Python tensorflow.contrib.slim 模块，assign_from_checkpoint_fn() 实例源码

def get_init_fn(self, checkpoint_path):
        """Returns a function run by the chief worker to warm-start the training."""
        checkpoint_exclude_scopes=["InceptionV4/Logits", "InceptionV4/AuxLogits"]

        exclusions = [scope.strip() for scope in checkpoint_exclude_scopes]

        variables_to_restore = []
        for var in slim.get_model_variables():
            excluded = False
            for exclusion in exclusions:
                if var.op.name.startswith(exclusion):
                    excluded = True
                    break
            if not excluded:
                variables_to_restore.append(var)

        return slim.assign_from_checkpoint_fn(
          checkpoint_path,
          variables_to_restore)

def train(self):

        s = tf.Session()

        init_fn = slim.assign_from_checkpoint_fn("./vgg_19.ckpt", slim.get_variables_to_restore(exclude = ['generate_image']))
        #optimizer = tf.train.AdamOptimizer(learning_rate = 1e-1, beta1 = 0.5, beta2 = 0.5).minimize(self.loss, var_list = [self.target])
        optimizer = tf.contrib.opt.ScipyOptimizerInterface(self.loss, options={'maxiter': 1000}, var_list = [self.target])

        s.run(tf.global_variables_initializer())
        init_fn(s)

        #for i in range(10000):
        #    _, loss_out = s.run([optimizer, self.loss])
        #    print("Current loss is: %.3f" %loss_out, end="\r")
        #print("")

        optimizer.minimize(s)
        loss_out = s.run(self.loss)
        print("Final loss: %.3f" %loss_out)

        plt.imshow(np.clip(s.run(self.target), 0, 255).astype(np.uint8))
        plt.show()

def create_init_fn_to_restore(master_checkpoint, train_dir):
    """Creates an init operations to restore weights from various checkpoints.
    master_checkpoint is path to a checkpoint which contains all weights for
    the whole model.
    """
    if master_checkpoint is None:
        return None

    # Warn the user if a checkpoint exists in the train_dir. Then we'll be
    # ignoring the checkpoint path anyway.
    if tf.train.latest_checkpoint(train_dir):
        tf.logging.info(
            'Ignoring --checkpoint_path because a checkpoint already exists in %s'
            % train_dir)
        return None

    if tf.gfile.IsDirectory(master_checkpoint):
        checkpoint_path = tf.train.latest_checkpoint(master_checkpoint)
    else:
        checkpoint_path = master_checkpoint

    tf.logging.info('Fine-tuning from %s' % checkpoint_path)
    return slim.assign_from_checkpoint_fn(checkpoint_path, slim.get_model_variables())

def load_model_from_checkpoint_fn(self, model_fn):
    """Load weights from file and keep in memory.

    Args:
      model_fn: saved model file.
    """
    # self.dm_model.use_graph()
    print "start loading from checkpoint file..."
    if self.vars_to_restore is None:
      self.vars_to_restore = slim.get_variables()
    restore_fn = slim.assign_from_checkpoint_fn(model_fn, self.vars_to_restore)
    print "restoring model from {}".format(model_fn)
    restore_fn(self.sess)
    print "model restored."

def load_model(ckpt_dir, variables_to_restore=None):
  """Load model weights.

  Assuming the model graph has been built.

  Args:
    ckpt_dir: checkpoint directory.
    variables_to_restore: which variables to load from checkpoint.
  """
  if not os.path.exists(ckpt_dir):
    print "checkpoint dir {} not exist.".format(ckpt_dir)
    return

  ckpts = glob.glob(os.path.join(ckpt_dir, "*.ckpt*"))
  ckpt = ckpts[0]

  if variables_to_restore is None:
    saver = tf.train.Saver()
  else:
    saver = tf.train.Saver(variables_to_restore)
  with tf.Session() as sess:
    # ckpt = tf.train.latest_checkpoint(ckpt_dir)
    if ckpt:
      saver.restore(sess, ckpt)
      print "model loaded from {}".format(ckpt)
    else:
      print "unable to load model from {}".format(ckpt)
  # another way.
  # slim.assign_from_checkpoint_fn(
  #     ckpt,
  #     variables_to_restore,
  #     ignore_missing_vars=True)

def use_inceptionv4(self):
        image_size = inception.inception_v4.default_image_size
        img_path = "../../data/misec_images/EnglishCockerSpaniel_simon.jpg"
        checkpoint_path = "../../data/trained_models/inception_v4/inception_v4.ckpt"

        with tf.Graph().as_default():

            image_string = tf.read_file(img_path)
            image = tf.image.decode_jpeg(image_string, channels=3)
            processed_image = inception_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)
            processed_images  = tf.expand_dims(processed_image, 0)

