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classifier_embed_contras.py
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import torch
import torch.nn as nn
from torch.autograd import Variable
import torch.optim as optim
import numpy as np
import util
from sklearn.preprocessing import MinMaxScaler
import sys
class CLASSIFIER:
# train_Y is interger
def __init__(self, _train_X, _train_Y, map_net, embed_size, data_loader, _nclass, _cuda, _lr=0.001, _beta1=0.5, _nepoch=20, _batch_size=100, generalized=True):
self.train_X = _train_X
self.train_Y = _train_Y
self.test_seen_feature = data_loader.test_seen_feature
self.test_seen_label = data_loader.test_seen_label
self.test_unseen_feature = data_loader.test_unseen_feature
self.test_unseen_label = data_loader.test_unseen_label
self.seenclasses = data_loader.seenclasses
self.unseenclasses = data_loader.unseenclasses
self.MapNet=map_net
self.batch_size = _batch_size
self.nepoch = _nepoch
self.nclass = _nclass
self.input_dim = embed_size
self.cuda = _cuda
self.model = LINEAR_LOGSOFTMAX(self.input_dim, self.nclass)
self.model.apply(util.weights_init)
self.criterion = nn.NLLLoss()
self.input = torch.FloatTensor(_batch_size, _train_X.size(1))
self.label = torch.LongTensor(_batch_size)
self.lr = _lr
self.beta1 = _beta1
# setup optimizer
self.optimizer = optim.Adam(self.model.parameters(), lr=_lr, betas=(_beta1, 0.999))
if self.cuda:
self.model.cuda()
self.criterion.cuda()
self.input = self.input.cuda()
self.label = self.label.cuda()
self.index_in_epoch = 0
self.epochs_completed = 0
self.ntrain = self.train_X.size()[0]
if generalized:
self.acc_seen, self.acc_unseen, self.H = self.fit()
else:
self.acc = self.fit_zsl()
def fit(self):
best_H = 0
best_seen = 0
best_unseen = 0
for epoch in range(self.nepoch):
for i in range(0, self.ntrain, self.batch_size):
self.model.zero_grad()
batch_input, batch_label = self.next_batch(self.batch_size)
self.input.copy_(batch_input)
self.label.copy_(batch_label)
embed, _ = self.MapNet(self.input)
output = self.model(embed)
loss = self.criterion(output, self.label)
loss.backward()
self.optimizer.step()
acc_seen = self.val_gzsl(self.test_seen_feature, self.test_seen_label, self.seenclasses)# 已见类准确率
acc_unseen = self.val_gzsl(self.test_unseen_feature, self.test_unseen_label, self.unseenclasses)
if (acc_seen+acc_unseen)==0:
print('a bug')
H=0
else:
H = 2*acc_seen*acc_unseen / (acc_seen+acc_unseen)# H = 2 × S × U/(S + U)
if H > best_H:
best_seen = acc_seen
best_unseen = acc_unseen
best_H = H
return best_seen, best_unseen, best_H
def next_batch(self, batch_size):
start = self.index_in_epoch
# shuffle the data at the first epoch
if self.epochs_completed == 0 and start == 0:
perm = torch.randperm(self.ntrain)
self.train_X = self.train_X[perm]
self.train_Y = self.train_Y[perm]
# the last batch
if start + batch_size > self.ntrain:
self.epochs_completed += 1
rest_num_examples = self.ntrain - start
if rest_num_examples > 0:
X_rest_part = self.train_X[start:self.ntrain]
Y_rest_part = self.train_Y[start:self.ntrain]
# shuffle the data
perm = torch.randperm(self.ntrain)
self.train_X = self.train_X[perm]
self.train_Y = self.train_Y[perm]
# start next epoch
start = 0
self.index_in_epoch = batch_size - rest_num_examples
end = self.index_in_epoch
X_new_part = self.train_X[start:end]
Y_new_part = self.train_Y[start:end]
#print(start, end)
if rest_num_examples > 0:
return torch.cat((X_rest_part, X_new_part), 0) , torch.cat((Y_rest_part, Y_new_part), 0)
else:
return X_new_part, Y_new_part
else:
self.index_in_epoch += batch_size
end = self.index_in_epoch
#print(start, end)
# from index start to index end-1
return self.train_X[start:end], self.train_Y[start:end]
def val_gzsl(self, test_X, test_label, target_classes):
start = 0
ntest = test_X.size()[0]
predicted_label = torch.LongTensor(test_label.size())
for i in range(0, ntest, self.batch_size):
end = min(ntest, start+self.batch_size)
with torch.no_grad():
if self.cuda:
embed, _ = self.MapNet(test_X[start:end].cuda())
output = self.model(embed)
else:
embed, _ = self.MapNet(test_X[start:end])
output = self.model(embed)
_, predicted_label[start:end] = torch.max(output, 1)
start = end
acc = self.compute_per_class_acc_gzsl(test_label, predicted_label, target_classes)
return acc
def compute_per_class_acc_gzsl(self, test_label, predicted_label, target_classes):
acc_per_class = 0
for i in target_classes:
idx = (test_label == i)
acc_per_class += float(torch.sum(test_label[idx] == predicted_label[idx])) / float(torch.sum(idx))
acc_per_class /= target_classes.size(0)
return acc_per_class
class LINEAR_LOGSOFTMAX(nn.Module):
def __init__(self, input_dim, nclass):
super(LINEAR_LOGSOFTMAX, self).__init__()
self.fc = nn.Linear(input_dim, nclass)
self.logic = nn.LogSoftmax(dim=1)
def forward(self, x):
o = self.logic(self.fc(x))
return o