Model predicting one classe only after running inference on test data

amy2: <p>i am using a cnn_svm architecture for multi-class classifcation , 3d resnet for feature extraction and svm as classifier i did some modification on my model</p>
<pre><code class="lang-auto">import math
from functools import partial

import torch
import torch.nn as nn
import torch.nn.functional as F


def get_inplanes():
    return [64, 128, 256, 512]


def conv3x3x3(in_planes, out_planes, stride=1):
    return nn.Conv3d(in_planes,
                     out_planes,
                     kernel_size=3,
                     stride=stride,
                     padding=1,
                     bias=False)


def conv1x1x1(in_planes, out_planes, stride=1):
    return nn.Conv3d(in_planes,
                     out_planes,
                     kernel_size=1,
                     stride=stride,
                     bias=False)


class BasicBlock(nn.Module):
    expansion = 1

    def __init__(self, in_planes, planes, stride=1, downsample=None):
        super().__init__()

        self.conv1 = conv3x3x3(in_planes, planes, stride)
        self.bn1 = nn.BatchNorm3d(planes)
        self.relu = nn.ReLU(inplace=True)
        self.conv2 = conv3x3x3(planes, planes)
        self.bn2 = nn.BatchNorm3d(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, in_planes, planes, stride=1, downsample=None):
        super().__init__()

        self.conv1 = conv1x1x1(in_planes, planes)
        self.bn1 = nn.BatchNorm3d(planes)
        self.conv2 = conv3x3x3(planes, planes, stride)
        self.bn2 = nn.BatchNorm3d(planes)
        self.conv3 = conv1x1x1(planes, planes * self.expansion)
        self.bn3 = nn.BatchNorm3d(planes * self.expansion)
        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


class ResNet(nn.Module):

    def __init__(self,
                 block,
                 layers,
                 block_inplanes,
                 n_input_channels=3,
                 conv1_t_size=7,
                 conv1_t_stride=1,
                 no_max_pool=False,
                 shortcut_type='B',
                 widen_factor=1.0,
                 n_classes=400):
        super().__init__()

        block_inplanes = [int(x * widen_factor) for x in block_inplanes]

        self.in_planes = block_inplanes[0]
        self.no_max_pool = no_max_pool

        self.conv1 = nn.Conv3d(n_input_channels,
                               self.in_planes,
                               kernel_size=(conv1_t_size, 7, 7),
                               stride=(conv1_t_stride, 2, 2),
                               padding=(conv1_t_size // 2, 3, 3),
                               bias=False)
        self.bn1 = nn.BatchNorm3d(self.in_planes)
        self.relu = nn.ReLU(inplace=True)
        self.maxpool = nn.MaxPool3d(kernel_size=3, stride=2, padding=1)
        self.layer1 = self._make_layer(block, block_inplanes[0], layers[0],
                                       shortcut_type)
        self.layer2 = self._make_layer(block,
                                       block_inplanes[1],
                                       layers[1],
                                       shortcut_type,
                                       stride=2)
        self.layer3 = self._make_layer(block,
                                       block_inplanes[2],
                                       layers[2],
                                       shortcut_type,
                                       stride=2)
        self.layer4 = self._make_layer(block,
                                       block_inplanes[3],
                                       layers[3],
                                       shortcut_type,
                                       stride=2)

        self.avgpool = nn.AdaptiveAvgPool3d((1, 1, 1))
       

        for m in self.modules():
            if isinstance(m, nn.Conv3d):
                nn.init.kaiming_normal_(m.weight,
                                        mode='fan_out',
                                        nonlinearity='relu')
            elif isinstance(m, nn.BatchNorm3d):
                nn.init.constant_(m.weight, 1)
                nn.init.constant_(m.bias, 0)

    def _downsample_basic_block(self, x, planes, stride):
        out = F.avg_pool3d(x, kernel_size=1, stride=stride)
        zero_pads = torch.zeros(out.size(0), planes - out.size(1), out.size(2),
                                out.size(3), out.size(4))
        if isinstance(out.data, torch.cuda.FloatTensor):
            zero_pads = zero_pads.cuda()

        out = torch.cat([out.data, zero_pads], dim=1)

        return out

    def _make_layer(self, block, planes, blocks, shortcut_type, stride=1):
        downsample = None
        if stride != 1 or self.in_planes != planes * block.expansion:
            if shortcut_type == 'A':
                downsample = partial(self._downsample_basic_block,
                                     planes=planes * block.expansion,
                                     stride=stride)
            else:
                downsample = nn.Sequential(
                    conv1x1x1(self.in_planes, planes * block.expansion, stride),
                    nn.BatchNorm3d(planes * block.expansion))

