modeling_chada_vit.py

"""
ChAda-ViT (i.e Channel Adaptive ViT) is a variant of ViT that can handle multi-channel images.
"""

import logging
import math
from typing import Optional, Union, Callable

import torch
import torch.nn as nn
from transformers import PreTrainedModel

from torch import Tensor
import torch.nn.functional as F
from torch.nn.modules.module import Module
from torch.nn.modules.activation import MultiheadAttention
from torch.nn.modules.dropout import Dropout
from torch.nn.modules.linear import Linear
from torch.nn.modules.normalization import LayerNorm

from .config_chada_vit import ChAdaViTConfig


def _get_activation_fn(activation: str) -> Callable[[Tensor], Tensor]:
    if activation == "relu":
        return F.relu
    elif activation == "gelu":
        return F.gelu

    raise RuntimeError("activation should be relu/gelu, not {}".format(activation))


def _no_grad_trunc_normal_(tensor, mean, std, a, b):
    """Copy & paste from PyTorch official master until it's in a few official releases - RW
    Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    """

    def norm_cdf(x):
        """Computes standard normal cumulative distribution function"""

        return (1.0 + math.erf(x / math.sqrt(2.0))) / 2.0

    if (mean < a - 2 * std) or (mean > b + 2 * std):
        logging.warn(
            "mean is more than 2 std from [a, b] in nn.init.trunc_normal_. "
            "The distribution of values may be incorrect.",
            stacklevel=2,
        )

    with torch.no_grad():
        # Values are generated by using a truncated uniform distribution and
        # then using the inverse CDF for the normal distribution.
        # Get upper and lower cdf values
        l = norm_cdf((a - mean) / std)
        u = norm_cdf((b - mean) / std)

        # Uniformly fill tensor with values from [l, u], then translate to
        # [2l-1, 2u-1].
        tensor.uniform_(2 * l - 1, 2 * u - 1)

        # Use inverse cdf transform for normal distribution to get truncated
        # standard normal
        tensor.erfinv_()

        # Transform to proper mean, std
        tensor.mul_(std * math.sqrt(2.0))
        tensor.add_(mean)

        # Clamp to ensure it's in the proper range
        tensor.clamp_(min=a, max=b)
        return tensor


def trunc_normal_(tensor, mean=0.0, std=1.0, a=-2.0, b=2.0):
    """Copy & paste from PyTorch official master until it's in a few official releases - RW
    Method based on https://people.sc.fsu.edu/~jburkardt/presentations/truncated_normal.pdf
    """

    return _no_grad_trunc_normal_(tensor, mean, std, a, b)


class TransformerEncoderLayer(Module):
    r"""
    Mostly copied from torch.nn.TransformerEncoderLayer, but with the following changes:
    - Added the possibility to retrieve the attention weights
    """

    __constants__ = ["batch_first", "norm_first"]

    def __init__(
        self,
        d_model: int,
        nhead: int,
        dim_feedforward: int = 2048,
        dropout: float = 0.1,
        activation: Union[str, Callable[[Tensor], Tensor]] = F.relu,
        layer_norm_eps: float = 1e-5,
        batch_first: bool = False,
        norm_first: bool = False,
        device=None,
        dtype=None,
    ) -> None:
        factory_kwargs = {"device": device, "dtype": dtype}
        super(TransformerEncoderLayer, self).__init__()
        self.self_attn = MultiheadAttention(
            embed_dim=d_model,
            num_heads=nhead,
            dropout=dropout,
            batch_first=batch_first,
            **factory_kwargs,
        )
        # Implementation of Feedforward model
        self.linear1 = Linear(d_model, dim_feedforward, **factory_kwargs)
        self.dropout = Dropout(dropout)
        self.linear2 = Linear(dim_feedforward, d_model, **factory_kwargs)

        self.norm_first = norm_first
        self.norm1 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
        self.norm2 = LayerNorm(d_model, eps=layer_norm_eps, **factory_kwargs)
        self.dropout1 = Dropout(dropout)
        self.dropout2 = Dropout(dropout)

        # Legacy string support for activation function.
        if isinstance(activation, str):
            activation = _get_activation_fn(activation)

