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DSN_FCN.py
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DSN_FCN.py
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import tensorflow as tf
import tensorflow.contrib.slim as slim
from tensorflow.python.framework import ops
from tensorflow.examples.tutorials.mnist import input_data
import numpy as np
import cPickle as pkl
from sklearn.manifold import TSNE
import matplotlib.pyplot as plt
import urllib
import os
import tarfile
import skimage
import skimage.io
import skimage.transform
from loss import *
from utils import *
from models import *
# PARAMETERS
batch_size = 100
training_epochs = 50
every_epoch = 1
#Data Import
mnistm_name = 'data/mnistm_data2.pkl'
if os.path.isfile(mnistm_name):
print ("[%s] ALREADY EXISTS. " % (mnistm_name))
else:
mnist = input_data.read_data_sets('data')
# OPEN BSDS500
f = tarfile.open(filename)
train_files = []
for name in f.getnames():
if name.startswith('BSR/BSDS500/data/images/train/'):
train_files.append(name)
print ("WE HAVE [%d] TRAIN FILES" % (len(train_files)))
# GET BACKGROUND
print ("GET BACKGROUND FOR MNIST-M")
background_data = []
for name in train_files:
try:
fp = f.extractfile(name)
bg_img = skimage.io.imread(fp)
background_data.append(bg_img)
except:
continue
print ("WE HAVE [%d] BACKGROUND DATA" % (len(background_data)))
rand = np.random.RandomState(42)
print ("BUILDING TRAIN SET...")
train = create_mnistm(mnist.train.images)
print ("BUILDING TEST SET...")
test = create_mnistm(mnist.test.images)
print ("BUILDING VALIDATION SET...")
valid = create_mnistm(mnist.validation.images)
# SAVE
print ("SAVE MNISTM DATA TO %s" % (mnistm_name))
with open(mnistm_name, 'w') as f:
pkl.dump({ 'train': train, 'test': test, 'valid': valid }, f, -1)
print ("DONE")
print ("LOADING MNIST")
mnist = input_data.read_data_sets('data', one_hot=True)
mnist_train = (mnist.train.images > 0).reshape(55000, 28, 28, 1).astype(np.uint8) * 255
mnist_train = np.concatenate([mnist_train, mnist_train, mnist_train], 3)
mnist_test = (mnist.test.images > 0).reshape(10000, 28, 28, 1).astype(np.uint8) * 255
mnist_test = np.concatenate([mnist_test, mnist_test, mnist_test], 3)
mnist_train_label = mnist.train.labels
mnist_test_label = mnist.test.labels
print ("LOADING MNISTM")
mnistm_name = 'data/mnistm_data2.pkl'
mnistm = pkl.load(open(mnistm_name))
mnistm_train = mnistm['train']
mnistm_test = mnistm['test']
mnistm_valid = mnistm['valid']
mnistm_train_label = mnist_train_label
mnistm_test_label = mnist_test_label
total_train = np.vstack([mnist_train, mnistm_train])
total_test = np.vstack([mnist_test, mnistm_test])
ntrain = mnist_train.shape[0]
ntest = mnist_test.shape[0]
total_train_domain = np.vstack([np.tile([1., 0.], [ntrain, 1]), np.tile([0., 1.], [ntrain, 1])])
total_test_domain = np.vstack([np.tile([1., 0.], [ntest, 1]), np.tile([0., 1.], [ntest, 1])])
n_total_train = total_train.shape[0]
n_total_test = total_test.shape[0]
# GET PIXEL MEAN
pixel_mean = np.vstack([mnist_train, mnistm_train]).mean((0, 1, 2))
# PLOT IMAGES
imshow_grid(mnist_train, shape=[5, 10])
imshow_grid(mnistm_train, shape=[5, 10])
# SOURCE AND TARGET DATA
source_train_img = np.concatenate((mnist_train,mnist_train),axis=0)
source_train_label = np.concatenate((mnist_train_label,mnist_train_label),axis=0)
source_test_img = mnist_test
source_test_label = mnist_test_label
target_train_img = np.concatenate((mnistm_train,mnistm_train),axis=0)
target_train_label= np.concatenate((mnistm_train_label,mnistm_train_label),axis=0)
target_test_img = mnistm_test
