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train_interactive_segmentation.py
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train_interactive_segmentation.py
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from __future__ import division
import os, time, cv2
import scipy.io as sio
import tensorflow as tf
import tensorflow.contrib.slim as slim
import numpy as np
from numpy import *
import scipy.linalg
from copy import copy, deepcopy
def lrelu(x):
return tf.maximum(x * 0.2, x)
def identity_initializer():
def _initializer(shape, dtype=tf.float32, partition_info=None):
array = np.zeros(shape, dtype=float)
cx, cy = shape[0] // 2, shape[1] // 2
for i in range(min(shape[2], shape[3])):
array[cx, cy, i, i] = 1
return tf.constant(array, dtype=dtype)
return _initializer
def nm(x):
w0 = tf.Variable(1.0, name='w0')
w1 = tf.Variable(0.0, name='w1')
return w0 * x + w1 * slim.batch_norm(x)
MEAN_VALUES = np.array([123.6800, 116.7790, 103.9390]).reshape((1, 1, 1, 3))
def build_net(ntype, nin, nwb=None, name=None):
if ntype == 'conv':
return tf.nn.relu(tf.nn.conv2d(nin, nwb[0], strides=[1, 1, 1, 1], padding='SAME', name=name) + nwb[1])
elif ntype == 'pool':
return tf.nn.avg_pool(nin, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
def get_weight_bias(vgg_layers, i):
weights = vgg_layers[i][0][0][2][0][0]
weights = tf.constant(weights)
bias = vgg_layers[i][0][0][2][0][1]
bias = tf.constant(np.reshape(bias, (bias.size)))
return weights, bias
def build_vgg19(input, reuse=False):
if reuse:
tf.get_variable_scope().reuse_variables()
net = {}
vgg_rawnet = scipy.io.loadmat('Models/imagenet-vgg-verydeep-19.mat')
vgg_layers = vgg_rawnet['layers'][0]
net['input'] = input - MEAN_VALUES
net['conv1_1'] = build_net('conv', net['input'], get_weight_bias(vgg_layers, 0), name='vgg_conv1_1')
net['conv1_2'] = build_net('conv', net['conv1_1'], get_weight_bias(vgg_layers, 2), name='vgg_conv1_2')
net['pool1'] = build_net('pool', net['conv1_2'])
net['conv2_1'] = build_net('conv', net['pool1'], get_weight_bias(vgg_layers, 5), name='vgg_conv2_1')
net['conv2_2'] = build_net('conv', net['conv2_1'], get_weight_bias(vgg_layers, 7), name='vgg_conv2_2')
net['pool2'] = build_net('pool', net['conv2_2'])
net['conv3_1'] = build_net('conv', net['pool2'], get_weight_bias(vgg_layers, 10), name='vgg_conv3_1')
net['conv3_2'] = build_net('conv', net['conv3_1'], get_weight_bias(vgg_layers, 12), name='vgg_conv3_2')
net['conv3_3'] = build_net('conv', net['conv3_2'], get_weight_bias(vgg_layers, 14), name='vgg_conv3_3')
net['conv3_4'] = build_net('conv', net['conv3_3'], get_weight_bias(vgg_layers, 16), name='vgg_conv3_4')
net['pool3'] = build_net('pool', net['conv3_4'])
net['conv4_1'] = build_net('conv', net['pool3'], get_weight_bias(vgg_layers, 19), name='vgg_conv4_1')
net['conv4_2'] = build_net('conv', net['conv4_1'], get_weight_bias(vgg_layers, 21), name='vgg_conv4_2')
net['conv4_3'] = build_net('conv', net['conv4_2'], get_weight_bias(vgg_layers, 23), name='vgg_conv4_3')
