From d426692b95c81a610d4eacab4a5ebbeee1932384 Mon Sep 17 00:00:00 2001
From: shaoniangu <shaoniangu@163.com>
Date: Fri, 5 Jul 2019 01:09:30 +0800
Subject: [PATCH] remove code that is irrelevant to the paper
---
layers/__init__.py | 77 ++----------
layers/cluster_loss.py | 269 -----------------------------------------
layers/range_loss.py | 232 -----------------------------------
3 files changed, 10 insertions(+), 568 deletions(-)
delete mode 100644 layers/cluster_loss.py
delete mode 100644 layers/range_loss.py
diff --git a/layers/__init__.py b/layers/__init__.py
index aa3ebcb..378af1e 100644
--- a/layers/__init__.py
+++ b/layers/__init__.py
@@ -7,22 +7,15 @@
import torch.nn.functional as F
from .triplet_loss import TripletLoss, CrossEntropyLabelSmooth
-from .cluster_loss import ClusterLoss
from .center_loss import CenterLoss
-from .range_loss import RangeLoss
def make_loss(cfg, num_classes): # modified by gu
sampler = cfg.DATALOADER.SAMPLER
if cfg.MODEL.METRIC_LOSS_TYPE == 'triplet':
triplet = TripletLoss(cfg.SOLVER.MARGIN) # triplet loss
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'cluster':
- cluster = ClusterLoss(cfg.SOLVER.CLUSTER_MARGIN, True, True, cfg.SOLVER.IMS_PER_BATCH // cfg.DATALOADER.NUM_INSTANCE, cfg.DATALOADER.NUM_INSTANCE)
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'triplet_cluster':
- triplet = TripletLoss(cfg.SOLVER.MARGIN) # triplet loss
- cluster = ClusterLoss(cfg.SOLVER.CLUSTER_MARGIN, True, True, cfg.SOLVER.IMS_PER_BATCH // cfg.DATALOADER.NUM_INSTANCE, cfg.DATALOADER.NUM_INSTANCE)
else:
- print('expected METRIC_LOSS_TYPE should be triplet, cluster, triplet_cluster'
+ print('expected METRIC_LOSS_TYPE should be triplet'
'but got {}'.format(cfg.MODEL.METRIC_LOSS_TYPE))
if cfg.MODEL.IF_LABELSMOOTH == 'on':
@@ -39,23 +32,11 @@ def make_loss(cfg, num_classes): # modified by gu
def loss_func(score, feat, target):
if cfg.MODEL.METRIC_LOSS_TYPE == 'triplet':
if cfg.MODEL.IF_LABELSMOOTH == 'on':
- return xent(score, target) + triplet(feat, target)[0] # new add by luo, open label smooth
+ return xent(score, target) + triplet(feat, target)[0]
else:
- return F.cross_entropy(score, target) + triplet(feat, target)[0] # new add by luo, no label smooth
-
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'cluster':
- if cfg.MODEL.IF_LABELSMOOTH == 'on':
- return xent(score, target) + cluster(feat, target)[0] # new add by luo, open label smooth
- else:
- return F.cross_entropy(score, target) + cluster(feat, target)[0] # new add by luo, no label smooth
-
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'triplet_cluster':
- if cfg.MODEL.IF_LABELSMOOTH == 'on':
- return xent(score, target) + triplet(feat, target)[0] + cluster(feat, target)[0] # new add by luo, open label smooth
- else:
- return F.cross_entropy(score, target) + triplet(feat, target)[0] + cluster(feat, target)[0] # new add by luo, no label smooth
+ return F.cross_entropy(score, target) + triplet(feat, target)[0]
else:
- print('expected METRIC_LOSS_TYPE should be triplet, cluster, triplet_cluster，'
+ print('expected METRIC_LOSS_TYPE should be triplet'
'but got {}'.format(cfg.MODEL.METRIC_LOSS_TYPE))
else:
print('expected sampler should be softmax, triplet or softmax_triplet, '
@@ -72,27 +53,12 @@ def make_loss_with_center(cfg, num_classes): # modified by gu
if cfg.MODEL.METRIC_LOSS_TYPE == 'center':
center_criterion = CenterLoss(num_classes=num_classes, feat_dim=feat_dim, use_gpu=True) # center loss
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'range_center':
- center_criterion = CenterLoss(num_classes=num_classes, feat_dim=feat_dim, use_gpu=True) # center_range loss
- range_criterion = RangeLoss(k=cfg.SOLVER.RANGE_K, margin=cfg.SOLVER.RANGE_MARGIN, alpha=cfg.SOLVER.RANGE_ALPHA,
- beta=cfg.SOLVER.RANGE_BETA, ordered=True, use_gpu=True,
- ids_per_batch=cfg.SOLVER.IMS_PER_BATCH // cfg.DATALOADER.NUM_INSTANCE,
