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Semantic_synthesis_v2.0.py
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Semantic_synthesis_v2.0.py
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import numpy as np
import tensorflow as tf
import vgg19
import utils
import os
from tqdm import tqdm
from scipy import misc
# from pathlib import Path
# pathlist = Path('../../3D-R2N2/ShapeNet/ShapeNetRendering/').glob('**/*00.png')
# os.path.dirname(str(path))
path = '../../chair_2.0_databset/'
def generator(path):
for j in range(100):
for i in range(1000):
# receive input_a
input_a = utils.load_image(path + 'chair_rotation_%.2d/chair_rotation_%.2d.png' % (j, j))
# input_a = utils.load_image('../../chair.png')
input_a = input_a[:,:,:3]
input_a = input_a.reshape((-1))
input_a = np.tile(input_a, 10)
input_a = input_a.reshape((10, 224, 224, 3))
# receive input_b
input_b = np.loadtxt(path + 'chair_rotation_%.2d/rendering_metadata_rotation_%.2d.txt' % (j, j))
# input_b = np.loadtxt(path + 'rendering_metadata_rotation_00.txt')
input_b = input_b[(i*10):(i*10+10),:]
# receive input_c
input_c = utils.load_image_com(path + 'chair_rotation_%.2d/chair_rotation_%.2d_%.2d.png' % (j, j, i*10))[:,:,:3]
# input_c = utils.load_image_com(path + 'chair_rotation_00_only_%.2d.png' % (i*10))[:,:,:3]
for k in range(1,10,1):
input_c = np.concatenate((input_c, utils.load_image_com(path + 'chair_rotation_%.2d/chair_rotation_%.2d_%.2d.png' % (j, j, (i*10 + k)))[:,:,:3]), axis = 0)
# input_c = np.concatenate((input_c, utils.load_image_com(path + 'chair_rotation_00_only_%.2d.png' % (i*10+k))[:,:,:3]), axis = 0)
input_c = np.reshape(input_c, (10, 224, 224, 3))
# generate data
yield (input_a, input_b, input_c)
# input_a = utils.load_image("")
# input_b = dataset from quaternions txt
# input_c = load image
# input_a = np.random.rand(6, 224, 224, 3)
# input_b = np.random.rand(6, 4)
# input_c = np.random.rand(6,137, 137, 3)
# define skip_connection layer
def skip_connection_block(input_a_layer, input_b_layer, resize, filters):
input_a = tf.image.resize_images(input_a_layer, [resize,resize])
output_skip_layer = tf.concat([input_a, input_b_layer],-1)
output_skip_layer = tf.layers.conv2d_transpose(inputs = output_skip_layer, filters = filters, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
# output_skip_layer = tf.layers.conv2d_transpose(inputs = output_skip_layer, filters = filters, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
return output_skip_layer
# define resnet_block
def residual_block(inputs_layer, nb_blocks, out_filters, strides):
for _ in range(nb_blocks):
shortcut = inputs_layer
# resnet_1_layer
inputs_layer = tf.layers.conv2d_transpose(inputs = inputs_layer, filters = out_filters, kernel_size = 3, strides = strides, padding='same', data_format='channels_last')
inputs_layer = tf.contrib.layers.batch_norm(inputs_layer)
inputs_layer = tf.nn.relu(inputs_layer)
# resnet_2_layer
inputs_layer = tf.layers.conv2d_transpose(inputs = inputs_layer, filters = out_filters, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
inputs_layer = tf.contrib.layers.batch_norm(inputs_layer)
inputs_layer = tf.nn.relu(inputs_layer)
# process shortcut
shortcut = tf.layers.conv2d_transpose(inputs = shortcut, filters = out_filters, kernel_size = 3, strides = strides, padding='same', data_format='channels_last')
# skip connection
inputs_layer += shortcut
# output resnet result
return inputs_layer
def VGG19_Synthesis_Network(input_img, input_Semantic):
# img encoder
vgg = vgg19.Vgg19()
vgg.build(input_img)
img_layer = tf.contrib.layers.flatten(vgg.pool5)
img_layer = tf.contrib.layers.fully_connected(img_layer, 512)
# Semantic encoder
Semantic_layer = tf.contrib.layers.fully_connected(input_Semantic, 512)
# Semantic synthesis
z = tf.concat([img_layer, Semantic_layer], -1)
