models.py

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

# 1. SRCNN
class SRCNN(nn.Module):
    def __init__(self):
        super(SRCNN, self).__init__()
        self.conv1 = nn.Conv2d(1, 64, kernel_size=9, padding=4)
        self.conv2 = nn.Conv2d(64, 32, kernel_size=5, padding=2)
        self.conv3 = nn.Conv2d(32, 1, kernel_size=5, padding=2)
        self.relu = nn.ReLU(inplace=True)

    def forward(self, x):
        # SRCNN typically takes an already upscaled (bicubic) input, but we can structure it safely
        if x.shape[2:] != (x.shape[2]*4, x.shape[3]*4):
            x = F.interpolate(x, scale_factor=4, mode='bicubic', align_corners=False)
        x = self.relu(self.conv1(x))
        x = self.relu(self.conv2(x))
        x = self.conv3(x)
        return x



# 3. Satlas (Placeholder architecture)
class SatlasSR(nn.Module):
    def __init__(self):
        super(SatlasSR, self).__init__()
        # NOTE: satlaspretrain models are Swin feature backbones, not native SuperResolution headers.
        # Randomly initialized wrapper convolutions will cause severe output noise (fucked channels). 
        # For demonstration without a trained SR head, this placeholder passes safely via bicubic upsampling.
        pass

    def forward(self, x):
        return F.interpolate(x, scale_factor=4, mode='bicubic', align_corners=False)

# 4. ESRGAN (RRDBNet)
class ResidualDenseBlock(nn.Module):
    def __init__(self, num_feat=64, num_grow_ch=32):
        super(ResidualDenseBlock, self).__init__()
        self.conv1 = nn.Conv2d(num_feat, num_grow_ch, 3, 1, 1)
        self.conv2 = nn.Conv2d(num_feat + num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv3 = nn.Conv2d(num_feat + 2 * num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv4 = nn.Conv2d(num_feat + 3 * num_grow_ch, num_grow_ch, 3, 1, 1)
        self.conv5 = nn.Conv2d(num_feat + 4 * num_grow_ch, num_feat, 3, 1, 1)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        x1 = self.lrelu(self.conv1(x))
        x2 = self.lrelu(self.conv2(torch.cat((x, x1), 1)))
        x3 = self.lrelu(self.conv3(torch.cat((x, x1, x2), 1)))
        x4 = self.lrelu(self.conv4(torch.cat((x, x1, x2, x3), 1)))
        x5 = self.conv5(torch.cat((x, x1, x2, x3, x4), 1))
        return x5 * 0.2 + x

class RRDB(nn.Module):
    def __init__(self, num_feat, num_grow_ch=32):
        super(RRDB, self).__init__()
        self.rdb1 = ResidualDenseBlock(num_feat, num_grow_ch)
        self.rdb2 = ResidualDenseBlock(num_feat, num_grow_ch)
        self.rdb3 = ResidualDenseBlock(num_feat, num_grow_ch)

    def forward(self, x):
        out = self.rdb1(x)
        out = self.rdb2(out)
        out = self.rdb3(out)
        return out * 0.2 + x

class RRDBNet(nn.Module):
    def __init__(self):
        super(RRDBNet, self).__init__()
        num_in_ch=3
        num_out_ch=3
        num_feat=64
        num_block=23
        num_grow_ch=32
        self.conv_first = nn.Conv2d(num_in_ch, num_feat, 3, 1, 1)
        self.body = nn.Sequential(*[RRDB(num_feat=num_feat, num_grow_ch=num_grow_ch) for _ in range(num_block)])
        self.conv_body = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_up1 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_up2 = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_hr = nn.Conv2d(num_feat, num_feat, 3, 1, 1)
        self.conv_last = nn.Conv2d(num_feat, num_out_ch, 3, 1, 1)
        self.lrelu = nn.LeakyReLU(negative_slope=0.2, inplace=True)

    def forward(self, x):
        feat = self.conv_first(x)
        body_feat = self.conv_body(self.body(feat))
        feat = feat + body_feat
        feat = self.lrelu(self.conv_up1(F.interpolate(feat, scale_factor=2, mode='nearest')))
        feat = self.lrelu(self.conv_up2(F.interpolate(feat, scale_factor=2, mode='nearest')))
        out = self.conv_last(self.lrelu(self.conv_hr(feat)))
        return out

def load_model(model_name, model_path, device):
    if not os.path.exists(model_path):
        return None
    
    if model_name == "srcnn":
        model = SRCNN()
    elif model_name == "satlas":
        model = SatlasSR()
    elif model_name == "esrgan":
        model = RRDBNet()
    else:
        return None

    try:
        state_dict = torch.load(model_path, map_location=device)
        
        # Extract params_ema if found (often standard for pretrained models like RealESRGAN)
        if 'params_ema' in state_dict:
            state_dict = state_dict['params_ema']
        elif 'params' in state_dict:
            state_dict = state_dict['params']

        # Attempt minimal state dict loading.
        # Strict=False to bypass mismatches in our placeholder architectures compared to actual weights
        model.load_state_dict(state_dict, strict=False)
        model.eval()
        model.to(device)
        return model
    except Exception as e:
        print(f"Error loading {model_name}: {e}")
        return None

def get_available_models(model_dir="models", device="cpu"):
    models = {}
    
    paths = {
        "srcnn": os.path.join(model_dir, "srcnn_x4.pth"),
        "satlas": os.path.join(model_dir, "aerial_swinb_si.pth"),
        "esrgan": os.path.join(model_dir, "RealESRGAN_x4plus.pth")
    }
    
    for name, path in paths.items():
        if os.path.exists(path):
            print(f"Loading {name}...")
            model = load_model(name, path, device)
            if model is not None:
                models[name] = model
        else:
            print(f"Model file for {name} not found at {path}. Skipping.")
            
    return models