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

        return nn.Sequential(*layers)

    def forward(self, x):
        x = self.conv1(x)
        x = self.bn1(x)
        x = self.relu(x)
        if not self.no_max_pool:
            x = self.maxpool(x)

        x = self.layer1(x)
        x = self.layer2(x)
        x = self.layer3(x)
        x = self.layer4(x)

        x = self.avgpool(x)

        feature_vector = x.view(x.size(0), -1)
       

        return feature_vector 
#now the resnet return the feautre vector without the classification result 

def generate_model(model_depth, **kwargs):
    assert model_depth in [10, 18, 34, 50, 101, 152, 200]

    if model_depth == 10:
        model = ResNet(BasicBlock, [1, 1, 1, 1], get_inplanes(), **kwargs)
    elif model_depth == 18:
        model = ResNet(BasicBlock, [2, 2, 2, 2], get_inplanes(), **kwargs)
    elif model_depth == 34:
        model = ResNet(BasicBlock, [3, 4, 6, 3], get_inplanes(), **kwargs)
    elif model_depth == 50:
        model = ResNet(Bottleneck, [3, 4, 6, 3], get_inplanes(), **kwargs)
    elif model_depth == 101:
        model = ResNet(Bottleneck, [3, 4, 23, 3], get_inplanes(), **kwargs)
    elif model_depth == 152:
        model = ResNet(Bottleneck, [3, 8, 36, 3], get_inplanes(), **kwargs)
    elif model_depth == 200:
        model = ResNet(Bottleneck, [3, 24, 36, 3], get_inplanes(), **kwargs)

    return model

</code></pre>
<p>i replaced the fully connected layer with feature_vector=   feature_vector = x.view(x.size(0), -1) after the avgpooling layer<br>
and then finetuned on a pretrained 3dresnet model also i used this code</p>
<pre><code class="lang-auto">def get_module_name(name):
    name = name.split('.')
    if name[0] == 'module':
        i = 1
    else:
        i = 0
    if name[i] == 'features':
        i += 1

    return name[i]


def get_fine_tuning_parameters(model, ft_begin_module):
    if not ft_begin_module:
        return model.parameters()

    parameters = []
    add_flag = False
    for k, v in model.named_parameters():
        if ft_begin_module == get_module_name(k):
            add_flag = True

        if add_flag:
            parameters.append({'params': v})

    return parameters


def generate_model(opt):
  
        model = resnet.generate_model(model_depth=opt.model_depth,
                                      n_input_channels=opt.n_input_channels,
                                      shortcut_type=opt.resnet_shortcut,
                                      conv1_t_size=opt.conv1_t_size,
                                      conv1_t_stride=opt.conv1_t_stride,
                                      no_max_pool=opt.no_max_pool,
                                      widen_factor=opt.resnet_widen_factor)
  

        return model



def load_pretrained_model(model, pretrain_path, model_name,n_finetune_classes):
    if pretrain_path:
        print('loading pretrained model {}'.format(pretrain_path))
        pretrain = torch.load(pretrain_path, map_location='cpu')
        
        # Load state dict, but exclude FC layer parameters if they exist
        state_dict = {k: v for k, v in pretrain['state_dict'].items() if 'fc' not in k and 'classifier' not in k}
        model.load_state_dict(state_dict, strict=False)  # Load only matching layers    
        model.fc = None

    return model

</code></pre>
<p>i removed the fully connected layer from the pretrained model also then in the main i define it as  this</p>
<pre><code class="lang-auto">    # Generate the resnet model with finetuning as a feature extractor
    model = generate_model(opt)
    if opt.pretrain_path:
        model = load_pretrained_model(model, opt.pretrain_path, opt.model,opt.n_finetune_classes)
        parameters = get_fine_tuning_parameters(model, opt.ft_begin_module)
    else:
        parameters = model.parameters()
    model.to(opt.device)
    
</code></pre>
<p>but in the inference step it’s only predicting one classe<br>
Am I using the model in the right way !!</p>