networks.py

import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.nn import init
from torchvision import models
import os
import numpy as np

class Options:
    def __init__(self):
        # Image dimensions
        self.fine_height = 256
        self.fine_width = 192
        
        # GMM parameters
        self.grid_size = 5
        self.input_nc = 22  # For extractionA
        self.input_nc_B = 1  # For extractionB
        
        # TOM parameters
        self.tom_input_nc = 26  # 3(agnostic) + 3(warped) + 1(mask) + 19(features)
        self.tom_output_nc = 4   # 3(rendered) + 1(composite mask)
        
        # Training settings
        self.use_dropout = False
        self.norm_layer = nn.BatchNorm2d

def weights_init_normal(m):
    classname = m.__class__.__name__
    if classname.find('Conv') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('Linear') != -1:
        init.normal_(m.weight.data, 0.0, 0.02)
    elif classname.find('BatchNorm') != -1:
        init.normal_(m.weight.data, 1.0, 0.02)
        init.constant_(m.bias.data, 0.0)

def init_weights(net, init_type='normal'):
    print(f'initialization method [{init_type}]')
    net.apply(weights_init_normal)

class FeatureExtraction(nn.Module):
    def __init__(self, input_nc, ngf=64, n_layers=3, norm_layer=nn.BatchNorm2d):
        super(FeatureExtraction, self).__init__()
        
        # Build feature extraction layers
        layers = [
            nn.Conv2d(input_nc, ngf, kernel_size=4, stride=2, padding=1),
            nn.ReLU(True),
            norm_layer(ngf)
        ]
        
        for i in range(n_layers):
            in_channels = min(2**i * ngf, 512)
            out_channels = min(2**(i+1) * ngf, 512)
            layers += [
                nn.Conv2d(in_channels, out_channels, kernel_size=4, stride=2, padding=1),
                nn.ReLU(True),
                norm_layer(out_channels)
            ]
        
        # Final processing blocks
        layers += [
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True),
            norm_layer(512),
            nn.Conv2d(512, 512, kernel_size=3, stride=1, padding=1),
            nn.ReLU(True)
        ]
        
        self.model = nn.Sequential(*layers)
        init_weights(self.model)

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

class FeatureL2Norm(nn.Module):
    def __init__(self):
        super(FeatureL2Norm, self).__init__()

    def forward(self, feature):
        epsilon = 1e-6
        norm = torch.pow(torch.sum(torch.pow(feature, 2), 1) + epsilon, 0.5).unsqueeze(1).expand_as(feature)
        return torch.div(feature, norm)

class FeatureCorrelation(nn.Module):
    def __init__(self):
        super(FeatureCorrelation, self).__init__()

    def forward(self, feature_A, feature_B):
        b, c, h, w = feature_A.size()
        feature_A = feature_A.transpose(2, 3).contiguous().view(b, c, h*w)
        feature_B = feature_B.view(b, c, h*w).transpose(1, 2)
        feature_mul = torch.bmm(feature_B, feature_A)
        return feature_mul.view(b, h, w, h*w).transpose(2, 3).transpose(1, 2)

class FeatureRegression(nn.Module):
    def __init__(self, input_nc=512, output_dim=6):
        super(FeatureRegression, self).__init__()
        self.conv = nn.Sequential(
            nn.Conv2d(input_nc, 512, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(512),
            nn.ReLU(inplace=True),
            nn.Conv2d(512, 256, kernel_size=4, stride=2, padding=1),
            nn.BatchNorm2d(256),
            nn.ReLU(inplace=True),
            nn.Conv2d(256, 128, kernel_size=3, padding=1),
            nn.BatchNorm2d(128),
            nn.ReLU(inplace=True),
            nn.Conv2d(128, 64, kernel_size=3, padding=1),
            nn.BatchNorm2d(64),
            nn.ReLU(inplace=True)
        )
        self.linear = nn.Linear(64 * 4 * 3, output_dim)
        self.tanh = nn.Tanh()

    def forward(self, x):
        x = self.conv(x)
        x = x.contiguous().view(x.size(0), -1)
        x = self.linear(x)
        return self.tanh(x)

class TpsGridGen(nn.Module):
    def __init__(self, out_h=256, out_w=192, grid_size=5):
        super(TpsGridGen, self).__init__()
        self.out_h = out_h
        self.out_w = out_w
        self.grid_size = grid_size
        self.N = grid_size * grid_size

        # Create regular grid of control points
        axis_coords = np.linspace(-1, 1, grid_size)
        P_Y, P_X = np.meshgrid(axis_coords, axis_coords)
        P_X = torch.FloatTensor(P_X.reshape(-1, 1))  # (N,1)
        P_Y = torch.FloatTensor(P_Y.reshape(-1, 1))  # (N,1)
        self.register_buffer('P_X', P_X)
        self.register_buffer('P_Y', P_Y)
        