        # We can't test self.activation in forward() in TorchScript,
        # so stash some information about it instead.
        if activation is F.relu:
            self.activation_relu_or_gelu = 1
        elif activation is F.gelu:
            self.activation_relu_or_gelu = 2
        else:
            self.activation_relu_or_gelu = 0
        self.activation = activation

    def __setstate__(self, state):
        super(TransformerEncoderLayer, self).__setstate__(state)
        if not hasattr(self, "activation"):
            self.activation = F.relu

    def forward(
        self,
        src: Tensor,
        src_mask: Optional[Tensor] = None,
        src_key_padding_mask: Optional[Tensor] = None,
        return_attention=False,
    ) -> Tensor:
        r"""Pass the input through the encoder layer.

        Args:
            src: the sequence to the encoder layer (required).
            src_mask: the mask for the src sequence (optional).
            src_key_padding_mask: the mask for the src keys per batch (optional).

        Shape:
            see the docs in Transformer class.
        """

        x = src
        if self.norm_first:
            attn, attn_weights = self._sa_block(
                x=self.norm1(x),
                attn_mask=src_mask,
                key_padding_mask=src_key_padding_mask,
                return_attention=return_attention,
            )
            if return_attention:
                return attn_weights
            x = x + attn
            x = x + self._ff_block(self.norm2(x))
        else:
            attn, attn_weights = self._sa_block(
                x=self.norm1(x),
                attn_mask=src_mask,
                key_padding_mask=src_key_padding_mask,
                return_attention=return_attention,
            )
            if return_attention:
                return attn_weights
            x = self.norm1(x + attn)
            x = self.norm2(x + self._ff_block(x))

        return x

    # self-attention block
    def _sa_block(
        self,
        x: Tensor,
        attn_mask: Optional[Tensor],
        key_padding_mask: Optional[Tensor],
        return_attention: bool = False,
    ) -> Tensor:
        x, attn_weights = self.self_attn(
            x,
            x,
            x,
            attn_mask=attn_mask,
            key_padding_mask=key_padding_mask,
            need_weights=return_attention,
            average_attn_weights=False,
        )
        return self.dropout1(x), attn_weights

    # feed forward block
    def _ff_block(self, x: Tensor) -> Tensor:
        x = self.linear2(self.dropout(self.activation(self.linear1(x))))
        return self.dropout2(x)


class TokenLearner(nn.Module):
    """Image to Patch Embedding"""

    def __init__(self, img_size=224, patch_size=16, in_chans=1, embed_dim=768):
        super().__init__()
        num_patches = (img_size // patch_size) * (img_size // patch_size)
        self.img_size = img_size
        self.patch_size = patch_size
        self.num_patches = num_patches

        self.proj = nn.Conv2d(in_chans, embed_dim, kernel_size=patch_size, stride=patch_size)

    def forward(self, x):
        x = self.proj(x)
        x = x.flatten(2)
        x = x.transpose(1, 2)
        return x


class ChAdaViTModel(PreTrainedModel):
    """Channel Adaptive Vision Transformer"""

    config_class = ChAdaViTConfig

    def __init__(self, config):
        super().__init__(config)

        # Embeddings dimension
        self.num_features = self.embed_dim = config.embed_dim

        # Num of maximum channels in the batch
        self.max_channels = config.max_number_channels

        # Tokenization module
        self.token_learner = TokenLearner(
            img_size=config.img_size[0],
            patch_size=config.patch_size,
            in_chans=config.in_chans,
            embed_dim=self.embed_dim,
        )
        num_patches = self.token_learner.num_patches

        self.cls_token = nn.Parameter(
            torch.zeros(1, 1, self.embed_dim)
        )  # (B, max_channels * num_tokens, embed_dim)
        self.channel_token = nn.Parameter(
            torch.zeros(1, self.max_channels, 1, self.embed_dim)
        )  # (B, max_channels, 1, embed_dim)
        self.pos_embed = nn.Parameter(
            torch.zeros(1, 1, num_patches + 1, self.embed_dim)
        )  # (B, max_channels, num_tokens, embed_dim)
        self.pos_drop = nn.Dropout(p=config.drop_rate)

