Contents Menu Expand Light mode Dark mode Auto light/dark mode Auto light/dark, in light mode Auto light/dark, in dark mode Skip to content
diffusionmodels 0.0.1 documentation
diffusionmodels 0.0.1 documentation
  • diffusion_models
    • diffusion_models.losses
      • diffusion_models.losses.kl_divergence
        • diffusion_models.losses.kl_divergence.gaussian_kl
        • diffusion_models.losses.kl_divergence.log_gaussian_kl
    • diffusion_models.models
      • diffusion_models.models.fp16_util
        • diffusion_models.models.fp16_util.convert_module_to_f16
        • diffusion_models.models.fp16_util.convert_module_to_f32
        • diffusion_models.models.fp16_util.make_master_params
        • diffusion_models.models.fp16_util.master_params_to_model_params
        • diffusion_models.models.fp16_util.model_grads_to_master_grads
        • diffusion_models.models.fp16_util.unflatten_master_params
        • diffusion_models.models.fp16_util.zero_grad
      • diffusion_models.models.mnist_enc
        • diffusion_models.models.mnist_enc.MNISTEncoder
      • diffusion_models.models.multicoil
        • diffusion_models.models.multicoil.MultiCoilConv2d
        • diffusion_models.models.multicoil.MultiCoilReducer
      • diffusion_models.models.nn
        • diffusion_models.models.nn.avg_pool_nd
        • diffusion_models.models.nn.checkpoint
        • diffusion_models.models.nn.conv_nd
        • diffusion_models.models.nn.linear
        • diffusion_models.models.nn.mean_flat
        • diffusion_models.models.nn.normalization
        • diffusion_models.models.nn.scale_module
        • diffusion_models.models.nn.timestep_embedding
        • diffusion_models.models.nn.update_ema
        • diffusion_models.models.nn.zero_module
        • diffusion_models.models.nn.CheckpointFunction
        • diffusion_models.models.nn.GroupNorm32
        • diffusion_models.models.nn.SiLU
      • diffusion_models.models.openai_unet
        • diffusion_models.models.openai_unet.AttentionBlock
        • diffusion_models.models.openai_unet.Downsample
        • diffusion_models.models.openai_unet.QKVAttention
        • diffusion_models.models.openai_unet.ResBlock
        • diffusion_models.models.openai_unet.SuperResModel
        • diffusion_models.models.openai_unet.TimestepBlock
        • diffusion_models.models.openai_unet.TimestepEmbedSequential
        • diffusion_models.models.openai_unet.UNetModel
        • diffusion_models.models.openai_unet.Upsample
      • diffusion_models.models.positional_encoding
        • diffusion_models.models.positional_encoding.PositionalEncoding
        • diffusion_models.models.positional_encoding.PositionalEncoding2D
      • diffusion_models.models.vae
        • diffusion_models.models.vae.ResNet18Decoder
        • diffusion_models.models.vae.ResNet18Encoder
        • diffusion_models.models.vae.ResNetBlock
        • diffusion_models.models.vae.ResNetDecoderBlock
        • diffusion_models.models.vae.SkipDownSampler
        • diffusion_models.models.vae.SkipUpSampler
        • diffusion_models.models.vae.VariationalAutoencoder
    • diffusion_models.mri_forward
      • diffusion_models.mri_forward.fft
        • diffusion_models.mri_forward.fft.to_imgspace
        • diffusion_models.mri_forward.fft.to_kspace
      • diffusion_models.mri_forward.noise
        • diffusion_models.mri_forward.noise.apply_add_gauss_noise
        • diffusion_models.mri_forward.noise.apply_mult_gauss_noise
        • diffusion_models.mri_forward.noise.independent_gauss_noise
      • diffusion_models.mri_forward.undersampling_mask
        • diffusion_models.mri_forward.undersampling_mask.gaussian_kernel1d_h
        • diffusion_models.mri_forward.undersampling_mask.gaussian_kernel1d_v
        • diffusion_models.mri_forward.undersampling_mask.gaussian_kernel2d
        • diffusion_models.mri_forward.undersampling_mask.naive_undersampling1d_h
        • diffusion_models.mri_forward.undersampling_mask.naive_undersampling1d_v
        • diffusion_models.mri_forward.undersampling_mask.naive_undersampling2d
        • diffusion_models.mri_forward.undersampling_mask.normalize_mask
        • diffusion_models.mri_forward.undersampling_mask.StochasticUndersamplingMask
        • diffusion_models.mri_forward.undersampling_mask.UndersamplingMask
    • diffusion_models.spine_dataset
      • diffusion_models.spine_dataset.base_dataset
        • diffusion_models.spine_dataset.base_dataset.BaseDataset
      • diffusion_models.spine_dataset.collations
        • diffusion_models.spine_dataset.collations.collate_fn
      • diffusion_models.spine_dataset.spatial_transformer
        • diffusion_models.spine_dataset.spatial_transformer.gauss_gen3D
        • diffusion_models.spine_dataset.spatial_transformer.SpatialTransformer
    • diffusion_models.utils
      • diffusion_models.utils.helpers
        • diffusion_models.utils.helpers.bytes_to_gb
        • diffusion_models.utils.helpers.complex_to_2channelfloat
        • diffusion_models.utils.helpers.dotdict
      • diffusion_models.utils.mp_setup
        • diffusion_models.utils.mp_setup.DDP_Proc_Group
      • diffusion_models.utils.trainer
        • diffusion_models.utils.trainer.DiscriminativeTrainer
        • diffusion_models.utils.trainer.GenerativeTrainer
        • diffusion_models.utils.trainer.Trainer
Back to top
View this page

diffusion_models.models.positional_encodingΒΆ

Classes

PositionalEncoding([d_model, dropout, max_len])

PositionalEncoding2D(*args, **kwargs)

Next
diffusion_models.models.positional_encoding.PositionalEncoding
Previous
diffusion_models.models.openai_unet.Upsample
Copyright © 2024, Lionel Peer
Made with Sphinx and @pradyunsg's Furo