def inference_deconvolution(image, keep_prob, training):
"""
FCN with deconvolution decoder definition
:param image: input image. Should have values in range 0-255
:param keep_prob: drop-out argument
"""
print("setting up initialized conv layers ...")
net = {};
num_conv = 4;
height = 7;
width = 3;
depth = 2;
height1 = 7;
width1 = 3;
depth1 = 2;
inpt = image;
output_depth = 64;
input_depth = inpt.get_shape().as_list()[4]
W_t = tf.Variable(tf.truncated_normal([height, width, depth, input_depth, output_depth], stddev=0.02))
tf.add_to_collection('lr_w', W_t)
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t))
output = utils.conv3d_strided_batch_normalization(inpt, W_t, training, stride=1)
net['conv_batch_normalization1'] = output
for i in range(1, num_conv):
output = tf.nn.avg_pool3d(output, ksize=[1, 2, 2, 2, 1], strides=[1, 2, 2, 2, 1], padding='SAME')
if i == (num_conv - 1):
height_mlp = 1;
width_mlp = 1;
depth_mlp = 1;
output_mlp = 8
W_t = tf.Variable(
tf.truncated_normal([height_mlp, width_mlp, depth_mlp, output.get_shape().as_list()[4], output_mlp],
stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t))
tf.add_to_collection('lr_w', W_t)
output = utils.conv3d_strided_batch_normalization(output, W_t, training, stride=1)
output_mlp = 64
W_t = tf.Variable(
tf.truncated_normal([height_mlp, width_mlp, depth_mlp, output.get_shape().as_list()[4], output_mlp],
stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t))
tf.add_to_collection('lr_w', W_t)
output = utils.conv3d_strided_batch_normalization(output, W_t, training, stride=1)
net['pool%s' % str(i)] = output
inpt = output;
output_depth = 64;
input_depth = inpt.get_shape().as_list()[4]
W_t = tf.Variable(tf.truncated_normal([height, width, depth, input_depth, output_depth], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t))
tf.add_to_collection('lr_w', W_t)
output = utils.conv3d_strided_batch_normalization(inpt, W_t, training, stride=1)
net['conv_batch_normalization%s' % str(i + 1)] = output
last_output_num = 64
# now to upscale to actual image size
deconv_shape1 = net["conv_batch_normalization3"].get_shape()
W_t1 = tf.Variable(
tf.truncated_normal([height1, width1, depth1, deconv_shape1[4].value, last_output_num], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t1))
tf.add_to_collection('lr_w', W_t1)
# b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
conv_t1 = utils.conv3d_transpose_strided_batch_normalization(output, W_t1, training, output_shape=tf.shape(
net["conv_batch_normalization3"]), stride=2)
try:
fuse_1 = tf.concat((conv_t1, net["conv_batch_normalization3"]), 4, name="fuse_1")
except Exception as e:
fuse_1 = tf.concat(4, (conv_t1, net["conv_batch_normalization3"]), name="fuse_1")
deconv_shape2 = net["conv_batch_normalization2"].get_shape()
W_t2 = tf.Variable(
tf.truncated_normal([height1, width1, depth1, deconv_shape2[4].value, 2 * deconv_shape1[4].value], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t2))
tf.add_to_collection('lr_w', W_t2)
# b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t2 = utils.conv3d_transpose_strided_batch_normalization(fuse_1, W_t2, training, output_shape=tf.shape(
net["conv_batch_normalization2"]), stride=2)
try:
fuse_2 = tf.concat((conv_t2, net["conv_batch_normalization2"]), 4, name="fuse_2")
except Exception as e:
fuse_2 = tf.concat(4, (conv_t2, net["conv_batch_normalization2"]), name="fuse_2")
deconv_shape3 = net["conv_batch_normalization1"].get_shape()
W_t3 = tf.Variable(
tf.truncated_normal([height1, width1, depth1, deconv_shape3[4].value, 2 * deconv_shape2[4].value], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t3))
tf.add_to_collection('lr_w', W_t3)
# b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t3 = utils.conv3d_transpose_strided_batch_normalization(fuse_2, W_t3, training, output_shape=tf.shape(
net["conv_batch_normalization1"]), stride=2)
try:
fuse_3 = tf.concat((conv_t3, net["conv_batch_normalization1"]), 4, name="fuse_3")
except Exception as e:
fuse_3 = tf.concat(4, (conv_t3, net["conv_batch_normalization1"]), name="fuse_3")
W_t4 = tf.Variable(
tf.truncated_normal([height1, width1, depth1, 2 * deconv_shape3[4].value, last_output_num], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t4))
tf.add_to_collection('lr_w', W_t4)
# b_t4 = tf.Variable(tf.constant(0.0, shape=[last_output_num]))
# tf.add_to_collection('lr_b', b_t4)
conv_t4 = utils.conv3d_strided_batch_normalization(fuse_3, W_t4, training, stride=1)
conv_t4 = tf.nn.dropout(conv_t4, keep_prob=keep_prob)
W_4 = tf.Variable(tf.truncated_normal([1, 1, 1, last_output_num, NUM_OF_CLASSESS], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_4))
tf.add_to_collection('lr_w', W_4)
b_4 = tf.Variable(tf.constant(0.0, shape=[NUM_OF_CLASSESS]))
tf.add_to_collection('lr_b', b_4)
conv_t4 = tf.nn.conv3d(conv_t4, filter=W_4, strides=[1, 1, 1, 1, 1], padding="SAME")
conv_t4 = tf.nn.bias_add(conv_t4, b_4)
weight_decay_sum = tf.add_n(tf.get_collection('weight_decay'))
lr_w_vars = tf.get_collection('lr_w')
lr_b_vars = tf.get_collection('lr_b')
return conv_t4, weight_decay_sum, lr_w_vars, lr_b_vars
def inference_median_unpool(image, keep_prob, training):
"""
FCN with median unpooling decoder definition
"""
print("setting up initialized conv layers ...")
