Bugfixes + Refactor + Faster inference (#119)

1. Replace naive pytorch local correlation with more efficient fused kernel (new dependency).
2. Fixes to the image loading in .match
3. Refactors.
4. Modern packaging with uv_build and pyproject.toml
5. Some regression testing.
6. PyTorch native flash attn
7. GP with cholesky solve instead of inverse

.gitignore

@@ -8,4 +8,5 @@ workspace*
 jobs/*
 *ignore_me*
 *.pth
-wandb*
+wandb*
+results/*

.python-version

@@ -0,0 +1 @@
+3.12

README.md

@@ -26,10 +26,15 @@
 </p>
 
 ## Setup/Install
-In your python environment (tested on Linux python 3.10), run:
+In your python environment (tested on Linux python 3.12), run:
 ```bash
 pip install -e .
 ```
+or 
+``bash
+uv sync
+``
+
 ## Demo / How to Use
 We provide two demos in the [demos folder](demo).
 Here's the gist of it:
@@ -111,6 +116,17 @@ IMC22 :'):
 | Tiny RoMa v1    | 42.2 |
 | RoMa    |  -   |
 
+## Reproducibility
+There are a few diffs in the current codebase compared to the original repo used to run experiments.
+
+1. The `scale_factor` used in the `match` method now is relative to the original training resolution of `560`. Previosly it was based on the set coarse resolution (which might or might not be `560`).
+2. Newer PyTorch, original code used something like `2.1`.
+3. Stochastic eval: both RANSAC and the chosen correspondences can affect results in `Mega1500`.
+4. Matrix inverse in GP has been replaced with cholesky decomp.
+
+That being said, if diff of results are $>0.5$ there probably is something wrong, please let me know.
+
+
 ## BibTeX
 If you find our models useful, please consider citing our paper!
 ```

experiments/roma_indoor.py

@@ -1,7 +1,7 @@
 import os
 import torch
 from argparse import ArgumentParser
-
+from warnings import warn
 from torch import nn
 from torch.utils.data import ConcatDataset
 import torch.distributed as dist
@@ -286,6 +286,8 @@ def test_scannet(model, name, resolution, sample_mode):
 
 if __name__ == "__main__":
     import warnings
+    warn('Current version of romatch is not tested for training, use at your own risk.')
+
     warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
     warnings.filterwarnings('ignore')#, category=UserWarning)#, message='WARNING batched routines are designed for small sizes.')
     os.environ["TORCH_CUDNN_V8_API_ENABLED"] = "1" # For BF16 computations

experiments/train_roma_outdoor.py

@@ -1,7 +1,7 @@
 import os
 import torch
 from argparse import ArgumentParser
-
+from warnings import warn
 from torch import nn
 from torch.utils.data import ConcatDataset
 import torch.distributed as dist
@@ -292,6 +292,7 @@ if __name__ == "__main__":
     os.environ["TORCH_CUDNN_V8_API_ENABLED"] = "1" # For BF16 computations
     os.environ["OMP_NUM_THREADS"] = "16"
     torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
+    warn('Current version of romatch is not tested for training, use at your own risk.')
     import romatch
     parser = ArgumentParser()
     parser.add_argument("--only_test", action='store_true')

pyproject.toml

@@ -0,0 +1,38 @@
+[project]
+name = "romatch"
+version = "0.1.0"
+description = "Robust Dense Feature Matching"
+readme = "README.md"
+authors = [
+    { name = "Johan Edstedt", email = "johan.edstedt@liu.se" }
+]
+requires-python = ">=3.9"
+dependencies = [
+    "albumentations",
+    "einops",
+    "fused-local-corr>=0.2.2",
+    "h5py",
+    "kornia",
+    "loguru",
+    "matplotlib",
+    "opencv-python",
+    "poselib>=2.0.4",
+    "timm",
+    "torch>=2.5.1",
+    "torchvision",
+    "tqdm",
+    "wandb",
+]
+
+[build-system]
+requires = ["uv_build>=0.8.14,<0.9.0"]
+build-backend = "uv_build"
+
+[tool.uv.build-backend]
+module-name = "romatch"
+module-root = ""
+
+[dependency-groups]
+dev = [
+    "ruff>=0.13.1",
+]

requirements.txt

@@ -11,4 +11,4 @@ h5py
 wandb
 timm
 poselib
-#xformers # Optional, used for memefficient attention
+fused-local-corr>=0.2.2

romatch/benchmarks/megadepth_dense_benchmark.py

@@ -43,61 +43,60 @@ class MegadepthDenseBenchmark:
 
     def benchmark(self, model, batch_size=8):
         model.train(False)
-        with torch.no_grad():
-            gd_tot = 0.0
-            pck_1_tot = 0.0
-            pck_3_tot = 0.0
-            pck_5_tot = 0.0
-            sampler = torch.utils.data.WeightedRandomSampler(
-                torch.ones(len(self.dataset)), replacement=False, num_samples=self.num_samples
+        gd_tot = 0.0
+        pck_1_tot = 0.0
+        pck_3_tot = 0.0
+        pck_5_tot = 0.0
+        sampler = torch.utils.data.WeightedRandomSampler(
+            torch.ones(len(self.dataset)), replacement=False, num_samples=self.num_samples
+        )
+        B = batch_size
+        dataloader = torch.utils.data.DataLoader(
+            self.dataset, batch_size=B, num_workers=batch_size, sampler=sampler
+        )
+        for idx, data in tqdm.tqdm(enumerate(dataloader), disable = romatch.RANK > 0):
+            im_A, im_B, depth1, depth2, T_1to2, K1, K2 = (
+                data["im_A"].cuda(),
+                data["im_B"].cuda(),
+                data["im_A_depth"].cuda(),
+                data["im_B_depth"].cuda(),
+                data["T_1to2"].cuda(),
+                data["K1"].cuda(),
+                data["K2"].cuda(),
             )
-            B = batch_size
-            dataloader = torch.utils.data.DataLoader(
-                self.dataset, batch_size=B, num_workers=batch_size, sampler=sampler
+            matches, certainty = model.match(im_A, im_B, batched=True)
+            gd, pck_1, pck_3, pck_5, prob = self.geometric_dist(
+                depth1, depth2, T_1to2, K1, K2, matches
             )
