def __init__(self, in_channels, out_channels, stride=1, base_width=64):
super().__init__()
width = int(out_channels * (base_width / 64.))
self.residual_function = nn.Sequential(
layers.Conv2d(in_channels, width, kernel_size=1, bias=False),
layers.BatchNorm2d(width),
nn.ReLU(inplace=True),
layers.Conv2d(width,
width,
stride=stride,
kernel_size=3,
padding=1,
bias=False),
layers.BatchNorm2d(width),
nn.ReLU(inplace=True),
layers.Conv2d(width,
out_channels * BottleNeck.expansion,
kernel_size=1,
bias=False),
layers.BatchNorm2d(out_channels * BottleNeck.expansion),
)
self.shortcut = layers.Identity2d(in_channels)
if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
self.shortcut = nn.Sequential(
layers.Conv2d(in_channels,
out_channels * BottleNeck.expansion,
stride=stride,
kernel_size=1,
bias=False),
layers.BatchNorm2d(out_channels * BottleNeck.expansion))
def __init__(self, f_in: int, f_out: int, downsample=False):
super(Block, self).__init__()
stride = 2 if downsample else 1
self.conv1 = layers.Conv2d(f_in,
f_out,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
self.bn1 = layers.BatchNorm2d(f_out)
self.conv2 = layers.Conv2d(f_out,
f_out,
kernel_size=3,
stride=1,
padding=1,
bias=False)
self.bn2 = layers.BatchNorm2d(f_out)
# No parameters for shortcut connections.
if downsample or f_in != f_out:
self.shortcut = nn.Sequential(
layers.Conv2d(f_in, f_out, kernel_size=1, stride=2,
bias=False), layers.BatchNorm2d(f_out))
else:
self.shortcut = layers.Identity2d(f_in)
def __init__(self, in_channels, out_channels, stride=1, base_width=64):
super().__init__()
#residual function
self.residual_function = nn.Sequential(
layers.Conv2d(in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=1,
bias=False), layers.BatchNorm2d(out_channels),
nn.ReLU(inplace=True),
layers.Conv2d(out_channels,
out_channels * BasicBlock.expansion,
kernel_size=3,
padding=1,
bias=False),
layers.BatchNorm2d(out_channels * BasicBlock.expansion))
#shortcut
self.shortcut = layers.Identity2d(in_channels)
#the shortcut output dimension is not the same with residual function
#use 1*1 convolution to match the dimension
if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
self.shortcut = nn.Sequential(
layers.Conv2d(in_channels,
out_channels * BasicBlock.expansion,
kernel_size=1,
stride=stride,
bias=False),
layers.BatchNorm2d(out_channels * BasicBlock.expansion))
def __init__(self,
in_channels,
out_channels,
stride=1,
base_width=64,
batch_norm=True):
super().__init__()
self.batch_norm = batch_norm
# residual function
layer_list = [
layers.Conv2d(
in_channels,
out_channels,
kernel_size=3,
stride=stride,
padding=1,
bias=False,
),
]
if self.batch_norm:
layer_list.append(layers.BatchNorm2d(out_channels))
layer_list += [
nn.ReLU(inplace=True),
layers.Conv2d(
out_channels,
out_channels * BasicBlock.expansion,
kernel_size=3,
padding=1,
bias=False,
),
]
if self.batch_norm:
layer_list.append(
layers.BatchNorm2d(out_channels * BasicBlock.expansion))
self.residual_function = nn.Sequential(*layer_list)
# shortcut
self.shortcut = layers.Identity2d(in_channels)
# the shortcut output dimension is not the same with residual function
# use 1*1 convolution to match the dimension
if stride != 1 or in_channels != BasicBlock.expansion * out_channels:
layer_list = [
layers.Conv2d(
in_channels,
out_channels * BasicBlock.expansion,
kernel_size=1,
stride=stride,
bias=False,
)
]
if self.batch_norm:
layer_list.append(
layers.BatchNorm2d(out_channels * BasicBlock.expansion))
self.shortcut = nn.Sequential(*layer_list)
def __init__(self,
in_channels,
out_channels,
stride=1,
base_width=64,
batch_norm=True):
super().__init__()
self.batch_norm = batch_norm
width = int(out_channels * (base_width / 64.0))
layer_list = [
layers.Conv2d(in_channels, width, kernel_size=1, bias=False),
]
if self.batch_norm:
layer_list.append(layers.BatchNorm2d(width))
layer_list += [
nn.ReLU(inplace=True),
layers.Conv2d(width,
width,
stride=stride,
kernel_size=3,
padding=1,
bias=False),
]
if self.batch_norm:
layer_list.append(layers.BatchNorm2d(width))
layer_list += [
nn.ReLU(inplace=True),
layers.Conv2d(width,
out_channels * BottleNeck.expansion,
kernel_size=1,
bias=False),
]
if self.batch_norm:
layer_list.append(
layers.BatchNorm2d(out_channels * BottleNeck.expansion))
self.residual_function = nn.Sequential(*layer_list)
self.shortcut = layers.Identity2d(in_channels)
if stride != 1 or in_channels != out_channels * BottleNeck.expansion:
layer_list = [
layers.Conv2d(
in_channels,
out_channels * BottleNeck.expansion,
stride=stride,
kernel_size=1,
bias=False,
),
]
if self.batch_norm:
layer_list.append(
layers.BatchNorm2d(out_channels * BottleNeck.expansion))
self.shortcut = nn.Sequential(*layer_list)