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本系列来总结Pytorch训练中的模型结构一些内容，包括模型的定义，模型参数化初始化方法，模型的保存与加载等
1 保存和加载
1）首先看Save源码
save使用pickle工具将模型对象序列化为pickle文件到disk
def save(obj, f, pickle_module=pickle, pickle_protocol=DEFAULT_PROTOCOL):
"""Saves an object to a disk file. 保存模型到disk
See also: :ref:`recommend-saving-models`
Args:
obj: saved object
f: a file-like object (has to implement write and flush) or a string
containing a file name 保存模型的文件对象或文件名
pickle_module: module used for pickling metadata and objects 使用python的pickle格式序列化模型
pickle_protocol: can be specified to override the default protocol pickle协议
.. warning::
If you are using Python 2, torch.save does NOT support StringIO.StringIO
as a valid file-like object. This is because the write method should return
the number of bytes written; StringIO.write() does not do this.
Please use something like io.BytesIO instead.
python2不支持StringIO.StringIO作为文件对象，因为其StringIO.write()不能返回write方法需要的写入字节个数
但可用io.BytesIO
Example:
>>> # Save to file
>>> x = torch.tensor([0, 1, 2, 3, 4])
>>> torch.save(x, 'tensor.pt')
>>> # Save to io.BytesIO buffer
>>> buffer = io.BytesIO()
>>> torch.save(x, buffer)
"""
调用底层_save方法，略微复杂，不继续探讨
return _with_file_like(f, "wb", lambda f: _save(obj, f, pickle_module, pickle_protocol))
使用这个save函数可以保存各种对象的模型、张量和字典；一般Pytorch保存模型后缀为：.pt 或 .pth 或 .pkl
2）在看Load源码
Load使用pickle的unpickle工具将pickle的对象文件反序列化为内存
def load(f, map_location=None, pickle_module=pickle, **pickle_load_args):
"""
User extensions can register their own location tags and tagging and
deserialization methods using `register_package`.
Args:
文件对象或文件名
f: a file-like object (has to implement read, readline, tell, and seek),
or a string containing a file name
一个函数： 可以是torch.device，字符串，指定的重映射位置
可以用来指定加载模型到GPU或CPU等， 默认GPU
map_location: a function, torch.device, string or a dict specifying how to remap storage locations
pickle格式类型：这里应该时反pickle序列化
pickle_module: module used for unpickling metadata and objects (has to
match the pickle_module used to serialize file)
可选字段：比如 ``encoding=...`` 在版本切换种，编码冲突可用
pickle_load_args: optional keyword arguments passed over to
``pickle_module.load`` and ``pickle_module.Unpickler``, e.g.,
``encoding=...``.
.. note::
When you call :meth:`torch.load()` on a file which contains GPU tensors, those tensors
will be loaded to GPU by default. You can call `torch.load(.., map_location='cpu')`
and then :meth:`load_state_dict` to avoid GPU RAM surge when loading a model checkpoint.
.. note::
In Python 3, when loading files saved by Python 2, you may encounter
``UnicodeDecodeError: 'ascii' codec can't decode byte 0x...``. This is
caused by the difference of handling in byte strings in Python2 and
Python 3. You may use extra ``encoding`` keyword argument to specify how
these objects should be loaded, e.g., ``encoding='latin1'`` decodes them
to strings using ``latin1`` encoding, and ``encoding='bytes'`` keeps them
as byte arrays which can be decoded later with ``byte_array.decode(...)``.
Example:
#默认加载到GPU
>>> torch.load('tensors.pt')
# Load all tensors onto the CPU
加载到CPU
>>> torch.load('tensors.pt', map_location=torch.device('cpu'))
# Load all tensors onto the CPU, using a function
用函数加载到CPU
>>> torch.load('tensors.pt', map_location=lambda storage, loc: storage)
# Load all tensors onto GPU 1
加载到GPU１
>>> torch.load('tensors.pt', map_location=lambda storage, loc: storage.cuda(1))
# Map tensors from GPU 1 to GPU 0
从GPU１映射到GPU０
>>> torch.load('tensors.pt', map_location={'cuda:1':'cuda:0'})
# Load tensor from io.BytesIO object
从 io.BytesIO对象加载
>>> with open('tensor.pt') as f:
buffer = io.BytesIO(f.read())
>>> torch.load(buffer)
"""
new_fd = False
if isinstance(f, str) or \
(sys.version_info[0] == 2 and isinstance(f, unicode)) or \
(sys.version_info[0] == 3 and isinstance(f, pathlib.Path)):
new_fd = True
f = open(f, 'rb')
try:
return _load(f, map_location, pickle_module, **pickle_load_args)
finally:
if new_fd:
f.close()
2 一般形式
从源码不难看出pytorch保存模型的方式多样，保存模型的后缀名也是多样的，但要注意使用哪种保存，就要使用对应的加载方式
一般我们常用到Pytorch加载和保存模型方式有以下几种种：
1）保存整个网络
torch.save(net, PATH)
model=torch.load(PATH)
这种方式重新加载的时候不需要自定义网络结构，保存时已经把网络结构保存了下来
2）保存网络参数（速度快，占空间少）
torch.save(net.state_dict(),PATH)
model_dict=model.load_state_dict(torch.load(PATH))
仅保存和加载模型参数，这种方式重新加载的时候需要自己定义网络，并且其中的参数名称与结构要与保存的模型中的一致（可以是部分网络，比如只使用VGG的前几层），相对灵活，便于对网络进行修改
3）保存更多参数
在实验中往往需要保存更多的信息，比如优化器的参数，那么可以采取下面的方法保存：
torch.save({
'epoch': epochID + 1,
'state_dict': model.state_dict(),
'best_loss': lossMIN,
'optimizer': optimizer.state_dict(),
'alpha': loss.alpha,
'gamma': loss.gamma
},checkpoint_path + '/m-' + launchTimestamp + '-' + str("%.4f" % lossMIN) + '.pth.tar')
以上包含的信息有，epochID, state_dict, min loss, optimizer, 自定义损失函数的两个参数；格式以字典的格式存储。对应加载的方式：
def load_checkpoint(model, checkpoint_PATH, optimizer):
if checkpoint != None:
model_CKPT = torch.load(checkpoint_PATH)
model.load_state_dict(model_CKPT['state_dict'])
print('loading checkpoint!')
