = grad.detach().clone().to(device)
# Perform optimizer step and check parity
zero_optim.step()
for p, ddp_p in zip(zero_model.parameters(), params_at_each_iter[iter]):
assert torch.allclose(p, ddp_p), \
"Parameters differ between using ZeRO and local optimizer"
iter += 1
def train(hyp, tb_writer, opt, device):
print(f'Hyperparameters {hyp}')
log_dir = tb_writer.log_dir if tb_writer else 'runs/evolution' # run directory
wdir = str(Path(log_dir) / 'weights') + os.sep # weights directory
os.makedirs(wdir, exist_ok=True)
last = wdir + 'last.pt'
best = wdir + 'best.pt'
results_file = log_dir + os.sep + 'results.txt'
# Save run settings
with open(Path(log_dir) / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(Path(log_dir) / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
epochs = opt.epochs # 300
batch_size = opt.batch_size # batch size per process.
total_batch_size = opt.total_batch_size
weights = opt.weights # initial training weights
local_rank = opt.local_rank
# TODO: Init DDP logging. Only the first process is allowed to log.
# Since I see lots of print here, the logging configuration is skipped here. We may see repeated outputs.
# Configure
init_seeds(2 + local_rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.FullLoader) # model dict
train_path = data_dict['train']
test_path = data_dict['val']
nc, names = (1, ['item']) if opt.single_cls else (int(
data_dict['nc']), data_dict['names']) # number classes, names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Remove previous results
if local_rank in [-1, 0]:
for f in glob.glob('*_batch*.jpg') + glob.glob(results_file):
os.remove(f)
# Create model
model = Model(opt.cfg, nc=nc).to(device)
# Image sizes
gs = int(max(model.stride)) # grid size (max stride)
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# Optimizer
nbs = 64 # nominal batch size
# the default DDP implementation is slow for accumulation according to: https://pytorch.org/docs/stable/notes/ddp.html
# all-reduce operation is carried out during loss.backward().
# Thus, there would be redundant all-reduce communications in a accumulation procedure,
# which means, the result is still right but the training speed gets slower.
# TODO: If acceleration is needed, there is an implementation of allreduce_post_accumulation
# in https://github.com/NVIDIA/DeepLearningExamples/blob/master/PyTorch/LanguageModeling/BERT/run_pretraining.py
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_parameters():
if v.requires_grad:
if '.bias' in k:
pg2.append(v) # biases
elif '.weight' in k and '.bn' not in k:
pg1.append(v) # apply weight decay
else:
pg0.append(v) # all else
if hyp['optimizer'] == 'adam': # https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
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 conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Load Model
# Avoid multiple downloads.
with torch_distributed_zero_first(local_rank):
google_utils.attempt_download(weights)
start_epoch, best_fitness = 0, 0.0
if weights.endswith('.pt'): # pytorch format
ckpt = torch.load(weights, map_location=device) # load checkpoint
# load model
try:
ckpt['model'] = {
k: v
for k, v in ckpt['model'].float().state_dict().items()
if k in model.state_dict()
and model.state_dict()[k].shape == v.shape
}
model.load_state_dict(ckpt['model'], strict=False)
except KeyError as e:
s = "%s is not compatible with %s. This may be due to model differences or %s may be out of date. " \
"Please delete or update %s and try again, or use --weights '' to train from scratch." \
% (weights, opt.cfg, weights, weights)
raise KeyError(s) from e
# load optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# load results
if ckpt.get('training_results') is not None:
with open(results_file, 'w') as file:
file.write(ckpt['training_results']) # write results.txt
# epochs
start_epoch = ckpt['epoch'] + 1
if epochs < start_epoch:
print(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt
# Mixed precision training https://github.com/NVIDIA/apex
if mixed_precision:
model, optimizer = amp.initialize(model,
optimizer,
opt_level='O1',
verbosity=0)
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
lf = lambda x: ((
(1 + math.cos(x * math.pi / epochs)) / 2)**1.0) * 0.9 + 0.1 # cosine
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# https://discuss.pytorch.org/t/a-problem-occured-when-resuming-an-optimizer/28822
# plot_lr_scheduler(optimizer, scheduler, epochs)
# DP mode
if device.type != 'cpu' and local_rank == -1 and torch.cuda.device_count(
) > 1:
model = torch.nn.DataParallel(model)
# Exponential moving average
# From https://github.com/rwightman/pytorch-image-models/blob/master/train.py:
# "Important to create EMA model after cuda(), DP wrapper, and AMP but before SyncBN and DDP wrapper"
# chenyzsjtu: ema should be placed before after SyncBN. As SyncBN introduces new modules.
if opt.sync_bn and device.type != 'cpu' and local_rank != -1:
print("SyncBN activated!")
