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caltech2randedge.py
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caltech2randedge.py
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from __future__ import print_function
import tensorflow as tf
import numpy as np
import random
import TensorflowUtils as utils
import read_MITSceneParsingDataParis as scene_parsing
import datetime
import BatchDatsetReaderCfar as dataset
from six.moves import xrange
import math
from scipy import signal
from scipy.interpolate import interp1d
FLAGS = tf.flags.FLAGS
tf.flags.DEFINE_integer("batch_size", "20", "batch size for training")
tf.flags.DEFINE_string("logs_dir", "/scratch1/ram095/nips20/logs_caltech2randedge/", "path to logs directory")
tf.flags.DEFINE_string("data_dir", "/scratch1/ram095/nips20/datasets/caltech2/caltech", "path to dataset")
tf.flags.DEFINE_float("learning_rate", "1e-4", "Learning rate for Adam Optimizer")
tf.flags.DEFINE_string("model_dir", "Model_zoo/", "Path to vgg model mat")
tf.flags.DEFINE_bool('debug', "False", "Debug mode: True/ False")
tf.flags.DEFINE_string('mode', "train", "Mode train/ test/ visualize")
MODEL_URL = 'http://www.vlfeat.org/matconvnet/models/beta16/imagenet-vgg-verydeep-19.mat'
MAX_ITERATION = int(1e5 + 1)
NUM_OF_CLASSESS = 3
IMAGE_SIZE = 64
def vgg_net(weights, image):
layers = (
'conv1_1', 'relu1_1', 'conv1_2', 'relu1_2', 'pool1',
'conv2_1', 'relu2_1', 'conv2_2', 'relu2_2', 'pool2',
'conv3_1', 'relu3_1', 'conv3_2', 'relu3_2', 'conv3_3',
'relu3_3', 'conv3_4', 'relu3_4', 'pool3',
'conv4_1', 'relu4_1', 'conv4_2', 'relu4_2', 'conv4_3',
'relu4_3', 'conv4_4', 'relu4_4', 'pool4',
'conv5_1', 'relu5_1', 'conv5_2', 'relu5_2', 'conv5_3',
'relu5_3', 'conv5_4', 'relu5_4'
)
net = {}
current = image
for i, name in enumerate(layers):
kind = name[:4]
if kind == 'conv':
kernels, bias = weights[i][0][0][0][0]
# matconvnet: weights are [width, height, in_channels, out_channels]
# tensorflow: weights are [height, width, in_channels, out_channels]
kernels = utils.get_variable(np.transpose(kernels, (1, 0, 2, 3)), name=name + "_w")
bias = utils.get_variable(bias.reshape(-1), name=name + "_b")
current = utils.conv2d_basic(current, kernels, bias)
elif kind == 'relu':
current = tf.nn.relu(current, name=name)
if FLAGS.debug:
utils.add_activation_summary(current)
elif kind == 'pool':
current = utils.avg_pool_2x2(current)
net[name] = current
return net
'''
def decoder(image):
model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL)
mean = model_data['normalization'][0][0][0]
mean_pixel = np.mean(mean, axis=(0, 1))
weights = np.squeeze(model_data['layers'])
processed_image = utils.process_image(image, mean_pixel)
with tf.variable_scope("decoder"):
image_net = vgg_net(weights, processed_image)
conv_final_layer = image_net["conv5_3"]
pool5 = utils.max_pool_2x2(conv_final_layer)
return pool5
'''
def conv_out_size_same(size, stride):
return int(math.ceil(float(size) / float(stride)))
def linear(input_, output_size, scope=None, stddev=0.02, bias_start=0.0, with_w=False):
shape = input_.get_shape().as_list()
with tf.variable_scope(scope or "Linear"):
try:
matrix = tf.get_variable("Matrix", [shape[1], output_size], tf.float32,
tf.random_normal_initializer(stddev=stddev))
except ValueError as err:
msg = "NOTE: Usually, this is due to an issue with the image dimensions. Did you correctly set '--crop' or '--input_height' or '--output_height'?"
