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generation_builder_with_matchingclassifier.py
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import random
import utils.interpolations as interpolations
import numpy as np
import tqdm
from utils.storage import save_statistics, build_experiment_folder
from tensorflow.contrib import slim
from dagan_networks_wgan_with_matchingclassifier import *
from utils.sampling_with_matchingclassifier import sample_generator, sample_two_dimensions_generator, \
sample_generator_store_into_file_for_classifier, sample_generator_store_into_file_for_quality, sample_generator_store_into_file, sample_generator_store_into_file_fid_generation_real
import time
import scipy.misc
import os
import cv2
import tensorflow as tf
def isNaN(num):
return num != num
def find_classes(root_dir):
retour=[]
for (root,dirs,files) in os.walk(root_dir):
for f in files:
if (f.endswith("png")):
# if (f.endswith("jpg")):
r=root.split('/')
lr=len(r)
retour.append((f,r[lr-2]+"/"+r[lr-1],root))
# print("== Found %d items "%len(retour))
return len(retour)
class ExperimentBuilder(object):
def __init__(self, args, data):
tf.reset_default_graph()
self.continue_from_epoch = args.continue_from_epoch
self.experiment_name = args.experiment_title
self.saved_models_filepath, self.log_path, self.save_image_path = build_experiment_folder(self.experiment_name)
self.num_gpus = args.num_of_gpus
self.batch_size = args.batch_size
# self.support_number = args.support_number
self.selected_classes = args.selected_classes
gen_depth_per_layer = args.generator_inner_layers
discr_depth_per_layer = args.discriminator_inner_layers
self.z_dim = args.z_dim
self.num_generations = args.num_generations
self.dropout_rate_value = args.dropout_rate_value
self.data = data
self.reverse_channels = False
if args.generation_layers == 6:
generator_layers = [64, 64, 128, 128, 256, 256]
gen_inner_layers = [gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer,
gen_depth_per_layer, gen_depth_per_layer]
generator_layer_padding = ["SAME", "SAME", "SAME", "SAME", "SAME", "SAME"]
else:
generator_layers = [64, 64, 128, 128]
gen_inner_layers = [gen_depth_per_layer, gen_depth_per_layer,
gen_depth_per_layer, gen_depth_per_layer]
generator_layer_padding = ["SAME", "SAME", "SAME", "SAME"]
discriminator_layers = [64, 64, 128, 128]
discr_inner_layers = [discr_depth_per_layer, discr_depth_per_layer, discr_depth_per_layer,
discr_depth_per_layer]
image_height = data.image_height
image_width = data.image_width
image_channel = data.image_channel
self.classes = tf.placeholder(tf.int32)
self.selected_classes = tf.placeholder(tf.int32)
self.support_number = tf.placeholder(tf.int32)
#### [self.input_x_i, self.input_y_i, self.input_global_y_i] --> [images, few shot label, global label]
## batch: [self.input_x_i, self.input_y_i, self.input_global_y_i]
## support: self.input_x_j, self.input_y_j, self.input_global_y_j]
## the input of discriminator: [self.input_x_j_selected, self.input_global_y_j_selected]
self.input_x_i = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, image_height, image_width,
image_channel], 'batch')
self.input_y_i = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, self.data.selected_classes],
'y_inputs_bacth')
self.input_global_y_i = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, self.data.testing_classes],
'y_inputs_bacth_global')
self.input_x_j = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size,
self.data.selected_classes * self.data.support_number,
image_height, image_width,
image_channel], 'support')
