def update_training_scores_history(perf_scores_history, model, loss, epoch,
num_best_scores):
""" Update the list of top training scores achieved so far, and log the best scores so far"""
model_sparsity, _, params_nnz_cnt = distiller.model_params_stats(model)
perf_scores_history.append(
distiller.MutableNamedTuple({
'params_nnz_cnt': -params_nnz_cnt,
'sparsity': model_sparsity,
'loss': loss,
'epoch': epoch
}))
# Keep perf_scores_history sorted from best to worst
# Sort by sparsity as main sort key, then sort by top1, top5 and epoch
def key(entry):
return (
entry['params_nnz_cnt'],
-entry['loss'],
entry['epoch'],
)
perf_scores_history.sort(key=key, reverse=True)
for score in perf_scores_history[:num_best_scores]:
msglogger.info(
'==> Best [Loss: %.3f Sparsity:%.2f Params: %d on epoch: %d]',
score.loss, score.sparsity, -score.params_nnz_cnt, score.epoch)
def update_training_scores_history(perf_scores_history, model, top1, top5, epoch, num_best_scores):
""" Update the list of top training scores achieved so far, and log the best scores so far"""
model_sparsity, _, params_nnz_cnt = distiller.model_params_stats(model)
perf_scores_history.append(distiller.MutableNamedTuple({'params_nnz_cnt': -params_nnz_cnt,
'sparsity': model_sparsity,
'top1': top1, 'top5': top5, 'epoch': epoch}))
# Keep perf_scores_history sorted from best to worst
# Sort by sparsity as main sort key, then sort by top1, top5 and epoch
perf_scores_history.sort(key=operator.attrgetter('params_nnz_cnt', 'top1', 'top5', 'epoch'), reverse=True)
for score in perf_scores_history[:num_best_scores]:
msglogger.info('==> Best [Top1: %.3f Top5: %.3f Sparsity:%.2f Params: %d on epoch: %d]',
score.top1, score.top5, score.sparsity, -score.params_nnz_cnt, score.epoch)
def step(self, model, epoch, **kwargs):
assert all(score in kwargs.keys() for score in ('top1', 'top5'))
model_sparsity, _, params_nnz_cnt = distiller.model_params_stats(model)
self.perf_scores_history.append(distiller.MutableNamedTuple({
'params_nnz_cnt': -params_nnz_cnt,
'sparsity': model_sparsity,
'top1': kwargs['top1'],
'top5': kwargs['top5'],
'epoch': epoch}))
# Keep perf_scores_history sorted from best to worst
self.perf_scores_history.sort(
key=operator.attrgetter('top1', 'top5', 'params_nnz_cnt', 'epoch'),
reverse=True)
def train(**kwargs):
opt._parse(kwargs)
train_writer = None
value_writer = None
if opt.vis:
train_writer = SummaryWriter(
log_dir='./runs/train_' +
datetime.now().strftime('%y%m%d-%H-%M-%S'))
value_writer = SummaryWriter(
log_dir='./runs/val_' + datetime.now().strftime('%y%m%d-%H-%M-%S'))
previous_loss = 1e10 # 上次学习的loss
best_precision = 0 # 最好的精确度
start_epoch = 0
lr = opt.lr
perf_scores_history = [] # 绩效分数
# step1: criterion and optimizer
# 1. 铰链损失（Hinge Loss）：主要用于支持向量机（SVM） 中；
# 2. 互熵损失 （Cross Entropy Loss，Softmax Loss ）：用于Logistic 回归与Softmax 分类中；
# 3. 平方损失（Square Loss）：主要是最小二乘法（OLS）中；
# 4. 指数损失（Exponential Loss） ：主要用于Adaboost 集成学习算法中；
# 5. 其他损失（如0-1损失，绝对值损失）
criterion = t.nn.CrossEntropyLoss().to(opt.device) # 损失函数
# step2: meters
train_losses = AverageMeter() # 误差仪表
train_top1 = AverageMeter() # top1 仪表
train_top5 = AverageMeter() # top5 仪表
pylogger = PythonLogger(msglogger)
# step3: configure model
model = getattr(models, opt.model)() # 获得网络结构
compression_scheduler = distiller.CompressionScheduler(model)
optimizer = model.get_optimizer(lr, opt.weight_decay) # 优化器
if opt.load_model_path:
# # 把所有的张量加载到CPU中
# t.load(opt.load_model_path, map_location=lambda storage, loc: storage)
# t.load(opt.load_model_path, map_location='cpu')
# # 把所有的张量加载到GPU 1中
# t.load(opt.load_model_path, map_location=lambda storage, loc: storage.cuda(1))
# # 把张量从GPU 1 移动到 GPU 0
# t.load(opt.load_model_path, map_location={'cuda:1': 'cuda:0'})
checkpoint = t.load(opt.load_model_path)
start_epoch = checkpoint["epoch"]
# compression_scheduler.load_state_dict(checkpoint['compression_scheduler'], False)
best_precision = checkpoint["best_precision"]
model.load_state_dict(checkpoint["state_dict"])
optimizer = checkpoint['optimizer']
model.to(opt.device) # 加载模型到 GPU
if opt.compress:
compression_scheduler = distiller.file_config(
model, optimizer, opt.compress, compression_scheduler) # 加载模型修剪计划表
model.to(opt.device)
# 学习速率调整器
lr_scheduler = get_scheduler(optimizer, opt)
# step4: data_image
train_data = DatasetFromFilename(opt.data_root, flag='train') # 训练集
val_data = DatasetFromFilename(opt.data_root, flag='test') # 验证集
train_dataloader = DataLoader(train_data,
opt.batch_size,
shuffle=True,
num_workers=opt.num_workers) # 训练集加载器
val_dataloader = DataLoader(val_data,
opt.batch_size,
shuffle=True,
num_workers=opt.num_workers) # 验证集加载器
# train
for epoch in range(start_epoch, opt.max_epoch):
model.train()
if opt.pruning:
compression_scheduler.on_epoch_begin(epoch) # epoch 开始修剪
