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import torch.nn as nn
__all__ = ['ResNet31']
def conv3x3(in_channel, out_channel, stride=1):
return nn.Conv2d(in_channel,
out_channel,
kernel_size=3,
stride=stride,
padding=1,
bias=False)
class BasicBlock(nn.Module):
expansion = 1
def __init__(self, in_channels, channels, stride=1, downsample=False):
super().__init__()
self.conv1 = conv3x3(in_channels, channels, stride)
self.bn1 = nn.BatchNorm2d(channels)
self.relu = nn.ReLU()
self.conv2 = conv3x3(channels, channels)
self.bn2 = nn.BatchNorm2d(channels)
self.downsample = downsample
if downsample:
self.downsample = nn.Sequential(
nn.Conv2d(in_channels,
channels * self.expansion,
1,
stride,
bias=False),
nn.BatchNorm2d(channels * self.expansion),
)
else:
self.downsample = nn.Sequential()
self.stride = stride
def forward(self, x):
residual = x
out = self.conv1(x)
out = self.bn1(out)
out = self.relu(out)
out = self.conv2(out)
out = self.bn2(out)
if self.downsample:
residual = self.downsample(x)
out += residual
out = self.relu(out)
return out
class ResNet31(nn.Module):
"""
Args:
in_channels (int): Number of channels of input image tensor.
layers (list[int]): List of BasicBlock number for each stage.
channels (list[int]): List of out_channels of Conv2d layer.
out_indices (None | Sequence[int]): Indices of output stages.
last_stage_pool (bool): If True, add `MaxPool2d` layer to last stage.
"""
def __init__(
self,
in_channels=3,
layers=[1, 2, 5, 3],
channels=[64, 128, 256, 256, 512, 512, 512],
out_indices=None,
last_stage_pool=False,
):
super(ResNet31, self).__init__()
assert isinstance(in_channels, int)
assert isinstance(last_stage_pool, bool)
self.out_indices = out_indices
self.last_stage_pool = last_stage_pool
# conv 1 (Conv Conv)
self.conv1_1 = nn.Conv2d(in_channels,
channels[0],
kernel_size=3,
stride=1,
padding=1)
self.bn1_1 = nn.BatchNorm2d(channels[0])
self.relu1_1 = nn.ReLU(inplace=True)
self.conv1_2 = nn.Conv2d(channels[0],
channels[1],
kernel_size=3,
stride=1,
padding=1)
self.bn1_2 = nn.BatchNorm2d(channels[1])
self.relu1_2 = nn.ReLU(inplace=True)
# conv 2 (Max-pooling, Residual block, Conv)
self.pool2 = nn.MaxPool2d(kernel_size=2,
stride=2,
padding=0,
ceil_mode=True)
self.block2 = self._make_layer(channels[1], channels[2], layers[0])
self.conv2 = nn.Conv2d(channels[2],
channels[2],
kernel_size=3,
stride=1,
padding=1)
self.bn2 = nn.BatchNorm2d(channels[2])
self.relu2 = nn.ReLU(inplace=True)
# conv 3 (Max-pooling, Residual block, Conv)
self.pool3 = nn.MaxPool2d(kernel_size=2,
stride=2,
padding=0,
ceil_mode=True)
self.block3 = self._make_layer(channels[2], channels[3], layers[1])
self.conv3 = nn.Conv2d(channels[3],
channels[3],
kernel_size=3,
stride=1,
padding=1)
self.bn3 = nn.BatchNorm2d(channels[3])
self.relu3 = nn.ReLU(inplace=True)
# conv 4 (Max-pooling, Residual block, Conv)
self.pool4 = nn.MaxPool2d(kernel_size=(2, 1),
stride=(2, 1),
padding=0,
ceil_mode=True)
self.block4 = self._make_layer(channels[3], channels[4], layers[2])
self.conv4 = nn.Conv2d(channels[4],
channels[4],
kernel_size=3,
stride=1,
padding=1)
self.bn4 = nn.BatchNorm2d(channels[4])
self.relu4 = nn.ReLU(inplace=True)
# conv 5 ((Max-pooling), Residual block, Conv)
self.pool5 = None
if self.last_stage_pool:
self.pool5 = nn.MaxPool2d(kernel_size=2,
stride=2,
padding=0,
ceil_mode=True)
self.block5 = self._make_layer(channels[4], channels[5], layers[3])
self.conv5 = nn.Conv2d(channels[5],
channels[5],
kernel_size=3,
stride=1,
padding=1)
self.bn5 = nn.BatchNorm2d(channels[5])
self.relu5 = nn.ReLU(inplace=True)
self.out_channels = channels[-1]
def _make_layer(self, input_channels, output_channels, blocks):
layers = []
for _ in range(blocks):
downsample = None
if input_channels != output_channels:
downsample = nn.Sequential(
nn.Conv2d(
input_channels,
output_channels,
kernel_size=1,
stride=1,
bias=False,
),
nn.BatchNorm2d(output_channels),
)
layers.append(
BasicBlock(input_channels,
output_channels,
downsample=downsample))
input_channels = output_channels
return nn.Sequential(*layers)
def forward(self, x):
x = self.conv1_1(x)
x = self.bn1_1(x)
x = self.relu1_1(x)
x = self.conv1_2(x)
x = self.bn1_2(x)
x = self.relu1_2(x)
outs = []
for i in range(4):
layer_index = i + 2
pool_layer = getattr(self, 'pool{}'.format(layer_index))
block_layer = getattr(self, 'block{}'.format(layer_index))
conv_layer = getattr(self, 'conv{}'.format(layer_index))
bn_layer = getattr(self, 'bn{}'.format(layer_index))
relu_layer = getattr(self, 'relu{}'.format(layer_index))
if pool_layer is not None:
x = pool_layer(x)
x = block_layer(x)
x = conv_layer(x)
x = bn_layer(x)
x = relu_layer(x)
outs.append(x)
if self.out_indices is not None:
return tuple([outs[i] for i in self.out_indices])
return x
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