        # Compute inverse matrix L^-1
        self.register_buffer('Li', self.compute_L_inverse(P_X, P_Y))
        
        # Create sampling grid
        grid_X, grid_Y = np.meshgrid(np.linspace(-1, 1, out_w), np.linspace(-1, 1, out_h))
        self.register_buffer('grid_X', torch.FloatTensor(grid_X).unsqueeze(0).unsqueeze(3))  # (1,H,W,1)
        self.register_buffer('grid_Y', torch.FloatTensor(grid_Y).unsqueeze(0).unsqueeze(3))  # (1,H,W,1)

    def compute_L_inverse(self, X, Y):
        N = X.size(0)
        Xmat = X.expand(N, N)
        Ymat = Y.expand(N, N)
        P_dist_squared = torch.pow(Xmat - Xmat.transpose(0, 1), 2) + torch.pow(Ymat - Ymat.transpose(0, 1), 2)
        P_dist_squared[P_dist_squared == 0] = 1  # Avoid log(0)
        K = torch.mul(P_dist_squared, torch.log(P_dist_squared))
        
        # Construct L matrix
        O = torch.FloatTensor(N, 1).fill_(1)
        Z = torch.FloatTensor(3, 3).fill_(0)
        P = torch.cat((O, X, Y), 1)
        L = torch.cat((torch.cat((K, P), 1), torch.cat((P.transpose(0, 1), Z), 1)), 0)
        return torch.inverse(L)

    def forward(self, theta):
        batch_size = theta.size(0)
        device = theta.device
        
        # Split theta into x and y components
        Q_X = theta[:, :self.N].contiguous().view(batch_size, self.N, 1)
        Q_Y = theta[:, self.N:].contiguous().view(batch_size, self.N, 1)
        Q_X = Q_X + self.P_X.expand_as(Q_X)
        Q_Y = Q_Y + self.P_Y.expand_as(Q_Y)
        
        # Compute weights
        W_X = torch.bmm(self.Li[:, :self.N, :self.N].expand(batch_size, -1, -1), Q_X)
        W_Y = torch.bmm(self.Li[:, :self.N, :self.N].expand(batch_size, -1, -1), Q_Y)
        
        # Repeat grid for batch processing
        grid_X = self.grid_X.expand(batch_size, -1, -1, -1).to(device)
        grid_Y = self.grid_Y.expand(batch_size, -1, -1, -1).to(device)
        
        # Compute transformed coordinates
        points_X = self.transform_points(grid_X, W_X, Q_X)
        points_Y = self.transform_points(grid_Y, W_Y, Q_Y)
        
        return torch.cat((points_X, points_Y), 3)

    def transform_points(self, grid, W, Q):
        batch_size, h, w, _ = grid.size()
        
        # Compute distance between grid points and control points
        grid_flat = grid.view(batch_size, -1, 1)
        P = torch.cat([self.P_X, self.P_Y], 1).unsqueeze(0).expand(batch_size, -1, -1).to(grid.device)
        delta = grid_flat - P
        
        # Compute U (radial basis function)
        dist_squared = torch.sum(torch.pow(delta, 2), 2, keepdim=True)
        dist_squared[dist_squared == 0] = 1  # Avoid log(0)
        U = torch.mul(dist_squared, torch.log(dist_squared))
        
        # Compute affine + non-affine transformation
        A = torch.cat([
            torch.ones(batch_size, h*w, 1, device=grid.device),
            grid_flat[:, :, 0:1],
            grid_flat[:, :, 1:2]
        ], 2)
        
        points = torch.bmm(A, Q.view(batch_size, 3, -1)) + torch.bmm(U, W.view(batch_size, self.N, -1))
        return points.view(batch_size, h, w, 1)

class GMM(nn.Module):
    def __init__(self, opt=None):
        super(GMM, self).__init__()
        if opt is None:
            opt = Options()
            
        self.extractionA = FeatureExtraction(opt.input_nc)
        self.extractionB = FeatureExtraction(opt.input_nc_B)
        self.l2norm = FeatureL2Norm()
        self.correlation = FeatureCorrelation()
        self.regression = FeatureRegression(input_nc=192, output_dim=2*opt.grid_size**2)
        self.gridGen = TpsGridGen(opt.fine_height, opt.fine_width, opt.grid_size)

    def forward(self, inputA, inputB):
        featureA = self.extractionA(inputA)
        featureB = self.extractionB(inputB)
        featureA = self.l2norm(featureA)
        featureB = self.l2norm(featureB)
        correlation = self.correlation(featureA, featureB)
        theta = self.regression(correlation)
        grid = self.gridGen(theta)
        return grid, theta

class UnetSkipConnectionBlock(nn.Module):
    def __init__(self, outer_nc, inner_nc, input_nc=None,
                 submodule=None, outermost=False, innermost=False, 
                 norm_layer=nn.InstanceNorm2d, use_dropout=False):
        super(UnetSkipConnectionBlock, self).__init__()
        self.outermost = outermost
        use_bias = norm_layer == nn.InstanceNorm2d

