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Types & classes180 in github.com/ali-vilab/AnyDoor

↓ 16 callersClassResnetBlock
ldm/modules/diffusionmodules/model.py:90
↓ 11 callersClassTimestepEmbedSequential
A sequential module that passes timestep embeddings to the children that support it as an extra input.
ldm/modules/diffusionmodules/openaimodel.py:73
↓ 10 callersClassResBlock
A residual block that can optionally change the number of channels. :param channels: the number of input channels. :param emb_channels: t
ldm/modules/diffusionmodules/openaimodel.py:162
↓ 8 callersClassNormalizeImage
Normlize image by given mean and std.
ldm/modules/midas/midas/transforms.py:197
↓ 8 callersClassTranspose
ldm/modules/midas/midas/vit.py:45
↓ 5 callersClassAttentionBlock
An attention block that allows spatial positions to attend to each other. Originally ported from here, but adapted to the N-d case. https
ldm/modules/diffusionmodules/openaimodel.py:277
↓ 5 callersClassCosineScheduler
dinov2/dinov2/utils/utils.py:68
↓ 5 callersClassFeatureFusionBlock_custom
Feature fusion block.
ldm/modules/midas/midas/blocks.py:291
↓ 5 callersClassSpatialTransformer
Transformer block for image-like data. First, project the input (aka embedding) and reshape to b, t, d. Then apply standard transform
ldm/modules/attention.py:278
↓ 5 callersClass_ConvBNReLU
iseg/coarse_mask_refine_util.py:11
↓ 4 callersClassDDIMSampler
cldm/ddim_hacked.py:10
↓ 4 callersClassDinoVisionTransformer
dinov2/dinov2/models/vision_transformer.py:45
↓ 4 callersClassDownsample
A downsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determini
ldm/modules/diffusionmodules/openaimodel.py:133
↓ 4 callersClassFeatureFusionBlock
Feature fusion block.
ldm/modules/midas/midas/blocks.py:194
↓ 4 callersClassLitEma
ldm/modules/ema.py:5
↓ 4 callersClassMetricLogger
dinov2/dinov2/logging/helpers.py:21
↓ 4 callersClassUpsample
ldm/modules/diffusionmodules/model.py:50
↓ 3 callersClassDecoder
ldm/modules/diffusionmodules/model.py:546
↓ 3 callersClassGaussianBlur
Apply Gaussian Blur to the PIL image.
dinov2/dinov2/data/transforms.py:13
↓ 3 callersClassInterpolate
Interpolation module.
ldm/modules/midas/midas/blocks.py:120
↓ 3 callersClassLatentRescaler
ldm/modules/diffusionmodules/model.py:739
↓ 3 callersClassModuleDictWithForward
dinov2/dinov2/eval/knn.py:240
↓ 3 callersClassUpsample
An upsampling layer with an optional convolution. :param channels: channels in the inputs and outputs. :param use_conv: a bool determinin
ldm/modules/diffusionmodules/openaimodel.py:90
↓ 2 callersClassAttnBlock
ldm/modules/diffusionmodules/model.py:152
↓ 2 callersClassDPTDepthModel
ldm/modules/midas/midas/dpt_depth.py:88
↓ 2 callersClassDownsample
ldm/modules/diffusionmodules/model.py:68
↓ 2 callersClassDropPath
Drop paths (Stochastic Depth) per sample (when applied in main path of residual blocks).
dinov2/dinov2/layers/drop_path.py:27
↓ 2 callersClassEncoder
ldm/modules/diffusionmodules/model.py:452
↓ 2 callersClassFSDPCheckpointer
dinov2/dinov2/fsdp/__init__.py:86
↓ 2 callersClassFashionTryonDataset
datasets/fashiontryon.py:12
↓ 2 callersClassImageDataDecoder
dinov2/dinov2/data/datasets/decoders.py:18
↓ 2 callersClassLayerScale
dinov2/dinov2/layers/layer_scale.py:16
↓ 2 callersClassLvisDataset
datasets/lvis.py:13
↓ 2 callersClassMVImageNetDataset
datasets/mvimagenet.py:11
↓ 2 callersClassMaybeToTensor
Convert a ``PIL Image`` or ``numpy.ndarray`` to tensor, or keep as is if already a tensor.
dinov2/dinov2/data/transforms.py:25
↓ 2 callersClassMoseDataset
datasets/mose.py:12
↓ 2 callersClassPrepareForNet
Prepare sample for usage as network input.
ldm/modules/midas/midas/transforms.py:211
↓ 2 callersClassQKVAttention
A module which performs QKV attention and splits in a different order.
ldm/modules/diffusionmodules/openaimodel.py:378
↓ 2 callersClassResidualConvUnit
Residual convolution module.
ldm/modules/midas/midas/blocks.py:155
↓ 2 callersClassResidualConvUnit_custom
Residual convolution module.
ldm/modules/midas/midas/blocks.py:231
↓ 2 callersClassResize
Resize sample to given size (width, height).
ldm/modules/midas/midas/transforms.py:48
↓ 2 callersClassSAMDataset
datasets/sam.py:12
↓ 2 callersClassSaliencyDataset
datasets/saliency_modular.py:11
↓ 2 callersClassSmoothedValue
Track a series of values and provide access to smoothed values over a window or the global series average.