            # Create the model, use the default arg scope to configure the batch norm parameters.
            with slim.arg_scope(inception.inception_v4_arg_scope()):
                logits, _ = inception.inception_v4(processed_images, num_classes=1001, is_training=False)
            probabilities = tf.nn.softmax(logits)

            init_fn = slim.assign_from_checkpoint_fn(
                checkpoint_path,
                slim.get_model_variables('InceptionV4'))

            with tf.Session() as sess:
                init_fn(sess)
                np_image, probabilities = sess.run([image, probabilities])
                probabilities = probabilities[0, 0:]
                sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]
                self.disp_names(sorted_inds,probabilities)

            plt.figure()
            plt.imshow(np_image.astype(np.uint8))
            plt.axis('off')
            plt.title(img_path)
            plt.show()



        return

def use_vgg16(self):

        with tf.Graph().as_default():
            image_size = vgg.vgg_16.default_image_size
            img_path = "../../data/misec_images/First_Student_IC_school_bus_202076.jpg"
            checkpoint_path = "../../data/trained_models/vgg16/vgg_16.ckpt"

            image_string = tf.read_file(img_path)
            image = tf.image.decode_jpeg(image_string, channels=3)
            processed_image = vgg_preprocessing.preprocess_image(image, image_size, image_size, is_training=False)
            processed_images  = tf.expand_dims(processed_image, 0)

            # Create the model, use the default arg scope to configure the batch norm parameters.
            with slim.arg_scope(vgg.vgg_arg_scope()):
                # 1000 classes instead of 1001.
                logits, _ = vgg.vgg_16(processed_images, num_classes=1000, is_training=False)
                probabilities = tf.nn.softmax(logits)

                init_fn = slim.assign_from_checkpoint_fn(
                    checkpoint_path,
                    slim.get_model_variables('vgg_16'))

                with tf.Session() as sess:
                    init_fn(sess)
                    np_image, probabilities = sess.run([image, probabilities])
                    probabilities = probabilities[0, 0:]
                    sorted_inds = [i[0] for i in sorted(enumerate(-probabilities), key=lambda x:x[1])]
                    self.disp_names(sorted_inds,probabilities,include_background=False)

                plt.figure()
                plt.imshow(np_image.astype(np.uint8))
                plt.axis('off')
                plt.title(img_path)
                plt.show()
        return

def create_resnet_model(img_dim):
    pre_image = tf.placeholder(tf.float32, [None, None, 3])
    processed_image = cnn_preprocessing.preprocess_for_eval(pre_image/255.0, img_dim, img_dim)

    images = tf.placeholder(tf.float32, [None, img_dim, img_dim, 3])
    # mean = tf.constant([123.68, 116.779, 103.939], dtype=tf.float32, shape=[1, 1, 1, 3], name='img_mean')
    # processed_images = images - mean
    with slim.arg_scope(resnet_utils.resnet_arg_scope()):
        probs, endpoints = resnet_v2.resnet_v2_152(images, num_classes=1001, is_training = False)
        print endpoints['resnet_v2_152/block4']

    init_fn = slim.assign_from_checkpoint_fn(
            'Data/CNNModels/resnet_v2_152.ckpt',
            slim.get_model_variables('resnet_v2_152'))

    sess = tf.Session()
    init_fn(sess)

    return {
        'images_placeholder' : images,
        'block4' : endpoints['resnet_v2_152/block4'],
        'session' : sess,
        'processed_image' : processed_image,
        'pre_image' : pre_image,
        'probs' : probs
    }

def main():
    model = config.get('config', 'model')
    yolo = importlib.import_module('model.' + model)
    width = config.getint(model, 'width')
    height = config.getint(model, 'height')
    with tf.Session() as sess:
        image = tf.placeholder(tf.float32, [1, height, width, 3], name='image')
        builder = yolo.Builder(args, config)
        builder(image)
        global_step = tf.contrib.framework.get_or_create_global_step()
        model_path = tf.train.latest_checkpoint(utils.get_logdir(config))
        tf.logging.info('load ' + model_path)
        slim.assign_from_checkpoint_fn(model_path, tf.global_variables())(sess)
        tf.logging.info('global_step=%d' % sess.run(global_step))
        path = os.path.expanduser(os.path.expandvars(args.path))
        if os.path.isfile(path):
            detect(sess, builder.model, builder.names, image, path)
            plt.show()
        else:
            for dirpath, _, filenames in os.walk(path):
                for filename in filenames:
                    if os.path.splitext(filename)[-1].lower() in args.exts:
                        _path = os.path.join(dirpath, filename)
                        print(_path)
                        detect(sess, builder.model, builder.names, image, _path)
                        plt.show()

def main():
    model = config.get('config', 'model')
    yolo = importlib.import_module('model.' + model)
    width = config.getint(model, 'width')
    height = config.getint(model, 'height')
    preprocess = getattr(importlib.import_module('detect'), args.preprocess)
    with tf.Session() as sess:
        ph_image = tf.placeholder(tf.float32, [1, height, width, 3], name='ph_image')
        builder = yolo.Builder(args, config)
        builder(ph_image)
        global_step = tf.contrib.framework.get_or_create_global_step()
        model_path = tf.train.latest_checkpoint(utils.get_logdir(config))
        tf.logging.info('load ' + model_path)
        slim.assign_from_checkpoint_fn(model_path, tf.global_variables())(sess)
        tf.logging.info('global_step=%d' % sess.run(global_step))
        tensors = [builder.model.conf, builder.model.xy_min, builder.model.xy_max]
        tensors = [tf.check_numerics(t, t.op.name) for t in tensors]
        cap = cv2.VideoCapture(0)
        try:
            while True:
                ret, image_bgr = cap.read()
                assert ret
                image_height, image_width, _ = image_bgr.shape
                scale = [image_width / builder.model.cell_width, image_height / builder.model.cell_height]
                image_rgb = cv2.cvtColor(image_bgr, cv2.COLOR_BGR2RGB)
                image_std = np.expand_dims(preprocess(cv2.resize(image_rgb, (width, height))).astype(np.float32), 0)
                feed_dict = {ph_image: image_std}
                conf, xy_min, xy_max = sess.run(tensors, feed_dict)
                boxes = utils.postprocess.non_max_suppress(conf[0], xy_min[0], xy_max[0], args.threshold, args.threshold_iou)
                for _conf, _xy_min, _xy_max in boxes:
                    index = np.argmax(_conf)
                    if _conf[index] > args.threshold:
                        _xy_min = (_xy_min * scale).astype(np.int)
                        _xy_max = (_xy_max * scale).astype(np.int)
                        cv2.rectangle(image_bgr, tuple(_xy_min), tuple(_xy_max), (255, 0, 255), 3)
                        cv2.putText(image_bgr, builder.names[index] + ' (%.1f%%)' % (_conf[index] * 100), tuple(_xy_min), cv2.FONT_HERSHEY_SIMPLEX, 0.6, (255, 255, 255), 2)
                cv2.imshow('detection', image_bgr)
                cv2.waitKey(1)
        finally:
            cv2.destroyAllWindows()
            cap.release()

