mmp_wise2526_franksim/mmp/a5/model.py

import torch
from torchvision import models
from torchvision.models import MobileNet_V2_Weights
from torch import nn


class MmpNet(torch.nn.Module):
    def __init__(self, num_widths: int, num_aspect_ratios: int, num_classes: int = 2):
        super().__init__()
        self.backbone = models.mobilenet_v2(
            weights=MobileNet_V2_Weights.DEFAULT
        ).features
        self.num_widths = num_widths
        self.num_aspect_ratios = num_aspect_ratios
        self.num_classes = num_classes

        with torch.no_grad():
            dummy = torch.zeros(1, 3, 224, 224)
            backbone_out = self.backbone(dummy)
            in_channels = backbone_out.shape[1]

        self.head = nn.Conv2d(
            in_channels=in_channels,
            kernel_size=3,
            out_channels=self.get_required_output_channels(),
            stride=1,
            padding=1,
        )

    def get_required_output_channels(self):
        return self.num_widths * self.num_aspect_ratios * self.num_classes

    def forward(self, x: torch.Tensor):
        x = self.backbone(x)
        x = self.head(x)
        b, out_c, h, w = x.shape
        x = x.view(b, self.num_widths, self.num_aspect_ratios, self.num_classes, h, w)
        x = x.permute(0, 1, 2, 4, 5, 3).contiguous()
        # Now: (batch, num_widths, num_aspect_ratios, h, w, num_classes)
        return x