target_test_label = mnistm_test_label
# DOMAIN ADVERSARIAL TRAINING
domain_train_img = total_train
domain_train_label = total_train_domain
imgshape = source_train_img.shape[1:4]
labelshape = source_train_label.shape[1]
print_npshape(source_train_img, "source_train_img")
print_npshape(source_train_label, "source_train_label")
print_npshape(source_test_img, "source_test_img")
print_npshape(source_test_label, "source_test_label")
print_npshape(target_test_img, "target_test_img")
print_npshape(target_test_label, "target_test_label")
print_npshape(domain_train_img, "domain_train_img")
print_npshape(domain_train_label, "domain_train_label")
print
imgshape
print
labelshape
flip_gradient = FlipGradientBuilder()
##Place Holder
source = tf.placeholder(tf.uint8, [None, imgshape[0], imgshape[1], imgshape[2]])
target = tf.placeholder(tf.uint8, [None, imgshape[0], imgshape[1], imgshape[2]])
source_target = tf.placeholder(tf.uint8, [None, imgshape[0], imgshape[1], imgshape[2]])
y = tf.placeholder(tf.float32, [None, labelshape])
d = tf.placeholder(tf.float32, [None, 2]) # DOMAIN LABEL
lr = tf.placeholder(tf.float32, [])
dw = tf.placeholder(tf.float32, [])
# DOMAIN ADVERSARIAL NEURAL NETWORK
feat_ext_dann = shared_encoder(source_target, name='dann_feat_ext')
class_pred_dann = class_pred_net(feat_ext_dann, name='dann_class_pred')
domain_pred_dann = domain_pred_net(feat_ext_dann, name='dann_domain_pred')
# NAIVE CONVOLUTIONAL NEURAL NETWORK
feat_ext_cnn = shared_encoder(source, name='cnn_feat_ext')
class_pred_cnn = class_pred_net(feat_ext_cnn, name='cnn_class_pred')
#Private & Shared Encoder
source_private_feat = private_source_encoder(source, name='source_private')
target_private_feat = private_target_encoder(target, name='target_private')
source_shared_feat = shared_encoder(source, name='source_shared')
target_shared_feat = shared_encoder(target, name='target_shared')
#Input for Decoder
target_concat_feat = tf.concat([target_shared_feat,target_private_feat],1)
source_concat_feat = tf.concat([source_shared_feat,source_private_feat],1)
#Decoder
#target_recon = small_decoder(target_concat_feat,28,28,3)
target_recon = shared_decoder(target_concat_feat,28,28,3)
source_recon = shared_decoder(source_concat_feat,28,28,3, reuse=True)
print ("MODEL READY")
t_weights = tf.trainable_variables()
# FUNCTIONS FOR DANN
class_loss_dann = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=class_pred_dann, labels=y))
domain_loss_dann = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=domain_pred_dann, labels=d))
target_recon_loss = tf.reduce_mean(tf.pow((tf.cast(target,tf.float32) - target_recon),2))
source_recon_loss = tf.reduce_mean(tf.pow((tf.cast(source,tf.float32) - source_recon),2))
target_diff_loss = difference_loss(target_private_feat,target_shared_feat)
source_diff_loss = difference_loss(source_private_feat,source_shared_feat)
losses = class_loss_dann + target_recon_loss + source_recon_loss + target_diff_loss + source_diff_loss
optm_class_dann = tf.train.MomentumOptimizer(lr, 0.9).minimize(losses)
optm_domain_dann = tf.train.MomentumOptimizer(lr, 0.9).minimize(domain_loss_dann)
accr_class_dann = tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(class_pred_dann, 1), tf.arg_max(y, 1)), tf.float32))
accr_domain_dann = tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(domain_pred_dann, 1), tf.arg_max(d, 1)), tf.float32))
# FUNCTIONS FOR CNN
class_loss_cnn = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=class_pred_cnn, labels=y))
optm_class_cnn = tf.train.MomentumOptimizer(lr, 0.9).minimize(class_loss_cnn)
