def create_model_and_criterion(args):
'''
Creating a model of predetermined architecture.
:return: model and its criterion
'''
# create model
model = models.__dict__[args.model]
model_config = {'dataset': args.dataset}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
model = model(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# load checkpoint
# model.load_state_dict(checkpoint['state_dict'])
# logging.info("loaded checkpoint '%s' (epoch %s)",
# args.eval_path, checkpoint['epoch'])
# if args.absorb_bn:
# search_absorb_bn(model, remove_bn=not args.calibrate_bn, verbose=True)
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', nn.CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
return model, criterion
def main():
global args, best_prec1, dtype
best_prec1 = 0
args = parser.parse_args()
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = os.path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_init,
world_size=args.world_size,
rank=args.local_rank)
args.local_rank = dist.get_rank()
args.world_size = dist.get_world_size()
if args.dist_backend == 'mpi':
# If using MPI, select all visible devices
args.device_ids = list(range(torch.cuda.device_count()))
else:
args.device_ids = [args.local_rank]
if not os.path.exists(save_path) and not (args.distributed
and args.local_rank > 0):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = os.path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
model = models.__dict__[args.model]
model_config = {'dataset': args.dataset}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
model = model(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# optionally resume from a checkpoint
if args.evaluate:
if not os.path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate)
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s' (epoch %s)", args.evaluate,
checkpoint['epoch'])
elif args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
results.load(os.path.join(checkpoint_file, 'results.csv'))
checkpoint_file = os.path.join(checkpoint_file,
'model_best.pth.tar')
if os.path.isfile(checkpoint_file):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(checkpoint_file)
if args.start_epoch < 0: # not explicitly set
args.start_epoch = checkpoint['epoch'] - 1
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s' (epoch %s)", checkpoint_file,
checkpoint['epoch'])
else:
logging.error("no checkpoint found at '%s'", args.resume)
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
# optimizer configuration
optim_regime = getattr(model, 'regime', [{
'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay
}])
optimizer = optim_regime if isinstance(optim_regime, OptimRegime) \
else OptimRegime(model, optim_regime, use_float_copy='half' in args.dtype)
trainer = Trainer(model,
criterion,
optimizer,
device_ids=args.device_ids,
device=args.device,
dtype=dtype,
distributed=args.distributed,
local_rank=args.local_rank,
grad_clip=args.grad_clip,
print_freq=args.print_freq,
adapt_grad_norm=args.adapt_grad_norm)
# Evaluation Data loading code
args.eval_batch_size = args.eval_batch_size if args.eval_batch_size > 0 else args.batch_size
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'val',
'augment': False,
'input_size': args.input_size,
'batch_size': args.eval_batch_size,
'shuffle': False,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
if args.evaluate:
results = trainer.validate(val_data.get_loader())
logging.info(results)
return
# Training Data loading code
train_data = DataRegime(getattr(model, 'data_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'train',
'augment': True,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': True,
'distributed': args.distributed,
'duplicates': args.duplicates,
'cutout': {
'holes': 1,
'length': 16
} if args.cutout else None
})
logging.info('optimization regime: %s', optim_regime)
args.start_epoch = max(args.start_epoch, 0)
trainer.training_steps = args.start_epoch * len(train_data)
for epoch in range(args.start_epoch, args.epochs):
trainer.epoch = epoch
train_data.set_epoch(epoch)
val_data.set_epoch(epoch)
logging.info('\nStarting Epoch: {0}\n'.format(epoch + 1))
# train for one epoch
train_results = trainer.train(train_data.get_loader(),
duplicates=train_data.get('duplicates'),
chunk_batch=args.chunk_batch)
# evaluate on validation set
val_results = trainer.validate(val_data.get_loader())
if args.distributed and args.local_rank > 0:
continue
# remember best prec@1 and save checkpoint
is_best = val_results['prec1'] > best_prec1
best_prec1 = max(val_results['prec1'], best_prec1)
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'best_prec1': best_prec1
},
is_best,
path=save_path)
logging.info('\nResults - Epoch: {0}\n'
'Training Loss {train[loss]:.4f} \t'
'Training Prec@1 {train[prec1]:.3f} \t'
'Training Prec@5 {train[prec5]:.3f} \t'
'Validation Loss {val[loss]:.4f} \t'
'Validation Prec@1 {val[prec1]:.3f} \t'
'Validation Prec@5 {val[prec5]:.3f} \t\n'.format(
epoch + 1, train=train_results, val=val_results))
values = dict(epoch=epoch + 1, steps=trainer.training_steps)
values.update({'training ' + k: v for k, v in train_results.items()})
values.update({'validation ' + k: v for k, v in val_results.items()})
results.add(**values)
results.plot(x='epoch',
y=['training loss', 'validation loss'],
legend=['training', 'validation'],
title='Loss',
ylabel='loss')
results.plot(x='epoch',
y=['training error1', 'validation error1'],
legend=['training', 'validation'],
title='Error@1',
ylabel='error %')
results.plot(x='epoch',
y=['training error5', 'validation error5'],
legend=['training', 'validation'],
title='Error@5',
ylabel='error %')
if 'grad' in train_results.keys():
results.plot(x='epoch',
y=['training grad'],
legend=['gradient L2 norm'],
title='Gradient Norm',
ylabel='value')
results.save()
def main_worker(args):
global best_prec1, dtype
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = os.path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if not os.path.exists(save_path) and not (args.distributed
and args.local_rank > 0):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = os.path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
if not os.path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate, map_location="cpu")
# Overrride configuration with checkpoint info
args.model = checkpoint.get('model', args.model)
args.model_config = checkpoint.get('config', args.model_config)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
# create model
model = models.__dict__[args.model]
model_config = {'dataset': args.dataset}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
model = model(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# load checkpoint
################
if args.pretrained:
state_dict = load_state_dict_from_url(model_urls[args.model],
progress=progress)
model.load_state_dict(state_dict)
# model = imagenet_extra_models.__dict__[arch](pretrained=pretrained) // from distiller
else:
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s' (epoch %s)", args.evaluate,
checkpoint['epoch'])
###########
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s' (epoch %s)", args.evaluate,
checkpoint['epoch'])
if args.absorb_bn:
search_absorb_bn(model, remove_bn=not args.calibrate_bn, verbose=True)
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', nn.CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
trainer = Trainer(model,
criterion,
device_ids=args.device_ids,
device=args.device,
dtype=dtype,
mixup=args.mixup,
print_freq=args.print_freq)
# Evaluation Data loading code
val_data = DataRegime(None,
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'val',
'augment': args.augment,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': False,
'duplicates': args.duplicates,
'autoaugment': args.autoaugment,
'cutout': {
'holes': 1,
'length': 16
} if args.cutout else None,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
if args.calibrate_bn:
train_data = DataRegime(None,
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'train',
'augment': True,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
trainer.calibrate_bn(train_data.get_loader(), num_steps=200)
results = trainer.validate(val_data.get_loader(),
average_output=args.avg_out)
logging.info(results)
print(results)
return results
def main_worker(args):
global best_prec1, dtype
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_init,
world_size=args.world_size,
rank=args.local_rank)
args.local_rank = dist.get_rank()
args.world_size = dist.get_world_size()
if args.dist_backend == 'mpi':
# If using MPI, select all visible devices
args.device_ids = list(range(torch.cuda.device_count()))
else:
args.device_ids = [args.local_rank]
# if not (args.distributed and args.local_rank > 0):
if not path.exists(save_path):
makedirs(save_path)
dump_args(args, path.join(save_path, 'args.txt'))
setup_logging(path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=False)
results_path = path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# All parameters to the model should be passed via this dict.