def __init__(self, content, style, content_names, style_names):
        """
            Suppose the content and style is a numpy array,
        """

        self.content_names = content_names
        self.style_names = style_names
        self.VGG_MEAN = [123.68, 116.78, 103.94]

        tf.reset_default_graph()
        content = tf.constant(content) - tf.reshape(tf.constant(self.VGG_MEAN), [1, 1, 3])
        _, self.content_layers = nets.vgg.vgg_19(tf.expand_dims(content, axis = 0), is_training = False, spatial_squeeze = False)

        layer_name, layer_value = zip(*filter(lambda x: x[0] in content_names,  self.content_layers.items()))
        init_fn = slim.assign_from_checkpoint_fn("./vgg_19.ckpt", slim.get_variables_to_restore())
        with tf.Session() as s, tf.device("/device:XLA_CPU:0"):
            init_fn(s)
            layer_value = s.run(layer_value)

        self.content_map = dict(zip(layer_name, layer_value))
        #print(content_map)

        tf.reset_default_graph()
        style = tf.constant(style) - tf.reshape(tf.constant(self.VGG_MEAN), [1, 1, 3])
        _, self.style_layers = nets.vgg.vgg_19(tf.expand_dims(style, axis = 0), is_training = False, spatial_squeeze =  False)
        layer_name, layer_value = zip(*filter(lambda x: x[0] in style_names,  self.style_layers.items()))
        init_fn = slim.assign_from_checkpoint_fn("./vgg_19.ckpt", slim.get_variables_to_restore())

        with tf.Session() as s, tf.device("/device:XLA_CPU:0"):
            init_fn(s)
            layer_value = s.run(layer_value)

        self.style_map = dict(zip(layer_name, layer_value))
        #print(content_map)

        tf.reset_default_graph()
        self.target = tf.Variable(np.random.randint(0, 256, content.shape), dtype = tf.float32, name = "generate_image")
        self._build_graph()

def train(self):
        img_size = [self.image_height, self.image_width, self.image_depth]
        train_batch = tf.train.shuffle_batch([read_tfrecord(self.train_file, img_size)],
                    batch_size = self.train_batch_size,
                    capacity = 3000,
                    num_threads = 2,
                    min_after_dequeue = 1000)
        test_batch = tf.train.shuffle_batch([read_tfrecord(self.test_file, img_size)],
                    batch_size = self.test_batch_size,
                    capacity = 500,
                    num_threads = 2,
                    min_after_dequeue = 300)
        init = tf.global_variables_initializer()
        init_fn = slim.assign_from_checkpoint_fn("resnet_v2_50.ckpt", slim.get_model_variables('resnet_v2'))
        saver = tf.train.Saver()
        with tf.Session() as sess:
            sess.run(init)
            init_fn(sess)
            train_writer = tf.summary.FileWriter(self.log_dir + "/train", sess.graph)
            test_writer  = tf.summary.FileWriter(self.log_dir + "/test", sess.graph)
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)
            inputs_test, outputs_gt_test = build_img_pair(sess.run(test_batch))
            for iter in range(self.max_iteration):
                inputs_train, outputs_gt_train = build_img_pair(sess.run(train_batch))
                # train with dynamic learning rate
                if iter <= 500:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-3, self.batch_size:self.train_batch_size})
                elif iter <= self.max_iteration - 1000:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:0.5e-3, self.batch_size:self.train_batch_size})
                else:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-4, self.batch_size:self.train_batch_size})
                # print training loss and test loss
                if iter%10 == 0:
                    summary_train = sess.run(self.summary, {self.input_data:inputs_train, self.gt:outputs_gt_train,
                                             self.batch_size:self.train_batch_size})
                    train_writer.add_summary(summary_train, iter)
                    train_writer.flush()
                    summary_test = sess.run(self.summary, {self.input_data:inputs_test, self.gt:outputs_gt_test,
                                             self.batch_size:self.test_batch_size})
                    test_writer.add_summary(summary_test, iter)
                    test_writer.flush()
                # record training loss and test loss
                if iter%10 == 0:
                    train_loss  = self.cross_entropy.eval({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                                    self.batch_size:self.train_batch_size})
                    test_loss   = self.cross_entropy.eval({self.input_data:inputs_test, self.gt:outputs_gt_test,
                                                    self.batch_size:self.test_batch_size})
                    print("iter step %d trainning batch loss %f"%(iter, train_loss))
                    print("iter step %d test loss %f\n"%(iter, test_loss))
                # record model
                if iter%100 == 0:
                    saver.save(sess, self.log_dir + "/model.ckpt", global_step=iter)
            coord.request_stop()
            coord.join(threads)

