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util.py
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util.py
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#import h5py
import numpy as np
import scipy.io as sio
import torch
from sklearn import preprocessing
import sys
from sklearn.cluster import KMeans
def weights_init(m):
classname = m.__class__.__name__
if classname.find('Linear') != -1:
m.weight.data.normal_(0.0, 0.02)
m.bias.data.fill_(0)
elif classname.find('BatchNorm') != -1:
m.weight.data.normal_(1.0, 0.02)
m.bias.data.fill_(0)
def map_label(label, classes):
mapped_label = torch.LongTensor(label.size())
for i in range(classes.size(0)):
mapped_label[label==classes[i]] = i
return mapped_label
class Logger(object):
def __init__(self, filename):
self.filename = filename
f = open(self.filename+'.log', "a")
f.close()
def write(self, message):
f = open(self.filename+'.log', "a")
f.write(message)
f.close()
class DATA_LOADER(object):
def __init__(self, opt):
if opt.matdataset:
if opt.dataset == 'imageNet1K':
self.read_matimagenet(opt)
else:
self.read_matdataset(opt)
self.index_in_epoch = 0
self.epochs_completed = 0
self.feature_dim = self.train_feature.shape[1]
self.att_dim = self.attribute.shape[1]
self.text_dim = self.att_dim
self.train_cls_num = self.seenclasses.shape[0]
self.test_cls_num = self.unseenclasses.shape[0]
self.tr_cls_centroid = np.zeros([self.seenclasses.shape[0], self.feature_dim], np.float32)
for i in range(self.seenclasses.shape[0]):
self.tr_cls_centroid[i] = np.mean(self.train_feature[torch.nonzero(self.train_mapped_label == i),:].numpy(), axis=0)
n_cluster = opt.n_clusters
real_proto = torch.zeros(n_cluster * self.train_cls_num, self.feature_dim)
for i in range(self.train_cls_num):
sample_idx = (self.train_mapped_label == i).nonzero().squeeze()
if sample_idx.numel() == 0:
real_proto[n_cluster * i: n_cluster * (i+1)] = torch.zeros(n_cluster, self.feature_dim)
else:
real_sample_cls = self.train_feature[sample_idx, :]
y_pred = KMeans(n_clusters=n_cluster, random_state=3).fit_predict(real_sample_cls)
for j in range(n_cluster):
real_proto[n_cluster*i+j] = torch.from_numpy(real_sample_cls[torch.nonzero(torch.from_numpy(y_pred)==j),:].mean(dim=0).cpu().numpy())
self.real_proto = real_proto
# not tested
def read_h5dataset(self, opt):
# read image feature
fid = h5py.File(opt.dataroot + "/" + opt.dataset + "/" + opt.image_embedding + ".hdf5", 'r')
feature = fid['feature'][()]
label = fid['label'][()]
trainval_loc = fid['trainval_loc'][()]
train_loc = fid['train_loc'][()]
val_unseen_loc = fid['val_unseen_loc'][()]
test_seen_loc = fid['test_seen_loc'][()]
test_unseen_loc = fid['test_unseen_loc'][()]
fid.close()
# read attributes
fid = h5py.File(opt.dataroot + "/" + opt.dataset + "/" + opt.class_embedding + ".hdf5", 'r')
self.attribute = fid['attribute'][()]
fid.close()
if not opt.validation:
self.train_feature = feature[trainval_loc]
self.train_label = label[trainval_loc]
self.test_unseen_feature = feature[test_unseen_loc]
self.test_unseen_label = label[test_unseen_loc]
self.test_seen_feature = feature[test_seen_loc]
self.test_seen_label = label[test_seen_loc]
else:
self.train_feature = feature[train_loc]
self.train_label = label[train_loc]
self.test_unseen_feature = feature[val_unseen_loc]
self.test_unseen_label = label[val_unseen_loc]
self.seenclasses = np.unique(self.train_label)
self.unseenclasses = np.unique(self.test_unseen_label)
self.nclasses = self.seenclasses.size(0)
def read_matimagenet(self, opt):
if opt.preprocessing:
print('MinMaxScaler...')