self.input_y_j = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size,
self.data.selected_classes * self.data.support_number,
self.data.selected_classes], 'y_inputs_support')
self.input_global_y_j = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size,
self.data.selected_classes * self.data.support_number,
self.data.testing_classes], 'y_inputs_support_global')
self.input_x_j_selected = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, image_height, image_width,
image_channel], 'support_discriminator')
self.input_global_y_j_selected = tf.placeholder(tf.float32,
[self.num_gpus, self.batch_size, self.data.testing_classes],
'y_inputs_support_discriminator')
# self.z_input = tf.placeholder(tf.float32, [self.batch_size*self.data.selected_classes, self.z_dim], 'z-input')
# self.z_input_2 = tf.placeholder(tf.float32, [self.batch_size*self.data.selected_classes, self.z_dim], 'z-input_2')
self.z_input = tf.placeholder(tf.float32, [self.batch_size, self.z_dim], 'z-input')
self.z_input_2 = tf.placeholder(tf.float32, [self.batch_size, self.z_dim], 'z-input_2')
self.training_phase = tf.placeholder(tf.bool, name='training-flag')
self.z1z2_training = tf.placeholder(tf.bool, name='z1z2_training-flag')
self.random_rotate = tf.placeholder(tf.bool, name='rotation-flag')
self.dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
self.is_z2 = args.is_z2
self.is_z2_vae = args.is_z2_vae
self.matching = args.matching
self.fce = args.fce
self.full_context_unroll_k = args.full_context_unroll_k
self.average_per_class_embeddings = args.average_per_class_embeddings
self.restore_path = args.restore_path
self.is_z2 = args.is_z2
self.is_z2_vae = args.is_z2_vae
self.loss_G = args.loss_G
self.loss_D = args.loss_D
self.loss_CLA = args.loss_CLA
self.loss_FSL = args.loss_FSL
self.loss_KL = args.loss_KL
self.loss_recons_B = args.loss_recons_B
self.loss_matching_G = args.loss_matching_G
self.loss_matching_D = args.loss_matching_D
self.loss_sim = args.loss_sim
self.strategy = args.strategy
#### training/validation/testin
time_1 = time.time()
dagan = DAGAN(batch_size=self.batch_size, input_x_i=self.input_x_i, input_x_j=self.input_x_j,
input_y_i=self.input_y_i, input_y_j=self.input_y_j, input_global_y_i=self.input_global_y_i,
input_global_y_j=self.input_global_y_j,
input_x_j_selected=self.input_x_j_selected,
input_global_y_j_selected=self.input_global_y_j_selected, \
selected_classes=self.data.selected_classes, support_num=self.data.support_number,
classes=self.data.testing_classes,
dropout_rate=self.dropout_rate, generator_layer_sizes=generator_layers,
generator_layer_padding=generator_layer_padding, num_channels=data.image_channel,
is_training=self.training_phase, augment=self.random_rotate,
discriminator_layer_sizes=discriminator_layers,
discr_inner_conv=discr_inner_layers, is_z2=self.is_z2, is_z2_vae=self.is_z2_vae,
gen_inner_conv=gen_inner_layers, num_gpus=self.num_gpus, z_dim=self.z_dim, z_inputs=self.z_input,
z_inputs_2=self.z_input_2,
use_wide_connections=args.use_wide_connections, fce=self.fce, matching=self.matching,
full_context_unroll_k=self.full_context_unroll_k,
average_per_class_embeddings=self.average_per_class_embeddings,
loss_G=self.loss_G, loss_D=self.loss_D, loss_KL=self.loss_KL, loss_recons_B=self.loss_recons_B,
loss_matching_G=self.loss_matching_G, loss_matching_D=self.loss_matching_D,
loss_CLA=self.loss_CLA, loss_FSL=self.loss_FSL, loss_sim=self.loss_sim,
z1z2_training=self.z1z2_training)
self.same_images = dagan.sample_same_images()