-            for idx, data in tqdm.tqdm(enumerate(dataloader), disable = romatch.RANK > 0):
-                im_A, im_B, depth1, depth2, T_1to2, K1, K2 = (
-                    data["im_A"].cuda(),
-                    data["im_B"].cuda(),
-                    data["im_A_depth"].cuda(),
-                    data["im_B_depth"].cuda(),
-                    data["T_1to2"].cuda(),
-                    data["K1"].cuda(),
-                    data["K2"].cuda(),
+            if romatch.DEBUG_MODE:
+                from romatch.utils.utils import tensor_to_pil
+                import torch.nn.functional as F
+                path = "vis"
+                H, W = model.get_output_resolution()
+                white_im = torch.ones((B,1,H,W),device="cuda")
+                im_B_transfer_rgb = F.grid_sample(
+                    im_B.cuda(), matches[:,:,:W, 2:], mode="bilinear", align_corners=False
                 )
-                matches, certainty = model.match(im_A, im_B, batched=True)
-                gd, pck_1, pck_3, pck_5, prob = self.geometric_dist(
-                    depth1, depth2, T_1to2, K1, K2, matches
-                )
-                if romatch.DEBUG_MODE:
-                    from romatch.utils.utils import tensor_to_pil
-                    import torch.nn.functional as F
-                    path = "vis"
-                    H, W = model.get_output_resolution()
-                    white_im = torch.ones((B,1,H,W),device="cuda")
-                    im_B_transfer_rgb = F.grid_sample(
-                        im_B.cuda(), matches[:,:,:W, 2:], mode="bilinear", align_corners=False
-                    )
-                    warp_im = im_B_transfer_rgb
-                    c_b = certainty[:,None]#(certainty*0.9 + 0.1*torch.ones_like(certainty))[:,None]
-                    vis_im = c_b * warp_im + (1 - c_b) * white_im
-                    for b in range(B):
-                        import os
-                        os.makedirs(f"{path}/{model.name}/{idx}_{b}_{H}_{W}",exist_ok=True)
-                        tensor_to_pil(vis_im[b], unnormalize=True).save(
-                            f"{path}/{model.name}/{idx}_{b}_{H}_{W}/warp.jpg")
-                        tensor_to_pil(im_A[b].cuda(), unnormalize=True).save(
-                            f"{path}/{model.name}/{idx}_{b}_{H}_{W}/im_A.jpg")
-                        tensor_to_pil(im_B[b].cuda(), unnormalize=True).save(
-                            f"{path}/{model.name}/{idx}_{b}_{H}_{W}/im_B.jpg")
+                warp_im = im_B_transfer_rgb
+                c_b = certainty[:,None]#(certainty*0.9 + 0.1*torch.ones_like(certainty))[:,None]
+                vis_im = c_b * warp_im + (1 - c_b) * white_im
+                for b in range(B):
+                    import os
+                    os.makedirs(f"{path}/{model.name}/{idx}_{b}_{H}_{W}",exist_ok=True)
+                    tensor_to_pil(vis_im[b], unnormalize=True).save(
+                        f"{path}/{model.name}/{idx}_{b}_{H}_{W}/warp.jpg")
+                    tensor_to_pil(im_A[b].cuda(), unnormalize=True).save(
+                        f"{path}/{model.name}/{idx}_{b}_{H}_{W}/im_A.jpg")
+                    tensor_to_pil(im_B[b].cuda(), unnormalize=True).save(
+                        f"{path}/{model.name}/{idx}_{b}_{H}_{W}/im_B.jpg")
 
 
-                gd_tot, pck_1_tot, pck_3_tot, pck_5_tot = (
-                    gd_tot + gd.mean(),
-                    pck_1_tot + pck_1,
-                    pck_3_tot + pck_3,
-                    pck_5_tot + pck_5,
-                )
+            gd_tot, pck_1_tot, pck_3_tot, pck_5_tot = (
+                gd_tot + gd.mean(),
+                pck_1_tot + pck_1,
+                pck_3_tot + pck_3,
+                pck_5_tot + pck_5,
+            )
         return {
             "epe": gd_tot.item() / len(dataloader),
             "mega_pck_1": pck_1_tot.item() / len(dataloader),

romatch/benchmarks/megadepth_pose_estimation_benchmark.py

@@ -3,9 +3,6 @@ import torch
 from romatch.utils import *
 from PIL import Image
 from tqdm import tqdm
-import torch.nn.functional as F
-import romatch
-import kornia.geometry.epipolar as kepi
 
 class MegaDepthPoseEstimationBenchmark:
     def __init__(self, data_root="data/megadepth", scene_names = None) -> None:
@@ -39,7 +36,7 @@ class MegaDepthPoseEstimationBenchmark:
                 poses = scene["poses"]
                 im_paths = scene["image_paths"]
                 pair_inds = range(len(pairs))
-                for pairind in tqdm(pair_inds):
+                for pairind in (pbar := tqdm(pair_inds, desc = "Current AUC: ?")):
                     idx1, idx2 = pairs[pairind][0]
                     K1 = intrinsics[idx1].copy()
                     T1 = poses[idx1].copy()
@@ -54,9 +51,6 @@ class MegaDepthPoseEstimationBenchmark:
                     dense_matches, dense_certainty = model.match(
                         im_A_path, im_B_path, K1.copy(), K2.copy(), T1_to_2.copy()
                     )
-                    sparse_matches,_ = model.sample(
-                        dense_matches, dense_certainty, 5_000
-                    )
                     
                     im_A = Image.open(im_A_path)
                     w1, h1 = im_A.size
@@ -70,10 +64,12 @@ class MegaDepthPoseEstimationBenchmark:
                         K1, K2 = K1.copy(), K2.copy()
                         K1[:2] = K1[:2] * scale1
                         K2[:2] = K2[:2] * scale2
-
-                    kpts1, kpts2 = model.to_pixel_coordinates(sparse_matches, h1, w1, h2, w2)
-                    kpts1, kpts2 = kpts1.cpu().numpy(), kpts2.cpu().numpy()
                     for _ in range(5):
+                        sparse_matches,_ = model.sample(