        # TransformerEncoder block
        dpr = [
            x.item() for x in torch.linspace(0, config.drop_path_rate, config.depth)
        ]  # stochastic depth decay rule
        self.blocks = nn.ModuleList(
            [
                TransformerEncoderLayer(
                    d_model=self.embed_dim,
                    nhead=config.num_heads,
                    dim_feedforward=2048,
                    dropout=dpr[i],
                    batch_first=True,
                )
                for i in range(config.depth)
            ]
        )
        self.norm = nn.LayerNorm(self.embed_dim)

        # Classifier head
        self.head = nn.Linear(self.embed_dim, config.num_classes) if config.num_classes > 0 else nn.Identity()

        # Return only the [CLS] token or all tokens
        self.return_all_tokens = config.return_all_tokens

        trunc_normal_(self.pos_embed, std=0.02)
        trunc_normal_(self.cls_token, std=0.02)
        trunc_normal_(self.channel_token, std=0.02)
        self.apply(self._init_weights)

    def _init_weights(self, m):
        if isinstance(m, nn.Linear):
            trunc_normal_(m.weight, std=0.02)
            if isinstance(m, nn.Linear) and m.bias is not None:
                nn.init.constant_(m.bias, 0)
        elif isinstance(m, nn.LayerNorm):
            nn.init.constant_(m.bias, 0)
            nn.init.constant_(m.weight, 1.0)

    def add_pos_encoding_per_channel(self, x, w, h, class_pos_embed: bool = False):
        """
        Adds num_patches positional embeddings to EACH of the channels.
        """
        npatch = x.shape[2]
        N = self.pos_embed.shape[2] - 1

        # --------------------- [CLS] positional encoding --------------------- #
        if class_pos_embed:
            return self.pos_embed[:, :, 0]

        # --------------------- Patches positional encoding --------------------- #
        # If the input size is the same as the training size, return the positional embeddings for the desired type
        if npatch == N and w == h:
            return self.pos_embed[:, :, 1:]

        # Otherwise, interpolate the positional encoding for the input tokens
        class_pos_embed = self.pos_embed[:, :, 0]
        patch_pos_embed = self.pos_embed[:, :, 1:]
        dim = x.shape[-1]
        w0 = w // self.token_learner.patch_size
        h0 = h // self.token_learner.patch_size
        # a small number is added by DINO team to avoid floating point error in the interpolation
        # see discussion at https://github.com/facebookresearch/dino/issues/8
        w0, h0 = w0 + 0.1, h0 + 0.1
        patch_pos_embed = nn.functional.interpolate(
            patch_pos_embed.reshape(1, int(math.sqrt(N)), int(math.sqrt(N)), dim).permute(0, 3, 1, 2),
            scale_factor=(w0 / math.sqrt(N), h0 / math.sqrt(N)),
            mode="bicubic",
        )
        assert int(w0) == patch_pos_embed.shape[-2] and int(h0) == patch_pos_embed.shape[-1]
        patch_pos_embed = patch_pos_embed.permute(0, 2, 3, 1).view(1, -1, dim)
        return patch_pos_embed.unsqueeze(0)

    def channel_aware_tokenization(self, x, index, list_num_channels, max_channels=10):
        B, nc, w, h = x.shape  # (B*num_channels, 1, w, h)

        # Tokenize through linear embedding
        tokens_per_channel = self.token_learner(x)

        # Concatenate tokens per channel in each image
        chunks = torch.split(tokens_per_channel, list_num_channels[index], dim=0)

        # Pad the tokens tensor with zeros for each image separately in the chunks list
        padded_tokens = [
            torch.cat(
                [
                    chunk,
                    torch.zeros(
                        (max_channels - chunk.size(0), chunk.size(1), chunk.size(2)),
                        device=chunk.device,
                    ),
                ],
                dim=0,
            )
            if chunk.size(0) < max_channels
            else chunk
            for chunk in chunks
        ]

        # Stack along the batch dimension
        padded_tokens = torch.stack(padded_tokens, dim=0)
        num_tokens = padded_tokens.size(2)

        # Reshape the patches embeddings on the channel dimension
        padded_tokens = padded_tokens.reshape(padded_tokens.size(0), -1, padded_tokens.size(3))

        # Compute the masking for avoiding self-attention on empty padded channels
        channel_mask = torch.all(padded_tokens == 0.0, dim=-1)