# self.summary, self.losses, self.accuracy, self.graph_ops = classifier.init_train()
self.total_train_batches = int(data.training_data_size / (self.batch_size * self.num_gpus))
self.total_val_batches = int(data.validation_data_size / (self.batch_size * self.num_gpus))
self.total_test_batches = int(data.testing_data_size / (self.batch_size * self.num_gpus))
self.total_gen_batches = int(data.testing_data_size / (self.batch_size * self.num_gpus))
self.init = tf.global_variables_initializer()
time_2 = time.time()
# print('time for constructing graph:',time_2 - time_1)
self.tensorboard_update_interval = int(self.total_train_batches / 1 / self.num_gpus)
self.total_epochs = 800
self.is_generation_for_classifier = args.is_generation_for_classifier
self.is_all_test_categories = args.is_all_test_categories
# if self.continue_from_epoch == -1:
# save_statistics(self.log_path, ['epoch', 'total_d_train_loss_mean', 'total_d_val_loss_mean',
# 'total_d_train_loss_std', 'total_d_val_loss_std',
# 'total_g_train_loss_mean', 'total_g_val_loss_mean',
# 'total_g_train_loss_std', 'total_g_val_loss_std'], create=True)
def run_experiment(self):
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
# with tf.Session() as sess:
time_4 = time.time()
sess.run(self.init)
print('time for initializing global parameters:', time.time() - time_4)
# self.train_writer = tf.summary.FileWriter("{}/train_logs/".format(self.log_path),
# graph=tf.get_default_graph())
# self.validation_writer = tf.summary.FileWriter("{}/validation_logs/".format(self.log_path),
# graph=tf.get_default_graph())
log_name = "z2vae{}_z2{}_g{}_d{}_kl{}_cla{}_fzl{}_reconsB{}_matchingG{}_matchingD{}_sim{}_Net_batchsize{}_zdim{}".format(
self.is_z2_vae, self.is_z2, self.loss_G, self.loss_D, self.loss_KL, self.loss_CLA, self.loss_FSL,
self.loss_recons_B, self.loss_matching_G, self.loss_matching_D, self.loss_sim, self.batch_size,
self.z_dim)
self.train_writer = tf.summary.FileWriter("{}/train_logs/{}".format(self.log_path, log_name),
graph=sess.graph)
self.validation_writer = tf.summary.FileWriter("{}/validation_logs/{}".format(self.log_path, log_name),
graph=sess.graph)
self.train_saver = tf.train.Saver()
self.val_saver = tf.train.Saver()
# variable_names = [v.name for v in tf.trainable_variables()]
# print(variable_names)
start_from_epoch = 0
if self.continue_from_epoch != -1:
start_from_epoch = self.continue_from_epoch
# checkpoint = "{}train_saved_model_{}_{}.ckpt".format(self.saved_models_filepath, self.experiment_name, self.continue_from_epoch)
checkpoint = self.restore_path
variables_to_restore = []
# for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES):
# variables_to_restore.append(var)
for var in tf.get_collection(tf.GraphKeys.GLOBAL_VARIABLES,scope='generator'):
variables_to_restore.append(var)
tf.logging.info('Fine-tuning from %s' % checkpoint)
fine_tune = slim.assign_from_checkpoint_fn(
checkpoint,
variables_to_restore,
ignore_missing_vars=True)
fine_tune(sess)
self.iter_done = 0
self.disc_iter = 1
self.gen_iter = 1
best_d_val_loss = np.inf
### z preprocess
self.spherical_interpolation = False
if self.spherical_interpolation:
self.z_vectors = interpolations.create_mine_grid(rows=1, cols=self.num_generations, dim=self.z_dim,
space=3, anchors=None, spherical=True, gaussian=True)
self.z_vectors_2 = interpolations.create_mine_grid(rows=1, cols=self.num_generations, dim=self.z_dim,
space=3, anchors=None, spherical=True, gaussian=True)
self.z_2d_vectors = interpolations.create_mine_grid(rows=self.num_generations,
cols=self.num_generations,
dim=100, space=3, anchors=None,
spherical=True, gaussian=True)