def train(self):
        img_size = [self.image_height, self.image_width, self.image_depth]
        train_batch = tf.train.shuffle_batch([read_tfrecord(self.train_file, img_size)],
                    batch_size = self.train_batch_size,
                    capacity = 3000,
                    num_threads = 2,
                    min_after_dequeue = 1000)
        test_batch = tf.train.shuffle_batch([read_tfrecord(self.test_file, img_size)],
                    batch_size = self.test_batch_size,
                    capacity = 500,
                    num_threads = 2,
                    min_after_dequeue = 300)
        init = tf.global_variables_initializer()
        init_fn = slim.assign_from_checkpoint_fn("resnet_v2_50.ckpt", slim.get_model_variables('resnet_v2'))
        saver = tf.train.Saver()
        with tf.Session() as sess:
            sess.run(init)
            init_fn(sess)
            train_writer = tf.summary.FileWriter(self.log_dir + "/train", sess.graph)
            test_writer  = tf.summary.FileWriter(self.log_dir + "/test", sess.graph)
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)
            inputs_test, outputs_gt_test = build_img_pair(sess.run(test_batch))
            for iter in range(self.max_iteration):
                inputs_train, outputs_gt_train = build_img_pair(sess.run(train_batch))
                # train with dynamic learning rate
                if iter <= 500:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-3, self.batch_size:self.train_batch_size})
                elif iter <= self.max_iteration - 1000:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:0.5e-3, self.batch_size:self.train_batch_size})
                else:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-4, self.batch_size:self.train_batch_size})
                # print training loss and test loss
                if iter%10 == 0:
                    summary_train = sess.run(self.summary, {self.input_data:inputs_train, self.gt:outputs_gt_train,
                                             self.batch_size:self.train_batch_size})
                    train_writer.add_summary(summary_train, iter)
                    train_writer.flush()
                    summary_test = sess.run(self.summary, {self.input_data:inputs_test, self.gt:outputs_gt_test,
                                             self.batch_size:self.test_batch_size})
                    test_writer.add_summary(summary_test, iter)
                    test_writer.flush()
                # record training loss and test loss
                if iter%10 == 0:
                    train_loss  = self.cross_entropy.eval({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                                    self.batch_size:self.train_batch_size})
                    test_loss   = self.cross_entropy.eval({self.input_data:inputs_test, self.gt:outputs_gt_test,
                                                    self.batch_size:self.test_batch_size})
                    print("iter step %d trainning batch loss %f"%(iter, train_loss))
                    print("iter step %d test loss %f\n"%(iter, test_loss))
                # record model
                if iter%100 == 0:
                    saver.save(sess, self.log_dir + "/model.ckpt", global_step=iter)
            coord.request_stop()
            coord.join(threads)

def train(self):
        img_size = [self.image_height, self.image_width, self.image_depth]
        train_batch = tf.train.shuffle_batch([read_tfrecord(self.train_file, img_size)],
                    batch_size = self.train_batch_size,
                    capacity = 2000,
                    num_threads = 2,
                    min_after_dequeue = 1000)
        test_batch = tf.train.shuffle_batch([read_tfrecord(self.test_file, img_size)],
                    batch_size = self.test_batch_size,
                    capacity = 500,
                    num_threads = 2,
                    min_after_dequeue = 300)
        init = tf.global_variables_initializer()
        init_fn = slim.assign_from_checkpoint_fn("vgg_16.ckpt", slim.get_model_variables('vgg_16'))
        saver = tf.train.Saver()
        with tf.Session() as sess:
            sess.run(init)
            init_fn(sess)
            train_writer = tf.summary.FileWriter(self.log_dir + "/train", sess.graph)
            test_writer  = tf.summary.FileWriter(self.log_dir + "/test", sess.graph)
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)
            inputs_test, outputs_gt_test = build_img_pair(sess.run(test_batch))
            for iter in range(self.max_iteration):
                inputs_train, outputs_gt_train = build_img_pair(sess.run(train_batch))
                # train with dynamic learning rate
                if iter <= 500:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train, self.is_training:True,
                                    self.learning_rate:1e-4})
                elif iter <= self.max_iteration - 1000:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train, self.is_training:True,
                                    self.learning_rate:0.5e-4})
                else:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train, self.is_training:True,
                                    self.learning_rate:1e-5})
                # print training loss and test loss
                if iter%10 == 0:
                    summary_train = sess.run(self.summary, {self.input_data:inputs_train, self.gt:outputs_gt_train,
                                             self.is_training:False})
                    train_writer.add_summary(summary_train, iter)
                    train_writer.flush()
                    summary_test = sess.run(self.summary, {self.input_data:inputs_test, self.gt:outputs_gt_test,
                                            self.is_training:False})
                    test_writer.add_summary(summary_test, iter)
                    test_writer.flush()
                # record training loss and test loss
                if iter%10 == 0:
                    train_loss  = self.cross_entropy.eval({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                                    self.is_training:False})
                    test_loss   = self.cross_entropy.eval({self.input_data:inputs_test, self.gt:outputs_gt_test,
                                                    self.is_training:False})
                    print("iter step %d trainning batch loss %f"%(iter, train_loss))
                    print("iter step %d test loss %f\n"%(iter, test_loss))
                # record model
                if iter%100 == 0:
                    saver.save(sess, self.log_dir + "/model.ckpt", global_step=iter)
            coord.request_stop()
            coord.join(threads)