scaler = preprocessing.MinMaxScaler()
matcontent = h5py.File(opt.dataroot + "/" + opt.dataset + "/" + opt.image_embedding + ".mat", 'r')
feature = scaler.fit_transform(np.array(matcontent['features']))
label = np.array(matcontent['labels']).astype(int).squeeze() - 1
feature_val = scaler.transform(np.array(matcontent['features_val']))
label_val = np.array(matcontent['labels_val']).astype(int).squeeze() - 1
matcontent.close()
matcontent = h5py.File('/BS/xian/work/data/imageNet21K/extract_res/res101_1crop_2hops_t.mat', 'r')
feature_unseen = scaler.transform(np.array(matcontent['features']))
label_unseen = np.array(matcontent['labels']).astype(int).squeeze() - 1
matcontent.close()
else:
matcontent = h5py.File(opt.dataroot + "/" + opt.dataset + "/" + opt.image_embedding + ".mat", 'r')
feature = np.array(matcontent['features'])
label = np.array(matcontent['labels']).astype(int).squeeze() - 1
feature_val = np.array(matcontent['features_val'])
label_val = np.array(matcontent['labels_val']).astype(int).squeeze() - 1
matcontent.close()
matcontent = sio.loadmat(opt.dataroot + "/" + opt.dataset + "/" + opt.class_embedding + ".mat")
self.attribute = torch.from_numpy(matcontent['w2v']).float()
self.train_feature = torch.from_numpy(feature).float()
self.train_label = torch.from_numpy(label).long()
self.test_seen_feature = torch.from_numpy(feature_val).float()
self.test_seen_label = torch.from_numpy(label_val).long()
self.test_unseen_feature = torch.from_numpy(feature_unseen).float()
self.test_unseen_label = torch.from_numpy(label_unseen).long()
self.ntrain = self.train_feature.size()[0]
self.seenclasses = torch.from_numpy(np.unique(self.train_label.numpy()))
self.unseenclasses = torch.from_numpy(np.unique(self.test_unseen_label.numpy()))
self.train_class = torch.from_numpy(np.unique(self.train_label.numpy()))
self.ntrain_class = self.seenclasses.size(0)
self.ntest_class = self.unseenclasses.size(0)
def read_matdataset(self, opt):
matcontent = sio.loadmat(opt.dataroot + "/" + opt.dataset + "/" + opt.image_embedding + ".mat")
feature = matcontent['features'].T
label = matcontent['labels'].astype(int).squeeze() - 1
matcontent = sio.loadmat(opt.dataroot + "/" + opt.dataset + "/" + opt.class_embedding + "_splits.mat")
# numpy array index starts from 0, matlab starts from 1
trainval_loc = matcontent['trainval_loc'].squeeze() - 1
train_loc = matcontent['train_loc'].squeeze() - 1
val_unseen_loc = matcontent['val_loc'].squeeze() - 1
test_seen_loc = matcontent['test_seen_loc'].squeeze() - 1
test_unseen_loc = matcontent['test_unseen_loc'].squeeze() - 1
self.attribute = torch.from_numpy(matcontent['att'].T).float()
if not opt.validation:
if opt.preprocessing:
if opt.standardization:
print('standardization...')
