outputs2, variables2, _ = tft.volume_bndo_flbias_l6_40(
inputs_reshape[2],
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[2])
if FUSION_MODE == 'vote':
predictions = [
outputs0['sm_out'], outputs1['sm_out'], outputs2['sm_out']
]
combined_prediction = tft.vote_fusion(predictions)
combined_prediction = tf.reshape(combined_prediction, [-1, 1])
elif FUSION_MODE == 'committe':
predictions = [
outputs0['sm_out'], outputs1['sm_out'], outputs2['sm_out']
]
combined_prediction = tft.committe_fusion(predictions)
elif FUSION_MODE == 'late':
features = [
outputs0['flattened_out'], outputs1['flattened_out'],
outputs2['flattened_out']
]
_, combined_prediction, variables_fusion = tft.late_fusion(
features, False)
else:
print("unknown fusion mode")
exit()
saver0 = tf.train.Saver(variables0)
saver1 = tf.train.Saver(variables1)
saver2 = tf.train.Saver(variables2)
if FUSION_MODE == 'late':
volumes_reshape[2],
False,
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[2])
if FUSION_MODE == 'late':
features = [
net_outs0['flattened_out'], net_outs1['flattened_out'],
net_outs2['fc1_out']
]
_, softmax_out, variables_fusion = tft.late_fusion(features, True)
elif FUSION_MODE == 'committe':
predictions = [
net_outs0['sm_out'], net_outs1['sm_out'], net_outs2['sm_out']
]
softmax_out = tft.committe_fusion(predictions)
correct_prediction = tf.equal(tf.argmax(softmax_out, 1),
tf.argmax(real_label, 1))
batch_accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
if not AUGMENTATION:
correct_output = open(
vision_path + "/correct_predictions_" + FUSION_MODE + "_subset9.log",
"w")
incorrect_output = open(
vision_path + "/incorrect_predictions_" + FUSION_MODE + "_subset9.log",
"w")
extract_volumes = [
ft.partial(mt.extract_volumes,
volume_shape=np.int_(