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
ema = torch_utils.ModelEMA(model) if local_rank in [-1, 0] else None
# DDP mode
if device.type != 'cpu' and local_rank != -1:
model = DDP(model, device_ids=[local_rank], output_device=local_rank)
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
local_rank=local_rank,
world_size=opt.world_size)
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Testloader
if local_rank in [-1, 0]:
# local_rank is set to -1. Because only the first process is expected to do evaluation.
testloader = create_dataloader(test_path,
imgsz_test,
total_batch_size,
gs,
opt,
hyp=hyp,
augment=False,
cache=opt.cache_images,
rect=True,
local_rank=-1,
world_size=opt.world_size)[0]
# Model parameters
hyp['cls'] *= nc / 80. # scale coco-tuned hyp['cls'] to current dataset
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.names = names
# Class frequency
# Only one check and log is needed.
if local_rank in [-1, 0]:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1.
# model._initialize_biases(cf.to(device))
plot_labels(labels, save_dir=log_dir)
if tb_writer:
# tb_writer.add_hparams(hyp, {}) # causes duplicate https://github.com/ultralytics/yolov5/pull/384
tb_writer.add_histogram('classes', c, 0)
# Check anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
# Start training
t0 = time.time()
nw = max(3 * nb,
1e3) # number of warmup iterations, max(3 epochs, 1k iterations)
maps = np.zeros(nc) # mAP per class
results = (
0, 0, 0, 0, 0, 0, 0
) # 'P', 'R', 'mAP', 'F1', 'val GIoU', 'val Objectness', 'val Classification'
scheduler.last_epoch = start_epoch - 1 # do not move
if local_rank in [0, -1]:
print('Image sizes %g train, %g test' % (imgsz, imgsz_test))
print('Using %g dataloader workers' % dataloader.num_workers)
print('Starting training for %g epochs...' % epochs)
# torch.autograd.set_detect_anomaly(True)
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
# When in DDP mode, the generated indices will be broadcasted to synchronize dataset.
if dataset.image_weights:
# Generate indices.
if local_rank in [-1, 0]:
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
# Broadcast.
if local_rank != -1:
indices = torch.zeros([dataset.n], dtype=torch.int)
if local_rank == 0:
indices[:] = torch.from_tensor(dataset.indices,
dtype=torch.int)
dist.broadcast(indices, 0)
if local_rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if local_rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
if local_rank in [-1, 0]:
print(
('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'GIoU', 'obj',
'cls', 'total', 'targets', 'img_size'))
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0 - 255 to 0.0 - 1.0
# Warmup
if ni <= nw:
xi = [0, nw] # 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, nbs / total_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)])
if 'momentum' in x:
x['momentum'] = np.interp(ni, xi,
[0.9, hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / 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 gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
pred = model(imgs)
# Loss
loss, loss_items = compute_loss(pred, targets.to(device), model)
# loss is scaled with batch size in func compute_loss. But in DDP mode, gradient is averaged between devices.
if local_rank != -1:
loss *= opt.world_size
if not torch.isfinite(loss):
print('WARNING: non-finite loss, ending training ', loss_items)
return results
# Backward
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
# Optimize
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
if ema is not None:
ema.update(model)
# Print
if local_rank in [-1, 0]:
mloss = (mloss * i + loss_items) / (i + 1
) # update mean losses
mem = '%.3gG' % (torch.cuda.memory_cached() / 1E9
if torch.cuda.is_available() else 0) # (GB)
s = ('%10s' * 2 +
'%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if ni < 3:
f = str(Path(log_dir) /
('train_batch%g.jpg' % ni)) # filename
result = plot_images(images=imgs,
targets=targets,
paths=paths,
fname=f)
if tb_writer and result is not None:
tb_writer.add_image(f,
result,
dataformats='HWC',
global_step=epoch)
# tb_writer.add_graph(model, imgs) # add model to tensorboard
# end batch ------------------------------------------------------------------------------------------------
# Scheduler
scheduler.step()
# Only the first process in DDP mode is allowed to log or save checkpoints.
if local_rank in [-1, 0]:
# mAP
if ema is not None:
ema.update_attr(
model,
include=['md', 'nc', 'hyp', 'gr', 'names', 'stride'])
final_epoch = epoch + 1 == epochs
if not opt.notest or final_epoch: # Calculate mAP
results, maps, times = test.test(
opt.data,
batch_size=total_batch_size,
imgsz=imgsz_test,
save_json=final_epoch
and opt.data.endswith(os.sep + 'coco.yaml'),
model=ema.ema.module
if hasattr(ema.ema, 'module') else ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=log_dir)
