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from torch import nn
class ConvLayer(nn.Module):
def __init__(self, input_c, output_c, bias=False, stride=1, padding=1, pool=False, dropout=0.):
super(ConvLayer, self).__init__()
layers = list()
layers.append(
nn.Conv2d(input_c, output_c, kernel_size=3, bias=bias, stride=stride, padding=padding,
padding_mode='replicate')
)
if pool:
layers.append(nn.MaxPool2d(kernel_size=2, stride=2))
layers.append(nn.BatchNorm2d(output_c))
layers.append(nn.ReLU())
if dropout > 0:
layers.append(nn.Dropout(dropout))
self.all_layers = nn.Sequential(*layers)
def forward(self, x):
return self.all_layers(x)
class CustomLayer(nn.Module):
def __init__(self, input_c, output_c, pool=True, residue=2, dropout=0.):
super(CustomLayer, self).__init__()
self.pool_block = ConvLayer(input_c, output_c, pool=pool, dropout=dropout)
self.res_block = None
if residue > 0:
layers = list()
for i in range(0, residue):
layers.append(ConvLayer(output_c, output_c, pool=False, dropout=dropout))
self.res_block = nn.Sequential(*layers)
def forward(self, x):
x = self.pool_block(x)
if self.res_block is not None:
x_ = x
x = self.res_block(x)
# += operator causes inplace errors in pytorch if done right after relu.
x = x + x_
return x
class Model(nn.Module):
def __init__(self, dropout=0.05):
super(Model, self).__init__()
self.network = nn.Sequential(
CustomLayer(3, 64, pool=False, residue=0, dropout=dropout),
CustomLayer(64, 128, pool=True, residue=2, dropout=dropout),
CustomLayer(128, 256, pool=True, residue=0, dropout=dropout),
CustomLayer(256, 512, pool=True, residue=2, dropout=dropout),
nn.MaxPool2d(kernel_size=4, stride=4),
nn.Flatten(),
nn.Linear(512, 10)
)
def forward(self, x):
return self.network(x)
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