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Functions202 in github.com/Parskatt/DeDoDe

⨍ Functions 202◇ Types & classes 41

DeDoDe/utils.py:334

DeDoDe/utils.py:344

DeDoDe/utils.py:350

(self, im_tuple)

DeDoDe/utils.py:366

DeDoDe/utils.py:376

(self, im_tuple)

DeDoDe/utils.py:389

(self, im_tuple)

DeDoDe/utils.py:401

DeDoDe/utils.py:408

(self, im_tuple)

DeDoDe/utils.py:420

(self, im_tuple)

DeDoDe/utils.py:434

Method__getitem__

(self, pair_idx)

DeDoDe/datasets/megadepth.py:108

( self, in_dim=6, hidden_dim=16, out_dim=2, dw=True, kernel_si

DeDoDe/decoder.py:21

(self, cliplimit = 2, blocksize = 8)

DeDoDe/utils.py:332

(self, cliplimit = 8, blocksize = 8)

DeDoDe/utils.py:342

DeDoDe/utils.py:363

DeDoDe/utils.py:386

(self, size, mode=InterpolationMode.BICUBIC, antialias = None)

DeDoDe/utils.py:397

(self, mean, std)

DeDoDe/utils.py:415

(self, transforms)

DeDoDe/utils.py:431

(self, dir=None, name="tmp")

DeDoDe/checkpoint.py:10

(self, pretrained=False, amp = False, amp_dtype = torch.float16)

DeDoDe/encoder.py:7

(self, amp = True, amp_dtype = torch.float16, dinov2_weights = None)

DeDoDe/encoder.py:50

(self, vgg_kwargs = None, dinov2_kwargs = None)

DeDoDe/encoder.py:78

(self, smoothing_size = 1, use_max_logit = False, entropy_target = 80, num_matches = 1024, j

DeDoDe/detectors/keypoint_loss.py:10

(self, encoder, decoder, *args, remove_borders = False, **kwargs)

DeDoDe/detectors/dedode_detector.py:13

( self, data_root, scene_info, ht=512, wt=512, min_overlap=0.0

DeDoDe/datasets/megadepth.py:14

(self, data_root="data/megadepth", loftr_ignore=True, imc21_ignore = True)

DeDoDe/datasets/megadepth.py:214

(self, encoder, decoder, *args, **kwargs)

DeDoDe/descriptors/dedode_descriptor.py:10

(self, detector, num_keypoints = 5000, normalize_descriptions = False, inv_temp = 1, device = get_best_device(

DeDoDe/descriptors/descriptor_loss.py:11

Args: img_size (int, tuple): input image size patch_size (int, tuple): patch size in_chans (int): number

DeDoDe/transformer/dinov2.py:44

(self, drop_prob=None)

DeDoDe/transformer/layers/drop_path.py:30

( self, dim: int, num_heads: int, mlp_ratio: float = 4.0, qkv_bias: bo

DeDoDe/transformer/layers/block.py:37

( self, in_dim, out_dim, use_bn=False, nlayers=3, hidden_dim=2

DeDoDe/transformer/layers/dino_head.py:14

( self, dim: int, init_values: Union[float, Tensor] = 1e-5, inplace: bool = Fa

DeDoDe/transformer/layers/layer_scale.py:17

( self, dim: int, num_heads: int = 8, qkv_bias: bool = False, proj_bia

DeDoDe/transformer/layers/attention.py:30

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

DeDoDe/transformer/layers/mlp.py:18

( self, in_features: int, hidden_features: Optional[int] = None, out_features:

DeDoDe/transformer/layers/swiglu_ffn.py:46

( self, img_size: Union[int, Tuple[int, int]] = 224, patch_size: Union[int, Tuple[int,

DeDoDe/transformer/layers/patch_embed.py:38

(self, dataset, num_samples = 1000, batch_size = 8, device = "cuda")

DeDoDe/benchmarks/nll_benchmark.py:8

(self, data_root="data/megadepth", scene_names = None)

DeDoDe/benchmarks/mega_pose_est.py:9

(self, dataset, num_samples = 1000, batch_size = 8, num_keypoints = 10_000, device = get_best_device())

DeDoDe/benchmarks/num_inliers.py:8

(self, data_root="data/megadepth", scene_names = None)

DeDoDe/benchmarks/mega_pose_est_mnn.py:9

DeDoDe/datasets/megadepth.py:80

DeDoDe/utils.py:358

DeDoDe/utils.py:369

DeDoDe/utils.py:379

DeDoDe/utils.py:392

DeDoDe/utils.py:404

DeDoDe/utils.py:426

DeDoDe/utils.py:439

Method_init_weights

DeDoDe/transformer/layers/dino_head.py:31

Methodattn_residual_func

DeDoDe/transformer/layers/block.py:83

Methodattn_residual_func

(x: Tensor, attn_bias=None)