err.args = err.args + (msg,)
raise
bias = tf.get_variable("bias", [output_size],
initializer=tf.constant_initializer(bias_start))
if with_w:
return tf.matmul(input_, matrix) + bias, matrix, bias
else:
return tf.matmul(input_, matrix) + bias
def deconv2d(input_, output_shape,
k_h=5, k_w=5, d_h=2, d_w=2, stddev=0.02,
name="deconv2d", with_w=False):
with tf.variable_scope(name):
# filter : [height, width, output_channels, in_channels]
w = tf.get_variable('w', [k_h, k_w, output_shape[-1], input_.get_shape()[-1]],
initializer=tf.random_normal_initializer(stddev=stddev))
try:
deconv = tf.nn.conv2d_transpose(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
# Support for verisons of TensorFlow before 0.7.0
except AttributeError:
deconv = tf.nn.deconv2d(input_, w, output_shape=output_shape,
strides=[1, d_h, d_w, 1])
biases = tf.get_variable('biases', [output_shape[-1]], initializer=tf.constant_initializer(0.0))
deconv = tf.reshape(tf.nn.bias_add(deconv, biases), deconv.get_shape())
if with_w:
return deconv, w, biases
else:
return deconv
def new_conv_layer( bottom, filter_shape, activation=tf.identity, padding='SAME', stride=1, name=None ):
with tf.variable_scope( name ):
w = tf.get_variable(
"W",
shape=filter_shape,
initializer=tf.random_normal_initializer(0., 0.005))
b = tf.get_variable(
"b",
shape=filter_shape[-1],
initializer=tf.constant_initializer(0.))
conv = tf.nn.conv2d( bottom, w, [1,stride,stride,1], padding=padding)
bias = activation(tf.nn.bias_add(conv, b))
return bias #relu
def new_deconv_layer(bottom, filter_shape, output_shape, activation=tf.identity, padding='SAME', stride=1, name=None):
with tf.variable_scope(name):
W = tf.get_variable(
"W",
shape=filter_shape,
initializer=tf.random_normal_initializer(0., 0.005))
b = tf.get_variable(
"b",
shape=filter_shape[-2],
initializer=tf.constant_initializer(0.))
deconv = tf.nn.conv2d_transpose( bottom, W, output_shape, [1,stride,stride,1], padding=padding)
bias = activation(tf.nn.bias_add(deconv, b))
return bias
def batchnorm(bottom, is_train, epsilon=1e-8, name=None):
bottom = tf.clip_by_value( bottom, -100., 100.)
depth = bottom.get_shape().as_list()[-1]
with tf.variable_scope(name):
gamma = tf.get_variable("gamma", [depth], initializer=tf.constant_initializer(1.))
beta = tf.get_variable("beta" , [depth], initializer=tf.constant_initializer(0.))
batch_mean, batch_var = tf.nn.moments(bottom, [0,1,2], name='moments')
ema = tf.train.ExponentialMovingAverage(decay=0.5)
def update():
with tf.control_dependencies([ema_apply_op]):
return tf.identity(batch_mean), tf.identity(batch_var)
ema_apply_op = ema.apply([batch_mean, batch_var])
ema_mean, ema_var = ema.average(batch_mean), ema.average(batch_var)
mean, var = tf.cond(
is_train,
update,
lambda: (ema_mean, ema_var) )
normed = tf.nn.batch_norm_with_global_normalization(bottom, mean, var, beta, gamma, epsilon, False)
return normed
def inference(images, keep_prob,z,e,is_train):
"""
Semantic segmentation network definition
:param image: input image. Should have values in range 0-255
:param keep_prob:
:return:
"""
encoderLayerNum = int(math.log(IMAGE_SIZE) / math.log(2))
encoderLayerNum = encoderLayerNum - 1 # minus 1 because the second last layer directly go from 4x4 to 1x1
print("encoderLayerNum=", encoderLayerNum)
encoderLayerNum = encoderLayerNum
decoderLayerNum = int(math.log(IMAGE_SIZE) / math.log(2))
decoderLayerNum = decoderLayerNum - 1
print("decoderLayerNum=", decoderLayerNum)
decoderLayerNum = decoderLayerNum
print("setting up vgg initialized conv layers ...")