optimizer.load_state_dict(model_CKPT['optimizer'])
return model, optimizer
但是，我们可能修改了一部分网络，比如加了一些，删除一些，等等，那么需要过滤这些参数，加载方式：
def load_checkpoint(model, checkpoint, optimizer, loadOptimizer):
if checkpoint != 'No':
print("loading checkpoint...")
model_dict = model.state_dict()
modelCheckpoint = torch.load(checkpoint)
pretrained_dict = modelCheckpoint['state_dict']
# 过滤操作
new_dict = {k: v for k, v in pretrained_dict.items() if k in model_dict.keys()}
model_dict.update(new_dict)
# 打印出来，更新了多少的参数
print('Total : {}, update: {}'.format(len(pretrained_dict), len(new_dict)))
model.load_state_dict(model_dict)
print("loaded finished!")
# 如果不需要更新优化器那么设置为false
if loadOptimizer == True:
optimizer.load_state_dict(modelCheckpoint['optimizer'])
print('loaded! optimizer')
else:
print('not loaded optimizer')
else:
print('No checkpoint is included')
return model, optimizer
3 CPN
1）CPN模型保存–train
save_model({
'epoch': epoch + 1,
'state_dict': model.state_dict(),
'optimizer' : optimizer.state_dict(),
}, checkpoint=args.checkpoint)
保存了一些必要训练参数和模型参数
2）CPN模型加载–test
checkpoint_file = os.path.join(args.checkpoint, args.test+'.pth.tar')
checkpoint = torch.load(checkpoint_file)
model.load_state_dict(checkpoint['state_dict'])
print("=> loaded checkpoint '{}' (epoch {})".format(checkpoint_file, checkpoint['epoch']))
测试模型时，我们只关注模型参数
3）CPN模型加载–resume
if args.resume:
if isfile(args.resume):
print("=> loading checkpoint '{}'".format(args.resume))
checkpoint = torch.load(args.resume)
pretrained_dict = checkpoint['state_dict']
model.load_state_dict(pretrained_dict)
args.start_epoch = checkpoint['epoch']
optimizer.load_state_dict(checkpoint['optimizer'])
print("=> loaded checkpoint '{}' (epoch {})"
.format(args.resume, checkpoint['epoch']))
logger = Logger(join(args.checkpoint, 'log.txt'), resume=True)
else:
print("=> no checkpoint found at '{}'".format(args.resume))
else:
logger = Logger(join(args.checkpoint, 'log.txt'))
logger.set_names(['Epoch', 'LR', 'Train Loss'])
resume是指接着某一次保存的模型继续训练，因为我们在训练中，可能中断或需要调调参数，就可以用这种方式；一般来说，它需要保存模型时保存当时的训练现场，就像caffe训练时保存的solverstate文件
4）CPN模型加载–finetuning
def resnet50(pretrained=False, **kwargs):
"""Constructs a ResNet-50 model.
Args:
pretrained (bool): If True, returns a model pre-trained on ImageNet
"""
model = ResNet(Bottleneck, [3, 4, 6, 3], **kwargs)
if pretrained:
print('Initialize with pre-trained ResNet')
from collections import OrderedDict
state_dict = model.state_dict()
pretrained_state_dict = model_zoo.load_url(model_urls['resnet50'])
for k, v in pretrained_state_dict.items():
if k not in state_dict:
continue
state_dict[k] = v
print('successfully load '+str(len(state_dict.keys()))+' keys')
model.load_state_dict(state_dict)
return model
finetuning与resume之间还是有点区别的；我们常常说的finetuning（迁移学习）本质就是加载预训练，继续训练；当然加载时，可能会根据需求选择参数，也可能会适当冻结部分参数等
4 细节补充
1）model.state_dict
pytorch 中的 state_dict 是一个简单的python的字典对象；在模型中，它将每一层与它的对应参数建立映射关系，如model的每一层的weights及偏置等等
注意：只有那些参数可以训练的layer才会被保存到模型的state_dict中，如卷积层,线性层等等
优化器对象Optimizer也有一个state_dict，它包含了优化器的状态以及被使用的超参数，如lr， momentum，weight_decay等
2）OrderedDict
collections模块中的有序字典；模型中，大部分字典对象都是用它，如Sequential：
# Example of using Sequential
model = nn.Sequential(
nn.Conv2d(1,20,5),
nn.ReLU(),
nn.Conv2d(20,64,5),
nn.ReLU()
)
# Example of using Sequential with OrderedDict
model = nn.Sequential(OrderedDict([
('conv1', nn.Conv2d(1,20,5)),
('relu1', nn.ReLU()),
('conv2', nn.Conv2d(20,64,5)),
('relu2', nn.ReLU())
]))
在Python中，dict这个数据结构由于hash的特性，是无序的，这在有的时候会给我们带来一些麻烦， 幸运的是，collections模块为我们提供了OrderedDict，当你要获得一个有序的字典对象时，用它就对了