third_party/RoMa/romatch/models/tiny.py

import torch
import torch.nn as nn
import torch.nn.functional as F
import os
import torch
from pathlib import Path
import math
import numpy as np

from torch import nn
from PIL import Image
from torchvision.transforms import ToTensor
from romatch.utils.kde import kde

class BasicLayer(nn.Module):
    """
        Basic Convolutional Layer: Conv2d -> BatchNorm -> ReLU
    """
    def __init__(self, in_channels, out_channels, kernel_size=3, stride=1, padding=1, dilation=1, bias=False, relu = True):
        super().__init__()
        self.layer = nn.Sequential(
                                        nn.Conv2d( in_channels, out_channels, kernel_size, padding = padding, stride=stride, dilation=dilation, bias = bias),
                                        nn.BatchNorm2d(out_channels, affine=False),
                                        nn.ReLU(inplace = True) if relu else nn.Identity()
                                    )

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

class TinyRoMa(nn.Module):
    """
        Implementation of architecture described in 
        "XFeat: Accelerated Features for Lightweight Image Matching, CVPR 2024."
    """

    def __init__(self, xfeat = None, 
                 freeze_xfeat = True, 
                 sample_mode = "threshold_balanced", 
                 symmetric = False, 
                 exact_softmax = False):
        super().__init__()
        del xfeat.heatmap_head, xfeat.keypoint_head, xfeat.fine_matcher
        if freeze_xfeat:
            xfeat.train(False)
            self.xfeat = [xfeat]# hide params from ddp
        else:
            self.xfeat = nn.ModuleList([xfeat])
        self.freeze_xfeat = freeze_xfeat
        match_dim = 256
        self.coarse_matcher = nn.Sequential(
            BasicLayer(64+64+2, match_dim,),
            BasicLayer(match_dim, match_dim,), 
            BasicLayer(match_dim, match_dim,), 
            BasicLayer(match_dim, match_dim,), 
            nn.Conv2d(match_dim, 3, kernel_size=1, bias=True, padding=0))
        fine_match_dim = 64
        self.fine_matcher = nn.Sequential(
            BasicLayer(24+24+2, fine_match_dim,),
            BasicLayer(fine_match_dim, fine_match_dim,), 
            BasicLayer(fine_match_dim, fine_match_dim,), 
            BasicLayer(fine_match_dim, fine_match_dim,), 
            nn.Conv2d(fine_match_dim, 3, kernel_size=1, bias=True, padding=0),)
        self.sample_mode = sample_mode
        self.sample_thresh = 0.05
        self.symmetric = symmetric
        self.exact_softmax = exact_softmax
    
    @property
    def device(self):
        return self.fine_matcher[-1].weight.device
    
    def preprocess_tensor(self, x):
        """ Guarantee that image is divisible by 32 to avoid aliasing artifacts. """
        H, W = x.shape[-2:]
        _H, _W = (H//32) * 32, (W//32) * 32
        rh, rw = H/_H, W/_W

        x = F.interpolate(x, (_H, _W), mode='bilinear', align_corners=False)
        return x, rh, rw        
    
    def forward_single(self, x):
        with torch.inference_mode(self.freeze_xfeat or not self.training):
            xfeat = self.xfeat[0]
            with torch.no_grad():
                x = x.mean(dim=1, keepdim = True)
                x = xfeat.norm(x)

            #main backbone
            x1 = xfeat.block1(x)
            x2 = xfeat.block2(x1 + xfeat.skip1(x))
            x3 = xfeat.block3(x2)
            x4 = xfeat.block4(x3)
            x5 = xfeat.block5(x4)
            x4 = F.interpolate(x4, (x3.shape[-2], x3.shape[-1]), mode='bilinear')
            x5 = F.interpolate(x5, (x3.shape[-2], x3.shape[-1]), mode='bilinear')
            feats = xfeat.block_fusion( x3 + x4 + x5 )
        if self.freeze_xfeat:
            return x2.clone(), feats.clone()
        return x2, feats

    def to_pixel_coordinates(self, coords, H_A, W_A, H_B = None, W_B = None):
        if coords.shape[-1] == 2:
            return self._to_pixel_coordinates(coords, H_A, W_A) 
        
        if isinstance(coords, (list, tuple)):
            kpts_A, kpts_B = coords[0], coords[1]
        else:
            kpts_A, kpts_B = coords[...,:2], coords[...,2:]
        return self._to_pixel_coordinates(kpts_A, H_A, W_A), self._to_pixel_coordinates(kpts_B, H_B, W_B)

    def _to_pixel_coordinates(self, coords, H, W):
        kpts = torch.stack((W/2 * (coords[...,0]+1), H/2 * (coords[...,1]+1)),axis=-1)
        return kpts
    
