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bsc-ConvDAE.py
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bsc-ConvDAE.py
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# -*- coding: utf-8 -*-
'''
【basic convolutional denoising autoencoder——bsc-ConvDAE】
--max_pool in decoder, non dropout layer
contains two modes:
unspervised: input data will be randomly masked(through dropout), inputs_ & targets_ are the same (noisy data)
supervised: inputs_ & targets_ are clean data and noisy data separately
Author: zhihong (z_zhi_hong@163.com)
Date: 20190505
Modified: zhihong_20210223
'''
# In[]:
# import modules
import numpy as np
import tensorflow as tf
from datetime import datetime
from time import time
import matplotlib.pyplot as plt
import os
from util import my_io
# In[]
# environment config
os.environ["CUDA_VISIBLE_DEVICES"] = "0"
config = tf.ConfigProto()
config.gpu_options.allow_growth=True
config.gpu_options.per_process_gpu_memory_fraction = 0.8
# In[]:
# graph reset
tf.reset_default_graph()
# In[]:
# parameters config
# flags
SUP_FLAG = 0 # supervised learning flag
# setting
epochs = 20
batch_size = 64
learning_rate = 0.001
pic_size = [64,64] # picture size, for sml
# pic_size = [28,28] # picture size, for N-MNIST
keep_prob_v = 0.7
if SUP_FLAG:
mask_prob_v = 0.0 # supervised mode: mask layer' drop probability.
else:
mask_prob_v = 0.3 # unsupervised mode: mask layer' drop probability.
# dataset path
# train_path = "./dataset/N_MNIST_pic/N_MNIST_pic_train.mat"
# test_path = "./dataset/N_MNIST_pic/N_MNIST_pic_test.mat"
train_path = "./dataset/single_molecule_localization/sml_train.mat"
test_path = "./dataset/single_molecule_localization/sml_test.mat"
timestamp = '{:%m-%d_%H-%M/}'.format(datetime.now())
model_root_path = "./model_data/"
if SUP_FLAG:
model_name = "bsc-ConvDAE(sup)"
else:
model_name = "bsc-ConvDAE(unsup)"
model_dir = model_name + "--" + timestamp
model_path = model_root_path + model_dir
if not os.path.isdir(model_path):
os.makedirs(model_path)
train_log_dir = './logs/train/bsc-ConvDAE_'+timestamp
test_log_dir = './logs/test/bsc-ConvDAE_'+timestamp
# In[]:
# functions
# tensorflow log summary
def summaryWriter(train_writer, test_writer, record_point, run_tensor, train_feed_dict, test_feed_dict, iter):
tr, tr_cost = sess.run([record_point, run_tensor], feed_dict=train_feed_dict)
te, te_cost = sess.run([record_point, run_tensor], feed_dict=test_feed_dict)
train_writer.add_summary(tr, iter)
test_writer.add_summary(te, iter)
print("Epoch:",iter,"Train cost:",tr_cost,"Test cost",te_cost)
# In[]:
# model
# activate function
act_fun = tf.nn.relu # inner layer act_fun
act_fun_out = tf.nn.relu # output layer act_fun, for slm
# act_fun_out = tf.nn.tanh # output layer act_fun, for N-MNIST
with tf.name_scope('inputs'):
inputs_ = tf.placeholder(tf.float32, (None, *pic_size, 1), name='inputs_')
targets_ = tf.placeholder(tf.float32, (None, *pic_size, 1), name='targets_')
keep_prob = tf.placeholder(tf.float32) #range 0.0-1.0
mask_prob = tf.placeholder(tf.float32) #range 0.0-1.0
# net structure
# Encoder
with tf.name_scope('encoder'):
drop = tf.nn.dropout(inputs_, 1-mask_prob) #unsupervised：randomly masked
conv1 = tf.layers.conv2d(drop, 64, (3,3), padding='same', activation=act_fun)
# conv1 = tf.nn.dropout(conv1, keep_prob)
max_p1 = tf.layers.max_pooling2d(conv1, (2,2), (2,2), padding='same')
conv2 = tf.layers.conv2d(max_p1, 32, (3,3), padding='same', activation=act_fun)
# conv2 = tf.nn.dropout(conv2, keep_prob)
max_p2 = tf.layers.max_pooling2d(conv2, (2,2), (2,2), padding='same')
conv3 = tf.layers.conv2d(max_p2, 16, (3,3), padding='same', activation=act_fun)
# conv3 = tf.nn.dropout(conv3, keep_prob)
max_p3 = tf.layers.max_pooling2d(conv3, (2,2), (2,2), padding='same')
# Decoder
with tf.name_scope('decoder'):
res4 = tf.image.resize_nearest_neighbor(max_p3, (16,16))
conv4 = tf.layers.conv2d(res4, 16, (3,3), padding='same', activation=act_fun)
# conv4 = tf.nn.dropout(conv4, keep_prob)
res5 = tf.image.resize_nearest_neighbor(conv4, (32,32))
conv5 = tf.layers.conv2d(res5, 32, (3,3), padding='same', activation=act_fun)
