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a6dd040 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 | import torch.nn as nn
class ResidualBlock(nn.Module):
def __init__(
self,
in_planes,
planes,
norm_layer=nn.InstanceNorm2d,
stride=1,
dilation=1,
):
super(ResidualBlock, self).__init__()
self.conv1 = nn.Conv2d(
in_planes,
planes,
kernel_size=3,
dilation=dilation,
padding=dilation,
stride=stride,
bias=False,
)
self.conv2 = nn.Conv2d(
planes,
planes,
kernel_size=3,
dilation=dilation,
padding=dilation,
bias=False,
)
self.relu = nn.ReLU(inplace=True)
self.norm1 = norm_layer(planes)
self.norm2 = norm_layer(planes)
if not stride == 1 or in_planes != planes:
self.norm3 = norm_layer(planes)
if stride == 1 and in_planes == planes:
self.downsample = None
else:
self.downsample = nn.Sequential(
nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride), self.norm3
)
def forward(self, x):
y = x
y = self.relu(self.norm1(self.conv1(y)))
y = self.relu(self.norm2(self.conv2(y)))
if self.downsample is not None:
x = self.downsample(x)
return self.relu(x + y)
class CNNEncoder(nn.Module):
def __init__(
self,
output_dim=128,
norm_layer=nn.InstanceNorm2d,
num_output_scales=1,
return_quarter=False, # return 1/4 resolution feature
lowest_scale=8, # lowest resolution, 1/8 or 1/4
return_all_scales=False,
**kwargs,
):
super(CNNEncoder, self).__init__()
self.num_scales = num_output_scales
self.return_quarter = return_quarter
self.lowest_scale = lowest_scale
self.return_all_scales = return_all_scales
feature_dims = [64, 96, 128]
self.conv1 = nn.Conv2d(
3, feature_dims[0], kernel_size=7, stride=2, padding=3, bias=False
) # 1/2
self.norm1 = norm_layer(feature_dims[0])
self.relu1 = nn.ReLU(inplace=True)
self.in_planes = feature_dims[0]
self.layer1 = self._make_layer(
feature_dims[0], stride=1, norm_layer=norm_layer
) # 1/2
if self.lowest_scale == 4:
stride = 1
else:
stride = 2
self.layer2 = self._make_layer(
feature_dims[1], stride=stride, norm_layer=norm_layer
) # 1/2 or 1/4
# lowest resolution 1/4 or 1/8
self.layer3 = self._make_layer(
feature_dims[2],
stride=2,
norm_layer=norm_layer,
) # 1/4 or 1/8
self.conv2 = nn.Conv2d(feature_dims[2], output_dim, 1, 1, 0)
for m in self.modules():
if isinstance(m, nn.Conv2d):
nn.init.kaiming_normal_(m.weight, mode="fan_out", nonlinearity="relu")
elif isinstance(m, (nn.BatchNorm2d, nn.InstanceNorm2d, nn.GroupNorm)):
if m.weight is not None:
nn.init.constant_(m.weight, 1)
if m.bias is not None:
nn.init.constant_(m.bias, 0)
def _make_layer(self, dim, stride=1, dilation=1, norm_layer=nn.InstanceNorm2d):
layer1 = ResidualBlock(
self.in_planes, dim, norm_layer=norm_layer, stride=stride, dilation=dilation
)
layer2 = ResidualBlock(
dim, dim, norm_layer=norm_layer, stride=1, dilation=dilation
)
layers = (layer1, layer2)
self.in_planes = dim
return nn.Sequential(*layers)
def forward(self, x):
output_all_scales = []
output = []
x = self.conv1(x)
x = self.norm1(x)
x = self.relu1(x)
x = self.layer1(x) # 1/2
if self.return_all_scales:
output_all_scales.append(x)
if self.num_scales >= 3:
output.append(x)
x = self.layer2(x) # 1/2 or 1/4
if self.return_quarter:
output.append(x)
if self.return_all_scales:
output_all_scales.append(x)
if self.num_scales >= 2:
output.append(x)
x = self.layer3(x) # 1/4 or 1/8
x = self.conv2(x)
if self.return_all_scales:
output_all_scales.append(x)
if self.return_all_scales:
return output_all_scales
if self.return_quarter:
output.append(x)
return output
if self.num_scales >= 1:
output.append(x)
return output
out = [x]
return out
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