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- from __future__ import absolute_import
-
- import torch
- from torch import nn
-
-
- class RangeLoss(nn.Module):
- """
- Range_loss = alpha * intra_class_loss + beta * inter_class_loss
- intra_class_loss is the harmonic mean value of the top_k largest distances beturn intra_class_pairs
- inter_class_loss is the shortest distance between different class centers
- """
- def __init__(self, k=2, margin=0.1, alpha=0.5, beta=0.5, use_gpu=True, ordered=True, ids_per_batch=32, imgs_per_id=4):
- super(RangeLoss, self).__init__()
- self.use_gpu = use_gpu
- self.margin = margin
- self.k = k
- self.alpha = alpha
- self.beta = beta
- self.ordered = ordered
- self.ids_per_batch = ids_per_batch
- self.imgs_per_id = imgs_per_id
-
- def _pairwise_distance(self, features):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, feature_dim)
- Return:
- pairwise distance matrix with shape(batch_size, batch_size)
- """
- n = features.size(0)
- dist = torch.pow(features, 2).sum(dim=1, keepdim=True).expand(n, n)
- dist = dist + dist.t()
- dist.addmm_(1, -2, features, features.t())
- dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
- return dist
-
- def _compute_top_k(self, features):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, feature_dim)
- Return:
- top_k largest distances
- """
- # reading the codes below can help understand better
- '''
- dist_array_2 = self._pairwise_distance(features)
- n = features.size(0)
- mask = torch.zeros(n, n)
- if self.use_gpu: mask=mask.cuda()
- for i in range(0, n):
- for j in range(i+1, n):
- mask[i, j] += 1
- dist_array_2 = dist_array_2 * mask
- dist_array_2 = dist_array_2.view(1, -1)
- dist_array_2 = dist_array_2[torch.gt(dist_array_2, 0)]
- top_k_2 = dist_array_2.sort()[0][-self.k:]
- print(top_k_2)
- '''
- dist_array = self._pairwise_distance(features)
- dist_array = dist_array.view(1, -1)
- top_k = dist_array.sort()[0][0, -self.k * 2::2] # Because there are 2 same value of same feature pair in the dist_array
- # print('top k intra class dist:', top_k)
- return top_k
-
- def _compute_min_dist(self, center_features):
- """
- Args:
- center_features: center matrix (before softmax) with shape (center_number, center_dim)
- Return:
- minimum center distance
- """
- '''
- # reading codes below can help understand better
- dist_array = self._pairwise_distance(center_features)
- n = center_features.size(0)
- mask = torch.zeros(n, n)
- if self.use_gpu: mask=mask.cuda()
- for i in range(0, n):
- for j in range(i + 1, n):
- mask[i, j] += 1
- dist_array *= mask
- dist_array = dist_array.view(1, -1)
- dist_array = dist_array[torch.gt(dist_array, 0)]
- min_inter_class_dist = dist_array.min()
- print(min_inter_class_dist)
- '''
- n = center_features.size(0)
- dist_array2 = self._pairwise_distance(center_features)
- min_inter_class_dist2 = dist_array2.view(1, -1).sort()[0][0][n] # exclude self compare, the first one is the min_inter_class_dist
- return min_inter_class_dist2
-
- def _calculate_centers(self, features, targets, ordered, ids_per_batch, imgs_per_id):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, feature_dim)
- targets: ground truth labels with shape (batch_size)
- ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id.
- ids_per_batch: num of different ids per batch
- imgs_per_id: num of images per id
- Return:
- center_features: center matrix (before softmax) with shape (center_number, center_dim)
- """
- if self.use_gpu:
- if ordered:
- if targets.size(0) == ids_per_batch * imgs_per_id:
- unique_labels = targets[0:targets.size(0):imgs_per_id]
- else:
- unique_labels = targets.cpu().unique().cuda()
- else:
- unique_labels = targets.cpu().unique().cuda()
- else:
- if ordered:
- if targets.size(0) == ids_per_batch * imgs_per_id:
- unique_labels = targets[0:targets.size(0):imgs_per_id]
- else:
- unique_labels = targets.unique()
- else:
- unique_labels = targets.unique()
-
- center_features = torch.zeros(unique_labels.size(0), features.size(1))
- if self.use_gpu:
- center_features = center_features.cuda()
-
- for i in range(unique_labels.size(0)):
- label = unique_labels[i]
- same_class_features = features[targets == label]
- center_features[i] = same_class_features.mean(dim=0)
- return center_features
-
- def _inter_class_loss(self, features, targets, ordered, ids_per_batch, imgs_per_id):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, feature_dim)
- targets: ground truth labels with shape (batch_size)
- margin: inter class ringe loss margin
- ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id.