net['conv4_4'] = build_net('conv', net['conv4_3'], get_weight_bias(vgg_layers, 25), name='vgg_conv4_4')
net['pool4'] = build_net('pool', net['conv4_4'])
net['conv5_1'] = build_net('conv', net['pool4'], get_weight_bias(vgg_layers, 28), name='vgg_conv5_1')
net['conv5_2'] = build_net('conv', net['conv5_1'], get_weight_bias(vgg_layers, 30), name='vgg_conv5_2')
# net['conv5_3']=build_net('conv',net['conv5_2'],get_weight_bias(vgg_layers,32),name='vgg_conv5_3')
# net['conv5_4']=build_net('conv',net['conv5_3'],get_weight_bias(vgg_layers,34),name='vgg_conv5_4')
# net['pool5']=build_net('pool',net['conv5_4'])
return net
def build(input, sz):
vgg19_features = build_vgg19(input[:, :, :, 0:3])
for layer_id in range(1, 6):
vgg19_f = vgg19_features['conv%d_2' % layer_id]
input = tf.concat([input, tf.image.resize_bilinear(vgg19_f, sz)], axis=3)
input = input / 255.0
net = slim.conv2d(input, 64, [1, 1], rate=1, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv0')
net = slim.conv2d(net, 64, [3, 3], rate=1, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv1')
net = slim.conv2d(net, 64, [3, 3], rate=2, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv2')
net = slim.conv2d(net, 64, [3, 3], rate=4, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv3')
net = slim.conv2d(net, 64, [3, 3], rate=8, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv4')
net = slim.conv2d(net, 64, [3, 3], rate=16, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv5')
net = slim.conv2d(net, 64, [3, 3], rate=32, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv6')
net = slim.conv2d(net, 64, [3, 3], rate=64, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv7')
net = slim.conv2d(net, 64, [3, 3], rate=128, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv8')
net = slim.conv2d(net, 64, [3, 3], rate=1, activation_fn=lrelu, normalizer_fn=nm,
weights_initializer=identity_initializer(), scope='g_conv9')
net = slim.conv2d(net, 6, [1, 1], rate=1, activation_fn=None, scope='g_conv_last')
return tf.tanh(net)
def prepare_data():
train_im_names = [line.rstrip() for line in open('./train.txt')]
val_im_names = [line.rstrip() for line in open('./val.txt')]
return train_im_names, val_im_names
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
im_path = "./img"
seg_path = "./inst"
train_im_names, val_im_names = prepare_data()
input = tf.placeholder(tf.float32, shape=[None, None, None, 7])
output = tf.placeholder(tf.float32, shape=[None, None, None, 1])
sz = tf.placeholder(tf.int32, shape=[2])
input_vgg = tf.placeholder(tf.float32, shape=[None, None, None, 3])
network = build(input, sz)
vgg19_network = build_vgg19(input_vgg)
# L2 Loss
loss_d1 = tf.reduce_mean(tf.square(tf.expand_dims(network[:, :, :, 0], axis=3) - output))
loss_d2 = tf.reduce_mean(tf.square(tf.expand_dims(network[:, :, :, 1], axis=3) - output))
loss_d3 = tf.reduce_mean(tf.square(tf.expand_dims(network[:, :, :, 2], axis=3) - output))
loss_d4 = tf.reduce_mean(tf.square(tf.expand_dims(network[:, :, :, 3], axis=3) - output))
loss_d5 = tf.reduce_mean(tf.square(tf.expand_dims(network[:, :, :, 4], axis=3) - output))
