Simplifying poison changes. Removing missed progress bar edits

Signed-off-by: GiulioZizzo <giulio.zizzo@yahoo.co.uk>

art/attacks/poisoning/backdoor_attack.py

@@ -79,7 +79,7 @@ def poison(  # pylint: disable=W0221
         poisoned = np.copy(x)
 
         if callable(self.perturbation):
-            return self.perturbation(poisoned, channels_first=channels_first), y_attack
+            return self.perturbation(poisoned), y_attack
 
         for perturb in self.perturbation:
             poisoned = perturb(poisoned)

art/attacks/poisoning/clean_label_backdoor_attack.py

@@ -142,9 +142,7 @@ def poison(  # pylint: disable=W0221
             logger.warning("%d indices without change: %s", len(idx_no_change), idx_no_change)
 
         # Add backdoor and poison with the same label
-        poisoned_input, _ = self.backdoor.poison(
-            perturbed_input, self.target, broadcast=broadcast, channels_first=channels_first
-        )
+        poisoned_input, _ = self.backdoor.poison(perturbed_input, self.target, broadcast=broadcast)
         data[selected_indices] = poisoned_input
 
         return data, estimated_labels

art/estimators/classification/pytorch.py

@@ -23,14 +23,12 @@
 
 import copy
 import logging
-import math
 import os
 import time
 from typing import Any, Dict, List, Optional, Tuple, Union, TYPE_CHECKING
 
 import numpy as np
 import six
-from tqdm.auto import tqdm
 
 from art import config
 from art.estimators.classification.classifier import (
@@ -397,8 +395,6 @@ def fit(  # pylint: disable=W0221
         import torch
         from torch.utils.data import TensorDataset, DataLoader
 
-        display_progress_bar = kwargs.get("display_progress_bar", False)
-
         # Set model mode
         self._model.train(mode=training_mode)
 

tests/attacks/poison/test_clean_label_backdoor_attack.py

@@ -36,15 +36,20 @@ def test_poison(art_warning, get_default_mnist_subset, image_dl_estimator, frame
         (x_train, y_train), (_, _) = get_default_mnist_subset
         classifier, _ = image_dl_estimator()
         target = to_categorical([9], 10)[0]
+        if framework in ["pytorch", "huggingface"]:
+            def mod(x):
+                original_dtype = x.dtype
+                x = add_pattern_bd(x, channels_first=True)
+                return x.astype(original_dtype)
+        else:
+            def mod(x):
+                original_dtype = x.dtype
+                x = add_pattern_bd(x)
+                return x.astype(original_dtype)
 
         backdoor = PoisoningAttackBackdoor(add_pattern_bd)
-
         attack = PoisoningAttackCleanLabelBackdoor(backdoor, classifier, target)
-        if framework in ["pytorch", "huggingface"]:
-            print('HERE!!')
-            poison_data, poison_labels = attack.poison(x_train, y_train, channels_first=True)
-        else:
-            poison_data, poison_labels = attack.poison(x_train, y_train)
+        poison_data, poison_labels = attack.poison(x_train, y_train)
 
         np.testing.assert_equal(poison_data.shape, x_train.shape)
         np.testing.assert_equal(poison_labels.shape, y_train.shape)