self.z_2d_vectors_2 = interpolations.create_mine_grid(rows=self.num_generations,
cols=self.num_generations,
dim=100, space=3, anchors=None,
spherical=True, gaussian=True)
else:
# self.z_vectors = np.random.normal(size=(self.num_generations, self.z_dim))
# self.z_vectors_2 = np.random.normal(size=(self.num_generations, self.z_dim))
# np.random.seed(0)
# self.z_2d_vectors = np.random.normal(size=(self.num_generations, self.z_dim))
# self.z_2d_vectors_2 = np.random.normal(size=(self.num_generations, self.z_dim))
self.z_vectors = np.random.uniform(-1., 1., size=(self.num_generations, self.z_dim))
self.z_vectors_2 = np.random.uniform(-1., 1., size=(self.num_generations, self.z_dim))
self.z_2d_vectors = np.random.uniform(-1., 1., size=(self.num_generations, self.z_dim))
self.z_2d_vectors_2 = np.random.uniform(-1., 1., size=(self.num_generations, self.z_dim))
# self.z_vectors_base = np.random.uniform(-1., 1., size=(1, self.z_dim))
# for i in range(self.num_generations):
# self.z_vectors[i] = self.z_vectors_base[0]
# inter = 16
# for i in range(self.num_generations-4):
# if i > 0:
# # fix
# # self.z_vectors[i,inter*i:inter*(i+1)] = self.z_vectors_2[i,:inter]
# # random
# indexs = [i for i in range(self.z_dim)]
# s_index = random.sample(indexs,inter)
# self.z_vectors[i,s_index] = self.z_vectors_2[i,s_index]
### interpolation
# index = np.random.choice(self.num_generations, size=2, replace=False)
# fixed_1 = self.z_vectors[index[0]]
# fixed_2 = self.z_vectors[index[1]]
# steps = 1 / self.num_generations
# for i in range(self.num_generations):
# current_steps = i * steps
# self.z_vectors[i] = current_steps*fixed_1 + (1 - current_steps )*fixed_2
self.support_vector = [x for x in range(10)]
### training with train set with parameter update, validation without training, epoch
if self.is_all_test_categories > 0:
sampler = sample_generator_store_into_file
else:
sampler = sample_generator_store_into_file_for_classifier
image_name = "z2vae{}_z2{}_g{}_d{}_kl{}_cla{}_fzl{}_reconsB{}_matchingG{}_matchingD{}_sim{}_Net_batchsize{}_zdim{}".format(
self.is_z2_vae, self.is_z2, self.loss_G, self.loss_D, self.loss_KL, self.loss_CLA, self.loss_FSL,
self.loss_recons_B, self.loss_matching_G, self.loss_matching_D, self.loss_sim, self.batch_size,
self.z_dim)
if self.continue_from_epoch >= 0:
# print('starting sampling')
similarities_list = []
f_encode_z_list = []
matching_feature_list = []
if (self.total_gen_batches < 10):
self.total_gen_batches = 10
##### setting hyper-parameters
samples_each_category = self.data.general_classification_samples
each_samples_generation = int(self.num_generations / samples_each_category)
total_testing_class = self.data.testing_classes
print('totol test class', total_testing_class)
print('{}_shot generation'.format(samples_each_category))
print('each shot generate {} images'.format(each_samples_generation))
testing_num = 10
with tqdm.tqdm(total=self.total_gen_batches) as pbar_samp:
for i in range(self.total_gen_batches):
file_name="{}/{}_{}_{}.png".format(self.save_image_path,image_name,self.continue_from_epoch,i)
print('file name', file_name)
root_quilty = file_name.split('//')[0] +'_forquality'
root_classifier = file_name.split('//')[0] +'_forclassifier'
root_realimages = file_name.split('//')[0] +'_realimages'
root_recons = file_name.split('//')[0] +'_reconsimages'
if find_classes(root_quilty) >= total_testing_class*self.num_generations and find_classes(root_classifier) >= total_testing_class*(self.num_generations+ testing_num + samples_each_category) and find_classes(root_realimages) >= total_testing_class*self.num_generations:
print('satisfied, finish generating')
break
x_test_i_selected_classes, x_test_j, y_test_i_selected_classes, y_test_j, y_global_test_i_selected_classes, y_global_test_j = self.data.get_test_batch()
# np.random.seed(i)
# self.z_vectors = np.random.normal(size=(self.num_generations, self.z_dim))
# self.z_vectors_2 = np.random.normal(size=(self.num_generations, self.z_dim))
if i >= 0:
for j in range(1):
before_sample = time.time()
x_test_i = x_test_i_selected_classes[:, :, j, :, :, :]
y_test_i = y_test_i_selected_classes[:, :, j, :]
y_global_test_i = y_global_test_i_selected_classes[:, :, j, :]
support_index = int(np.random.choice(self.data.support_number, size=1))
x_test_j_selected = x_test_j[:, :, support_index, :, :, :]
y_test_j_selected = y_test_j[:, :, support_index, :]
y_global_test_j_selected = y_global_test_j[:, :, support_index, :]
images, x_total_images, _, _ = sample_generator_store_into_file_fid_generation_real(num_generations=self.num_generations,
sess=sess,
same_images=self.same_images,
input_a=self.input_x_i,
input_b=self.input_x_j,
input_y_i=self.input_y_i,
input_y_j=self.input_y_j,
input_global_y_i=self.input_global_y_i,
input_global_y_j=self.input_global_y_j,
classes=self.classes,
classes_selected=self.selected_classes,
number_support=self.support_number,
z_input=self.z_input,
z_input_2=self.z_input_2,
# selected_global_x_j = self.input_x_j_selected,
# selected_global_y_j=self.input_global_y_j_selected,
# conditional_inputs=x_test_i,
# y_input_i = y_test_i,
# conditional_inputs=x_test_j_selected,
# y_input_i=y_test_j_selected,
conditional_inputs=x_test_i,
y_input_i=y_test_i,
support_input=x_test_j,
y_input_j=y_test_j,
y_global_input_i=y_global_test_i,
y_global_input_j=y_global_test_j,
classes_number=self.data.testing_classes,
selected_classes=self.data.selected_classes,
support_number=self.data.support_number,
# input_global_x_j_selected = x_test_j_selected,
# input_global_y_j_selected = y_global_test_j_selected,
z_vectors=self.z_vectors,
z_vectors_2=self.z_vectors_2,
data=self.data,
batch_size=self.batch_size,
file_name="{}/{}_{}_{}.png".format(
self.save_image_path,
image_name,
self.continue_from_epoch,
i),
dropout_rate=self.dropout_rate,
dropout_rate_value=self.dropout_rate_value,
training_phase=self.training_phase,
z1z2_training=self.z1z2_training,
is_training=False,
training_z1z2=False)
# print('images shape',np.shape(images))
if self.is_all_test_categories > 0:
##### setting the stop threshold
##### storing the test images into correspoding
#### for classifier
image_size = x_total_images.shape[-2]
for j in range(self.batch_size):
current_path_quality = file_name.split('//')[0] +'_forquality' + '/{}/'.format(np.argmax(y_global_test_i[:, j]))
current_path_classifier = file_name.split('//')[0] + '_forclassifier' + '/{}/'.format(np.argmax(y_global_test_i[:, j]))
current_path_realimages = file_name.split('//')[0] + '_realimages' + '/{}/'.format(np.argmax(y_global_test_i[:, j]))
current_path_recons = file_name.split('//')[0] + '_recons' + '/{}/'.format(np.argmax(y_global_test_i[:, j]))
###### madir path to store images
if not os.path.exists(current_path_quality):
os.makedirs(current_path_quality)
if not os.path.exists(current_path_classifier):
os.makedirs(current_path_classifier)
if not os.path.exists(current_path_realimages):
os.makedirs(current_path_realimages)
if not os.path.exists(current_path_recons):
os.makedirs(current_path_recons)
###### dividing genrarated image maps into corresponding file path
for k in range(1 + self.data.support_number + 1 + 1 + each_samples_generation ):
