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dsrc_main.py
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# Deep Sparse Representation-based Classification
# https://arxiv.org/abs/1904.11093
# Mahdi Abavisani
# mahdi.abavisani@rutgers.edu
# Built upon https://github.com/panji1990/Deep-subspace-clustering-networks
# and https://github.com/mahdiabavisani/Deep-multimodal-subspace-clustering-networks
#
# Citation: M. Abavisani and V. M. Patel, "Deep sparse representation-based clas- sification,"
# IEEE Signal Processing Letters, vol. 26, no. 6, pp. 948-952, June 2019.
# DOI:10.1109/LSP.2019.2913022
import tensorflow as tf
import numpy as np
from tensorflow.contrib import layers
import scipy.io as sio
import argparse
import random
class ConvAE(object):
def __init__(self, n_input, kernel_size, n_hidden, reg_constant1=1.0, re_constant2=1.0, batch_size=200, train_size=100,reg=None, \
denoise=False, model_path=None, restore_path=None, \
logs_path='./logs'):
self.n_input = n_input
self.kernel_size = kernel_size
self.n_hidden = n_hidden
self.batch_size = batch_size
self.train_size = train_size
self.test_size = batch_size - train_size
self.reg = reg
self.model_path = model_path
self.restore_path = restore_path
self.iter = 0
tf.set_random_seed(2019)
weights = self._initialize_weights()
# input required to be fed
self.train = tf.placeholder(tf.float32, [None, self.n_input[0], self.n_input[1], 1])
self.test = tf.placeholder(tf.float32, [None, self.n_input[0], self.n_input[1], 1])
self.learning_rate = tf.placeholder(tf.float32, [],name='learningRate')
self.x = tf.concat([self.train, self.test], axis=0) #Concat testing and training samples
latent, latents, shape = self.encoder(self.x, weights)
latent_shape = tf.shape(latent)
# Slice the latent space features to separate training and testing latent features
latent_train = tf.slice(latent,[0,0,0,0],[self.train_size, latent_shape[1], latent_shape[2], latent_shape[3]])
latent_test = tf.slice(latent,[self.train_size,0,0,0],[self.test_size, latent_shape[1], latent_shape[2], latent_shape[3]])
# Vectorize the features
z_train = tf.reshape(latent_train, [self.train_size, -1])
z_test = tf.reshape(latent_test, [self.test_size, -1])
z = tf.reshape(latent, [self.batch_size, -1])
Coef = weights['Coef'] # This is \theta in the paper
z_test_c = tf.matmul(Coef, z_train)
z_c = tf.concat([z_train, z_test_c], axis=0)
latent_c_test = tf.reshape(z_test_c, tf.shape(latent_test))
latent_c_pretrain = tf.concat([latent_train, latent_test], axis=0) # used in pretraining stage
latent_c = tf.concat([latent_train, latent_c_test], axis=0) # used in the main model
self.x_r_pretrain = self.decoder(latent_c_pretrain, weights, shape) # used in pretraining stage
self.x_r = self.decoder(latent_c, weights, shape) # used in the main model
self.Coef_test = Coef
self.AE = tf.concat([z_train, z_test], axis=0) # Autoencoder features to be used in benchmarks comparison
# l_2 reconstruction loss
self.loss_pretrain = tf.reduce_sum(tf.pow(tf.subtract(self.x, self.x_r_pretrain), 2.0))
self.reconst_cost_x = tf.reduce_sum(tf.pow(tf.subtract(self.x, self.x_r), 2.0))
tf.summary.scalar("recons_loss", self.reconst_cost_x)
self.reg_losses = tf.reduce_sum(tf.pow(Coef, 2.0))
tf.summary.scalar("reg_loss", reg_constant1 * self.reg_losses)
self.selfexpress_losses = 0.5 * tf.reduce_sum(tf.pow(tf.subtract(z_c, z), 2.0))
tf.summary.scalar("selfexpress_loss", re_constant2 * self.selfexpress_losses)
# TOTAL LOSS
self.loss = self.reconst_cost_x + reg_constant1 * self.reg_losses + 0.5 * re_constant2 * self.selfexpress_losses
self.merged_summary_op = tf.summary.merge_all()
self.optimizer = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(
self.loss) # GradientDescentOptimizer #AdamOptimizer
self.optimizer_pretrain = tf.train.AdamOptimizer(learning_rate=self.learning_rate).minimize(
self.loss_pretrain) # GradientDescentOptimizer #AdamOptimizer
self.init = tf.global_variables_initializer()
tfconfig = tf.ConfigProto(allow_soft_placement=True)
tfconfig.gpu_options.allow_growth = True
self.sess = tf.InteractiveSession(config=tfconfig)
self.sess.run(self.init)
self.saver = tf.train.Saver([v for v in tf.trainable_variables() if not (v.name.startswith("Coef"))]) # to save the pretrained model
self.summary_writer = tf.summary.FileWriter(logs_path, graph=tf.get_default_graph())
def _initialize_weights(self):
'''
initializes weights for the model and soters them in a dictionary.
