def main():
if len(args['device_ids']) > 0:
torch.cuda.set_device(args['device_ids'][0])
test_loader = data.DataLoader(imageLabelLoader(args['data_path'],
dataName=args['domainB'],
phase='val'),
batch_size=args['batch_size'],
num_workers=args['num_workers'],
shuffle=False)
gym = deeplabGanS2TWithRefine4()
gym.initialize(args)
gym.load(
'/home/ben/mathfinder/PROJECT/AAAI2017/our_Method/v3/deeplab_feature_adaptation/checkpoints/Lip_to_July_g1/best_Ori_on_B_model.pth'
)
gym.eval()
matrix = ConfusionMatrix(args['label_nums'])
for i, (image, label) in enumerate(test_loader):
label = label.cuda(async=True)
target_var = torch.autograd.Variable(label, volatile=True)
gym.test(False, image)
output = gym.output
matrix = update_confusion_matrix(matrix, output.data, label)
print(matrix.avg_f1score())
print(matrix.f1score())
def test_model(model_path: str, stop_word_file="data\stopwords_eng.txt"):
print("starting testing")
token_filter = TokenFilter(stop_word_file)
tokenizer = Tokenizer()
with open(model_path, "rb") as fb:
bayes = pickle.load(fb)
cf = ConfusionMatrix(["positive", "negative"])
for path in paths:
for file in os.listdir(path[0]):
with open(path[0] + "/" + file) as f:
tokens = tokenizer.tokenize(f)
bag_of_words = create_bag_of_words(token_filter, tokens, True)
predicted_class = bayes.predict_class(bag_of_words)
print("Predicted: " + predicted_class + ", Real: " + path[1])
cf.add_prediction(path[1], predicted_class)
print(cf)
print("Accuracy: " + str(cf.accuracy_average()))
print("Recall: " + str(cf.recall_average()))
print("Precision: " + str(cf.precision_average()))
print("F-Measure: " + str(cf.f_measure_average()))
def validate(val_loader, model, criterion, adaptation):
# switch to evaluate mode
run_time = time.time()
matrix = ConfusionMatrix(args['label_nums'])
loss = 0
for i, (images, labels) in enumerate(val_loader):
labels = labels.cuda(async=True)
target_var = torch.autograd.Variable(labels, volatile=True)
model.test(adaptation, images)
output = model.output
loss += criterion(output, target_var)/args['batch_size']
matrix = update_confusion_matrix(matrix, output.data, labels)
loss /= (i+1)
run_time = time.time() - run_time
logger.info('=================================================')
logger.info('val:'
'loss: {0:.4f}\t'
'accuracy: {1:.4f}\t'
'fg_accuracy: {2:.4f}\t'
'avg_precision: {3:.4f}\t'
'avg_recall: {4:.4f}\t'
'avg_f1score: {5:.4f}\t'
'run_time:{run_time:.2f}\t'
.format(loss.data[0], matrix.accuracy(),
matrix.fg_accuracy(), matrix.avg_precision(), matrix.avg_recall(), matrix.avg_f1score(),run_time=run_time))
logger.info('=================================================')
return matrix.all_acc()
def train(A_train_loader, B_train_loader, model, epoch):
# switch to train mode
model.train()
for i, (A_image, A_label) in enumerate(A_train_loader):
B_image = next(iter(B_train_loader))
model.set_input({'A': A_image, 'A_label': A_label, 'B': B_image})
model.forward()
model.optimize_parameters()
output = model.output
if i % args['print_freq'] == 0:
matrix = ConfusionMatrix()
update_confusion_matrix(matrix, output.data, A_label)
print('Time: {time}\t'
'Epoch/Iter: [{epoch}/{iter}]\t'
'loss: {loss:.4f}\t'
'acc: {accuracy:.4f}\t'
'fg_acc: {fg_accuracy:.4f}\t'
'avg_prec: {avg_precision:.4f}\t'
'avg_rec: {avg_recall:.4f}\t'
'avg_f1: {avg_f1core:.4f}\t'
'loss_G: {loss_G:.4f}\t'
