之前一直不清楚怎么查看模型的参数和结构，现在学习了一下。
首先搞个resnet20出来
import torch import torch.nn as nn import torch.nn.functional as F from torch.nn import init from models.res_utils import DownsampleA, DownsampleC, DownsampleD import math class ResNetBasicblock(nn.Module): expansion = 1 """ RexNet basicblock (https://github.com/facebook/fb.resnet.torch/blob/master/models/resnet.lua) """ def __init__(self, inplanes, planes, stride=1, downsample=None): super(ResNetBasicblock, self).__init__() self.conv_a = nn.Conv2d(inplanes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn_a = nn.BatchNorm2d(planes) self.conv_b = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False) self.bn_b = nn.BatchNorm2d(planes) self.downsample = downsample def forward(self, x): residual = x basicblock = self.conv_a(x) basicblock = self.bn_a(basicblock) basicblock = F.relu(basicblock, inplace=True) basicblock = self.conv_b(basicblock) basicblock = self.bn_b(basicblock) if self.downsample is not None: residual = self.downsample(x) return F.relu(residual + basicblock, inplace=True) class CifarResNet(nn.Module): """ ResNet optimized for the Cifar dataset, as specified in https://arxiv.org/abs/1512.03385.pdf """ def __init__(self, block, depth, num_classes): """ Constructor Args: depth: number of layers. num_classes: number of classes base_ base width """ super(CifarResNet, self).__init__() #Model type specifies number of layers for CIFAR-10 and CIFAR-100 model assert (depth - 2) % 6 == 0, 'depth should be one of 20, 32, 44, 56, 110' layer_blocks = (depth - 2) // 6 print ('CifarResNet : Depth : {} , Layers for each block : {}'.format(depth, layer_blocks)) self.num_classes = num_classes self.conv_1_3x3 = nn.Conv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False) self.bn_1 = nn.BatchNorm2d(16) self.inplanes = 16 self.stage_1 = self._make_layer(block, 16, layer_blocks, 1) self.stage_2 = self._make_layer(block, 32, layer_blocks, 2) self.stage_3 = self._make_layer(block, 64, layer_blocks, 2) self.avgpool = nn.AvgPool2d(8) self.classifier = nn.Linear(64*block.expansion, num_classes) for m in self.modules(): if isinstance(m, nn.Conv2d): n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels m.weight.data.normal_(0, math.sqrt(2. / n)) #m.bias.data.zero_() elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) m.bias.data.zero_() elif isinstance(m, nn.Linear): init.kaiming_normal(m.weight) m.bias.data.zero_() def _make_layer(self, block, planes, blocks, stride=1): downsample = None if stride != 1 or self.inplanes != planes * block.expansion: downsample = DownsampleA(self.inplanes, planes * block.expansion, stride) layers = [] layers.append(block(self.inplanes, planes, stride, downsample)) self.inplanes = planes * block.expansion for i in range(1, blocks): layers.append(block(self.inplanes, planes)) return nn.Sequential(*layers) def forward(self, x): x = self.conv_1_3x3(x) x = F.relu(self.bn_1(x), inplace=True) x = self.stage_1(x) x = self.stage_2(x) x = self.stage_3(x) x = self.avgpool(x) x = x.view(x.size(0), -1) return self.classifier(x) def resnet20(num_classes=10): """Constructs a ResNet-20 model for CIFAR-10 (by default) Args: num_classes (uint): number of classes """ model = CifarResNet(ResNetBasicblock, 20, num_classes) return model
DownsampleA其实是这个东西
class DownsampleA(nn.Module): def __init__(self, nIn, nOut, stride): super(DownsampleA, self).__init__() assert stride == 2 self.avg = nn.AvgPool2d(kernel_size=1, stride=stride) def forward(self, x): x = self.avg(x) return torch.cat((x, x.mul(0)), 1)