model_config = {
'dataset': args.dataset,
'dp_type': args.dropout_type,
'dp_percentage': args.dropout_perc,
'dropout': args.drop_rate,
'device': args.device
}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
# create Resnet model
model = resnet(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# # optionally resume from a checkpoint
# if args.evaluate:
# if not path.isfile(args.evaluate):
# parser.error('invalid checkpoint: {}'.format(args.evaluate))
# checkpoint = torch.load(args.evaluate, map_location="cpu")
# # Overrride configuration with checkpoint info
# args.model = checkpoint.get('model', args.model)
# args.model_config = checkpoint.get('config', args.model_config)
# # load checkpoint
# model.load_state_dict(checkpoint['state_dict'])
# logging.info("loaded checkpoint '%s' (epoch %s)",
# args.evaluate, checkpoint['epoch'])
#
# if args.resume:
# checkpoint_file = args.resume
# if path.isdir(checkpoint_file):
# results.load(path.join(checkpoint_file, 'results.csv'))
# checkpoint_file = path.join(
# checkpoint_file, 'model_best.pth.tar')
# if path.isfile(checkpoint_file):
# logging.info("loading checkpoint '%s'", args.resume)
# checkpoint = torch.load(checkpoint_file, map_location="cpu")
# if args.start_epoch < 0: # not explicitly set
# args.start_epoch = checkpoint['epoch']
# best_prec1 = checkpoint['best_prec1']
# model.load_state_dict(checkpoint['state_dict'])
# optim_state_dict = checkpoint.get('optim_state_dict', None)
# logging.info("loaded checkpoint '%s' (epoch %s)",
# checkpoint_file, checkpoint['epoch'])
# else:
# logging.error("no checkpoint found at '%s'", args.resume)
# else:
# optim_state_dict = None
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
# optimizer configuration
optim_regime = getattr(model, 'regime', [{
'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay
}])
optimizer = optim_regime if isinstance(optim_regime, OptimRegime) \
else OptimRegime(model, optim_regime, use_float_copy='half' in args.dtype)
# if optim_state_dict is not None:
# optimizer.load_state_dict(optim_state_dict)
trainer = Trainer(model,
criterion,
optimizer,
device_ids=args.device_ids,
device=args.device,
dtype=dtype,
distributed=args.distributed,
local_rank=args.local_rank,
mixup=args.mixup,
loss_scale=args.loss_scale,
grad_clip=args.grad_clip,
print_freq=args.print_freq,
adapt_grad_norm=args.adapt_grad_norm)
if args.tensorwatch:
trainer.set_watcher(filename=path.abspath(
path.join(save_path, 'tensorwatch.log')),
port=args.tensorwatch_port)
# Evaluation Data loading code
args.eval_batch_size = args.eval_batch_size if args.eval_batch_size > 0 else args.batch_size
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'val',
'augment': False,
'input_size': args.input_size,
'batch_size': args.eval_batch_size,
'shuffle': False,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
if args.evaluate:
results = trainer.validate(val_data.get_loader())
logging.info(results)
return
# Training Data loading code
train_data_defaults = {
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'train',
'augment': True,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': True,
'distributed': args.distributed,
'duplicates': args.duplicates,
'autoaugment': args.autoaugment,
'cutout': {
'holes': 1,
'length': 16
} if args.cutout else None
}
if hasattr(model, 'sampled_data_regime'):
sampled_data_regime = model.sampled_data_regime
probs, regime_configs = zip(*sampled_data_regime)
regimes = []
for config in regime_configs:
defaults = {**train_data_defaults}
defaults.update(config)
regimes.append(DataRegime(None, defaults=defaults))
train_data = SampledDataRegime(regimes, probs)
else:
train_data = DataRegime(getattr(model, 'data_regime', None),
defaults=train_data_defaults)
logging.info('optimization regime: %s', optim_regime)
args.start_epoch = max(args.start_epoch, 0)
trainer.training_steps = args.start_epoch * len(train_data)
for epoch in range(args.start_epoch, args.epochs):
trainer.epoch = epoch
train_data.set_epoch(epoch)
val_data.set_epoch(epoch)
logging.info('\nStarting Epoch: {0}\n'.format(epoch + 1))
# train for one epoch
train_results = trainer.train(train_data.get_loader(),
chunk_batch=args.chunk_batch)
# evaluate on validation set
val_results = trainer.validate(val_data.get_loader())
# # save weights heatmap
# w = model._modules['layer3']._modules['5']._modules['conv2']._parameters['weight'].view(64, -1).cpu().detach().numpy()
# heat_maps_dir = 'C:\\Users\\Pavel\\Desktop\\targeted_dropout_pytorch\\pics\\experiment_0'
# plot = sns.heatmap(w, center=0)
# name = str(datetime.now()).replace(':', '_').replace('-', '_').replace('.', '_').replace(' ', '_') + '.png'
# plot.get_figure().savefig(path.join(heat_maps_dir, name))
# plt.clf()
if args.distributed and args.local_rank > 0:
continue
# remember best prec@1 and save checkpoint
is_best = val_results['prec1'] > best_prec1
best_prec1 = max(val_results['prec1'], best_prec1)
if args.drop_optim_state:
optim_state_dict = None
else:
optim_state_dict = optimizer.state_dict()
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'optim_state_dict': optim_state_dict,
'best_prec1': best_prec1
},
is_best,
path=save_path,
save_all=False)
logging.info('\nResults - Epoch: {0}\n'
'Training Loss {train[loss]:.4f} \t'
'Training Prec@1 {train[prec1]:.3f} \t'
'Training Prec@5 {train[prec5]:.3f} \t'
'Validation Loss {val[loss]:.4f} \t'
'Validation Prec@1 {val[prec1]:.3f} \t'
'Validation Prec@5 {val[prec5]:.3f} \t\n'.format(
epoch + 1, train=train_results, val=val_results))
values = dict(epoch=epoch + 1, steps=trainer.training_steps)
values.update({'training ' + k: v for k, v in train_results.items()})
values.update({'validation ' + k: v for k, v in val_results.items()})
results.add(**values)
results.plot(x='epoch',
y=['training loss', 'validation loss'],
legend=['training', 'validation'],
title='Loss',
ylabel='loss')
results.plot(x='epoch',
y=['training error1', 'validation error1'],
legend=['training', 'validation'],
title='Error@1',
ylabel='error %')
results.plot(x='epoch',
y=['training error5', 'validation error5'],
legend=['training', 'validation'],
title='Error@5',
ylabel='error %')
if 'grad' in train_results.keys():
results.plot(x='epoch',
y=['training grad'],
legend=['gradient L2 norm'],
title='Gradient Norm',
ylabel='value')
results.save()
def main_worker(args, ml_logger):
global best_prec1, dtype
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_init,
world_size=args.world_size,
rank=args.local_rank)
args.local_rank = dist.get_rank()
args.world_size = dist.get_world_size()
if args.dist_backend == 'mpi':
# If using MPI, select all visible devices
args.device_ids = list(range(torch.cuda.device_count()))
else:
args.device_ids = [args.local_rank]
if not (args.distributed and args.local_rank > 0):
if not path.exists(args.save_path):
makedirs(args.save_path)
export_args_namespace(args, path.join(args.save_path, 'config.json'))
setup_logging(path.join(args.save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = path.join(args.save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
logging.info("saving to %s", args.save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
model = models.__dict__[args.model]
model_config = {'dataset': args.dataset}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
model = model(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
if args.resume:
checkpoint_file = args.resume
if path.isdir(checkpoint_file):
results.load(path.join(checkpoint_file, 'results.csv'))
checkpoint_file = path.join(checkpoint_file, 'model_best.pth.tar')
if path.isfile(checkpoint_file):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(checkpoint_file, map_location="cpu")
if args.start_epoch < 0: # not explicitly set
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optim_state_dict = checkpoint.get('optim_state_dict', None)
logging.info("loaded checkpoint '%s' (epoch %s)", checkpoint_file,
checkpoint['epoch'])
else:
logging.error("no checkpoint found at '%s'", args.resume)
else:
optim_state_dict = None
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
# optimizer configuration
optim_regime = getattr(model, 'regime', [{
'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay
}])
optimizer = optim_regime if isinstance(optim_regime, OptimRegime) \
else OptimRegime(model, optim_regime, use_float_copy='half' in args.dtype)
if optim_state_dict is not None:
optimizer.load_state_dict(optim_state_dict)
trainer = Trainer(
model,
criterion,
optimizer,
device_ids=args.device_ids,
device=args.device,
dtype=dtype,
print_freq=args.print_freq,
distributed=args.distributed,
local_rank=args.local_rank,
mixup=args.mixup,
cutmix=args.cutmix,
loss_scale=args.loss_scale,
grad_clip=args.grad_clip,
adapt_grad_norm=args.adapt_grad_norm,
)
# Evaluation Data loading code
args.eval_batch_size = args.eval_batch_size if args.eval_batch_size > 0 else args.batch_size
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'val',
'augment': False,
'input_size': args.input_size,
'batch_size': args.eval_batch_size,
'shuffle': False,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
# Training Data loading code
train_data_defaults = {
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'train',
'augment': True,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': True,
'distributed': args.distributed,
'duplicates': args.duplicates,
'autoaugment': args.autoaugment,
'cutout': {
'holes': 1,
'length': 16
} if args.cutout else None
}
if hasattr(model, 'sampled_data_regime'):
sampled_data_regime = model.sampled_data_regime
probs, regime_configs = zip(*sampled_data_regime)
regimes = []
for config in regime_configs:
defaults = {**train_data_defaults}
defaults.update(config)
regimes.append(DataRegime(None, defaults=defaults))
train_data = SampledDataRegime(regimes, probs)
else:
train_data = DataRegime(getattr(model, 'data_regime', None),
defaults=train_data_defaults)
logging.info('optimization regime: %s', optim_regime)
logging.info('data regime: %s', train_data)
