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import torch.nn as nn
import torch.nn.functional as F
from openrec.modeling.common import Activation
class ConvBNLayer(nn.Module):
def __init__(
self,
in_channels,
out_channels,
kernel_size,
stride=1,
groups=1,
is_vd_mode=False,
act=None,
):
super(ConvBNLayer, self).__init__()
self.act = act
self.is_vd_mode = is_vd_mode
self._pool2d_avg = nn.AvgPool2d(kernel_size=stride,
stride=stride,
padding=0,
ceil_mode=False)
self._conv = nn.Conv2d(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
stride=1 if is_vd_mode else stride,
padding=(kernel_size - 1) // 2,
groups=groups,
bias=False,
)
self._batch_norm = nn.BatchNorm2d(out_channels, )
if self.act is not None:
self._act = Activation(act_type=act, inplace=True)
def forward(self, inputs):
if self.is_vd_mode:
inputs = self._pool2d_avg(inputs)
y = self._conv(inputs)
y = self._batch_norm(y)
if self.act is not None:
y = self._act(y)
return y
class BottleneckBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None,
):
super(BottleneckBlock, self).__init__()
self.scale = 4
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
act='relu',
)
self.conv1 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
)
self.conv2 = ConvBNLayer(
in_channels=out_channels,
out_channels=out_channels * self.scale,
kernel_size=1,
act=None,
)
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels * self.scale,
kernel_size=1,
stride=stride,
is_vd_mode=not if_first and stride[0] != 1,
)
self.shortcut = shortcut
self.out_channels = out_channels * self.scale
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
conv2 = self.conv2(conv1)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = short + conv2
y = F.relu(y)
return y
class BasicBlock(nn.Module):
def __init__(
self,
in_channels,
out_channels,
stride,
shortcut=True,
if_first=False,
name=None,
):
super(BasicBlock, self).__init__()
self.stride = stride
self.scale = 1
self.conv0 = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=3,
stride=stride,
act='relu',
)
self.conv1 = ConvBNLayer(in_channels=out_channels,
out_channels=out_channels,
kernel_size=3,
act=None)
if not shortcut:
self.short = ConvBNLayer(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=1,
stride=stride,
is_vd_mode=not if_first and stride[0] != 1,
)
self.shortcut = shortcut
self.out_channels = out_channels * self.scale
def forward(self, inputs):
y = self.conv0(inputs)
conv1 = self.conv1(y)
if self.shortcut:
short = inputs
else:
short = self.short(inputs)
y = short + conv1
y = F.relu(y)
return y
class ResNet(nn.Module):
def __init__(self, in_channels=3, layers=50, **kwargs):
super(ResNet, self).__init__()
self.layers = layers
supported_layers = [18, 34, 50, 101, 152, 200]
assert layers in supported_layers, 'supported layers are {} but input layer is {}'.format(
supported_layers, layers)
if layers == 18:
depth = [2, 2, 2, 2]
elif layers == 34 or layers == 50:
depth = [3, 4, 6, 3]
elif layers == 101:
depth = [3, 4, 23, 3]
elif layers == 152:
depth = [3, 8, 36, 3]
elif layers == 200:
depth = [3, 12, 48, 3]
if layers >= 50:
block_class = BottleneckBlock
else:
block_class = BasicBlock
num_filters = [64, 128, 256, 512]
self.conv1_1 = ConvBNLayer(
in_channels=in_channels,
out_channels=32,
kernel_size=3,
stride=1,
act='relu',
)
self.conv1_2 = ConvBNLayer(in_channels=32,
out_channels=32,
kernel_size=3,
stride=1,
act='relu')
self.conv1_3 = ConvBNLayer(in_channels=32,
out_channels=64,
kernel_size=3,
stride=1,
act='relu')
self.pool2d_max = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# self.block_list = list()
self.block_list = nn.Sequential()
in_channels = 64
for block in range(len(depth)):
shortcut = False
for i in range(depth[block]):
if layers in [101, 152, 200] and block == 2:
if i == 0:
conv_name = 'res' + str(block + 2) + 'a'
else:
conv_name = 'res' + str(block + 2) + 'b' + str(i)
else:
conv_name = 'res' + str(block + 2) + chr(97 + i)
if i == 0 and block != 0:
stride = (2, 1)
else:
stride = (1, 1)
block_instance = block_class(
in_channels=in_channels,
out_channels=num_filters[block],
stride=stride,
shortcut=shortcut,
if_first=block == i == 0,
name=conv_name,
)
shortcut = True
in_channels = block_instance.out_channels
# self.block_list.append(bottleneck_block)
self.block_list.add_module('bb_%d_%d' % (block, i),
block_instance)
self.out_channels = num_filters[block]
self.out_pool = nn.MaxPool2d(kernel_size=2, stride=2, padding=0)
def forward(self, inputs):
y = self.conv1_1(inputs)
y = self.conv1_2(y)
y = self.conv1_3(y)
y = self.pool2d_max(y)
for block in self.block_list:
y = block(y)
y = self.out_pool(y)
return y
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