def train(self):
        img_size = [self.image_height, self.image_width, self.image_depth]
        train_batch = tf.train.shuffle_batch([read_tfrecord(self.train_file, img_size)],
                    batch_size = self.train_batch_size,
                    capacity = 3000,
                    num_threads = 2,
                    min_after_dequeue = 1000)
        test_batch = tf.train.shuffle_batch([read_tfrecord(self.test_file, img_size)],
                    batch_size = self.test_batch_size,
                    capacity = 500,
                    num_threads = 2,
                    min_after_dequeue = 300)
        init = tf.global_variables_initializer()
        init_fn = slim.assign_from_checkpoint_fn("resnet_v2_50.ckpt", slim.get_model_variables('resnet_v2'))
        saver = tf.train.Saver()
        with tf.Session() as sess:
            sess.run(init)
            init_fn(sess)
            train_writer = tf.summary.FileWriter(self.log_dir + "/train", sess.graph)
            test_writer  = tf.summary.FileWriter(self.log_dir + "/test", sess.graph)
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)
            inputs_test, outputs_gt_test = build_img_pair(sess.run(test_batch))
            for iter in range(self.max_iteration):
                inputs_train, outputs_gt_train = build_img_pair(sess.run(train_batch))
                # train with dynamic learning rate
                if iter <= 500:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-3, self.batch_size:self.train_batch_size})
                elif iter <= self.max_iteration - 1000:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:0.5e-3, self.batch_size:self.train_batch_size})
                else:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-4, self.batch_size:self.train_batch_size})
                # print training loss and test loss
                if iter%10 == 0:
                    summary_train = sess.run(self.summary, {self.input_data:inputs_train, self.gt:outputs_gt_train,
                                             self.batch_size:self.train_batch_size})
                    train_writer.add_summary(summary_train, iter)
                    train_writer.flush()
                    summary_test = sess.run(self.summary, {self.input_data:inputs_test, self.gt:outputs_gt_test,
                                             self.batch_size:self.test_batch_size})
                    test_writer.add_summary(summary_test, iter)
                    test_writer.flush()
                # record training loss and test loss
                if iter%10 == 0:
                    train_loss  = self.cross_entropy.eval({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                                    self.batch_size:self.train_batch_size})
                    test_loss   = self.cross_entropy.eval({self.input_data:inputs_test, self.gt:outputs_gt_test,
                                                    self.batch_size:self.test_batch_size})
                    print("iter step %d trainning batch loss %f"%(iter, train_loss))
                    print("iter step %d test loss %f\n"%(iter, test_loss))
                # record model
                if iter%100 == 0:
                    saver.save(sess, self.log_dir + "/model.ckpt", global_step=iter)
            coord.request_stop()
            coord.join(threads)

def train(self):
        img_size = [self.image_height, self.image_width, self.image_depth]
        train_batch = tf.train.shuffle_batch([read_tfrecord(self.train_file, img_size)],
                    batch_size = self.train_batch_size,
                    capacity = 2000,
                    num_threads = 2,
                    min_after_dequeue = 1000)
        test_batch = tf.train.shuffle_batch([read_tfrecord(self.test_file, img_size)],
                    batch_size = self.test_batch_size,
                    capacity = 500,
                    num_threads = 2,
                    min_after_dequeue = 300)
        init = tf.global_variables_initializer()
        init_fn = slim.assign_from_checkpoint_fn("vgg_16.ckpt", slim.get_model_variables('vgg_16'))
        saver = tf.train.Saver()
        with tf.Session() as sess:
            sess.run(init)
            init_fn(sess)
            train_writer = tf.summary.FileWriter(self.log_dir + "/train", sess.graph)
            test_writer  = tf.summary.FileWriter(self.log_dir + "/test", sess.graph)
            coord = tf.train.Coordinator()
            threads = tf.train.start_queue_runners(sess=sess, coord=coord)
            inputs_test, outputs_gt_test = build_img_pair(sess.run(test_batch))
            for iter in range(self.max_iteration):
                inputs_train, outputs_gt_train = build_img_pair(sess.run(train_batch))
                # train with dynamic learning rate
                if iter <= 500:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-4, self.is_training:True})
                elif iter <= self.max_iteration - 1000:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:0.5e-4, self.is_training:True})
                else:
                    self.train_step.run({self.input_data:inputs_train, self.gt:outputs_gt_train,
                                    self.learning_rate:1e-5, self.is_training:True})
                # print training loss and test loss
                if iter%10 == 0:
                    summary_train = sess.run(self.summary, {self.input_data:inputs_train, self.gt:outputs_gt_train, self.is_training:False})
                    train_writer.add_summary(summary_train, iter)
                    train_writer.flush()
                    summary_test = sess.run(self.summary, {self.input_data:inputs_test, self.gt:outputs_gt_test, self.is_training:False})
                    test_writer.add_summary(summary_test, iter)
                    test_writer.flush()
                # record training loss and test loss
                if iter%10 == 0:
                    train_loss  = self.cross_entropy.eval({self.input_data:inputs_train, self.gt:outputs_gt_train, self.is_training:False})
                    test_loss   = self.cross_entropy.eval({self.input_data:inputs_test, self.gt:outputs_gt_test, self.is_training:False})
                    print("iter step %d trainning batch loss %f"%(iter, train_loss))
                    print("iter step %d test loss %f\n"%(iter, test_loss))
                # record model
                if iter%100 == 0:
                    saver.save(sess, self.log_dir + "/model.ckpt", global_step=iter)
            coord.request_stop()
            coord.join(threads)

def train(self,
            train_anchor_batch,
            train_pos_batch,
            train_neg_batch,
            train_params,
            preprocessed=True):
    """Training process of the matcher.