+                            dense_matches, dense_certainty, 5_000
+                        )
+                        kpts1, kpts2 = model.to_pixel_coordinates(sparse_matches, h1, w1, h2, w2)
+                        kpts1, kpts2 = kpts1.cpu().numpy(), kpts2.cpu().numpy()
                         shuffling = np.random.permutation(np.arange(len(kpts1)))
                         kpts1 = kpts1[shuffling]
                         kpts2 = kpts2[shuffling]
@@ -98,6 +94,8 @@ class MegaDepthPoseEstimationBenchmark:
                         tot_e_t.append(e_t)
                         tot_e_R.append(e_R)
                         tot_e_pose.append(e_pose)
+                        pbar.set_description(f"Current AUC: {pose_auc(tot_e_pose, thresholds)}")
+
             tot_e_pose = np.array(tot_e_pose)
             auc = pose_auc(tot_e_pose, thresholds)
             acc_5 = (tot_e_pose < 5).mean()

romatch/benchmarks/megadepth_pose_estimation_benchmark_poselib.py

@@ -3,9 +3,6 @@ import torch
 from romatch.utils import *
 from PIL import Image
 from tqdm import tqdm
-import torch.nn.functional as F
-import romatch
-import kornia.geometry.epipolar as kepi
 
 # wrap cause pyposelib is still in dev
 # will add in deps later

romatch/models/encoders.py

@@ -1,67 +1,16 @@
-from typing import Optional, Union
 import torch
-from torch import device
 import torch.nn as nn
-import torch.nn.functional as F
 import torchvision.models as tvm
-import gc
 from romatch.utils.utils import get_autocast_params
 
-
-class ResNet50(nn.Module):
-    def __init__(self, pretrained=False, high_res = False, weights = None, 
-                 dilation = None, freeze_bn = True, anti_aliased = False, early_exit = False, amp = False, amp_dtype = torch.float16) -> None:
-        super().__init__()
-        if dilation is None:
-            dilation = [False,False,False]
-        if anti_aliased:
-            pass
-        else:
-            if weights is not None:
-                self.net = tvm.resnet50(weights = weights,replace_stride_with_dilation=dilation)
-            else:
-                self.net = tvm.resnet50(pretrained=pretrained,replace_stride_with_dilation=dilation)
-            
-        self.high_res = high_res
-        self.freeze_bn = freeze_bn
-        self.early_exit = early_exit
-        self.amp = amp
-        self.amp_dtype = amp_dtype
-
-    def forward(self, x, **kwargs):
-        autocast_device, autocast_enabled, autocast_dtype = get_autocast_params(x.device, self.amp, self.amp_dtype)
-        with torch.autocast(autocast_device, enabled=autocast_enabled, dtype = autocast_dtype):
-            net = self.net
-            feats = {1:x}
-            x = net.conv1(x)
-            x = net.bn1(x)
-            x = net.relu(x)
-            feats[2] = x 
-            x = net.maxpool(x)
-            x = net.layer1(x)
-            feats[4] = x 
-            x = net.layer2(x)
-            feats[8] = x
-            if self.early_exit:
-                return feats
-            x = net.layer3(x)
-            feats[16] = x
-            x = net.layer4(x)
-            feats[32] = x
-            return feats
-
-    def train(self, mode=True):
-        super().train(mode)
-        if self.freeze_bn:
-            for m in self.modules():
-                if isinstance(m, nn.BatchNorm2d):
-                    m.eval()
-                pass
-
 class VGG19(nn.Module):
-    def __init__(self, pretrained=False, amp = False, amp_dtype = torch.float16) -> None:
+    def __init__(self, pretrained=True, amp = False, amp_dtype = torch.float16) -> None:
         super().__init__()
-        self.layers = nn.ModuleList(tvm.vgg19_bn(pretrained=pretrained).features[:40])
+        if pretrained:
+            weights = tvm.vgg.VGG19_BN_Weights.IMAGENET1K_V1
+        else:
+            weights = None
+        self.layers = nn.ModuleList(tvm.vgg19_bn(weights=weights).features[:40])
         self.amp = amp
         self.amp_dtype = amp_dtype
 
@@ -78,7 +27,7 @@ class VGG19(nn.Module):
             return feats
 
 class CNNandDinov2(nn.Module):
-    def __init__(self, cnn_kwargs = None, amp = False, use_vgg = False, dinov2_weights = None, amp_dtype = torch.float16):
+    def __init__(self, cnn_kwargs = None, amp = False, dinov2_weights = None, amp_dtype = torch.float16):
         super().__init__()
         if dinov2_weights is None:
             dinov2_weights = torch.hub.load_state_dict_from_url("https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth", map_location="cpu")
@@ -93,10 +42,7 @@ class CNNandDinov2(nn.Module):
         dinov2_vitl14 = vit_large(**vit_kwargs).eval()
         dinov2_vitl14.load_state_dict(dinov2_weights)
         cnn_kwargs = cnn_kwargs if cnn_kwargs is not None else {}
-        if not use_vgg:
-            self.cnn = ResNet50(**cnn_kwargs)
-        else:
-            self.cnn = VGG19(**cnn_kwargs)
+        self.cnn = VGG19(**cnn_kwargs)
         self.amp = amp
         self.amp_dtype = amp_dtype
         if self.amp:

romatch/models/model_zoo/__init__.py

@@ -1,6 +1,8 @@
 from typing import Union
 import torch
 from .roma_models import roma_model, tiny_roma_v1_model
+from loguru import logger
+
 
 weight_urls = {
     "romatch": {
@@ -10,64 +12,104 @@ weight_urls = {
     "tiny_roma_v1": {
         "outdoor": "https://github.com/Parskatt/storage/releases/download/roma/tiny_roma_v1_outdoor.pth",
     },
-    "dinov2": "https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth", #hopefully this doesnt change :D
+    "dinov2": "https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth",  # hopefully this doesnt change :D
 }
 
-def tiny_roma_v1_outdoor(device, weights = None, xfeat = None):
+
