def make_layers(cfg, network_width_multiplier, batch_norm=False, groups=1):
layers = []
in_channels = 3
for v in cfg:
if v == 'M':
layers += [nn.MaxPool2d(kernel_size=2, stride=2)]
else:
if in_channels == 3:
conv2d = nl.SharableConv2d(in_channels,
int(v * network_width_multiplier),
kernel_size=3,
padding=1,
bias=False)
else:
conv2d = nl.SharableConv2d(in_channels,
int(v * network_width_multiplier),
kernel_size=3,
padding=1,
bias=False,
groups=groups)
if batch_norm:
layers += [
conv2d,
nn.BatchNorm2d(int(v * network_width_multiplier)),
nn.ReLU(inplace=True)
]
else:
layers += [conv2d, nn.ReLU(inplace=True)]
in_channels = int(v * network_width_multiplier)
layers += [
View(-1,
int(512 * network_width_multiplier) * 7 * 7),
nl.SharableLinear(
int(512 * network_width_multiplier) * 7 * 7,
int(4096 * network_width_multiplier)),
nn.ReLU(True),
# We need Dropout() for 224x224
nn.Dropout(),
nl.SharableLinear(int(4096 * network_width_multiplier),
int(4096 * network_width_multiplier)),
nn.ReLU(True),
nn.Dropout()
]
return nn.Sequential(*layers)
def conv1x1(in_planes, out_planes, stride=1):
"""1x1 convolution"""
return nl.SharableConv2d(in_planes,
out_planes,
kernel_size=1,
stride=stride,
bias=False)
def conv3x3(in_planes, out_planes, stride=1, groups=1, dilation=1):
"""3x3 convolution with padding"""
return nl.SharableConv2d(in_planes,
out_planes,
kernel_size=3,
stride=stride,
padding=dilation,
groups=groups,
bias=False,
dilation=dilation)
def __init__(self,
block,
layers,
dataset_history,
dataset2num_classes,
network_width_multiplier,
shared_layer_info,
num_classes=1000,
zero_init_residual=False,
groups=1,
width_per_group=64,
replace_stride_with_dilation=None,
norm_layer=None):
super(ResNet, self).__init__()
if norm_layer is None:
norm_layer = nn.BatchNorm2d
self._norm_layer = norm_layer
self.network_width_multiplier = network_width_multiplier
self.shared_layer_info = shared_layer_info
self.inplanes = int(64 * network_width_multiplier)
self.dilation = 1
if replace_stride_with_dilation is None:
# each element in the tuple indicates if we should replace
# the 2x2 stride with a dilated convolution instead
replace_stride_with_dilation = [False, False, False]
if len(replace_stride_with_dilation) != 3:
raise ValueError("replace_stride_with_dilation should be None "
"or a 3-element tuple, got {}".format(
replace_stride_with_dilation))
self.groups = groups
self.base_width = width_per_group
self.conv1 = nl.SharableConv2d(3,
self.inplanes,
kernel_size=7,
stride=2,
padding=3,
bias=False)
self.bn1 = norm_layer(self.inplanes)
self.relu = nn.ReLU(inplace=True)
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.layer1 = self._make_layer(block, network_width_multiplier * 64,
layers[0])
self.layer2 = self._make_layer(block,
network_width_multiplier * 128,
layers[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
network_width_multiplier * 256,
layers[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
network_width_multiplier * 512,
layers[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.datasets, self.classifiers = dataset_history, nn.ModuleList()
self.dataset2num_classes = dataset2num_classes
# we delete default self.classifier for imagenet, because we manually add it in packnet_imagenet_main.py
if self.datasets:
self._reconstruct_classifiers()
for m in self.modules():
if isinstance(m, nl.SharableConv2d):
#nn.init.constant_(m.weight, 0)
nn.init.normal_(m.weight, 0, 0.001)
#nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
elif isinstance(m, (nn.BatchNorm2d, nn.GroupNorm)):
nn.init.constant_(m.weight, 1)
nn.init.constant_(m.bias, 0)
# Zero-initialize the last BN in each residual branch,
# so that the residual branch starts with zeros, and each residual block behaves like an identity.
# This improves the model by 0.2~0.3% according to https://arxiv.org/abs/1706.02677
if zero_init_residual:
for m in self.modules():
if isinstance(m, Bottleneck):
nn.init.constant_(m.bn3.weight, 0)
elif isinstance(m, BasicBlock):
nn.init.constant_(m.bn2.weight, 0)