# img decoder
z = tf.expand_dims(z,1)
z = tf.expand_dims(z,1)
# output_1 = tf.layers.conv2d_transpose(inputs = z, filters = 512, kernel_size = 3, strides = (2,2), padding='same', data_format='channels_last', activation = tf.nn.relu)
# output_2 = tf.layers.conv2d_transpose(inputs = output_1, filters = 256, kernel_size = 3, strides = (2,2), padding='same', data_format='channels_last', activation = tf.nn.relu)
# output_3 = tf.layers.conv2d_transpose(inputs = output_2, filters = 256, kernel_size = 3, strides = (2,2), padding='same', data_format='channels_last', activation = tf.nn.relu)
# output_4 = tf.layers.conv2d_transpose(inputs = output_3, filters = 128, kernel_size = 3, strides = (2,2), padding='same', data_format='channels_last', activation = tf.nn.relu)
# output_5 = tf.layers.conv2d_transpose(inputs = output_4, filters = 64, kernel_size = 3, strides = (2,2), padding='same', data_format='channels_last', activation = tf.nn.relu)
# output_6 = tf.layers.conv2d_transpose(inputs = output_5, filters = 32, kernel_size = 3, strides = (2,2), padding='same', data_format='channels_last', activation = tf.nn.relu)
# output_7 = tf.layers.conv2d_transpose(inputs = output_6, filters = 3, kernel_size = 3, strides = (2,2), padding='same', data_format='channels_last', activation = tf.nn.relu)
output_1 = residual_block(inputs_layer = z, nb_blocks = 1, out_filters = 512, strides = 2)
output_2 = tf.layers.conv2d_transpose(inputs = output_1, filters = 512, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
output_2 = tf.contrib.layers.batch_norm(output_2)
output_2 = tf.nn.relu(output_2)
output_2 = residual_block(inputs_layer = output_2, nb_blocks = 1, out_filters = 256, strides = 2)
output_3 = tf.layers.conv2d_transpose(inputs = output_2, filters = 256, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
output_3 = tf.contrib.layers.batch_norm(output_3)
output_3 = tf.nn.relu(output_3)
output_3 = residual_block(inputs_layer = output_3, nb_blocks = 1, out_filters = 256, strides = 2)
output_4 = tf.layers.conv2d_transpose(inputs = output_3, filters = 128, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
output_4 = tf.contrib.layers.batch_norm(output_4)
output_4 = tf.nn.relu(output_4)
output_4 = residual_block(inputs_layer = output_4, nb_blocks = 1, out_filters = 128, strides = 2)
output_5 = tf.layers.conv2d_transpose(inputs = output_4, filters = 128, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
output_5 = tf.contrib.layers.batch_norm(output_5)
output_5 = tf.nn.relu(output_5)
output_5 = residual_block(inputs_layer = output_5, nb_blocks = 1, out_filters = 64, strides = 2)
output_6 = tf.layers.conv2d_transpose(inputs = output_5, filters = 64, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
output_6 = tf.contrib.layers.batch_norm(output_6)
output_6 = tf.nn.relu(output_6)
output_6 = residual_block(inputs_layer = output_6, nb_blocks = 1, out_filters = 32, strides = 2)
output_7 = tf.layers.conv2d_transpose(inputs = output_6, filters = 32, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
output_7 = tf.contrib.layers.batch_norm(output_7)
output_7 = tf.nn.relu(output_7)
output_7 = residual_block(inputs_layer = output_7, nb_blocks = 1, out_filters = 3, strides = 2)
output_8 = tf.layers.conv2d_transpose(inputs = output_7, filters = 3, kernel_size = 3, strides = 1, padding='same', data_format='channels_last')
output_8 = tf.contrib.layers.batch_norm(output_8)
output_8 = tf.nn.relu(output_8)
# output_3 = skip_connection_block(output_2, vgg.pool5, 7, 256)
# output_4 = skip_connection_block(output_3,vgg.pool4,14, 256)
# output_5 = skip_connection_block(output_4,vgg.pool3,28, 128)
# output_6 = skip_connection_block(output_5,vgg.pool2,56, 64)
# output_7 = skip_connection_block(output_6,vgg.pool1,112, 32)
# output
output_8 = tf.image.resize_images(output_8, [224,224])
