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# Copyright (c) Meta Platforms, Inc. and affiliates.
# Copyright 2025 AI for Oncology Research Group. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
#
# References:
# https://github.com/facebookresearch/dino/blob/master/vision_transformer.py
# https://github.com/rwightman/pytorch-image-models/tree/master/timm/models/vision_transformer.py
from typing import Callable, Optional
import torch
from torch import nn
from .helpers import make_2tuple, make_3tuple
class PatchEmbed(nn.Module):
"""Patch embedding layer for Vision Transformers for 2D images.
This layer divides the input image into patches and projects them into a higher-dimensional space.
Parameters
----------
img_size : int or tuple[int, int], optional
Size of the input image. If an integer is provided, it is assumed to be square (img_size, img_size).
If a tuple is provided, it should be of the form (height, width), by default 224.
patch_size : int or tuple[int, int], optional
Size of the patches to be extracted from the input image. If an integer is provided, it is assumed to be square
(patch_size, patch_size). If a tuple is provided, it should be of the form (height, width), by default 16.
in_chans : int, optional
Number of input channels in the image, by default 3 (for RGB images).
embed_dim : int, optional
Dimension of the embedding space to which the patches will be projected, by default 768.
norm_layer : Callable, optional
Normalization layer to apply to the embeddings, by default None. If None, no normalization is applied.
flatten_embedding : bool, optional
Whether to flatten the embedding output, by default True.
"""
def __init__(
self,
img_size: int | tuple[int, int] = 224,
patch_size: int | tuple[int, int] = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten_embedding: bool = True,
) -> None:
"""Inits :class:`PatchEmbed`.
Parameters
----------
img_size : int or tuple[int, int], optional
Size of the input image. If an integer is provided, it is assumed to be square (img_size, img_size).
If a tuple is provided, it should be of the form (height, width), by default 224.
patch_size : int or tuple[int, int], optional
Size of the patches to be extracted from the input image. If an integer is provided, it is assumed to be square
(patch_size, patch_size). If a tuple is provided, it should be of the form (height, width), by default 16.
in_chans : int, optional
Number of input channels in the image, by default 3 (for RGB images).
embed_dim : int, optional
Dimension of the embedding space to which the patches will be projected, by default 768.
norm_layer : Callable, optional
Normalization layer to apply to the embeddings, by default None. If None, no normalization is applied.
flatten_embedding : bool, optional
Whether to flatten the embedding output, by default True.
"""
super().__init__()
image_HW = make_2tuple(img_size)
patch_HW = make_2tuple(patch_size)
patch_grid_size = (
image_HW[0] // patch_HW[0],
image_HW[1] // patch_HW[1],
)
self.img_size = image_HW
self.patch_size = patch_HW
self.patches_resolution = patch_grid_size
self.num_patches = patch_grid_size[0] * patch_grid_size[1]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.flatten_embedding = flatten_embedding
self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_HW, stride=patch_HW)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass of :class:`PatchEmbed`.
Parameters
----------
x : torch.Tensor
Input tensor of shape (B, C, H, W) where B is the batch size, C is the number of channels,
H is the height, and W is the width of the input image.
Raises
------
ValueError
If the input image dimensions are not compatible with the patch size.
"""
_, _, H, W = x.shape
patch_H, patch_W = self.patch_size
if H % patch_H != 0:
raise ValueError(f"Input image height {H} is not a multiple of patch height {patch_H}")
if W % patch_W != 0:
raise ValueError(f"Input image width {W} is not a multiple of patch width: {patch_W}")
x = self.proj(x) # B C H W
H, W = x.size(2), x.size(3)
x = x.flatten(2).transpose(1, 2) # B HW C
x = self.norm(x)
if not self.flatten_embedding:
x = x.reshape(-1, H, W, self.embed_dim) # B H W C
return x
def flops(self) -> float:
"""Calculate the number of floating point operations (FLOPs) for the patch embedding layer.
Returns
-------
float
The number of FLOPs for the patch embedding layer.
"""
Ho, Wo = self.patches_resolution
flops = Ho * Wo * self.embed_dim * self.in_chans * (self.patch_size[0] * self.patch_size[1])
if not isinstance(self.norm, nn.Identity):
flops += Ho * Wo * self.embed_dim
return flops
class PatchEmbed3d(nn.Module):
"""Patch embedding layer for Vision Transformers for 3D images.
This layer divides the input 3D image volume into patches and projects them into a higher-dimensional space.
Parameters
----------
img_size : int or tuple[int, int, int], optional
Size of the input image volume. If an integer is provided, it is assumed to be cubic (img_size, img_size, img_size).