args.start_epoch = max(args.start_epoch, 0)
trainer.training_steps = args.start_epoch * len(train_data)
if 'zeroBN' in model_config: #hot start
num_steps = int(len(train_data.get_loader()) * 0.5)
trainer.train(train_data.get_loader(),
chunk_batch=args.chunk_batch,
num_steps=num_steps)
for m in model.modules():
if isinstance(m, ZeroBN):
m.max_sparsity = args.max_sparsity
m.max_cos_sim = args.max_cos_sim
if args.preserve_cosine:
if args.layers_cos_sim1 in m.fullName:
m.preserve_cosine = args.preserve_cosine
m.cos_sim = args.cos_sim1
if args.layers_cos_sim2 in m.fullName:
m.preserve_cosine = args.preserve_cosine
m.cos_sim = args.cos_sim2
if args.layers_cos_sim3 in m.fullName:
m.preserve_cosine = args.preserve_cosine
m.cos_sim = args.cos_sim3
if args.min_cos_sim:
if args.layers_min_cos_sim1 in m.fullName:
m.min_cos_sim = args.min_cos_sim
m.cos_sim_min = args.cos_sim_min1
if args.layers_min_cos_sim2 in m.fullName:
m.min_cos_sim = args.min_cos_sim
m.cos_sim_min = args.cos_sim_min2
for epoch in range(args.start_epoch, args.epochs):
trainer.epoch = epoch
train_data.set_epoch(epoch)
val_data.set_epoch(epoch)
logging.info('\nStarting Epoch: {0}\n'.format(epoch + 1))
if 'zeroBN' in model_config:
trainer.collectStat(train_data.get_loader(),
num_steps=1,
prunRatio=args.stochasticPrunning,
cos_sim=args.cos_sim,
cos_sim_max=args.cos_sim_max)
trainer.collectStat(train_data.get_loader(),
num_steps=1,
prunRatio=args.stochasticPrunning,
cos_sim=args.cos_sim,
cos_sim_max=args.cos_sim_max)
# torch.cuda.empty_cache()
train_results = trainer.train(train_data.get_loader(),
ml_logger,
chunk_batch=args.chunk_batch)
# evaluate on validation set
val_results = trainer.validate(val_data.get_loader())
ml_logger.log_metric('Val Acc1', val_results['prec1'], step='auto')
# torch.cuda.empty_cache()
if args.distributed and args.local_rank > 0:
continue
# remember best prec@1 and save checkpoint
is_best = val_results['prec1'] > best_prec1
best_prec1 = max(val_results['prec1'], best_prec1)
if args.drop_optim_state:
optim_state_dict = None
else:
optim_state_dict = optimizer.state_dict()
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'optim_state_dict': optim_state_dict,
'best_prec1': best_prec1
},
is_best,
path=args.save_path,
save_all=args.save_all)
logging.info('\nResults - Epoch: {0}\n'
'Training Loss {train[loss]:.4f} \t'
'Training Prec@1 {train[prec1]:.3f} \t'
'Training Prec@5 {train[prec5]:.3f} \t'
'Validation Loss {val[loss]:.4f} \t'
'Validation Prec@1 {val[prec1]:.3f} \t'
'Validation Prec@5 {val[prec5]:.3f} \t\n'.format(
epoch + 1, train=train_results, val=val_results))
values = dict(epoch=epoch + 1, steps=trainer.training_steps)
values.update({'training ' + k: v for k, v in train_results.items()})
values.update({'validation ' + k: v for k, v in val_results.items()})
results.add(**values)
results.plot(x='epoch',
y=['training loss', 'validation loss'],
legend=['training', 'validation'],
title='Loss',
ylabel='loss')
results.plot(x='epoch',
y=['training error1', 'validation error1'],
legend=['training', 'validation'],
title='Error@1',
ylabel='error %')
results.plot(x='epoch',
y=['training error5', 'validation error5'],
legend=['training', 'validation'],
title='Error@5',
ylabel='error %')
if 'grad' in train_results.keys():
results.plot(x='epoch',
y=['training grad'],
legend=['gradient L2 norm'],
title='Gradient Norm',
ylabel='value')
results.save()
def main_worker(args):
global best_prec1, dtype
acc = -1
loss = -1
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = os.path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_init,
world_size=args.world_size, rank=args.local_rank)
args.local_rank = dist.get_rank()
args.world_size = dist.get_world_size()
if args.dist_backend == 'mpi':
# If using MPI, select all visible devices
args.device_ids = list(range(torch.cuda.device_count()))
else:
args.device_ids = [args.local_rank]
if not os.path.exists(save_path) and not (args.distributed and args.local_rank > 0):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = os.path.join(save_path, 'results')
results = ResultsLog(
results_path, title='Training Results - %s' % args.save)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
model = models.__dict__[args.model]
dataset_type = 'imagenet' if 'imagenet_calib' in args.dataset else args.dataset
model_config = {'dataset': dataset_type}
if args.model_config is not '':
if isinstance(args.model_config, dict):
for k, v in args.model_config.items():
if k not in model_config.keys():
model_config[k] = v
else:
args_dict = literal_eval(args.model_config)
for k, v in args_dict.items():
model_config[k] = v
if (args.absorb_bn or args.load_from_vision or args.pretrained) and not args.batch_norn_tuning:
if args.load_from_vision:
import torchvision
exec_lfv_str = 'torchvision.models.' + args.load_from_vision + '(pretrained=True)'
model = eval(exec_lfv_str)
else:
if not os.path.isfile(args.absorb_bn):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
model = model(**model_config)
checkpoint = torch.load(args.absorb_bn,map_location=lambda storage, loc: storage)
checkpoint = checkpoint['state_dict'] if 'state_dict' in checkpoint.keys() else checkpoint
sd={}
for key in checkpoint.keys():
key_clean=key.split('module.1.')[1]
sd[key_clean]=checkpoint[key]
checkpoint = sd
model.load_state_dict(checkpoint,strict=False)
if args.load_from_vision or ('batch_norm' in model_config and model_config['batch_norm']):
logging.info('Creating absorb_bn state dict')
search_absorbe_bn(model)
filename_ab = args.absorb_bn+'.absorb_bn' if args.absorb_bn else save_path+'/'+args.model+'.absorb_bn'
torch.save(model.state_dict(),filename_ab)
if not args.load_from_vision: return
else:
filename_bn = save_path+'/'+args.model+'.with_bn'
torch.save(model.state_dict(),filename_bn)
if args.load_from_vision: return
model = model(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# optionally resume from a checkpoint
if args.evaluate:
if not os.path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate, map_location="cpu")
# Overrride configuration with checkpoint info
args.model = checkpoint.get('model', args.model)
#args.model_config = checkpoint.get('config', args.model_config)
if not model_config['batch_norm']:
search_absorbe_fake_bn(model)
# load checkpoint
if 'state_dict' in checkpoint.keys():
if any([True for key in checkpoint['state_dict'].keys() if 'module.1.' in key]):
sd={}
for key in checkpoint['state_dict'].keys():
key_clean=key.split('module.1.')[1]
sd[key_clean]=checkpoint['state_dict'][key]
model.load_state_dict(sd,strict=False)
else:
model.load_state_dict(checkpoint['state_dict'],strict=False)
logging.info("loaded checkpoint '%s'", args.evaluate)
else:
model.load_state_dict(checkpoint,strict=False)
logging.info("loaded checkpoint '%s'",args.evaluate)
if args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
results.load(os.path.join(checkpoint_file, 'results.csv'))
checkpoint_file = os.path.join(
checkpoint_file, 'model_best.pth.tar')
if os.path.isfile(checkpoint_file):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(checkpoint_file)
if args.start_epoch < 0: # not explicitly set
args.start_epoch = checkpoint['epoch'] - 1 if 'epoch' in checkpoint.keys() else 0
best_prec1 = checkpoint['best_prec1'] if 'best_prec1' in checkpoint.keys() else -1
sd = checkpoint['state_dict'] if 'state_dict' in checkpoint.keys() else checkpoint
model.load_state_dict(sd,strict=False)
logging.info("loaded checkpoint '%s' (epoch %s)",
checkpoint_file, args.start_epoch)
else:
logging.error("no checkpoint found at '%s'", args.resume)
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
# optimizer configuration
optim_regime = getattr(model, 'regime', [{'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay}])
if args.fine_tune or args.prune:
if not args.resume: args.start_epoch=0
if args.update_only_th:
#optim_regime = [
# {'epoch': 0, 'optimizer': 'Adam', 'lr': 1e-4}]
optim_regime = [
{'epoch': 0, 'optimizer': 'SGD', 'lr': 1e-1},
{'epoch': 10, 'lr': 1e-2},
{'epoch': 15, 'lr': 1e-3}]
else:
optim_regime = [
{'epoch': 0, 'optimizer': 'SGD', 'lr': 1e-4, 'momentum': 0.9},
{'epoch': 2, 'lr': 1e-5, 'momentum': 0.9},
{'epoch': 10, 'lr': 1e-6, 'momentum': 0.9}]
optimizer = optim_regime if isinstance(optim_regime, OptimRegime) \
else OptimRegime(model, optim_regime, use_float_copy='half' in args.dtype)
# Training Data loading code
train_data = DataRegime(getattr(model, 'data_regime', None),
defaults={'datasets_path': args.datasets_dir, 'name': args.dataset, 'split': 'train', 'augment': False,
'input_size': args.input_size, 'batch_size': args.batch_size, 'shuffle': True,
'num_workers': args.workers, 'pin_memory': True, 'drop_last': True,
'distributed': args.distributed, 'duplicates': args.duplicates, 'autoaugment': args.autoaugment,
'cutout': {'holes': 1, 'length': 16} if args.cutout else None})
prunner = None
trainer = Trainer(model,prunner, criterion, optimizer,
device_ids=args.device_ids, device=args.device, dtype=dtype,
distributed=args.distributed, local_rank=args.local_rank, mixup=args.mixup, loss_scale=args.loss_scale,
grad_clip=args.grad_clip, print_freq=args.print_freq, adapt_grad_norm=args.adapt_grad_norm,epoch=args.start_epoch)
# Evaluation Data loading code
args.eval_batch_size = args.eval_batch_size if args.eval_batch_size > 0 else args.batch_size
dataset_type = 'imagenet' if 'imagenet_calib' in args.dataset else args.dataset
#dataset_type = 'imagenet' if args.dataset =='imagenet_calib' else args.dataset
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={'datasets_path': args.datasets_dir, 'name': dataset_type, 'split': 'val', 'augment': False,
'input_size': args.input_size, 'batch_size': args.eval_batch_size, 'shuffle': True,
'num_workers': args.workers, 'pin_memory': True, 'drop_last': False})
cached_input_output = {}
cached_layer_names = []
if args.adaprune:
generate_masks_from_model(model)
def hook(module, input, output):
if module not in cached_input_output:
cached_input_output[module] = []
