def do_admmtrain(args,model,train_loader,test_loader,sparsity_type,prune_ratios,masks,base_model_path,admm_path):
"""====================="""
""" multi-rho admm train"""
"""====================="""
initial_rho = args.rho
current_rho = initial_rho
if args.admm:
for i in range(args.rho_num):
current_rho = initial_rho * 10 ** i
if i == 0:
print("Loading" + base_model_path)
model.load_state_dict(torch.load(base_model_path)) # admm train need basline model
model.cuda()
else:
print("Loading: "+admm_path+"/cifar_vgg{}_{}_{}_{}.pt".format(args.depth, current_rho / 10, args.config_file, args.optmzr))
model.load_state_dict(torch.load(admm_path+"/cifar_vgg{}_{}_{}_{}.pt".format(args.depth, current_rho / 10, args.config_file, args.optmzr)))
model.cuda()
ADMM = admm.ADMM(model, sparsity_type,prune_ratios, rho = current_rho)
admm.admm_initialization(args, ADMM=ADMM, model=model) # intialize Z variable
# admm train
best_prec1 = 0.
lr = args.lr / 10
if args.optmzr == "adam":
optimizer = optim.Adam(model.parameters(), lr=lr, weight_decay=args.weight_decay)
if args.optmzr == "sgd":
optimizer = optim.SGD(model.parameters(), lr=lr, momentum=args.momentum, weight_decay=args.weight_decay)
for epoch in range(1, args.epochs + 1):
print("current rho: {}".format(current_rho))
train(args, ADMM, model, device, train_loader, optimizer, epoch, writer,masks)
prec1 = test(args, model, device, test_loader)
best_prec1 = max(prec1, best_prec1)
print("Best Acc: {:.4f}".format(best_prec1))
print("Saving model: " + admm_path+"/cifar_vgg{}_{}_{}_{}.pt".format(args.depth, current_rho, args.config_file, args.optmzr))
torch.save(model.state_dict(), admm_path+"/cifar_vgg{}_{}_{}_{}.pt".format(args.depth, current_rho, args.config_file, args.optmzr))
return admm_path+"/cifar_vgg{}_{}_{}_{}.pt".format(args.depth, current_rho, args.config_file, args.optmzr)
def train(hyp):
# batch_time = AverageMeter()
# data_time = AverageMeter()
# losses = AverageMeter()
cfg = opt.cfg
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
imgsz_min, imgsz_max, imgsz_test = opt.img_size # img sizes (min, max, test)
# Image Sizes
gs = 32 # (pixels) grid size
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)
# Initialize model
model = Darknet(cfg).to(device)
# 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
start_epoch = 0
best_fitness = 0.0
# attempt_download(weights)
if opt.freeze_layers:
output_layer_indices = [idx - 1 for idx, module in enumerate(model.module_list) if isinstance(module, YOLOLayer)]
freeze_layer_indices = [x for x in range(len(model.module_list)) if
(x not in output_layer_indices) and
(x - 1 not in output_layer_indices)]
for idx in freeze_layer_indices:
for parameter in model.module_list[idx].parameters():
parameter.requires_grad_(False)
# 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) * 0.95 + 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)
# 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)
# 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=batch_size,
num_workers=nw,
shuffle=not opt.rect,
# Shuffle=True unless rectangular training is used
pin_memory=True,
collate_fn=dataset.collate_fn)
# 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)
initial_rho = opt.rho
t0 = time.time()
"""====================="""
""" multi-rho admm train"""
"""====================="""
if opt.admm:
opt.notest = True
# 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 Exception 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)
print(e)
raise KeyError(s) from e
del chkpt
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
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
model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# 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
ema = torch_utils.ModelEMA(model)
# Start training
nb = len(dataloader) # number of batches
n_burn = max(int(0.7 * nb), 500) # burn-in iterations, max(0.7 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'
