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pruned_resnet.py
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pruned_resnet.py
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# copy from pytorch-torchvision-models-resnet
import math
import torch.nn as nn
__all__ = ['PrunedResNet', 'PrunedBasicBlock', 'PrunedBottleneck']
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 PrunedBasicBlock(nn.Module):
expansion = 1
def __init__(self, inplanes, planes, pruning_rate, stride=1, downsample=None):
super(PrunedBasicBlock, self).__init__()
self.name = "resnet-basic"
self.pruned_channel_plane = int(planes - math.floor(planes * pruning_rate))
self.conv1 = conv3x3(inplanes, self.pruned_channel_plane, stride)
self.bn1 = nn.BatchNorm2d(self.pruned_channel_plane)
self.relu = nn.ReLU(inplace=True)
self.conv2 = conv3x3(self.pruned_channel_plane, planes)
self.bn2 = nn.BatchNorm2d(planes)
self.downsample = downsample
self.stride = stride
self.block_index = 0
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 out
class PrunedBottleneck(nn.Module):
expansion = 4
def __init__(self, inplanes, planes, pruning_rate, stride=1, downsample=None):
super(PrunedBottleneck, self).__init__()
self.name = "resnet-bottleneck"
self.pruned_channel_plane = int(planes - math.floor(planes * pruning_rate))
self.conv1 = nn.Conv2d(inplanes, self.pruned_channel_plane, kernel_size=1, bias=False)
self.bn1 = nn.BatchNorm2d(self.pruned_channel_plane)
self.conv2 = nn.Conv2d(self.pruned_channel_plane, self.pruned_channel_plane, kernel_size=3, stride=stride,
padding=1, bias=False)
self.bn2 = nn.BatchNorm2d(self.pruned_channel_plane)
self.conv3 = nn.Conv2d(self.pruned_channel_plane, 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
self.block_index = 0
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 out
class PrunedResNet(nn.Module):
def __init__(self, depth, pruning_rate, num_classes=1000):
self.inplanes = 64
super(PrunedResNet, self).__init__()
if depth < 50:
block = PrunedBasicBlock
else:
block = PrunedBottleneck
if depth == 18:
layers = [2, 2, 2, 2]
elif depth == 34:
layers = [3, 4, 6, 3]
elif depth == 50:
layers = [3, 4, 6, 3]
elif depth == 101:
layers = [3, 4, 23, 3]
elif depth == 152:
layers = [3, 8, 36, 3]
self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, 64, layers[0], pruning_rate)
self.layer2 = self._make_layer(block, 128, layers[1], pruning_rate, stride=2)
self.layer3 = self._make_layer(block, 256, layers[2], pruning_rate, stride=2)
self.layer4 = self._make_layer(block, 512, layers[3], pruning_rate, stride=2)
self.avgpool = nn.AvgPool2d(7, stride=1)
self.fc = nn.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, nn.BatchNorm2d):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
def _make_layer(self, block, planes, blocks, pruning_rate, 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, pruning_rate, stride, downsample))
self.inplanes = planes * block.expansion
for i in range(1, blocks):
layers.append(block(self.inplanes, planes, pruning_rate))
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 = x.view(x.size(0), -1)
x = self.fc(x)
return x