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resnet.py
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import torch
import torch.nn as nn
import torch.nn.functional as F
import math
from torch.autograd import Variable
import torch.nn.init as init
def to_var(x, requires_grad=True):
if torch.cuda.is_available():
x = x.cuda()
return Variable(x, requires_grad=requires_grad)
class MetaModule(nn.Module):
# adopted from: Adrien Ecoffet https://github.com/AdrienLE
def params(self):
for name, param in self.named_params(self):
yield param
def named_leaves(self):
return []
def named_submodules(self):
return []
def named_params(self, curr_module=None, memo=None, prefix=''):
if memo is None:
memo = set()
if hasattr(curr_module, 'named_leaves'):
for name, p in curr_module.named_leaves():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
else:
for name, p in curr_module._parameters.items():
if p is not None and p not in memo:
memo.add(p)
yield prefix + ('.' if prefix else '') + name, p
for mname, module in curr_module.named_children():
submodule_prefix = prefix + ('.' if prefix else '') + mname
for name, p in self.named_params(module, memo, submodule_prefix):
yield name, p
def update_params(self, lr_inner, first_order=False, source_params=None, detach=False):
if source_params is not None:
for tgt, src in zip(self.named_params(self), source_params):
name_t, param_t = tgt
# name_s, param_s = src
# grad = param_s.grad
# name_s, param_s = src
grad = src
if first_order:
grad = to_var(grad.detach().data)
tmp = param_t - lr_inner * grad
self.set_param(self, name_t, tmp)
else:
for name, param in self.named_params(self):
if not detach:
grad = param.grad
if first_order:
grad = to_var(grad.detach().data)
tmp = param - lr_inner * grad
self.set_param(self, name, tmp)
else:
param = param.detach_() # https://blog.csdn.net/qq_39709535/article/details/81866686
self.set_param(self, name, param)
def set_param(self, curr_mod, name, param):
if '.' in name:
n = name.split('.')
module_name = n[0]
rest = '.'.join(n[1:])
for name, mod in curr_mod.named_children():
if module_name == name:
self.set_param(mod, rest, param)
break
else:
setattr(curr_mod, name, param)
def detach_params(self):
for name, param in self.named_params(self):
self.set_param(self, name, param.detach())
def copy(self, other, same_var=False):
for name, param in other.named_params():
if not same_var:
param = to_var(param.data.clone(), requires_grad=True)
self.set_param(name, param)
class MetaLinear(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Linear(*args, **kwargs)
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
def forward(self, x):
return F.linear(x, self.weight, self.bias)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class MetaConv2d(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.Conv2d(*args, **kwargs)
self.in_channels = ignore.in_channels
self.out_channels = ignore.out_channels
self.stride = ignore.stride
self.padding = ignore.padding
self.dilation = ignore.dilation
self.groups = ignore.groups
self.kernel_size = ignore.kernel_size
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
if ignore.bias is not None:
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
else:
self.register_buffer('bias', None)
def forward(self, x):
return F.conv2d(x, self.weight, self.bias, self.stride, self.padding, self.dilation, self.groups)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class MetaConvTranspose2d(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.ConvTranspose2d(*args, **kwargs)
self.stride = ignore.stride
self.padding = ignore.padding
self.dilation = ignore.dilation
self.groups = ignore.groups
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
if ignore.bias is not None:
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
else:
self.register_buffer('bias', None)
def forward(self, x, output_size=None):
output_padding = self._output_padding(x, output_size)
return F.conv_transpose2d(x, self.weight, self.bias, self.stride, self.padding,
output_padding, self.groups, self.dilation)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
class MetaBatchNorm2d(MetaModule):
def __init__(self, *args, **kwargs):
super().__init__()
ignore = nn.BatchNorm2d(*args, **kwargs)
self.num_features = ignore.num_features
self.eps = ignore.eps
self.momentum = ignore.momentum
self.affine = ignore.affine
self.track_running_stats = ignore.track_running_stats
if self.affine:
self.register_buffer('weight', to_var(ignore.weight.data, requires_grad=True))
self.register_buffer('bias', to_var(ignore.bias.data, requires_grad=True))
if self.track_running_stats:
self.register_buffer('running_mean', torch.zeros(self.num_features))
self.register_buffer('running_var', torch.ones(self.num_features))
else:
self.register_parameter('running_mean', None)
self.register_parameter('running_var', None)
def forward(self, x):
return F.batch_norm(x, self.running_mean, self.running_var, self.weight, self.bias,
self.training or not self.track_running_stats, self.momentum, self.eps)
def named_leaves(self):
return [('weight', self.weight), ('bias', self.bias)]
def _weights_init(m):
classname = m.__class__.__name__
# print(classname)
if isinstance(m, MetaLinear) or isinstance(m, MetaConv2d):
init.kaiming_normal(m.weight)
class LambdaLayer(MetaModule):
def __init__(self, lambd):
super(LambdaLayer, self).__init__()
self.lambd = lambd
def forward(self, x):
return self.lambd(x)
class ShortcutLayer(MetaModule):
def __init__(self, planes):
super(ShortcutLayer, self).__init__()
self.planes = planes
def forward(self, x):
return F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, self.planes//4, self.planes//4), "constant", 0)
class BasicBlock(MetaModule):
expansion = 1
def __init__(self, in_planes, planes, stride=1, option='A'):
super(BasicBlock, self).__init__()
self.conv1 = MetaConv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = MetaBatchNorm2d(planes)
self.conv2 = MetaConv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = MetaBatchNorm2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != planes:
if option == 'A':
"""
For CIFAR10 ResNet paper uses option A.