'''
all_weights = dict()
all_weights['enc_w0'] = tf.get_variable("enc_w0",
shape=[self.kernel_size[0], self.kernel_size[0], 1,
self.n_hidden[0]],
initializer=layers.xavier_initializer_conv2d())
all_weights['enc1_b0'] = tf.Variable(tf.zeros([self.n_hidden[0]], dtype=tf.float32))
all_weights['enc_b0'] = tf.Variable(tf.zeros([self.n_hidden[0]], dtype=tf.float32))
all_weights['enc_w1'] = tf.get_variable("enc_w1",
shape=[self.kernel_size[1], self.kernel_size[1],
self.n_hidden[0],
self.n_hidden[1]],
initializer=layers.xavier_initializer_conv2d())
all_weights['enc_b1'] = tf.Variable(tf.zeros([self.n_hidden[1]], dtype=tf.float32))
all_weights['enc_w2'] = tf.get_variable("enc_w2",
shape=[self.kernel_size[2], self.kernel_size[2],
self.n_hidden[1],
self.n_hidden[2]],
initializer=layers.xavier_initializer_conv2d())
all_weights['enc_b2'] = tf.Variable(tf.zeros([self.n_hidden[2]], dtype=tf.float32))
all_weights['dec_w0'] = tf.get_variable("dec1_w0",
shape=[self.kernel_size[2], self.kernel_size[2],
self.n_hidden[1],
self.n_hidden[3]],
initializer=layers.xavier_initializer_conv2d())
all_weights['dec_b0'] = tf.Variable(tf.zeros([self.n_hidden[1]], dtype=tf.float32))
all_weights['dec_w1'] = tf.get_variable("dec1_w1",
shape=[self.kernel_size[1], self.kernel_size[1],
self.n_hidden[0],
self.n_hidden[1]],
initializer=layers.xavier_initializer_conv2d())
all_weights['dec_b1'] = tf.Variable(tf.zeros([self.n_hidden[0]], dtype=tf.float32))
all_weights['dec_w2'] = tf.get_variable("dec1_w2",
shape=[self.kernel_size[0], self.kernel_size[0], 1,
self.n_hidden[0]],
initializer=layers.xavier_initializer_conv2d())
all_weights['dec_b2'] = tf.Variable(tf.zeros([1], dtype=tf.float32))
all_weights['enc_w3'] = tf.get_variable("enc_w3",
shape=[self.kernel_size[3], self.kernel_size[3],
self.n_hidden[2],
self.n_hidden[3]],
initializer=layers.xavier_initializer_conv2d())
all_weights['enc_b3'] = tf.Variable(tf.zeros([self.n_hidden[3]], dtype=tf.float32))
all_weights['Coef'] = tf.Variable(1.0e-4 * tf.ones([self.test_size, self.train_size], tf.float32), name='Coef')
return all_weights
# Building the encoder
def encoder(self, X, weights):
shapes = []
# Encoder Hidden layer with relu activation #1
shapes.append(X.get_shape().as_list())
layer1 = tf.nn.bias_add(
tf.nn.conv2d(X, weights['enc_w0'], strides=[1, 2, 2, 1], padding='SAME'),
weights['enc_b0'])
layer1 = tf.nn.relu(layer1)
layer2 = tf.nn.bias_add(
tf.nn.conv2d(layer1, weights['enc_w1'], strides=[1, 1, 1, 1], padding='SAME'),
weights['enc_b1'])
layer2 = tf.nn.relu(layer2)
layer3 = tf.nn.bias_add(
tf.nn.conv2d(layer2, weights['enc_w2'], strides=[1, 2, 2, 1], padding='SAME'),
weights['enc_b2'])
layer3 = tf.nn.relu(layer3)
latents = layer3
print(layer3.shape)
shapes.append(layer1.get_shape().as_list())
shapes.append(layer2.get_shape().as_list())
layer3_in = layer3
latent = tf.nn.conv2d(layer3_in, weights['enc_w3'], strides=[1, 1, 1, 1], padding='SAME')
latent = tf.nn.relu(latent)
shapes.append(latent.get_shape().as_list())
return latent, latents, shapes
# Building the decoder
def decoder(self, z, weights, shapes):
# Encoder Hidden layer with relu activation #1
shape_de1 = shapes[2]
layer1 = tf.add(tf.nn.conv2d_transpose(z, weights['dec_w0'], tf.stack(
[tf.shape(self.x)[0], shape_de1[1], shape_de1[2], shape_de1[3]]), \
strides=[1, 2, 2, 1], padding='SAME'), weights['dec_b0'])
layer1 = tf.nn.relu(layer1)
shape_de2 = shapes[1]
layer2 = tf.add(tf.nn.conv2d_transpose(layer1, weights['dec_w1'], tf.stack(