# Explicitly keep the shape.
# Write
with open(results_file, 'a') as f:
f.write(
s + '%10.4g' * 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)
# 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 save:
with open(results_file, 'r') as f: # create checkpoint
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
f.read(),
'model':
ema.ema.module if hasattr(ema, 'module') else ema.ema,
'optimizer':
None if final_epoch else optimizer.state_dict()
}
# Save last, best and delete
torch.save(ckpt, last)
if (best_fitness == fi) and not final_epoch:
torch.save(ckpt, best)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if local_rank in [-1, 0]:
# Strip optimizers
n = ('_'
if len(opt.name) and not opt.name.isnumeric() else '') + opt.name
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
# Finish
if not opt.evolve:
plot_results() # save as results.png
print('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
dist.destroy_process_group() if local_rank not in [-1, 0] else None
torch.cuda.empty_cache()
return results
def _test_zero_model_parallel(self, parameters_as_bucket_view: bool):
# Use two processes each with two GPUs
assert self.rank < 2
NUM_EPOCHS = 3
NUM_INPUTS = 5
LR = 0.01
torch.manual_seed(0)
torch.cuda.manual_seed(0)
class ModelParallelModel(torch.nn.Module):
def __init__(self, dev0, dev1):
super().__init__()
self.dev0 = dev0
self.dev1 = dev1
self.net0 = torch.nn.Linear(10, 10).to(dev0)
self.relu = torch.nn.ReLU()
self.net1 = torch.nn.Linear(10, 5).to(dev1)
def forward(self, x):
x = x.to(self.dev0)
x = self.relu(self.net0(x))
x = x.to(self.dev1)
return self.net1(x)
class LocalModel(torch.nn.Module):
def __init__(self):
super().__init__()
self.net0 = torch.nn.Linear(10, 10)
self.relu = torch.nn.ReLU()
self.net1 = torch.nn.Linear(10, 5)
def forward(self, x):
return self.net1(self.relu(self.net0(x)))
dev0 = 2 * self.rank
dev1 = 2 * self.rank + 1
mp_model = ModelParallelModel(dev0, dev1)
ddp_model = DDP(mp_model)
local_model = LocalModel()
cpu_device = torch.device("cpu")
# Ensure the parameters are the same across the two models
local_model.net0.weight = torch.nn.Parameter(mp_model.net0.weight.detach().clone().to(cpu_device))
local_model.net0.bias = torch.nn.Parameter(mp_model.net0.bias.detach().clone().to(cpu_device))
local_model.net1.weight = torch.nn.Parameter(mp_model.net1.weight.detach().clone().to(cpu_device))
local_model.net1.bias = torch.nn.Parameter(mp_model.net1.bias.detach().clone().to(cpu_device))
# Compare parity between DDP with model parallelism using ZeRO and
# a local model using a local optimizer
zero_optim = ZeroRedundancyOptimizer(
ddp_model.parameters(),
optimizer_class=torch.optim.Adam,
parameters_as_bucket_view=parameters_as_bucket_view,
lr=LR
)
local_optim = torch.optim.Adam(local_model.parameters(), lr=LR)
inputs = [torch.randn(20, 10) for _ in range(NUM_INPUTS)]
for _ in range(NUM_EPOCHS):
for input in inputs:
def closure_local():
local_optim.zero_grad()
local_loss = local_model(input).abs().sum()
local_loss.backward()
return local_loss
def closure_ddp():
zero_optim.zero_grad()
ddp_loss = ddp_model(input).abs().sum()
ddp_loss.backward()
return ddp_loss
local_loss = cast(torch.Tensor, local_optim.step(closure=closure_local))
ddp_loss = cast(torch.Tensor, zero_optim.step(closure=closure_ddp)).to(cpu_device)
assert torch.allclose(
local_loss, ddp_loss
), "Losses differ between local optim and ZeroRedundancyOptimizer"
for local_p, ddp_p in zip(local_model.parameters(), ddp_model.parameters()):
ddp_p = ddp_p.to(cpu_device)
assert torch.allclose(local_p, ddp_p), "Models differ after a step"
def train(hyp, opt, device, tb_writer=None):
logger.info(
colorstr('hyperparameters: ') + ', '.join(f'{k}={v}'
for k, v in hyp.items()))
save_dir, epochs, batch_size, total_batch_size, weights, rank = \
Path(opt.save_dir), opt.epochs, opt.batch_size, opt.total_batch_size, opt.weights, opt.global_rank
# Directories
wdir = save_dir / 'weights'
wdir.mkdir(parents=True, exist_ok=True) # make dir
last = wdir / 'last.pt'
best = wdir / 'best.pt'
results_file = save_dir / 'results.txt'
# Save run settings
with open(save_dir / 'hyp.yaml', 'w') as f:
yaml.dump(hyp, f, sort_keys=False)
with open(save_dir / 'opt.yaml', 'w') as f:
yaml.dump(vars(opt), f, sort_keys=False)
# Configure
plots = not opt.evolve # create plots
cuda = device.type != 'cpu'
init_seeds(2 + rank)
with open(opt.data) as f:
data_dict = yaml.load(f, Loader=yaml.SafeLoader) # data dict
is_coco = opt.data.endswith('coco.yaml')
# Logging- Doing this before checking the dataset. Might update data_dict
loggers = {'wandb': None} # loggers dict
if rank in [-1, 0]:
opt.hyp = hyp # add hyperparameters
run_id = torch.load(weights).get('wandb_id') if weights.endswith(
'.pt') and os.path.isfile(weights) else None
wandb_logger = WandbLogger(opt,
Path(opt.save_dir).stem, run_id, data_dict)
loggers['wandb'] = wandb_logger.wandb
data_dict = wandb_logger.data_dict
if wandb_logger.wandb:
weights, epochs, hyp = opt.weights, opt.epochs, opt.hyp # WandbLogger might update weights, epochs if resuming
nc = 1 if opt.single_cls else int(data_dict['nc']) # number of classes
names = ['item'] if opt.single_cls and len(
data_dict['names']) != 1 else data_dict['names'] # class names
assert len(names) == nc, '%g names found for nc=%g dataset in %s' % (
len(names), nc, opt.data) # check
# Model
pretrained = weights.endswith('.pt')
if pretrained:
with torch_distributed_zero_first(rank):
attempt_download(weights) # download if not found locally
ckpt = torch.load(weights, map_location=device) # load checkpoint
model = Model(opt.cfg or ckpt['model'].yaml,
ch=3,
nc=nc,
anchors=hyp.get('anchors')).to(device) # create
exclude = [
'anchor'
] if (opt.cfg or hyp.get('anchors')) and not opt.resume else [
] # exclude keys
state_dict = ckpt['model'].float().state_dict() # to FP32
state_dict = intersect_dicts(state_dict,
model.state_dict(),
exclude=exclude) # intersect
model.load_state_dict(state_dict, strict=False) # load
logger.info(
'Transferred %g/%g items from %s' %
(len(state_dict), len(model.state_dict()), weights)) # report
else:
model = Model(opt.cfg, ch=3, nc=nc,
anchors=hyp.get('anchors')).to(device) # create
with torch_distributed_zero_first(rank):
check_dataset(data_dict) # check
train_path = data_dict['train']
test_path = data_dict['val']
# Freeze
freeze = [] # parameter names to freeze (full or partial)
for k, v in model.named_parameters():
v.requires_grad = True # train all layers
if any(x in k for x in freeze):
print('freezing %s' % k)
v.requires_grad = False
# Optimizer
nbs = 64 # nominal batch size
accumulate = max(round(nbs / total_batch_size),
1) # accumulate loss before optimizing
hyp['weight_decay'] *= total_batch_size * accumulate / nbs # scale weight_decay
logger.info(f"Scaled weight_decay = {hyp['weight_decay']}")
pg0, pg1, pg2 = [], [], [] # optimizer parameter groups
for k, v in model.named_modules():
if hasattr(v, 'bias') and isinstance(v.bias, nn.Parameter):
pg2.append(v.bias) # biases
if isinstance(v, nn.BatchNorm2d):
pg0.append(v.weight) # no decay
elif hasattr(v, 'weight') and isinstance(v.weight, nn.Parameter):
pg1.append(v.weight) # apply decay
if opt.adam:
optimizer = optim.Adam(pg0,
lr=hyp['lr0'],
betas=(hyp['momentum'],
0.999)) # adjust beta1 to momentum
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)
logger.info('Optimizer groups: %g .bias, %g conv.weight, %g other' %
(len(pg2), len(pg1), len(pg0)))
del pg0, pg1, pg2
# Scheduler https://arxiv.org/pdf/1812.01187.pdf
# https://pytorch.org/docs/stable/_modules/torch/optim/lr_scheduler.html#OneCycleLR
if opt.linear_lr:
lf = lambda x: (1 - x / (epochs - 1)) * (1.0 - hyp['lrf']) + hyp[
'lrf'] # linear
else:
lf = one_cycle(1, hyp['lrf'], epochs) # cosine 1->hyp['lrf']
scheduler = lr_scheduler.LambdaLR(optimizer, lr_lambda=lf)
# plot_lr_scheduler(optimizer, scheduler, epochs)
# EMA
ema = ModelEMA(model) if rank in [-1, 0] else None
# Resume
start_epoch, best_fitness = 0, 0.0
if pretrained:
# Optimizer
if ckpt['optimizer'] is not None:
optimizer.load_state_dict(ckpt['optimizer'])
best_fitness = ckpt['best_fitness']
# EMA
if ema and ckpt.get('ema'):
ema.ema.load_state_dict(ckpt['ema'].float().state_dict())
ema.updates = ckpt['updates']
# Results
if ckpt.get('training_results') is not None:
results_file.write_text(
ckpt['training_results']) # write results.txt
# Epochs
start_epoch = ckpt['epoch'] + 1
if opt.resume:
assert start_epoch > 0, '%s training to %g epochs is finished, nothing to resume.' % (
weights, epochs)
if epochs < start_epoch:
logger.info(
'%s has been trained for %g epochs. Fine-tuning for %g additional epochs.'