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)
for i in range(opt.rho_num):
current_rho = initial_rho * 10 ** i
ADMM = admm.ADMM(model, file_name="./prune_config/" + opt.config_file + ".yaml", rho=current_rho)
admm.admm_initialization(opt, ADMM=ADMM, model=model) # intialize Z variable
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
print("current rho: {}".format(current_rho))
model.train()
masks = {}
if opt.masked_retrain and not opt.combine_progressive:
print("full acc re-train masking")
for name, W in (model.module.named_parameters() if type(
model) is torch.nn.parallel.DistributedDataParallel else model.named_parameters()):
if name not in ADMM.prune_ratios:
continue
above_threshold, W = admm.weight_pruning(opt, W, ADMM.prune_ratios[name])
W.data = W
masks[name] = above_threshold
elif opt.combine_progressive:
print("progressive admm-train/re-train masking")
for name, W in (model.module.named_parameters() if type(
model) is torch.nn.parallel.DistributedDataParallel else model.named_parameters()):
weight = W.cpu().detach().numpy()
non_zeros = weight != 0
non_zeros = non_zeros.astype(np.float32)
zero_mask = torch.from_numpy(non_zeros).cuda()
W = torch.from_numpy(weight).cuda()
W.data = W
masks[name] = zero_mask
# 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
targets = targets.to(device)
# 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
pred = 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
# Backward
loss *= batch_size / 64 # scale loss
admm.z_u_update(opt, ADMM, model, device, dataloader, optimizer, epoch, imgs, i,
tb_writer) # update Z and U variables
loss, admm_loss, mixed_loss = admm.append_admm_loss(opt, ADMM, model,
loss) # append admm losss
if mixed_precision:
with amp.scale_loss(mixed_loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
mixed_loss.backward()
if opt.combine_progressive:
with torch.no_grad():
for name, W in (model.module.named_parameters() if type(
model) is torch.nn.parallel.DistributedDataParallel else model.named_parameters()):
if name in masks:
W.grad *= masks[name]
# Optimize
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
ema.update(model)
# Print
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 + '%10.3g' * 6) % ('%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
pbar.set_description(s)
# Plot
# if ni < 1:
# f = 'train_batch%g.jpg' % i # 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
if opt.admm:
admm.admm_adjust_learning_rate(optimizer, epoch, opt)
else:
scheduler.step()
# Process epoch results
ema.update_attr(model)
final_epoch = epoch + 1 == epochs
if not opt.notest: # Calculate mAP #or final_epoch
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,
save_json=final_epoch and is_coco,
single_cls=opt.single_cls,
dataloader=testloader,
multi_label=ni > n_burn)
# 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)
# 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
# end epoch ----------------------------------------------------------------------------------------------------
# end training
# admm_adjust_learning_rate ----------------------------------------------------------------------------------------------------
admm.admm_adjust_learning_rate(optimizer, epoch, opt)
# end admm_adjust_learning_rate ----------------------------------------------------------------------------------------------------
print("Saving model.")
torch.save(
model.module.state_dict() if type(model) is nn.parallel.DistributedDataParallel else model.state_dict(),
"./model_pruned/yolov4_{}_{}_{}.pt".format(
current_rho, opt.config_file, opt.sparsity_type))
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
"""=============="""
"""masked retrain"""
"""=============="""
if opt.masked_retrain:
ADMM = admm.ADMM(model, file_name="./prune_config/" + opt.config_file + ".yaml", rho=initial_rho)
if not opt.resume:
# possible weights are '*.pt', 'yolov3-spp.pt', 'yolov3-tiny.pt' etc.
print("\n>_ Loading file: ./model_pruned/yolov4_{}_{}_{}.pt".format(initial_rho * 10 ** (opt.rho_num - 1), opt.config_file, opt.sparsity_type))
chkpt = torch.load("./model_pruned/yolov4_{}_{}_{}.pt".format(initial_rho * 10 ** (opt.rho_num - 1), opt.config_file, opt.sparsity_type), map_location=device)
# 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, strict=False) #['model']
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() from e
#----------------------------------------------hard prune------------------------------------------------
admm.hard_prune(opt, ADMM, model)
#----------------------------------------------hard prune------------------------------------------------
else:
try:
chkpt = torch.load(weights, map_location=device)
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() from e
# load optimizer
if chkpt['optimizer'] is not None:
optimizer.load_state_dict(chkpt['optimizer'])
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
start_epoch = chkpt['epoch'] + 1
del chkpt
# Initialize distributed training
if device.type != 'cpu' and torch.cuda.device_count() > 1 and torch.distributed.is_available():
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
model = torch.nn.parallel.DistributedDataParallel(model, find_unused_parameters=True)
model.yolo_layers = model.module.yolo_layers # move yolo layer indices to top level
# 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
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'
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)
for epoch in range(start_epoch, epochs): # epoch ------------------------------------------------------------------
model.train()
if opt.masked_retrain and not opt.combine_progressive:
print("full acc re-train masking")
masks = {}
for name, W in (model.module.named_parameters() if type(
model) is torch.nn.parallel.DistributedDataParallel else model.named_parameters()):
if name not in ADMM.prune_ratios:
continue
above_threshold, W = admm.weight_pruning(opt, W, ADMM.prune_ratios[name])
W.data = W
masks[name] = above_threshold
elif opt.combine_progressive:
print("progressive admm-train/re-train masking")
masks = {}
for name, W in (model.module.named_parameters() if type(
model) is torch.nn.parallel.DistributedDataParallel else model.named_parameters()):
weight = W.cpu().detach().numpy()
non_zeros = weight != 0
non_zeros = non_zeros.astype(np.float32)
zero_mask = torch.from_numpy(non_zeros).cuda()
W = torch.from_numpy(weight).cuda()
W.data = W
masks[name] = zero_mask
# 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
targets = targets.to(device)
# 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
pred = 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
# Backward
loss *= batch_size / 64 # scale loss
if mixed_precision:
with amp.scale_loss(loss, optimizer) as scaled_loss:
scaled_loss.backward()
else:
loss.backward()
if opt.combine_progressive:
with torch.no_grad():
for name, W in (model.module.named_parameters() if type(
model) is torch.nn.parallel.DistributedDataParallel else model.named_parameters()):
if name in masks:
W.grad *= masks[name]
if opt.masked_retrain:
with torch.no_grad():
for name, W in (model.module.named_parameters() if type(
model) is torch.nn.parallel.DistributedDataParallel else model.named_parameters()):
if name in masks:
W.grad *= masks[name]
# Optimize
if ni % accumulate == 0:
optimizer.step()
optimizer.zero_grad()
ema.update(model)
# Print
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 + '%10.3g' * 6) % (
'%g/%g' % (epoch, epochs - 1), mem, *mloss, len(targets), img_size)
pbar.set_description(s)
# Plot
if ni < 1:
f = 'train_batch%g.jpg' % i # 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
ema.update_attr(model)
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,
save_json=final_epoch and is_coco,
single_cls=opt.single_cls,
dataloader=testloader,
multi_label=ni > n_burn)
# 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)
# Update best mAP
fi = fitness(np.array(results).reshape(1, -1)) # fitness_i = weighted combination of [P, R, mAP, F1]
if fi > best_fitness: #results[2]
best_fitness = fi #results[2]
print("\n>_ Got better accuracy {:.3f}% now...\n".format(results[2]))
# torch.save(ema.ema.module.state_dict() if hasattr(model, 'module') else ema.ema.state_dict(),
# "./model_retrained/yolov4_retrained_acc_{:.3f}_{}rhos_{}_{}.pt".format(results[2], opt.rho_num, opt.config_file, opt.sparsity_type))
# Save model
save = (not opt.nosave) or (final_epoch and not opt.evolve)
if save:
with open(results_file, 'r') as f: # create checkpoint
chkpt = {'epoch': epoch,
'best_fitness': best_fitness,
'training_results': f.read(),
'model': ema.ema.module.state_dict() if hasattr(model,
'module') else ema.ema.state_dict(),
'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
test_sparsity(model)
print("Best Acc: {:.4f}".format(results[2]))
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 main():
global args, best_prec1
args = parser.parse_args()
args.save_name = 'loushu'
if args.logger:
logging.basicConfig(level=logging.INFO, format='%(message)s')
logger = logging.getLogger()
try:
os.makedirs("logger", exist_ok=True)
except TypeError:
raise Exception("Direction not create!")