current_iamge = images[image_size * j:image_size * (j + 1), image_size * (k):image_size * (k + 1)]
if k < 1 + self.data.support_number + 1 + 1 + 3:
current_name_recons = current_path_recons + '{}.png'.format(k)
scipy.misc.imsave(current_name_recons, current_iamge)
# print('here',np.shape(current_iamge))
if (current_iamge.shape[-2] < 128):
current_iamge = cv2.resize(current_iamge, (128, 128), interpolation=cv2.INTER_LINEAR)
if self.data.support_number -1 < k < self.data.support_number + 2:
#### classifier ####
if (find_classes(current_path_classifier) < (self.num_generations + samples_each_category + testing_num)):
current_name_classifier = current_path_classifier + 'c{}'.format(i) + '_conditionalreal{}.png'.format(k)
scipy.misc.imsave(current_name_classifier, current_iamge)
else:
print('category_{} classifier generation finished '.format(np.argmax(y_global_test_i[:, j])))
#### realimages ####
if (find_classes(current_path_realimages) < (self.num_generations)):
conditional_num = 1
for m in range(conditional_num):
current_name_realimages = current_path_realimages + 'c{}'.format(i) +'_conditionalreal{}_{}.png'.format(k,m)
scipy.misc.imsave(current_name_realimages, current_iamge)
else:
print('category_{} real images generation finished '.format(np.argmax(y_global_test_i[:, j])))
# elif k > self.data.support_number + 1 + 1:
else :
#### classifier ####
if (find_classes(current_path_classifier) < (self.num_generations + samples_each_category + testing_num)):
current_name_classifier = current_path_classifier + file_name.split('//')[-1].split('.png')[
0] + '_sample{}.png'.format(k)
scipy.misc.imsave(current_name_classifier, current_iamge)
else:
print('category_{} classifier generation finished '.format(np.argmax(y_global_test_i[:, j])))
#### quality ####
if (find_classes(current_path_quality) < self.num_generations):
current_name_quality = current_path_quality + file_name.split('//')[-1].split('.png')[
0] + '_sample{}.png'.format(k)
scipy.misc.imsave(current_name_quality, current_iamge)
else:
print('category_{} quality generation finished '.format(np.argmax(y_global_test_i[:, j])))
# print('testing number',np.shape(x_test_i_selected_classes)[2])
#### for real_images file
if np.shape(x_test_i_selected_classes)[2] > each_samples_generation - 1:
threshold = each_samples_generation - 1
else:
threshold = np.shape(x_test_i_selected_classes)[2]
for k in range(threshold):
current_iamge = x_test_i_selected_classes[0,j,k]
current_iamge = self.data.reconstruct_original(current_iamge) * 255
current_name_realimages = current_path_realimages + 'a{}'.format(i)+'_real{}.png'.format(k)
if (find_classes(current_path_realimages) < (self.num_generations)):
scipy.misc.imsave(current_name_realimages, current_iamge)
else:
print('category_{} real images generation finished '.format(np.argmax(y_global_test_i[:, j])))
#### for classifier file
# print('1111111',np.shape(x_test_i_selected_classes),np.max(x_test_i_selected_classes[0,0,0]), np.max(x_test_i_selected_classes[0,0,1]))
for k in range(np.shape(x_test_i_selected_classes)[2]):
if ( find_classes(current_path_classifier) < (self.num_generations + samples_each_category + testing_num)):
if (find_classes(current_path_classifier) >= (self.num_generations + samples_each_category)):
current_iamge = x_test_i_selected_classes[0,j,k]
current_iamge = self.data.reconstruct_original(current_iamge) * 255
current_name_classifier = current_path_classifier + 'a{}'.format(i) + '_real{}.png'.format(k)
scipy.misc.imsave(current_name_classifier, current_iamge)