#model_data = utils.get_model_data(FLAGS.model_dir, MODEL_URL)
#mean = model_data['normalization'][0][0][0]
#mean_pixel = np.mean(mean, axis=(0, 1))
#weights = np.squeeze(model_data['layers'])
#processed_image = utils.process_image(image, mean_pixel)
with tf.variable_scope("encoder", reuse = tf.AUTO_REUSE):
previousFeatureMap = images
previousDepth = 3
depth = 64
for layer in range(1, encoderLayerNum):
print("build_reconstruction encoder layer=", layer)
conv = tf.nn.dropout(new_conv_layer(previousFeatureMap, [4,4,previousDepth,depth], stride=2, name=("conv" + str(layer))),keep_prob)
bn = tf.nn.leaky_relu(batchnorm(conv, is_train, name=("bn" + str(layer))))
previousFeatureMap = bn
previousDepth = depth
depth = depth * 2
# last layer
conv = new_conv_layer(previousFeatureMap, [4,4,previousDepth,4000], stride=2, padding='VALID', name=('conv' + str(encoderLayerNum)))
bn = tf.nn.leaky_relu(batchnorm(conv, is_train, name=("bn" + str(encoderLayerNum))))
previousDepth = 4000
depth = 64 * pow(2,decoderLayerNum-2)
featureMapSize = 4
deconv = tf.nn.dropout(new_deconv_layer( bn, [4,4,depth,previousDepth], [FLAGS.batch_size,featureMapSize,featureMapSize,depth], padding='VALID', stride=2, name=("deconv" + str(decoderLayerNum))),keep_prob)
#debn_ = tf.nn.relu(batchnorm(deconv, is_train, name=("debn" + str(decoderLayerNum))))
z_ = z/tf.norm(z)
debn_ = tf.nn.relu(batchnorm(deconv, is_train, name=("debn" + str(decoderLayerNum))))
debn = tf.concat([debn_,tf.tile(z_,[1,4,4,1])],axis = 3) + e
with tf.variable_scope("decoder", reuse = tf.AUTO_REUSE):
print("#################################")
print(debn)
previousFeatureMap = debn
previousDepth = 552
depth = depth / 2
featureMapSize = featureMapSize *2
for layer in range(decoderLayerNum-1,1, -1):
print("build_reconstruction decoder layer=", layer)
deconv = new_deconv_layer( previousFeatureMap, [4,4,depth,previousDepth], [FLAGS.batch_size,featureMapSize,featureMapSize,depth], stride=2, name=("deconv" + str(layer)))
debn = tf.nn.relu(batchnorm(deconv, is_train, name=('debn'+ str(layer))))
previousFeatureMap = debn
previousDepth = depth
depth = depth / 2
featureMapSize = featureMapSize *2
recon = tf.nn.tanh(new_deconv_layer( debn, [4,4,3,previousDepth], [FLAGS.batch_size,64,64,3], stride=2, name="recon"))
'''
conv1 = new_conv_layer(images, [4,4,3,64], stride=2, name="conv1" )
bn1 = tf.nn.leaky_relu(batchnorm(conv1, is_train, name='bn1'))
conv2 = new_conv_layer(bn1, [4,4,64,64], stride=2, name="conv2" )
bn2 = tf.nn.leaky_relu(batchnorm(conv2, is_train, name='bn2'))
conv3 = new_conv_layer(bn2, [4,4,64,128], stride=2, name="conv3")
bn3 = tf.nn.leaky_relu(batchnorm(conv3, is_train, name='bn3'))
conv4 = new_conv_layer(bn3, [4,4,128,256], stride=2, name="conv4")
bn4 = tf.nn.leaky_relu(batchnorm(conv4, is_train, name='bn4'))
conv5 = new_conv_layer(bn4, [4,4,256,512], stride=2, name="conv5")
bn5 = tf.nn.leaky_relu(batchnorm(conv5, is_train, name='bn5'))
conv6 = new_conv_layer(bn5, [4,4,512,4000], stride=2, padding='VALID', name='conv6')
bn6 = tf.nn.leaky_relu(batchnorm(conv6, is_train, name='bn6'))
deconv4 = new_deconv_layer( bn6, [4,4,512,4000], conv5.get_shape().as_list(), padding='VALID', stride=2, name="deconv4")
debn4 = tf.nn.relu(batchnorm(deconv4, is_train, name='debn4'))
deconv3 = new_deconv_layer( debn4, [4,4,256,512], conv4.get_shape().as_list(), stride=2, name="deconv3")
debn3 = tf.nn.relu(batchnorm(deconv3, is_train, name='debn3'))