net = {};
num_conv = 4;
height = 7;
width = 3;
depth = 3;
height1 = 7;
width1 = 3;
depth1 = 3;
pool_sz = [2, 2, 2]
inpt = image;
output_depth = 64;
input_depth = inpt.get_shape().as_list()[4]
W_t = tf.Variable(tf.truncated_normal([height, width, depth, input_depth, output_depth], stddev=0.02))
tf.add_to_collection('lr_w', W_t)
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t))
output = utils.conv3d_strided_batch_normalization(inpt, W_t, training, stride=1)
net['conv_batch_normalization1'] = output
for i in range(1, num_conv):
print(output.get_shape())
output, argmax = utils.median_pool_3d_with_argmedian(output, pool_sz)
print(argmax.get_shape())
net['pool%s' % str(i)] = output
net['pool_argmax%s' % str(i)] = argmax
inpt = output;
output_depth = 64;
input_depth = inpt.get_shape().as_list()[4]
W_t = tf.Variable(tf.truncated_normal([height, width, depth, input_depth, output_depth], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t))
tf.add_to_collection('lr_w', W_t)
output = utils.conv3d_strided_batch_normalization(inpt, W_t, training, stride=1)
net['conv_batch_normalization%s' % str(i + 1)] = output
last_output_num = 64
# now to upscale to actual image size
unpool1 = utils.unpool_layer_batch_unraveled_indices(output, net['pool_argmax3'], pool_sz)
try:
fuse_1 = tf.concat((unpool1, net["conv_batch_normalization3"]), 4, name="fuse_1")
except Exception as e:
fuse_1 = tf.concat(4, (unpool1, net["conv_batch_normalization3"]), name="fuse_1")
deconv_shape1 = net["conv_batch_normalization3"].get_shape()
W_t1 = tf.Variable(
tf.truncated_normal([height1, width1, depth1, 2 * deconv_shape1[4].value, last_output_num], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t1))
tf.add_to_collection('lr_w', W_t1)
# b_t1 = utils.bias_variable([deconv_shape1[3].value], name="b_t1")
conv_t1 = utils.conv3d_strided_batch_normalization(fuse_1, W_t1, training, stride=1)
unpool2 = utils.unpool_layer_batch_unraveled_indices(conv_t1, net['pool_argmax2'], pool_sz)
try:
fuse_2 = tf.concat((unpool2, net["conv_batch_normalization2"]), 4, name="fuse_2")
except Exception as e:
fuse_2 = tf.concat(4, (unpool2, net["conv_batch_normalization2"]), name="fuse_2")
deconv_shape2 = net["conv_batch_normalization2"].get_shape()
W_t2 = tf.Variable(
tf.truncated_normal([height1, width1, depth1, 2 * deconv_shape1[4].value, deconv_shape2[4].value, ],
stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t2))
tf.add_to_collection('lr_w', W_t2)
# b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t2 = utils.conv3d_strided_batch_normalization(fuse_2, W_t2, training, stride=1)
unpool3 = utils.unpool_layer_batch_unraveled_indices(conv_t2, net['pool_argmax1'], pool_sz)
deconv_shape3 = net["conv_batch_normalization1"].get_shape()
try:
fuse_3 = tf.concat((unpool3, net["conv_batch_normalization1"]), 4, name="fuse_3")
except Exception as e:
fuse_3 = tf.concat(4, (unpool3, net["conv_batch_normalization1"]), name="fuse_3")
W_t3 = tf.Variable(
tf.truncated_normal([height1, width1, depth1, 2 * deconv_shape2[4].value, deconv_shape3[4].value, ],
stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t3))
tf.add_to_collection('lr_w', W_t3)
# b_t2 = utils.bias_variable([deconv_shape2[3].value], name="b_t2")
conv_t3 = utils.conv3d_strided_batch_normalization(fuse_3, W_t3, training, stride=1)
# W_t4 = tf.Variable(tf.truncated_normal([height1, width1, depth1, 2*deconv_shape3[4].value, last_output_num], stddev=0.02))
# tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_t3))
# tf.add_to_collection('lr_w', W_t4)
# #b_t4 = tf.Variable(tf.constant(0.0, shape=[last_output_num]))
# #tf.add_to_collection('lr_b', b_t4)
# conv_t4 = utils.conv3d_strided_batch_normalization(conv_t3, W_t4, training, stride=1)
# conv_t4 = tf.nn.dropout(conv_t4, keep_prob=keep_prob)
W_4 = tf.Variable(tf.truncated_normal([1, 1, 1, last_output_num, NUM_OF_CLASSESS], stddev=0.02))
tf.add_to_collection('weight_decay', tf.nn.l2_loss(W_4))
tf.add_to_collection('lr_w', W_4)
b_4 = tf.Variable(tf.constant(0.0, shape=[NUM_OF_CLASSESS]))
tf.add_to_collection('lr_b', b_4)
conv_t4 = tf.nn.conv3d(conv_t3, filter=W_4, strides=[1, 1, 1, 1, 1], padding="SAME")
conv_t4 = tf.nn.bias_add(conv_t4, b_4)
weight_decay_sum = tf.add_n(tf.get_collection('weight_decay'))
lr_w_vars = tf.get_collection('lr_w')
lr_b_vars = tf.get_collection('lr_b')
return conv_t3, conv_t4, weight_decay_sum, lr_w_vars, lr_b_vars