    Each input data should have same shape.

    Args:
      train_anchor_batch: anchor batch.
      train_pos_batch: positive batch.
      train_neg_batch: negative batch.
      train_params: commons.TrainTestParams object.
      preprocessed: if data has been preprocessed.
    """
    self.check_dm_model_exist()
    self.dm_model.use_graph()
    # get embedding for all batches.
    all_batches = tf.concat(
        0, [train_anchor_batch, train_pos_batch, train_neg_batch])
    if not preprocessed:
      all_batches = self.dm_model.preprocess(all_batches)
    all_feats, _ = self.build_model(all_batches)
    anchor_feats, pos_feats, neg_feats = tf.split(all_feats, 3, axis=0)
    self.set_key_vars(train_params.restore_scopes_exclude,
                      train_params.train_scopes)
    self.compute_losses(anchor_feats, pos_feats, neg_feats, train_params)
    init_fn = None
    if train_params.fine_tune:
      # self.vars_to_restore is supposed to be set in set_key_vars
      print("[dm_matcher.train: info] Trying to restore variables: {}".format(
          self.vars_to_restore))
      init_fn = slim.assign_from_checkpoint_fn(train_params.custom["model_fn"],
                                               self.vars_to_restore)
    if not train_params.resume_training:
      data_manager.remove_dir(train_params.train_log_dir)
    if train_params.use_regularization:
      regularization_loss = tf.add_n(tf.losses.get_regularization_losses())
      tf.summary.scalar("losses/regularization_loss", regularization_loss)

    total_loss = tf.losses.get_total_loss(
        add_regularization_losses=train_params.use_regularization)
    base_model.train_model_given_loss(
        total_loss, self.vars_to_train, train_params, init_fn=init_fn)

  # TODO(jiefeng): to load weights from file.

def train(self,
            train_input_batch,
            train_label_batch,
            train_params,
            preprocessed=True):
    """Training process of the classifier.

    Args:
      train_input_batch: input batch for training.
      train_label_batch: class id for training.
      train_params: commons.TrainTestParams object.
      preprocessed: if train data has been preprocessed.
    """
    assert train_input_batch is not None, "train input batch is none"
    assert train_label_batch is not None, "train label batch is none"
    assert isinstance(
        train_params,
        commons.TrainTestParams), "train params is not a valid type"
    self.check_dm_model_exist()
    # self.dm_model.use_graph()
    model_params = self.dm_model.net_params
    if not preprocessed:
      train_input_batch = self.dm_model.preprocess(train_input_batch)
    pred_logits, endpoints = self.build_model(train_input_batch)
    self.set_key_vars(train_params.restore_scopes_exclude,
                      train_params.train_scopes)
    comp_train_accuracy(pred_logits, train_label_batch)
    tf.assert_equal(
        tf.reduce_max(train_label_batch),
        tf.convert_to_tensor(
            model_params.cls_num, dtype=tf.int64))
    onehot_labels = tf.one_hot(
        train_label_batch, model_params.cls_num, on_value=1.0, off_value=0.0)
    # onehot_labels = slim.one_hot_encoding(train_label_batch,
    #                                       model_params.cls_num)
    onehot_labels = tf.squeeze(onehot_labels)
    self.compute_losses(onehot_labels, pred_logits, endpoints)
    init_fn = None
    if train_params.fine_tune and not train_params.resume_training:
      init_fn = slim.assign_from_checkpoint_fn(train_params.custom["model_fn"],
                                               self.vars_to_restore)
    # this would not work if a tensorboard is running...
    if not train_params.resume_training:
      data_manager.remove_dir(train_params.train_log_dir)
    # display regularization loss.
    if train_params.use_regularization:
      regularization_loss = tf.add_n(tf.losses.get_regularization_losses())
      tf.summary.scalar("losses/regularization_loss", regularization_loss)

    total_loss = tf.losses.get_total_loss(
        add_regularization_losses=train_params.use_regularization)
    base_model.train_model_given_loss(
        total_loss, self.vars_to_train, train_params, init_fn=init_fn)

def __get_init_fn(self):
        """Returns a function run by the chief worker to warm-start the training.

        Note that the init_fn is only run when initializing the model during the very
        first global step.

