1. torch.nn与torch.nn.functional之间的区别和联系
https://blog.csdn.net/GZHermit/article/details/78730856
nn和nn.functional之间的差别如下，我们以conv2d的定义为例
torch.nn.Conv2d
import torch.nn.functional as F class Conv2d(_ConvNd): def __init__(self, in_channels, out_channels, kernel_size, stride=1, padding=0, dilation=1, groups=1, bias=True): kernel_size = _pair(kernel_size) stride = _pair(stride) padding = _pair(padding) dilation = _pair(dilation) super(Conv2d, self).__init__( in_channels, out_channels, kernel_size, stride, padding, dilation, False, _pair(0), groups, bias) def forward(self, input): return F.conv2d(input, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
torch.nn.functional.conv2d
def conv2d(input, weight, bias=None, stride=1, padding=0, dilation=1, groups=1): if input is not None and input.dim() != 4: raise ValueError("Expected 4D tensor as input, got {}D tensor instead.".format(input.dim())) f = _ConvNd(_pair(stride), _pair(padding), _pair(dilation), False, _pair(0), groups, torch.backends.cudnn.benchmark, torch.backends.cudnn.deterministic, torch.backends.cudnn.enabled) return f(input, weight, bias)
区别：
1. nn.Conv2d是一个类；F.conv2d是一个函数
联系：
nn.Conv2d的forword()函数是用F.conv2d()实现的，两者功能并无区别。
(在Module类里的__call__实现了forward()函数的调用，所以当实例化nn.Conv2d类时，forward()函数也被执行了，详细可阅读torch源码)
为什么要有这样的两种实现方式同时存在呢？
原因其实在于，为了兼顾灵活性和便利性。
在建图过程中，往往有两种层，一种如全连接层，卷积层等，当中有 Variable, 另一种如 Pooling层，ReLU层，当中没有 Variable.
如果所有的层都用 nn.functional 来定义，那么所有的Variable， 如 weights, bias 等，都需要用户手动定义，非常不便；
如果所有的层都用 nn 来定义，那么即便是简单的计算都需要建类来做，而这些可以用更为简单的函数来代替。
综上，在定义网络的时候，如果层内有 Variable, 那么用 nn 定义， 反之，则用 nn.functional定义。
2. ‘model.eval()’ vs ‘with torch.no_grad()’
https://discuss.pytorch.org/t/model-eval-vs-with-torch-no-grad/19615
1. model.eval() will notify all your layers that you are in eval mode, that way, batchnorm or dropout layers will work in eval model instead of training mode.
model.eval()会告知模型中的所有layers, 目前处在eval模式，batchnorm和dropout层等都会在eval模式中工作。
2. torch.no_grad() impacts the autograd engine and deactivate it. It will reduce memory usage and speed up computations but you won’t be able to backprop (which you don’t want in an eval script).
torch.no_grad() 会影响 autograd 引擎，并关闭它。这样会降低内存的使用并且加速计算。但是将不可以使用backprop.
3. nn.Sequential() vs nn.moduleList
https://blog.csdn.net/e01528/article/details/84397174
对于cnn前馈神经网络如果前馈一次写一个forward函数会有些麻烦，在此就有两种简化方式，ModuleList和Sequential。
其中Sequential是一个特殊的module，它包含几个子Module，前向传播时会将输入一层接一层的传递下去。
ModuleList也是一个特殊的module，可以包含几个子module，可以像用list一样使用它，但不能直接把输入传给ModuleList。
3.1 nn.Sequential()
1. 模型的建立方式：
import torch import torch.nn as nn from torch.autograd import Variable ''' nn.Sequential ''' net1 = nn.Sequential() net1.add_module('conv', nn.Conv2d(3, 3, 3)) # net1.add_module('conv2', nn.Conv2d(3, 3, 2)) net1.add_module('batchnorm', nn.BatchNorm2d(3)) net1.add_module('activation_layer', nn.ReLU()) print("net1:") print(net1) # net1: # Sequential( # (conv): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) # (batchnorm): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # (activation_layer): ReLU() # ) net2 = nn.Sequential( nn.Conv2d(3, 3, 3), nn.BatchNorm2d(3), nn.ReLU() ) print("net2:") print(net2) # net2: # Sequential( # (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) # (1): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # (2): ReLU() # ) from collections import OrderedDict net3 = nn.Sequential(OrderedDict([ ('conv', nn.Conv2d(3, 3, 3)), ('batchnorm', nn.BatchNorm2d(3)), ('activation_layer', nn.ReLU()) ])) print("net3:") print(net3) # net3: # Sequential( # (conv): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) # (batchnorm): BatchNorm2d(3, eps=1e-05, momentum=0.1, affine=True, track_running_stats=True) # (activation_layer): ReLU() # )