# Meanwhile store data in the RAM.
cached_input_output[module].append((input[0].detach().cpu(), output.detach().cpu()))
print(module.__str__()[:70])
handlers = []
count = 0
global_val = calc_global_prune_val(model,args.sparsity_level) if args.global_pruning else None
for name,m in model.named_modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
m.quantize = False
if count < 1000:
handlers.append(m.register_forward_hook(hook))
count += 1
cached_layer_names.append(name)
# Store input/output for all quantizable layers
trainer.validate(train_data.get_loader(),num_steps=1)
print("Input/outputs cached")
for handler in handlers:
handler.remove()
mse_df = pd.DataFrame(index=np.arange(len(cached_input_output)), columns=['name', 'shape', 'mse_before', 'mse_after'])
print_freq = 100
masks_dict = {}
for i, layer in enumerate(cached_input_output):
layer.name = cached_layer_names[i]
print("\nOptimize {}:{} for shape of {}".format(i, layer.name , layer.weight.shape))
sparsity_level = 1 if args.keep_first_last and (i==0 or i==len(cached_input_output)) else args.sparsity_level
prune_topk = args.prune_bs if args.keep_first_last and (i==0 or i==len(cached_input_output)) else args.prune_topk
mse_before, mse_after, snr_before, snr_after, kurt_in, kurt_w, mask= \
optimize_layer(layer, cached_input_output[layer], args.optimize_weights,bs=args.prune_bs,topk=prune_topk,extract_topk=args.prune_extract_topk, \
unstructured=args.unstructured,sparsity_level=sparsity_level,global_val=global_val,conf_level=args.conf_level)
masks_dict[layer.name ] = mask
print("\nMSE before optimization: {}".format(mse_before))
print("MSE after optimization: {}".format(mse_after))
mse_df.loc[i, 'name'] = layer.name
mse_df.loc[i, 'shape'] = str(layer.weight.shape)
mse_df.loc[i, 'mse_before'] = mse_before
mse_df.loc[i, 'mse_after'] = mse_after
mse_df.loc[i, 'snr_before'] = snr_before
mse_df.loc[i, 'snr_after'] = snr_after
mse_df.loc[i, 'kurt_in'] = kurt_in
mse_df.loc[i, 'kurt_w'] = kurt_w
if i > 0 and i % print_freq == 0:
print('\n')
val_results = trainer.validate(val_data.get_loader())
logging.info(val_results)
total_sparsity = calc_masks_sparsity(masks_dict)
mse_csv = args.evaluate + '.mse.csv'
mse_df.to_csv(mse_csv)
filename = args.evaluate + '.adaprune'
torch.save(model.state_dict(), filename)
cached_input_output = None
val_results = trainer.validate(val_data.get_loader())
logging.info(val_results)
trainer.cal_bn_stats(train_data.get_loader())
val_results = trainer.validate(val_data.get_loader())
logging.info(val_results)
elif args.batch_norn_tuning:
for m in model.modules():
if isinstance(m, nn.Conv2d) or isinstance(m, nn.Linear):
m.quantize = False
exec_lfv_str = 'torchvision.models.' + args.load_from_vision + '(pretrained=True)'
model_orig = eval(exec_lfv_str)
model_orig.to(args.device, dtype)
search_copy_bn_params(model_orig)
layers_orig = dict([(n, m) for n, m in model_orig.named_modules() if isinstance(m, nn.Conv2d)])
layers_q = dict([(n, m) for n, m in model.named_modules() if isinstance(m, nn.Conv2d)])
for l in layers_orig:
conv_orig = layers_orig[l]
conv_q = layers_q[l]
conv_q.register_parameter('gamma', nn.Parameter(conv_orig.gamma.clone()))
conv_q.register_parameter('beta', nn.Parameter(conv_orig.beta.clone()))
del model_orig
search_add_bn(model)
print("Run BN tuning")
for tt in range(args.tuning_iter):
print(tt)
trainer.cal_bn_stats(train_data.get_loader())
search_absorbe_tuning_bn(model)
filename = args.evaluate + '.bn_tuning'
print("Save model to: {}".format(filename))
torch.save(model.state_dict(), filename)
val_results = trainer.validate(val_data.get_loader())
logging.info(val_results)
if args.res_log is not None:
if not os.path.exists(args.res_log):
df = pd.DataFrame()
else:
df = pd.read_csv(args.res_log, index_col=0)
ckp = ntpath.basename(args.evaluate)
df.loc[ckp, 'acc_bn_tuning'] = val_results['prec1']
df.loc[ckp, 'loss_bn_tuning'] = val_results['loss']
df.to_csv(args.res_log)
else:
#print('Please Choose one of the following ....')
if model_config['measure']:
results = trainer.validate(train_data.get_loader(),rec=args.rec)
else:
if args.evaluate_init_configuration:
results = trainer.validate(val_data.get_loader())
if args.res_log is not None:
if not os.path.exists(args.res_log):
df = pd.DataFrame()
else:
df = pd.read_csv(args.res_log, index_col=0)
ckp = ntpath.basename(args.evaluate)
df.loc[ckp, 'acc_base'] = results['prec1']
df.loc[ckp, 'loss_base'] = results['loss']
df.to_csv(args.res_log)
if args.evaluate_init_configuration:
logging.info(results)
return acc, loss
def main_worker(args):
global best_prec1, dtype
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
model_config = {'dataset': args.dataset, 'batch': args.batch_size}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
##autoname
fname = auto_name(args, model_config)
args.save = fname
monitor = args.monitor
print(fname)
save_path = path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend, init_method=args.dist_init,
world_size=args.world_size, rank=args.local_rank)
args.local_rank = dist.get_rank()
args.world_size = dist.get_world_size()
if args.dist_backend == 'mpi':
# If using MPI, select all visible devices
args.device_ids = list(range(torch.cuda.device_count()))
else:
args.device_ids = [args.local_rank]
if not (args.distributed and args.local_rank > 0):
if not args.dry:
if not path.exists(save_path):
makedirs(save_path)
export_args_namespace(args, path.join(save_path, 'config.json'))
if monitor > 0 and not args.dry:
events_path = "runs/%s" % fname
my_file = Path(events_path)
if my_file.is_file():
os.remove(events_path)
writer = SummaryWriter(log_dir=events_path ,comment=str(args))
model_config['writer'] = writer
model_config['monitor'] = monitor
else:
monitor = 0
writer = None
if args.dry:
model = models.__dict__[args.model]
model = model(**model_config)
print("created model with configuration: %s" % model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
print("number of parameters: %d" % num_parameters)
return
setup_logging(path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
model = models.__dict__[args.model]
model = model(**model_config)
if args.sync_bn:
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# optionally resume from a checkpoint
if args.evaluate:
if not path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate, map_location="cpu")
# Overrride configuration with checkpoint info
args.model = checkpoint.get('model', args.model)
args.model_config = checkpoint.get('config', args.model_config)
# load checkpoint
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s' (epoch %s)",
args.evaluate, checkpoint['epoch'])
if args.resume:
checkpoint_file = args.resume
if path.isdir(checkpoint_file):
results.load(path.join(checkpoint_file, 'results.csv'))
checkpoint_file = path.join(
checkpoint_file, 'model_best.pth.tar')
if path.isfile(checkpoint_file):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(checkpoint_file, map_location="cpu")
if args.start_epoch < 0: # not explicitly set
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optim_state_dict = checkpoint.get('optim_state_dict', None)
logging.info("loaded checkpoint '%s' (epoch %s)",
checkpoint_file, checkpoint['epoch'])
else:
logging.error("no checkpoint found at '%s'", args.resume)
else:
optim_state_dict = None
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
# optimizer configuration
optim_regime = getattr(model, 'regime', [{'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay}])
optimizer = optim_regime if isinstance(optim_regime, OptimRegime) \
else OptimRegime(model, optim_regime, use_float_copy='half' in args.dtype)
if optim_state_dict is not None:
optimizer.load_state_dict(optim_state_dict)
trainer = Trainer(model, criterion, optimizer,
device_ids=args.device_ids, device=args.device, dtype=dtype, print_freq=args.print_freq,
distributed=args.distributed, local_rank=args.local_rank, mixup=args.mixup, cutmix=args.cutmix,
loss_scale=args.loss_scale, grad_clip=args.grad_clip, adapt_grad_norm=args.adapt_grad_norm, writer = writer, monitor = monitor)
if args.tensorwatch:
trainer.set_watcher(filename=path.abspath(path.join(save_path, 'tensorwatch.log')),
port=args.tensorwatch_port)
# Evaluation Data loading code
args.eval_batch_size = args.eval_batch_size if args.eval_batch_size > 0 else args.batch_size
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={'datasets_path': args.datasets_dir, 'name': args.dataset, 'split': 'val', 'augment': False,
'input_size': args.input_size, 'batch_size': args.eval_batch_size, 'shuffle': False,
'num_workers': args.workers, 'pin_memory': True, 'drop_last': False})
if args.evaluate:
results = trainer.validate(val_data.get_loader())
logging.info(results)
return
# Training Data loading code
train_data_defaults = {'datasets_path': args.datasets_dir, 'name': args.dataset, 'split': 'train', 'augment': True,
