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ldr2hdr_net.py
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ldr2hdr_net.py
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from ldr2hdr_ops import *
import tensorflow as tf
from gradient_reversal import gradient_reversal
class LDR2HDR_Net():
def __init__(self, fc_dim, im_height, deconv_method):
"""
Args:
doSkipLinks:
doSigmoidLast: if input is linearHDR, use sigmoid for last layer
fc_dim: Dimension of fully connect layer. [64]
"""
self.fc_dim = fc_dim
self.doSigmoidLast = doSigmoidLast
self.deconv_method = deconv_method
self.im_height = im_height
self.im_width = 2 * im_height
def encoder(self, inputs, isTraining, reuse=False):
with tf.variable_scope('Encoders', reuse=reuse):
fc_dim = self.fc_dim
''' encoder '''
with tf.variable_scope('encoder1'):
self.conv1 = conv2d(inputs, output_channels=64, k_h=7, k_w=7, pool_method='stride')
self.conv1 = tf.nn.elu(batch_norm(self.conv1, isTraining), name='activation')
# Layer2
with tf.variable_scope('encoder2'):
self.conv2 = conv2d(self.conv1, output_channels=128, k_h=5, k_w=5, pool_method='stride')
self.conv2 = tf.nn.elu(batch_norm(self.conv2, isTraining), name='activation')
# Layer3
with tf.variable_scope('encoder3'):
self.conv3 = conv2d(self.conv2, output_channels=256, k_h=3, k_w=3, pool_method='stride')
self.conv3 = tf.nn.elu(batch_norm(self.conv3, isTraining), name='activation')
# Layer4
with tf.variable_scope('encoder4'):
self.conv4 = conv2d(self.conv3, output_channels=256, k_h=3, k_w=3, pool_method='stride')
self.conv4 = tf.nn.elu(batch_norm(self.conv4, isTraining), name='activation')
# Fully connected layer1
with tf.variable_scope('feature'):
fc = fc2d(self.conv4, fc_dim, name='fc2d')
fc = tf.nn.elu(batch_norm(fc, isTraining), name='activation')
self.fc = fc
# Apply Dropout
keep_prob = 0.5
self.fc = tf.nn.dropout(self.fc, keep_prob, name='dropout')
return self.fc
def decoder(self, isTraining, reuse=False):
fc = self.fc
conv4 = self.conv4[:, 0:self.im_height / 32, :, :]
conv3 = self.conv3[:, 0:self.im_height / 16, :, :]
conv2 = self.conv2[:, 0:self.im_height / 8, :, :]
conv1 = self.conv1[:, 0:self.im_height / 4, :, :]
with tf.variable_scope('Decoders', reuse=reuse):
# de FC layer
# Reshape fc to fit fully connected layer input
with tf.variable_scope('defc'):
defc = dfc2d(fc, out_height=conv4.get_shape()[1].value,
out_width=conv4.get_shape()[2].value,
out_channels=conv4.get_shape()[3].value, name='dfc_skyonly')
defc = tf.add(defc, conv4, name='ResidualMatch')
defc = tf.nn.elu(batch_norm(defc, isTraining), name='activation')
''' decoder '''
with tf.variable_scope('decoder4'):
deconv4 = deconv2d(defc, output_channels=256, output_imshape=[2 * self.im_height / 32, 2 * self.im_width / 16], k_h=3, k_w=3, method=self.deconv_method)
deconv4 = tf.add(deconv4, conv3, name='ResidualMatch')
deconv4 = tf.nn.elu(batch_norm(deconv4, isTraining), name='activation')
with tf.variable_scope('decoder3'):
deconv3 = deconv2d(deconv4, output_channels=128, output_imshape=[2 * self.im_height / 16, 2 * self.im_width / 8], k_h=3, k_w=3, method=self.deconv_method)
deconv3 = tf.add(deconv3, conv2, name='ResidualMatch')
deconv3 = tf.nn.elu(batch_norm(deconv3, isTraining), name='activation')
with tf.variable_scope('decoder2'):
deconv2 = deconv2d(deconv3, output_channels=64, output_imshape=[2 * self.im_height / 8, 2 * self.im_width / 4], k_h=5, k_w=5, method=self.deconv_method)
deconv2 = tf.add(deconv2, conv1, name='ResidualMatch')
deconv2 = tf.nn.elu(batch_norm(deconv2, isTraining), name='activation')
with tf.variable_scope('decoder1'):
deconv1 = deconv2d(deconv2, output_channels=64, output_imshape=[2 * self.im_height / 4, 2 * self.im_width / 2], k_h=7, k_w=7, method=self.deconv_method, name='decoder1')
deconv1 = tf.nn.elu(batch_norm(deconv1, isTraining), name='decoder1_activation')
# Output
out = conv2d(deconv1, output_channels=3, k_h=1, k_w=1, pool_method=None, name='3ChannelImg')
out = tf.nn.sigmoid(out, name='OutputImg')
return out
def sunPredictior(self, isTraining, reuse=False):
fc = self.fc
with tf.variable_scope('SunPosition', reuse=reuse):
if self.doFCNorFC == 'FC':
fc_fcn = lambda fc, dims: fc2d(fc, dims)
elif self.doFCNorFC == 'FCN':
fc_fcn = lambda fc, dims: conv2d(fc, output_channels=dims, k_h=1, k_w=1, pool_method=None, padding='VALID')
with tf.variable_scope('fc1'):
sunpos_fc1 = fc_fcn(fc, 32)
sunpos_fc1 = tf.nn.elu(batch_norm(sunpos_fc1, isTraining), name='activation')
with tf.variable_scope('fc2'):
sunpos_fc2 = fc_fcn(sunpos_fc1, 16)
sunpos_fc2 = tf.nn.elu(batch_norm(sunpos_fc2, isTraining), name='activation')
with tf.variable_scope('fc5'):
sunPos = fc_fcn(sunpos_fc2, 1)
sunPos = tf.nn.relu(sunPos, name='activation')
sunPos = tf.squeeze(sunPos, [1, 2], name='output') # [N,1,1,D]->[N, D] or [N, D]
return sunPos
def discriminator_da(self, isTraining, lambdar, reuse=False):
fc = gradient_reversal(self.fc, lambdar)
with tf.variable_scope("Discriminator", reuse=reuse):
with tf.variable_scope('fc1'):
domain_fc = fc2d(fc, 32)
domain_fc = tf.nn.elu(domain_fc, name='activation')
with tf.variable_scope('fc2'):
domain_logit = fc2d(domain_fc, 2)
domain_fc = tf.nn.softmax(domain_logit, name='activation')
domain_fc = tf.squeeze(domain_fc, [1, 2], name='domain_out')
return domain_fc, domain_logit # (BATCH, 2)
def pred(self, inputs, isTraining, reuse=False):
fc = self.encoder(inputs=inputs, isTraining=isTraining, reuse=reuse)
sunPos = self.sunPredictior(isTraining=isTraining, reuse=reuse)
outImg = self.decoder(isTraining=isTraining, reuse=reuse)
return outImg, sunPos, fc
def usage():
sess = tf.Session()
generator = LDR2HDR_Net(fc_dim=64, im_height=64, deconv_method='upsample')
x = tf.placeholder(tf.float32, [32, 64, 128, 3], name='InputImage')
outImg, sunPos, fc = generator.pred(inputs=x, isTraining=True)