% (weights, ckpt['epoch'], epochs))
epochs += ckpt['epoch'] # finetune additional epochs
del ckpt, state_dict
# Image sizes
gs = max(int(model.stride.max()), 32) # grid size (max stride)
nl = model.model[
-1].nl # number of detection layers (used for scaling hyp['obj'])
imgsz, imgsz_test = [check_img_size(x, gs) for x in opt.img_size
] # verify imgsz are gs-multiples
# DP mode
if cuda and rank == -1 and torch.cuda.device_count() > 1:
model = torch.nn.DataParallel(model)
# SyncBatchNorm
if opt.sync_bn and cuda and rank != -1:
model = torch.nn.SyncBatchNorm.convert_sync_batchnorm(model).to(device)
logger.info('Using SyncBatchNorm()')
# Trainloader
dataloader, dataset = create_dataloader(train_path,
imgsz,
batch_size,
gs,
opt,
hyp=hyp,
augment=True,
cache=opt.cache_images,
rect=opt.rect,
rank=rank,
world_size=opt.world_size,
workers=opt.workers,
image_weights=opt.image_weights,
quad=opt.quad,
prefix=colorstr('train: '))
mlc = np.concatenate(dataset.labels, 0)[:, 0].max() # max label class
nb = len(dataloader) # number of batches
assert mlc < nc, 'Label class %g exceeds nc=%g in %s. Possible class labels are 0-%g' % (
mlc, nc, opt.data, nc - 1)
# Process 0
if rank in [-1, 0]:
testloader = create_dataloader(
test_path,
imgsz_test,
batch_size * 2,
gs,
opt, # testloader
hyp=hyp,
cache=opt.cache_images and not opt.notest,
rect=True,
rank=-1,
world_size=opt.world_size,
workers=opt.workers,
pad=0.5,
prefix=colorstr('val: '))[0]
if not opt.resume:
labels = np.concatenate(dataset.labels, 0)
c = torch.tensor(labels[:, 0]) # classes
# cf = torch.bincount(c.long(), minlength=nc) + 1. # frequency
# model._initialize_biases(cf.to(device))
if plots:
plot_labels(labels, names, save_dir, loggers)
if tb_writer:
tb_writer.add_histogram('classes', c, 0)
# Anchors
if not opt.noautoanchor:
check_anchors(dataset,
model=model,
thr=hyp['anchor_t'],
imgsz=imgsz)
model.half().float() # pre-reduce anchor precision
# DDP mode
if cuda and rank != -1:
model = DDP(
model,
device_ids=[opt.local_rank],
output_device=opt.local_rank,
# nn.MultiheadAttention incompatibility with DDP https://github.com/pytorch/pytorch/issues/26698
find_unused_parameters=any(
isinstance(layer, nn.MultiheadAttention)
for layer in model.modules()))
# Model parameters
hyp['box'] *= 3. / nl # scale to layers
hyp['cls'] *= nc / 80. * 3. / nl # scale to classes and layers
hyp['obj'] *= (imgsz / 640)**2 * 3. / nl # scale to image size and layers
hyp['label_smoothing'] = opt.label_smoothing
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) * nc # attach class weights
model.names = names
# Start training
t0 = time.time()
nw = max(round(hyp['warmup_epochs'] * nb),
1000) # number of warmup iterations, max(3 epochs, 1k iterations)
# nw = min(nw, (epochs - start_epoch) / 2 * nb) # limit warmup to < 1/2 of training
maps = np.zeros(nc) # mAP per class
results = (0, 0, 0, 0, 0, 0, 0
) # P, R, [email protected], [email protected], val_loss(box, obj, cls)
scheduler.last_epoch = start_epoch - 1 # do not move
scaler = amp.GradScaler(enabled=cuda)
compute_loss = ComputeLoss(model) # init loss class
logger.info(f'Image sizes {imgsz} train, {imgsz_test} test\n'
f'Using {dataloader.num_workers} dataloader workers\n'
f'Logging results to {save_dir}\n'
f'Starting training for {epochs} epochs...')
for epoch in range(
start_epoch, epochs
): # epoch ------------------------------------------------------------------
model.train()
# Update image weights (optional)
if opt.image_weights:
# Generate indices
if rank in [-1, 0]:
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
if rank != -1:
indices = (torch.tensor(dataset.indices)
if rank == 0 else torch.zeros(dataset.n)).int()
dist.broadcast(indices, 0)
if rank != 0:
dataset.indices = indices.cpu().numpy()
# Update mosaic border
# b = int(random.uniform(0.25 * imgsz, 0.75 * imgsz + gs) // gs * gs)
# dataset.mosaic_border = [b - imgsz, -b] # height, width borders
mloss = torch.zeros(4, device=device) # mean losses
if rank != -1:
dataloader.sampler.set_epoch(epoch)
pbar = enumerate(dataloader)
logger.info(
('\n' + '%10s' * 8) % ('Epoch', 'gpu_mem', 'box', 'obj', 'cls',
'total', 'labels', 'img_size'))
if rank in [-1, 0]:
pbar = tqdm(pbar, total=nb) # progress bar
optimizer.zero_grad()
for i, (
imgs, targets, paths, _
) in pbar: # batch -------------------------------------------------------------
ni = i + nb * epoch # number integrated batches (since train start)
imgs = imgs.to(device, non_blocking=True).float(
) / 255.0 # uint8 to float32, 0-255 to 0.0-1.0
# Warmup
if ni <= nw:
xi = [0, nw] # x interp
# model.gr = np.interp(ni, xi, [0.0, 1.0]) # iou loss ratio (obj_loss = 1.0 or iou)
accumulate = max(
1,
np.interp(ni, xi, [1, nbs / total_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, [
hyp['warmup_bias_lr'] if j == 2 else 0.0,
x['initial_lr'] * lf(epoch)
])
if 'momentum' in x:
x['momentum'] = np.interp(
ni, xi, [hyp['warmup_momentum'], hyp['momentum']])
# Multi-scale
if opt.multi_scale:
sz = random.randrange(imgsz * 0.5,
imgsz * 1.5 + gs) // gs * gs # size
sf = sz / 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 gs-multiple)
imgs = F.interpolate(imgs,
size=ns,
mode='bilinear',
align_corners=False)
# Forward
with amp.autocast(enabled=cuda):
pred = model(imgs) # forward
loss, loss_items = compute_loss(
pred, targets.to(device)) # loss scaled by batch_size
if rank != -1:
loss *= opt.world_size # gradient averaged between devices in DDP mode
if opt.quad:
loss *= 4.
