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Running
on
Zero
File size: 3,987 Bytes
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import torch.nn as nn
def _make_scratch(in_shape, out_shape, groups=1, expand=False):
scratch = nn.Module()
out_shape1 = out_shape
out_shape2 = out_shape
out_shape3 = out_shape
if len(in_shape) >= 4:
out_shape4 = out_shape
if expand:
out_shape1 = out_shape
out_shape2 = out_shape * 2
out_shape3 = out_shape * 4
if len(in_shape) >= 4:
out_shape4 = out_shape * 8
scratch.layer1_rn = nn.Conv2d(in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
scratch.layer2_rn = nn.Conv2d(in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
scratch.layer3_rn = nn.Conv2d(in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
if len(in_shape) >= 4:
scratch.layer4_rn = nn.Conv2d(in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups)
return scratch
class ResidualConvUnit(nn.Module):
"""Residual convolution module.
"""
def __init__(self, features, activation, bn):
"""Init.
Args:
features (int): number of features
"""
super().__init__()
self.bn = bn
self.groups=1
self.conv1 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
self.conv2 = nn.Conv2d(features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups)
if self.bn == True:
self.bn1 = nn.BatchNorm2d(features)
self.bn2 = nn.BatchNorm2d(features)
self.activation = activation
self.skip_add = nn.quantized.FloatFunctional()
def forward(self, x):
"""Forward pass.
Args:
x (tensor): input
Returns:
tensor: output
"""
out = self.activation(x)
out = self.conv1(out)
if self.bn == True:
out = self.bn1(out)
out = self.activation(out)
out = self.conv2(out)
if self.bn == True:
out = self.bn2(out)
if self.groups > 1:
out = self.conv_merge(out)
return self.skip_add.add(out, x)
class FeatureFusionBlock(nn.Module):
"""Feature fusion block.
"""
def __init__(
self,
features,
activation,
deconv=False,
bn=False,
expand=False,
align_corners=True,
size=None
):
"""Init.
Args:
features (int): number of features
"""
super(FeatureFusionBlock, self).__init__()
self.deconv = deconv
self.align_corners = align_corners
self.groups=1
self.expand = expand
out_features = features
if self.expand == True:
out_features = features // 2
self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
self.skip_add = nn.quantized.FloatFunctional()
self.size=size
def forward(self, *xs, size=None):
"""Forward pass.
Returns:
tensor: output
"""
output = xs[0]
if len(xs) == 2:
res = self.resConfUnit1(xs[1])
output = self.skip_add.add(output, res)
output = self.resConfUnit2(output)
if (size is None) and (self.size is None):
modifier = {"scale_factor": 2}
elif size is None:
modifier = {"size": self.size}
else:
modifier = {"size": size}
output = nn.functional.interpolate(output, **modifier, mode="bilinear", align_corners=self.align_corners)
output = self.out_conv(output)
return output
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