'input_size': args.input_size, 'batch_size': args.batch_size, 'shuffle': True,
'num_workers': args.workers, 'pin_memory': True, 'drop_last': True,
'distributed': args.distributed, 'duplicates': args.duplicates, 'autoaugment': args.autoaugment,
'cutout': {'holes': 1, 'length': 16} if args.cutout else None}
if hasattr(model, 'sampled_data_regime'):
sampled_data_regime = model.sampled_data_regime
probs, regime_configs = zip(*sampled_data_regime)
regimes = []
for config in regime_configs:
defaults = {**train_data_defaults}
defaults.update(config)
regimes.append(DataRegime(None, defaults=defaults))
train_data = SampledDataRegime(regimes, probs)
else:
train_data = DataRegime(
getattr(model, 'data_regime', None), defaults=train_data_defaults)
logging.info('optimization regime: %s', optim_regime)
logging.info('data regime: %s', train_data)
args.start_epoch = max(args.start_epoch, 0)
trainer.training_steps = args.start_epoch * len(train_data)
if not args.covmat == "":
try:
int_covmat = int(args.covmat)
if int_covmat < 0:
total_layers = len([name for name, layer in model.named_children()])
int_covmat = total_layers + int_covmat
child_cnt = 0
except ValueError:
int_covmat = None
def calc_covmat(x_, partitions = 64):
L = x_.shape[0] // partitions
non_diags = []
diags = []
for p1 in range(partitions):
for p2 in range(partitions):
x = x_[p1*L:(p1+1)*L]
y = x_[p2*L:(p2+1)*L]
X = torch.matmul(x,y.transpose(0,1))
if p1 == p2:
mask = torch.eye(X.shape[0],dtype=torch.bool)
non_diag = X[~mask].reshape(-1).cpu()
diag = X[mask].reshape(-1).cpu()
non_diags.append(non_diag)
diags.append(diag)
else:
non_diag = X.reshape(-1).cpu()
non_diags.append(diag)
diags = torch.cat(diags)
non_diags = torch.cat(non_diags)
diag_var = diags.var()
non_diag_var = non_diags.var()
diags = diags - diags.mean()
non_diags = non_diags - non_diags.mean()
##import pdb; pdb.set_trace()
diag_small_ratio = (diags < -diags.std()).to(dtype = torch.float).mean()
non_diag_small_ratio = (non_diags < -non_diags.std()).to(dtype = torch.float).mean()
return diag_var, non_diag_var, diag_small_ratio, non_diag_small_ratio
global diag_var_mean
global non_diag_var_mean
global var_count
var_count = 0
diag_var_mean = 0
non_diag_var_mean = 0
def report_covmat_hook(module, input, output):
global diag_var_mean
global non_diag_var_mean
global var_count
flatten_output = output.reshape([-1,1]).detach()
diag_var, non_diag_var, diag_small_ratio, non_diag_small_ratio = calc_covmat(flatten_output)
diag_var_mean = diag_var_mean + diag_var
non_diag_var_mean = non_diag_var_mean + non_diag_var
var_count = var_count + 1
if var_count % 10 == 1:
print("diag_var = %.02f (%.02f), ratio: %.02f , non_diag_var = %0.2f (%.02f), ratio: %.02f" % (diag_var, diag_var_mean/var_count, diag_small_ratio , non_diag_var, non_diag_var_mean/var_count, non_diag_small_ratio ))
for name, layer in model.named_children():
if int_covmat is None:
condition = (name == args.covmat)
else:
condition = (child_cnt == int_covmat)
child_cnt = child_cnt + 1
if condition:
layer.register_forward_hook( report_covmat_hook)
for epoch in range(args.start_epoch, args.epochs):
trainer.epoch = epoch
train_data.set_epoch(epoch)
val_data.set_epoch(epoch)
logging.info('\nStarting Epoch: {0}\n'.format(epoch + 1))
# train for one epoch
train_results = trainer.train(train_data.get_loader(),
chunk_batch=args.chunk_batch)
# evaluate on validation set
val_results = trainer.validate(val_data.get_loader())
if args.distributed and args.local_rank > 0:
continue
# remember best prec@1 and save checkpoint
is_best = val_results['prec1'] > best_prec1
best_prec1 = max(val_results['prec1'], best_prec1)
if args.drop_optim_state:
optim_state_dict = None
else:
optim_state_dict = optimizer.state_dict()
save_checkpoint({
'epoch': epoch + 1,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'optim_state_dict': optim_state_dict,
'best_prec1': best_prec1
}, is_best, path=save_path, save_all=args.save_all)
logging.info('\nResults - Epoch: {0}\n'
'Training Loss {train[loss]:.4f} \t'
'Training Prec@1 {train[prec1]:.3f} \t'
'Training Prec@5 {train[prec5]:.3f} \t'
'Validation Loss {val[loss]:.4f} \t'
'Validation Prec@1 {val[prec1]:.3f} \t'
'Validation Prec@5 {val[prec5]:.3f} \t\n'
.format(epoch + 1, train=train_results, val=val_results))
if writer is not None:
writer.add_scalar('Train/Loss', train_results['loss'], epoch)
writer.add_scalar('Train/Prec@1', train_results['prec1'], epoch)
writer.add_scalar('Train/Prec@5', train_results['prec5'], epoch)
writer.add_scalar('Val/Loss', val_results['loss'], epoch)
writer.add_scalar('Val/Prec@1', val_results['prec1'], epoch)
writer.add_scalar('Val/Prec@5', val_results['prec5'], epoch)
# tmplr = optimizer.get_lr()
# writer.add_scalar('HyperParameters/learning-rate', tmplr, epoch)
values = dict(epoch=epoch + 1, steps=trainer.training_steps)
values.update({'training ' + k: v for k, v in train_results.items()})
values.update({'validation ' + k: v for k, v in val_results.items()})
results.add(**values)
results.plot(x='epoch', y=['training loss', 'validation loss'],
legend=['training', 'validation'],
title='Loss', ylabel='loss')
results.plot(x='epoch', y=['training error1', 'validation error1'],
legend=['training', 'validation'],
title='Error@1', ylabel='error %')
results.plot(x='epoch', y=['training error5', 'validation error5'],
legend=['training', 'validation'],
title='Error@5', ylabel='error %')
if 'grad' in train_results.keys():
results.plot(x='epoch', y=['training grad'],
legend=['gradient L2 norm'],
title='Gradient Norm', ylabel='value')
results.save()
logging.info(f'\nBest Validation Accuracy (top1): {best_prec1}')
if writer:
writer.close()
def main_worker(args):
global best_prec1, dtype
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_init,
world_size=args.world_size,
rank=args.local_rank)
args.local_rank = dist.get_rank()
args.world_size = dist.get_world_size()
if args.dist_backend == 'mpi':
# If using MPI, select all visible devices
args.device_ids = list(range(torch.cuda.device_count()))
else:
args.device_ids = [args.local_rank]
if not (args.distributed and args.local_rank > 0):
if not path.exists(save_path):
makedirs(save_path)
export_args_namespace(args, path.join(save_path, 'config.json'))
setup_logging(path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
grad_stats_path = path.join(save_path, 'grad_stats')
grad_stats = ResultsLog(grad_stats_path,
title='collect grad stats - %s' % args.save)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
model = models.__dict__[args.model]
model_config = {'dataset': args.dataset}
if args.model_config is not '':
model_config = dict(model_config, **literal_eval(args.model_config))
if args.enable_scheduler:
model_config['fp8_dynamic'] = True
if args.smart_loss_scale_only:
model_config['smart_loss_scale_only'] = True
if args.smart_loss_scale_and_exp_bits:
model_config['smart_loss_scale_and_exp_bits'] = True
model = model(**model_config)
quantize_modules_name = [
n for n, m in model.named_modules() if isinstance(m, nn.Conv2d)
]
fp8_scheduler = FP8TrainingScheduler(
model,
model_config,
args,
collect_stats_online=False,
start_to_collect_stats_in_epoch=3,
collect_stats_every_epochs=10,
online_update=False,
first_update_with_stats_from_epoch=4,
start_online_update_in_epoch=3,
update_every_epochs=1,
update_loss_scale=True,
update_exp_bit_width=args.smart_loss_scale_and_exp_bits,
stats_path=
"/data/moran/ConvNet_lowp_0/convNet.pytorch/results/2020-05-16_01-44-22/results.csv", # ResNet18- cifar10
# stats_path = "/data/moran/ConvNet_lowp_0/convNet.pytorch/results/2020-05-19_01-27-57/results.csv", # ResNet18- ImageNet
quantize_modules_name=quantize_modules_name,
enable_scheduler=False)
if args.sync_bn:
model = nn.SyncBatchNorm.convert_sync_batchnorm(model)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# optionally resume from a checkpoint
if args.evaluate:
if not path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate, map_location="cpu")
# Overrride configuration with checkpoint info
args.model = checkpoint.get('model', args.model)
args.model_config = checkpoint.get('config', args.model_config)
# load checkpoint
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s' (epoch %s)", args.evaluate,
checkpoint['epoch'])
if args.resume:
checkpoint_file = args.resume
if path.isdir(checkpoint_file):
results.load(path.join(checkpoint_file, 'results.csv'))
checkpoint_file = path.join(checkpoint_file, 'model_best.pth.tar')
if path.isfile(checkpoint_file):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(checkpoint_file, map_location="cpu")
if args.start_epoch < 0: # not explicitly set
args.start_epoch = checkpoint['epoch']
best_prec1 = checkpoint['best_prec1']
model.load_state_dict(checkpoint['state_dict'])
optim_state_dict = checkpoint.get('optim_state_dict', None)