# Backward
scaler.scale(loss).backward()
# Optimize
if ni % accumulate == 0:
scaler.step(optimizer) # optimizer.step
scaler.update()
optimizer.zero_grad()
if ema:
ema.update(model)
# Print
if rank in [-1, 0]:
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 +
'%10.4g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem,
*mloss, targets.shape[0], imgs.shape[-1])
pbar.set_description(s)
# Plot
if plots and ni < 3:
f = save_dir / f'train_batch{ni}.jpg' # filename
Thread(target=plot_images,
args=(imgs, targets, paths, f),
daemon=True).start()
# if tb_writer:
# tb_writer.add_image(f, result, dataformats='HWC', global_step=epoch)
# tb_writer.add_graph(torch.jit.trace(model, imgs, strict=False), []) # add model graph
elif plots and ni == 10 and wandb_logger.wandb:
wandb_logger.log({
"Mosaics": [
wandb_logger.wandb.Image(str(x), caption=x.name)
for x in save_dir.glob('train*.jpg') if x.exists()
]
})
# end batch ------------------------------------------------------------------------------------------------
# end epoch ----------------------------------------------------------------------------------------------------
# Scheduler
lr = [x['lr'] for x in optimizer.param_groups] # for tensorboard
scheduler.step()
# DDP process 0 or single-GPU
if rank in [-1, 0]:
# mAP
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
wandb_logger.current_epoch = epoch + 1
results, maps, times = test.test(data_dict,
batch_size=batch_size * 2,
imgsz=imgsz_test,
model=ema.ema,
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
verbose=nc < 50
and final_epoch,
plots=plots and final_epoch,
wandb_logger=wandb_logger,
compute_loss=compute_loss,
is_coco=is_coco)
# Write
with open(results_file, 'a') as f:
f.write(s + '%10.4g' * 7 % results +
'\n') # append metrics, val_loss
if len(opt.name) and opt.bucket:
os.system('gsutil cp %s gs://%s/results/results%s.txt' %
(results_file, opt.bucket, opt.name))
# Log
tags = [
'train/box_loss',
'train/obj_loss',
'train/cls_loss', # train loss
'metrics/precision',
'metrics/recall',
'metrics/mAP_0.5',
'metrics/mAP_0.5:0.95',
'val/box_loss',
'val/obj_loss',
'val/cls_loss', # val loss
'x/lr0',
'x/lr1',
'x/lr2'
] # params
for x, tag in zip(list(mloss[:-1]) + list(results) + lr, tags):
if tb_writer:
tb_writer.add_scalar(tag, x, epoch) # tensorboard
if wandb_logger.wandb:
wandb_logger.log({tag: x}) # W&B
# Update best mAP
fi = fitness(np.array(results).reshape(
1, -1)) # weighted combination of [P, R, [email protected], [email protected]]
if fi > best_fitness:
best_fitness = fi
wandb_logger.end_epoch(best_result=best_fitness == fi)
# Save model
if (not opt.nosave) or (final_epoch and not opt.evolve): # if save
ckpt = {
'epoch':
epoch,
'best_fitness':
best_fitness,
'training_results':
results_file.read_text(),
'model':
deepcopy(
model.module if is_parallel(model) else model).half(),
'ema':
deepcopy(ema.ema).half(),
'updates':
ema.updates,
'optimizer':
optimizer.state_dict(),
'wandb_id':
wandb_logger.wandb_run.id if wandb_logger.wandb else None
}
# Save last, best and delete
torch.save(ckpt, last)
if best_fitness == fi:
torch.save(ckpt, best)
if wandb_logger.wandb:
if ((epoch + 1) % opt.save_period == 0
and not final_epoch) and opt.save_period != -1:
wandb_logger.log_model(last.parent,
opt,
epoch,
fi,
best_model=best_fitness == fi)
del ckpt
# end epoch ----------------------------------------------------------------------------------------------------
# end training
if rank in [-1, 0]:
# Plots
if plots:
plot_results(save_dir=save_dir) # save as results.png
if wandb_logger.wandb:
files = [
'results.png', 'confusion_matrix.png',
*[f'{x}_curve.png' for x in ('F1', 'PR', 'P', 'R')]
]
wandb_logger.log({
"Results": [
wandb_logger.wandb.Image(str(save_dir / f), caption=f)
for f in files if (save_dir / f).exists()
]
})
# Test best.pt
logger.info('%g epochs completed in %.3f hours.\n' %
(epoch - start_epoch + 1, (time.time() - t0) / 3600))
if opt.data.endswith('coco.yaml') and nc == 80: # if COCO
for m in (last,
best) if best.exists() else (last): # speed, mAP tests
results, _, _ = test.test(opt.data,
batch_size=batch_size * 2,
imgsz=imgsz_test,
conf_thres=0.001,
iou_thres=0.7,
model=attempt_load(m, device).half(),