    def pos_embed(self, corr_volume: torch.Tensor):
        B, H1, W1, H0, W0 = corr_volume.shape 
        grid = torch.stack(
                torch.meshgrid(
                    torch.linspace(-1+1/W1,1-1/W1, W1), 
                    torch.linspace(-1+1/H1,1-1/H1, H1), 
                    indexing = "xy"), 
                dim = -1).float().to(corr_volume).reshape(H1*W1, 2)
        down = 4
        if not self.training and not self.exact_softmax:
            grid_lr = torch.stack(
                torch.meshgrid(
                    torch.linspace(-1+down/W1,1-down/W1, W1//down), 
                    torch.linspace(-1+down/H1,1-down/H1, H1//down), 
                    indexing = "xy"), 
                dim = -1).float().to(corr_volume).reshape(H1*W1 //down**2, 2)
            cv = corr_volume
            best_match = cv.reshape(B,H1*W1,H0,W0).argmax(dim=1) # B, HW, H, W
            P_lowres = torch.cat((cv[:,::down,::down].reshape(B,H1*W1 // down**2,H0,W0), best_match[:,None]),dim=1).softmax(dim=1)
            pos_embeddings = torch.einsum('bchw,cd->bdhw', P_lowres[:,:-1], grid_lr)
            pos_embeddings += P_lowres[:,-1] * grid[best_match].permute(0,3,1,2)
            #print("hej")
        else:
            P = corr_volume.reshape(B,H1*W1,H0,W0).softmax(dim=1) # B, HW, H, W
            pos_embeddings = torch.einsum('bchw,cd->bdhw', P, grid)
        return pos_embeddings
    
    def visualize_warp(self, warp, certainty, im_A = None, im_B = None, 
                       im_A_path = None, im_B_path = None, symmetric = True, save_path = None, unnormalize = False):
        device = warp.device
        H,W2,_ = warp.shape
        W = W2//2 if symmetric else W2
        if im_A is None:
            from PIL import Image
            im_A, im_B = Image.open(im_A_path).convert("RGB"), Image.open(im_B_path).convert("RGB")
        if not isinstance(im_A, torch.Tensor):
            im_A = im_A.resize((W,H))
            im_B = im_B.resize((W,H))    
            x_B = (torch.tensor(np.array(im_B)) / 255).to(device).permute(2, 0, 1)
            if symmetric:
                x_A = (torch.tensor(np.array(im_A)) / 255).to(device).permute(2, 0, 1)
        else:
            if symmetric:
                x_A = im_A
            x_B = im_B
        im_A_transfer_rgb = F.grid_sample(
        x_B[None], warp[:,:W, 2:][None], mode="bilinear", align_corners=False
        )[0]
        if symmetric:
            im_B_transfer_rgb = F.grid_sample(
            x_A[None], warp[:, W:, :2][None], mode="bilinear", align_corners=False
            )[0]
            warp_im = torch.cat((im_A_transfer_rgb,im_B_transfer_rgb),dim=2)
            white_im = torch.ones((H,2*W),device=device)
        else:
            warp_im = im_A_transfer_rgb
            white_im = torch.ones((H, W), device = device)
        vis_im = certainty * warp_im + (1 - certainty) * white_im
        if save_path is not None:
            from romatch.utils import tensor_to_pil
            tensor_to_pil(vis_im, unnormalize=unnormalize).save(save_path)
        return vis_im
     
    def corr_volume(self, feat0, feat1):
        """
            input:
                feat0 -> torch.Tensor(B, C, H, W)
                feat1 -> torch.Tensor(B, C, H, W)
            return:
                corr_volume -> torch.Tensor(B, H, W, H, W)
        """
        B, C, H0, W0 = feat0.shape
        B, C, H1, W1 = feat1.shape
        feat0 = feat0.view(B, C, H0*W0)
        feat1 = feat1.view(B, C, H1*W1)
        corr_volume = torch.einsum('bci,bcj->bji', feat0, feat1).reshape(B, H1, W1, H0 , W0)/math.sqrt(C) #16*16*16
        return corr_volume
    
    @torch.inference_mode()
    def match_from_path(self, im0_path, im1_path):
        device = self.device
        im0 = ToTensor()(Image.open(im0_path))[None].to(device)
        im1 = ToTensor()(Image.open(im1_path))[None].to(device)
        return self.match(im0, im1, batched = False)
    
    @torch.inference_mode()
    def match(self, im0, im1, *args, batched = True):
        # stupid
        if isinstance(im0, (str, Path)):
            return self.match_from_path(im0, im1)
        elif isinstance(im0, Image.Image):
            batched = False
            device = self.device
            im0 = ToTensor()(im0)[None].to(device)
            im1 = ToTensor()(im1)[None].to(device)
 
