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resnet.py
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resnet.py
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'''ResNet in PyTorch.
For Pre-activation ResNet, see 'preact_resnet.py'.
Reference:
[1] Kaiming He, Xiangyu Zhang, Shaoqing Ren, Jian Sun
Deep Residual Learning for Image Recognition. arXiv:1512.03385
'''
import torch
import torch.nn as nn
from pruning.layers import MaskedLinear, MaskedConv2d, Flatten, MaskedBN1d, MaskedBN2d
import math
import numpy as np
import pdb
import torch.nn.functional as F
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_planes, planes, stride=1):
super(BasicBlock, self).__init__()
self.conv1 = MaskedConv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn1 = MaskedBN2d(planes)
self.conv2 = MaskedConv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
self.bn2 = MaskedBN2d(planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
MaskedConv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
MaskedBN2d(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 Bottleneck(nn.Module):
expansion = 4
def __init__(self, in_planes, planes, stride=1):
super(Bottleneck, self).__init__()
self.conv1 = MaskedConv2d(in_planes, planes, kernel_size=1, bias=False)
self.bn1 = MaskedBN2d(planes)
self.conv2 = MaskedConv2d(planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
self.bn2 = MaskedBN2d(planes)
self.conv3 = MaskedConv2d(planes, self.expansion*planes, kernel_size=1, bias=False)
self.bn3 = MaskedBN2d(self.expansion*planes)
self.shortcut = nn.Sequential()
if stride != 1 or in_planes != self.expansion*planes:
self.shortcut = nn.Sequential(
MaskedConv2d(in_planes, self.expansion*planes, kernel_size=1, stride=stride, bias=False),
MaskedBN2d(self.expansion*planes)
)
def forward(self, x):
out = F.relu(self.bn1(self.conv1(x)))
out = F.relu(self.bn2(self.conv2(out)))
out = self.bn3(self.conv3(out))
out += self.shortcut(x)
out = F.relu(out)
return out
class ResNet(nn.Module):
def __init__(self, block, num_blocks, num_classes=10):
super(ResNet, self).__init__()
self.in_planes = 64
self.conv1 = MaskedConv2d(3, 64, kernel_size=3, stride=1, padding=1, bias=False)
self.bn1 = MaskedBN2d(64)
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.linear = MaskedLinear(512*block.expansion, num_classes)
self.index = 0
for m in self.modules():
#print(m.__class__.__name__)
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))
self.index += 1
elif isinstance(m, nn.BatchNorm2d):
m.weight.data.fill_(1)
m.bias.data.zero_()
self.index += 1
elif isinstance(m, nn.Linear):
m.bias.data.zero_()
self.index += 1
else:
pass
#print("index init:", self.index)
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 = self.layer4(out)
out = F.avg_pool2d(out, 4)
out = out.view(out.size(0), -1)
out = self.linear(out)
#out = self.model(x)
return out
class ResNet18(nn.Module):
def __init__(self):
super(ResNet18, self).__init__()
self.model = ResNet(BasicBlock, [2,2,2,2])
self.index = self.model.index
def forward(self, x):
out = self.model(x)
return out
def set_masks(self, masks, transfer=False):
index = np.array([0])
for layer in self.model.modules():
#print(m.__class__.__name__)
if isinstance(layer, (nn.Conv2d, nn.Linear, nn.BatchNorm2d, nn.BatchNorm1d)):
#print(layer.__class__.__name__)
layer.set_mask(torch.from_numpy(masks[index[0]]))
index[0] +=1
assert index[0] == self.index, "seif.index["+str(self.index)+"] !="\
"mask index["+str(index[0])+"]"
class ResNet34(nn.Module):
def __init__(self):
super(ResNet18, self).__init__()
self.model = ResNet(BasicBlock, [3,4,6,3])
self.index = self.model.index
def forward(self, x):
out = self.model(x)
return out
def set_masks(self, masks, transfer=False):
index = np.array([0])
for layer in self.model.modules():
#print(m.__class__.__name__)
if isinstance(layer, (nn.Conv2d, nn.Linear, nn.BatchNorm2d, nn.BatchNorm1d)):
#print(layer.__class__.__name__)
layer.set_mask(torch.from_numpy(masks[index[0]]))
index[0] +=1
assert index[0] == self.index, "seif.index["+str(self.index)+"] !="\
"mask index["+str(index[0])+"]"
def ResNet101():
return ResNet(Bottleneck, [3,4,23,3])
def ResNet152():
return ResNet(Bottleneck, [3,8,36,3])