code/lake_detection_deep_learning/trainer/models/swinv2Cnn.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from einops import rearrange
import timm

# ---------------------------------------------------------
#                  Basic CNN Blocks
# ---------------------------------------------------------

class DoubleConv(nn.Module):
    def __init__(self, in_ch, out_ch):
        super().__init__()
        self.block = nn.Sequential(
            nn.Conv2d(in_ch, out_ch, 3, padding=1, bias=False),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
            nn.Conv2d(out_ch, out_ch, 3, padding=1, bias=False),
            nn.BatchNorm2d(out_ch),
            nn.ReLU(inplace=True),
        )

    def forward(self, x):
        return self.block(x)


class UpBlock(nn.Module):
    """
    Upsample (bilinear) + concat skip + DoubleConv
    NO transposed convolutions → no grid artifacts
    """
    def __init__(self, in_ch, skip_ch, out_ch):
        super().__init__()
        self.conv = DoubleConv(in_ch + skip_ch, out_ch)

    def forward(self, x, skip):
        x = F.interpolate(x, size=skip.shape[2:], mode="bilinear", align_corners=False)
        x = torch.cat([x, skip], dim=1)
        return self.conv(x)

# ---------------------------------------------------------
#                  SwinV2 + CNN Decoder
# ---------------------------------------------------------

class model(nn.Module):
    def __init__(
        self,
        in_channels=3,
        num_classes=15,
        freeze_encoder=False,
    ):
        super().__init__()

        # -------------------------------
        # Encoder (SwinV2)
        # -------------------------------
        self.encoder = timm.create_model(
            "swinv2_tiny_window8_256",
            pretrained=True,
            features_only=True,
            out_indices=(0, 1, 2, 3),
        )

        if freeze_encoder:
            for p in self.encoder.parameters():
                p.requires_grad = False

        # Replace patch embedding to accept custom input channels
        old_proj = self.encoder.patch_embed.proj
        self.encoder.patch_embed.proj = nn.Conv2d(
            in_channels=in_channels,
            out_channels=old_proj.out_channels,
            kernel_size=old_proj.kernel_size,
            stride=old_proj.stride,
            padding=old_proj.padding,
            bias=old_proj.bias is not None,
        )

        # Encoder channel sizes
        c0, c1, c2, c3 = self.encoder.feature_info.channels()

        # -------------------------------
        # CNN Decoder (artifact-free)
        # -------------------------------
        self.up3 = UpBlock(c3, c2, c2)  # 1/32 → 1/16
        self.up2 = UpBlock(c2, c1, c1)  # 1/16 → 1/8
        self.up1 = UpBlock(c1, c0, c0)  # 1/8 → 1/4

        self.refine = nn.Sequential(
            nn.Conv2d(c0, c0, 3, padding=1),
            nn.ReLU(inplace=True),
            nn.Conv2d(c0, c0, 3, padding=1),
            nn.ReLU(inplace=True),
        )

        self.head = nn.Conv2d(c0, num_classes, kernel_size=1)

    # ---------------------------------------------------------
    #                        Forward
    # ---------------------------------------------------------
    def forward(self, x):
        f0, f1, f2, f3 = self.encoder(x)

        # Swin outputs are (B, H, W, C)
        f0 = rearrange(f0, "b h w c -> b c h w")
        f1 = rearrange(f1, "b h w c -> b c h w")
        f2 = rearrange(f2, "b h w c -> b c h w")
        f3 = rearrange(f3, "b h w c -> b c h w")

        # Decoder
        d3 = self.up3(f3, f2)
        d2 = self.up2(d3, f1)
        d1 = self.up1(d2, f0)

        d1 = self.refine(d1)

        out = F.interpolate(
            d1, size=x.shape[2:], mode="bilinear", align_corners=False
        )

        return self.head(out)