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# Copyright (c) Owkin, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
# Copyright (c) Owkin, Inc.
# All rights reserved.
#
# This source code is licensed under the license found in the
# LICENSE file in the root directory of this source tree.
import warnings
from typing import List, Optional, Tuple, Union
import torch
from torch import nn
class MLP(torch.nn.Sequential):
"""MLP Module.
Parameters
----------
in_features: int
Features (model input) dimension.
out_features: int = 1
Prediction (model output) dimension.
hidden: Optional[List[int]] = None
Dimension of hidden layer(s).
dropout: Optional[List[float]] = None
Dropout rate(s).
activation: Optional[torch.nn.Module] = torch.nn.Sigmoid
MLP activation.
bias: bool = True
Add bias to MLP hidden layers.
Raises
------
ValueError
If ``hidden`` and ``dropout`` do not share the same length.
"""
def __init__(
self,
in_features: int,
out_features: int,
hidden: Optional[List[int]] = None,
dropout: Optional[List[float]] = None,
activation: Optional[torch.nn.Module] = torch.nn.Sigmoid(),
bias: bool = True,
):
if dropout is not None:
if hidden is not None:
assert len(hidden) == len(
dropout
), "hidden and dropout must have the same length"
else:
raise ValueError(
"hidden must have a value and have the same length as dropout if dropout is given."
)
d_model = in_features
layers = []
if hidden is not None:
for i, h in enumerate(hidden):
seq = [torch.nn.Linear(d_model, h, bias=bias)]
d_model = h
if activation is not None:
seq.append(activation)
if dropout is not None:
seq.append(torch.nn.Dropout(dropout[i]))
layers.append(torch.nn.Sequential(*seq))
layers.append(torch.nn.Linear(d_model, out_features))
super(MLP, self).__init__(*layers)
class MaskedLinear(torch.nn.Linear):
"""
Linear layer to be applied tile wise.
This layer can be used in combination with a mask
to prevent padding tiles from influencing the values of a subsequent
activation.
Example:
>>> module = Linear(in_features=128, out_features=1) # With Linear
>>> out = module(slide)
>>> wrong_value = torch.sigmoid(out) # Value is influenced by padding
>>> module = MaskedLinear(in_features=128, out_features=1, mask_value='-inf') # With MaskedLinear
>>> out = module(slide, mask) # Padding now has the '-inf' value
>>> correct_value = torch.sigmoid(out) # Value is not influenced by padding as sigmoid('-inf') = 0
Parameters
----------
in_features: int
size of each input sample
out_features: int
size of each output sample
mask_value: Union[str, int]
value to give to the mask
bias: bool = True
If set to ``False``, the layer will not learn an additive bias.
"""
def __init__(
self,
in_features: int,
out_features: int,
mask_value: Union[str, float],
bias: bool = True,
):
super(MaskedLinear, self).__init__(
in_features=in_features, out_features=out_features, bias=bias
)
self.mask_value = mask_value
def forward(
self, x: torch.Tensor, mask: Optional[torch.BoolTensor] = None
): # pylint: disable=arguments-renamed
"""Forward pass.
Parameters
----------
x: torch.Tensor
Input tensor, shape (B, SEQ_LEN, IN_FEATURES).
mask: Optional[torch.BoolTensor] = None
True for values that were padded, shape (B, SEQ_LEN, 1),
Returns
-------
x: torch.Tensor
(B, SEQ_LEN, OUT_FEATURES)
"""
x = super(MaskedLinear, self).forward(x)
if mask is not None:
x = x.masked_fill(mask, float(self.mask_value))
return x
def extra_repr(self):
return (
f"in_features={self.in_features}, out_features={self.out_features}, "
f"mask_value={self.mask_value}, bias={self.bias is not None}"
)
class TilesMLP(torch.nn.Module):
"""MLP to be applied to tiles to compute scores.
This module can be used in combination of a mask
to prevent padding from influencing the scores values.
Parameters
----------
in_features: int
size of each input sample
out_features: int
size of each output sample
hidden: Optional[List[int]] = None
Number of hidden layers and their respective number of features.
bias: bool = True
If set to ``False``, the layer will not learn an additive bias.
activation: torch.nn.Module = torch.nn.Sigmoid()
MLP activation function
dropout: Optional[torch.nn.Module] = None
Optional dropout module. Will be interlaced with the linear layers.