deconv2 = new_deconv_layer( debn3, [4,4,128,256], conv3.get_shape().as_list(), stride=2, name="deconv2")
debn2 = tf.nn.relu(batchnorm(deconv2, is_train, name='debn2'))
deconv1 = new_deconv_layer( debn2, [4,4,64,128], conv2.get_shape().as_list(), stride=2, name="deconv1")
debn1 = tf.nn.relu(batchnorm(deconv1, is_train, name='debn1'))
recon = new_deconv_layer( debn1, [4,4,3,64], [batch_size,64,64,3], stride=2, name="recon")
print("##########################################")
print(recon)
'''
return recon, debn_
def predictor_(h,z, is_train):
z_tiled = tf.tile(z,[1,4,4,1])
concat = tf.concat([h,z_tiled],axis = 3)
conv1 = new_conv_layer(concat, [3,3,1024,512], stride=1, padding='VALID', name=('pred_conv_1'))
bn = tf.nn.leaky_relu(batchnorm(conv1, is_train, name=("pred_bn_1")))
bn_ln = tf.reshape(bn,[FLAGS.batch_size,-1])
fc1 = tf.expand_dims(tf.expand_dims(tf.layers.dense(bn_ln,10),1),1)
# bn2 = tf.nn.leaky_relu(batchnorm(fc1, is_train, name=("pred_bn_2")))
# z_pred = tf.clip_by_value(tf.nn.tanh(fc1),-0.1,0.1)
z_pred = fc1
return z_pred
def predictor(h,z,e, is_train):
# z_tiled = tf.tile(z,[1,4,4,1])
with tf.variable_scope("predictor", reuse = tf.AUTO_REUSE):
concat = tf.concat([tf.contrib.layers.flatten(h),tf.contrib.layers.flatten(z)],axis = 1, name = "pred_concat") + tf.reshape(e,[FLAGS.batch_size,-1])
# concat = tf.reshape(tf.concat([h,z_tiled],axis = 3),[FLAGS.batch_size,-1])
fc1 = tf.nn.leaky_relu(tf.layers.dense(concat,512), name = "pred_fc1")
# bn = tf.nn.leaky_relu(batchnorm(fc1, is_train, name=("pred_bn_1")))
fc2 = tf.nn.leaky_relu(tf.layers.dense(fc1,512), name = "pred_fc2")
# bn2 = tf.nn.leaky_relu(batchnorm(fc2, is_train, name=("pred_bn_2")))
fc3 = tf.expand_dims(tf.expand_dims(tf.layers.dense(fc2,40),1),1, name = "pred_fc3")
# bn2 = tf.nn.leaky_relu(batchnorm(fc1, is_train, name=("pred_bn_2")))
# z_pred = tf.clip_by_value(tf.nn.tanh(fc1),-0.1,0.1)
z_pred = tf.nn.tanh(fc3)
return z_pred
def train(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def train_predictor(loss_val, var_list):
optimizer = tf.train.AdamOptimizer(FLAGS.learning_rate)
grads = optimizer.compute_gradients(loss_val, var_list=var_list)
if FLAGS.debug:
# print(len(var_list))
for grad, var in grads:
utils.add_gradient_summary(grad, var)
return optimizer.apply_gradients(grads)
def train_z(loss,Z):
return tf.gradients(ys = loss, xs = Z)
def random_mask(input_size):
x1 = random.randint(5,20)
w1 = random.randint(20, 34)
y1 = random.randint(5, 20)
h1 = random.randint(20, 34)
mask = np.zeros((1,64,64,1))
mask[:,x1:x1+w1,y1:y1+h1,:] = 1.0
mask2 = np.zeros((1,64,64,1))
mask2[:,x1-5:w1+x1+5,y1-5:h1+y1+5,:] = 1.0
mask2 = mask2 - mask
return mask, mask2
def main(argv=None):
keep_probability = tf.placeholder(tf.float32, name="keep_probabilty")
image = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="input_image")
annotation = tf.placeholder(tf.float32, shape=[None, IMAGE_SIZE, IMAGE_SIZE, 3], name="annotation")
z = tf.placeholder(tf.float32, shape=[None, 1, 1, 40], name="z")
mask = tf.placeholder(tf.float32, shape=[None, 64, 64, 1], name="mask")
mask2 = tf.placeholder(tf.float32, shape=[None, 64, 64, 1], name="mask2")
z_new = tf.placeholder(tf.float32, shape=[None, 1, 1, 40], name="z_new")
istrain = tf.placeholder(tf.bool)
#z_lip = tf.placeholder(tf.float32, shape=[None, 1, 1, 10], name="z_lip")
#z_lip_inv = tf.placeholder(tf.float32, shape=[None, 1, 1, 10], name="z_lip_inv")
e = tf.placeholder(tf.float32, shape=[None, 4, 4, 552], name="e")
e_p = tf.placeholder(tf.float32, shape=[None, 1, 1, 8232], name="e_p")
