def fc_orth(input_shape, num_classes, dense_classifier=False, pretrained=False, L=6, N=100, nonlinearity=nn.ReLU()):
def _orthogonal_init(m):
if (type(m) == Layers.layers.Linear):
torch.nn.init.orthogonal_(m.weight)
size = np.prod(input_shape)
# Linear feature extractor
modules = [nn.Flatten()]
modules.append(layers.Linear(size, N))
modules.append(nonlinearity)
for i in range(L - 2):
modules.append(layers.Linear(N, N))
modules.append(nonlinearity)
# Linear classifier
if dense_classifier:
modules.append(nn.Linear(N, num_classes))
else:
modules.append(layers.Linear(N, num_classes))
model = nn.Sequential(*modules)
model.apply(_orthogonal_init)
# Pretrained model
if pretrained:
print("WARNING: this model does not have pretrained weights.")
return model
def fc(input_shape,
num_classes,
dense_classifier=False,
pretrained=False,
L=5,
N=256,
nonlinearity=nn.ReLU()):
size = np.prod(input_shape)
# Linear feature extractor
modules = [nn.Flatten()]
modules.append(layers.Linear(size, N))
modules.append(nonlinearity)
for i in range(L - 2):
modules.append(layers.Linear(N, N))
modules.append(nonlinearity)
# Linear classifier
if dense_classifier:
modules.append(nn.Linear(N, num_classes))
else:
modules.append(layers.Linear(N, num_classes))
model = nn.Sequential(*modules)
# Pretrained model
if pretrained:
print("WARNING: this model does not have pretrained weights.")
return model
def __init__(self, features, num_classes=1000, init_weights=True):
super(VGG, self).__init__()
self.features = features
self.avgpool = nn.AdaptiveAvgPool2d((7, 7))
self.classifier = nn.Sequential(
layers.Linear(512 * 7 * 7, 4096),
nn.ReLU(True),
nn.Dropout(),
layers.Linear(4096, 4096),
nn.ReLU(True),
nn.Dropout(),
layers.Linear(4096, num_classes),
)
if init_weights:
self._initialize_weights()
def conv(input_shape,
num_classes,
dense_classifier=False,
pretrained=False,
L=3,
N=32,
nonlinearity=nn.ReLU()):
channels, width, height = input_shape
# Convolutional feature extractor
modules = []
modules.append(layers.Conv2d(channels, N, kernel_size=3, padding=3 // 2))
modules.append(nonlinearity)
for i in range(L - 2):
modules.append(layers.Conv2d(N, N, kernel_size=3, padding=3 // 2))
modules.append(nonlinearity)
# Linear classifier
modules.append(nn.Flatten())
if dense_classifier:
modules.append(nn.Linear(N * width * height, num_classes))
else:
modules.append(layers.Linear(N * width * height, num_classes))
model = nn.Sequential(*modules)
# Pretrained model
if pretrained:
print("WARNING: this model does not have pretrained weights.")
return model
def __init__(self, plan, num_classes, dense_classifier):
super(ResNet, self).__init__()
# Initial convolution.
current_filters = plan[0][0]
self.conv = layers.Conv2d(
3, current_filters, kernel_size=3, stride=1, padding=1, bias=False
)
self.bn = layers.BatchNorm2d(current_filters)
# The subsequent blocks of the ResNet.
blocks = []
for segment_index, (filters, num_blocks) in enumerate(plan):
for block_index in range(num_blocks):
downsample = segment_index > 0 and block_index == 0
blocks.append(Block(current_filters, filters, downsample))
current_filters = filters
self.blocks = nn.Sequential(*blocks)
self.fc = layers.Linear(plan[-1][0], num_classes)
if dense_classifier:
self.fc = nn.Linear(plan[-1][0], num_classes)
self._initialize_weights()
def __init__(self,
plan,
conv,
num_classes=10,
dense_classifier=False,
path=False):
super(VGG, self).__init__()
layer_list = []
filters = 3
self.path = path
for spec in plan:
if spec == 'M':
if self.path:
layer_list.append(nn.LPPool2d(1.0, kernel_size=2,
stride=2))
else:
layer_list.append(nn.MaxPool2d(kernel_size=2, stride=2))
else:
layer_list.append(conv(filters, spec))
filters = spec
self.layers = nn.Sequential(*layer_list)
self.fc = layers.Linear(512, num_classes)
if dense_classifier:
self.fc = nn.Linear(512, num_classes)
self._initialize_weights()
def __init__(self,
block,
num_block,
base_width,
num_classes=200,
dense_classifier=False):
super().__init__()
self.in_channels = 64
self.conv1 = nn.Sequential(
layers.Conv2d(3, 64, kernel_size=3, padding=1, bias=False),
layers.BatchNorm2d(64), nn.ReLU(inplace=True))
#we use a different inputsize than the original paper
#so conv2_x's stride is 1
self.conv2_x = self._make_layer(block, 64, num_block[0], 1, base_width)
self.conv3_x = self._make_layer(block, 128, num_block[1], 2,
base_width)
self.conv4_x = self._make_layer(block, 256, num_block[2], 2,
base_width)
self.conv5_x = self._make_layer(block, 512, num_block[3], 2,
base_width)
self.avg_pool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = layers.Linear(512 * block.expansion, num_classes)
if dense_classifier:
self.fc = nn.Linear(512 * block.expansion, num_classes)
self._initialize_weights()
def __init__(self,
block,
layer_list,
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 = layers.BatchNorm2d
self._norm_layer = norm_layer
self.inplanes = 64
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 = layers.Conv2d(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, 64, layer_list[0])
self.layer2 = self._make_layer(block,
128,
layer_list[1],
stride=2,
dilate=replace_stride_with_dilation[0])
self.layer3 = self._make_layer(block,
256,
layer_list[2],
stride=2,
dilate=replace_stride_with_dilation[1])
self.layer4 = self._make_layer(block,
512,
layer_list[3],
stride=2,
dilate=replace_stride_with_dilation[2])
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = layers.Linear(512 * block.expansion, num_classes)
for m in self.modules():
if isinstance(m, layers.Conv2d):
nn.init.kaiming_normal_(m.weight,
mode='fan_out',
nonlinearity='relu')
elif isinstance(m, (layers.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)