"""
def __init__(
self,
in_features: int,
out_features: int = 1,
hidden: Optional[List[int]] = None,
bias: bool = True,
activation: torch.nn.Module = torch.nn.Sigmoid(),
dropout: Optional[torch.nn.Module] = None,
):
super(TilesMLP, self).__init__()
self.hidden_layers = torch.nn.ModuleList()
if hidden is not None:
for h in hidden:
self.hidden_layers.append(
MaskedLinear(in_features, h, bias=bias, mask_value="-inf")
)
self.hidden_layers.append(activation)
if dropout:
self.hidden_layers.append(dropout)
in_features = h
self.hidden_layers.append(
torch.nn.Linear(in_features, out_features, bias=bias)
)
def forward(
self, x: torch.Tensor, mask: Optional[torch.BoolTensor] = None
):
"""Forward pass.
Parameters
----------
x: torch.Tensor
(B, N_TILES, IN_FEATURES)
mask: Optional[torch.BoolTensor] = None
(B, N_TILES), True for values that were padded.
Returns
-------
x: torch.Tensor
(B, N_TILES, OUT_FEATURES)
"""
for layer in self.hidden_layers:
if isinstance(layer, MaskedLinear):
x = layer(x, mask)
else:
x = layer(x)
return x
class ExtremeLayer(torch.nn.Module):
"""Extreme layer.
Returns concatenation of n_top top tiles and n_bottom bottom tiles
.. warning::
If top tiles or bottom tiles is superior to the true number of
tiles in the input then padded tiles will be selected and their value
will be 0.
Parameters
----------
n_top: Optional[int] = None
Number of top tiles to select
n_bottom: Optional[int] = None
Number of bottom tiles to select
dim: int = 1
Dimension to select top/bottom tiles from
return_indices: bool = False
Whether to return the indices of the extreme tiles
Raises
------
ValueError
If ``n_top`` and ``n_bottom`` are set to ``None`` or both are 0.
"""
def __init__(
self,
n_top: Optional[int] = None,
n_bottom: Optional[int] = None,
dim: int = 1,
return_indices: bool = False,
):
super(ExtremeLayer, self).__init__()
if not (n_top is not None or n_bottom is not None):
raise ValueError("one of n_top or n_bottom must have a value.")
if not (
(n_top is not None and n_top > 0)
or (n_bottom is not None and n_bottom > 0)
):
raise ValueError("one of n_top or n_bottom must have a value > 0.")
self.n_top = n_top
self.n_bottom = n_bottom
self.dim = dim
self.return_indices = return_indices
def forward(
self, x: torch.Tensor, mask: Optional[torch.BoolTensor] = None
) -> Union[torch.Tensor, Tuple[torch.Tensor, torch.Tensor]]:
"""Forward pass.
Parameters
----------
x: torch.Tensor
Input tensor, shape (B, N_TILES, IN_FEATURES).
mask: Optional[torch.BoolTensor]
True for values that were padded, shape (B, N_TILES, 1).
Warnings
--------
If top tiles or bottom tiles is superior to the true number of tiles in
the input then padded tiles will be selected and their value will be 0.
Returns
-------
values: torch.Tensor
Extreme tiles, shape (B, N_TOP + N_BOTTOM).
indices: torch.Tensor
If ``self.return_indices=True``, return extreme tiles' indices.
"""
if (
self.n_top
and self.n_bottom
and ((self.n_top + self.n_bottom) > x.shape[self.dim])
):
warnings.warn(
f"Sum of tops is larger than the input tensor shape for dimension {self.dim}: "
f"{self.n_top + self.n_bottom} > {x.shape[self.dim]}. "
f"Values will appear twice (in top and in bottom)"
)
top, bottom = None, None
top_idx, bottom_idx = None, None
if mask is not None:
if self.n_top:
top, top_idx = x.masked_fill(mask, float("-inf")).topk(
k=self.n_top, sorted=True, dim=self.dim
)
top_mask = top.eq(float("-inf"))
if top_mask.any():
warnings.warn(
"The top tiles contain masked values, they will be set to zero."
)
top[top_mask] = 0
if self.n_bottom:
bottom, bottom_idx = x.masked_fill(mask, float("inf")).topk(
k=self.n_bottom, largest=False, sorted=True, dim=self.dim
)
bottom_mask = bottom.eq(float("inf"))
if bottom_mask.any():
warnings.warn(
"The bottom tiles contain masked values, they will be set to zero."
)
bottom[bottom_mask] = 0
else:
if self.n_top:
top, top_idx = x.topk(k=self.n_top, sorted=True, dim=self.dim)
if self.n_bottom:
bottom, bottom_idx = x.topk(
k=self.n_bottom, largest=False, sorted=True, dim=self.dim
)
if top is not None and bottom is not None:
values = torch.cat([top, bottom], dim=self.dim)
indices = torch.cat([top_idx, bottom_idx], dim=self.dim)
elif top is not None:
values = top
indices = top_idx
elif bottom is not None:
values = bottom
indices = bottom_idx
else:
raise ValueError
if self.return_indices:
return values, indices
else:
return values
def extra_repr(self) -> str:
"""Format representation."""
return f"n_top={self.n_top}, n_bottom={self.n_bottom}"
class Chowder(nn.Module):
"""Chowder MIL model (See [1]_).