loss_d6 = tf.reduce_mean(tf.square(tf.expand_dims(network[:, :, :, 5], axis=3) - output))
loss = tf.reduce_min([loss_d1, loss_d2, loss_d3, loss_d4, loss_d5, loss_d6]) + 0.0025 * (
32 * loss_d1 + 16 * loss_d2 + 8 * loss_d3 + 4 * loss_d4 + 2 * loss_d5 + 1 * loss_d6)
# L1 Loss
loss2_d1 = tf.reduce_mean(tf.abs(tf.expand_dims(network[:, :, :, 0], axis=3) - output))
loss2_d2 = tf.reduce_mean(tf.abs(tf.expand_dims(network[:, :, :, 1], axis=3) - output))
loss2_d3 = tf.reduce_mean(tf.abs(tf.expand_dims(network[:, :, :, 2], axis=3) - output))
loss2_d4 = tf.reduce_mean(tf.abs(tf.expand_dims(network[:, :, :, 3], axis=3) - output))
loss2_d5 = tf.reduce_mean(tf.abs(tf.expand_dims(network[:, :, :, 4], axis=3) - output))
loss2_d6 = tf.reduce_mean(tf.abs(tf.expand_dims(network[:, :, :, 5], axis=3) - output))
loss2 = tf.reduce_min([loss2_d1, loss2_d2, loss2_d3, loss2_d4, loss2_d5, loss2_d6]) + 0.0025 * (
32 * loss2_d1 + 16 * loss2_d2 + 8 * loss2_d3 + 4 * loss2_d4 + 2 * loss2_d5 + 1 * loss2_d6)
# IoU Loss
nw1 = tf.expand_dims(network[:, :, :, 0], axis=3)
nw2 = tf.expand_dims(network[:, :, :, 1], axis=3)
nw3 = tf.expand_dims(network[:, :, :, 2], axis=3)
nw4 = tf.expand_dims(network[:, :, :, 3], axis=3)
nw5 = tf.expand_dims(network[:, :, :, 4], axis=3)
nw6 = tf.expand_dims(network[:, :, :, 5], axis=3)
iou_d1 = 1 - tf.reduce_mean(tf.multiply(nw1, output)) / (tf.reduce_mean(tf.maximum(nw1, output)) + 1e-6)
iou_d2 = 1 - tf.reduce_mean(tf.multiply(nw2, output)) / (tf.reduce_mean(tf.maximum(nw2, output)) + 1e-6)
iou_d3 = 1 - tf.reduce_mean(tf.multiply(nw3, output)) / (tf.reduce_mean(tf.maximum(nw3, output)) + 1e-6)
iou_d4 = 1 - tf.reduce_mean(tf.multiply(nw4, output)) / (tf.reduce_mean(tf.maximum(nw4, output)) + 1e-6)
iou_d5 = 1 - tf.reduce_mean(tf.multiply(nw5, output)) / (tf.reduce_mean(tf.maximum(nw5, output)) + 1e-6)
iou_d6 = 1 - tf.reduce_mean(tf.multiply(nw6, output)) / (tf.reduce_mean(tf.maximum(nw6, output)) + 1e-6)
loss_iou = tf.reduce_min([iou_d1, iou_d2, iou_d3, iou_d4, iou_d5, iou_d6]) + 0.0025 * (
32 * iou_d1 + 16 * iou_d2 + 8 * iou_d3 + 4 * iou_d4 + 2 * iou_d5 + 1 * iou_d6)
# add positive/negative clicks as soft constraints
ct_mask = tf.cast(input[:, :, :, 3], dtype=tf.bool) & tf.cast(input[:, :, :, 4], dtype=tf.bool)
ct_mask = tf.tile(tf.expand_dims(~ct_mask, axis=3), [1, 1, 1, 6])
ct_mask = tf.cast(ct_mask, dtype=tf.float32)
ct_mask /= tf.reduce_mean(ct_mask)
output_tile = tf.tile(output, [1, 1, 1, 6])
ct_loss = tf.reduce_mean(tf.abs(network - output_tile) * ct_mask)
all_loss = loss_iou + ct_loss
opt = tf.train.AdamOptimizer(learning_rate=0.0001).minimize(all_loss,
var_list=[var for var in tf.trainable_variables() if
var.name.startswith('g_')])
saver = tf.train.Saver(max_to_keep=1000)
sess.run(tf.initialize_all_variables())
ckpt = tf.train.get_checkpoint_state("result64_vgg19_RDL6_IoU_dt_pt_ct_tanh")
if ckpt:
print('loaded ' + ckpt.model_checkpoint_path)
saver.restore(sess, ckpt.model_checkpoint_path)
input_images = [None] * len(train_im_names)
