hi guys,
i would like to add a softmax form my ResNet model i don’t know how?
there is the model :
import torch
import torch.nn as nn
class block(nn.Module):
def __init__(
self, in_channels, intermediate_channels, identity_downsample=None, stride=1
):
super(block, self).__init__()
self.expansion = 4
self.conv1 = nn.Conv2d(
in_channels, intermediate_channels, kernel_size=1, stride=1, padding=0
)
self.bn1 = nn.BatchNorm2d(intermediate_channels)
self.conv2 = nn.Conv2d(
intermediate_channels,
intermediate_channels,
kernel_size=3,
stride=stride,
padding=1,
)
self.bn2 = nn.BatchNorm2d(intermediate_channels )
self.conv3 = nn.Conv2d(
intermediate_channels,
intermediate_channels * self.expansion,
kernel_size=1,
stride=1,
padding=0,
)
self.bn3 = nn.BatchNorm2d(intermediate_channels * self.expansion)
self.relu = nn.ReLU()
self.identity_downsample = identity_downsample
self.stride = stride
def forward(self, x):
identity = x.clone()
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x)
x = self.relu(x)
x = self.conv3(x)
x = self.bn3(x)
if self.identity_downsample is not None:
identity = self.identity_downsample(identity)
x += identity
x = self.relu(x)
return x
class ResNet(nn.Module):
def __init__(self, block, layers, image_channels, num_classes):
super(ResNet, self).__init__()
self.in_channels = 10
self.conv1 = nn.Conv2d(image_channels, 10, kernel_size=3, stride=2, padding=3)
self.bn1 = nn.BatchNorm2d(10)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
self.softmax = nn.Softmax(dim=1)
# Essentially the entire ResNet architecture are in these 4 lines below
self.layer1 = self._make_layer(
block, layers[0], intermediate_channels=32, stride=1
)
self.layer2 = self._make_layer(
block, layers[1], intermediate_channels=64, stride=2
)
self.layer3 = self._make_layer(
block, layers[2], intermediate_channels=128, stride=2
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
self.fc = nn.Linear(128 * 4, num_classes)
def forward(self, x):
x = self.conv1(x)
x = self.bn1(x)
x = self.relu(x)
x = self.maxpool(x)
x = self.layer1(x)
x = self.layer2(x)
x = self.layer3(x)
x = self.avgpool(x)
x = x.reshape(x.shape[0], -1)
x = self.fc(x)
x = self.softmax(x)
return x
def _make_layer(self, block, num_residual_blocks, intermediate_channels, stride):
identity_downsample = None
layers = []
if stride != 1 or self.in_channels != intermediate_channels * 4:
identity_downsample = nn.Sequential(
nn.Conv2d(
self.in_channels,
intermediate_channels * 4,
kernel_size=1,
stride=stride,
),
nn.BatchNorm2d(intermediate_channels * 4),
)
layers.append(
block(self.in_channels, intermediate_channels, identity_downsample, stride)
)
self.in_channels = intermediate_channels * 4
for i in range(num_residual_blocks - 1):
layers.append(block(self.in_channels, intermediate_channels))
return nn.Sequential(*layers)
def ResNet50(img_channel=10, num_classes=2):
return ResNet(block, [4,9,4], img_channel, num_classes)
loss_fn = nn.CrossEntropyLoss()
import torch.optim as optim
net = ResNet50(img_channel=10, num_classes=2)
optimizer = torch.optim.Adam(net.parameters(), lr = 0.001)