Source code for

@author: Junguang Jiang
from typing import Optional
import torch.nn as nn
import torch

[docs]def classifier_discrepancy(predictions1: torch.Tensor, predictions2: torch.Tensor) -> torch.Tensor: r"""The `Classifier Discrepancy` in `Maximum Classifier Discrepancy for Unsupervised Domain Adaptation (CVPR 2018) <>`_. The classfier discrepancy between predictions :math:`p_1` and :math:`p_2` can be described as: .. math:: d(p_1, p_2) = \dfrac{1}{K} \sum_{k=1}^K | p_{1k} - p_{2k} |, where K is number of classes. Args: predictions1 (torch.Tensor): Classifier predictions :math:`p_1`. Expected to contain raw, normalized scores for each class predictions2 (torch.Tensor): Classifier predictions :math:`p_2` """ return torch.mean(torch.abs(predictions1 - predictions2))
[docs]def entropy(predictions: torch.Tensor) -> torch.Tensor: r"""Entropy of N predictions :math:`(p_1, p_2, ..., p_N)`. The definition is: .. math:: d(p_1, p_2, ..., p_N) = -\dfrac{1}{K} \sum_{k=1}^K \log \left( \dfrac{1}{N} \sum_{i=1}^N p_{ik} \right) where K is number of classes. .. note:: This entropy function is specifically used in MCD and different from the usual :meth:`~tllib.modules.entropy.entropy` function. Args: predictions (torch.Tensor): Classifier predictions. Expected to contain raw, normalized scores for each class """ return -torch.mean(torch.log(torch.mean(predictions, 0) + 1e-6))
[docs]class ImageClassifierHead(nn.Module): r"""Classifier Head for MCD. Args: in_features (int): Dimension of input features num_classes (int): Number of classes bottleneck_dim (int, optional): Feature dimension of the bottleneck layer. Default: 1024 Shape: - Inputs: :math:`(minibatch, F)` where F = `in_features`. - Output: :math:`(minibatch, C)` where C = `num_classes`. """ def __init__(self, in_features: int, num_classes: int, bottleneck_dim: Optional[int] = 1024, pool_layer=None): super(ImageClassifierHead, self).__init__() self.num_classes = num_classes if pool_layer is None: self.pool_layer = nn.Sequential( nn.AdaptiveAvgPool2d(output_size=(1, 1)), nn.Flatten() ) else: self.pool_layer = pool_layer self.head = nn.Sequential( nn.Dropout(0.5), nn.Linear(in_features, bottleneck_dim), nn.BatchNorm1d(bottleneck_dim), nn.ReLU(), nn.Dropout(0.5), nn.Linear(bottleneck_dim, bottleneck_dim), nn.BatchNorm1d(bottleneck_dim), nn.ReLU(), nn.Linear(bottleneck_dim, num_classes) ) def forward(self, inputs: torch.Tensor) -> torch.Tensor: return self.head(self.pool_layer(inputs))


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