logging.info("loaded checkpoint '%s' (epoch %s)", checkpoint_file,
checkpoint['epoch'])
else:
logging.error("no checkpoint found at '%s'", args.resume)
else:
optim_state_dict = None
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', CrossEntropyLoss)(**loss_params)
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
# optimizer configuration
optim_regime = getattr(model, 'regime', [{
'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay
}])
optimizer = optim_regime if isinstance(optim_regime, OptimRegime) \
else OptimRegime(model, optim_regime, use_float_copy='half' in args.dtype)
if optim_state_dict is not None:
optimizer.load_state_dict(optim_state_dict)
trainer = Trainer(model,
criterion,
optimizer,
device_ids=args.device_ids,
device=args.device,
dtype=dtype,
print_freq=args.print_freq,
distributed=args.distributed,
local_rank=args.local_rank,
mixup=args.mixup,
cutmix=args.cutmix,
loss_scale=args.loss_scale,
grad_clip=args.grad_clip,
adapt_grad_norm=args.adapt_grad_norm,
enable_input_grad_statistics=True,
exp_bits=args.exp_bits,
fp_bits=args.fp_bits)
if args.tensorwatch:
trainer.set_watcher(filename=path.abspath(
path.join(save_path, 'tensorwatch.log')),
port=args.tensorwatch_port)
# Evaluation Data loading code
args.eval_batch_size = args.eval_batch_size if args.eval_batch_size > 0 else args.batch_size
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'val',
'augment': False,
'input_size': args.input_size,
'batch_size': args.eval_batch_size,
'shuffle': False,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
if args.evaluate:
results = trainer.validate(val_data.get_loader())
logging.info(results)
return
# Training Data loading code
train_data_defaults = {
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'train',
'augment': True,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': True,
'distributed': args.distributed,
'duplicates': args.duplicates,
'autoaugment': args.autoaugment,
'cutout': {
'holes': 1,
'length': 16
} if args.cutout else None
}
if hasattr(model, 'sampled_data_regime'):
sampled_data_regime = model.sampled_data_regime
probs, regime_configs = zip(*sampled_data_regime)
regimes = []
for config in regime_configs:
defaults = {**train_data_defaults}
defaults.update(config)
regimes.append(DataRegime(None, defaults=defaults))
train_data = SampledDataRegime(regimes, probs)
else:
train_data = DataRegime(getattr(model, 'data_regime', None),
defaults=train_data_defaults)
logging.info('optimization regime: %s', optim_regime)
logging.info('data regime: %s', train_data)
args.start_epoch = max(args.start_epoch, 0)
trainer.training_steps = args.start_epoch * len(train_data)
for epoch in range(args.start_epoch, args.epochs):
trainer.epoch = epoch
train_data.set_epoch(epoch)
val_data.set_epoch(epoch)
logging.info('\nStarting Epoch: {0}\n'.format(epoch + 1))
fp8_scheduler.schedule_before_epoch(epoch)
# train for one epoch
# pdb.set_trace()
train_results, meters_grad = trainer.train(
train_data.get_loader(),
chunk_batch=args.chunk_batch,
scheduled_instructions=fp8_scheduler.scheduled_instructions)
# evaluate on validation set
if args.calibrate_bn:
train_data = DataRegime(None,
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'train',
'augment': True,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': True,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
trainer.calibrate_bn(train_data.get_loader(), num_steps=200)
val_results, _ = trainer.validate(val_data.get_loader())
if args.distributed and args.local_rank > 0:
continue
# remember best prec@1 and save checkpoint
is_best = val_results['prec1'] > best_prec1
best_prec1 = max(val_results['prec1'], best_prec1)
if args.drop_optim_state:
optim_state_dict = None
else:
optim_state_dict = optimizer.state_dict()
save_checkpoint(
{
'epoch': epoch + 1,
'model': args.model,
'config': args.model_config,
'state_dict': model.state_dict(),
'optim_state_dict': optim_state_dict,
'best_prec1': best_prec1
},
is_best,
path=save_path,
save_all=args.save_all)
logging.info('\nResults - Epoch: {0}\n'
'Training Loss {train[loss]:.4f} \t'
'Training Prec@1 {train[prec1]:.3f} \t'
'Training Prec@5 {train[prec5]:.3f} \t'
'Validation Loss {val[loss]:.4f} \t'
'Validation Prec@1 {val[prec1]:.3f} \t'
'Validation Prec@5 {val[prec5]:.3f} \t\n'.format(
epoch + 1, train=train_results, val=val_results))
values = dict(epoch=epoch + 1, steps=trainer.training_steps)
values.update({'training ' + k: v for k, v in train_results.items()})
values.update({'validation ' + k: v for k, v in val_results.items()})
values.update(
{'grad mean ' + k: v['mean'].avg
for k, v in meters_grad.items()})
values.update(
{'grad std ' + k: v['std'].avg
for k, v in meters_grad.items()})
results.add(**values)
# stats was collected
if fp8_scheduler.scheduled_instructions['collect_stat']:
grad_stats_values = dict(epoch=epoch + 1)
grad_stats_values.update({
'grad mean ' + k: v['mean'].avg
for k, v in meters_grad.items()
})
grad_stats_values.update({
'grad std ' + k: v['std'].avg
for k, v in meters_grad.items()
})
grad_stats.add(**grad_stats_values)
fp8_scheduler.update_stats(grad_stats)
results.plot(x='epoch',
y=['training loss', 'validation loss'],
legend=['training', 'validation'],
title='Loss',
ylabel='loss')
results.plot(x='epoch',
y=['training error1', 'validation error1'],
legend=['training', 'validation'],
title='Error@1',
ylabel='error %')
results.plot(x='epoch',
y=['training error5', 'validation error5'],
legend=['training', 'validation'],
title='Error@5',
ylabel='error %')
if 'grad' in train_results.keys():
results.plot(x='epoch',
y=['training grad'],
legend=['gradient L2 norm'],
title='Gradient Norm',
ylabel='value')
results.save()
grad_stats.save()
def main_worker(args):
global best_prec1, dtype
acc = -1
loss = -1
best_prec1 = 0
dtype = torch_dtypes.get(args.dtype)
random.seed(args.seed)
np.random.seed(args.seed)
torch.manual_seed(args.seed)
time_stamp = datetime.now().strftime('%Y-%m-%d_%H-%M-%S')
if args.evaluate:
args.results_dir = '/tmp'
if args.save is '':
args.save = time_stamp
save_path = os.path.join(args.results_dir, args.save)
args.distributed = args.local_rank >= 0 or args.world_size > 1
if args.distributed:
dist.init_process_group(backend=args.dist_backend,
init_method=args.dist_init,
world_size=args.world_size,
rank=args.local_rank)
args.local_rank = dist.get_rank()
args.world_size = dist.get_world_size()
if args.dist_backend == 'mpi':
# If using MPI, select all visible devices
args.device_ids = list(range(torch.cuda.device_count()))
else:
args.device_ids = [args.local_rank]
if not os.path.exists(save_path) and not (args.distributed
and args.local_rank > 0):
os.makedirs(save_path)
setup_logging(os.path.join(save_path, 'log.txt'),
resume=args.resume is not '',
dummy=args.distributed and args.local_rank > 0)
results_path = os.path.join(save_path, 'results')
results = ResultsLog(results_path,
title='Training Results - %s' % args.save)
logging.info("saving to %s", save_path)
logging.debug("run arguments: %s", args)
logging.info("creating model %s", args.model)
if 'cuda' in args.device and torch.cuda.is_available():
torch.cuda.manual_seed_all(args.seed)
torch.cuda.set_device(args.device_ids[0])
cudnn.benchmark = True
else:
args.device_ids = None
# create model
model = models.__dict__[args.model]
dataset_type = 'imagenet' if args.dataset == 'imagenet_calib' else args.dataset
model_config = {'dataset': dataset_type}
if args.model_config is not '':
if isinstance(args.model_config, dict):
for k, v in args.model_config.items():
if k not in model_config.keys():
model_config[k] = v
else:
args_dict = literal_eval(args.model_config)
for k, v in args_dict.items():
model_config[k] = v
if (args.absorb_bn or args.load_from_vision
or args.pretrained) and not args.batch_norn_tuning:
if args.load_from_vision:
import torchvision
exec_lfv_str = 'torchvision.models.' + args.load_from_vision + '(pretrained=True)'
model = eval(exec_lfv_str)
if 'pytcv' in args.model:
from pytorchcv.model_provider import get_model as ptcv_get_model
exec_lfv_str = 'ptcv_get_model("' + args.load_from_vision + '", pretrained=True)'
model_pytcv = eval(exec_lfv_str)
model = convert_pytcv_model(model, model_pytcv)
else:
if not os.path.isfile(args.absorb_bn):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
model = model(**model_config)
checkpoint = torch.load(args.absorb_bn,
map_location=lambda storage, loc: storage)
checkpoint = checkpoint[
'state_dict'] if 'state_dict' in checkpoint.keys(
) else checkpoint
model.load_state_dict(checkpoint, strict=False)
if 'batch_norm' in model_config and not model_config['batch_norm']:
logging.info('Creating absorb_bn state dict')
search_absorbe_bn(model)
filename_ab = args.absorb_bn + '.absorb_bn' if args.absorb_bn else save_path + '/' + args.model + '.absorb_bn'
torch.save(model.state_dict(), filename_ab)