        if input_nc is None:
            input_nc = outer_nc
            
        downconv = nn.Conv2d(input_nc, inner_nc, kernel_size=4,
                             stride=2, padding=1, bias=use_bias)
        downrelu = nn.LeakyReLU(0.2, True)
        downnorm = norm_layer(inner_nc)
        uprelu = nn.ReLU(True)
        upnorm = norm_layer(outer_nc)

        if outermost:
            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
                                        kernel_size=4, stride=2,
                                        padding=1)
            down = [downconv]
            up = [uprelu, upconv, nn.Tanh()]
            model = down + [submodule] + up
        elif innermost:
            upconv = nn.ConvTranspose2d(inner_nc, outer_nc,
                                        kernel_size=4, stride=2,
                                        padding=1, bias=use_bias)
            down = [downrelu, downconv]
            up = [uprelu, upconv, upnorm]
            model = down + up
        else:
            upconv = nn.ConvTranspose2d(inner_nc * 2, outer_nc,
                                        kernel_size=4, stride=2,
                                        padding=1, bias=use_bias)
            down = [downrelu, downconv, downnorm]
            up = [uprelu, upconv, upnorm]

            if use_dropout:
                model = down + [submodule] + up + [nn.Dropout(0.5)]
            else:
                model = down + [submodule] + up

        self.model = nn.Sequential(*model)

    def forward(self, x):
        if self.outermost:
            return self.model(x)
        else:
            return torch.cat([x, self.model(x)], 1)

class UnetGenerator(nn.Module):
    def __init__(self, input_nc, output_nc, num_downs, ngf=64,
                 norm_layer=nn.InstanceNorm2d, use_dropout=False):
        super(UnetGenerator, self).__init__()
        
        # Build UNet structure
        unet_block = UnetSkipConnectionBlock(
            ngf * 8, ngf * 8, input_nc=None, submodule=None,
            norm_layer=norm_layer, innermost=True)
        
        for i in range(num_downs - 5):
            unet_block = UnetSkipConnectionBlock(
                ngf * 8, ngf * 8, input_nc=None, submodule=unet_block,
                norm_layer=norm_layer, use_dropout=use_dropout)
                
        unet_block = UnetSkipConnectionBlock(
            ngf * 4, ngf * 8, input_nc=None, submodule=unet_block,
            norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(
            ngf * 2, ngf * 4, input_nc=None, submodule=unet_block,
            norm_layer=norm_layer)
        unet_block = UnetSkipConnectionBlock(
            ngf, ngf * 2, input_nc=None, submodule=unet_block,
            norm_layer=norm_layer)
            
        self.model = UnetSkipConnectionBlock(
            output_nc, ngf, input_nc=input_nc, submodule=unet_block,
            outermost=True, norm_layer=norm_layer)

    def forward(self, input):
        return self.model(input)

class TOM(nn.Module):
    def __init__(self, opt=None):
        super(TOM, self).__init__()
        if opt is None:
            opt = Options()
        
        self.unet = UnetGenerator(
            input_nc=opt.tom_input_nc,
            output_nc=opt.tom_output_nc,
            num_downs=6,
            norm_layer=nn.InstanceNorm2d
        )

    def forward(self, x):
        output = self.unet(x)
        p_rendered, m_composite = torch.split(output, [3, 1], dim=1)
        p_rendered = torch.tanh(p_rendered)
        m_composite = torch.sigmoid(m_composite)
        return p_rendered, m_composite

def save_checkpoint(model, save_path):
    if not os.path.exists(os.path.dirname(save_path)):
        os.makedirs(os.path.dirname(save_path))
    torch.save(model.state_dict(), save_path)

def load_checkpoint(model, checkpoint_path, strict=True):
    if not os.path.exists(checkpoint_path):
        raise FileNotFoundError(f"Checkpoint file not found: {checkpoint_path}")
    
    state_dict = torch.load(checkpoint_path, map_location=torch.device('cpu'))
    
    # Filter out unexpected keys
    model_state_dict = model.state_dict()
    filtered_state_dict = {k: v for k, v in state_dict.items()
                         if k in model_state_dict and v.size() == model_state_dict[k].size()}
    
    # Load filtered state dict
    model.load_state_dict(filtered_state_dict, strict=strict)
    
    # Print warnings
    missing = [k for k in model_state_dict if k not in state_dict]
    unexpected = [k for k in state_dict if k not in model_state_dict]
    size_mismatch = [k for k in state_dict 
                    if k in model_state_dict and state_dict[k].size() != model_state_dict[k].size()]
    
    if missing:
        print(f"Missing keys: {missing}")
    if unexpected:
        print(f"Unexpected keys: {unexpected}")
    if size_mismatch:
        print(f"Size mismatch: {size_mismatch}")