output_masks = [None] * len(train_im_names)
# For displaying the losses
all = np.zeros(30000, dtype=float)
all2 = np.zeros(30000, dtype=float)
all_iou = np.zeros(30000, dtype=float)
all_d1 = np.zeros(30000, dtype=float)
all_d2 = np.zeros(30000, dtype=float)
all_d3 = np.zeros(30000, dtype=float)
all_d4 = np.zeros(30000, dtype=float)
all_d5 = np.zeros(30000, dtype=float)
all_d6 = np.zeros(30000, dtype=float)
for epoch in range(1, 101):
if os.path.isdir("result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/%04d" % epoch):
continue
cnt = 0
for id in np.random.permutation(len(train_im_names)):
# for id in np.random.permutation(1):
if input_images[id] is None:
# The input image
input_images[id] = cv2.imread(im_path + "/" + train_im_names[id] + ".jpg", -1)
if output_masks[id] is None:
# The SBD Groundtruth mask
mat_contents = sio.loadmat(seg_path + "/" + train_im_names[id] + ".mat")
tmpstr = mat_contents['GTinst']
tmpmat = tmpstr[0, 0]
output_masks[id] = tmpmat['Segmentation']
output_mask = deepcopy(output_masks[id])
output_mask[output_mask == 255] = 0
num_obj = output_mask.max()
for obj_id in range(num_obj):
st = time.time()
# random clicks
input_pos = cv2.imread("./train" + "/" + train_im_names[id] + "/ints/%03d_%03d_pos.png" % (
obj_id + 1, np.random.randint(1, 16)), -1)
input_neg = cv2.imread("./train" + "/" + train_im_names[id] + "/ints/%03d_%03d_neg.png" % (
obj_id + 1, np.random.randint(1, 16)), -1)
input_pos_clks = deepcopy(input_pos)
input_neg_clks = deepcopy(input_neg)
input_pos_clks[input_pos != 0] = 255
input_neg_clks[input_neg != 0] = 255
if np.sum(input_pos == 0) == 0:
continue
input_image = np.expand_dims(np.float32(np.concatenate(
[input_images[id], np.expand_dims(input_pos, axis=2), np.expand_dims(input_neg, axis=2),
np.expand_dims(input_pos_clks, axis=2), np.expand_dims(input_neg_clks, axis=2)], axis=2)), axis=0)
_, iH, iW, _ = input_image.shape
output_image = deepcopy(output_mask)
output_image[output_mask != (obj_id + 1)] = 0
output_image[output_mask == (obj_id + 1)] = 255
output_image = np.expand_dims(np.expand_dims(np.float32(output_image), axis=0), axis=3) / 255.0
_, current, current2, current3, d1, d2, d3, d4, d5, d6 = sess.run(
[opt, loss, loss2, loss_iou, iou_d1, iou_d2, iou_d3, iou_d4, iou_d5, iou_d6],
feed_dict={input: input_image, sz: [iH, iW], output: output_image})
all[cnt] = current * 255.0 * 255.0 # squared in 255 range (remember the network takes [0,1]
all2[cnt] = current2 * 255.0 # changed to 255 in error
all_iou[cnt] = current3
all_d1[cnt] = d1
all_d2[cnt] = d2
all_d3[cnt] = d3
all_d4[cnt] = d4
all_d5[cnt] = d5
all_d6[cnt] = d6
cnt += 1
print("%d %d l2: %.4f l1: %.4f IoU: %.4f d1-6: %.4f %.4f %.4f %.4f %.4f %.4f time: %.4f %s" % (
epoch, cnt, np.mean(all[np.where(all)]), np.mean(all2[np.where(all2)]), np.mean(all_iou[np.where(all_iou)]),
np.mean(all_d1[np.where(all_d1)]),
np.mean(all_d2[np.where(all_d2)]), np.mean(all_d3[np.where(all_d3)]), np.mean(all_d4[np.where(all_d4)]),
np.mean(all_d5[np.where(all_d5)]), np.mean(all_d6[np.where(all_d6)]),
