# Copyright (c) MONAI Consortium
# 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.
from typing import Sequence, Union
import torch
import torch.nn as nn
from monai.networks.blocks.patchembedding import PatchEmbeddingBlock
from monai.networks.blocks.transformerblock import TransformerBlock
__all__ = ["ViT"]
[docs]class ViT(nn.Module):
"""
Vision Transformer (ViT), based on: "Dosovitskiy et al.,
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
ViT supports Torchscript but only works for Pytorch after 1.8.
"""
[docs] def __init__(
self,
in_channels: int,
img_size: Union[Sequence[int], int],
patch_size: Union[Sequence[int], int],
hidden_size: int = 768,
mlp_dim: int = 3072,
num_layers: int = 12,
num_heads: int = 12,
pos_embed: str = "conv",
classification: bool = False,
num_classes: int = 2,
dropout_rate: float = 0.0,
spatial_dims: int = 3,
post_activation="Tanh",
qkv_bias: bool = False,
) -> None:
"""
Args:
in_channels: dimension of input channels.
img_size: dimension of input image.
patch_size: dimension of patch size.
hidden_size: dimension of hidden layer.
mlp_dim: dimension of feedforward layer.
num_layers: number of transformer blocks.
num_heads: number of attention heads.
pos_embed: position embedding layer type.
classification: bool argument to determine if classification is used.
num_classes: number of classes if classification is used.
dropout_rate: faction of the input units to drop.
spatial_dims: number of spatial dimensions.
post_activation: add a final acivation function to the classification head when `classification` is True.
Default to "Tanh" for `nn.Tanh()`. Set to other values to remove this function.
qkv_bias: apply bias to the qkv linear layer in self attention block
Examples::
# for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone
>>> net = ViT(in_channels=1, img_size=(96,96,96), pos_embed='conv')
# for 3-channel with image size of (128,128,128), 24 layers and classification backbone
>>> net = ViT(in_channels=3, img_size=(128,128,128), pos_embed='conv', classification=True)
# for 3-channel with image size of (224,224), 12 layers and classification backbone
>>> net = ViT(in_channels=3, img_size=(224,224), pos_embed='conv', classification=True, spatial_dims=2)
"""
super().__init__()
if not (0 <= dropout_rate <= 1):
raise ValueError("dropout_rate should be between 0 and 1.")
if hidden_size % num_heads != 0:
raise ValueError("hidden_size should be divisible by num_heads.")
self.classification = classification
self.patch_embedding = PatchEmbeddingBlock(
in_channels=in_channels,
img_size=img_size,
patch_size=patch_size,
hidden_size=hidden_size,
num_heads=num_heads,
pos_embed=pos_embed,
dropout_rate=dropout_rate,
spatial_dims=spatial_dims,
)
self.blocks = nn.ModuleList(
[TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate, qkv_bias) for i in range(num_layers)]
)
self.norm = nn.LayerNorm(hidden_size)
if self.classification:
self.cls_token = nn.Parameter(torch.zeros(1, 1, hidden_size))
if post_activation == "Tanh":
self.classification_head = nn.Sequential(nn.Linear(hidden_size, num_classes), nn.Tanh())
else:
self.classification_head = nn.Linear(hidden_size, num_classes) # type: ignore
[docs] def forward(self, x):
x = self.patch_embedding(x)
if hasattr(self, "cls_token"):
cls_token = self.cls_token.expand(x.shape[0], -1, -1)
x = torch.cat((cls_token, x), dim=1)
hidden_states_out = []
for blk in self.blocks:
x = blk(x)
hidden_states_out.append(x)
x = self.norm(x)
if hasattr(self, "classification_head"):
x = self.classification_head(x[:, 0])
return x, hidden_states_out