+def tiny_roma_v1_outdoor(device, weights=None, xfeat=None):
     if weights is None:
         weights = torch.hub.load_state_dict_from_url(
-            weight_urls["tiny_roma_v1"]["outdoor"],
-            map_location=device)
+            weight_urls["tiny_roma_v1"]["outdoor"], map_location=device
+        )
     if xfeat is None:
         xfeat = torch.hub.load(
-            'verlab/accelerated_features', 
-            'XFeat', 
-            pretrained = True, 
-            top_k = 4096).net
+            "verlab/accelerated_features", "XFeat", pretrained=True, top_k=4096
+        ).net
 
-    return tiny_roma_v1_model(weights = weights, xfeat = xfeat).to(device) 
+    return tiny_roma_v1_model(weights=weights, xfeat=xfeat).to(device)
 
-def roma_outdoor(device, weights=None, dinov2_weights=None, coarse_res: Union[int,tuple[int,int]] = 560, upsample_res: Union[int,tuple[int,int]] = 864, amp_dtype: torch.dtype = torch.float16):
+
+def roma_outdoor(
+    device,
+    weights=None,
+    dinov2_weights=None,
+    coarse_res: Union[int, tuple[int, int]] = 560,
+    upsample_res: Union[int, tuple[int, int]] = 864,
+    amp_dtype: torch.dtype = torch.float16,
+    symmetric=True,
+    use_custom_corr=False,
+    upsample_preds=True,
+):
     if isinstance(coarse_res, int):
         coarse_res = (coarse_res, coarse_res)
-    if isinstance(upsample_res, int):    
+    if isinstance(upsample_res, int):
         upsample_res = (upsample_res, upsample_res)
 
-    if str(device) == 'cpu':
+    if str(device) == "cpu":
         amp_dtype = torch.float32
 
     assert coarse_res[0] % 14 == 0, "Needs to be multiple of 14 for backbone"
     assert coarse_res[1] % 14 == 0, "Needs to be multiple of 14 for backbone"
-    
+
     if weights is None:
-        weights = torch.hub.load_state_dict_from_url(weight_urls["romatch"]["outdoor"],
-                                                     map_location=device)
+        weights = torch.hub.load_state_dict_from_url(
+            weight_urls["romatch"]["outdoor"], map_location=device
+        )
     if dinov2_weights is None:
-        dinov2_weights = torch.hub.load_state_dict_from_url(weight_urls["dinov2"],
-                                                     map_location=device)
-    model = roma_model(resolution=coarse_res, upsample_preds=True,
-               weights=weights,dinov2_weights = dinov2_weights,device=device, amp_dtype=amp_dtype)
+        dinov2_weights = torch.hub.load_state_dict_from_url(
+            weight_urls["dinov2"], map_location=device
+        )
+    model = roma_model(
+        resolution=coarse_res,
+        upsample_preds=upsample_preds,
+        weights=weights,
+        dinov2_weights=dinov2_weights,
+        device=device,
+        amp_dtype=amp_dtype,
+        symmetric=symmetric,
+        use_custom_corr=use_custom_corr,
+    )
     model.upsample_res = upsample_res
-    print(f"Using coarse resolution {coarse_res}, and upsample res {model.upsample_res}")
+    logger.info(
+        f"Using coarse resolution {coarse_res}, and upsample res {model.upsample_res}"
+    )
     return model
 
-def roma_indoor(device, weights=None, dinov2_weights=None, coarse_res: Union[int,tuple[int,int]] = 560, upsample_res: Union[int,tuple[int,int]] = 864, amp_dtype: torch.dtype = torch.float16):
+
+def roma_indoor(
+    device,
+    weights=None,
+    dinov2_weights=None,
+    coarse_res: Union[int, tuple[int, int]] = 560,
+    upsample_res: Union[int, tuple[int, int]] = 864,
+    amp_dtype: torch.dtype = torch.float16,
+):
     if isinstance(coarse_res, int):
         coarse_res = (coarse_res, coarse_res)
-    if isinstance(upsample_res, int):    
+    if isinstance(upsample_res, int):
         upsample_res = (upsample_res, upsample_res)
 
     assert coarse_res[0] % 14 == 0, "Needs to be multiple of 14 for backbone"
     assert coarse_res[1] % 14 == 0, "Needs to be multiple of 14 for backbone"
-    
+
     if weights is None:
-        weights = torch.hub.load_state_dict_from_url(weight_urls["romatch"]["indoor"],
-                                                     map_location=device)
+        weights = torch.hub.load_state_dict_from_url(
+            weight_urls["romatch"]["indoor"], map_location=device
+        )
     if dinov2_weights is None:
-        dinov2_weights = torch.hub.load_state_dict_from_url(weight_urls["dinov2"],
-                                                     map_location=device)
-    model = roma_model(resolution=coarse_res, upsample_preds=True,
-               weights=weights,dinov2_weights = dinov2_weights,device=device, amp_dtype=amp_dtype)
+        dinov2_weights = torch.hub.load_state_dict_from_url(
+            weight_urls["dinov2"], map_location=device
+        )
+    model = roma_model(
+        resolution=coarse_res,
+        upsample_preds=True,
+        weights=weights,
+        dinov2_weights=dinov2_weights,
+        device=device,
+        amp_dtype=amp_dtype,
+    )
     model.upsample_res = upsample_res
-    print(f"Using coarse resolution {coarse_res}, and upsample res {model.upsample_res}")
+    logger.info(
+        f"Using coarse resolution {coarse_res}, and upsample res {model.upsample_res}"
+    )
     return model

romatch/models/model_zoo/roma_models.py

@@ -1,114 +1,110 @@
+from functools import partial
 import warnings
 import torch.nn as nn
 import torch
-from romatch.models.matcher import *
+from romatch.models.matcher import (
+    ConvRefiner,
+    CosKernel,
+    GP,
+    Decoder,
+    RegressionMatcher,
+)
 from romatch.models.transformer import Block, TransformerDecoder, MemEffAttention
-from romatch.models.encoders import *
+from romatch.models.encoders import CNNandDinov2
 from romatch.models.tiny import TinyRoMa
 
