/
classification_builder.py
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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 = self.data.image_width
image_width = self.data.image_width
image_channels = self.data.image_channel
self.classes = tf.placeholder(tf.int32)
self.input_x_i = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size*self.data.selected_classes, image_height, image_width,
image_channels], 'inputs-1')
self.input_y = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size*self.data.selected_classes, self.data.selected_classes],
'y_inputs-1')
self.input_y_general = tf.placeholder(tf.float32, [self.num_gpus, self.batch_size * self.data.selected_classes,
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 = 5*545 / (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.is_fewshot_setting = args.is_fewshot_setting
# self.Nway = self.data.selected_classes
# self.kshot = args.general_classification_samples
self.tensorboard_update_interval = int(self.total_test_batches / 10 / self.num_gpus)
classifier = densenet_classifier(input_x_i=self.input_x_i, input_y=self.input_y,
classes=self.data.selected_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)
print('{}_way_{}_shot'.format(self.data.selected_classes,self.data.general_classification_samples))
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())
print('Load parameters from basemodel')
variables = tf.global_variables()
vars_restore = [var for var in variables
if not "Momentum" in var.name and
not "global_step" in var.name and var.name.split('/')[0]!='logits']
# print('load weights',vars_restore)
### remove fc weights to load
# tf.contrib.framework.get_variables("logits/fc/weights")
# print('network variable', vars_restore)
saver_restore = tf.train.Saver(vars_restore, max_to_keep=10000)
checkpoint = './pretrained-resnet18model/model.ckpt'
# checkpoint = './EVALUATION/AugmentedClassifier/animals/pretrained/saved_models/Pretrain_on_Source_Domain_9_0.5907641649246216.ckpt'
# reader = tf.train.NewCheckpointReader(checkpoint)
# var_to_shape_map = reader.get_variable_to_shape_map()
# for key in var_to_shape_map:
# print("store model name: ", key)
fine_tune = slim.assign_from_checkpoint_fn(
checkpoint,
vars_restore,
ignore_missing_vars=True)
fine_tune(sess)
# saver_restore.restore(sess, checkpoint)
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_test_classification_i, x_train_classification_i, y_test, y_train, _, _ = self.data.get_train_batch()
else:
x_test_classification_i, x_train_classification_i, y_test, y_train, _, _, = self.data.get_test_batch()
x_train_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_test_i = np.reshape(x_test_classification_i,
[x_test_classification_i.shape[0],
x_test_classification_i.shape[1] *
x_test_classification_i.shape[2], \
x_test_classification_i.shape[3],
x_test_classification_i.shape[4],
x_test_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_test_i = np.reshape(x_test_i,
# [x_test_i.shape[0] * x_test_i.shape[1], \
# x_test_i.shape[2], x_test_i.shape[3], x_test_i.shape[4]])
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_test_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)