From 9f07f2a87194ec30a4ace02cbd81206039f3f1ad Mon Sep 17 00:00:00 2001
From: amad-person <e0072565@u.nus.edu>
Date: Fri, 6 Nov 2020 16:42:31 +0800
Subject: [PATCH] Add Seq2SeqAttackInputData data structure

---
 .../data_structures.py                        | 80 ++++++++++++++++++-
 1 file changed, 79 insertions(+), 1 deletion(-)

diff --git a/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py b/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py
index 2f7c205..fd549d2 100644
--- a/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py
+++ b/tensorflow_privacy/privacy/membership_inference_attack/data_structures.py
@@ -18,7 +18,7 @@ import enum
 import glob
 import os
 import pickle
-from typing import Any, Iterable, Union
+from typing import Any, Iterable, Union, Iterator
 
 from dataclasses import dataclass
 import numpy as np
@@ -374,6 +374,84 @@ def _append_array_shape(arr: np.array, arr_name: str, result):
     result.append(' %s with shape: %s,' % (arr_name, arr.shape))
 
 
+def _is_generator(gen, gen_name):
+  """Checks whether gen is a generator."""
+  if gen is not None and not isinstance(gen, Iterator):
+    raise ValueError('%s should be a generator.' % gen_name)
+
+
+@dataclass
+class Seq2SeqAttackInputData:
+  """Input data for running an attack on seq2seq models.
+
+  This includes only the data, and not configuration.
+  """
+  logits_train: Iterator[np.ndarray] = None
+  logits_test: Iterator[np.ndarray] = None
+
+  # Contains ground-truth token indices for the target sequences.
+  labels_train: Iterator[np.ndarray] = None
+  labels_test: Iterator[np.ndarray] = None
+
+  # Denotes size of the target sequence vocabulary.
+  vocab_size: int = None
+
+  # Train, test size = number of batches in training, test set.
+  # These values need to be supplied by the user as logits, labels
+  # are lazy loaded for seq2seq models.
+  train_size: int = 0
+  test_size: int = 0
+
+  def validate(self):
+    """Validates the inputs."""
+
+    if (self.logits_train is None) != (self.logits_test is None):
+      raise ValueError(
+          'logits_train and logits_test should both be either set or unset')
+
+    if (self.labels_train is None) != (self.labels_test is None):
+      raise ValueError(
+          'labels_train and labels_test should both be either set or unset')
+
+    if self.logits_train is None or self.labels_train is None:
+      raise ValueError(
+          'Labels, logits of training, test sets should all be set')
+
+    if (self.vocab_size is None or self.train_size is None
+        or self.test_size is None):
+      raise ValueError(
+          'vocab_size, train_size, test_size should all be set')
+
+    if self.vocab_size is not None and not int:
+      raise ValueError('vocab_size should be of integer type')
+
+    if self.train_size is not None and not int:
+      raise ValueError('train_size should be of integer type')
+
+    if self.test_size is not None and not int:
+      raise ValueError('test_size should be of integer type')
+
+    _is_generator(self.logits_train, 'logits_train')
+    _is_generator(self.logits_test, 'logits_test')
+    _is_generator(self.labels_train, 'labels_train')
+    _is_generator(self.labels_test, 'labels_test')
+
+  def __str__(self):
+    """Return the shapes of variables that are not None."""
+    result = ['AttackInputData(']
+
+    if self.vocab_size is not None and self.train_size is not None:
+      result.append('logits_train with shape (%d, num_sequences, num_tokens, %d)' % (self.train_size, self.vocab_size))
+      result.append('labels_train with shape (%d, num_sequences, num_tokens, 1)' % self.train_size)
+
+    if self.vocab_size is not None and self.test_size is not None:
+      result.append('logits_test with shape (%d, num_sequences, num_tokens, %d)' % (self.test_size, self.vocab_size))
+      result.append('labels_test with shape (%d, num_sequences, num_tokens, 1)' % self.test_size)
+
+    result.append(')')
+    return '\n'.join(result)
+
+
 @dataclass
 class RocCurve:
   """Represents ROC curve of a membership inference classifier."""