[tf.shape(self.x)[0], shape_de2[1], shape_de2[2], shape_de2[3]]), \
strides=[1, 1, 1, 1], padding='SAME'), weights['dec_b1'])
layer2 = tf.nn.relu(layer2)
shape_de3 = shapes[0]
layer3 = tf.add(tf.nn.conv2d_transpose(layer2, weights['dec_w2'], tf.stack(
[tf.shape(self.x)[0], shape_de3[1], shape_de3[2], shape_de3[3]]), \
strides=[1, 2, 2, 1], padding='SAME'), weights['dec_b2'])
layer3 = tf.nn.relu(layer3)
recons = layer3
return recons
def partial_fit(self, X,Y, lr):
cost, summary, _, Coef = self.sess.run(
(self.reconst_cost_x, self.merged_summary_op, self.optimizer, self.Coef_test), feed_dict={self.learning_rate:lr,self.train:Y,self.test:X})
self.summary_writer.add_summary(summary, self.iter)
self.iter = self.iter + 1
return cost, Coef
def pretrain_step(self, X,Y, lr):
cost, summary, _ = self.sess.run(
(self.reconst_cost_x, self.merged_summary_op, self.optimizer_pretrain), feed_dict={self.learning_rate:lr,self.train:Y,self.test:X})
self.summary_writer.add_summary(summary, self.iter)
self.iter = self.iter + 1
return cost
def initlization(self):
self.sess.run(self.init)
def reconstruct(self, X):
return self.sess.run(self.x_r, feed_dict={self.x:X})
def transform(self, X,Y):
return self.sess.run(self.AE, feed_dict={self.train:Y,self.test:X})
def save_model(self):
save_path = self.saver.save(self.sess, self.model_path)
print ("model saved in file: %s" % save_path)
def restore(self):
self.saver.restore(self.sess, self.restore_path)
print ("model restored")
def thrC(C, ro=0.1):
if ro < 1:
N1 = C.shape[0]
N2 = C.shape[1]
Cp = np.zeros((N1, N2))
S = np.abs(np.sort(-np.abs(C), axis=0))
Ind = np.argsort(-np.abs(C), axis=0)
for i in range(N2):
cL1 = np.sum(S[:, i]).astype(float)
stop = False
csum = 0
t = 0
while (stop == False):
csum = csum + S[t, i]
if csum > ro * cL1:
stop = True
Cp[Ind[0:t + 1, i], i] = C[Ind[0:t + 1, i], i]
t = t + 1
else:
Cp = C
return Cp
def err_rate(gt_s, s):
err_x = np.sum(gt_s[:] != s[:])
missrate = err_x.astype(float) / (gt_s.shape[0])
return missrate
def testing(Img_test,Img_train, train_labels,test_labels, CAE, num_class,args):
Img_test = np.array(Img_test)
Img_test = Img_test.astype(float)
Img_train = np.array(Img_train)
Img_train = Img_train.astype(float)
train_labels = np.array(train_labels[:])
train_labels = train_labels - train_labels.min() + 1
train_labels = np.squeeze(train_labels)
test_labels = np.array(test_labels[:])
test_labels = test_labels - test_labels.min() + 1
test_labels = np.squeeze(test_labels)
CAE.initlization()
max_step = args.max_step # 500 + num_class*25# 100+num_class*20
pretrain_max_step = args.pretrain_step
display_step = args.display_step #max_step
lr = 1.0e-3
epoch = 0
class_ = np.zeros(np.max(test_labels))
prediction = np.zeros(len(test_labels))
ACC =[]
Cost=[]
while epoch < pretrain_max_step:
epoch = epoch + 1
cost = CAE.pretrain_step(Img_test,Img_train, lr) #
if epoch % display_step == 0:
print ("pretrtain epoch: %.1d" % epoch, "cost: %.8f" % (cost / float(batch_size)))
while epoch < max_step:
epoch = epoch + 1
cost, Coef = CAE.partial_fit(Img_test,Img_train, lr) #
if epoch % display_step == 0:
print ("epoch: %.1d" % epoch, "cost: %.8f" % (cost / float(batch_size)))
Coef = thrC(Coef)
Coef= np.abs(Coef)
for test_sample in range(0,len(test_labels)):
x = Coef[test_sample,:]
for l in range(1,np.max(test_labels)+1):
l_idx = np.array([j for j in range(0,len(train_labels)) if train_labels[j]==l])
l_idx= l_idx.astype(int)