# conv5 = tf.nn.dropout(conv5, keep_prob)
res6 = tf.image.resize_nearest_neighbor(conv5, (64,64))
conv6 = tf.layers.conv2d(res6, 64, (3,3), padding='same', activation=act_fun)
# conv6 = tf.nn.dropout(conv6, keep_prob)
# logits and outputs
with tf.name_scope('outputs'):
logits_ = tf.layers.conv2d(conv6, 1, (3,3), padding='same', activation=None)
outputs_ = act_fun_out(logits_, name='outputs_')
# loss and Optimizer
with tf.name_scope('loss'):
# loss = tf.nn.sigmoid_cross_entropy_with_logits(labels=targets_, logits=logits_)
# loss = tf.reduce_sum(tf.square(targets_ - outputs_))
loss = tf.losses.mean_squared_error(targets_ , outputs_)
cost = tf.reduce_mean(loss)
tf.summary.scalar('cost', cost)
with tf.name_scope('train'):
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)
# In[]:
# load data
train_data = my_io.load_mat(train_path)
test_data = my_io.load_mat(test_path)
train_x = train_data['data'].astype('float32')
test_x = test_data['data'].astype('float32')
if SUP_FLAG==0:
train_y = train_x
test_y = test_x
else:
train_y = train_data['data_gt'].astype('float32')
test_y = test_data['data_gt'].astype('float32')
print('train_x: ', train_x.shape, '\ttrain_y: ', train_y.shape,
'\ntest_x: ', test_x.shape, '\ttest_y: ', test_y.shape)
# to avoid OOM, use part of test dataset for testing
# test_idx = np.linspace(0,len(test_x)-1,1000).astype('int32')
test_idx = np.linspace(1,len(test_x)-1,100).astype('int32')
test_x1 = test_x[test_idx].reshape((-1, *pic_size, 1))
test_y1 = test_y[test_idx].reshape((-1, *pic_size, 1))
# data disp
#for k in range(5):
# plt.subplot(2,5,k+1)
# plt.imshow(train_x[k])
# plt.title('train_x_%d'%(k+1))
# plt.xticks([])
# plt.yticks([])
# plt.subplot(2,5,k+6)
# plt.imshow(train_y[k])
# plt.title('train_y_%d'%(k+1))
# plt.xticks([])
# plt.yticks([])
# In[]
# initialize
sess = tf.Session(config=config)
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
writer_tr = tf.summary.FileWriter(train_log_dir, sess.graph)
writer_te = tf.summary.FileWriter(test_log_dir)
merged = tf.summary.merge_all()
# In[]:
# train
test_feed_dict={inputs_: test_x1, targets_: test_y1, keep_prob: 1.0, mask_prob:0.0}
time_start = time()
summaryWriter(writer_tr, writer_te, merged, cost, test_feed_dict, test_feed_dict, 0)
for e in range(1, 1+epochs):
for batch_x, batch_y in my_io.batch_iter(batch_size, train_x, train_y, throw_insufficient=True):
x = batch_x.reshape((-1, *pic_size, 1))
y = batch_y.reshape((-1, *pic_size, 1))
train_feed_dict = {inputs_: x, targets_: y, keep_prob: keep_prob_v, mask_prob: mask_prob_v}
sess.run(optimizer, feed_dict=train_feed_dict)
if e%10 == 0:
time_cost = time()-time_start
summaryWriter(writer_tr, writer_te, merged, cost, train_feed_dict, test_feed_dict, e)
res_imgs = sess.run(outputs_, feed_dict={inputs_: test_x1, targets_: test_y1,keep_prob:1.0, mask_prob: 0.0})
res_imgs = np.squeeze(res_imgs)
data_save = {'reconstructed': res_imgs}
my_io.save_mat(test_log_dir+'/'+ test_path.split('/')[-1][0:-4]+'_epoch'+str(e)+'.mat', data_save)
print('Time:', time_cost, ' Reconstruction test data saved to :',test_log_dir + '\n')
if e%20 == 0 and e!=0:
saver.save(sess, model_path+'my_model',global_step=e, write_meta_graph=False)
# saver.save(sess,model_path+'my_model')
print('epoch %d model saved to:'%e, model_path+'my_model\n')
summaryWriter(writer_tr, writer_te, merged, cost, train_feed_dict, test_feed_dict, e)
saver.save(sess,model_path+'my_model')
print('epoch: %d model saved to:'%e, model_path+'my_model')
# In[61]:
# test
start = 0
end = len(test_x)-1
idx = np.linspace(start, end, 10).astype('int32') # show 10 results at equal intervals
in_imgs = test_x[idx]
gt_imgs = test_y[idx]
reconstructed = sess.run(outputs_, feed_dict={inputs_: in_imgs.reshape((10, *pic_size, 1)), keep_prob: 1.0, mask_prob:0.0})
reconstructed = np.squeeze(reconstructed)
fig, axes = plt.subplots(nrows=3, ncols=10, sharex=True, sharey=True, figsize=(20,4))
for images, row in zip([in_imgs, reconstructed, gt_imgs], axes):
for img, ax in zip(images, row):
ax.imshow(img.reshape((*pic_size)), cmap='gray')
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
fig.tight_layout(pad=0.1)
plt.show()
# In[ ]:
# release
sess.close()