train_losses.reset() # 重置仪表
train_top1.reset() # 重置仪表
# print('训练数据集大小', len(train_dataloader))
total_samples = len(train_dataloader.sampler)
steps_per_epoch = math.ceil(total_samples / opt.batch_size)
train_progressor = ProgressBar(mode="Train ",
epoch=epoch,
total_epoch=opt.max_epoch,
model_name=opt.model,
lr=lr,
total=len(train_dataloader))
lr = lr_scheduler.get_lr()[0]
for ii, (data, labels, img_path, tag) in enumerate(train_dataloader):
if not check_date(img_path, tag, msglogger): return
if opt.pruning:
compression_scheduler.on_minibatch_begin(
epoch, ii, steps_per_epoch, optimizer) # batch 开始修剪
train_progressor.current = ii + 1 # 训练集当前进度
# train model
input = data.to(opt.device)
target = labels.to(opt.device)
if train_writer:
grid = make_grid(
(input.data.cpu() * 0.225 + 0.45).clamp(min=0, max=1))
train_writer.add_image('train_images', grid,
ii * (epoch + 1)) # 训练图片
score = model(input) # 网络结构返回值
# 计算损失
loss = criterion(score, target)
if opt.pruning:
# Before running the backward phase, we allow the scheduler to modify the loss
# (e.g. add regularization loss)
agg_loss = compression_scheduler.before_backward_pass(
epoch,
ii,
steps_per_epoch,
loss,
optimizer=optimizer,
return_loss_components=True) # 模型修建误差
loss = agg_loss.overall_loss
train_losses.update(loss.item(), input.size(0))
# loss = criterion(score[0], target) # 计算损失 Inception3网络
optimizer.zero_grad() # 参数梯度设成0
loss.backward() # 反向传播
optimizer.step() # 更新参数
if opt.pruning:
compression_scheduler.on_minibatch_end(epoch, ii,
steps_per_epoch,
optimizer) # batch 结束修剪
precision1_train, precision5_train = accuracy(
score, target, topk=(1, 5)) # top1 和 top5 的准确率
# writer.add_graph(model, input)
# precision1_train, precision2_train = accuracy(score[0], target, topk=(1, 2)) # Inception3网络
train_losses.update(loss.item(), input.size(0))
train_top1.update(precision1_train[0].item(), input.size(0))
train_top5.update(precision5_train[0].item(), input.size(0))
train_progressor.current_loss = train_losses.avg
train_progressor.current_top1 = train_top1.avg
train_progressor.current_top5 = train_top5.avg
train_progressor() # 打印进度
if ii % opt.print_freq == 0:
if train_writer:
train_writer.add_scalar('loss', train_losses.avg,
ii * (epoch + 1)) # 训练误差
train_writer.add_text(
'top1', 'train accuracy top1 %s' % train_top1.avg,
ii * (epoch + 1)) # top1准确率文本
train_writer.add_scalars(
'accuracy', {
'top1': train_top1.avg,
'top5': train_top5.avg,
'loss': train_losses.avg
}, ii * (epoch + 1))
# train_progressor.done() # 保存训练结果为txt
# validate and visualize
if opt.pruning:
distiller.log_weights_sparsity(model, epoch,
loggers=[pylogger]) # 打印模型修剪结果
compression_scheduler.on_epoch_end(epoch, optimizer) # epoch 结束修剪
val_loss, val_top1, val_top5 = val(model, criterion, val_dataloader,
epoch, value_writer, lr) # 校验模型
sparsity = distiller.model_sparsity(model)
perf_scores_history.append(
distiller.MutableNamedTuple(
{
'sparsity': sparsity,
'top1': val_top1,
'top5': val_top5,
'epoch': epoch + 1,
'lr': lr,
'loss': val_loss
}, ))
# 保持绩效分数历史记录从最好到最差的排序
# 按稀疏度排序为主排序键，然后按top1、top5、epoch排序
perf_scores_history.sort(key=operator.attrgetter(
'sparsity', 'top1', 'top5', 'epoch'),
reverse=True)
for score in perf_scores_history[:1]:
msglogger.info(
'==> Best [Top1: %.3f Top5: %.3f Sparsity: %.2f on epoch: %d Lr: %f Loss: %f]',
score.top1, score.top5, score.sparsity, score.epoch, lr,
score.loss)
best_precision = max(perf_scores_history[0].top1,
best_precision) # 最大top1 准确率
is_best = epoch + 1 == perf_scores_history[
0].epoch # 当前epoch 和最佳epoch 一样
if is_best:
model.save({
"epoch":
epoch + 1,
"model_name":
opt.model,
"state_dict":
model.state_dict(),
"best_precision":
best_precision,
"optimizer":
optimizer,
"valid_loss": [val_loss, val_top1, val_top5],
'compression_scheduler':
compression_scheduler.state_dict(),
}) # 保存模型
# update learning rate
lr_scheduler.step(epoch) # 更新学习效率
# 如果训练误差比上次大 降低学习效率
# if train_losses.val > previous_loss:
# lr = lr * opt.lr_decay
# # 当loss大于上一次loss,降低学习率
# for param_group in optimizer.param_groups:
# param_group['lr'] = lr
#
# previous_loss = train_losses.val
t.cuda.empty_cache() # 这个命令是清除没用的临时变量的
def main():
global msglogger
check_pytorch_version()
args = parser.parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(sys.argv, gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
best_epochs = [distiller.MutableNamedTuple({'epoch': 0, 'top1': 0, 'sparsity': 0})
for i in range(args.num_best_scores)]
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.workers > 1:
msglogger.error('ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1')
exit(1)
# Use a well-known seed, for repeatability of experiments
torch.manual_seed(0)
random.seed(0)
np.random.seed(0)
cudnn.deterministic = True
