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classification_builder.py
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import utils.interpolations as interpolations
import tqdm
from utils.storage import *
from tensorflow.contrib import slim
from dagan_networks_wgan_with_matchingclassifier import *
from utils.sampling_with_matchingclassifier import *
import sys
# sys.path.append("./models/research/slim")
# from nets.mobilenet import mobilenet_v2
slim = tf.contrib.slim
from densenet_classifier import densenet_classifier
# pretrained_resnet = './models/pretrained_model/resnet_v2_50_2017_04_14/resnet_v2_50.ckpt'
pretrained_resnet = './models/pretrained_model/resnet_v1_50.ckpt'
checkpoint_exclude_scopes = 'resnet_v1_50/logits'
model_name= 'resnet_v1_50'
checkpoint_path = './models/pretrained_model/resnet_v1_50.ckpt'
checkpoint_exclude_scopes = 'resnet_v1_50/logits'
#### for mobilenet
class ExperimentBuilder(object):
def __init__(self, parser, data):
tf.reset_default_graph()
args = parser.parse_args()
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
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
self.support_number = args.support_number
self.classification_total_epoch = args.classification_total_epoch
image_channel = data.image_channel
self.use_wide_connections = args.use_wide_connections
self.pretrain = args.pretrain
generator_layers = [64, 64, 128, 128]
self.discriminator_layers = [64, 64, 128, 128]
gen_inner_layers = [gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer, gen_depth_per_layer]
self.discr_inner_layers = [discr_depth_per_layer, discr_depth_per_layer, discr_depth_per_layer,
discr_depth_per_layer]
generator_layer_padding = ["SAME", "SAME", "SAME", "SAME"]
image_height = data.x.shape[2]
image_width = data.x.shape[3]
image_channels = data.x.shape[4]
self.classes = tf.placeholder(tf.int32)
self.input_x_i = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, image_height, image_width,
image_channels], 'inputs-1')
if self.pretrain>0:
self.input_y = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, self.data.training_classes], 'y_inputs-1')
else:
self.input_y = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, self.data.testing_classes], 'y_inputs-1')
self.input_x_j = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size, self.support_number ,image_height, image_width,
image_channels], 'inputs-2-same-class')
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.random_rotate = tf.placeholder(tf.bool, name='rotation-flag')
self.dropout_rate = tf.placeholder(tf.float32, name='dropout-prob')
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.total_train_batches = data.training_data_size / (self.batch_size * self.num_gpus)
self.total_val_batches = data.validation_data_size / (self.batch_size * self.num_gpus)
self.total_test_batches = data.testing_data_size / (self.batch_size * self.num_gpus)
self.total_gen_batches = data.generation_data_size / (self.batch_size * self.num_gpus)
self.spherical_interpolation = True
self.tensorboard_update_interval = int(self.total_test_batches/10/self.num_gpus)
if self.pretrain>0:
classifier = densenet_classifier(input_x_i=self.input_x_i, input_y=self.input_y, classes=self.data.training_classes,
batch_size=self.batch_size, layer_sizes=self.discriminator_layers, inner_layers=self.discr_inner_layers, num_gpus=self.num_gpus,
use_wide_connections=self.use_wide_connections,
is_training=self.training_phase, augment=self.random_rotate,dropout_rate=self.dropout_rate)
else:
classifier = densenet_classifier(input_x_i=self.input_x_i, input_y=self.input_y, classes=self.data.testing_classes,
batch_size=self.batch_size, layer_sizes=self.discriminator_layers, inner_layers=self.discr_inner_layers, num_gpus=self.num_gpus,
use_wide_connections=self.use_wide_connections,
is_training=self.training_phase, augment=self.random_rotate,dropout_rate=self.dropout_rate)