        B,C,H0,W0 = im0.shape
        B,C,H1,W1 = im1.shape
        self.train(False)
        corresps = self.forward({"im_A":im0, "im_B":im1})
        #return 1,1
        flow = F.interpolate(
            corresps[4]["flow"], 
            size = (H0, W0), 
            mode = "bilinear", align_corners = False).permute(0,2,3,1).reshape(B,H0,W0,2)
        grid = torch.stack(
            torch.meshgrid(
                torch.linspace(-1+1/W0,1-1/W0, W0), 
                torch.linspace(-1+1/H0,1-1/H0, H0), 
                indexing = "xy"), 
            dim = -1).float().to(flow.device).expand(B, H0, W0, 2)
        
        certainty = F.interpolate(corresps[4]["certainty"], size = (H0,W0), mode = "bilinear", align_corners = False)
        warp, cert = torch.cat((grid, flow), dim = -1), certainty[:,0].sigmoid()
        if batched:
            return warp, cert
        else:
            return warp[0], cert[0]

    def sample(
        self,
        matches,
        certainty,
        num=5_000,
    ):
        H,W,_ = matches.shape
        if "threshold" in self.sample_mode:
            upper_thresh = self.sample_thresh
            certainty = certainty.clone()
            certainty[certainty > upper_thresh] = 1
        matches, certainty = (
            matches.reshape(-1, 4),
            certainty.reshape(-1),
        )
        expansion_factor = 4 if "balanced" in self.sample_mode else 1
        good_samples = torch.multinomial(certainty, 
                        num_samples = min(expansion_factor*num, len(certainty)), 
                        replacement=False)
        good_matches, good_certainty = matches[good_samples], certainty[good_samples]
        if "balanced" not in self.sample_mode:
            return good_matches, good_certainty 
        use_half = True if matches.device.type == "cuda" else False
        down = 1 if matches.device.type == "cuda" else 8
        density = kde(good_matches, std=0.1, half = use_half, down = down)
        p = 1 / (density+1)
        p[density < 10] = 1e-7 # Basically should have at least 10 perfect neighbours, or around 100 ok ones
        balanced_samples = torch.multinomial(p, 
                        num_samples = min(num,len(good_certainty)), 
                        replacement=False)
        return good_matches[balanced_samples], good_certainty[balanced_samples]
        
            
    def forward(self, batch):
        """
            input:
                x -> torch.Tensor(B, C, H, W) grayscale or rgb images
            return:

        """
        im0 = batch["im_A"]
        im1 = batch["im_B"]
        corresps = {}
        im0, rh0, rw0 = self.preprocess_tensor(im0)
        im1, rh1, rw1 = self.preprocess_tensor(im1)
        B, C, H0, W0 = im0.shape
        B, C, H1, W1 = im1.shape
        to_normalized = torch.tensor((2/W1, 2/H1, 1)).to(im0.device)[None,:,None,None]
 
        if im0.shape[-2:] == im1.shape[-2:]:
            x = torch.cat([im0, im1], dim=0)
            x = self.forward_single(x)
            feats_x0_c, feats_x1_c = x[1].chunk(2)
            feats_x0_f, feats_x1_f = x[0].chunk(2)
        else:
            feats_x0_f, feats_x0_c = self.forward_single(im0)
            feats_x1_f, feats_x1_c = self.forward_single(im1)
        corr_volume = self.corr_volume(feats_x0_c, feats_x1_c)
        coarse_warp = self.pos_embed(corr_volume)
        coarse_matches = torch.cat((coarse_warp, torch.zeros_like(coarse_warp[:,-1:])), dim=1)
        feats_x1_c_warped = F.grid_sample(feats_x1_c, coarse_matches.permute(0, 2, 3, 1)[...,:2], mode = 'bilinear', align_corners = False)
        coarse_matches_delta = self.coarse_matcher(torch.cat((feats_x0_c, feats_x1_c_warped, coarse_warp), dim=1))
        coarse_matches = coarse_matches + coarse_matches_delta * to_normalized
        corresps[8] = {"flow": coarse_matches[:,:2], "certainty": coarse_matches[:,2:]}
        coarse_matches_up = F.interpolate(coarse_matches, size = feats_x0_f.shape[-2:], mode = "bilinear", align_corners = False)        
        coarse_matches_up_detach = coarse_matches_up.detach()#note the detach
        feats_x1_f_warped = F.grid_sample(feats_x1_f, coarse_matches_up_detach.permute(0, 2, 3, 1)[...,:2], mode = 'bilinear', align_corners = False)
        fine_matches_delta = self.fine_matcher(torch.cat((feats_x0_f, feats_x1_f_warped, coarse_matches_up_detach[:,:2]), dim=1))
        fine_matches = coarse_matches_up_detach+fine_matches_delta * to_normalized
        corresps[4] = {"flow": fine_matches[:,:2], "certainty": fine_matches[:,2:]}
        return corresps