- imgs_per_id=cfg.DATALOADER.NUM_INSTANCE)
-
elif cfg.MODEL.METRIC_LOSS_TYPE == 'triplet_center':
triplet = TripletLoss(cfg.SOLVER.MARGIN) # triplet loss
center_criterion = CenterLoss(num_classes=num_classes, feat_dim=feat_dim, use_gpu=True) # center loss
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'triplet_range_center':
- triplet = TripletLoss(cfg.SOLVER.MARGIN) # triplet loss
- center_criterion = CenterLoss(num_classes=num_classes, feat_dim=feat_dim, use_gpu=True) # center_range loss
- range_criterion = RangeLoss(k=cfg.SOLVER.RANGE_K, margin=cfg.SOLVER.RANGE_MARGIN, alpha=cfg.SOLVER.RANGE_ALPHA,
- beta=cfg.SOLVER.RANGE_BETA, ordered=True, use_gpu=True,
- ids_per_batch=cfg.SOLVER.IMS_PER_BATCH // cfg.DATALOADER.NUM_INSTANCE,
- imgs_per_id=cfg.DATALOADER.NUM_INSTANCE)
else:
- print('expected METRIC_LOSS_TYPE with center should be center, '
- 'range_center,triplet_center, triplet_range_center '
+ print('expected METRIC_LOSS_TYPE with center should be center, triplet_center'
'but got {}'.format(cfg.MODEL.METRIC_LOSS_TYPE))
if cfg.MODEL.IF_LABELSMOOTH == 'on':
@@ -103,45 +69,22 @@ def make_loss_with_center(cfg, num_classes): # modified by gu
if cfg.MODEL.METRIC_LOSS_TYPE == 'center':
if cfg.MODEL.IF_LABELSMOOTH == 'on':
return xent(score, target) + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) # new add by luo, open label smooth
+ cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target)
else:
return F.cross_entropy(score, target) + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) # new add by luo, no label smooth
-
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'range_center':
- if cfg.MODEL.IF_LABELSMOOTH == 'on':
- return xent(score, target) + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) + \
- cfg.SOLVER.RANGE_LOSS_WEIGHT * range_criterion(feat, target)[0] # new add by luo, open label smooth
- else:
- return F.cross_entropy(score, target) + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) + \
- cfg.SOLVER.RANGE_LOSS_WEIGHT * range_criterion(feat, target)[0] # new add by luo, no label smooth
+ cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target)
elif cfg.MODEL.METRIC_LOSS_TYPE == 'triplet_center':
if cfg.MODEL.IF_LABELSMOOTH == 'on':
return xent(score, target) + \
triplet(feat, target)[0] + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) # new add by luo, open label smooth
+ cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target)
else:
return F.cross_entropy(score, target) + \
triplet(feat, target)[0] + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) # new add by luo, no label smooth
-
- elif cfg.MODEL.METRIC_LOSS_TYPE == 'triplet_range_center':
- if cfg.MODEL.IF_LABELSMOOTH == 'on':
- return xent(score, target) + \
- triplet(feat, target)[0] + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) + \
- cfg.SOLVER.RANGE_LOSS_WEIGHT * range_criterion(feat, target)[0] # new add by luo, open label smooth
- else:
- return F.cross_entropy(score, target) + \
- triplet(feat, target)[0] + \
- cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target) + \
- cfg.SOLVER.RANGE_LOSS_WEIGHT * range_criterion(feat, target)[0] # new add by luo, no label smooth
+ cfg.SOLVER.CENTER_LOSS_WEIGHT * center_criterion(feat, target)
else:
- print('expected METRIC_LOSS_TYPE with center should be center,'
- ' range_center, triplet_center, triplet_range_center '
+ print('expected METRIC_LOSS_TYPE with center should be center, triplet_center'
'but got {}'.format(cfg.MODEL.METRIC_LOSS_TYPE))
return loss_func, center_criterion
\ No newline at end of file
diff --git a/layers/cluster_loss.py b/layers/cluster_loss.py
deleted file mode 100644
index 6318389..0000000
--- a/layers/cluster_loss.py
+++ /dev/null
@@ -1,269 +0,0 @@
-from __future__ import absolute_import
-
-import torch
-from torch import nn