'loss_D: {loss_D:.4f}\t'.format(
time=time.strftime("%Y-%m-%d_%H:%M:%S",
time.localtime()),
epoch=epoch,
iter=i + epoch * len(A_train_loader),
loss=model.loss_P.data[0],
accuracy=matrix.accuracy(),
fg_accuracy=matrix.fg_accuracy(),
avg_precision=matrix.avg_precision(),
avg_recall=matrix.avg_recall(),
avg_f1core=matrix.avg_f1score(),
loss_G=model.loss_G.data[0],
loss_D=model.loss_D.data[0]))
def main():
if len(args['device_ids']) > 0:
torch.cuda.set_device(args['device_ids'][0])
test_loader = data.DataLoader(imageLabelLoader(args['data_path'], dataName=args['domainB'], phase='test'),
batch_size=args['batch_size'],
num_workers=args['num_workers'], shuffle=False)
gym = deeplabG1G2()
gym.initialize(args)
gym.load('/home/ben/mathfinder/PROJECT/AAAI2017/our_Method/v3/deeplab_feature_adaptation/checkpoints/lr_g1=0.00001_lr_g2=0.00000001_interval_g1=6_interval_d1=6_net_D=lsganMultOutput_D_if_adaptive=True/best_Ori_on_B_model.pth')
gym.eval()
matrix = ConfusionMatrix(args['label_nums'])
for i, (image, label) in enumerate(test_loader):
label = label.cuda(async=True)
target_var = torch.autograd.Variable(label, volatile=True)
gym.test(image)
output = gym.output
matrix = update_confusion_matrix(matrix, output.data, label)
print(matrix.all_acc())
def train(train_loader, model, criterion, optimizer, epoch):
# switch to train mode
model.train()
for i, (images, labels) in enumerate(train_loader):
run_time = time.time()
labels = labels.cuda(async=True)
input_var = torch.autograd.Variable(images)
target_var = torch.autograd.Variable(labels)
# compute output
output = model.forward(input_var)
loss = criterion(output, target_var) / args['batch_size']
# compute gradient and do SGD step
optimizer.zero_grad()
loss.backward()
optimizer.step()
if i % args['print_freq'] == 0:
matrix = ConfusionMatrix()
update_confusion_matrix(matrix, output.data, labels)
run_time = time.time() - run_time
print('Epoch/Iter: [{epoch}/{iter}]\t'
'loss: {loss:.4f}\t'
'acc: {accuracy:.4f}\t'
'fg_acc: {fg_accuracy:.4f}\t'
'avg_prec: {avg_precision:.4f}\t'
'avg_rec: {avg_recall:.4f}\t'
'avg_f1: {avg_f1:.4f}\t'
'run_time:{run_time:.2f}\t'.format(
epoch=epoch,
iter=i + epoch * len(train_loader),
loss=loss.data[0],
accuracy=matrix.accuracy(),
fg_accuracy=matrix.fg_accuracy(),
avg_precision=matrix.avg_precision(),
avg_recall=matrix.avg_recall(),
avg_f1core=matrix.avg_f1score(),
run_time=run_time))
def main():
makedirs.mkdirs(os.path.join(args['checkpoints_dir'], args['name']))
if len(args['device_ids']) > 0:
torch.cuda.set_device(args['device_ids'][0])
A_train_loader = data.DataLoader(imageLabelLoader(args['data_path'],dataName=args['domainA'], phase='train'), batch_size=args['batch_size'],
num_workers=args['num_workers'], shuffle=True)
A_val_loader = data.DataLoader(imageLabelLoader(args['data_path'], dataName=args['domainA'], phase='val'), batch_size=args['batch_size'],
num_workers=args['num_workers'], shuffle=False)
B_train_loader = data.DataLoader(imageLoader(args['data_path'], dataName=args['domainB'], phase='train+unlabel'),
batch_size=args['batch_size'],
num_workers=args['num_workers'], shuffle=True)
B_val_loader = data.DataLoader(imageLabelLoader(args['data_path'], dataName=args['domainB'], phase='val'),
batch_size=args['batch_size'],
num_workers=args['num_workers'], shuffle=False)
model = deeplabGanWithRefine()
