class PDN_S(nn.Module):
def __init__(self, last_kernel_size=384,with_bn=False) -> None:
super().__init__()
# Layer Name Stride Kernel Size Number of Kernels Padding Activation
# Conv-1 1×1 4×4 128 3 ReLU
# AvgPool-1 2×2 2×2 128 1 -
# Conv-2 1×1 4×4 256 3 ReLU
# AvgPool-2 2×2 2×2 256 1 -
# Conv-3 1×1 3×3 256 1 ReLU
# Conv-4 1×1 4×4 384 0 -
self.with_bn = with_bn
self.conv1 = nn.Conv2d(3, 128, kernel_size=4, stride=1, padding=3)
self.conv2 = nn.Conv2d(128, 256, kernel_size=4, stride=1, padding=3)
self.conv3 = nn.Conv2d(256, 256, kernel_size=3, stride=1, padding=1)
self.conv4 = nn.Conv2d(256, last_kernel_size, kernel_size=4, stride=1, padding=0)
self.avgpool1 = nn.AvgPool2d(kernel_size=2, stride=2, padding=1)
self.avgpool2 = nn.AvgPool2d(kernel_size=2, stride=2, padding=1)
if self.with_bn:
self.bn1 = nn.BatchNorm2d(128)
self.bn2 = nn.BatchNorm2d(256)
self.bn3 = nn.BatchNorm2d(256)
self.bn4 = nn.BatchNorm2d(last_kernel_size)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x) if self.with_bn else x
x = F.relu(x)
x = self.avgpool1(x)
x = self.conv2(x)
x = self.bn2(x) if self.with_bn else x
x = F.relu(x)
x = self.avgpool2(x)
x = self.conv3(x)
x = self.bn3(x) if self.with_bn else x
x = F.relu(x)
x = self.conv4(x)
x = self.bn4(x) if self.with_bn else x
return xPatch Description Network (PDN) Receptive Field Calculation
In EfficientAD anomaly detection, the authors use the Patch Description Network (PDN) in Figure 2.
They make the statement:
“Each output neuron has a receptive field of 33× 33 pixels and thus each output feature vector describes a 33× 33 patch.”
Calculate the receptive fields across the PDN so that you can support this statement.
In [2]: