text: # Copyright (c) 2018 Mengye Ren, Eleni Triantafillou, Sachin Ravi, Jake Snell,
  # Kevin Swersky, Joshua B. Tenenbaum, Hugo Larochelle, Richars S. Zemel.
  #
  # Permission is hereby granted, free of charge, to any person obtaining a copy
  # of this software and associated documentation files (the "Software"), to deal
  # in the Software without restriction, including without limitation the rights
  # to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
  # copies of the Software, and to permit persons to whom the Software is
  # furnished to do so, subject to the following conditions:
  #
  # The above copyright notice and this permission notice shall be included in all
  # copies or substantial portions of the Software.
  #
  # THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
  # IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
  # FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
  # AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
  # LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
  # OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
  # SOFTWARE.
  # =============================================================================
  """
  A prototypical network with K-means to refine unlabeled examples.
  
  Author: Mengye Ren (mren@cs.toronto.edu)
  
  In a single episode, the model computes the mean representation of the
  positive refereene images as prototypes and then refine the representation by
  running a few steps of soft K-means iterations.
  """
  import tensorflow as tf
  
  from fewshot.models.kmeans_utils import assign_cluster, update_cluster, compute_logits
  from fewshot.models.model_factory import RegisterModel
  from fewshot.models.nnlib import concat
  from fewshot.models.refine_model import RefineModel
  from fewshot.models.basic_model_RW import BasicModelRW
  
  
  from fewshot.utils import logger
  from fewshot.models.SSL_utils import get_dist_filter
  log = logger.get()
  
  
  @RegisterModel("kmeans-refine")
  class KMeansRefineModel(RefineModel):
  
    def compute_output(self):
      """See `model.py` for documentation."""
  
      nclasses = self.nway
      num_cluster_steps = self.config.num_cluster_steps
      h_train, h_unlabel, h_test = self.encode(
          self.x_train, self.x_unlabel, self.x_test)
      y_train = self.y_train
      protos = self._compute_protos(nclasses, h_train, y_train)
      logits = compute_logits(protos, h_test)
  
      # Hard assignment for training images.
      prob_train = [None] * nclasses
      for kk in range(nclasses):
        # [B, N, 1]
        prob_train[kk] = tf.expand_dims(
            tf.cast(tf.equal(y_train, kk), h_train.dtype), 2)
      prob_train = concat(prob_train, 2)
  
      h_all = concat([h_train, h_unlabel], 1)
  
      logits_list = []
      logits_list.append(compute_logits(protos, h_test))
  
      # Run clustering.
      for tt in range(num_cluster_steps):
        # Label assignment.
        prob_unlabel = assign_cluster(protos, h_unlabel)
        entropy = tf.reduce_sum(
            -prob_unlabel * tf.log(prob_unlabel), [2], keep_dims=True)
        prob_all = concat([prob_train, prob_unlabel], 1)
        prob_all = tf.stop_gradient(prob_all)
        protos = update_cluster(h_all, prob_all)
        # protos = tf.cond(
        #     tf.shape(self._x_unlabel)[1] > 0,
        #     lambda: update_cluster(h_all, prob_all), lambda: protos)
        logits_list.append(compute_logits(protos, h_test))
  
        self._logits = logits_list
        super().compute_output()
  
  ###################################################################
  
  @RegisterModel("kmeans-filter")
  class KMeansFilterModel(RefineModel):
    def predict(self):
      super(RefineModel, self).predict()
      with tf.name_scope('Predict/VAT'):
        self._unlabel_logits = compute_logits(self._ssl_protos, self.h_unlabel)
  
    def compute_output(self):
      """See `model.py` for documentation."""
      logits = self._unlabel_logits
  
      s = tf.shape(logits)
      s = s[0]
      p = self.h_unlabel
      filter = get_dist_filter(logits)
      ####################################
  
      nclasses = self.nway
      num_cluster_steps = self.config.num_cluster_steps
      h_train, h_unlabel, h_test = self.encode(
          self.x_train, self.x_unlabel, self.x_test)
      y_train = self.y_train
      protos = self._compute_protos(nclasses, h_train, y_train)
      logits = compute_logits(protos, h_test)
  
      # Hard assignment for training images.
      prob_train = [None] * nclasses
      for kk in range(nclasses):
        # [B, N, 1]
        prob_train[kk] = tf.expand_dims(
            tf.cast(tf.equal(y_train, kk), h_train.dtype), 2)
      prob_train = concat(prob_train, 2)
  
      h_all = concat([h_train, h_unlabel], 1)
  
      logits_list = []
      logits_list.append(compute_logits(protos, h_test))
  
      # Run clustering.
      for tt in range(num_cluster_steps):
        # Label assignment.
        prob_unlabel = assign_cluster(protos, h_unlabel)
        prob_unlabel = tf.squeeze(prob_unlabel)
        prob_unlabel = prob_unlabel * tf.expand_dims(filter, 1)
        prob_unlabel = tf.expand_dims(prob_unlabel, 0)
        entropy = tf.reduce_sum(
            -prob_unlabel * tf.log(prob_unlabel), [2], keep_dims=True)
        prob_all = concat([prob_train, prob_unlabel], 1)
        prob_all = tf.stop_gradient(prob_all)
        protos = update_cluster(h_all, prob_all)
        # protos = tf.cond(
        #     tf.shape(self._x_unlabel)[1] > 0,
        #     lambda: update_cluster(h_all, prob_all), lambda: protos)
        logits_list.append(compute_logits(protos, h_test))
  
        self._logits = logits_list
        super().compute_output()