time.time() - st, os.getcwd().split('/')[-2]))
os.makedirs("result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/%04d" % epoch)
target = open("result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/%04d/score.txt" % epoch, 'w')
target.write("%f\n%f\n%f" % (
np.mean(all[np.where(all)]), np.mean(all2[np.where(all2)]), np.mean(all_iou[np.where(all_iou)])))
target.close()
saver.save(sess, "result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/model.ckpt")
saver.save(sess, "result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/%04d/model.ckpt" % epoch)
# validation
all_test = np.zeros(100, dtype=float)
all2_test = np.zeros(100, dtype=float)
all_iou_test = np.zeros(100, dtype=float)
target = open("result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/%04d/test_score.txt" % epoch, 'w')
for id in range(100):
input_image = cv2.imread(im_path + "/" + val_im_names[id] + ".jpg", -1)
input_pos = cv2.imread("./val" + "/" + val_im_names[id] + "/ints/%03d_%03d_pos.png" % (1, 1), -1)
input_neg = cv2.imread("./val" + "/" + val_im_names[id] + "/ints/%03d_%03d_neg.png" % (1, 1), -1)
input_pos_clks = deepcopy(input_pos)
input_neg_clks = deepcopy(input_neg)
input_pos_clks[input_pos != 0] = 255
input_neg_clks[input_neg != 0] = 255
output_gt = cv2.imread("./val" + "/" + val_im_names[id] + "/objs/%05d.png" % 1, -1)
output_gt = np.expand_dims(np.expand_dims(np.float32(output_gt), axis=0), axis=3) / 255.0
iH, iW, _ = input_image.shape
input_image = np.expand_dims(np.float32(np.concatenate(
[input_image, np.expand_dims(input_pos, axis=2), np.expand_dims(input_neg, axis=2),
np.expand_dims(input_pos_clks, axis=2), np.expand_dims(input_neg_clks, axis=2)], axis=2)), axis=0)
st = time.time()
output_image, loss_test, loss2_test, iou_test = sess.run([network, loss, loss2, loss_iou],
feed_dict={input: input_image, sz: [iH, iW],
output: output_gt})
all_test[id] = loss_test * 255.0 * 255.0
all2_test[id] = loss2_test * 255
all_iou_test[id] = iou_test
target.write("%f %f %f\n" % (all_test[id], all2_test[id], all_iou_test[id]))
print("%.3f" % (time.time() - st))
output_image = np.minimum(np.maximum(output_image, 0.0), 1.0)
for output_d in range(6):
save_image = input_image[0, :, :, 0:3] / 255.0
save_image[:, :, 0] = (save_image[:, :, 0] + 0.5 * output_image[0, :, :, output_d])
save_image[:, :, 1] = (save_image[:, :, 1] + 0.5 * output_image[0, :, :, output_d])
save_image[:, :, 2] = (save_image[:, :, 2] + 0.5 * output_image[0, :, :, output_d])
save_image = np.minimum(np.maximum(save_image, 0.0), 1.0) * 255.0
cv2.imwrite(
"result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/%04d/%s_%02d_BW.png" % (epoch, val_im_names[id], output_d),
np.uint8(output_image[0, :, :, output_d] * 255.0))
cv2.imwrite("result64_vgg19_RDL6_IoU_dt_pt_ct_tanh/%04d/%s_%02d.jpg" % (epoch, val_im_names[id], output_d),
np.uint8(save_image))
target.write("Mean: %f %f %f\n" % (np.mean(all_test[np.where(all_test)]), np.mean(all2_test[np.where(all2_test)]),
np.mean(all_iou_test[np.where(all_iou_test)])))
target.close()