class ResNet18(nn.Module):
def __init__(self, n_classes):
super(ResNet18, self).__init__()
self.dropout_percentage = 0.5
self.relu = nn.ReLU()
# BLOCK-1 (starting block) input=(224x224) output=(56x56)
self.conv1 = nn.Conv2d(in_channels=3, out_channels=64, kernel_size=(7,7), stride=(2,2), padding=(3,3))
self.batchnorm1 = nn.BatchNorm2d(64)
self.maxpool1 = nn.MaxPool2d(kernel_size=(3,3), stride=(2,2), padding=(1,1))
# BLOCK-2 (1) input=(56x56) output = (56x56)
self.conv2_1_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm2_1_1 = nn.BatchNorm2d(64)
self.conv2_1_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm2_1_2 = nn.BatchNorm2d(64)
self.dropout2_1 = nn.Dropout(p=self.dropout_percentage)
# BLOCK-2 (2)
self.conv2_2_1 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm2_2_1 = nn.BatchNorm2d(64)
self.conv2_2_2 = nn.Conv2d(in_channels=64, out_channels=64, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm2_2_2 = nn.BatchNorm2d(64)
self.dropout2_2 = nn.Dropout(p=self.dropout_percentage)
# BLOCK-3 (1) input=(56x56) output = (28x28)
self.conv3_1_1 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(3,3), stride=(2,2), padding=(1,1))
self.batchnorm3_1_1 = nn.BatchNorm2d(128)
self.conv3_1_2 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm3_1_2 = nn.BatchNorm2d(128)
self.concat_adjust_3 = nn.Conv2d(in_channels=64, out_channels=128, kernel_size=(1,1), stride=(2,2), padding=(0,0))
self.dropout3_1 = nn.Dropout(p=self.dropout_percentage)
# BLOCK-3 (2)
self.conv3_2_1 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm3_2_1 = nn.BatchNorm2d(128)
self.conv3_2_2 = nn.Conv2d(in_channels=128, out_channels=128, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm3_2_2 = nn.BatchNorm2d(128)
self.dropout3_2 = nn.Dropout(p=self.dropout_percentage)
# BLOCK-4 (1) input=(28x28) output = (14x14)
self.conv4_1_1 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(3,3), stride=(2,2), padding=(1,1))
self.batchnorm4_1_1 = nn.BatchNorm2d(256)
self.conv4_1_2 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm4_1_2 = nn.BatchNorm2d(256)
self.concat_adjust_4 = nn.Conv2d(in_channels=128, out_channels=256, kernel_size=(1,1), stride=(2,2), padding=(0,0))
self.dropout4_1 = nn.Dropout(p=self.dropout_percentage)
# BLOCK-4 (2)
self.conv4_2_1 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm4_2_1 = nn.BatchNorm2d(256)
self.conv4_2_2 = nn.Conv2d(in_channels=256, out_channels=256, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm4_2_2 = nn.BatchNorm2d(256)
self.dropout4_2 = nn.Dropout(p=self.dropout_percentage)
# BLOCK-5 (1) input=(14x14) output = (7x7)
self.conv5_1_1 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(3,3), stride=(2,2), padding=(1,1))
self.batchnorm5_1_1 = nn.BatchNorm2d(512)
self.conv5_1_2 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm5_1_2 = nn.BatchNorm2d(512)
self.concat_adjust_5 = nn.Conv2d(in_channels=256, out_channels=512, kernel_size=(1,1), stride=(2,2), padding=(0,0))
self.dropout5_1 = nn.Dropout(p=self.dropout_percentage)
# BLOCK-5 (2)
self.conv5_2_1 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm5_2_1 = nn.BatchNorm2d(512)
self.conv5_2_2 = nn.Conv2d(in_channels=512, out_channels=512, kernel_size=(3,3), stride=(1,1), padding=(1,1))
self.batchnorm5_2_2 = nn.BatchNorm2d(512)
self.dropout5_2 = nn.Dropout(p=self.dropout_percentage)
# Final Block input=(7x7)
self.avgpool = nn.AvgPool2d(kernel_size=(7,7), stride=(1,1))
self.fc = nn.Linear(in_features=1*1*512, out_features=1000)
self.out = nn.Linear(in_features=1000, out_features=n_classes) # END
def forward(self, x):
# block 1 --> Starting block
x = self.relu(self.batchnorm1(self.conv1(x)))
op1 = self.maxpool1(x)
# block2 - 1
x = self.relu(self.batchnorm2_1_1(self.conv2_1_1(op1))) # conv2_1
x = self.batchnorm2_1_2(self.conv2_1_2(x))
# conv2_1
x = self.dropout2_1(x)
# block2 - Adjust - No adjust in this layer as dimensions are already same
# block2 - Concatenate 1
op2_1 = self.relu(x + op1)
# block2 - 2
x = self.relu(self.batchnorm2_2_1(self.conv2_2_1(op2_1))) # conv2_2
x = self.batchnorm2_2_2(self.conv2_2_2(x))
# conv2_2
x = self.dropout2_2(x)
# op - block2
op2 = self.relu(x + op2_1)
# block3 - 1[Convolution block]
x = self.relu(self.batchnorm3_1_1(self.conv3_1_1(op2))) # conv3_1
x = self.batchnorm3_1_2(self.conv3_1_2(x))
# conv3_1
x = self.dropout3_1(x)
# block3 - Adjust
op2 = self.concat_adjust_3(op2) # SKIP CONNECTION
# block3 - Concatenate 1
op3_1 = self.relu(x + op2)
# block3 - 2[Identity Block]
x = self.relu(self.batchnorm3_2_1(self.conv3_2_1(op3_1))) # conv3_2
x = self.batchnorm3_2_2(self.conv3_2_2(x))
# conv3_2
x = self.dropout3_2(x)
# op - block3
op3 = self.relu(x + op3_1)
# block4 - 1[Convolition block]
x = self.relu(self.batchnorm4_1_1(self.conv4_1_1(op3))) # conv4_1
x = self.batchnorm4_1_2(self.conv4_1_2(x))
# conv4_1
x = self.dropout4_1(x)
# block4 - Adjust
op3 = self.concat_adjust_4(op3) # SKIP CONNECTION
# block4 - Concatenate 1
op4_1 = self.relu(x + op3)
# block4 - 2[Identity Block]
x = self.relu(self.batchnorm4_2_1(self.conv4_2_1(op4_1))) # conv4_2
x = self.batchnorm4_2_2(self.conv4_2_2(x))
# conv4_2
x = self.dropout4_2(x)
# op - block4
op4 = self.relu(x + op4_1)
# block5 - 1[Convolution Block]
x = self.relu(self.batchnorm5_1_1(self.conv5_1_1(op4))) # conv5_1
x = self.batchnorm5_1_2(self.conv5_1_2(x))
# conv5_1
x = self.dropout5_1(x)
# block5 - Adjust
op4 = self.concat_adjust_5(op4) # SKIP CONNECTION
# block5 - Concatenate 1
op5_1 = self.relu(x + op4)
# block5 - 2[Identity Block]
x = self.relu(self.batchnorm5_2_1(self.conv5_2_1(op5_1))) # conv5_2
x = self.batchnorm5_2_1(self.conv5_2_1(x))
# conv5_2
x = self.dropout5_2(x)
# op - block5
op5 = self.relu(x + op5_1)
# FINAL BLOCK - classifier
x = self.avgpool(op5)
x = x.reshape(x.shape[0], -1)
x = self.relu(self.fc(x))
x = self.out(x)
return x