Example:
>>> module = Chowder(in_features=128, out_features=1, n_top=5, n_bottom=5)
>>> logits, extreme_scores = module(slide, mask=mask)
>>> scores = module.score_model(slide, mask=mask)
Parameters
----------
in_features: int
Features (model input) dimension.
out_features: int
Controls the number of scores and, by extension, the number of out_features.
n_top: int
Number of tiles with hightest scores that are selected and fed to the MLP.
n_bottom: int
Number of tiles with lowest scores that are selected and fed to the MLP.
tiles_mlp_hidden: Optional[List[int]] = None
Number of units for layers in the first MLP applied tile wise to compute
a score for each tiles from the tile features.
If `None`, a linear layer is used to compute tile scores.
If e.g. `[128, 64]`, the tile scores are computed with a MLP of dimension
features_dim -> 128 -> 64 -> 1.
mlp_hidden: Optional[List[int]] = None
Number of units for layers of the second MLP that combine top and bottom
scores and outputs a final prediction at the slide-level. If `None`, a
linear layer is used to compute the prediction from the extreme scores.
If e.g. `[128, 64]`, the prediction is computed
with a MLP n_top + n_bottom -> 128 -> 64 -> 1.
mlp_dropout: Optional[List[float]] = None
Dropout that is used for each layer of the MLP. If `None`, no dropout
is used.
mlp_activation: Optional[torch.nn.Module] = torch.nn.Sigmoid
Activation that is used after each layer of the MLP.
bias: bool = True
Whether to add bias for layers of the tiles MLP.
References
----------
.. [1] Pierre Courtiol, Eric W. Tramel, Marc Sanselme, and Gilles Wainrib. Classification
and disease localization in histopathology using only global labels: A weakly-supervised
approach. CoRR, abs/1802.02212, 2018.
"""
def __init__(
self,
in_features: int,
out_features: int,
n_top: Optional[int] = None,
n_bottom: Optional[int] = None,
tiles_mlp_hidden: Optional[List[int]] = None,
mlp_hidden: Optional[List[int]] = None,
mlp_dropout: Optional[List[float]] = None,
mlp_activation: Optional[torch.nn.Module] = torch.nn.Sigmoid(),
bias: bool = True,
) -> None:
super(Chowder, self).__init__()
if n_top is None and n_bottom is None:
raise ValueError(
"At least one of `n_top` or `n_bottom` must not be None."
)
if mlp_dropout is not None:
if mlp_hidden is not None:
assert len(mlp_hidden) == len(
mlp_dropout
), "mlp_hidden and mlp_dropout must have the same length"
else:
raise ValueError(
"mlp_hidden must have a value and have the same length as mlp_dropout if mlp_dropout is given."
)
self.score_model = TilesMLP(
in_features,
hidden=tiles_mlp_hidden,
bias=bias,
out_features=out_features,
)
self.score_model.apply(self.weight_initialization)
self.extreme_layer = ExtremeLayer(n_top=n_top, n_bottom=n_bottom)
mlp_in_features = n_top + n_bottom
self.mlp = MLP(
mlp_in_features,
1,
hidden=mlp_hidden,
dropout=mlp_dropout,
activation=mlp_activation,
)
self.mlp.apply(self.weight_initialization)
@staticmethod
def weight_initialization(module: torch.nn.Module) -> None:
"""Initialize weights for the module using Xavier initialization method,
"Understanding the difficulty of training deep feedforward neural networks",
Glorot, X. & Bengio, Y. (2010)."""
if isinstance(module, torch.nn.Linear):
torch.nn.init.xavier_uniform_(module.weight)
if module.bias is not None:
module.bias.data.fill_(0.0)
def forward(
self, features: torch.Tensor, mask: Optional[torch.BoolTensor] = None
) -> torch.Tensor:
"""
Parameters
----------
features: torch.Tensor
(B, N_TILES, IN_FEATURES)
mask: Optional[torch.BoolTensor] = None
(B, N_TILES, 1), True for values that were padded.
Returns
-------
logits, extreme_scores: Tuple[torch.Tensor, torch.Tensor]:
(B, OUT_FEATURES), (B, N_TOP + N_BOTTOM, OUT_FEATURES)
"""
scores = self.score_model(x=features[..., 3:], mask=mask)
extreme_scores = self.extreme_layer(
x=scores, mask=mask
) # (B, N_TOP + N_BOTTOM, OUT_FEATURES)
# Apply MLP to the N_TOP + N_BOTTOM scores.
y = self.mlp(extreme_scores.transpose(1, 2)) # (B, OUT_FEATURES, 1)
return y.squeeze(2)
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