        Returns:
            An init function run by the supervisor.
        """

        if self.checkpoint_path is None:
            return None

        # Warn the user if a checkpoint exists in the train_dir. Then we'll be
        # ignoring the checkpoint anyway.
        if tf.train.latest_checkpoint(self.train_dir):
            tf.logging.info(
                    'Ignoring --checkpoint_path because a checkpoint already exists in %s'
                    % self.train_dir)
            return None

        exclusions = []
        if self.checkpoint_exclude_scopes:
            exclusions = [scope.strip()
                                        for scope in self.checkpoint_exclude_scopes.split(',')]

        # TODO(sguada) variables.filter_variables()
        variables_to_restore = []
        for var in slim.get_model_variables():
            excluded = False
            for exclusion in exclusions:
                if var.op.name.startswith(exclusion):
                    excluded = True
                    break
            if not excluded:
                variables_to_restore.append(var)

        if tf.gfile.IsDirectory(self.checkpoint_path):
            checkpoint_path = tf.train.latest_checkpoint(self.checkpoint_path)
        else:
            checkpoint_path = self.checkpoint_path

        tf.logging.info('Fine-tuning from %s' % checkpoint_path)

        return slim.assign_from_checkpoint_fn(
                checkpoint_path,
                variables_to_restore,
                ignore_missing_vars=self.ignore_missing_vars)

def use_fined_model(self):
        image_size = inception.inception_v4.default_image_size
        batch_size = 3
        flowers_data_dir = "../../data/flower"
        train_dir = '/tmp/inception_finetuned/'

        with tf.Graph().as_default():
            tf.logging.set_verbosity(tf.logging.INFO)

            dataset = flowers.get_split('train', flowers_data_dir)
            images, images_raw, labels = self.load_batch(dataset, height=image_size, width=image_size)

            # Create the model, use the default arg scope to configure the batch norm parameters.
            with slim.arg_scope(inception.inception_v4_arg_scope()):
                logits, _ = inception.inception_v4(images, num_classes=dataset.num_classes, is_training=True)

            probabilities = tf.nn.softmax(logits)

            checkpoint_path = tf.train.latest_checkpoint(train_dir)
            init_fn = slim.assign_from_checkpoint_fn(
              checkpoint_path,
              slim.get_variables_to_restore())

            with tf.Session() as sess:
                with slim.queues.QueueRunners(sess):
                    sess.run(tf.initialize_local_variables())
                    init_fn(sess)
                    np_probabilities, np_images_raw, np_labels = sess.run([probabilities, images_raw, labels])

                    for i in range(batch_size): 
                        image = np_images_raw[i, :, :, :]
                        true_label = np_labels[i]
                        predicted_label = np.argmax(np_probabilities[i, :])
                        predicted_name = dataset.labels_to_names[predicted_label]
                        true_name = dataset.labels_to_names[true_label]

                        plt.figure()
                        plt.imshow(image.astype(np.uint8))
                        plt.title('Ground Truth: [%s], Prediction [%s]' % (true_name, predicted_name))
                        plt.axis('off')
                        plt.show()
                return

def __get_init_fn(self):
        """Returns a function run by the chief worker to warm-start the training.

        Note that the init_fn is only run when initializing the model during the very
        first global step.

        Returns:
            An init function run by the supervisor.
        """  

        if self.checkpoint_path is None:
            return None

        # Warn the user if a checkpoint exists in the train_dir. Then we'll be
        # ignoring the checkpoint anyway.


        if tf.train.latest_checkpoint(self.train_dir):
            tf.logging.info(
                    'Ignoring --checkpoint_path because a checkpoint already exists in %s'
                    % self.train_dir)
            return None

        exclusions = []
        if self.checkpoint_exclude_scopes:
            exclusions = [scope.strip()
                                        for scope in self.checkpoint_exclude_scopes.split(',')]

        # TODO(sguada) variables.filter_variables()
        variables_to_restore = []
        all_variables = slim.get_model_variables()
        if self.fine_tune_vgg16:
            global_step = slim.get_or_create_global_step()
            all_variables.append(global_step)
        for var in all_variables:
            excluded = False

            for exclusion in exclusions:
                if var.op.name.startswith(exclusion):
                    excluded = True
                    break
            if not excluded:
                variables_to_restore.append(var)

        if tf.gfile.IsDirectory(self.checkpoint_path):
            checkpoint_path = tf.train.latest_checkpoint(self.checkpoint_path)
        else:
            checkpoint_path = self.checkpoint_path

        tf.logging.info('Fine-tuning from %s' % checkpoint_path)

        return slim.assign_from_checkpoint_fn(
                checkpoint_path,
                variables_to_restore,
                ignore_missing_vars=self.ignore_missing_vars)