logger.addHandler(
logging.FileHandler(
strftime('logger/GSC_%m-%d-%Y-%H:%M_id_') + str(uuid.uuid4()) +
'.log', 'a'))
global print
print = logger.info
print("The config arguments showed as below:")
print(args)
use_cuda = not args.no_cuda and torch.cuda.is_available()
device = torch.device("cuda" if use_cuda else "cpu")
# Check the save_dir exists or not
if not os.path.exists(args.save_dir):
os.makedirs(args.save_dir)
print("Current network is {}".format(args.arch))
# Start a new TensorFlow session.
sess = tf.InteractiveSession()
# Begin by making sure we have the training data we need. If you already have
# training data of your own, use `--data_url= ` on the command line to avoid
# downloading.
model_settings = models.prepare_model_settings(
len(input_data.prepare_words_list(args.wanted_words.split(','))),
args.sample_rate, args.clip_duration_ms, args.window_size_ms,
args.window_stride_ms, args.dct_coefficient_count)
print(model_settings)
audio_processor = input_data.AudioProcessor(args.data_url, args.data_dir,
args.silence_percentage,
args.unknown_percentage,
args.wanted_words.split(','),
args.validation_percentage,
args.testing_percentage,
model_settings)
# fingerprint_size = model_settings['fingerprint_size']
# label_count = model_settings['label_count']
# train_set_size = audio_processor.set_size('training')
# print('set_size=%d', train_set_size)
# valid_set_size = audio_processor.set_size('validation')
# print('set_size=%d', valid_set_size)
time_shift_samples = int((args.time_shift_ms * args.sample_rate) / 1000)
# train_loader = torch.utils.data.DataLoader(
# GSCDataset(args.data_url, args.data_dir, args.silence_percentage, args.unknown_percentage,
# args.wanted_words.split(','), args.validation_percentage, args.testing_percentage,
# model_settings, sess, args.arch, mode="training", background_frequency=args.background_frequency,
# background_volume_range=args.background_frequency, time_shift=time_shift_samples), shuffle=True,
# batch_size=args.batch_size, num_workers=args.workers)
# print("train set size: {}".format(len(train_loader.dataset)))
val_loader = torch.utils.data.DataLoader(GSCDataset(
args.data_url,
args.data_dir,
args.silence_percentage,
args.unknown_percentage,
args.wanted_words.split(','),
args.validation_percentage,
args.testing_percentage,
model_settings,
sess,
args.arch,
mode="validation"),
batch_size=args.batch_size,
num_workers=args.workers)
print("validation set size: {}".format(len(val_loader.dataset)))
test_loader = torch.utils.data.DataLoader(GSCDataset(
args.data_url,
args.data_dir,
args.silence_percentage,
args.unknown_percentage,
args.wanted_words.split(','),
args.validation_percentage,
args.testing_percentage,
model_settings,
sess,
args.arch,
mode="testing"),
batch_size=args.batch_size,
num_workers=args.workers)
print("test set size: {}".format(len(test_loader.dataset)))
#model = models.create_model(model_settings, args.arch, args.model_size_info)
model = models.create_model(model_settings, args.arch,
args.model_size_info, args.save_act_value,
args.save_act_dir)
model.cuda()
# optionally resume from a checkpoint
if args.resume:
if os.path.isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume, map_location='cuda:0')
try:
model.load_state_dict(checkpoint)
except:
print("Trying load with dict 'state_dict'")
try:
model.load_state_dict(checkpoint['state_dict'])
except:
print("Cann't load model")
return
else:
print("=> no checkpoint found at '{}'".format(args.resume))
return
cudnn.benchmark = True
# define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
if args.optimizer_type == "sgd":
optimizer = torch.optim.SGD(model.parameters(),
args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay,
nesterov=True)
elif args.optimizer_type == "adam":
optimizer = torch.optim.Adam(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
elif args.optimizer_type == "adamw":
optimizer = AdamW(model.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
elif args.optimizer_type == "rmsprop":
optimizer = torch.optim.RMSprop(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
elif args.optimizer_type == "adagrad":
optimizer = torch.optim.Adagrad(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
elif args.optimizer_type == "adadelta":
optimizer = torch.optim.Adadelta(model.parameters(),
args.lr,
weight_decay=args.weight_decay)
else:
raise ValueError("The optimizer type is not defined!")