self.summary, self.losses, self.accuracy, self.graph_ops = classifier.init_train()
self.init = tf.global_variables_initializer()
def run_experiment(self):
with tf.Session(config=tf.ConfigProto(allow_soft_placement=True)) as sess:
sess.run(self.init)
# self.train_writer = tf.summary.FileWriter("{}/train_classification_logs/".format(self.log_path),
# graph=tf.get_default_graph())
# self.valid_writer = tf.summary.FileWriter("{}/validation_classification_logs/".format(self.log_path),
# graph=tf.get_default_graph())
self.saver = tf.train.Saver()
self.iter_done = 0
best_d_val_loss = np.inf
lowest_d_val_accuracy = 0
with tqdm.tqdm(total=self.classification_total_epoch) as pbar_e:
for e in range(self.classification_total_epoch):
train_loss = []
train_acc = []
test_loss = []
test_acc =[]
with tqdm.tqdm(total=self.total_test_batches) as pbar_samp:
for iter in range(int(self.total_test_batches)):
if self.pretrain>0:
x_train_classification_i, x_train_i,_,_,y_test,y_train = self.data.get_train_batch()
else:
x_train_classification_i, x_train_i,_,_,y_test,y_train = self.data.get_test_batch()
x_train_classification_i = np.reshape(x_train_classification_i,[x_train_classification_i.shape[0],x_train_classification_i.shape[1]*x_train_classification_i.shape[2],\
x_train_classification_i.shape[3],x_train_classification_i.shape[4],x_train_classification_i.shape[5]])
x_train_i = np.reshape(x_train_i,[x_train_i.shape[0],x_train_i.shape[1]*x_train_i.shape[2],\
x_train_i.shape[3],x_train_i.shape[4],x_train_i.shape[5]])
y_train = np.reshape(y_train,[y_train.shape[0],y_train.shape[1]*y_train.shape[2],y_train.shape[3]])
y_test = np.reshape(y_test,[y_test.shape[0],y_test.shape[1]*y_test.shape[2],y_test.shape[3]])
_,train_loss_value, train_acc_value, train_summary = sess.run([self.graph_ops["loss_opt_op"],self.losses["losses"], self.accuracy,self.summary],
feed_dict={self.input_x_i: x_train_i,
self.input_y: y_train,
self.dropout_rate: self.dropout_rate_value,
self.training_phase: True, self.random_rotate: True})
test_loss_value, test_acc_value, test_summary = sess.run([self.losses["losses"], self.accuracy,self.summary],
feed_dict={self.input_x_i: x_train_classification_i,
self.input_y: y_test,
self.dropout_rate: self.dropout_rate_value,
self.training_phase: False, self.random_rotate: False})
train_loss.append(train_loss_value)
train_acc.append(train_acc_value)
test_loss.append(test_loss_value)
test_acc.append(test_acc_value)
# if iter % (self.tensorboard_update_interval) == 0:
# self.train_writer.add_summary(train_summary, global_step=self.iter_done)
# self.valid_writer.add_summary(test_summary, global_step=self.iter_done)
total_train_loss_mean = np.mean(train_loss)
total_train_accuracy_mean = np.mean(train_acc)
total_test_loss_mean = np.mean(test_loss)
total_test_accuracy_mean = np.mean(test_acc)
iter_out = "{},total_test_loss: {}, total_test_accuracy: {} ".format(iter,total_test_loss_mean,total_test_accuracy_mean)
pbar_e.set_description(iter_out)
pbar_e.update(1)
# train_save_path = self.saver.save(sess, "{}/train_saved_model_{}_{}.ckpt".format(
# self.saved_models_filepath,
# self.experiment_name, e))
### validation selection, according to the loss of discriminator.
model_name = 'Pretrain_on_Source_Domain'
if total_test_loss_mean < best_d_val_loss:
best_d_val_loss = total_test_loss_mean
val_save_path = self.saver.save(sess, "{}/{}_{}_{}.ckpt".format(
self.saved_models_filepath,
model_name, e, total_test_accuracy_mean))
print("Saved current best val model at", val_save_path)
if total_test_accuracy_mean > lowest_d_val_accuracy:
lowest_d_val_accuracy = total_test_accuracy_mean
val_save_path = self.saver.save(sess, "{}/{}_{}_{}.ckpt".format(
self.saved_models_filepath,
model_name, e, total_test_accuracy_mean))
print("Saved current best val model at", val_save_path)
pbar_e.update(1)