def main():
    model = config.get('config', 'model')
    cachedir = utils.get_cachedir(config)
    with open(os.path.join(cachedir, 'names'), 'r') as f:
        names = [line.strip() for line in f]
    width = config.getint(model, 'width')
    height = config.getint(model, 'height')
    yolo = importlib.import_module('model.' + model)
    cell_width, cell_height = utils.calc_cell_width_height(config, width, height)
    tf.logging.info('(width, height)=(%d, %d), (cell_width, cell_height)=(%d, %d)' % (width, height, cell_width, cell_height))
    with tf.Session() as sess:
        paths = [os.path.join(cachedir, profile + '.tfrecord') for profile in args.profile]
        num_examples = sum(sum(1 for _ in tf.python_io.tf_record_iterator(path)) for path in paths)
        tf.logging.warn('num_examples=%d' % num_examples)
        image_rgb, labels = utils.data.load_image_labels(paths, len(names), width, height, cell_width, cell_height, config)
        image_std = tf.image.per_image_standardization(image_rgb)
        image_rgb = tf.cast(image_rgb, tf.uint8)
        ph_image = tf.placeholder(image_std.dtype, [1] + image_std.get_shape().as_list(), name='ph_image')
        global_step = tf.contrib.framework.get_or_create_global_step()
        builder = yolo.Builder(args, config)
        builder(ph_image)
        variables_to_restore = slim.get_variables_to_restore()
        ph_labels = [tf.placeholder(l.dtype, [1] + l.get_shape().as_list(), name='ph_' + l.op.name) for l in labels]
        with tf.name_scope('total_loss') as name:
            builder.create_objectives(ph_labels)
            total_loss = tf.losses.get_total_loss(name=name)
        tf.global_variables_initializer().run()
        coord = tf.train.Coordinator()
        threads = tf.train.start_queue_runners(sess, coord)
        _image_rgb, _image_std, _labels = sess.run([image_rgb, image_std, labels])
        coord.request_stop()
        coord.join(threads)
        feed_dict = dict([(ph, np.expand_dims(d, 0)) for ph, d in zip(ph_labels, _labels)])
        feed_dict[ph_image] = np.expand_dims(_image_std, 0)
        logdir = utils.get_logdir(config)
        assert os.path.exists(logdir)
        model_path = tf.train.latest_checkpoint(logdir)
        tf.logging.info('load ' + model_path)
        slim.assign_from_checkpoint_fn(model_path, variables_to_restore)(sess)
        tf.logging.info('global_step=%d' % sess.run(global_step))
        tf.logging.info('total_loss=%f' % sess.run(total_loss, feed_dict))
        _ = Drawer(sess, names, builder.model.cell_width, builder.model.cell_height, _image_rgb, _labels, builder.model, feed_dict)
        plt.show()

def _get_init_fn():
  """Returns a function run by the chief worker to warm-start the training.

  Note that the init_fn is only run when initializing the model during the very
  first global step.

  Returns:
    An init function run by the supervisor.
  """
  if FLAGS.checkpoint_path is None:
    return None

  # Warn the user if a checkpoint exists in the train_dir. Then we'll be
  # ignoring the checkpoint anyway.
  if tf.train.latest_checkpoint(FLAGS.train_dir):
    tf.logging.info(
      'Ignoring --checkpoint_path because a checkpoint already exists in %s'
      % FLAGS.train_dir)
    return None

  exclusions = []
  if FLAGS.checkpoint_exclude_scopes:
    exclusions = [scope.strip()
                  for scope in FLAGS.checkpoint_exclude_scopes.split(',')]

  # TODO(sguada) variables.filter_variables()
  variables_to_restore = []
  for var in slim.get_model_variables():
    excluded = False
    for exclusion in exclusions:
      if var.op.name.startswith(exclusion):
        excluded = True
        break
    if not excluded:
      variables_to_restore.append(var)

  if tf.gfile.IsDirectory(FLAGS.checkpoint_path):
    checkpoint_path = tf.train.latest_checkpoint(FLAGS.checkpoint_path)
  else:
    checkpoint_path = FLAGS.checkpoint_path

  tf.logging.info('Fine-tuning from %s' % checkpoint_path)

  return slim.assign_from_checkpoint_fn(
    checkpoint_path,
    variables_to_restore,
    ignore_missing_vars=FLAGS.ignore_missing_vars)

def export_to_h5(checkpoint_dir, export_path, images, end_points, num_samples,
                 batch_size, sact):
  """Exports ponder cost maps and other useful info to an HDF5 file."""
  output_file = h5py.File(export_path, 'w')

  output_file.attrs['block_scopes'] = end_points['block_scopes']
  keys_to_tensors = {}
  for block_scope in end_points['block_scopes']:
    for k in ('{}/ponder_cost'.format(block_scope),
              '{}/num_units'.format(block_scope),
              '{}/halting_distribution'.format(block_scope),
              '{}/flops'.format(block_scope)):
      keys_to_tensors[k] = end_points[k]
  keys_to_tensors['images'] = images
  keys_to_tensors['flops'] = end_points['flops']

  if sact:
    keys_to_tensors['ponder_cost_map'] = sact_map(end_points, 'ponder_cost')
    keys_to_tensors['num_units_map'] = sact_map(end_points, 'num_units')

  keys_to_datasets = {}
  for key, tensor in keys_to_tensors.iteritems():
    sh = tensor.get_shape().as_list()
    sh[0] = num_samples
    print(key, sh)
    keys_to_datasets[key] = output_file.create_dataset(
        key, sh, compression='lzf')

  variables_to_restore = slim.get_model_variables()
  checkpoint_path = tf.train.latest_checkpoint(checkpoint_dir)
  assert checkpoint_path is not None
  init_fn = slim.assign_from_checkpoint_fn(checkpoint_path,
                                           variables_to_restore)

  sv = tf.train.Supervisor(
      graph=tf.get_default_graph(),
      logdir=None,
      summary_op=None,
      summary_writer=None,
      global_step=None,
      saver=None)

  assert num_samples % batch_size == 0
  num_batches = num_samples // batch_size

  with sv.managed_session('', start_standard_services=False) as sess:
    init_fn(sess)
    sv.start_queue_runners(sess)

    for i in range(num_batches):
      tf.logging.info('Evaluating batch %d/%d', i + 1, num_batches)
      end_points_out = sess.run(keys_to_tensors)
      for key, dataset in keys_to_datasets.iteritems():
        dataset[i * batch_size:(i + 1) * batch_size, ...] = end_points_out[key]