model.initialize(args)
# multi GPUS
# model = torch.nn.DataParallel(model,device_ids=args['device_ids']).cuda()
Iter = 0
if args['resume']:
if os.path.isfile(args['resume']):
logger.info("=> loading checkpoint '{}'".format(args['resume']))
model.load(args['resume'])
else:
print("=> no checkpoint found at '{}'".format(args['resume']))
best_Ori_on_B = 0
best_Ada_on_B = 0
model.train()
for epoch in range(args['n_epoch']):
# train(A_train_loader, B_train_loader, model, epoch)
# switch to train mode
for i, (A_image, A_label) in enumerate(A_train_loader):
Iter += 1
B_image = next(iter(B_train_loader))
model.set_input({'A': A_image, 'A_label': A_label, 'B': B_image})
model.optimize_parameters()
output = model.output
if i % args['print_freq'] == 0:
matrix = ConfusionMatrix()
update_confusion_matrix(matrix, output.data, A_label)
logger.info('Time: {time}\t'
'Epoch/Iter: [{epoch}/{Iter}]\t'
'loss: {loss:.4f}\t'
'loss_R: {loss_R:.4f}\t'
'acc: {accuracy:.4f}\t'
'fg_acc: {fg_accuracy:.4f}\t'
'avg_prec: {avg_precision:.4f}\t'
'avg_rec: {avg_recall:.4f}\t'
'avg_f1: {avg_f1core:.4f}\t'
'loss_G: {loss_G:.4f}\t'
'loss_D: {loss_D:.4f}\t'.format(
time=time.strftime("%Y-%m-%d_%H:%M:%S", time.localtime()),
epoch=epoch, Iter=Iter, loss=model.loss_P.data[0],
loss_R=model.loss_R.data[0], accuracy=matrix.accuracy(),
fg_accuracy=matrix.fg_accuracy(), avg_precision=matrix.avg_precision(),
avg_recall=matrix.avg_recall(), avg_f1core=matrix.avg_f1score(),
loss_G=model.loss_G.data[0], loss_D=model.loss_D.data[0]))
if Iter % 1000 == 0:
model.eval()
acc_Ori_on_A = validate(A_val_loader, model, nn.CrossEntropyLoss(size_average=False), False)
acc_Ori_on_B = validate(B_val_loader, model, nn.CrossEntropyLoss(size_average=False), False)
acc_Ada_on_B = validate(B_val_loader, model, nn.CrossEntropyLoss(size_average=False), True)
prec_Ori_on_B = acc_Ori_on_B['avg_f1score']
prec_Ada_on_B = acc_Ada_on_B['avg_f1score']
is_best = prec_Ori_on_B > best_Ori_on_B
best_Ori_on_B = max(prec_Ori_on_B, best_Ori_on_B)
if is_best:
model.save('best_Ori_on_B', Iter=Iter, epoch=epoch, acc={'acc_Ori_on_A':acc_Ori_on_A, 'acc_Ori_on_B':acc_Ori_on_B, 'acc_Ada_on_B':acc_Ada_on_B})
is_best = prec_Ada_on_B > best_Ada_on_B
best_Ada_on_B = max(prec_Ada_on_B, best_Ada_on_B)
if is_best:
model.save('best_Ada_on_B', Iter=Iter, epoch=epoch, acc={'acc_Ori_on_A':acc_Ori_on_A, 'acc_Ori_on_B':acc_Ori_on_B, 'acc_Ada_on_B':acc_Ada_on_B})
model.train()
from sentiment_classifier import SentimentClassifier
from data import create_bows_from_path
from util.confusion_matrix import ConfusionMatrix
from util.object_util import load_object
MODEL_PATH = "../data/model/"
TEST_DATA_PATH = "../data/test/"
# load model
bayes: SentimentClassifier = load_object(MODEL_PATH + "classifier.model")
cf: ConfusionMatrix = ConfusionMatrix(bayes.classes)
# load bows
# collect bag_of_words for positive samples
positive_bow_list = []
positive_test_path = TEST_DATA_PATH + "pos/"
positive_bow_list = create_bows_from_path(positive_test_path)
for bow in positive_bow_list:
clazz: str = bayes.predict_class(bow)
cf.add_prediction("positive", clazz)
negative_bow_list = []
negative_test_path = TEST_DATA_PATH + "neg/"
negative_bow_list = create_bows_from_path(negative_test_path)
for bow in negative_bow_list:
clazz: str = bayes.predict_class(bow)
cf.add_prediction("negative", clazz)
