adrd/nn/vitautoenc.py

# 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 monai.networks.blocks.patchembedding import PatchEmbeddingBlock
from monai.networks.layers import Conv
from monai.utils import ensure_tuple_rep

from typing import Sequence, Union
import torch
import torch.nn as nn

from ..nn.blocks import TransformerBlock
from icecream import ic
ic.disable()

__all__ = ["ViTAutoEnc"]


class ViTAutoEnc(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>"

    Modified to also give same dimension outputs as the input size of the image
    """

    def __init__(
        self,
        in_channels: int,
        img_size: Union[Sequence[int], int],
        patch_size: Union[Sequence[int], int],
        out_channels: int = 1,
        deconv_chns: int = 16,
        hidden_size: int = 768,
        mlp_dim: int = 3072,
        num_layers: int = 12,
        num_heads: int = 12,
        pos_embed: str = "conv",
        dropout_rate: float = 0.0,
        spatial_dims: int = 3,
    ) -> None:
        """
        Args:
            in_channels: dimension of input channels or the number of channels for input
            img_size: dimension of input image.
            patch_size: dimension of patch size.
            hidden_size: dimension of hidden layer.
            out_channels: number of output channels.
            deconv_chns: number of channels for the deconvolution layers.
            mlp_dim: dimension of feedforward layer.
            num_layers: number of transformer blocks.
            num_heads: number of attention heads.
            pos_embed: position embedding layer type.
            dropout_rate: faction of the input units to drop.
            spatial_dims: number of spatial dimensions.

        Examples::

            # for single channel input with image size of (96,96,96), conv position embedding and segmentation backbone
            # It will provide an output of same size as that of the input
            >>> net = ViTAutoEnc(in_channels=1, patch_size=(16,16,16), img_size=(96,96,96), pos_embed='conv')

            # for 3-channel with image size of (128,128,128), output will be same size as of input
            >>> net = ViTAutoEnc(in_channels=3, patch_size=(16,16,16), img_size=(128,128,128), pos_embed='conv')

        """

        super().__init__()

        self.patch_size = ensure_tuple_rep(patch_size, spatial_dims)
        self.spatial_dims = spatial_dims
        self.hidden_size = hidden_size
        
        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=self.spatial_dims,
        )
        self.blocks = nn.ModuleList(
            [TransformerBlock(hidden_size, mlp_dim, num_heads, dropout_rate) for i in range(num_layers)]
        )
        self.norm = nn.LayerNorm(hidden_size)

        new_patch_size = [4] * self.spatial_dims
        conv_trans = Conv[Conv.CONVTRANS, self.spatial_dims]
        # self.conv3d_transpose* is to be compatible with existing 3d model weights.
        self.conv3d_transpose = conv_trans(hidden_size, deconv_chns, kernel_size=new_patch_size, stride=new_patch_size)
        self.conv3d_transpose_1 = conv_trans(
            in_channels=deconv_chns, out_channels=out_channels, kernel_size=new_patch_size, stride=new_patch_size
        )

    def forward(self, x, return_emb=False, return_hiddens=False):
        """
        Args:
            x: input tensor must have isotropic spatial dimensions,
                such as ``[batch_size, channels, sp_size, sp_size[, sp_size]]``.
        """
        spatial_size = x.shape[2:]
        x = self.patch_embedding(x)
        hidden_states_out = []
        for blk in self.blocks:
            x = blk(x)
            hidden_states_out.append(x)
        x = self.norm(x)
        x = x.transpose(1, 2)
        if return_emb:
            return x
        d = [s // p for s, p in zip(spatial_size, self.patch_size)]
        x = torch.reshape(x, [x.shape[0], x.shape[1], *d])
        x = self.conv3d_transpose(x)
        x = self.conv3d_transpose_1(x)
        if return_hiddens:
            return x, hidden_states_out
        return x
    
    def get_last_selfattention(self, x):
        """
        Args:
            x: input tensor must have isotropic spatial dimensions,
                such as ``[batch_size, channels, sp_size, sp_size[, sp_size]]``.
        """
        x = self.patch_embedding(x)
        ic(x.size())
        for i, blk in enumerate(self.blocks):
            if i < len(self.blocks) - 1:
                x = blk(x)
                x.size()
            else:
                return blk(x, return_attention=True)
            
    def load(self, ckpt_path, map_location='cpu', checkpoint_key='state_dict'):
        """
        Args:
            ckpt_path: path to the pretrained weights
            map_location: device to load the checkpoint on
        """
        state_dict = torch.load(ckpt_path, map_location=map_location)
        ic(state_dict['epoch'], state_dict['train_loss'])
        if checkpoint_key in state_dict:
            print(f"Take key {checkpoint_key} in provided checkpoint dict")
            state_dict = state_dict[checkpoint_key]
        # remove `module.` prefix
        state_dict = {k.replace("module.", ""): v for k, v in state_dict.items()}
        # remove `backbone.` prefix induced by multicrop wrapper
        state_dict = {k.replace("backbone.", ""): v for k, v in state_dict.items()}
        msg = self.load_state_dict(state_dict, strict=False)
        print('Pretrained weights found at {} and loaded with msg: {}'.format(ckpt_path, msg))