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error('ERROR: Argument --gpus must be a comma-separated list of integers only')
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error('ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
if 'cinic' in args.arch:
args.dataset = 'cinic10'
else:
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset in ['cifar10', 'cinic10'] else 1000
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
#model = create_model(args.pretrained, args.dataset, args.arch,
# parallel=not args.load_serialized, device_ids=args.gpus)
model = create_model(False, args.dataset, args.arch, device_ids=args.gpus) # Get arch state_dict
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s', args.earlyexit_thresholds)
# We can optionally resume from a checkpoint
if args.resume:
#model, compression_scheduler, start_epoch = apputils.load_checkpoint(
# model, chkpt_file=args.resume)
# Load Pre-trained Model
chkpt_file=args.resume
print("=> loading checkpoint %s" % chkpt_file)
checkpoint = torch.load(chkpt_file)
model.load_state_dict(checkpoint['net'])
# Define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().cuda()
optimizer = torch.optim.SGD(model.parameters(), lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.ADC:
return automated_deep_compression(model, criterion, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.workers, args.validation_size, args.deterministic)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler), len(test_loader.sampler))
activations_collectors = create_activation_stats_collectors(model, collection_phase=args.activation_stats)
if args.sensitivity is not None:
return sensitivity_analysis(model, criterion, test_loader, pylogger, args)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger, activations_collectors, args)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
compression_scheduler = distiller.file_config(model, optimizer, args.compress)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
model.cuda()
else:
compression_scheduler = distiller.CompressionScheduler(model)
args.kd_policy = None
if args.kd_teacher:
teacher = create_model(args.kd_pretrained, args.dataset, args.kd_teacher, device_ids=args.gpus)
if args.kd_resume:
teacher, _, _ = apputils.load_checkpoint(teacher, chkpt_file=args.kd_resume)
dlw = distiller.DistillationLossWeights(args.kd_distill_wt, args.kd_student_wt, args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(args.kd_policy, starting_epoch=args.kd_start_epoch, ending_epoch=args.epochs,
frequency=1)
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
for epoch in range(start_epoch, start_epoch + args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
with collectors_context(activations_collectors["train"]) as collectors:
train(train_loader, model, criterion, optimizer, epoch, compression_scheduler,
loggers=[tflogger, pylogger], args=args)
distiller.log_weights_sparsity(model, epoch, loggers=[tflogger, pylogger])
distiller.log_activation_statsitics(epoch, "train", loggers=[tflogger],
collector=collectors["sparsity"])
if args.masks_sparsity:
msglogger.info(distiller.masks_sparsity_tbl_summary(model, compression_scheduler))
# evaluate on validation set
with collectors_context(activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(val_loader, model, criterion, [pylogger], args, epoch)
distiller.log_activation_statsitics(epoch, "valid", loggers=[tflogger],
collector=collectors["sparsity"])
save_collectors_data(collectors, msglogger.logdir)
stats = ('Peformance/Validation/',
OrderedDict([('Loss', vloss),
('Top1', top1),
('Top5', top5)]))
distiller.log_training_progress(stats, None, epoch, steps_completed=0, total_steps=1, log_freq=1,
loggers=[tflogger])
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
# remember best top1 and save checkpoint
#sparsity = distiller.model_sparsity(model)
is_best = top1 > best_epochs[0].top1
if is_best:
best_epochs[0].epoch = epoch
best_epochs[0].top1 = top1
#best_epoch.sparsity = sparsity
best_epochs = sorted(best_epochs, key=lambda score: score.top1)
for score in reversed(best_epochs):
if score.top1 > 0:
msglogger.info('==> Best Top1: %.3f on Epoch: %d', score.top1, score.epoch)
apputils.save_checkpoint(epoch, args.arch, model, optimizer, compression_scheduler,
best_epochs[0].top1, is_best, args.name, msglogger.logdir)
# Finally run results on the test set
test(test_loader, model, criterion, [pylogger], activations_collectors, args=args)
def train(self):
previous_loss = 1e10 # 上次学习的loss
lr = self.opt.lr
perf_scores_history = []
pylogger = PythonLogger(msglogger)
self.train_load_model()
self.load_compress()
self.create_write()
lr_scheduler = get_scheduler(self.optimizer, opt)
for epoch in range(self.start_epoch, self.opt.max_epoch):