        # Destack to obtain the original number of channels
        padded_tokens = padded_tokens.reshape(-1, max_channels, num_tokens, padded_tokens.size(-1))

        # Add the [POS] token to the embed patch tokens
        padded_tokens = padded_tokens + self.add_pos_encoding_per_channel(
            padded_tokens, w, h, class_pos_embed=False
        )

        # Add the [CHANNEL] token to the embed patch tokens
        if max_channels == self.max_channels:
            channel_tokens = self.channel_token.expand(padded_tokens.shape[0], -1, padded_tokens.shape[2], -1)
            padded_tokens = padded_tokens + channel_tokens

        # Restack the patches embeddings on the channel dimension
        embeddings = padded_tokens.reshape(padded_tokens.size(0), -1, padded_tokens.size(3))

        # Expand the [CLS] token to the batch dimension
        cls_tokens = self.cls_token.expand(embeddings.shape[0], -1, -1)

        # Add [POS] positional encoding to the [CLS] token
        cls_tokens = cls_tokens + self.add_pos_encoding_per_channel(embeddings, w, h, class_pos_embed=True)

        # Concatenate the [CLS] token to the embed patch tokens
        embeddings = torch.cat([cls_tokens, embeddings], dim=1)

        # Adding a False value to the beginning of each channel_mask to account for the [CLS] token
        channel_mask = torch.cat(
            [
                torch.tensor([False], device=channel_mask.device).expand(channel_mask.size(0), 1),
                channel_mask,
            ],
            dim=1,
        )

        return self.pos_drop(embeddings), channel_mask

    def forward(self, x, index, list_num_channels):
        # Apply the TokenLearner module to obtain learnable tokens
        x, channel_mask = self.channel_aware_tokenization(
            x, index, list_num_channels
        )  # (B*num_channels, embed_dim)

        # Apply the self-attention layers with masked self-attention
        for blk in self.blocks:
            x = blk(
                x, src_key_padding_mask=channel_mask
            )  # Use src_key_padding_mask to mask out padded tokens

        # Normalize
        x = self.norm(x)

        if self.return_all_tokens:
            # Create a mask to select non-masked tokens (excluding CLS token)
            non_masked_tokens_mask = ~channel_mask[:, 1:]
            non_masked_tokens = x[:, 1:][non_masked_tokens_mask]
            return non_masked_tokens  # return non-masked tokens (excluding CLS token)
        else:
            return x[:, 0]  # return only the [CLS] token

    def channel_token_sanity_check(self, x):
        """
        Helper function to check consistency of channel tokens.
        """
        # 1. Compare Patches Across Different Channels
        print("Values for the first patch across different channels:")
        for ch in range(10):  # Assuming 10 channels
            print(f"Channel {ch + 1}:", x[0, ch, 0, :5])  # Print first 5 values of the embedding for brevity

        print("\n")

        # 2. Compare Patches Within the Same Channel
        for ch in range(10):
            is_same = torch.all(x[0, ch, 0] == x[0, ch, 1])
            print(f"First and second patch embeddings are the same for Channel {ch + 1}: {is_same.item()}")

        # 3. Check Consistency Across Batch
        print("Checking consistency of channel tokens across the batch:")
        for ch in range(10):
            is_consistent = torch.all(x[0, ch, 0] == x[1, ch, 0])
            print(
                f"Channel token for first patch is consistent between first and second image for Channel {ch + 1}: {is_consistent.item()}"
            )

    def get_last_selfattention(self, x):
        x, channel_mask = self.channel_aware_tokenization(x, index=0, list_num_channels=[1], max_channels=1)
        for i, blk in enumerate(self.blocks):
            if i < len(self.blocks) - 1:
                x = blk(x, src_key_padding_mask=channel_mask)
            else:
                # return attention of the last block
                return blk(x, src_key_padding_mask=channel_mask, return_attention=True)

    def get_intermediate_layers(self, x, n=1):
        x, channel_mask = self.channel_aware_tokenization(x)
        # return the output tokens from the `n` last blocks
        output = []
        for i, blk in enumerate(self.blocks):
            x = blk(x, src_key_padding_mask=channel_mask)
            if len(self.blocks) - i <= n:
                output.append(self.norm(x))
        return output