DeDoDe/transformer/layers/block.py:213

Methodbenchmark

(self, keypoint_model, matching_model, model_name = None, resolution = None, scale_intrinsics = True, calibrat

DeDoDe/benchmarks/mega_pose_est.py:26

Methodbenchmark

(self, detector_model, descriptor_model, matcher_model, model_name = None, resolution = None, scale_intrinsics

DeDoDe/benchmarks/mega_pose_est_mnn.py:26

Methodcompute_consistency

(self, logits_A, logits_B_to_A, mask = None)

DeDoDe/detectors/keypoint_loss.py:27

Functionconditional_softmax_matcher

(desc_A: tuple['B','N','C'], desc_B: tuple['B','M','C'], inv_temperature = 1, normalize = False)

DeDoDe/utils.py:704

Functiondedode_detector_B

(device = get_best_device(), weights = None)

DeDoDe/model_zoo/dedode_models.py:11

(*args, **kwargs)

DeDoDe/transformer/dinov2.py:113

Methodffn_residual_func

DeDoDe/transformer/layers/block.py:86

Methodffn_residual_func

(x: Tensor, attn_bias=None)

DeDoDe/transformer/layers/block.py:216

DeDoDe/transformer/layers/patch_embed.py:84

(self, features, context = None, scale = None)

DeDoDe/decoder.py:13

DeDoDe/decoder.py:82

(self, x, **kwargs)

DeDoDe/encoder.py:14

(self, x, **kwargs)

DeDoDe/encoder.py:38

DeDoDe/encoder.py:68

DeDoDe/encoder.py:84

(self, outputs, batch)

DeDoDe/detectors/keypoint_loss.py:178

(self, outputs, batch)

DeDoDe/descriptors/descriptor_loss.py:66

DeDoDe/transformer/dinov2.py:37

(self, *args, is_training=False, **kwargs)

DeDoDe/transformer/dinov2.py:291

DeDoDe/transformer/layers/drop_path.py:34

(self, x_or_x_list)

DeDoDe/transformer/layers/block.py:245

DeDoDe/transformer/layers/dino_head.py:37

(self, x: Tensor)

DeDoDe/transformer/layers/layer_scale.py:27

(self, x: Tensor, attn_bias=None)

DeDoDe/transformer/layers/attention.py:66

(self, x: Tensor)

DeDoDe/transformer/layers/mlp.py:35

(self, x: Tensor)

DeDoDe/transformer/layers/swiglu_ffn.py:29

(self, x: Tensor)

DeDoDe/transformer/layers/patch_embed.py:69

Functionget_homog_warp

(Homog, H, W, device = get_best_device())

DeDoDe/utils.py:672

Methodget_intermediate_layers

( self, x: torch.Tensor, n: Union[int, Sequence] = 1, # Layers or n last layers to ta

DeDoDe/transformer/dinov2.py:265

Functionget_pose

DeDoDe/utils.py:600

Functioninit_weights_vit_timm

ViT weight initialization, original timm impl (for reproducibility)

DeDoDe/transformer/dinov2.py:299

Functionjacobi_determinant

(warp, certainty, R = 3, device = get_best_device(), dtype = torch.float32)

DeDoDe/utils.py:153

Methodmasked_softmax

(self, logits, mask)

DeDoDe/detectors/keypoint_loss.py:73

Functionnewton_step

(f:tuple["B","H","W"], inds, device = get_best_device())

DeDoDe/utils.py:105

Functionrecover_pose

(E, kpts0, kpts1, K0, K1, mask)

DeDoDe/utils.py:27

Functionrotate_intrinsic

DeDoDe/utils.py:244

Functionrotate_pose_inplane

DeDoDe/utils.py:250

Functionscale_intrinsics

DeDoDe/utils.py:266

DeDoDe/utils.py:593

Methodto_normalized_coords

(self, x, H, W)

DeDoDe/detectors/dedode_detector.py:75

Methodto_normalized_coords

(self, x_A, x_B, H_A, W_A, H_B, W_B)

DeDoDe/matchers/dual_softmax_matcher.py:37

Methodto_pixel_coords

(self, x_A, x_B, H_A, W_A, H_B, W_B)

DeDoDe/matchers/dual_softmax_matcher.py:34

Methodtracks_to_detections

(self, tracks3D, pose, intrinsics, H, W)

DeDoDe/datasets/megadepth.py:99

Functiontrain_k_epochs

( start_epoch, end_epoch, dataloader, model, objective, optimizer, lr_scheduler )

DeDoDe/train.py:65

Functionunnormalize_coords

DeDoDe/utils.py:237

Functionvit_base

(patch_size=16, **kwargs)

DeDoDe/transformer/dinov2.py:320

Functionvit_giant2

Close to ViT-giant, with embed-dim 1536 and 24 heads => embed-dim per head 64

DeDoDe/transformer/dinov2.py:346

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