self.model.train()
self.load_data()
if self.opt.pruning:
self.compression_scheduler.on_epoch_begin(epoch) # epoch 开始修剪
self.train_losses.reset() # 重置仪表
self.train_top1.reset() # 重置仪表
# print('训练数据集大小', len(train_dataloader))
total_samples = len(self.train_dataloader.sampler)
steps_per_epoch = math.ceil(total_samples / self.opt.batch_size)
train_progressor = ProgressBar(mode="Train ",
epoch=epoch,
total_epoch=self.opt.max_epoch,
model_name=self.opt.model,
total=len(self.train_dataloader))
lr = lr_scheduler.get_lr()
for ii, (data, labels,
img_path) in enumerate(self.train_dataloader):
if self.opt.pruning:
self.compression_scheduler.on_minibatch_begin(
epoch, ii, steps_per_epoch,
self.optimizer) # batch 开始修剪
train_progressor.current = ii + 1 # 训练集当前进度
# train model
input = data.to(self.opt.device)
target = labels.to(self.opt.device)
score = self.model(input) # 网络结构返回值
loss = self.criterion(score, target) # 计算损失
if self.opt.pruning:
# Before running the backward phase, we allow the scheduler to modify the loss
# (e.g. add regularization loss)
agg_loss = self.compression_scheduler.before_backward_pass(
epoch,
ii,
steps_per_epoch,
loss,
optimizer=self.optimizer,
return_loss_components=True) # 模型修建误差
loss = agg_loss.overall_loss
self.train_losses.update(loss.item(), input.size(0))
# loss = criterion(score[0], target) # 计算损失 Inception3网络
self.optimizer.zero_grad() # 参数梯度设成0
loss.backward() # 反向传播
self.optimizer.step() # 更新参数
if opt.pruning:
self.compression_scheduler.on_minibatch_end(
epoch, ii, steps_per_epoch,
self.optimizer) # batch 结束修剪
precision1_train, precision5_train = accuracy(
score, target, topk=(1, 5)) # top1 和 top5 的准确率
# precision1_train, precision2_train = accuracy(score[0], target, topk=(1, 2)) # Inception3网络
self.train_losses.update(loss.item(), input.size(0))
self.train_top1.update(precision1_train[0].item(),
input.size(0))
self.train_top5.update(precision5_train[0].item(),
input.size(0))
train_progressor.current_loss = self.train_losses.avg
train_progressor.current_top1 = self.train_top1.avg
train_progressor.current_top5 = self.train_top5.avg
train_progressor() # 打印进度
if (ii + 1) % self.opt.print_freq == 0:
self.visualization_train(input, ii, epoch)
if self.opt.pruning:
distiller.log_weights_sparsity(self.model,
epoch,
loggers=[pylogger]) # 打印模型修剪结果
self.compression_scheduler.on_epoch_end(
epoch, self.optimizer) # epoch 结束修剪
val_loss, val_top1, val_top5 = val(self.model, self.criterion,
self.val_dataloader, epoch,
self.value_writer) # 校验模型
sparsity = distiller.model_sparsity(self.model)
perf_scores_history.append(
distiller.MutableNamedTuple(
{
'sparsity': sparsity,
'top1': val_top1,
'top5': val_top5,
'epoch': epoch + 1,
'lr': lr,
'loss': val_loss
}, ))
# 保持绩效分数历史记录从最好到最差的排序
# 按稀疏度排序为主排序键，然后按top1、top5、epoch排序
perf_scores_history.sort(key=operator.attrgetter(
'sparsity', 'top1', 'top5', 'epoch'),
reverse=True)
for score in perf_scores_history[:1]:
msglogger.info(
'==> Best [Top1: %.3f Top5: %.3f Sparsity: %.2f on epoch: %d Lr: %f Loss: %f]',
score.top1, score.top5, score.sparsity, score.epoch, lr,
score.loss)
is_best = epoch == perf_scores_history[
0].epoch # 当前epoch 和最佳epoch 一样
self.best_precision = max(perf_scores_history[0].top1,
self.best_precision) # 最大top1 准确率
if is_best:
self.train_save_model(epoch, val_loss, val_top1, val_top5)
# update learning rate
lr = lr_scheduler.get_lr()
# # 如果训练误差比上次大 降低学习效率
# if self.train_losses.val > previous_loss:
# lr = lr * self.opt.lr_decay
# # 当loss大于上一次loss,降低学习率
# for param_group in self.optimizer.param_groups:
# param_group['lr'] = lr
#
# previous_loss = self.train_losses.val
t.cuda.empty_cache()
def main():
script_dir = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
global msglogger
# Parse arguments
args = parser.get_parser().parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(
os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
apputils.log_execution_env_state(args.compress,
msglogger.logdir,
gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
best_epochs = list()
if args.deterministic:
if args.loaders is None:
args.loaders = 1
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.loaders > 1:
msglogger.error(
'ERROR: Setting --deterministic requires setting --loaders to 0 or 1'
)
exit(1)
# Use a well-known seed, for repeatability of experiments
distiller.set_deterministic()
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.use_cpu or (args.gpus is None
and not torch.cuda.is_available()) or (args.gpus
== ''):
# Set GPU index to -1 if using CPU
args.device = 'cpu'
args.gpus = -1
else:
args.device = 'cuda'
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error(
'ERROR: Argument --gpus must be a comma-separated list of integers only'
)