# pred_annotation, logits = inference(image, keep_probability,z)
# tf.summary.image("input_image", image, max_outputs=2)
# tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
# tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
# loss = tf.reduce_mean((tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits,
# labels=tf.squeeze(annotation, squeeze_dims=[3]),
# name="entropy")))
# mask_ = tf.ones([FLAGS.batch_size,32,64,3])
# mask = tf.pad(mask_, [[0,0],[0,32],[0,0],[0,0]])
# mask2__ = tf.ones([FLAGS.batch_size,78,78,3])
# mask2_ = tf.pad(mask2__, [[0,0],[25,25],[25,25],[0,0]])
# mask2 = mask2_ - mask
zero = tf.zeros([20,1,1,8232])
logits, h = inference((1-mask)*image + mask*1.0, keep_probability,z,0.0,istrain)
logits_e, h_e = inference((1-mask)*image + mask*1.0, keep_probability,z,e,istrain)
#logits_lip,_ = inference((1-mask)*image + mask*0.0, keep_probability,z_lip,istrain )
#logits_lip_inv,_ = inference((1-mask)*image + mask*0.0, keep_probability,z_lip_inv,istrain )
z_pred = predictor(h,z,zero,istrain)
z_pred_e = predictor(h,z,e_p,istrain)
# z_pred_lip = predictor(h,z_lip,istrain)
# z_pred_lip_inv = predictor(h,z_lip_inv,istrain)
# logits = inference(image, keep_probability,z,istrain)
tf.summary.image("input_image", image, max_outputs=2)
tf.summary.image("ground_truth", tf.cast(annotation, tf.uint8), max_outputs=2)
# tf.summary.image("pred_annotation", tf.cast(pred_annotation, tf.uint8), max_outputs=2)
# lossz = 0.1 * tf.reduce_mean(tf.reduce_sum(tf.abs(z),[1,2,3]))
# lossz = 0.1 * tf.reduce_mean(tf.abs(z))
# loss_all = tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square((image - logits)),[1,2,3])))
# loss_all = tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs(image - logits)),1))
# loss_mask = 0.8*tf.reduce_mean(tf.sqrt(tf.reduce_sum(tf.square((image - logits)*mask),[1,2,3])))
loss_mask = tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs((annotation - logits)*mask)),1))
loss_mask2 = tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs((annotation - logits)*mask2)),1))
loss = 0.5*loss_mask + loss_mask2
# loss = tf.reduce_mean(tf.squared_difference(logits ,annotation ))
loss_summary = tf.summary.scalar("entropy", loss)
# zloss = tf.reduce_mean(tf.losses.cosine_distance(tf.contrib.layers.flatten(z_new) ,tf.contrib.layers.flatten(z_pred),axis =1))
zloss_ = tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs((z_pred - z_new))),1))
# zloss_lip = tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs((z_pred - z_pred_lip))),1))
# zloss_lip_inv = -tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs((z_pred - z_pred_lip_inv))),1))
# z_loss = zloss_ + 0.1* zloss_lip# + zloss_lip_inv
lip_loss_dec = tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs((logits - logits_e))),1))
loss = loss + 0.1*lip_loss_dec
lip_loss_pred = tf.reduce_mean(tf.reduce_sum(tf.contrib.layers.flatten(tf.abs((z_pred - z_pred_e))),1))
zloss = zloss_ + 0.1*lip_loss_pred
grads = train_z(loss_mask,z)
trainable_var = tf.trainable_variables()
trainable_z_pred_var = tf.trainable_variables(scope="predictor")
trainable_d_pred_var = tf.trainable_variables(scope="decoder")
print(trainable_z_pred_var)
if FLAGS.debug:
for var in trainable_var:
utils.add_to_regularization_and_summary(var)
train_op = train(loss, trainable_var)
train_pred = train_predictor(zloss,trainable_z_pred_var)
print("Setting up summary op...")
summary_op = tf.summary.merge_all()
print("Setting up image reader...")