class_[int(l-1)] = sum(np.abs(x[l_idx]))
prediction[test_sample] = np.argmax(class_) +1
prediction = np.array(prediction)
missrate_x = err_rate(test_labels, prediction)
acc_x = 1 - missrate_x
print("accuracy: %.4f" % acc_x)
ACC.append(acc_x)
Cost.append(cost / float(batch_size))
if False: # change to ture to save values in a mat file
sio.savemat('./coef.mat', dict(ACC=ACC,Coef=Coef,Cost=Cost))
return acc_x, Coef
def get_train_test_data(data,training_rate=0.8):
'''
Extracts features and labels from the dictionary "data," and splits the samples
into training and testing sets.
Input:
data: dictionary containing two keys: {feature, Label}
data['features'] : vectorized features (1024 x N)
data['Label'] : groundtruth labels (1 x N)
rate: ratio of the # of training samples to the total # of samples
Output:
training and testing sets.
'''
Label = data['Label']
Label = np.squeeze(np.array(Label))
training_size = int(training_rate * len(Label))
perm = np.random.permutation(len(Label))
training_idx = perm[:training_size]
testing_idx = perm[training_size:]
train_labels = Label[training_idx]
test_labels = Label[testing_idx]
I_test = []
I_train = []
img = data['features']
training_img = img[:,training_idx]
testing_img = img[:,testing_idx]
for i in range(training_img.shape[1]):
temp = np.reshape(training_img[:, i], [32, 32])
I_train.append(temp)
Img_train = np.transpose(np.array(I_train), [0, 2, 1])
Img_train = np.expand_dims(Img_train[:], 3)
for i in range(testing_img.shape[1]):
temp = np.reshape(testing_img[:, i], [32, 32])
I_test.append(temp)
Img_test = np.transpose(np.array(I_test), [0, 2, 1])
Img_test = np.expand_dims(Img_test[:], 3)
return Img_train,Img_test,train_labels,test_labels,Label
if __name__ == '__main__':
random.seed(2019)
parser = argparse.ArgumentParser(description='')
parser.add_argument('--mat', dest='mat', default='umd', help='path of the dataset')
parser.add_argument('--model', dest='model', default='umd',
help='name of the model to be saved')
parser.add_argument('--rate', dest='rate', type=float, default=0.8, help='Pecentage of samples ')
parser.add_argument('--epoch', dest='max_step', type=int, default=10000, help='Max # training epochs')
parser.add_argument('--pretrain_step', dest='pretrain_step', type=int, default=1000, help='Max # of pretraining epochs ')
parser.add_argument('--display_step', dest='display_step', type=int, default=1000, help='frequency of reports')
args = parser.parse_args()
# load face images and labels
datapath = './data/' + args.mat + '.mat'
data = sio.loadmat(datapath)
# Split the data into training and testing sets
[Im_train,Im_test,train_labels,test_labels,Label] = get_train_test_data(data,training_rate=args.rate)
# face image clustering
n_input = [32, 32]
kernel_size = [5,3,3,1]
n_hidden = [10, 20, 30,30]
iter_loop = 0
num_class = Label.max()
batch_size = len(Label)
training_size = len(train_labels)
# These regularization values work best if the features are intensity values between 0-225
reg1 = 1.0 # random.uniform(1, 10)
reg2 = 8.0 # random.uniform(1, 10)
model_path = './models/' + args.model + '.ckpt'
logs_path = './logs'
tf.reset_default_graph()
CAE = ConvAE(n_input=n_input, n_hidden=n_hidden, reg_constant1=reg1, re_constant2=reg2, \
kernel_size=kernel_size, batch_size=batch_size, train_size=training_size,model_path=model_path, restore_path=model_path,
logs_path=logs_path)
ACC, C = testing(Im_test,Im_train, train_labels, test_labels, CAE, num_class,args)