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error(
'ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
if args.loaders is None:
active_gpus = args.gpus if args.gpus is not None else torch.cuda.device_count(
)
args.loaders = max(parser.DEFAULT_LOADERS_COUNT,
parser.DEFAULT_LOADERS_COUNT * active_gpus)
msglogger.debug('Number of data loaders set to: {}'.format(args.loaders))
# Infer the dataset from the model name
args.dataset = 'cifar10' if 'cifar' in args.arch else 'imagenet'
args.num_classes = 10 if args.dataset == 'cifar10' else 1000
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
model = create_model(args.pretrained,
args.dataset,
args.arch,
parallel=not args.load_serialized,
device_ids=args.gpus)
compression_scheduler = None
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s',
args.earlyexit_thresholds)
# We can optionally resume from a checkpoint
optimizer = None
resumed_training_steps = None
if args.resume or args.load_state_dict:
if args.resume and not args.reset_optimizer:
# initiate SGD with dummy lr
optimizer = torch.optim.SGD(model.parameters(), lr=0.36787944117)
model, compression_scheduler, optimizer, start_epoch, resumed_training_steps = apputils.load_checkpoint(
model, args.resume or args.load_state_dict, optimizer=optimizer)
model.to(args.device)
# Define loss function (criterion) and optimizer
criterion = nn.CrossEntropyLoss().to(args.device)
if optimizer is not None:
# optimizer was resumed from checkpoint
# check if user has tried to set optimizer arguments
# if so, ignore arguments with a warning.
optimizer_group_args = [
'lr', 'learning-rate', 'momentum', 'weight-decay', 'wd'
]
user_optim_args = [
x for x in optimizer_group_args for arg in sys.argv
if arg.startswith('--' + x)
]
if user_optim_args:
msglogger.warning(
'{} optimizer arguments are ignored.'.format(user_optim_args))
msglogger.info(
'setting optimizer arguments when optimizer is resumed '
'from checkpoint is forbidden.')
else:
optimizer = torch.optim.SGD(model.parameters(),
lr=args.lr,
momentum=args.momentum,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.AMC:
return automated_deep_compression(model, criterion, optimizer,
pylogger, args)
if args.greedy:
return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports.
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
activations_collectors = create_activation_stats_collectors(
model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info(
'\tStats will be collected for {:.1%} of test dataset'.format(
args.qe_calibration))
msglogger.info(
'\tSetting constant seeds and converting model to serialized execution'
)
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(
create_quantization_stats_collector(model))
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
train_loader, val_loader, test_loader, _ = apputils.load_data(
args.dataset, os.path.expanduser(args.data), args.batch_size,
args.loaders, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size,
args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler),
len(test_loader.sampler))
args.trainset_print_period = parser.getPrintPeriod(
args, len(train_loader.sampler), args.batch_size)
args.validset_print_period = parser.getPrintPeriod(args,
len(val_loader.sampler),
args.batch_size)
args.testset_print_period = parser.getPrintPeriod(args,
len(test_loader.sampler),
args.batch_size)
if args.sensitivity is not None:
sensitivities = np.arange(args.sensitivity_range[0],
args.sensitivity_range[1],
args.sensitivity_range[2])
return sensitivity_analysis(model, criterion, test_loader, pylogger,
args, sensitivities)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger,
activations_collectors, args,
compression_scheduler)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
compression_scheduler = distiller.file_config(
model, optimizer, args.compress, compression_scheduler,
(start_epoch - 1) if
(args.resume and not args.reset_optimizer) else None)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
model.to(args.device)
elif compression_scheduler is None:
compression_scheduler = distiller.CompressionScheduler(model)
if args.thinnify:
#zeros_mask_dict = distiller.create_model_masks_dict(model)
assert args.resume is not None, "You must use --resume to provide a checkpoint file to thinnify"
distiller.remove_filters(model,
compression_scheduler.zeros_mask_dict,
args.arch,
args.dataset,
optimizer=None)
apputils.save_checkpoint(0,
args.arch,
model,
optimizer=None,
scheduler=compression_scheduler,
name="{}_thinned".format(
args.resume.replace(".pth.tar", "")),
dir=msglogger.logdir)
print(
"Note: your model may have collapsed to random inference, so you may want to fine-tune"
)
return
args.kd_policy = None
if args.kd_teacher:
teacher = create_model(args.kd_pretrained,