train_records, valid_records = scene_parsing.read_dataset(FLAGS.data_dir)
print(len(train_records))
print(len(valid_records))
print("Setting up dataset reader")
image_options = {'resize': True, 'resize_size': IMAGE_SIZE}
if FLAGS.mode == 'train':
train_dataset_reader = dataset.BatchDatset(train_records, image_options)
validation_dataset_reader = dataset.BatchDatset(valid_records, image_options)
sess = tf.Session()
print("Setting up Saver...")
saver = tf.train.Saver()
# create two summary writers to show training loss and validation loss in the same graph
# need to create two folders 'train' and 'validation' inside FLAGS.logs_dir
train_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/train', sess.graph)
validation_writer = tf.summary.FileWriter(FLAGS.logs_dir + '/validation')
sess.run(tf.global_variables_initializer())
ckpt = tf.train.get_checkpoint_state(FLAGS.logs_dir)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess, ckpt.model_checkpoint_path)
print("Model restored...")
saved =True
if FLAGS.mode == "train":
for itr in xrange(MAX_ITERATION):
train_images, train_annotations = train_dataset_reader.next_batch(FLAGS.batch_size)
print(np.max(train_images))
# z_ = np.reshape(signal.gaussian(200, std=1),(FLAGS.batch_size,1,1,10))-0.5
z_ = np.random.uniform(low=-1.0, high=1.0, size=(FLAGS.batch_size,1,1,40))
# train_images[train_images < 0.] = -1.
# train_annotations[train_annotations < 0.] = -1.
# train_images[train_images >= 0.] = 1.0
# train_annotations[train_annotations >= 0.] = 1.0
x1 = random.randint(0, 10)
w1 = random.randint(30, 54)
y1 = random.randint(0, 10)
h1 = random.randint(30, 54)
cond = random.randint(0, 10)
# saved = True
if False:
saved = False
train_images_m, train_annotations_m = train_dataset_reader.get_random_batch(FLAGS.batch_size)
train_images_m[train_images_m < 0.] = -1.
train_annotations_m[train_annotations_m < 0.] = -1.
train_images_m[train_images_m >= 0.] = 1.0
train_annotations_m[train_annotations_m >= 0.] = 1.0
train_images = (train_images + 1.)/2.0*255.0
train_annotations = (train_annotations + 1.)/2.0*255.0
train_images_m = (train_images_m + 1.)/2.0*255.0
train_annotations_m = (train_annotations_m + 1.)/2.0*255.0
train_images_m[:,32:,:,:] = 0
train_annotations_m[:,32:,:,:] = 0
train_images = np.clip((train_images + train_images_m),0.0,255.0)
train_annotations = np.clip((train_annotations + train_annotations_m),0.0,255.0)
'''
train_images[train_images < 0.] = -1.
train_annotations[train_annotations < 0.] = -1.
train_images[train_images >= 0.] = 1.0
train_annotations[train_annotations >= 0.] = 1.0
'''
train_annotations_ = np.squeeze(train_annotations,axis = 3)
train_images_ = train_images
train_images = train_images/127.5 - 1.0
train_annotations = train_annotations/127.5 - 1.0
# for itr_ in range(FLAGS.batch_size):
# utils.save_image(train_images_[itr_].astype(np.uint8), FLAGS.logs_dir, name="inp_" + str(5+itr_) )
# utils.save_image(train_annotations_[itr_].astype(np.uint8), FLAGS.logs_dir, name="gt_" + str(5+itr_) )
# train_images[:,x1:w1,y1:h1,:] = 0
# print(train_images)
r_m, r_m2 = random_mask(64)
#feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.85, z: z_,mask:r_m, istrain:True }
#train_images[:,50:100,50:100,:] =0
v = 0
# print(train_images)
error_dec = np.random.normal(0.0,0.001,(FLAGS.batch_size,4,4,552))
error_dec_ = np.random.normal(0.0,0.001,(FLAGS.batch_size,1,1,8232))