else:
filename_bn = save_path + '/' + args.model + '.with_bn'
torch.save(model.state_dict(), filename_bn)
if (args.load_from_vision
or args.absorb_bn) and not args.evaluate_init_configuration:
return
if 'inception' in args.model:
model = model(init_weights=False, **model_config)
else:
model = model(**model_config)
logging.info("created model with configuration: %s", model_config)
num_parameters = sum([l.nelement() for l in model.parameters()])
logging.info("number of parameters: %d", num_parameters)
# optionally resume from a checkpoint
if args.evaluate:
if not os.path.isfile(args.evaluate):
parser.error('invalid checkpoint: {}'.format(args.evaluate))
checkpoint = torch.load(args.evaluate, map_location="cpu")
# Overrride configuration with checkpoint info
args.model = checkpoint.get('model', args.model)
args.model_config = checkpoint.get('config', args.model_config)
if not model_config['batch_norm']:
search_absorbe_fake_bn(model)
# load checkpoint
if 'state_dict' in checkpoint.keys():
model.load_state_dict(checkpoint['state_dict'])
logging.info("loaded checkpoint '%s'", args.evaluate)
else:
model.load_state_dict(checkpoint, strict=False)
logging.info("loaded checkpoint '%s'", args.evaluate)
if args.resume:
checkpoint_file = args.resume
if os.path.isdir(checkpoint_file):
results.load(os.path.join(checkpoint_file, 'results.csv'))
checkpoint_file = os.path.join(checkpoint_file,
'model_best.pth.tar')
if os.path.isfile(checkpoint_file):
logging.info("loading checkpoint '%s'", args.resume)
checkpoint = torch.load(checkpoint_file)
if args.start_epoch < 0: # not explicitly set
args.start_epoch = checkpoint[
'epoch'] - 1 if 'epoch' in checkpoint.keys() else 0
best_prec1 = checkpoint[
'best_prec1'] if 'best_prec1' in checkpoint.keys() else -1
sd = checkpoint['state_dict'] if 'state_dict' in checkpoint.keys(
) else checkpoint
model.load_state_dict(sd, strict=False)
logging.info("loaded checkpoint '%s' (epoch %s)", checkpoint_file,
args.start_epoch)
else:
logging.error("no checkpoint found at '%s'", args.resume)
# define loss function (criterion) and optimizer
loss_params = {}
if args.label_smoothing > 0:
loss_params['smooth_eps'] = args.label_smoothing
criterion = getattr(model, 'criterion', CrossEntropyLoss)(**loss_params)
if args.kld_loss:
criterion = nn.KLDivLoss(reduction='mean')
criterion.to(args.device, dtype)
model.to(args.device, dtype)
# Batch-norm should always be done in float
if 'half' in args.dtype:
FilterModules(model, module=is_bn).to(dtype=torch.float)
# optimizer configuration
optim_regime = getattr(model, 'regime', [{
'epoch': 0,
'optimizer': args.optimizer,
'lr': args.lr,
'momentum': args.momentum,
'weight_decay': args.weight_decay
}])
if args.fine_tune or args.prune:
if not args.resume: args.start_epoch = 0
if args.update_only_th:
#optim_regime = [
# {'epoch': 0, 'optimizer': 'Adam', 'lr': 1e-4}]
optim_regime = [{
'epoch': 0,
'optimizer': 'SGD',
'lr': 1e-1
}, {
'epoch': 10,
'lr': 1e-2
}, {
'epoch': 15,
'lr': 1e-3
}]
else:
optim_regime = [{
'epoch': 0,
'optimizer': 'SGD',
'lr': 1e-4,
'momentum': 0.9
}, {
'epoch': 2,
'lr': 1e-5,
'momentum': 0.9
}, {
'epoch': 10,
'lr': 1e-6,
'momentum': 0.9
}]
optimizer = optim_regime if isinstance(optim_regime, OptimRegime) \
else OptimRegime(model, optim_regime, use_float_copy='half' in args.dtype)
# Training Data loading code
train_data = DataRegime(getattr(model, 'data_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': args.dataset,
'split': 'train',
'augment': False,
'input_size': args.input_size,
'batch_size': args.batch_size,
'shuffle': not args.seq_adaquant,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': True,
'distributed': args.distributed,
'duplicates': args.duplicates,
'autoaugment': args.autoaugment,
'cutout': {
'holes': 1,
'length': 16
} if args.cutout else None,
'inception_prep': 'inception' in args.model
})
if args.names_sp_layers is None and args.layers_precision_dict is None:
args.names_sp_layers = [
key[:-7] for key in model.state_dict().keys()
if 'weight' in key and 'running' not in key and (
'conv' in key or 'downsample.0' in key or 'fc' in key)
]
if args.keep_first_last:
args.names_sp_layers = [
name for name in args.names_sp_layers if name != 'conv1'
and name != 'fc' and name != 'Conv2d_1a_3x3.conv'
]
args.names_sp_layers = [
k for k in args.names_sp_layers if 'downsample' not in k
] if args.ignore_downsample else args.names_sp_layers
if args.num_sp_layers == 0 and not args.keep_first_last:
args.names_sp_layers = []
if args.layers_precision_dict is not None:
print(args.layers_precision_dict)
prunner = None
trainer = Trainer(model,
prunner,
criterion,
optimizer,
device_ids=args.device_ids,
device=args.device,
dtype=dtype,
distributed=args.distributed,
local_rank=args.local_rank,
mixup=args.mixup,
loss_scale=args.loss_scale,
grad_clip=args.grad_clip,
print_freq=args.print_freq,
adapt_grad_norm=args.adapt_grad_norm,
epoch=args.start_epoch,
update_only_th=args.update_only_th,
optimize_rounding=args.optimize_rounding)
# Evaluation Data loading code
args.eval_batch_size = args.eval_batch_size if args.eval_batch_size > 0 else args.batch_size
dataset_type = 'imagenet' if args.dataset == 'imagenet_calib' else args.dataset
val_data = DataRegime(getattr(model, 'data_eval_regime', None),
defaults={
'datasets_path': args.datasets_dir,
'name': dataset_type,
'split': 'val',
'augment': False,
'input_size': args.input_size,
'batch_size': args.eval_batch_size,
'shuffle': True,
'num_workers': args.workers,
'pin_memory': True,
'drop_last': False
})
if args.evaluate or args.resume:
from utils.layer_sensativity import search_replace_layer, extract_save_quant_state_dict, search_replace_layer_from_dict
if args.layers_precision_dict is not None:
model = search_replace_layer_from_dict(
model, ast.literal_eval(args.layers_precision_dict))
else:
model = search_replace_layer(model,
args.names_sp_layers,
num_bits_activation=args.nbits_act,
num_bits_weight=args.nbits_weight)
cached_input_output = {}
quant_keys = [
'.weight', '.bias', '.equ_scale', '.quantize_input.running_zero_point',
'.quantize_input.running_range', '.quantize_weight.running_zero_point',
'.quantize_weight.running_range',
'.quantize_input1.running_zero_point', '.quantize_input1.running_range'
'.quantize_input2.running_zero_point', '.quantize_input2.running_range'
]
if args.adaquant:
def Qhook(name, module, input, output):
if module not in cached_qinput:
cached_qinput[module] = []
# Meanwhile store data in the RAM.
cached_qinput[module].append(input[0].detach().cpu())
# print(name)
def hook(name, module, input, output):
if module not in cached_input_output:
cached_input_output[module] = []
# Meanwhile store data in the RAM.
cached_input_output[module].append(
(input[0].detach().cpu(), output.detach().cpu()))
# print(name)
from models.modules.quantize import QConv2d, QLinear
handlers = []
count = 0
for name, m in model.named_modules():
if isinstance(m, QConv2d) or isinstance(m, QLinear):
#if isinstance(m, QConv2d) or isinstance(m, QLinear):
# if isinstance(m, QConv2d):
m.quantize = False
if count < 1000:
# if (isinstance(m, QConv2d) and m.groups == 1) or isinstance(m, QLinear):
handlers.append(
m.register_forward_hook(partial(hook, name)))
count += 1
# Store input/output for all quantizable layers
trainer.validate(train_data.get_loader())
print("Input/outputs cached")
for handler in handlers:
handler.remove()
for m in model.modules():
if isinstance(m, QConv2d) or isinstance(m, QLinear):
m.quantize = True
mse_df = pd.DataFrame(
index=np.arange(len(cached_input_output)),
columns=['name', 'bit', 'shape', 'mse_before', 'mse_after'])
print_freq = 100
for i, layer in enumerate(cached_input_output):
if i > 0 and args.seq_adaquant:
count = 0
cached_qinput = {}
for name, m in model.named_modules():
if layer.name == name:
if count < 1000:
handler = m.register_forward_hook(
partial(Qhook, name))
count += 1
# Store input/output for all quantizable layers
trainer.validate(train_data.get_loader())
print("cashed quant Input%s" % layer.name)
cached_input_output[layer][0] = (
cached_qinput[layer][0], cached_input_output[layer][0][1])
handler.remove()
print("\nOptimize {}:{} for {} bit of shape {}".format(
i, layer.name, layer.num_bits, layer.weight.shape))
mse_before, mse_after, snr_before, snr_after, kurt_in, kurt_w = \
optimize_layer(layer, cached_input_output[layer], args.optimize_weights, batch_size=args.batch_size, model_name=args.model)
print("\nMSE before optimization: {}".format(mse_before))
print("MSE after optimization: {}".format(mse_after))
mse_df.loc[i, 'name'] = layer.name
mse_df.loc[i, 'bit'] = layer.num_bits
mse_df.loc[i, 'shape'] = str(layer.weight.shape)
mse_df.loc[i, 'mse_before'] = mse_before
mse_df.loc[i, 'mse_after'] = mse_after
mse_df.loc[i, 'snr_before'] = snr_before
mse_df.loc[i, 'snr_after'] = snr_after
mse_df.loc[i, 'kurt_in'] = kurt_in
