def prepare_networks(gpu, image_batch, nb_classes):
mean_img = tf.constant([123.68, 116.779, 103.939],
dtype=tf.float32,
shape=[1, 1, 1, 3],
name='img_mean')
scores = []
with tf.variable_scope('ResNet18'):
with tf.device('/gpu:' + gpu):
score = utils_resnet.ResNet18(image_batch - mean_img,
phase='train',
num_outputs=nb_classes)
scores.append(score)
scope = tf.get_variable_scope()
scope.reuse_variables()
# First score and initialization
variables_graph = tf.get_collection(tf.GraphKeys.WEIGHTS, scope='ResNet18')
scores_stored = []
with tf.variable_scope('store_ResNet18'):
with tf.device('/gpu:' + gpu):
score = utils_resnet.ResNet18(image_batch - mean_img,
phase='test',
num_outputs=nb_classes)
scores_stored.append(score)
scope = tf.get_variable_scope()
scope.reuse_variables()
variables_graph2 = tf.get_collection(tf.GraphKeys.WEIGHTS,
scope='store_ResNet18')
return variables_graph, variables_graph2, scores, scores_stored
def reading_data_and_preparing_network(files_from_cl, gpu, itera, batch_size, train_path, labels_dic, mixing, nb_groups, nb_cl, save_path):
image_train, label_train,file_string = utils_data.read_data_test(train_path,labels_dic, mixing,files_from_cl=files_from_cl)
image_batch, label_batch,file_string_batch = tf.train.batch([image_train, label_train,file_string], batch_size=batch_size, num_threads=8)
label_batch_one_hot = tf.one_hot(label_batch,nb_groups*nb_cl)
### Network and loss function
mean_img = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
with tf.variable_scope('ResNet18'):
with tf.device('/cpu:'+gpu):
scores = utils_resnet.ResNet18(image_batch-mean_img, phase='test',num_outputs=nb_cl*nb_groups)
graph = tf.get_default_graph()
op_feature_map = graph.get_operation_by_name('ResNet18/pool_last/avg').outputs[0]
loss_class = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(labels=label_batch_one_hot, logits=scores))
### Initilization
params = dict(cPickle.load(open(save_path+'model-iteration'+str(nb_cl)+'-%i.pickle' % itera, 'rb')))
inits = utils_resnet.get_weight_initializer(params)
return inits,scores,label_batch,loss_class,file_string_batch,op_feature_map