# z_l_inv = z_ + np.random.normal(0.0,0.1)
# feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.85, z: z_, e:error_dec, mask:r_m, istrain:True }
# z_l = z_ + np.random.normal(0.0,0.001)
# lloss,_ = sess.run([lip_loss, train_lip ], feed_dict=feed_dict)
# z_l = z_ + np.random.normal(0.0,0.001)
# print("Step: %d, lip_loss:%g" % (itr,lloss))
for p in range(20):
z_ol = np.copy(z_)
# z_l = z_ol + np.random.normal(0.0,0.001)
# print("666666666666666666666666666666666666666")
feed_dict = {image: train_images, annotation: train_annotations, keep_probability: 0.85, z: z_,e:error_dec, mask:r_m,mask2:r_m2, istrain:True }
# lloss,_ = sess.run([lip_loss, train_lip ], feed_dict=feed_dict)
# print("Step: %d, z_step: %d, lip_loss:%g" % (itr,p,lloss))
z_loss, summ = sess.run([loss,loss_summary], feed_dict=feed_dict)
print("Step: %d, z_step: %d, Train_loss:%g" % (itr,p,z_loss))
# print(z_)
g = sess.run([grads],feed_dict=feed_dict)
v_prev = np.copy(v)
# print(g[0][0].shape)
v = 0.001*v - 0.1*g[0][0]
z_ += 0.001 * v_prev + (1+0.001)*v
z_ = np.clip(z_, -10.0, 10.0)
'''
m = interp1d([-10.0,10.0],[-1.0,1.0])
print(np.max(z_))
print(np.min(z_))
z_ol_interp = m(z_ol)
z_interp = m(z_)
_,z_pred_loss =sess.run([train_pred,zloss],feed_dict={image: train_images,mask:r_m,z:z_ol_interp,z_new:z_interp,e_p:error_dec_,istrain:True,keep_probability: 0.85})
print("Step: %d, z_step: %d, z_pred_loss:%g" % (itr,p,z_pred_loss))
'''
# _,z_pred_loss =sess.run([train_pred,zloss],feed_dict={image: train_images,mask:r_m,z:z_ol,z_new:z_,istrain:True,keep_probability: 0.85})
# print("Step: %d, z_step: %d, z_pred_loss:%g" % (itr,p,z_pred_loss))
# z_ = np.clip(z_, -1.0, 1.0)
# print(v.shape)
# print(z_.shape)
feed_dict = {image: train_images, annotation: train_annotations, keep_probability:0.85,mask:r_m,e:error_dec, z: z_,mask2:r_m2, istrain:True }
sess.run(train_op, feed_dict=feed_dict)
if itr % 10 == 0:
train_loss, summary_str = sess.run([loss, loss_summary], feed_dict=feed_dict)
print("Step: %d, Train_loss:%g" % (itr, train_loss))
train_writer.add_summary(summary_str, itr)
if itr % 500 == 0:
valid_images, valid_annotations = validation_dataset_reader.next_batch(FLAGS.batch_size)
# valid_annotations[valid_annotations < 0.] = -1.
# valid_images[valid_images < 0.] = -1.
# valid_annotations[valid_annotations >= 0.] = 1.0
# valid_images[valid_images >= 0.] = 1.0
x1 = random.randint(0, 10)
w1 = random.randint(30, 54)
y1 = random.randint(0, 10)
h1 = random.randint(30, 54)
# valid_images[:,x1:w1,y1:h1,:] = 0
valid_loss, summary_sva = sess.run([loss, loss_summary], feed_dict={image: valid_images,mask:r_m, annotation: valid_annotations,
keep_probability: 1.0, z: z_,e:error_dec, istrain:False,mask2:r_m2 })
print("%s ---> Validation_loss: %g" % (datetime.datetime.now(), valid_loss))
# add validation loss to TensorBoard
validation_writer.add_summary(summary_sva, itr)
saver.save(sess, FLAGS.logs_dir + "model_z_center_7.ckpt", 500)
elif FLAGS.mode == "visualize":
valid_images, valid_annotations = validation_dataset_reader.get_random_batch(20)
# valid_annotations[valid_annotations < 0.] = -1.0
# valid_images[valid_images < 0.] = -1.0
# valid_annotations[valid_annotations >= 0.] = 1.0
# valid_images[valid_images >= 0.] = 1.0
x1 = random.randint(0, 10)
w1 = random.randint(30, 54)
y1 = random.randint(0, 10)
h1 = random.randint(30, 54)
# valid_images[:,x1:w1,y1:h1,:] = 0
r_m, r_m2 = random_mask(64)