-import torch.nn.functional as F
-
-
-class ClusterLoss(nn.Module):
- def __init__(self, margin=10, use_gpu=True, ordered=True, ids_per_batch=16, imgs_per_id=4):
- super(ClusterLoss, self).__init__()
- self.use_gpu = use_gpu
- self.margin = margin
- self.ordered = ordered
- self.ids_per_batch = ids_per_batch
- self.imgs_per_id = imgs_per_id
-
- def _euclidean_dist(self, x, y):
- """
- Args:
- x: pytorch Variable, with shape [m, d]
- y: pytorch Variable, with shape [n, d]
- Returns:
- dist: pytorch Variable, with shape [m, n]
- """
- m, n = x.size(0), y.size(0)
- xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
- yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
- dist = xx + yy
- dist.addmm_(1, -2, x, y.t())
- dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
- return dist
-
- def _cluster_loss(self, features, targets, ordered=True, ids_per_batch=16, imgs_per_id=4):
- """
- 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:
- cluster_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()
-
- inter_min_distance = torch.zeros(unique_labels.size(0))
- intra_max_distance = torch.zeros(unique_labels.size(0))
- center_features = torch.zeros(unique_labels.size(0), features.size(1))
-
- if self.use_gpu:
- inter_min_distance = inter_min_distance.cuda()
- intra_max_distance = intra_max_distance.cuda()
- center_features = center_features.cuda()
-
- index = torch.range(0, unique_labels.size(0) - 1)
- 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)
- intra_class_distance = self._euclidean_dist(center_features[index==i], same_class_features)
- # print('intra_class_distance', intra_class_distance)
- intra_max_distance[i] = intra_class_distance.max()
- # print('intra_max_distance:', intra_max_distance)
-
- for i in range(unique_labels.size(0)):
- inter_class_distance = self._euclidean_dist(center_features[index==i], center_features[index != i])
- # print('inter_class_distance', inter_class_distance)
- inter_min_distance[i] = inter_class_distance.min()
- # print('inter_min_distance:', inter_min_distance)
- cluster_loss = torch.mean(torch.relu(intra_max_distance - inter_min_distance + self.margin))
- return cluster_loss, intra_max_distance, inter_min_distance
-
- 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:
- cluster_loss
- """
- assert features.size(0) == targets.size(0), "features.size(0) is not equal to targets.size(0)"
- cluster_loss, cluster_dist_ap, cluster_dist_an = self._cluster_loss(features, targets, self.ordered, self.ids_per_batch, self.imgs_per_id)
- return cluster_loss, cluster_dist_ap, cluster_dist_an
-
-
-class ClusterLoss_local(nn.Module):
- def __init__(self, margin=10, use_gpu=True, ordered=True, ids_per_batch=32, imgs_per_id=4):
- super(ClusterLoss_local, self).__init__()
- self.use_gpu = use_gpu
- self.margin = margin
- self.ordered = ordered
- self.ids_per_batch = ids_per_batch
- self.imgs_per_id = imgs_per_id
-
- def _euclidean_dist(self, x, y):
- """
- Args:
- x: pytorch Variable, with shape [m, d]
- y: pytorch Variable, with shape [n, d]
- Returns:
- dist: pytorch Variable, with shape [m, n]
- """
- m, n = x.size(0), y.size(0)
- xx = torch.pow(x, 2).sum(1, keepdim=True).expand(m, n)
- yy = torch.pow(y, 2).sum(1, keepdim=True).expand(n, m).t()
- dist = xx + yy
- dist.addmm_(1, -2, x, y.t())
- dist = dist.clamp(min=1e-12).sqrt() # for numerical stability
- return dist
-
- def _shortest_dist(self, dist_mat):
- """Parallel version.
- Args:
- dist_mat: pytorch Variable, available shape:
- 1) [m, n]
- 2) [m, n, N], N is batch size
- 3) [m, n, *], * can be arbitrary additional dimensions
- Returns:
- dist: three cases corresponding to `dist_mat`:
- 1) scalar
- 2) pytorch Variable, with shape [N]
- 3) pytorch Variable, with shape [*]
- """
- m, n = dist_mat.size()[:2]
- # Just offering some reference for accessing intermediate distance.