# output_8 = skip_connection_block(output_7,input_img,224, 3)
return output_8
# placeholder of networks
input_img = tf.placeholder(tf.float32, [None, 224, 224, 3])
input_Semantic = tf.placeholder(tf.float32, [None, 6])
output_img = tf.placeholder(tf.float32, [None, 224, 224, 3])
# output of networks
model = VGG19_Synthesis_Network(input_img, input_Semantic)
# loss of networks
loss = tf.reduce_mean(tf.square(model - output_img))
# optimizer of networks
train_step = tf.train.AdamOptimizer(0.001).minimize(loss)
# path of checkpoint
saver = tf.train.Saver()
checkpoint_dir_1 = 'model_weight_rotation_v2.0'
checkpoint_dir_2 = 'model_weight_rotation_new_6/'
# GPU using rate
# config = tf.ConfigProto()
# config.gpu_options.allow_growth = True
# config.gpu_options.per_process_gpu_memory_fraction = 0.9
with tf.Session() as sess:
# initializer variables
sess.run(tf.global_variables_initializer())
sess.graph.finalize()
# checkpoint loading
flag_x = 0
if(flag_x):
ckpt = tf.train.get_checkpoint_state(checkpoint_dir_2)
if ckpt and ckpt.model_checkpoint_path:
saver.restore(sess,ckpt.model_checkpoint_path)
for epoch in range(8):
# generate database
generator_result = generator(path)
# training model
for i in tqdm(range(100000)):
input_a, input_b, input_c = generator_result.__next__()
sess.run(train_step,feed_dict={input_img:input_a, input_Semantic:input_b, output_img:input_c})
if ((i + 1) % 50 == 0):
print([i+1],sess.run(loss, feed_dict={input_img:input_a, input_Semantic:input_b, output_img:input_c}))
# print loss of epoch
print([epoch+1],sess.run(loss, feed_dict={input_img:input_a, input_Semantic:input_b, output_img:input_c}))
# save 5th epoch weight
if ((epoch+1) % 4 == 0):
saver.save(sess, checkpoint_dir_1 + 'model_ckpt', global_step=epoch+1)
# output image for testing
# for i in range(50):
# # input_image_test
# input_test = utils.load_image('../../' + 'chair.png')
# input_test = input_test[:,:,:3]
# # input_test = input_test.reshape((-1))
# # input_test = np.tile(input_test, 10)
# input_test = input_test.reshape((1, 224, 224, 3))
# # input_Semantic_test
# input_Semantic_test = np.loadtxt('only_rotation_test_example.txt')
# # input_Semantic_test = np.loadtxt(path + 'rendering_metadata_rotation_00.txt')
# # input_Semantic_test = input_Semantic_test.reshape((1,6))
# input_Semantic_test = input_Semantic_test[(i*1):(i*1+1),:]
# # input_c_test
# input_c_test = utils.load_image_com('../../chair_only_rotation_test_true/chair_only_rotation_true/' + 'chair_rotation_00_only_%.2d.png' % i)[:,:,:3]
# # input_c_test = utils.load_image_com('../../chair_only_rotation_test_true/chair_only_rotation_true/' + 'chair_rotation_00_only_%.2d.png' % (i*10))[:,:,:3]
# # for k in range(1,10,1):
# # # input_c = np.concatenate((input_c, utils.load_image_com(path + 'chair_rotation_%.2d/chair_rotation_%.2d_trans_%.2d.png' % (j, j, (i*8 + k)))[:,:,:3]), axis = 0)
# # input_c_test = np.concatenate((input_c_test, utils.load_image_com('../../chair_only_rotation_test_true/chair_only_rotation_true/' + 'chair_rotation_00_only_%.2d.png' % (i*10+k))[:,:,:3]), axis = 0)
# # input_c_test = np.reshape(input_c_test, (10, 224, 224, 3))
# input_c_test = input_c_test.reshape((1,224,224,3))
# # plot output_model
# output_test_image = sess.run(model,feed_dict={input_img:input_test, input_Semantic:input_Semantic_test})
# output_test_image = output_test_image.reshape((224, 224, 3))
# print([i+1],sess.run(loss, feed_dict={input_img:input_test, input_Semantic:input_Semantic_test, output_img:input_c_test}))
# # misc.imsave('../../chairr1_test_test.png' , output_test_image)
# misc.imsave('../../chair_only_rotation_test_true/chair_only_rotation_test/chair_only_rotation_test_%.2d.png' % i , output_test_image)