Hy guys I want to create a model like this:
Prova716×181 6.41 KB
I need to know if I made mistakes. Because I am not convinced of the code:
import torch.nn as nn
from torchvision.models.utils import load_state_dict_from_url
import math
# ResNet###### BLOCCHI #####################################
def conv3x3(in_planes, out_planes, stride=1):
    """convoluzione 3x3   con padding di 1"""
    return nn.Conv2d(in_planes, out_planes, kernel_size=3, stride=stride,padding=1, bias=False)

class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(BasicBlock, self).__init__()
        self.conv1 = conv3x3(inplanes, planes, stride)
        self.bn1 = nn.BatchNorm2d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3(planes, planes)
        self.bn2 = nn.BatchNorm2d(planes)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)
        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


class Bottleneck(nn.Module):
    expansion = 4

    def __init__(self, inplanes, planes, stride=1, downsample=None):
        super(Bottleneck, self).__init__()
        self.conv1 = nn.Conv2d(inplanes, planes, kernel_size=1, bias=False)
        self.bn1 = nn.BatchNorm2d(planes)
        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=stride,
                               padding=1, bias=False)
        self.bn2 = nn.BatchNorm2d(planes)
        self.conv3 = nn.Conv2d(planes, planes * 4, kernel_size=1, bias=False)
        self.bn3 = nn.BatchNorm2d(planes * 4)
        self.relu = nn.ReLU(inplace=True)
        self.downsample = downsample
        self.stride = stride

    def forward(self, x):
        residual = x

        out = self.conv1(x)

        out = self.bn1(out)
        out = self.relu(out)

        out = self.conv2(out)
        out = self.bn2(out)
        out = self.relu(out)

        out = self.conv3(out)
        out = self.bn3(out)

        if self.downsample is not None:
            residual = self.downsample(x)

        out += residual
        out = self.relu(out)

        return out


###############################################################

#################ENCODER CON RESNET###########################

###############################################################
class Encoder(nn.Module):

    def __init__(self, block, layers):
        self.inplanes = 64
        super(Encoder, self).__init__()
        self.conv1 = nn.Conv2d(3, 64, kernel_size=7, stride=2, padding=3,
                               bias=False)
        self.bn1 = nn.BatchNorm2d(64)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool2d(kernel_size=3, stride=2, padding=1)  # , return_indices = True)
        self.layer1 = self._make_layer(block, 64, layers[0])
        self.layer2 = self._make_layer(block, 128, layers[1], stride=2)
        self.layer3 = self._make_layer(block, 256, layers[2], stride=2)
        self.layer4 = self._make_layer(block, 512, layers[3], stride=2)
        self.avgpool = nn.AvgPool2d(7, stride=1)
        self.fc = nn.Linear(512 * block.expansion, 1000)
        for m in self.modules():
            if isinstance(m, nn.Conv2d):
                n = m.kernel_size[0] * m.kernel_size[1] * m.out_channels
                m.weight.data.normal_(0, math.sqrt(2. / n))
            elif isinstance(m, nn.BatchNorm2d):
                m.weight.data.fill_(1)
                m.bias.data.zero_()

    def _make_layer(self, block, planes, blocks, stride=1):
        downsample = None
        if stride != 1 or self.inplanes != planes * block.expansion:
            downsample = nn.Sequential(
                nn.Conv2d(self.inplanes, planes * block.expansion,kernel_size=1, stride=stride, bias=False),
                nn.BatchNorm2d(planes * block.expansion),
            )

        layers = []
        layers.append(block(self.inplanes, planes, stride, downsample))
        self.inplanes = planes * block.expansion
        for i in range(1, blocks):
            layers.append(block(self.inplanes, planes))

        return nn.Sequential(*layers)

    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.avgpool(x)
        x = x.view(x.size(0), -1)
        x = self.fc(x)

        return x


def create_encoder(pretrained = False):
    model = Encoder(Bottleneck, [3, 4, 6, 3]) #Con questi parametri è una resnet50

    if pretrained:
        state_dict = load_state_dict_from_url('https://download.pytorch.org/models/resnext50_32x4d-7cdf4587.pth') #URL verificato
        model.load_state_dict(state_dict)

    ######Aggiungo layer di encoder come suggerito
    model.fc = nn.Sequential(
            nn.Linear(2048, 1024),
            nn.ReLU(),
            nn.Linear(1024, 512),
            nn.ReLU(),
            nn.Linear(512, 256),
            nn.ReLU(),
            nn.Linear(256, 128),
            nn.ReLU(),
            nn.Linear(128, 64),
            nn.ReLU())

    return model

encoder = create_encoder(True)


################USCITA ENCODER = 7x7x64

class AutoEncoder(nn.Module):
    def __init__(self,):
        super(AutoEncoder, self).__init__()
        self.encoder = encoder
        self.decoder = nn.Sequential(
            nn.ConvTranspose2d(64, 64, 3),
            nn.ConvTranspose2d(64, 64, 3),
            nn.ReLU(),
            nn.ConvTranspose2d(64, 128, 3),
            nn.ConvTranspose2d(128, 128, 3),
            nn.ReLU(),
            nn.ConvTranspose2d(128, 256, 3),
            nn.ConvTranspose2d(256, 256, 3),
            nn.ReLU(),
            nn.ConvTranspose2d(256, 512, 3),
            nn.ConvTranspose2d(512, 512, 3),
            nn.ReLU(),
            nn.ConvTranspose2d(512, 1024, 3),
            nn.ConvTranspose2d(1024, 3, 3))

    def forward(self,x):
        code = self.encoder(x)
        reconstructed = self.decoder(code)

        return code, reconstructed

class Ciccio(nn.Module):
    def __init__(self):
        super(Ciccio, self)
        self.encoder = encoder
        self.ciccio = nn.Sequential(nn.Linear(64, 2), nn.Relu())


    def forward(self,x):
        code = self.encoder(x)
        ciccio = self.ciccio(code)
        return ciccio

class Pippo(nn.Module):
    def __init__(self):
        super(Pippo, self)
        self.encoder = encoder
        self.pippo = nn.Sequential(nn.Linear(64, 2), nn.Relu())

    def forward(self, x):
        code = self.encoder(x)
        pippo = self.pippo(code)
        return pippo

class final_model(nn.Module):
    def __init__(self):
        self.AutoEncoder = AutoEncoder()
        self.Ciccio = Ciccio()
        self.Pippo = Pippo()

    def forward(self, x):
        code , rec = self.AutoEncoder(x)
        Ciccio = self.Ciccio(x)
        Pippo = self.Pippo(x)
        #control dimension in runtime
        return code, rec, Ciccio, Pippo


I edit the code of Resnet because in future I think to modify it entirely.
Sorry for my bad English