- dist = [[0 for _ in range(n)] for _ in range(m)]
- for i in range(m):
- for j in range(n):
- if (i == 0) and (j == 0):
- dist[i][j] = dist_mat[i, j]
- elif (i == 0) and (j > 0):
- dist[i][j] = dist[i][j - 1] + dist_mat[i, j]
- elif (i > 0) and (j == 0):
- dist[i][j] = dist[i - 1][j] + dist_mat[i, j]
- else:
- dist[i][j] = torch.min(dist[i - 1][j], dist[i][j - 1]) + dist_mat[i, j]
- dist = dist[-1][-1]
- return dist
-
- def _local_dist(self, x, y):
- """
- Args:
- x: pytorch Variable, with shape [M, m, d]
- y: pytorch Variable, with shape [N, n, d]
- Returns:
- dist: pytorch Variable, with shape [M, N]
- """
- M, m, d = x.size()
- N, n, d = y.size()
- x = x.contiguous().view(M * m, d)
- y = y.contiguous().view(N * n, d)
- # shape [M * m, N * n]
- dist_mat = self._euclidean_dist(x, y)
- dist_mat = (torch.exp(dist_mat) - 1.) / (torch.exp(dist_mat) + 1.)
- # shape [M * m, N * n] -> [M, m, N, n] -> [m, n, M, N]
- dist_mat = dist_mat.contiguous().view(M, m, N, n).permute(1, 3, 0, 2)
- # shape [M, N]
- dist_mat = self._shortest_dist(dist_mat)
- return dist_mat
-
- def _cluster_loss(self, features, targets,ordered=True, ids_per_batch=32, imgs_per_id=4):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, H, 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:
- cluster_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()
-
- inter_min_distance = torch.zeros(unique_labels.size(0))
- intra_max_distance = torch.zeros(unique_labels.size(0))
- center_features = torch.zeros(unique_labels.size(0), features.size(1), features.size(2))
-
- if self.use_gpu:
- inter_min_distance = inter_min_distance.cuda()
- intra_max_distance = intra_max_distance.cuda()
- center_features = center_features.cuda()
-
- index = torch.range(0, unique_labels.size(0) - 1)
- 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)
- intra_class_distance = self._local_dist(center_features[index==i], same_class_features)
- # print('intra_class_distance', intra_class_distance)
- intra_max_distance[i] = intra_class_distance.max()
- # print('intra_max_distance:', intra_max_distance)
-
- for i in range(unique_labels.size(0)):
- inter_class_distance = self._local_dist(center_features[index==i], center_features[index != i])
- # print('inter_class_distance', inter_class_distance)
- inter_min_distance[i] = inter_class_distance.min()
- # print('inter_min_distance:', inter_min_distance)
-
- cluster_loss = torch.mean(torch.relu(intra_max_distance - inter_min_distance + self.margin))
- return cluster_loss, intra_max_distance, inter_min_distance
-
- def forward(self, features, targets):
- """
- Args:
- features: prediction matrix (before softmax) with shape (batch_size, H, 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:
- cluster_loss
- """
- assert features.size(0) == targets.size(0), "features.size(0) is not equal to targets.size(0)"
- cluster_loss, cluster_dist_ap, cluster_dist_an = self._cluster_loss(features, targets, self.ordered, self.ids_per_batch, self.imgs_per_id)
- return cluster_loss, cluster_dist_ap, cluster_dist_an
-
-
-if __name__ == '__main__':
- use_gpu = True
- cluster_loss = ClusterLoss(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 = cluster_loss(features, targets)
- print(loss)
-
- cluster_loss_local = ClusterLoss_local(use_gpu=use_gpu, ids_per_batch=4, imgs_per_id=4)
- features = torch.rand(16, 8, 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, 8, 2048).cuda()
- targets = torch.Tensor([0, 0, 0, 0, 1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3]).cuda()
- loss = cluster_loss_local(features, targets)
- print(loss)
diff --git a/layers/range_loss.py b/layers/range_loss.py
deleted file mode 100644
index de6f587..0000000
--- a/layers/range_loss.py
+++ /dev/null
@@ -1,232 +0,0 @@
-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)