- ids_per_batch: num of different ids per batch
- imgs_per_id: num of images per id
- Return:
- inter_class_loss
- """
- center_features = self._calculate_centers(features, targets, ordered, ids_per_batch, imgs_per_id)
- min_inter_class_center_distance = self._compute_min_dist(center_features)
- # print('min_inter_class_center_dist:', min_inter_class_center_distance)
- return torch.relu(self.margin - min_inter_class_center_distance)
-
- def _intra_class_loss(self, features, targets, ordered, ids_per_batch, imgs_per_id):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, feature_dim)
- targets: ground truth labels with shape (batch_size)
- ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id.
- ids_per_batch: num of different ids per batch
- imgs_per_id: num of images per id
- Return:
- intra_class_loss
- """
- if self.use_gpu:
- if ordered:
- if targets.size(0) == ids_per_batch * imgs_per_id:
- unique_labels = targets[0:targets.size(0):imgs_per_id]
- else:
- unique_labels = targets.cpu().unique().cuda()
- else:
- unique_labels = targets.cpu().unique().cuda()
- else:
- if ordered:
- if targets.size(0) == ids_per_batch * imgs_per_id:
- unique_labels = targets[0:targets.size(0):imgs_per_id]
- else:
- unique_labels = targets.unique()
- else:
- unique_labels = targets.unique()
-
- intra_distance = torch.zeros(unique_labels.size(0))
- if self.use_gpu:
- intra_distance = intra_distance.cuda()
-
- for i in range(unique_labels.size(0)):
- label = unique_labels[i]
- same_class_distances = 1.0 / self._compute_top_k(features[targets == label])
- intra_distance[i] = self.k / torch.sum(same_class_distances)
- # print('intra_distace:', intra_distance)
- return torch.sum(intra_distance)
-
- def _range_loss(self, features, targets, ordered, ids_per_batch, imgs_per_id):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, feature_dim)
- targets: ground truth labels with shape (batch_size)
- ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id.
- ids_per_batch: num of different ids per batch
- imgs_per_id: num of images per id
- Return:
- range_loss
- """
- inter_class_loss = self._inter_class_loss(features, targets, ordered, ids_per_batch, imgs_per_id)
- intra_class_loss = self._intra_class_loss(features, targets, ordered, ids_per_batch, imgs_per_id)
- range_loss = self.alpha * intra_class_loss + self.beta * inter_class_loss
- return range_loss, intra_class_loss, inter_class_loss
-
- def forward(self, features, targets):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, feature_dim)
- targets: ground truth labels with shape (batch_size)
- ordered: bool type. If the train data per batch are formed as p*k, where p is the num of ids per batch and k is the num of images per id.
- ids_per_batch: num of different ids per batch
- imgs_per_id: num of images per id
- Return:
- range_loss
- """
- assert features.size(0) == targets.size(0), "features.size(0) is not equal to targets.size(0)"
- if self.use_gpu:
- features = features.cuda()
- targets = targets.cuda()
-
- range_loss, intra_class_loss, inter_class_loss = self._range_loss(features, targets, self.ordered, self.ids_per_batch, self.imgs_per_id)
- return range_loss, intra_class_loss, inter_class_loss
-
-
- if __name__ == '__main__':
- use_gpu = False
- range_loss = RangeLoss(use_gpu=use_gpu, ids_per_batch=4, imgs_per_id=4)
- features = torch.rand(16, 2048)
- targets = torch.Tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])
- if use_gpu:
- features = torch.rand(16, 2048).cuda()
- targets = torch.Tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]).cuda()
- loss = range_loss(features, targets)
- print(loss)
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