2. 获取子Module对象
# get the sub module by the name or index print("Get the sub module by the name or index:") print(net1.conv) print(net2[0]) print(net3.conv) # Get the sub module by the name or index: # Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) # Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) # Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1))
3. 调用模型
# use the model input = Variable(torch.rand(1, 3, 4, 4)) output1 = net1(input) output2 = net2(input) output3 = net3(input) output4 = net3.activation_layer(net1.batchnorm(net1.conv(input))) print("output1:", output1) print("output2:", output2) print("output3:", output3) print("output4:", output4) # output1: tensor([[[[0.0000, 0.1066], # [0.0075, 0.1379]], # [[0.0558, 0.9517], # [0.0000, 0.0000]], # [[0.5355, 0.0000], # [0.4478, 0.0000]]]], grad_fn=) # output2: tensor([[[[0.4227, 0.3509], # [0.0868, 0.0000]], # [[0.0000, 0.0034], # [0.0038, 0.0000]], # [[0.0000, 0.0000], # [0.4002, 0.1882]]]], grad_fn=) # output3: tensor([[[[0.0000, 0.0000], # [0.4779, 0.0000]], # [[0.0000, 1.5064], # [0.0000, 0.1515]], # [[0.7417, 0.0000], # [0.3366, 0.0000]]]], grad_fn=) # output4: tensor([[[[0.0000, 0.1066], # [0.0075, 0.1379]], # [[0.0558, 0.9517], # [0.0000, 0.0000]], # [[0.5355, 0.0000], # [0.4478, 0.0000]]]], grad_fn=)
3.2 nn.moduleList
它被设计用来存储任意数量的nn. module。
如果在构造函数__init__中用到list、tuple、dict等对象时，一定要思考是否应该用ModuleList或ParameterList代替。
1. 可以采用迭代或下标索引方式获取Module
# 1. support index and enumerate class MyModule(nn.Module): def __init__(self): super(MyModule, self).__init__() self.linears = nn.ModuleList([nn.Linear(10, 10) for i in range(10)]) def forward(self, x): for i, l in enumerate(self.linears): x = self.linears[i // 2](x) + l(x) return x
2. extend 和 append方法
nn.moduleList定义对象后，有extend和append方法，用法和python中一样。
extend是添加另一个modulelist ；
append是添加另一个module。
# 2. extend a modulelist; attend a module class LinearNet(nn.Module): """docstring for LinearNet""" def __init__(self, input_size, num_layers, layers_size, output_size): super(LinearNet, self).__init__() self.linears = nn.ModuleList([nn.Linear(input_size, layers_size)]) self.linears.extend([nn.Linear(layers_size, layers_size) for i in range(1, num_layers - 1)]) self.linears.append(nn.Linear(layers_size, output_size)) model1 = LinearNet(5, 3, 4, 2) print("---model LinearNet---") print(model1) print() # ---model LinearNet--- # LinearNet( # (linears): ModuleList( # (0): Linear(in_features=5, out_features=4, bias=True) # (1): Linear(in_features=4, out_features=4, bias=True) # (2): Linear(in_features=4, out_features=2, bias=True) # ) # )
3. 建立以及使用方法
# 3. create and use -- not implement the forward modellist = nn.ModuleList([nn.Linear(3, 4), nn.ReLU(), nn.Linear(4, 2)]) input = Variable(torch.randn(1, 3)) for model in modellist: input = model(input) # output = modellist(input) --> wrong 因为modellist没有实现forward方法
4. ModuleList与list的区别
普通list中的子module并不能被主module所识别，而ModuleList中的子module能够被主module所识别。这意味着如果用list保存子module，将无法调整其参数，因其未加入到主module的参数中。
除ModuleList之外还有ParameterList，其是一个可以包含多个parameter的类list对象。在实际应用中，使用方式与ModuleList类似。
class MyModule_list(nn.Module): """docstring for MyModule_list""" def __init__(self): super(MyModule_list, self).__init__() self.list = [nn.Linear(3, 4), nn.ReLU()] self.module_list = nn.ModuleList([nn.Conv2d(3, 3, 3), nn.ReLU()]) def forward(self): pass model = MyModule_list() print(model) # MyModule_list( # (module_list): ModuleList( # (0): Conv2d(3, 3, kernel_size=(3, 3), stride=(1, 1)) # (1): ReLU() # ) # ) # 只有ModuleList的信息，并没有list的信息 for name, param in model.named_parameters(): print(name, param.size()) # module_list.0.weight torch.Size([3, 3, 3, 3]) # module_list.0.bias torch.Size([3]) # 只有ModuleList的信息，并没有list的信息