-def tiny_roma_v1_model(weights = None, freeze_xfeat=False, exact_softmax=False, xfeat = None):
+
+def tiny_roma_v1_model(
+    weights=None, freeze_xfeat=False, exact_softmax=False, xfeat=None
+):
     model = TinyRoMa(
-        xfeat = xfeat,
-        freeze_xfeat=freeze_xfeat, 
-        exact_softmax=exact_softmax)
+        xfeat=xfeat, freeze_xfeat=freeze_xfeat, exact_softmax=exact_softmax
+    )
     if weights is not None:
         model.load_state_dict(weights)
     return model
 
-def roma_model(resolution, upsample_preds, device = None, weights=None, dinov2_weights=None, amp_dtype: torch.dtype=torch.float16, **kwargs):
-    # romatch weights and dinov2 weights are loaded seperately, as dinov2 weights are not parameters
-    #torch.backends.cuda.matmul.allow_tf32 = True # allow tf32 on matmul TODO: these probably ruin stuff, should be careful
-    #torch.backends.cudnn.allow_tf32 = True # allow tf32 on cudnn
-    warnings.filterwarnings('ignore', category=UserWarning, message='TypedStorage is deprecated')
+
+def roma_model(
+    resolution,
+    upsample_preds,
+    device=None,
+    weights=None,
+    dinov2_weights=None,
+    amp_dtype: torch.dtype = torch.float16,
+    use_custom_corr=False,
+    symmetric=True,
+    **kwargs,
+):
+    warnings.filterwarnings(
+        "ignore", category=UserWarning, message="TypedStorage is deprecated"
+    )
     gp_dim = 512
     feat_dim = 512
     decoder_dim = gp_dim + feat_dim
     cls_to_coord_res = 64
     coordinate_decoder = TransformerDecoder(
-        nn.Sequential(*[Block(decoder_dim, 8, attn_class=MemEffAttention) for _ in range(5)]), 
-        decoder_dim, 
+        nn.Sequential(
+            *[Block(decoder_dim, 8, attn_class=MemEffAttention) for _ in range(5)]
+        ),
+        decoder_dim,
         cls_to_coord_res**2 + 1,
         is_classifier=True,
-        amp = True,
-        pos_enc = False,)
+        amp=True,
+        pos_enc=False,
+    )
     dw = True
     hidden_blocks = 8
     kernel_size = 5
     displacement_emb = "linear"
     disable_local_corr_grad = True
-    
+    partial_conv_refiner = partial(
+        ConvRefiner,
+        kernel_size=kernel_size,
+        dw=dw,
+        hidden_blocks=hidden_blocks,
+        displacement_emb=displacement_emb,
+        corr_in_other=True,
+        amp=True,
+        disable_local_corr_grad=disable_local_corr_grad,
+        bn_momentum=0.01,
+        use_custom_corr=use_custom_corr,
+    )
+
     conv_refiner = nn.ModuleDict(
         {
-            "16": ConvRefiner(
-                2 * 512+128+(2*7+1)**2,
-                2 * 512+128+(2*7+1)**2,
+            "16": partial_conv_refiner(
+                2 * 512 + 128 + (2 * 7 + 1) ** 2,
+                2 * 512 + 128 + (2 * 7 + 1) ** 2,
                 2 + 1,
-                kernel_size=kernel_size,
-                dw=dw,
-                hidden_blocks=hidden_blocks,
-                displacement_emb=displacement_emb,
                 displacement_emb_dim=128,
-                local_corr_radius = 7,
-                corr_in_other = True,
-                amp = True,
-                disable_local_corr_grad = disable_local_corr_grad,
-                bn_momentum = 0.01,
+                local_corr_radius=7,
             ),
-            "8": ConvRefiner(
-                2 * 512+64+(2*3+1)**2,
-                2 * 512+64+(2*3+1)**2,
+            "8": partial_conv_refiner(
+                2 * 512 + 64 + (2 * 3 + 1) ** 2,
+                2 * 512 + 64 + (2 * 3 + 1) ** 2,
                 2 + 1,
-                kernel_size=kernel_size,
-                dw=dw,
-                hidden_blocks=hidden_blocks,
-                displacement_emb=displacement_emb,
                 displacement_emb_dim=64,
-                local_corr_radius = 3,
-                corr_in_other = True,
-                amp = True,
-                disable_local_corr_grad = disable_local_corr_grad,
-                bn_momentum = 0.01,
+                local_corr_radius=3,
             ),
-            "4": ConvRefiner(
-                2 * 256+32+(2*2+1)**2,
-                2 * 256+32+(2*2+1)**2,
+            "4": partial_conv_refiner(
+                2 * 256 + 32 + (2 * 2 + 1) ** 2,
+                2 * 256 + 32 + (2 * 2 + 1) ** 2,
                 2 + 1,
-                kernel_size=kernel_size,
-                dw=dw,
-                hidden_blocks=hidden_blocks,
-                displacement_emb=displacement_emb,
                 displacement_emb_dim=32,
-                local_corr_radius = 2,
-                corr_in_other = True,
-                amp = True,
-                disable_local_corr_grad = disable_local_corr_grad,
-                bn_momentum = 0.01,
+                local_corr_radius=2,
             ),
-            "2": ConvRefiner(
-                2 * 64+16,
-                128+16,
+            "2": partial_conv_refiner(
+                2 * 64 + 16,
+                128 + 16,
                 2 + 1,
-                kernel_size=kernel_size,
-                dw=dw,
-                hidden_blocks=hidden_blocks,
-                displacement_emb=displacement_emb,
                 displacement_emb_dim=16,
-                amp = True,
-                disable_local_corr_grad = disable_local_corr_grad,
-                bn_momentum = 0.01,
             ),
-            "1": ConvRefiner(
+            "1": partial_conv_refiner(
                 2 * 9 + 6,
                 24,
                 2 + 1,
-                kernel_size=kernel_size,
-                dw=dw,
-                hidden_blocks = hidden_blocks,
-                displacement_emb = displacement_emb,
-                displacement_emb_dim = 6,
-                amp = True,