scaler = preprocessing.StandardScaler()
else:
scaler = preprocessing.MinMaxScaler()
_train_feature = scaler.fit_transform(feature[trainval_loc])
_test_seen_feature = scaler.transform(feature[test_seen_loc])
_test_unseen_feature = scaler.transform(feature[test_unseen_loc])
self.train_feature = torch.from_numpy(_train_feature).float()
mx = self.train_feature.max()
self.train_feature.mul_(1/mx)
self.train_label = torch.from_numpy(label[trainval_loc]).long()
self.test_unseen_feature = torch.from_numpy(_test_unseen_feature).float()
self.test_unseen_feature.mul_(1/mx)
self.test_unseen_label = torch.from_numpy(label[test_unseen_loc]).long()
self.test_seen_feature = torch.from_numpy(_test_seen_feature).float()
self.test_seen_feature.mul_(1/mx)
self.test_seen_label = torch.from_numpy(label[test_seen_loc]).long()
else:
self.train_feature = torch.from_numpy(feature[trainval_loc]).float()
self.train_label = torch.from_numpy(label[trainval_loc]).long()
self.test_unseen_feature = torch.from_numpy(feature[test_unseen_loc]).float()
self.test_unseen_label = torch.from_numpy(label[test_unseen_loc]).long()
self.test_seen_feature = torch.from_numpy(feature[test_seen_loc]).float()
self.test_seen_label = torch.from_numpy(label[test_seen_loc]).long()
else:
self.train_feature = torch.from_numpy(feature[train_loc]).float()
self.train_label = torch.from_numpy(label[train_loc]).long()
self.test_unseen_feature = torch.from_numpy(feature[val_unseen_loc]).float()
self.test_unseen_label = torch.from_numpy(label[val_unseen_loc]).long()
self.seenclasses = torch.from_numpy(np.unique(self.train_label.numpy()))
self.unseenclasses = torch.from_numpy(np.unique(self.test_unseen_label.numpy()))
self.ntrain = self.train_feature.size()[0]
self.ntrain_class = self.seenclasses.size(0)
self.ntest_class = self.unseenclasses.size(0)
self.train_class = self.seenclasses.clone()
self.allclasses = torch.arange(0, self.ntrain_class+self.ntest_class).long()
self.train_mapped_label = map_label(self.train_label, self.seenclasses)
self.train_att = self.attribute[self.seenclasses].numpy()
self.test_att = self.attribute[self.unseenclasses].numpy()
self.train_cls_num = self.ntrain_class
self.test_cls_num = self.ntest_class
def next_batch_one_class(self, batch_size):
if self.index_in_epoch == self.ntrain_class:
self.index_in_epoch = 0
perm = torch.randperm(self.ntrain_class)
self.train_class[perm] = self.train_class[perm]
iclass = self.train_class[self.index_in_epoch]
idx = self.train_label.eq(iclass).nonzero().squeeze()
perm = torch.randperm(idx.size(0))
idx = idx[perm]
iclass_feature = self.train_feature[idx]
iclass_label = self.train_label[idx]
self.index_in_epoch += 1
return iclass_feature[0:batch_size], iclass_label[0:batch_size], self.attribute[iclass_label[0:batch_size]]
def next_batch(self, batch_size):
idx = torch.randperm(self.ntrain)[0:batch_size]
batch_feature = self.train_feature[idx]
batch_label = self.train_label[idx]
batch_att = self.attribute[batch_label]
return batch_feature, batch_label, batch_att
# select batch samples by randomly drawing batch_size classes
def next_batch_uniform_class(self, batch_size):
batch_class = torch.LongTensor(batch_size)
for i in range(batch_size):
idx = torch.randperm(self.ntrain_class)[0]
batch_class[i] = self.train_class[idx]
batch_feature = torch.FloatTensor(batch_size, self.train_feature.size(1))
batch_label = torch.LongTensor(batch_size)
batch_att = torch.FloatTensor(batch_size, self.attribute.size(1))
for i in range(batch_size):
iclass = batch_class[i]
idx_iclass = self.train_label.eq(iclass).nonzero().squeeze()
idx_in_iclass = torch.randperm(idx_iclass.size(0))[0]
idx_file = idx_iclass[idx_in_iclass]
batch_feature[i] = self.train_feature[idx_file]
batch_label[i] = self.train_label[idx_file]
batch_att[i] = self.attribute[batch_label[i]]
return batch_feature, batch_label, batch_att