False,
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[1])
net_outs2, variables2, _ = tft.volume_bndo_flbias_l6_40(
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",
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':
saver_fusion = tf.train.Saver(variables_fusion)
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
saver0.restore(sess, net_files[0])
saver1.restore(sess, net_files[1])
saver2.restore(sess, net_files[2])
positive_confidence,
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[1])
net_outs2, variables2, _ = tft.volume_bndo_flbias_l6_40(
volumes_reshape[2],
False,
positive_confidence,
dropout_rate=0.0,
batch_normalization_statistic=False,
bn_params=bn_params[2])
features = [
net_outs0['flattened_out'], net_outs1['flattened_out'],
net_outs2['fc1_out']
]
net_out, sm_out, fusion_variables = tft.late_fusion(features, True,
positive_confidence)
loss = tf.nn.softmax_cross_entropy_with_logits(logits=net_out,
labels=real_label)
if type(LOSS_BALANCING) == float:
balancing_factor = LOSS_BALANCING
else:
balancing_factor = positive_confidence
balancing = real_label[:, 0] + tf.pow(tf.constant(-1, dtype=tf.float32),
real_label[:, 0]) * tf.constant(
balancing_factor, tf.float32)
modulation = tf.pow(
real_label[:, 0] +
tf.pow(tf.constant(-1, dtype=tf.float32), real_label[:, 0]) * sm_out[:, 0],
tf.constant(2, dtype=tf.float32))
if LOSS_BALANCING:
loss = balancing * loss