mse_df.loc[i, 'kurt_w'] = kurt_w
mse_csv = args.evaluate + '.mse.csv'
mse_df.to_csv(mse_csv)
filename = args.evaluate + '.adaquant'
torch.save(model.state_dict(), filename)
train_data = None
cached_input_output = None
val_results = trainer.validate(val_data.get_loader())
logging.info(val_results)
if args.res_log is not None:
if not os.path.exists(args.res_log):
df = pd.DataFrame()
else:
df = pd.read_csv(args.res_log, index_col=0)
ckp = ntpath.basename(args.evaluate)
if args.cmp is not None:
ckp += '_{}'.format(args.cmp)
adaquant_type = 'adaquant_seq' if args.seq_adaquant else 'adaquant_parallel'
df.loc[ckp, 'acc_' + adaquant_type] = val_results['prec1']
df.to_csv(args.res_log)
# print(df)
elif args.per_layer:
# Store input/output for all quantizable layers
calib_all_8_results = trainer.validate(train_data.get_loader())
print('########## All 8bit results ###########', calib_all_8_results)
int8_opt_model_state_dict = torch.load(args.int8_opt_model_path)
int4_opt_model_state_dict = torch.load(args.int4_opt_model_path)
per_layer_results = {}
args.names_sp_layers = [
key[:-7] for key in model.state_dict().keys()
if 'weight' in key and 'running' not in key and 'quantize' not in
key and ('conv' in key or 'downsample.0' in key or 'fc' in key)
]
for layer_idx, layer in enumerate(args.names_sp_layers):
model.load_state_dict(int8_opt_model_state_dict, strict=False)
model = search_replace_layer(model, [layer],
num_bits_activation=args.nbits_act,
num_bits_weight=args.nbits_weight)
layer_keys = [
key for key in int8_opt_model_state_dict
for qpkey in quant_keys if layer + qpkey == key
]
for key in layer_keys:
model.state_dict()[key].copy_(int4_opt_model_state_dict[key])
calib_results = trainer.validate(train_data.get_loader())
model = search_replace_layer(model, [layer],
num_bits_activation=8,
num_bits_weight=8)
print('finished %d out of %d' %
(layer_idx, len(args.names_sp_layers)))
logging.info(layer)
logging.info(calib_results)
per_layer_results[layer] = {
'base precision':
8,
'replaced precision':
args.nbits_act,
'replaced layer':
layer,
'accuracy':
calib_results['prec1'],
'loss':
calib_results['loss'],
'Parameters Size [Elements]':
model.state_dict()[layer + '.weight'].numel(),
'MACs':
'-'
}
torch.save(
per_layer_results, args.evaluate + '.per_layer_accuracy.A' +
str(args.nbits_act) + '.W' + str(args.nbits_weight))
all_8_dict = {
'base precision': 8,
'replaced precision': args.nbits_act,
'replaced layer': '-',
'accuracy': calib_all_8_results['prec1'],
'loss': calib_all_8_results['loss'],
'Parameters Size [Elements]': '-',
'MACs': '-'
}
columns = [key for key in all_8_dict]
with open(
args.evaluate + '.per_layer_accuracy.A' + str(args.nbits_act) +
'.W' + str(args.nbits_weight) + '.csv', "w") as f:
f.write(",".join(columns) + "\n")
col = [str(all_8_dict[c]) for c in all_8_dict.keys()]
f.write(",".join(col) + "\n")
for layer in per_layer_results:
r = per_layer_results[layer]
col = [str(r[c]) for c in r.keys()]
f.write(",".join(col) + "\n")
elif args.mixed_builder:
if isinstance(args.names_sp_layers, list):
print('loading int8 model" ', args.int8_opt_model_path)
int8_opt_model_state_dict = torch.load(args.int8_opt_model_path)
print('loading int4 model" ', args.int4_opt_model_path)
int4_opt_model_state_dict = torch.load(args.int4_opt_model_path)
model.load_state_dict(int8_opt_model_state_dict, strict=False)
model = search_replace_layer(model,
args.names_sp_layers,
num_bits_activation=args.nbits_act,
num_bits_weight=args.nbits_weight)
for layer_idx, layer in enumerate(args.names_sp_layers):
layer_keys = [
key for key in int8_opt_model_state_dict
for qpkey in quant_keys if layer + qpkey == key
]
for key in layer_keys:
model.state_dict()[key].copy_(
int4_opt_model_state_dict[key])
print('switched layer %s to 4 bit' % (layer))
elif isinstance(args.names_sp_layers, dict):
quant_models = {}
base_precision = args.precisions[0]
for m, prec in zip(args.opt_model_paths, args.precisions):
print('For precision={}, loading {}'.format(prec, m))
quant_models[prec] = torch.load(m)
model.load_state_dict(quant_models[base_precision], strict=False)
for layer_name, nbits_list in args.names_sp_layers.items():
model = search_replace_layer(model, [layer_name],
num_bits_activation=nbits_list[0],
num_bits_weight=nbits_list[0])
layer_keys = [
key for key in quant_models[base_precision]
for qpkey in quant_keys if layer_name + qpkey == key
]
for key in layer_keys:
model.state_dict()[key].copy_(
quant_models[nbits_list[0]][key])
print('switched layer {} to {} bit'.format(
layer_name, nbits_list[0]))
if os.environ.get('DEBUG') == 'True':
from utils.layer_sensativity import check_quantized_model
fp_names = check_quantized_model(trainer.model)
if len(fp_names) > 0:
logging.info('Found FP32 layers in the model:')
logging.info(fp_names)
if args.eval_on_train:
mixedIP_results = trainer.validate(train_data.get_loader())
else:
mixedIP_results = trainer.validate(val_data.get_loader())
torch.save(
{
'state_dict': model.state_dict(),
'config-ip': args.names_sp_layers
}, args.evaluate + '.mixed-ip-results.' + args.suffix)
logging.info(mixedIP_results)
acc = mixedIP_results['prec1']
loss = mixedIP_results['loss']
elif args.batch_norn_tuning:
from utils.layer_sensativity import search_replace_layer, extract_save_quant_state_dict, search_replace_layer_from_dict
from models.modules.quantize import QConv2d
if args.layers_precision_dict is not None:
model = search_replace_layer_from_dict(
model, literal_eval(args.layers_precision_dict))
else:
model = search_replace_layer(model,
args.names_sp_layers,
num_bits_activation=args.nbits_act,
num_bits_weight=args.nbits_weight)
exec_lfv_str = 'torchvision.models.' + args.load_from_vision + '(pretrained=True)'
model_orig = eval(exec_lfv_str)
model_orig.to(args.device, dtype)
search_copy_bn_params(model_orig)
layers_orig = dict([(n, m) for n, m in model_orig.named_modules()
if isinstance(m, nn.Conv2d)])
layers_q = dict([(n, m) for n, m in model.named_modules()
if isinstance(m, QConv2d)])
for l in layers_orig:
conv_orig = layers_orig[l]
conv_q = layers_q[l]
conv_q.register_parameter('gamma',
nn.Parameter(conv_orig.gamma.clone()))
conv_q.register_parameter('beta',
nn.Parameter(conv_orig.beta.clone()))
del model_orig
search_add_bn(model)
print("Run BN tuning")
for tt in range(args.tuning_iter):
print(tt)
trainer.cal_bn_stats(train_data.get_loader())
search_absorbe_tuning_bn(model)
filename = args.evaluate + '.bn_tuning'
print("Save model to: {}".format(filename))
torch.save(model.state_dict(), filename)
val_results = trainer.validate(val_data.get_loader())
logging.info(val_results)
if args.res_log is not None:
if not os.path.exists(args.res_log):
df = pd.DataFrame()
else:
df = pd.read_csv(args.res_log, index_col=0)
ckp = ntpath.basename(args.evaluate)
df.loc[ckp, 'acc_bn_tuning'] = val_results['prec1']
df.loc[ckp, 'loss_bn_tuning'] = val_results['loss']
df.to_csv(args.res_log)
# print(df)
elif args.bias_tuning:
for epoch in range(args.epochs):
trainer.epoch = epoch
train_data.set_epoch(epoch)
val_data.set_epoch(epoch)
logging.info('\nStarting Epoch: {0}\n'.format(epoch + 1))
# train for one epoch
repeat_train = 20 if args.update_only_th else 1
for tt in range(repeat_train):
print(tt)
train_results = trainer.train(
train_data.get_loader(),
duplicates=train_data.get('duplicates'),
chunk_batch=args.chunk_batch)
logging.info(train_results)
val_results = trainer.validate(val_data.get_loader())
logging.info(val_results)
if args.res_log is not None:
if not os.path.exists(args.res_log):
df = pd.DataFrame()
else:
df = pd.read_csv(args.res_log, index_col=0)
ckp = ntpath.basename(args.evaluate)
if 'bn_tuning' in ckp:
ckp = ckp.replace('.bn_tuning', '')
df.loc[ckp, 'acc_bias_tuning'] = val_results['prec1']
df.to_csv(args.res_log)
# import pdb; pdb.set_trace()
else:
#print('Please Choose one of the following ....')
if model_config['measure']:
results = trainer.validate(train_data.get_loader(), rec=args.rec)
# results = trainer.validate(val_data.get_loader())
# print(results)
else:
if args.evaluate_init_configuration:
results = trainer.validate(val_data.get_loader())
if args.res_log is not None:
if not os.path.exists(args.res_log):
df = pd.DataFrame()
else:
df = pd.read_csv(args.res_log, index_col=0)
ckp = ntpath.basename(args.evaluate)
if args.cmp is not None:
ckp += '_{}'.format(args.cmp)
df.loc[ckp, 'acc_base'] = results['prec1']
df.to_csv(args.res_log)
if args.extract_bias_mean:
file_name = 'bias_mean_measure' if model_config[
'measure'] else 'bias_mean_quant'
torch.save(trainer.bias_mean, file_name)
if model_config['measure']:
filename = args.evaluate + '.measure'
if 'perC' in args.model_config: filename += '_perC'
torch.save(model.state_dict(), filename)
logging.info(results)
else:
if args.evaluate_init_configuration:
logging.info(results)
return acc, loss