所以最后网络结构是预处理的conv层和bn层，以及接下去的三个stage，每个stage分别是三层，最后是avgpool和全连接层
1、model.named_parameters()，迭代打印model.named_parameters()将会打印每一次迭代元素的名字和param
for name, param in net.named_parameters(): print(name,param.requires_grad) param.requires_grad = False # conv_1_3x3.weight False bn_1.weight False bn_1.bias False stage_1.0.conv_a.weight False stage_1.0.bn_a.weight False stage_1.0.bn_a.bias False stage_1.0.conv_b.weight False stage_1.0.bn_b.weight False stage_1.0.bn_b.bias False stage_1.1.conv_a.weight False stage_1.1.bn_a.weight False stage_1.1.bn_a.bias False stage_1.1.conv_b.weight False stage_1.1.bn_b.weight False stage_1.1.bn_b.bias False stage_1.2.conv_a.weight False stage_1.2.bn_a.weight False stage_1.2.bn_a.bias False stage_1.2.conv_b.weight False stage_1.2.bn_b.weight False stage_1.2.bn_b.bias False stage_2.0.conv_a.weight False stage_2.0.bn_a.weight False stage_2.0.bn_a.bias False stage_2.0.conv_b.weight False stage_2.0.bn_b.weight False stage_2.0.bn_b.bias False stage_2.1.conv_a.weight False stage_2.1.bn_a.weight False stage_2.1.bn_a.bias False stage_2.1.conv_b.weight False stage_2.1.bn_b.weight False stage_2.1.bn_b.bias False stage_2.2.conv_a.weight False stage_2.2.bn_a.weight False stage_2.2.bn_a.bias False stage_2.2.conv_b.weight False stage_2.2.bn_b.weight False stage_2.2.bn_b.bias False stage_3.0.conv_a.weight False stage_3.0.bn_a.weight False stage_3.0.bn_a.bias False stage_3.0.conv_b.weight False stage_3.0.bn_b.weight False stage_3.0.bn_b.bias False stage_3.1.conv_a.weight False stage_3.1.bn_a.weight False stage_3.1.bn_a.bias False stage_3.1.conv_b.weight False stage_3.1.bn_b.weight False stage_3.1.bn_b.bias False stage_3.2.conv_a.weight False stage_3.2.bn_a.weight False stage_3.2.bn_a.bias False stage_3.2.conv_b.weight False stage_3.2.bn_b.weight False stage_3.2.bn_b.bias False classifier.weight False classifier.bias False
并且可以更改参数的可训练属性，第一次打印是True，这是第二次，就是False了
2、model.parameters()，迭代打印model.parameters()将会打印每一次迭代元素的param而不会打印名字，这是他和named_parameters的区别，两者都可以用来改变requires_grad的属性
for index, param in enumerate(net.parameters()): print(param.shape) # torch.Size([16, 3, 3, 3]) torch.Size([16]) torch.Size([16]) torch.Size([16, 16, 3, 3]) torch.Size([16]) torch.Size([16]) torch.Size([16, 16, 3, 3]) torch.Size([16]) torch.Size([16]) torch.Size([16, 16, 3, 3]) torch.Size([16]) torch.Size([16]) torch.Size([16, 16, 3, 3]) torch.Size([16]) torch.Size([16]) torch.Size([16, 16, 3, 3]) torch.Size([16]) torch.Size([16]) torch.Size([16, 16, 3, 3]) torch.Size([16]) torch.Size([16]) torch.Size([32, 16, 3, 3]) torch.Size([32]) torch.Size([32]) torch.Size([32, 32, 3, 3]) torch.Size([32]) torch.Size([32]) torch.Size([32, 32, 3, 3]) torch.Size([32]) torch.Size([32]) torch.Size([32, 32, 3, 3]) torch.Size([32]) torch.Size([32]) torch.Size([32, 32, 3, 3]) torch.Size([32]) torch.Size([32]) torch.Size([32, 32, 3, 3]) torch.Size([32]) torch.Size([32]) torch.Size([64, 32, 3, 3]) torch.Size([64]) torch.Size([64]) torch.Size([64, 64, 3, 3]) torch.Size([64]) torch.Size([64]) torch.Size([64, 64, 3, 3]) torch.Size([64]) torch.Size([64]) torch.Size([64, 64, 3, 3]) torch.Size([64]) torch.Size([64]) torch.Size([64, 64, 3, 3]) torch.Size([64]) torch.Size([64]) torch.Size([64, 64, 3, 3]) torch.Size([64]) torch.Size([64]) torch.Size([10, 64]) torch.Size([10])
可以看出这些参数的尺寸