if args.evaluate:
# validate(val_loader, model, criterion)
validate_by_step(args, audio_processor, model, criterion,
model_settings, sess)
#test(test_loader, model, criterion)
test_by_step(args, audio_processor, model, criterion, model_settings,
sess)
return
# scheduler = torch.optim.lr_scheduler.CosineAnnealingLR(optimizer, T_max=args.epochs * len(train_loader),
# eta_min=4e-08)
if args.admm_quant:
name_list = []
for name, w in model.named_parameters():
if "weight" or "bias" in name:
name_list.append(name)
print("Quantized Layer name list is :")
print(", ".join(name_list))
print("Before quantized:")
validate_by_step(args, audio_processor, model, criterion,
model_settings, sess)
admm.admm_initialization(args, model, device, name_list, print)
print("After quantized:")
validate_quant_by_step(args, audio_processor, model, criterion,
model_settings, sess, name_list, device)
for epoch in range(args.start_epoch, args.epochs):
if args.lr_scheduler == 'default':
adjust_learning_rate(optimizer, epoch)
elif args.lr_scheduler == 'cosine':
pass
admm_quant_train_by_step(args, audio_processor, model, criterion,
optimizer, epoch, model_settings,
time_shift_samples, sess, name_list,
device)
# evaluate on validation set
print("After Quantized:")
prec1, quantized_model = validate_quant_by_step(
args, audio_processor, model, criterion, model_settings, sess,
name_list, device)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
if is_best:
path_name = os.path.join(
args.save_dir,
'{arch}_{type}_{num_bits}bits_quantized_GSC_acc_{prec1:.3f}_{add}.pt'
.format(arch=args.arch,
type=args.quant_type,
num_bits=args.num_bits,
prec1=best_prec1,
add=args.save_name))
new_path_name = os.path.join(
args.save_dir,
'{arch}_{type}_{num_bits}bits_quantized_GSC_acc_{prec1:.3f}_{add}.pt'
.format(arch=args.arch,
type=args.quant_type,
num_bits=args.num_bits,
prec1=prec1,
add=args.save_name))
if os.path.isfile(path_name):
os.remove(path_name)
best_prec1 = prec1
save_checkpoint(quantized_model, new_path_name)
print("Admm training, best top 1 acc {best_prec1:.3f}".format(
best_prec1=best_prec1))
print("Best testing dataset:")
test_by_step(args, audio_processor, quantized_model, criterion,
model_settings, sess)
else:
print(
"Admm training, best top 1 acc {best_prec1:.3f}, current top 1 acc {prec1:.3f}"
.format(best_prec1=best_prec1, prec1=prec1))
else:
for epoch in range(args.start_epoch, args.epochs):
if args.lr_scheduler == 'default':
adjust_learning_rate(optimizer, epoch)
elif args.lr_scheduler == 'cosine':
pass
# scheduler.step()
# train for one epoch
train_by_step(args, audio_processor, model, criterion, optimizer,
epoch, model_settings, time_shift_samples, sess)
# evaluate on validation set
# prec1 = validate(val_loader, model, criterion)
prec1 = validate_by_step(args, audio_processor, model, criterion,
model_settings, sess)
# remember best prec@1 and save checkpoint
is_best = prec1 > best_prec1
if is_best:
path_name = os.path.join(
args.save_dir,
'{arch}_GSC_acc_{prec1:.3f}_{add}.pt'.format(
arch=args.arch, prec1=best_prec1, add=args.save_name))
new_path_name = os.path.join(
args.save_dir,
'{arch}_GSC_acc_{prec1:.3f}_{add}.pt'.format(
arch=args.arch, prec1=prec1, add=args.save_name))
if os.path.isfile(path_name):
os.remove(path_name)
best_prec1 = prec1
save_checkpoint(model, new_path_name)
print(
"Current best validation accuracy {best_prec1:.3f}".format(
best_prec1=best_prec1))
else:
print("Current validation accuracy {prec1:.3f}, "
"best validation accuracy {best_prec1:.3f}".format(
prec1=prec1, best_prec1=best_prec1))
# test(test_loader, model, criterion)
test_by_step(args, audio_processor, model, criterion, model_settings,
sess)
criterion = nn.CrossEntropyLoss()
ADMM = None
config = None
if args.admm or args.masked_retrain:
config = admm.Config(args, model)
print(config.prune_ratios)
for name,_ in model.named_parameters():
if name in config.prune_ratios:
print('{} will be pruned'.format(name))
else:
print('{} willnot be pruned'.format(name))
if args.admm:
ADMM = admm.ADMM(model, config)
admm.admm_initialization(args, ADMM, model) # intialize Z, U variable
###############################################################################
# Training code
###############################################################################
def repackage_hidden(h):
"""Wraps hidden states in new Tensors, to detach them from their history."""