single_cls=opt.single_cls,
dataloader=testloader,
save_dir=save_dir,
save_json=True,
plots=False,
is_coco=is_coco)
# Strip optimizers
final = best if best.exists() else last # final model
for f in last, best:
if f.exists():
strip_optimizer(f) # strip optimizers
if opt.bucket:
os.system(f'gsutil cp {final} gs://{opt.bucket}/weights') # upload
if wandb_logger.wandb and not opt.evolve: # Log the stripped model
wandb_logger.wandb.log_artifact(
str(final),
type='model',
name='run_' + wandb_logger.wandb_run.id + '_model',
aliases=['last', 'best', 'stripped'])
wandb_logger.finish_run()
else:
dist.destroy_process_group()
torch.cuda.empty_cache()
return results
def train_step(self, rank, start_time, return_dict, writer):
device = torch.device("cuda:" + str(rank))
print('Running on device: ', device)
torch.cuda.set_device(device)
torch.set_default_tensor_type(torch.FloatTensor)
self.setup(rank, self.args.num_processes)
if self.cfg.MC_DQL:
transition = namedtuple('Transition', ('episode'))
else:
transition = namedtuple(
'Transition',
('state', 'action', 'reward', 'next_state', 'done'))
memory = TransitionData_ts(capacity=self.args.t_max,
storage_object=transition)
env = SpGcnEnv(self.args,
device,
writer=writer,
writer_counter=self.global_writer_quality_count,
win_event_counter=self.global_win_event_count)
# Create shared network
# model = GcnEdgeAC_1(self.cfg, self.args.n_raw_channels, self.args.n_embedding_features, 1, device, writer=writer)
model = GcnEdgeAC_3(self.cfg,
self.args.n_raw_channels,
self.args.n_embedding_features,
1,
device,
writer=writer)
# model = GcnEdgeAC(self.cfg, self.args.n_raw_channels, self.args.n_embedding_features, 1, device, writer=writer)
model.cuda(device)
shared_model = DDP(model,
device_ids=[model.device],
find_unused_parameters=True)
# dloader = DataLoader(MultiDiscSpGraphDsetBalanced(no_suppix=False, create=False), batch_size=1, shuffle=True, pin_memory=True,
# num_workers=0)
dloader = DataLoader(SpgDset(),
batch_size=self.cfg.batch_size,
shuffle=True,
pin_memory=True,
num_workers=0)
# Create optimizer for shared network parameters with shared statistics
# optimizer = CstmAdam(shared_model.parameters(), lr=self.args.lr, betas=self.args.Adam_betas,
# weight_decay=self.args.Adam_weight_decay)
######################
self.action_range = 1
self.device = torch.device(device)
self.discount = 0.5
self.critic_tau = self.cfg.critic_tau
self.actor_update_frequency = self.cfg.actor_update_frequency
self.critic_target_update_frequency = self.cfg.critic_target_update_frequency
self.batch_size = self.cfg.batch_size
self.log_alpha = torch.tensor(np.log(self.cfg.init_temperature)).to(
self.device)
self.log_alpha.requires_grad = True
# set target entropy to -|A|
######################
# optimizers
OptimizerContainer = namedtuple('OptimizerContainer',
('actor', 'critic', 'temperature'))
actor_optimizer = torch.optim.Adam(
shared_model.module.actor.parameters(),
lr=self.cfg.actor_lr,
betas=self.cfg.actor_betas)
critic_optimizer = torch.optim.Adam(
shared_model.module.critic.parameters(),
lr=self.cfg.critic_lr,
betas=self.cfg.critic_betas)
temp_optimizer = torch.optim.Adam([self.log_alpha],
lr=self.cfg.alpha_lr,
betas=self.cfg.alpha_betas)
optimizers = OptimizerContainer(actor_optimizer, critic_optimizer,
temp_optimizer)
if self.args.fe_extr_warmup and rank == 0 and not self.args.test_score_only:
fe_extr = shared_model.module.fe_ext
fe_extr.cuda(device)
self.fe_extr_warm_start_1(fe_extr, writer=writer)
# self.fe_extr_warm_start(fe_extr, writer=writer)
if self.args.model_name == "" and not self.args.no_save:
torch.save(fe_extr.state_dict(),
os.path.join(self.save_dir, 'agent_model_fe_extr'))
elif not self.args.no_save:
torch.save(fe_extr.state_dict(),
os.path.join(self.save_dir, self.args.model_name))
dist.barrier()
for param in model.fe_ext.parameters():
param.requires_grad = False
if self.args.model_name != "":
shared_model.load_state_dict(
torch.load(os.path.join(self.save_dir, self.args.model_name)))
elif self.args.model_fe_name != "":
shared_model.module.fe_ext.load_state_dict(
torch.load(os.path.join(self.save_dir,
self.args.model_fe_name)))
elif self.args.fe_extr_warmup:
print('loaded fe extractor')
shared_model.module.fe_ext.load_state_dict(
torch.load(os.path.join(self.save_dir, 'agent_model_fe_extr')))
if not self.args.test_score_only:
quality = self.args.stop_qual_scaling + self.args.stop_qual_offset
best_quality = np.inf
last_quals = []
while self.global_count.value() <= self.args.T_max:
if self.global_count.value() == 78:
a = 1
self.update_env_data(env, dloader, device)
# waff_dis = torch.softmax(env.edge_features[:, 0].squeeze() + 1e-30, dim=0)
# waff_dis = torch.softmax(env.gt_edge_weights + 0.5, dim=0)
waff_dis = torch.softmax(torch.ones_like(
env.b_gt_edge_weights),
dim=0)
loss_weight = torch.softmax(env.b_gt_edge_weights + 1, dim=0)
env.reset()
# self.target_entropy = - float(env.gt_edge_weights.shape[0])
self.target_entropy = -8.0
env.stop_quality = self.stop_qual_rule.apply(
self.global_count.value(), quality)
if self.cfg.temperature_regulation == 'follow_quality':
self.alpha = self.eps_rule.apply(self.global_count.value(),
quality)
print(self.alpha.item())
with open(os.path.join(self.save_dir,
'runtime_cfg.yaml')) as info:
args_dict = yaml.full_load(info)
if args_dict is not None:
if 'safe_model' in args_dict:
self.args.safe_model = args_dict['safe_model']
args_dict['safe_model'] = False
if 'add_noise' in args_dict:
self.args.add_noise = args_dict['add_noise']
if 'critic_lr' in args_dict and args_dict[
'critic_lr'] != self.cfg.critic_lr:
self.cfg.critic_lr = args_dict['critic_lr']
adjust_learning_rate(critic_optimizer,
self.cfg.critic_lr)
if 'actor_lr' in args_dict and args_dict[
'actor_lr'] != self.cfg.actor_lr:
self.cfg.actor_lr = args_dict['actor_lr']
adjust_learning_rate(actor_optimizer,
self.cfg.actor_lr)
if 'alpha_lr' in args_dict and args_dict[
'alpha_lr'] != self.cfg.alpha_lr:
self.cfg.alpha_lr = args_dict['alpha_lr']
adjust_learning_rate(temp_optimizer,
self.cfg.alpha_lr)
with open(os.path.join(self.save_dir, 'runtime_cfg.yaml'),
"w") as info:
yaml.dump(args_dict, info)
if self.args.safe_model:
best_quality = quality
if rank == 0:
if self.args.model_name_dest != "":
torch.save(
shared_model.state_dict(),
os.path.join(self.save_dir,
self.args.model_name_dest))
else:
torch.save(
shared_model.state_dict(),
os.path.join(self.save_dir, 'agent_model'))
state = env.get_state()
while not env.done:
# Calculate policy and values
post_input = True if (
self.global_count.value() +
1) % 15 == 0 and env.counter == 0 else False
round_n = env.counter
# sample action for data collection
distr = None
if self.global_count.value() < self.cfg.num_seed_steps:
action = torch.rand_like(env.b_current_edge_weights)
else:
distr, _, _, action = self.agent_forward(
env,
shared_model,
state=state,
grad=False,
post_input=post_input)
logg_dict = {'temperature': self.alpha.item()}
if distr is not None:
logg_dict['mean_loc'] = distr.loc.mean().item()
logg_dict['mean_scale'] = distr.scale.mean().item()
if self.global_count.value(
) >= self.cfg.num_seed_steps and memory.is_full():
self._step(memory,
optimizers,
env,
shared_model,
self.global_count.value(),
writer=writer)
self.global_writer_loss_count.increment()
next_state, reward, quality = env.execute_action(
action, logg_dict)
last_quals.append(quality)
if len(last_quals) > 10:
last_quals.pop(0)
if self.args.add_noise:
noise = torch.randn_like(reward) * self.alpha.item()
reward = reward + noise
memory.push(self.state_to_cpu(state), action, reward,
self.state_to_cpu(next_state), env.done)
# Train the network
# self._step(memory, shared_model, env, optimizer, loss_weight, off_policy=True, writer=writer)
# reward = self.args.reward_clip and min(max(reward, -1), 1) or reward # Optionally clamp rewards
# done = done or episode_length >= self.args.max_episode_length # Stop episodes at a max length
state = next_state
self.global_count.increment()
dist.barrier()
if rank == 0:
if not self.args.cross_validate_hp and not self.args.test_score_only and not self.args.no_save:
# pass
if self.args.model_name_dest != "":
torch.save(
shared_model.state_dict(),
os.path.join(self.save_dir, self.args.model_name_dest))
print('saved')
else:
torch.save(shared_model.state_dict(),
os.path.join(self.save_dir, 'agent_model'))
self.cleanup()
return sum(last_quals) / 10