"""
#self.shortcut = LambdaLayer(lambda x:F.pad(x[:, :, ::2, ::2], (0, 0, 0, 0, planes//4, planes//4), "constant", 0))
self.shortcut = ShortcutLayer(planes=planes)
elif option == 'B':
self.shortcut = nn.Sequential(
MetaConv2d(in_planes, self.expansion * planes, kernel_size=1, stride=stride, bias=False),
MetaBatchNorm2d(self.expansion * planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.bn2(self.conv2(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet32(MetaModule):
def __init__(self, num_classes, block=BasicBlock, num_blocks=[5, 5, 5]):
super(ResNet32, self).__init__()
self.in_planes = 16
self.conv1 = MetaConv2d(3, 16, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = MetaBatchNorm2d(16)
self.layer1 = self._make_layer(block, 16, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 32, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 64, num_blocks[2], stride=2)
self.linear = MetaLinear(64, num_classes)
self.apply(_weights_init)
def _make_layer(self, block, planes, num_blocks, stride):
strides = [stride] + [1]*(num_blocks-1)
layers = []
for stride in strides:
layers.append(block(self.in_planes, planes, stride))
self.in_planes = planes * block.expansion
return nn.Sequential(*layers)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = self.layer1(out)
out = self.layer2(out)
out = self.layer3(out)
out = F.avg_pool2d(out, out.size()[3])
out = out.view(out.size(0), -1)
out = self.linear(out)
return out
class Bottleneck(MetaModule):
# Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
# while original implementation places the stride at the first 1x1 convolution(self.conv1)
# according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
# This variant is also known as ResNet V1.5 and improves accuracy according to
# https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
expansion = 4
def __init__(self, inplanes, planes, stride=1, downsample=None, groups=1,
base_width=64, dilation=1, norm_layer=None):
super(Bottleneck, self).__init__()
width = int(planes * (base_width / 64.)) * groups
self.conv1 = MetaConv2d(inplanes, width, kernel_size=1, stride=1, bias=False)
self.bn1 = MetaBatchNorm2d(width)
self.conv2 = MetaConv2d(width, width, kernel_size=3,
stride=stride, padding=1, groups=groups, bias=False)
self.bn2 = MetaBatchNorm2d(width)
self.conv3 = MetaConv2d(width,planes*self.expansion, kernel_size=1, stride=1, bias=False)
self.bn3 = MetaBatchNorm2d(planes*self.expansion)
self.relu = nn.ReLU(inplace=True)
self.downsample = downsample
self.stride = stride
def forward(self, x):
identity = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
out = self.relu(out)
out = self.conv3(out)
out = self.bn3(out)
if self.downsample is not None:
identity = self.downsample(x)
out += identity
out = self.relu(out)
return out
class ResNet50(MetaModule):
def __init__(self, num_classes, block=Bottleneck, num_blocks=[3, 4, 6, 3]):
super(ResNet50, self).__init__()
self.in_planes = 64
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.conv1 = MetaConv2d(3, self.in_planes, kernel_size=7,
stride=2, padding=3, bias=False)
self.bn1 = MetaBatchNorm2d(self.in_planes)
self.layer1 = self._make_layer(block, 64, num_blocks[0], stride=1)
self.layer2 = self._make_layer(block, 128, num_blocks[1], stride=2)
self.layer3 = self._make_layer(block, 256, num_blocks[2], stride=2)
self.layer4 = self._make_layer(block, 512, num_blocks[3], stride=2)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
#self.linear = MetaLinear(64, num_classes)
self.linear = MetaLinear(512 * block.expansion, num_classes)
#self.apply(_weights_init)
def _make_layer(self, block, planes, num_blocks, stride, dilate= False):
downsample = None
norm_layer = MetaBatchNorm2d
if stride != 1 or self.in_planes != planes * block.expansion:
downsample = nn.Sequential(
MetaConv2d(self.in_planes, planes * block.expansion, kernel_size=1, stride=stride, bias=False),
norm_layer(planes * block.expansion),
)
layers = []
layers.append(block(self.in_planes, planes, stride, downsample))
self.in_planes = planes * block.expansion
for _ in range(1, num_blocks):
layers.append(block(self.in_planes, planes))
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.layer4(x)
x = self.avgpool(x)
x = torch.flatten(x, 1)
x = self.linear(x)
return x
class VNet(MetaModule):
def __init__(self, input, hidden1, output):
super(VNet, self).__init__()
self.linear1 = MetaLinear(input, hidden1)
self.relu1 = nn.ReLU(inplace=True)
self.linear2 = MetaLinear(hidden1, output)
# self.linear3 = MetaLinear(hidden2, output)
def forward(self, x):
x = self.linear1(x)
x = self.relu1(x)
# x = self.linear2(x)
# x = self.relu1(x)
out = self.linear2(x)
return F.sigmoid(out)