Hi,
I’m implementing Stochastic Depth on ResNet. But got the TypeError in forward(). Is there a way to fix this?
Thank you in advance!
“”"
Resnet for cifar dataset.
Ported from
GitHub - facebookarchive/fb.resnet.torch: Torch implementation of ResNet from http://arxiv.org/abs/1512.03385 and training scripts
and
https://github.com/pytorch/vision/blob/master/torchvision/models/resnet.py
“”"
import torch
import torch.nn as nn
import mathall = [‘resnet’, ‘sto_depth_resnet’]
def conv3x3(in_planes, out_planes, stride=1):
“3x3 convolution with padding”
return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride, padding=1, bias=False)class BasicBlock(nn.Module):
expansion = 1def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x): residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) return outclass Bottleneck(nn.Module):
expansion = 4def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x): residual = 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: residual = self.downsample(x) out += residual out = self.relu(out) return outclass BasicBlock_stochastic_depth(nn.Module):
expansion = 1def __init__(self, inplanes, planes, stride=1, downsample=None): super(BasicBlock_stochastic_depth, self).__init__() self.conv1 = conv3x3(inplanes, planes, stride) self.bn1 = nn.BatchNorm2d(planes) self.relu = nn.ReLU(inplace=True) self.conv2 = conv3x3(planes, planes) self.bn2 = nn.BatchNorm2d(planes) self.downsample = downsample self.stride = stride def forward(self, x, active, prob): if self.training: if active == 1: print('active') residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out += residual out = self.relu(out) else: print('inactive') out = x if self.downsample is not None: out = self.downsample(out) out = self.relu(out) else: residual = x out = self.conv1(x) out = self.bn1(out) out = self.relu(out) out = self.conv2(out) out = self.bn2(out) if self.downsample is not None: residual = self.downsample(x) out = prob * out + residual out = self.relu(out) return outclass Bottleneck_stochastic_depth(nn.Module):
expansion = 4def __init__(self, inplanes, planes, stride=1, downsample=None): super(Bottleneck_stochastic_depth, self).__init__() self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False) self.bn1 = nn.BatchNorm2d(planes) self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False) self.bn2 = nn.BatchNorm2d(planes) self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False) self.bn3 = nn.BatchNorm2d(planes * 4) self.relu = nn.ReLU(inplace=True) self.downsample = downsample self.stride = stride def forward(self, x, active, prob): if self.training: if active == 1: print('active') residual = 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: residual = self.downsample(x) out += residual out = self.relu(out) else: print('inactive') out = x if self.downsample is not None: out = self.downsample(out) out = self.relu(out) else: residual = 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: residual = self.downsample(x) out = prob * out + residual out = self.relu(out) return outclass ResNet(nn.Module):
def __init__(self, depth, num_classes=1000, block_name='BasicBlock'): super(ResNet, self).__init__() # Model type specifies number of layers for CIFAR-10 model if block_name.lower() == 'basicblock': assert(depth - 2) % 6 == 0, 'When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202' n = (depth - 2) // 6 block = BasicBlock elif block_name.lower() == 'bottleneck': assert (depth == 2) % 9 == 0, 'When use bottleneck, depth should be 9n + 2, e.g. 20, 29, 47, 56, 110, 1199' n = (depth - 2) // 9 block = Bottleneck else: raise ValueError('block_name should be Basicblock or Bottleneck') self.inplanes = 16 self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(16) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, 16, n) self.layer2 = self._make_layer(block, 32, n, stride=2) self.layer3 = self._make_layer(block, 64, n, stride=2) self.avgpool = nn.AvgPool2d(8) self.fc = 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)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) 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 = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion) ) 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.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.layer1(x) x = self.layer2(x) x = self.layer3(x) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return xclass ResNetSD(nn.Module):
def __init__(self, depth, num_classes=1000, block_name='BasicBlock'): super(ResNetSD, self).__init__() # Model type specifies number of layers for CIFAR-10 model if block_name.lower() == 'basicblock': assert(depth - 2) % 6 == 0, 'When use basicblock, depth should be 6n+2, e.g. 20, 32, 44, 56, 110, 1202' n = (depth - 2) // 6 block = BasicBlock_stochastic_depth elif block_name.lower() == 'bottleneck': assert (depth == 2) % 9 == 0, 'When use bottleneck, depth should be 9n + 2, e.g. 20, 29, 47, 56, 110, 1199' n = (depth - 2) // 9 block = Bottleneck_stochastic_depth else: raise ValueError('block_name should be Basicblock or Bottleneck') self.probabilities = torch.tensor(0.5) self.actives = torch.bernoulli(self.probabilities) self.inplanes = 16 self.conv1 = nn.Conv2d(3, 16, kernel_size=3, padding=1, bias=False) self.bn1 = nn.BatchNorm2d(16) self.relu = nn.ReLU(inplace=True) self.layer1 = self._make_layer(block, 16, n) self.layer2 = self._make_layer(block, 32, n, stride=2) self.layer3 = self._make_layer(block, 64, n, stride=2) self.avgpool = nn.AvgPool2d(8) self.fc = 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)) elif isinstance(m, nn.BatchNorm2d): m.weight.data.fill_(1) 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 = nn.Sequential( nn.Conv2d(self.inplanes, planes * block.expansion, kernel_size=1, stride=stride, bias=False), nn.BatchNorm2d(planes * block.expansion) ) 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.conv1(x) x = self.bn1(x) x = self.relu(x) x = self.layer1(x, self.actives, self.probabilities) x = self.layer2(x, self.actives, self.probabilities) x = self.layer3(x, self.actives, self.probabilities) x = self.avgpool(x) x = x.view(x.size(0), -1) x = self.fc(x) return xdef resnet(**kwargs):
“”"
Constructs a ResNet model.
:param kwargs:
:return:
“”"
return ResNet(**kwargs)def sto_depth_resnet(**kwargs):
return ResNetSD(**kwargs)if name == “main”:
import timeitbatch_size = 512 model = sto_depth_resnet(depth=20, num_classes=1000) data = torch.rand(batch_size, 3, 32, 32).cuda() print('data.device = ', data.device) model = torch.nn.DataParallel(model).cuda() for m in model.modules(): print(m) start = timeit.default_timer() out = model(data) end = timeit.default_timer() print('Done.')