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# 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 __future__ import annotations
import torch.nn as nn
from monai.networks.layers import get_act_layer
from monai.utils import look_up_option
SUPPORTED_DROPOUT_MODE = {"vit", "swin"}
class MLPBlock(nn.Module):
"""
A multi-layer perceptron block, based on: "Dosovitskiy et al.,
An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale <https://arxiv.org/abs/2010.11929>"
"""
def __init__(
self, hidden_size: int, mlp_dim: int, dropout_rate: float = 0.0, act: tuple | str = "GELU", dropout_mode="vit"
) -> None:
"""
Args:
hidden_size: dimension of hidden layer.
mlp_dim: dimension of feedforward layer. If 0, `hidden_size` will be used.
dropout_rate: fraction of the input units to drop.
act: activation type and arguments. Defaults to GELU. Also supports "GEGLU" and others.
dropout_mode: dropout mode, can be "vit" or "swin".
"vit" mode uses two dropout instances as implemented in
https://github.com/google-research/vision_transformer/blob/main/vit_jax/models.py#L87
"swin" corresponds to one instance as implemented in
https://github.com/microsoft/Swin-Transformer/blob/main/models/swin_mlp.py#L23
"""
super().__init__()
if not (0 <= dropout_rate <= 1):
raise ValueError("dropout_rate should be between 0 and 1.")
mlp_dim = mlp_dim or hidden_size
self.linear1 = nn.Linear(hidden_size, mlp_dim) if act != "GEGLU" else nn.Linear(hidden_size, mlp_dim * 2)
self.linear2 = nn.Linear(mlp_dim, hidden_size)
self.fn = get_act_layer(act)
self.drop1 = nn.Dropout(dropout_rate)
dropout_opt = look_up_option(dropout_mode, SUPPORTED_DROPOUT_MODE)
if dropout_opt == "vit":
self.drop2 = nn.Dropout(dropout_rate)
elif dropout_opt == "swin":
self.drop2 = self.drop1
else:
raise ValueError(f"dropout_mode should be one of {SUPPORTED_DROPOUT_MODE}")
def forward(self, x):
x = self.fn(self.linear1(x))
x = self.drop1(x)
x = self.linear2(x)
x = self.drop2(x)
return x
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