I have a single model (Net ) that contains two separate ResNet models(without FC , the FC is after the contact )
the input of net1 are images with resolution (51,50,4) and the input of net2 are images with resolution (15,15,40)
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, bias=False
)
self.bn1 = nn.BatchNorm2d(intermediate_channels)
self.conv2 = nn.Conv2d(
intermediate_channels,
intermediate_channels,
kernel_size=3,
stride=stride,
padding=1,
bias=False
)
self.bn2 = nn.BatchNorm2d(intermediate_channels)
self.conv3 = nn.Conv2d(
intermediate_channels,
intermediate_channels * self.expansion,
kernel_size=1,
stride=1,
padding=0,
bias=False
)
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.expansion = 4
self.in_channels = 64
self.conv1 = nn.Conv2d(image_channels, 64, kernel_size=1, stride=1, padding=0, bias=False)
self.bn1 = nn.BatchNorm2d(64)
self.relu = nn.ReLU()
self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)
# Essentially the entire ResNet architecture are in these 4 lines below
self.layer1 = self._make_layer(
block, layers[0], intermediate_channels=64, stride=1
)
self.layer2 = self._make_layer(
block, layers[1], intermediate_channels=128, stride=2
)
self.layer3 = self._make_layer(
block, layers[2], intermediate_channels=128, stride=2
)
self.layer4 = self._make_layer(
block, layers[3], intermediate_channels=128, stride=2
)
self.layer5 = self._make_layer(
block, layers[4], intermediate_channels=256, stride=2
)
self.avgpool = nn.AdaptiveAvgPool2d((1, 1))
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.layer4(x)
x = self.layer5(x)
x = self.avgpool(x)
x = x.reshape(x.shape[0], -1)
return x
def _make_layer(self, block, num_residual_blocks, intermediate_channels, stride):
identity_downsample = None
layers = []
# Either if we half the input space for ex, 56x56 -> 28x28 (stride=2), or channels changes
# we need to adapt the Identity (skip connection) so it will be able to be added
# to the layer that's ahead
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,
bias=False
),
nn.BatchNorm2d(intermediate_channels * 4),
)
layers.append(
block(self.in_channels, intermediate_channels, identity_downsample, stride)
)
# The expansion size is always 4 for ResNet 50,101,152
self.in_channels = intermediate_channels * 4
# For example for first resnet layer: 256 will be mapped to 64 as intermediate layer,
# then finally back to 256. Hence no identity downsample is needed, since stride = 1,
# and also same amount of channels.
for i in range(num_residual_blocks - 1):
layers.append(block(self.in_channels, intermediate_channels))
return nn.Sequential(*layers)
def ResNet50(img_channel=4, num_classes=2):
return ResNet(block, [3, 4,6, 3,3], img_channel, num_classes)
import torch.optim as optim
net1 = ResNet50(img_channel=4, num_classes=2)
when i turn this model the accuracy is 0,114%
who can help me to find the problem in my code ? thank you