def build_network(): network = resnet_model.resnet_v1( resnet_depth=params['resnet_depth'], num_classes=params['num_label_classes'], dropblock_size=params['dropblock_size'], dropblock_keep_probs=dropblock_keep_probs, data_format=params['data_format']) return network(inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
def build_network(): network = resnet_model.resnet_v1( resnet_depth=FLAGS.resnet_depth, num_classes=FLAGS.num_label_classes, dropblock_size=FLAGS.dropblock_size, dropblock_keep_probs=dropblock_keep_probs, data_format=FLAGS.data_format) return network(inputs=features, is_training=(mode == tf.estimator.ModeKeys.TRAIN))
def test_load_resnet18_v1(self): network = resnet_model.resnet_v1(resnet_depth=18, num_classes=10, data_format='channels_last') input_bhw3 = tf.placeholder(tf.float32, [1, 28, 28, 3]) resnet_output = network(inputs=input_bhw3, is_training=True) sess = tf.Session() sess.run(tf.global_variables_initializer()) _ = sess.run(resnet_output, feed_dict={input_bhw3: np.random.randn(1, 28, 28, 3)})
def test_load_resnet_rs(self): network = resnet_model.resnet_v1(resnet_depth=50, num_classes=10, data_format='channels_last', se_ratio=0.25, drop_connect_rate=0.2, use_resnetd_stem=True, resnetd_shortcut=True, dropout_rate=0.2, replace_stem_max_pool=True) input_bhw3 = tf.placeholder(tf.float32, [1, 28, 28, 3]) resnet_output = network(inputs=input_bhw3, is_training=True) sess = tf.Session() sess.run(tf.global_variables_initializer()) _ = sess.run(resnet_output, feed_dict={input_bhw3: np.random.randn(1, 28, 28, 3)})