def up_layer(x1, x2, in_size1, in_size2, out_size, i):
# Up 1
W1 = utils.weight_variable([2, 2, 2, in_size2, in_size1],
name="W_u_" + str(i) + "_1")
b1 = utils.bias_variable([in_size2], name="b_u_" + str(i) + "_1")
deco1 = utils.conv3d_transpose_strided(x1,
W1,
b1,
output_shape=tf.shape(x2))
relu1 = tf.nn.relu(deco1, name="relu_d_" + str(i) + "_1")
# Concat
conc1 = tf.concat([relu1, x2],
-1) # concat along the channels dimension
# Conv1
W2 = utils.weight_variable([3, 3, 3, in_size2 * 2, out_size],
name="W_u_" + str(i) + "_2")
b2 = utils.bias_variable([out_size], name="b_u_" + str(i) + "_2")
conv1 = utils.conv3d_basic(conc1, W2, b2)
relu2 = tf.nn.relu(conv1, name="relu_u_" + str(i) + "_2")
relu2 = tf.nn.dropout(relu2, keep_prob=keep_prob)
# Conv2
W3 = utils.weight_variable([3, 3, 3, out_size, out_size],
name="W_u_" + str(i) + "_3")
b3 = utils.bias_variable([out_size], name="b_u_" + str(i) + "_3")
conv3 = utils.conv3d_basic(relu2, W3, b3)
relu3 = tf.nn.relu(conv3, name="relu_u_" + str(i) + "_3")
relu3 = tf.nn.dropout(relu3, keep_prob=keep_prob)
return relu3
