Dubhe/model_measuring/kamal/transferability/depara/attribution_map.py

"""
 Copyright 2020 Tianshu AI Platform. All Rights Reserved.

 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.
 =============================================================
"""

import torch
from typing import Type, Callable
from captum.attr import Attribution
from captum.attr import NoiseTunnel


def with_norm(func: Callable[[torch.Tensor], torch.Tensor], x: torch.Tensor, square: bool = False):
    x = func(x)
    x = torch.norm(x.flatten(1), dim=1, p=2)
    if square:
        x = torch.pow(x, 2)
    return x


def attribution_map(
    func: Callable[[torch.Tensor], torch.Tensor],
    attribution_type: Type,
    with_noise: bool,
    probe_data: torch.Tensor,
    norm_square: bool = False,
    **attribution_kwargs
) -> torch.Tensor:
    """
    Calculate attribution map with given attribution type(algorithm).
    Args:
        model: pytorch module
        attribution_type: attribution algorithm, e.g. IntegratedGradients, InputXGradient, ...
        with_noise: whether to add noise tunnel
        probe_data: input data to model
        device: torch.device("cuda: 0")
        attribution_kwargs: other kwargs for attribution method
    Return: attribution map
    """
    attribution: Attribution = attribution_type(lambda x: with_norm(func, x, norm_square))
    if with_noise:
        attribution = NoiseTunnel(attribution)
    attr_map = attribution.attribute(
        inputs=probe_data,
        target=None,
        **attribution_kwargs
    )
    return attr_map.detach()

def attr_map_distance(map_1: torch.Tensor, map_2: torch.Tensor):
    if map_1.shape != map_2.shape:
        map_1 = torch.nn.functional.interpolate( map_1, size=map_2.shape[-2:] )
    #dist = torch.dist(map_1.flatten(1), map_2.flatten(1), p=2).mean()
    dist = 1 - torch.cosine_similarity(map_1.flatten(1), map_2.flatten(1)).mean()
    return dist.item()

def attr_map_similarity(map_1: torch.Tensor, map_2: torch.Tensor):
    assert(map_1.shape == map_2.shape)
    dist = torch.cosine_similarity(map_1.flatten(1), map_2.flatten(1)).mean()
    return dist.item()


if __name__ == "__main__":
    import captum

    def ff(x):
        return x ** 2

    m = attribution_map(
        ff,
        captum.attr.InputXGradient,
        with_noise=False,
        probe_data=torch.tensor([[1, 2, 3, 4]], dtype=torch.float, requires_grad=True),
        norm_square=True
    )
    print(m)