args.dataset,
args.kd_teacher,
device_ids=args.gpus)
if args.kd_resume:
teacher = apputils.load_checkpoint(teacher,
chkpt_file=args.kd_resume)[0]
dlw = distiller.DistillationLossWeights(args.kd_distill_wt,
args.kd_student_wt,
args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(
model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(
args.kd_policy, range(args.kd_start_epoch, args.epochs, 1))
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
if getattr(compression_scheduler, 'global_policy_end_epoch',
None) is not None:
if compression_scheduler.global_policy_end_epoch >= (start_epoch +
args.epochs):
msglogger.warning(
'scheduler requires at least {} epochs, but only {} are sanctioned'
.format(compression_scheduler.global_policy_end_epoch,
args.epochs))
accumulated_training_steps = resumed_training_steps if resumed_training_steps is not None else 0
for epoch in range(start_epoch, start_epoch + args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
with collectors_context(activations_collectors["train"]) as collectors:
try:
train(train_loader,
model,
criterion,
optimizer,
epoch,
accumulated_training_steps,
compression_scheduler,
loggers=[tflogger, pylogger],
args=args)
except RuntimeError as e:
if ('cuda out of memory' in str(e).lower()):
msglogger.error(
'CUDA memory failure has been detected.\n'
'Sometimes it helps to decrease batch size.\n'
'e.g. Add the following flag to your call: --batch-size={}'
.format(args.batch_size // 10))
raise
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
distiller.log_activation_statsitics(
epoch,
"train",
loggers=[tflogger],
collector=collectors["sparsity"])
if args.masks_sparsity:
msglogger.info(
distiller.masks_sparsity_tbl_summary(
model, compression_scheduler))
accumulated_training_steps += math.ceil(
len(train_loader.sampler) / train_loader.batch_size)
# evaluate on validation set
with collectors_context(activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(val_loader, model, criterion,
[pylogger], args, epoch)
distiller.log_activation_statsitics(
epoch,
"valid",
loggers=[tflogger],
collector=collectors["sparsity"])
save_collectors_data(collectors, msglogger.logdir)
stats = ('Performance/Validation/',
OrderedDict([('Loss', vloss), ('Top1', top1),
('Top5', top5)]))
tflogger.log_training_progress(stats, epoch, None)
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
if getattr(compression_scheduler, 'global_policy_end_epoch',
None) is None or (
compression_scheduler.global_policy_end_epoch <= epoch):
# Update the list of top scores achieved since all policies have concluded
if top1 > 0:
best_epochs.append(
distiller.MutableNamedTuple({
'top1': top1,
'top5': top5,
'epoch': epoch
}))
# Keep best_epochs sorted from best to worst
# Sort by top1 first, secondary sort by top5, and so forth
best_epochs.sort(key=operator.attrgetter('top1', 'top5', 'epoch'),
reverse=True)
for score in best_epochs[:args.num_best_scores]:
msglogger.info('==> Best Top1: %.3f Top5: %.3f on epoch: %d',
score.top1, score.top5, score.epoch)
is_best = best_epochs and (epoch == best_epochs[0].epoch)
apputils.save_checkpoint(epoch, args.arch, model, optimizer,
compression_scheduler,
best_epochs[0].top1 if best_epochs else None,
is_best, args.name, msglogger.logdir,
accumulated_training_steps)
# Finally run results on the test set
test(test_loader,
model,
criterion, [pylogger],
activations_collectors,
args=args)
def main():
script_dir = os.path.dirname(__file__)
module_path = os.path.abspath(os.path.join(script_dir, '..', '..'))
global msglogger
# Parse arguments
args = parser.get_parser().parse_args()
if not os.path.exists(args.output_dir):
os.makedirs(args.output_dir)
msglogger = apputils.config_pylogger(
os.path.join(script_dir, 'logging.conf'), args.name, args.output_dir)
# Log various details about the execution environment. It is sometimes useful
# to refer to past experiment executions and this information may be useful.
# 记录有关执行环境的各种详细信息。有时是有用的
# 参考过去的实验执行，这些信息可能有用。
apputils.log_execution_env_state(args.compress,
msglogger.logdir,
gitroot=module_path)
msglogger.debug("Distiller: %s", distiller.__version__)
start_epoch = 0
perf_scores_history = []
if args.deterministic:
# Experiment reproducibility is sometimes important. Pete Warden expounded about this
# in his blog: https://petewarden.com/2018/03/19/the-machine-learning-reproducibility-crisis/
# In Pytorch, support for deterministic execution is still a bit clunky.
if args.workers > 1:
msglogger.error(
'ERROR: Setting --deterministic requires setting --workers/-j to 0 or 1'
) # 错误：设置--确定性要求将--workers/-j设置为0或1
exit(1) # 正常退出程序
# Use a well-known seed, for repeatability of experiments 使用一种众所周知的种子，用于实验的重复性。
distiller.set_deterministic()
else:
# This issue: https://github.com/pytorch/pytorch/issues/3659
# Implies that cudnn.benchmark should respect cudnn.deterministic, but empirically we see that
# results are not re-produced when benchmark is set. So enabling only if deterministic mode disabled.
cudnn.benchmark = True
if args.cpu or not torch.cuda.is_available():
# Set GPU index to -1 if using CPU
args.device = 'cpu'
args.gpus = -1
else:
args.device = 'cuda'
if args.gpus is not None:
try:
args.gpus = [int(s) for s in args.gpus.split(',')]
except ValueError:
msglogger.error(
'ERROR: Argument --gpus must be a comma-separated list of integers only'
)
exit(1)
available_gpus = torch.cuda.device_count()
for dev_id in args.gpus:
if dev_id >= available_gpus:
msglogger.error(
'ERROR: GPU device ID {0} requested, but only {1} devices available'
.format(dev_id, available_gpus))
exit(1)
# Set default device in case the first one on the list != 0
torch.cuda.set_device(args.gpus[0])
# Infer the dataset from the model name
args.dataset = 'cousm'
if args.earlyexit_thresholds:
args.num_exits = len(args.earlyexit_thresholds) + 1
args.loss_exits = [0] * args.num_exits
args.losses_exits = []
args.exiterrors = []
# Create the model
model = ResNet152()
# model = torch.nn.DataParallel(model, device_ids=args.gpus) # 并行GPU
model.to(args.device)
compression_scheduler = None # 压缩调度
# Create a couple of logging backends. TensorBoardLogger writes log files in a format
# that can be read by Google's Tensor Board. PythonLogger writes to the Python logger.
# 创建两个日志后端 TensorBoardLogger以Google的Tensor板可以读取的格式写入日志文件。python logger将写入python记录器。
tflogger = TensorBoardLogger(msglogger.logdir)
pylogger = PythonLogger(msglogger)
# capture thresholds for early-exit training
if args.earlyexit_thresholds:
msglogger.info('=> using early-exit threshold values of %s',
args.earlyexit_thresholds)
# We can optionally resume from a checkpoint
if args.resume: # 加载训练模型
# checkpoint = torch.load(args.resume)
# model.load_state_dict(checkpoint['state_dict'])
model, compression_scheduler, start_epoch = apputils.load_checkpoint(
model, chkpt_file=args.resume)
model.to(args.device)
# Define loss function (criterion) and optimizer # 定义损失函数和优化器SGD
criterion = nn.CrossEntropyLoss().to(args.device)
# optimizer = torch.optim.SGD(model.fc.parameters(), lr=args.lr,
# momentum=args.momentum,
# weight_decay=args.weight_decay)
optimizer = torch.optim.Adam(model.model.fc.parameters(),
lr=args.lr,
weight_decay=args.weight_decay)
msglogger.info('Optimizer Type: %s', type(optimizer))
msglogger.info('Optimizer Args: %s', optimizer.defaults)
if args.AMC: # 自动化的深层压缩
return automated_deep_compression(model, criterion, optimizer,
pylogger, args)
if args.greedy: # 贪婪的
return greedy(model, criterion, optimizer, pylogger, args)
# This sample application can be invoked to produce various summary reports. # 可以调用此示例应用程序来生成各种摘要报告。
if args.summary:
return summarize_model(model, args.dataset, which_summary=args.summary)
# 激活统计收集器
activations_collectors = create_activation_stats_collectors(
model, *args.activation_stats)
if args.qe_calibration:
msglogger.info('Quantization calibration stats collection enabled:')
msglogger.info(
'\tStats will be collected for {:.1%} of test dataset'.format(
args.qe_calibration))
msglogger.info(
'\tSetting constant seeds and converting model to serialized execution'
)
distiller.set_deterministic()
model = distiller.make_non_parallel_copy(model)
activations_collectors.update(
create_quantization_stats_collector(model)) # 量化统计收集器
args.evaluate = True
args.effective_test_size = args.qe_calibration
# Load the datasets: the dataset to load is inferred from the model name passed
# in args.arch. The default dataset is ImageNet, but if args.arch contains the
# substring "_cifar", then cifar10 is used.
# 加载数据集：从传递的模型名称推断要加载的数据集
train_loader, val_loader, test_loader, _ = get_data_loaders(
datasets_fn, r'/home/tian/Desktop/image_yasuo', args.batch_size,
args.workers, args.validation_split, args.deterministic,
args.effective_train_size, args.effective_valid_size,
args.effective_test_size)
msglogger.info('Dataset sizes:\n\ttraining=%d\n\tvalidation=%d\n\ttest=%d',
len(train_loader.sampler), len(val_loader.sampler),
len(test_loader.sampler))
# 可以调用此示例应用程序来对模型执行敏感性分析。输出保存到csv和png。
if args.sensitivity is not None:
sensitivities = np.arange(args.sensitivity_range[0],
args.sensitivity_range[1],
args.sensitivity_range[2])
return sensitivity_analysis(model, criterion, test_loader, pylogger,
args, sensitivities)
if args.evaluate:
return evaluate_model(model, criterion, test_loader, pylogger,
activations_collectors, args,
compression_scheduler)
if args.compress:
# The main use-case for this sample application is CNN compression. Compression
# requires a compression schedule configuration file in YAML.
# #这个示例应用程序的主要用例是CNN压缩
# #需要yaml中的压缩计划配置文件。
compression_scheduler = distiller.file_config(model, optimizer,
args.compress,
compression_scheduler)
# Model is re-transferred to GPU in case parameters were added (e.g. PACTQuantizer)
# 如果添加了参数（如PactQualifier），则模型会重新传输到GPU。
model.to(args.device)
elif compression_scheduler is None:
compression_scheduler = distiller.CompressionScheduler(model) # 压缩计划程序
if args.thinnify:
# zeros_mask_dict = distiller.create_model_masks_dict(model)
assert args.resume is not None, "You must use --resume to provide a checkpoint file to thinnify" # 必须使用--resume提供检查点文件以细化
distiller.remove_filters(model,
compression_scheduler.zeros_mask_dict,
args.arch,
args.dataset,
optimizer=None)
apputils.save_checkpoint(0,
args.arch,
model,
optimizer=None,
scheduler=compression_scheduler,
name="{}_thinned".format(
args.resume.replace(".pth.tar", "")),
dir=msglogger.logdir)
print(
"Note: your model may have collapsed to random inference, so you may want to fine-tune"
) # 注意：您的模型可能已折叠为随机推理，因此您可能需要对其进行微调。
return
args.kd_policy = None # 蒸馏
if args.kd_teacher:
teacher = create_model(args.kd_pretrained,
args.dataset,
args.kd_teacher,
device_ids=args.gpus)
if args.kd_resume:
teacher, _, _ = apputils.load_checkpoint(teacher,
chkpt_file=args.kd_resume)
dlw = distiller.DistillationLossWeights(args.kd_distill_wt,
args.kd_student_wt,
args.kd_teacher_wt)
args.kd_policy = distiller.KnowledgeDistillationPolicy(
model, teacher, args.kd_temp, dlw)
compression_scheduler.add_policy(args.kd_policy,
starting_epoch=args.kd_start_epoch,
ending_epoch=args.epochs,
frequency=1)
msglogger.info('\nStudent-Teacher knowledge distillation enabled:')
msglogger.info('\tTeacher Model: %s', args.kd_teacher)
msglogger.info('\tTemperature: %s', args.kd_temp)
msglogger.info('\tLoss Weights (distillation | student | teacher): %s',
' | '.join(['{:.2f}'.format(val) for val in dlw]))
msglogger.info('\tStarting from Epoch: %s', args.kd_start_epoch)
lr = args.lr
lr_decay = 0.5
for epoch in range(start_epoch, args.epochs):
# This is the main training loop.
msglogger.info('\n')
if compression_scheduler:
compression_scheduler.on_epoch_begin(epoch)
# Train for one epoch
with collectors_context(activations_collectors["train"]) as collectors:
train(train_loader,
model,
criterion,
optimizer,
epoch,
compression_scheduler,
loggers=[tflogger, pylogger],
args=args)
distiller.log_weights_sparsity(model,
epoch,
loggers=[tflogger, pylogger])
distiller.log_activation_statsitics(
epoch,
"train",
loggers=[tflogger],
collector=collectors["sparsity"])
if args.masks_sparsity: # 打印掩盖稀疏表 在end of each epoch
msglogger.info(
distiller.masks_sparsity_tbl_summary(
model, compression_scheduler))
# evaluate on validation set
with collectors_context(activations_collectors["valid"]) as collectors:
top1, top5, vloss = validate(val_loader, model, criterion,
[pylogger], args, epoch)
distiller.log_activation_statsitics(
epoch,
"valid",
loggers=[tflogger],
collector=collectors["sparsity"])
save_collectors_data(collectors, msglogger.logdir)
stats = ('Peformance/Validation/',
OrderedDict([('Loss', vloss), ('Top1', top1),
('Top5', top5)]))
distiller.log_training_progress(stats,
None,
epoch,
steps_completed=0,
total_steps=1,
log_freq=1,
loggers=[tflogger])
if compression_scheduler:
compression_scheduler.on_epoch_end(epoch, optimizer)
# Update the list of top scores achieved so far, and save the checkpoint # 更新到目前为止获得的最高分数列表，并保存检查点
sparsity = distiller.model_sparsity(model)
perf_scores_history.append(
distiller.MutableNamedTuple({
'sparsity': sparsity,
'top1': top1,
'top5': top5,
'epoch': epoch
}))
# Keep perf_scores_history sorted from best to worst
# Sort by sparsity as main sort key, then sort by top1, top5 and epoch
# 保持绩效分数历史记录从最好到最差的排序
# 按稀疏度排序为主排序键，然后按top1、top5、epoch排序
perf_scores_history.sort(key=operator.attrgetter(
'sparsity', 'top1', 'top5', 'epoch'),
reverse=True)
for score in perf_scores_history[:args.num_best_scores]:
msglogger.info(
'==> Best [Top1: %.3f Top5: %.3f Sparsity: %.2f on epoch: %d]',
score.top1, score.top5, score.sparsity, score.epoch)
is_best = epoch == perf_scores_history[0].epoch
apputils.save_checkpoint(epoch, args.arch, model, optimizer,
compression_scheduler,
perf_scores_history[0].top1, is_best,
args.name, msglogger.logdir)
if not is_best:
lr = lr * lr_decay
# 当loss大于上一次loss,降低学习率
for param_group in optimizer.param_groups:
param_group['lr'] = lr
# Finally run results on the test set # 最后在测试集上运行结果
test(test_loader,
model,
criterion, [pylogger],
activations_collectors,
args=args)