accr_class_cnn = tf.reduce_mean(tf.cast(tf.equal(tf.arg_max(class_pred_cnn, 1), tf.arg_max(y, 1)), tf.float32))
print ("FUNCTIONS READY")
config = tf.ConfigProto()
config.gpu_options.allow_growth = True
sess = tf.Session(config=config)
tf.set_random_seed(0)
init = tf.global_variables_initializer()
sess.run(init)
print ("SESSION OPENED")
# PARAMETERS
batch_size = 100
training_epochs = 50
every_epoch = 1
num_batch = int(ntrain/ batch_size)
total_iter = training_epochs * num_batch
for epoch in range(training_epochs):
randpermlist = np.random.permutation(ntrain)
for i in range(num_batch):
# REVERSAL WEIGHT AND LEARNING RATE SCHEDULE
curriter = epoch * num_batch + i
p = float(curriter) / float(total_iter)
dw_val = 2. / (1. + np.exp(-10. * p)) - 1
lr_val = 0.01 / (1. + 10 * p) ** 0.75
# OPTIMIZE DANN: CLASS-CLASSIFIER
#randidx_class = randpermlist[i * batch_size:min((i + 1) * batch_size, ntrain - 1)]
randidx_class = randpermlist[i * batch_size:min((i + 1) * batch_size, ntrain-1)]
randidx_domain = randpermlist[i * batch_size:min((i + 1) * batch_size, ntrain-1)]
# randidx_domain = np.random.permutation(n_total_train)[:batch_size]
batch_source = source_train_img[randidx_class]
batch_target = target_train_img[randidx_class]
batch_x_domain = total_train[randidx_domain]
batch_y_class = source_train_label[randidx_class, :]
#print(batch_source.shape)
#print(batch_target.shape)
#print(batch_x_domain.shape)
#print(batch_y_class.shape)
feeds_class = {source: batch_source,source_target: batch_x_domain, target:batch_target, y: batch_y_class, lr: lr_val, dw: dw_val}
#feeds_class = {source: batch_source, target:batch_target, y: batch_y_class, lr: lr_val, dw: dw_val}
_, lossclass_val_dann = sess.run([optm_class_dann, class_loss_dann], feed_dict=feeds_class)
# OPTIMIZE DANN: DOMAIN-CLASSIFER
randidx_domain = np.random.permutation(n_total_train)[:batch_size]
batch_x_domain = total_train[randidx_domain]
batch_d_domain = total_train_domain[randidx_domain, :]
feeds_domain = {source_target: batch_x_domain, d: batch_d_domain, lr: lr_val, dw: dw_val}
_, lossdomain_val_dann = sess.run([optm_domain_dann, domain_loss_dann], feed_dict=feeds_domain)
# OPTIMIZE DANN: CLASS-CLASSIFIER
_, lossclass_val_cnn = sess.run([optm_class_cnn, class_loss_cnn], feed_dict=feeds_class)
if epoch % every_epoch == 0:
# CHECK BOTH LOSSES
print("[%d/%d][%d/%d] p: %.3f lossclass_val: %.3e, lossdomain_val: %.3e"
% (epoch, training_epochs, curriter, total_iter, p, lossdomain_val_dann, lossclass_val_dann))
# CHECK ACCUARACIES OF BOTH SOURCE AND TARGET
#feed_source = {source: batch_source, source_target: batch_x_domain, target:batch_target, y: batch_y_class, lr: lr_val, dw: dw_val}
#feed_target = {source: batch_source, source_target: batch_x_domain, target: batch_target, y: batch_y_class,lr: lr_val, dw: dw_val}
feed_source = {source_target: source_test_img, y: source_test_label}
feed_target = {source_target: target_test_img, y: target_test_label}
feed_source_cnn = {source: source_test_img, y: source_test_label}
feed_target_cnn = {source: target_test_img, y: target_test_label}
accr_source_dann = sess.run(accr_class_dann, feed_dict=feed_source)
accr_target_dann = sess.run(accr_class_dann, feed_dict=feed_target)
accr_source_cnn = sess.run(accr_class_cnn, feed_dict=feed_source_cnn)
accr_target_cnn = sess.run(accr_class_cnn, feed_dict=feed_target_cnn)
print(" DANN: SOURCE ACCURACY: %.3f TARGET ACCURACY: %.3f"
% (accr_source_dann, accr_target_dann))
print(" CNN: SOURCE ACCURACY: %.3f TARGET ACCURACY: %.3f"
% (accr_source_cnn, accr_target_cnn))