If a tuple is provided, it should be of the form (depth, height, width), by default 224.
patch_size : int or tuple[int, int, int], optional
Size of the patches to be extracted from the input image volume. If an integer is provided, it is assumed to be cubic
(patch_size, patch_size, patch_size). If a tuple is provided, it should be of the form (depth, height, width), by default 16.
in_chans : int, optional
Number of input channels in the image volume, by default 3 (for RGB images).
embed_dim : int, optional
Dimension of the embedding space to which the patches will be projected, by default 768.
norm_layer : Callable, optional
Normalization layer to apply to the embeddings, by default None. If None, no normalization is applied.
flatten_embedding : bool, optional
Whether to flatten the embedding output, by default True.
"""
def __init__(
self,
img_size: int | tuple[int, int, int] = 224,
patch_size: int | tuple[int, int, int] = 16,
in_chans: int = 3,
embed_dim: int = 768,
norm_layer: Optional[Callable] = None,
flatten_embedding: bool = True,
) -> None:
"""Inits :class:`PatchEmbed3d`.
Parameters
----------
img_size : int or tuple[int, int, int], optional
Size of the input image volume. If an integer is provided, it is assumed to be cubic
(img_size, img_size, img_size).
If a tuple is provided, it should be of the form (depth, height, width), by default 224.
patch_size : int or tuple[int, int, int], optional
Size of the patches to be extracted from the input image volume. If an integer is provided, it is
assumed to be cubic (patch_size, patch_size, patch_size). If a tuple is provided, it should be of the
form (depth, height, width), by default 16.
in_chans : int, optional
Number of input channels in the image volume, by default 3 (for RGB images).
embed_dim : int, optional
Dimension of the embedding space to which the patches will be projected, by default 768.
norm_layer : Callable, optional
Normalization layer to apply to the embeddings, by default None. If None, no normalization is applied.
flatten_embedding : bool, optional
Whether to flatten the embedding output, by default True.
"""
super().__init__()
image_DHW = make_3tuple(img_size)
patch_DHW = make_3tuple(patch_size)
patch_grid_size = (
image_DHW[0] // patch_DHW[0],
image_DHW[1] // patch_DHW[1],
image_DHW[2] // patch_DHW[2],
)
self.img_size = image_DHW
self.patch_size = patch_DHW
self.patches_resolution = patch_grid_size
self.num_patches = patch_grid_size[0] * patch_grid_size[1] * patch_grid_size[2]
self.in_chans = in_chans
self.embed_dim = embed_dim
self.flatten_embedding = flatten_embedding
self.proj = nn.Conv3d(in_chans, embed_dim, kernel_size=patch_DHW, stride=patch_DHW)
self.norm = norm_layer(embed_dim) if norm_layer else nn.Identity()
def forward(self, x: torch.Tensor) -> torch.Tensor:
"""Forward pass of :class:`PatchEmbed3d`.
Parameters
----------
x : torch.Tensor
Input tensor of shape (B, C, D, H, W) where B is the batch size, C is the number of channels,
D is the depth, H is the height, and W is the width of the input volume.
Raises
------
ValueError
If the input volume dimensions are not compatible with the patch size.
"""
_, _, D, H, W = x.shape
patch_D, patch_H, patch_W = self.patch_size
if D % patch_D != 0:
raise ValueError(f"Input volume depth {D} is not a multiple of patch depth {patch_D}")
if H % patch_H != 0:
raise ValueError(f"Input volume height {H} is not a multiple of patch height {patch_H}")
if W % patch_W != 0:
raise ValueError(f"Input volume width {W} is not a multiple of patch width {patch_W}")
x = self.proj(x) # B C D H W
D, H, W = x.size(2), x.size(3), x.size(4)
x = x.flatten(2).transpose(1, 2) # B (DHW) C
x = self.norm(x)
if not self.flatten_embedding:
x = x.reshape(-1, D, H, W, self.embed_dim) # B D H W C
return x
def flops(self) -> float:
"""Calculate the number of floating point operations (FLOPs) for the patch embedding 3D layer.
Returns
-------
float
The number of FLOPs for the patch embedding layer.
"""
Do, Ho, Wo = self.patches_resolution
flops = (
Do
* Ho
* Wo
* self.embed_dim
* self.in_chans
* (self.patch_size[0] * self.patch_size[1] * self.patch_size[2])
)
if not isinstance(self.norm, nn.Identity):
flops += Do * Ho * Wo * self.embed_dim
return flops
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