def test_process(model, pre_file, data_type, epochs=20, dataset='ucf101',
classes=101, cross_index=1, seq_len=3, batch_size=16, split_sequence=False):
model.load_weights('weights/{}_{}e_cr{}.h5'.format(pre_file,epochs,cross_index))
out_file = r'{}database/{}-test{}-split{}.pickle'.format(data_output_path,dataset,seq_len,cross_index)
with open(out_file,'rb') as f2:
keys = pickle.load(f2)
len_samples = len(keys)
print('-'*40)
print('{} testing'.format(pre_file))
print 'Number samples: {}'.format(len_samples)
print('-'*40)
Y_test = gd.getClassData(keys)
steps = int(np.ceil(len_samples*1.0/batch_size))
time_start = time.time()
y_pred = model.predict_generator(
gd.getTrainData(
keys=keys,batch_size=batch_size,dataset=dataset,classes=classes,train='test',data_type=data_type,split_sequence=split_sequence),
max_queue_size=20,
steps=steps)
run_time = time.time() - time_start
with open('results/{}_{}e_cr{}.pickle'.format(pre_file,epochs,cross_index),'wb') as fw3:
pickle.dump([y_pred, Y_test],fw3)
y_classes = y_pred.argmax(axis=-1)
print(classification_report(Y_test, y_classes, digits=6))
print 'Run time: {}'.format(run_time)
print('-' * 40)
print 'MobileNet Optical #{} stream only: Testing'.format(opt_size)
print('-' * 40)
print 'Number samples: {}'.format(len_samples)
# score = result_model.evaluate_generator(
# gd.getTrainData(
# keys,
# batch_size,
# classes,
# 2,
# 'test',
# opt_size),
# max_queue_size=3,
# steps=len_samples/batch_size
# )
# print('Test loss:', score[0])
# print('Test accuracy:', score[1])
Y_test = gd.getClassData(keys)
y_pred = result_model.predict_generator(
gd.getTrainData(keys, batch_size, classes, 2, 'test', opt_size),
max_queue_size=3,
steps=int(np.ceil(len_samples * 1.0 / batch_size)))
y_classes = y_pred.argmax(axis=-1)
print 'Score per samples'
print(classification_report(Y_test, y_classes, digits=6))
print 'Score per video'
print(gd.getScorePerVideo(y_pred, keys))
with open(out_file, 'rb') as f2:
keys = pickle.load(f2)
# else:
# with open(out_file,'rb') as f1:
# keys_cross = pickle.load(f1)
# keys_train, keys = gd.get_data_cross_validation(keys_cross,cross_index)
len_samples = len(keys)
print('-' * 40)
print 'MobileNet Temporal{} stream: Testing'.format(opt_size)
print('-' * 40)
print 'Number samples: {}'.format(len_samples)
if server:
Y_test = gd.getClassData(keys)
steps = int(np.ceil(len_samples * 1.0 / batch_size))
else:
Y_test = gd.getClassData(keys, 10 * batch_size)
steps = 10
time_start = time.time()
y_pred = result_model.predict_generator(gd.getTrainData(
keys=keys,
batch_size=batch_size,
classes=classes,
mode=1,
train='test',
opt_size=[opt_size],
seq=True),
def test_process(model,
pre_file,
data_type,
epochs=20,
dataset='ucf101',
classes=101,
cross_index=1,
seq_len=3,
batch_size=1,
split_sequence=False):
print(
glob.glob('weights/' + pre_file + '-{:02d}-*.hdf5'.format(epochs))[0])
model.load_weights(
glob.glob('weights/' + pre_file + '-{:02d}-*.hdf5'.format(epochs))[0])
y_pred = np.zeros((3783, 101))
for i in range(9):
out_file = r'{}database/{}-test{}-split{}-test0'.format(
data_output_path, dataset, seq_len,
cross_index) + str(i) + '.pickle'
with open(out_file, 'rb') as f2:
keys = pickle.load(f2)
len_samples = len(keys)
print('-' * 40)
print('{} testing'.format(pre_file))
print('Number samples: {}'.format(len_samples))
print('-' * 40)
Y_test = gd.getClassData(keys)
steps = int(np.ceil(len_samples * 1.0 / batch_size))
time_start = time.time()
y_ = model.predict_generator(gd.getTrainData(
keys=keys,
batch_size=batch_size,
dataset=dataset,
classes=classes,
train='test',
data_type=data_type,
split_sequence=split_sequence),
max_queue_size=20,
steps=steps)
run_time = time.time() - time_start
#y_ = y[2]
y_p = []
for i in range(0, 10 * len_samples, 10):
temp = np.sum(y_[i:i + 9], axis=0) * 1.0 / 10
y_p.append(temp)
y_pred = y_pred + y_p
with open(
'results/{}_{}e_cr{}.pickle'.format(pre_file, epochs, cross_index),
'wb') as fw3:
pickle.dump([y_pred, Y_test], fw3)
print(np.array(y_pred).shape)
y_classes = np.array(y_pred).argmax(axis=-1)
print(classification_report(Y_test, y_classes, digits=6))
confusion_mtx = confusion_matrix(Y_test, y_classes)
np.savetxt('results/{}_{}e_cr{}.csv'.format(pre_file, epochs, cross_index),
confusion_mtx,
delimiter=",")
print('Run time: {}'.format(run_time))