if isinstance(h, torch.Tensor):
return h.detach()
else:
return tuple(repackage_hidden(v) for v in h)
# get_batch subdivides the source data into chunks of length args.bptt.
# If source is equal to the example output of the batchify function, with
def admm_prune(args, pre_mask, task, train_loader):
"""
bag of tricks set-ups
"""
initial_rho = args.rho
criterion = CrossEntropyLossMaybeSmooth(smooth_eps=args.smooth_eps).cuda()
args.smooth = args.smooth_eps > 0.0
args.mixup = args.alpha > 0.0
optimizer_init_lr = args.warmup_lr if args.warmup else args.lr
optimizer = None
if args.optmzr == 'sgd':
optimizer = torch.optim.SGD(model.parameters(),
optimizer_init_lr,
momentum=0.9,
weight_decay=1e-4)
elif args.optmzr == 'adam':
optimizer = torch.optim.Adam(model.parameters(), optimizer_init_lr)
'''
Set learning rate
'''
scheduler = None
if args.lr_scheduler == 'cosine':
scheduler = optim.lr_scheduler.CosineAnnealingLR(
optimizer,
T_max=args.epochs_prune * len(train_loader),
eta_min=4e-08)
elif args.lr_scheduler == 'default':
# my learning rate scheduler for cifar, following https://github.com/kuangliu/pytorch-cifar
epoch_milestones = [65, 100, 130, 190, 220, 250, 280]
"""
Set the learning rate of each parameter task to the initial lr decayed
by gamma once the number of epoch reaches one of the milestones
"""
scheduler = optim.lr_scheduler.MultiStepLR(
optimizer,
milestones=[i * len(train_loader) for i in epoch_milestones],
gamma=0.5)
else:
raise Exception("unknown lr scheduler")
if args.warmup:
scheduler = GradualWarmupScheduler(optimizer,
multiplier=args.lr / args.warmup_lr,
total_iter=args.warmup_epochs *
len(train_loader),
after_scheduler=scheduler)
# backup model weights
if args.heritage_weight or args.adaptive_mask:
model_backup = copy.deepcopy(model.state_dict())
# get mask for training & set pre-trained (for previous tasks) weights to be zero
if pre_mask:
pre_mask = mask_reverse(args, pre_mask)
set_model_mask(model, pre_mask)
'''
if heritage or adaptive, copy weights back to model
not for first task
'''
if args.heritage_weight or args.adaptive_mask:
if args.mask:
with torch.no_grad():
for name, W in (model.named_parameters()):
if name in args.pruned_layer:
W.data += model_backup[name].data * args.mask[
name].cuda()
'''
Start Pruning...
'''
for i in range(args.rho_num):
current_rho = initial_rho * 10**i
if args.config_file:
config = "./profile/" + args.config_file + ".yaml"
elif args.config_setting:
config = args.prune_ratios
else:
raise Exception("must provide a config setting.")
ADMM = admm.ADMM(args, model, config=config, rho=current_rho)
admm.admm_initialization(args, ADMM=ADMM,
model=model) # intialize Z variable
# admm train
best_prec1 = 0.
for epoch in range(1, args.epochs_prune + 1):
print("current rho: {}".format(current_rho))
prune_train(args, pre_mask, ADMM, train_loader, criterion,
optimizer, scheduler, epoch)
prec1 = pipeline.test_model(args, model)
best_prec1 = max(prec1, best_prec1)
print("Best Acc: {:.4f}%".format(best_prec1))
save_path = os.path.join(args.save_path_exp, 'task' + str(task))
torch.save(
model.state_dict(),
save_path + "/prunned_{}{}_{}_{}_{}_{}.pt".format(
args.arch, args.depth, current_rho, args.config_file,
args.optmzr, args.sparsity_type))