-                disable_local_corr_grad = disable_local_corr_grad,
-                bn_momentum = 0.01,
+                displacement_emb_dim=6,
             ),
         }
     )
@@ -133,38 +129,47 @@ def roma_model(resolution, upsample_preds, device = None, weights=None, dinov2_w
     proj4 = nn.Sequential(nn.Conv2d(256, 256, 1, 1), nn.BatchNorm2d(256))
     proj2 = nn.Sequential(nn.Conv2d(128, 64, 1, 1), nn.BatchNorm2d(64))
     proj1 = nn.Sequential(nn.Conv2d(64, 9, 1, 1), nn.BatchNorm2d(9))
-    proj = nn.ModuleDict({
-        "16": proj16,
-        "8": proj8,
-        "4": proj4,
-        "2": proj2,
-        "1": proj1,
-        })
+    proj = nn.ModuleDict(
+        {
+            "16": proj16,
+            "8": proj8,
+            "4": proj4,
+            "2": proj2,
+            "1": proj1,
+        }
+    )
     displacement_dropout_p = 0.0
     gm_warp_dropout_p = 0.0
-    decoder = Decoder(coordinate_decoder, 
-                      gps, 
-                      proj, 
-                      conv_refiner, 
-                      detach=True, 
-                      scales=["16", "8", "4", "2", "1"], 
-                      displacement_dropout_p = displacement_dropout_p,
-                      gm_warp_dropout_p = gm_warp_dropout_p)
-    
+    decoder = Decoder(
+        coordinate_decoder,
+        gps,
+        proj,
+        conv_refiner,
+        detach=True,
+        scales=["16", "8", "4", "2", "1"],
+        displacement_dropout_p=displacement_dropout_p,
+        gm_warp_dropout_p=gm_warp_dropout_p,
+    )
+
     encoder = CNNandDinov2(
-        cnn_kwargs = dict(
-            pretrained=False,
-            amp = True),
-        amp = True,
-        use_vgg = True,
-        dinov2_weights = dinov2_weights,
+        cnn_kwargs=dict(pretrained=False, amp=True),
+        amp=True,
+        dinov2_weights=dinov2_weights,
         amp_dtype=amp_dtype,
     )
-    h,w = resolution
-    symmetric = True
+    h, w = resolution
     attenuate_cert = True
     sample_mode = "threshold_balanced"
-    matcher = RegressionMatcher(encoder, decoder, h=h, w=w, upsample_preds=upsample_preds, 
-                                symmetric = symmetric, attenuate_cert = attenuate_cert, sample_mode = sample_mode, **kwargs).to(device)
+    matcher = RegressionMatcher(
+        encoder,
+        decoder,
+        h=h,
+        w=w,
+        upsample_preds=upsample_preds,
+        symmetric=symmetric,
+        attenuate_cert=attenuate_cert,
+        sample_mode=sample_mode,
+        **kwargs,
+    ).to(device)
     matcher.load_state_dict(weights)
     return matcher

romatch/models/transformer/layers/attention.py

@@ -12,6 +12,7 @@ import logging
 
 from torch import Tensor
 from torch import nn
+import torch
 
 
 logger = logging.getLogger("dinov2")
@@ -47,6 +48,20 @@ class Attention(nn.Module):
         self.proj_drop = nn.Dropout(proj_drop)
 
     def forward(self, x: Tensor) -> Tensor:
+        # use new pytorch native attn
+        qkv = self.qkv(x)
+        B, N, _ = qkv.shape
+        C = self.qkv.in_features
+
+        qkv = qkv.reshape(B, N, 3, self.num_heads, C // self.num_heads)
+        q, k, v = torch.unbind(qkv, 2)
+        q, k, v = [t.transpose(1, 2) for t in [q, k, v]]
+        x = torch.nn.functional.scaled_dot_product_attention(q, k, v)
+        x = x.transpose(1, 2).reshape([B, N, C])
+        x = self.proj(x)
+        x = self.proj_drop(x)
+        return x
+        # old code below
         B, N, C = x.shape
         qkv = self.qkv(x).reshape(B, N, 3, self.num_heads, C // self.num_heads).permute(2, 0, 3, 1, 4)
 

romatch/utils/local_correlation.py

@@ -1,48 +1,143 @@
+from typing import Literal
 import torch
 import torch.nn.functional as F
+import local_corr
 
-def local_correlation(
+
+def local_corr_wrapper(
+    feature0: torch.Tensor,
+    feature1: torch.Tensor,
+    coords: torch.Tensor,
+    local_window: torch.Tensor,
+    B,
+    K,
+    c,
+    r,
+    h,
+    w,
+    device,
+    padding_mode="zeros",
+    sample_mode: Literal["bilinear", "nearest"] = "bilinear",
+    dtype=torch.float32,
+):
+    assert padding_mode == "zeros"
+    warp = (coords[..., None, :] + local_window[:, None, None]).reshape(B, h * w, K, 2)
+    corr = (
+        local_corr.local_corr(
+            feature0.reshape(B, c, h * w).permute(0, 2, 1).float() / (c**0.5),
+            feature1.permute(0, 2, 3, 1).clone().detach().float(),
+            warp.clone().detach(),
+            mode=sample_mode,
+            normalized_coords=True,
+        )
+        .permute(0, 2, 1)
+        .reshape(B, K, h, w)
+    )
+    return corr, warp
+
+
+def shitty_native_torch_local_corr(
     feature0,
     feature1,
-    local_radius,
+    warp,
+    local_window,
+    B,
+    K,
+    c,
+    r,
+    h,
+    w,
+    device,
     padding_mode="zeros",
-    flow = None,
-    sample_mode = "bilinear",
+    sample_mode="bilinear",
+    dtype=torch.float32,
 ):
-    r = local_radius
-    K = (2*r+1)**2
-    B, c, h, w = feature0.size()
-    corr = torch.empty((B,K,h,w), device = feature0.device, dtype=feature0.dtype)
-    if flow is None:
-        # If flow is None, assume feature0 and feature1 are aligned
-        coords = torch.meshgrid(
-                (
-                    torch.linspace(-1 + 1 / h, 1 - 1 / h, h, device=feature0.device),
-                    torch.linspace(-1 + 1 / w, 1 - 1 / w, w, device=feature0.device),
-                ),
-                indexing = 'ij'
-                )
-        coords = torch.stack((coords[1], coords[0]), dim=-1)[
-            None
-        ].expand(B, h, w, 2)
-    else:
-        coords = flow.permute(0,2,3,1) # If using flow, sample around flow target.