dinov2/dinov2/logging/helpers.py:134
↓ 2 callersClassTargetDecoder
dinov2/dinov2/data/datasets/decoders.py:27
↓ 2 callersClassUVODataset
datasets/uvo.py:12
↓ 2 callersClassUVOValDataset
datasets/uvo_val.py:12
↓ 2 callersClassVIPSegDataset
datasets/vipseg.py:13
↓ 2 callersClassVitonHDDataset
datasets/vitonhd.py:12
↓ 2 callersClassYoutubeVISDataset
datasets/ytb_vis.py:11
↓ 2 callersClassYoutubeVOSDataset
datasets/ytb_vos.py:11
↓ 1 callersClassAddReadout
ldm/modules/midas/midas/vit.py:18
↓ 1 callersClassAllClassifiers
dinov2/dinov2/eval/linear.py:204
↓ 1 callersClassBaselineModel
iseg/coarse_mask_refine_util.py:243
↓ 1 callersClassBasicTransformerBlock
ldm/modules/attention.py:246
↓ 1 callersClassBlockChunk
dinov2/dinov2/models/vision_transformer.py:38
↓ 1 callersClassDDIMSampler
ldm/models/diffusion/ddim.py:10
↓ 1 callersClassDINOLoss
dinov2/dinov2/loss/dino_clstoken_loss.py:13
↓ 1 callersClassDPM_Solver
ldm/models/diffusion/dpm_solver/dpm_solver.py:319
↓ 1 callersClassDataAugmentationDINO
dinov2/dinov2/data/augmentations.py:20
↓ 1 callersClassDatasetWithEnumeratedTargets
dinov2/dinov2/data/adapters.py:12
↓ 1 callersClassDiagonalGaussianDistribution
ldm/modules/distributions/distributions.py:24
↓ 1 callersClassDictKeysModule
dinov2/dinov2/eval/knn.py:187
↓ 1 callersClassDiffusionWrapper
ldm/models/diffusion/ddpm.py:1324
↓ 1 callersClassEpochSampler
dinov2/dinov2/data/samplers.py:18
↓ 1 callersClassFeedForward
ldm/modules/attention.py:59
↓ 1 callersClassFrozenCLIPEmbedder
Uses the CLIP transformer encoder for text (from huggingface)
ldm/modules/encoders/modules.py:107
↓ 1 callersClassFrozenT5Embedder
Uses the T5 transformer encoder for text
ldm/modules/encoders/modules.py:77
↓ 1 callersClassGEGLU
ldm/modules/attention.py:49
↓ 1 callersClassGroupNorm32
ldm/modules/diffusionmodules/util.py:217
↓ 1 callersClassImageLogger
cldm/logger.py:11
↓ 1 callersClassImageNetReaLAccuracy
dinov2/dinov2/eval/metrics.py:72
↓ 1 callersClassInfiniteSampler
dinov2/dinov2/data/samplers.py:79
↓ 1 callersClassKoLeoLoss
Kozachenko-Leonenko entropic loss regularizer from Sablayrolles et al. - 2018 - Spreading vectors for similarity search
dinov2/dinov2/loss/koleo_loss.py:19
↓ 1 callersClassLRASPP
Lite R-ASPP
iseg/coarse_mask_refine_util.py:164
↓ 1 callersClassLinearClassifier
Linear layer to train on top of frozen features
dinov2/dinov2/eval/linear.py:186
↓ 1 callersClassLinearPostprocessor
dinov2/dinov2/eval/linear.py:217
↓ 1 callersClassLogRegModule
dinov2/dinov2/eval/log_regression.py:109
↓ 1 callersClassMaskingGenerator
dinov2/dinov2/data/masking.py:12
↓ 1 callersClassMemoryEfficientAttnBlock
Uses xformers efficient implementation, see https://github.com/MatthieuTPHR/diffusers/blob/d80b531ff8060ec1ea982b65a1b8df70f73aa67c/s
ldm/modules/diffusionmodules/model.py:205
↓ 1 callersClassMemoryEfficientCrossAttentionWrapper
ldm/modules/diffusionmodules/model.py:271
↓ 1 callersClassMidasNet
Network for monocular depth estimation.
ldm/modules/midas/midas/midas_net.py:12
↓ 1 callersClassMidasNet_small
Network for monocular depth estimation.
ldm/modules/midas/midas/midas_net_custom.py:12
↓ 1 callersClassMobileNetV2
iseg/coarse_mask_refine_util.py:67
↓ 1 callersClassMobileSeg
iseg/coarse_mask_refine_util.py:190
↓ 1 callersClassModelWithIntermediateLayers
dinov2/dinov2/eval/utils.py:31
↓ 1 callersClassModelWithNormalize
dinov2/dinov2/eval/utils.py:22
↓ 1 callersClassNoiseScheduleVP
ldm/models/diffusion/dpm_solver/dpm_solver.py:7
↓ 1 callersClassProjectReadout
ldm/modules/midas/midas/vit.py:31
↓ 1 callersClassQKVAttentionLegacy
A module which performs QKV attention. Matches legacy QKVAttention + input/ouput heads shaping
ldm/modules/diffusionmodules/openaimodel.py:346
↓ 1 callersClassSSLMetaArch
dinov2/dinov2/train/ssl_meta_arch.py:34
↓ 1 callersClassScaleLayer
iseg/coarse_mask_refine_util.py:228
↓ 1 callersClassShardedInfiniteSampler
dinov2/dinov2/data/samplers.py:166
↓ 1 callersClassSlice
ldm/modules/midas/midas/vit.py:9
↓ 1 callersClassTupleDecoder
dinov2/dinov2/data/datasets/decoders.py:35
↓ 1 callersClass_ClassEntry
dinov2/dinov2/data/datasets/image_net_22k.py:29
↓ 1 callersClass_Entry
dinov2/dinov2/data/datasets/image_net_22k.py:35
↓ 1 callersClass_LinearClassifierWrapper
dinov2/hubconf.py:100
↓ 1 callersClass_TorchDistributedEnvironment
dinov2/dinov2/distributed/__init__.py:152
↓ 1 callersClassiBOTPatchLoss
dinov2/dinov2/loss/ibot_patch_loss.py:35
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