# z_ = np.zeros(low=-1.0, high=1.0, size=(FLAGS.batch_size,1,1,10))
# z_ = np.reshape(signal.gaussian(200, std=1),(FLAGS.batch_size,1,1,10))-0.5
z_ = np.random.uniform(low=-1.0, high=1.0, size=(FLAGS.batch_size,1,1,40))
feed_dict = {image: valid_images, annotation: valid_annotations, keep_probability: 0.85, z: z_, istrain:False,mask:r_m,mask2:r_m2 }
v= 0
m__ = interp1d([-10.0,10.0],[-1.0,1.0])
z_ = m__(z_)
# feed_dict = {image: valid_images, annotation: valid_annotations, keep_probability: 0.85, z: z_, istrain:False,mask:r_m }
for p in range(20):
z_ol = np.copy(z_)
# print("666666666666666666666666666666666666666")
# print(z_)
# feed_dict = {image: valid_images, annotation: valid_annotations, keep_probability: 0.85, z: z_, istrain:False,mask:r_m }
# z_loss, summ = sess.run([loss,loss_summary], feed_dict=feed_dict)
# print("z_step: %d, Train_loss:%g" % (p,z_loss))
# z_, z_pred_loss = sess.run(z_pred,zlossfeed_dict = {image: valid_images, annotation: valid_annotations, keep_probability: 1.0, z:z_ol, istrain:False,mask:r_m})
# print(z_)
g = sess.run([grads],feed_dict=feed_dict)
v_prev = np.copy(v)
# print(g[0][0].shape)
v = 0.001*v - 0.1*g[0][0]
z_ = z_ol + 0.001 * v_prev + (1+0.001)*v
# z_ = z_ol + 0.001 * v_prev + (1+0.001)*v
# print("z_____________")
# print(z__)
# print("z_")
# print(z_)
# m__ = interp1d([-10.0,10.0],[-1.0,1.0])
# z_ol = m__(z_ol)
# z_ = sess.run(z_pred,feed_dict = {image: valid_images, annotation: valid_annotations, keep_probability: 0.85, z:z_ol, istrain:False,mask:r_m})
# m_ = interp1d([-1.0,1.0],[-10.0,10.0])
# z_ = m_(z_)
# z_ = np.clip(z_, -1.0, 1.0)
# print(z_pred_loss)
# m_ = interp1d([-1.0,1.0],[-10.0,10.0])
# z_ = m_(z_)
pred = sess.run(logits, feed_dict={image: valid_images, annotation: valid_annotations,z:z_, istrain:False,mask:r_m,mask2:r_m2,
keep_probability: 0.85})
valid_images_masked = ((1-r_m)*valid_images + 1.)/2.0*255
# valid_images = (valid_images +1.)/2.0*255
# predicted_patch = sess.run(mask) * pred
# pred = valid_images_masked + predicted_patch
pred_ = (pred +1.)/2.0*255
# pred = pred + 1./2.0*255
pred = valid_images_masked *(1-r_m) + pred_ * r_m
valid_annotations_ = (valid_annotations +1.)/2.0*255
# pred = np.squeeze(pred, axis=3)
print(np.max(pred))
print(valid_images.shape)
print(valid_annotations.shape)
print(pred.shape)
# for itr in range(FLAGS.batch_size):
# utils.save_image(valid_images_masked[itr].astype(np.uint8), FLAGS.logs_dir, name="inp_" + str(5+itr))
# utils.save_image(valid_annotations[itr].astype(np.uint8), FLAGS.logs_dir, name="gt_" + str(5+itr))
# utils.save_image(pred[itr].astype(np.uint8), FLAGS.logs_dir, name="predz_" + str(5+itr))
# utils.save_image(valid_images_masked[itr].astype(np.uint8), FLAGS.logs_dir, name="inp_" + str(5+itr)+'_' + str(p) )
# utils.save_image(valid_annotations_[itr].astype(np.uint8), FLAGS.logs_dir, name="gt_" + str(5+itr)+'_' + str(p) )
# utils.save_image(pred[itr].astype(np.uint8), FLAGS.logs_dir, name="predz_" + str(5+itr)+'_' + str(p) )
# print("Saved image: %d" % itr)
for itr in range(FLAGS.batch_size):
utils.save_image(valid_images_masked[itr].astype(np.uint8), FLAGS.logs_dir, name="inp_" + str(5+itr) )
utils.save_image(pred[itr].astype(np.uint8), FLAGS.logs_dir, name="predz_" + str(5+itr) )
utils.save_image(valid_annotations_[itr].astype(np.uint8), FLAGS.logs_dir, name="gt_" + str(5+itr) )
if __name__ == "__main__":
tf.app.run()