-    local_window = torch.meshgrid(
-                (
-                    torch.linspace(-2*local_radius/h, 2*local_radius/h, 2*r+1, device=feature0.device),
-                    torch.linspace(-2*local_radius/w, 2*local_radius/w, 2*r+1, device=feature0.device),
-                ),
-                indexing = 'ij'
-                )
-    local_window = torch.stack((local_window[1], local_window[0]), dim=-1)[
-            None
-        ].expand(1, 2*r+1, 2*r+1, 2).reshape(1, (2*r+1)**2, 2)
+    corr = torch.empty((B, K, h, w), device=device, dtype=dtype)
     for _ in range(B):
         with torch.no_grad():
-            local_window_coords = (coords[_,:,:,None]+local_window[:,None,None]).reshape(1,h,w*(2*r+1)**2,2)
+            local_window_coords = (
+                warp[_, :, :, None] + local_window[:, None, None]
+            ).reshape(1, h, w * K, 2)
             window_feature = F.grid_sample(
-                feature1[_:_+1], local_window_coords, padding_mode=padding_mode, align_corners=False, mode = sample_mode, #
+                feature1[_ : _ + 1],
+                local_window_coords,
+                padding_mode=padding_mode,
+                align_corners=False,
+                mode=sample_mode,  #
             )
-            window_feature = window_feature.reshape(c,h,w,(2*r+1)**2)
-        corr[_] = (feature0[_,...,None]/(c**.5)*window_feature).sum(dim=0).permute(2,0,1)
-    return corr
+            window_feature = window_feature.reshape(c, h, w, K)
+        corr[_] = (
+            (feature0[_, ..., None] / (c**0.5) * window_feature)
+            .sum(dim=0)
+            .permute(2, 0, 1)
+        )
+    return corr, None
+
+
+def local_correlation(
+    feature0: torch.Tensor,  # (B x C x H x W)
+    feature1: torch.Tensor,  # (B x C x H x W)
+    local_radius: int,
+    warp: torch.Tensor,  # (B x 2 x H x W)
+    *,
+    use_custom_corr: bool,
+    padding_mode="zeros",
+    sample_mode: Literal["bilinear", "nearest"] = "bilinear",
+):
+    r = local_radius
+    K = (2 * r + 1) ** 2
+    B, c, h, w = feature0.size()
+    warp = warp.permute(0, 2, 3, 1)
+    device = feature0.device
+    dtype = feature0.dtype
+    local_window = torch.meshgrid(
+        [
+            torch.linspace(
+                -2 * local_radius / h, 2 * local_radius / h, 2 * r + 1, device=device
+            ),
+            torch.linspace(
+                -2 * local_radius / w, 2 * local_radius / w, 2 * r + 1, device=device
+            ),
+        ],
+        indexing="ij",
+    )
+    local_window = (
+        torch.stack((local_window[1], local_window[0]), dim=-1)[None]
+        .expand(1, 2 * r + 1, 2 * r + 1, 2)
+        .reshape(1, K, 2)
+    )
+    if not use_custom_corr:
+        corr, corr_coords = shitty_native_torch_local_corr(
+            feature0,
+            feature1,
+            warp,
+            local_window,
+            B,
+            K,
+            c,
+            r,
+            h,
+            w,
+            device,
+            padding_mode,
+            sample_mode,
+            dtype,
+        )
+    else:
+        corr, corr_coords = local_corr_wrapper(
+            feature0,
+            feature1,
+            warp,
+            local_window,
+            B,
+            K,
+            c,
+            r,
+            h,
+            w,
+            device,
+            padding_mode,
+            sample_mode,
+            dtype,
+        )
+    return corr

romatch/utils/utils.py

@@ -7,7 +7,6 @@ from torchvision import transforms
 from torchvision.transforms.functional import InterpolationMode
 import torch.nn.functional as F
 from PIL import Image
-import kornia
 
 def recover_pose(E, kpts0, kpts1, K0, K1, mask):
     best_num_inliers = 0
@@ -144,7 +143,7 @@ def pose_auc(errors, thresholds):
         last_index = np.searchsorted(errors, t)
         r = np.r_[recall[:last_index], recall[last_index - 1]]
         e = np.r_[errors[:last_index], t]
-        aucs.append(np.trapz(r, x=e) / t)
+        aucs.append(np.trapz(r, x=e).item() / t)
     return aucs
 
 

setup.py

@@ -1,9 +0,0 @@
-from setuptools import setup, find_packages
-
-setup(
-    name="romatch",
-    packages=find_packages(include=("romatch*",)),
-    version="0.0.2",
-    author="Johan Edstedt",
-    install_requires=open("requirements.txt", "r").read().split("\n"),
-)

tests/test_match_modes.py

@@ -0,0 +1,75 @@
+import torch
+from PIL import Image
+from romatch.models.matcher import RegressionMatcher
+
+
+def test_bs_one_tensor_inputs(model: RegressionMatcher, device, coarse_res: int, upsample_res: int):
+    model.match(
+        torch.randn(1, 3, coarse_res, coarse_res).to(device),
+        torch.randn(1, 3, coarse_res, coarse_res).to(device),
+        im_A_high_res=torch.randn(1, 3, upsample_res, upsample_res).to(device),
+        im_B_high_res=torch.randn(1, 3, upsample_res, upsample_res).to(device),
+    )
+
+
+def test_bs_8_tensor_inputs(model: RegressionMatcher, device, coarse_res: int, upsample_res: int):
+    model.match(
+        torch.randn(8, 3, coarse_res, coarse_res).to(device),
+        torch.randn(8, 3, coarse_res, coarse_res).to(device),
+        im_A_high_res=torch.randn(8, 3, upsample_res, upsample_res).to(device),
+        im_B_high_res=torch.randn(8, 3, upsample_res, upsample_res).to(device),
+    )
+
+
+def test_pil_inputs(model: RegressionMatcher):
+    model.match(Image.open("assets/toronto_A.jpg"), Image.open("assets/toronto_B.jpg"))
+
+
+def test_str_inputs(model: RegressionMatcher):
+    model.match("assets/toronto_A.jpg", "assets/toronto_B.jpg")
+
+
+if __name__ == "__main__":
+    from romatch import roma_outdoor
+
+    coarse_res = 560
+    upsample_res = 1152
+    for device in [torch.device("cuda")]:
+        model = roma_outdoor(
+            device=device,
+            coarse_res=coarse_res,
+            upsample_res=upsample_res,
+            use_custom_corr=True,
+            symmetric=True,
+            upsample_preds=True,
+        )
+        for is_symmetric in [True, False]:
+            for upsample_preds in [True, False]:
+                for batched in [True, False]:
+                    model.symmetric = is_symmetric
+                    model.upsample_preds = upsample_preds
+                    model.batched = batched
+                    test_bs_one_tensor_inputs(model, device, coarse_res, upsample_res)
+                    test_bs_8_tensor_inputs(model, device, coarse_res, upsample_res)
+                    test_pil_inputs(model)
+                    test_str_inputs(model)
+                    print(f"Done with {is_symmetric=}, {upsample_preds=}, {batched=}, {device=}")
+
+    for device in [torch.device("cpu")]:
+        model = roma_outdoor(
+            device=device,
+            coarse_res=coarse_res,
+            upsample_res=upsample_res,
+            use_custom_corr=True,
+            symmetric=True,
+            upsample_preds=True,
+        )
+        model.symmetric = is_symmetric
+        model.upsample_preds = upsample_preds
+        model.batched = batched
+        model.device = device
+        test_bs_one_tensor_inputs(model, device, coarse_res, upsample_res)
+        test_bs_8_tensor_inputs(model, device, coarse_res, upsample_res)
+        test_pil_inputs(model)
+        test_str_inputs(model)
+        print(f"Done with {is_symmetric=}, {upsample_preds=}, {batched=}, {device=}")

tests/test_mega1500.py

@@ -0,0 +1,22 @@
+from romatch.benchmarks import MegaDepthPoseEstimationBenchmark
+import numpy as np
+
+def test_mega1500(model, name):
+    mega1500_benchmark = MegaDepthPoseEstimationBenchmark("data/megadepth")
+    mega1500_results = mega1500_benchmark.benchmark(model, model_name=name)
+    return mega1500_results
+
+if __name__ == "__main__":
+    from romatch import roma_outdoor
+    device = "cuda"
+    model = roma_outdoor(device = device, coarse_res = 672, upsample_res = 1344, use_custom_corr=True)
+    experiment_name = "roma_latest"
+    results = test_mega1500(model, experiment_name)
+    print(results)
+    # gotten on 3.12 env with torch 2.8.0
+    reference_scores = [0.6271474434923545, 0.7673889435429945, 0.8642099162282599] # slightly worse.
+    # old_reference_scores = [0.6235757679569996, 0.7648007367330985, 0.8630483724961098]
+    assert np.isclose(results[0], reference_scores[0], atol=3e-1 / 100)
+    assert np.isclose(results[1], reference_scores[1], atol=2e-1 / 100)
+    assert np.isclose(results[2], reference_scores[2], atol=1e-1 / 100)
+    

tests/test_mega1500_poselib.py

@@ -0,0 +1,14 @@
+from romatch.benchmarks import Mega1500PoseLibBenchmark
+
+def test_mega1500_poselib(model, name):
+    mega1500_benchmark = Mega1500PoseLibBenchmark("data/megadepth")
+    mega1500_results = mega1500_benchmark.benchmark(model, model_name=name)
+    return mega1500_results
+
+if __name__ == "__main__":
+    from romatch import roma_outdoor
+    device = "cuda"
+    model = roma_outdoor(device = device, coarse_res = 672, upsample_res = 1344, use_custom_corr=True)
+    experiment_name = "roma_latest"
+    results = test_mega1500_poselib(model, experiment_name)
+    print(results)

tests/test_mega_dense.py

@@ -0,0 +1,21 @@
+from romatch.benchmarks import MegadepthDenseBenchmark
+import numpy as np
+
+def test_mega_dense(model, name):
+    mega_dense_benchmark = MegadepthDenseBenchmark("data/megadepth", h = 560, w = 560)
+    mega_dense_results = mega_dense_benchmark.benchmark(model)
+    return mega_dense_results
+
+if __name__ == "__main__":
+    from romatch import roma_outdoor
+    device = "cuda"
+    model = roma_outdoor(device = device, coarse_res = 560, use_custom_corr=True, symmetric = False, upsample_preds = False)
+    experiment_name = "roma_latest"
+    results = test_mega_dense(model, experiment_name)
+    print(results)
+    # gotten on 3.12 env with torch 2.8.0
+    reference_results = {'epe': 1.581197752074192, 'mega_pck_1': 0.8516846923828125, 'mega_pck_3': 0.9566336059570313, 'mega_pck_5': 0.9714825439453125}
+    assert np.isclose(results['epe'], reference_results['epe'], atol=1e-1)
+    assert np.isclose(results['mega_pck_1'], reference_results['mega_pck_1'], atol=2e-1 / 100)
+    assert np.isclose(results['mega_pck_3'], reference_results['mega_pck_3'], atol=2e-1 / 100)
+    assert np.isclose(results['mega_pck_5'], reference_results['mega_pck_5'], atol=2e-1 / 100)

tests/test_roma_coarse_inference_time.py

@@ -0,0 +1,32 @@
+from romatch import roma_outdoor
+import torch
+from tqdm import tqdm
+import time
+
+
+def test_inference_time(model, name):
+    T = 100
+    im_A = torch.randn(8, 3, 560, 560).to(device)
+    im_B = torch.randn(8, 3, 560, 560).to(device)
+    # burn in
+    for i in range(10):
+        model.match(im_A, im_B, batched=True)
+    start_time = time.time()
+    for t in tqdm(range(T)):
+        model.match(im_A, im_B, batched=True)
+    end_time = time.time()
+    return (end_time - start_time) / T
+
+
+if __name__ == "__main__":
+    device = "cuda"
+    model = roma_outdoor(
+        device=device,
+        coarse_res=560,
+        upsample_res=None,
+        symmetric=True,
+        upsample_preds=False,
+        use_custom_corr=True,
+    )
+    experiment_name = "roma_latest"
+    results = test_inference_time(model, experiment_name)

tests/test_roma_coarse_inference_time_cpu.py

@@ -0,0 +1,29 @@
+from romatch import roma_outdoor
+import torch
+from tqdm import tqdm
+import time
+
+
+def test_inference_time(model, name):
+    T = 5
+    im_A = torch.randn(8, 3, 560, 560).to(device)
+    im_B = torch.randn(8, 3, 560, 560).to(device)
+    start_time = time.time()
+    for t in tqdm(range(T)):
+        model.match(im_A, im_B, batched=True)
+    end_time = time.time()
+    return (end_time - start_time) / T
+
+
+if __name__ == "__main__":
+    device = "cpu"
+    model = roma_outdoor(
+        device=device,
+        coarse_res=560,
+        upsample_res=None,
+        symmetric=True,
+        upsample_preds=False,
+        use_custom_corr=True,
+    )
+    experiment_name = "roma_latest"
+    results = test_inference_time(model, experiment_name)

tests/test_roma_upsample_inference_time.py

@@ -0,0 +1,47 @@
+from romatch import roma_outdoor
+import torch
+from tqdm import tqdm
+import time
+
+
+def test_inference_time(model, name):
+    T = 1000
+    im_A = torch.randn(8, 3, 560, 560).to(device)
+    im_B = torch.randn(8, 3, 560, 560).to(device)
+    im_A_high_res = torch.randn(8, 3, 864, 864).to(device)
+    im_B_high_res = torch.randn(8, 3, 864, 864).to(device)
+    # burn in
+    for i in range(10):
+        model.match(
+            im_A,
+            im_B,
+            im_A_high_res=im_A_high_res,
+            im_B_high_res=im_B_high_res,
+            batched=True,
+        )
+    start_time = time.time()
+    for t in tqdm(range(T)):
+        model.match(
+            im_A,
+            im_B,
+            im_A_high_res=im_A_high_res,
+            im_B_high_res=im_B_high_res,
+            batched=True,
+        )
+    end_time = time.time()
+    return (end_time - start_time) / T
+
+
+if __name__ == "__main__":
+    device = "cuda"
+    model = roma_outdoor(
+        device=device,
+        coarse_res=560,
+        upsample_res=864,
+        symmetric=True,
+        upsample_preds=True,
+        use_custom_corr=True,
+        amp_dtype=torch.bfloat16,
+    )
+    experiment_name = "roma_latest"
+    results = test_inference_time(model, experiment_name)

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