fix image downloader failing with automl format on deserialize due to…

… missing label transformations

.github/workflows/CI-e2e-notebooks-text-vision.yml

@@ -90,7 +90,7 @@ jobs:
       - name: Install pytorch with python 3.7
         shell: bash -l {0}
         run: |
-          conda install --yes --quiet "pytorch==1.13.1" "torchvision<0.15" captum cpuonly -c pytorch
+          conda install --yes --quiet "pytorch==1.13.1" "torchvision<0.15" cpuonly -c pytorch
 
       - name: Setup tools
         shell: bash -l {0}
@@ -102,6 +102,7 @@ jobs:
       - name: Install dependencies
         shell: bash -l {0}
         run: |
+          pip install captum
           pip install -r requirements-dev.txt
           pip install -v -e .
         working-directory: raiwidgets

.github/workflows/CI-notebook-vision.yml

@@ -52,25 +52,25 @@ jobs:
         name: Install pytorch on non-MacOS with python 3.7
         shell: bash -l {0}
         run: |
-          conda install --yes --quiet "pytorch==1.13.1" "torchvision<0.15" captum cpuonly -c pytorch
+          conda install --yes --quiet "pytorch==1.13.1" "torchvision<0.15" cpuonly -c pytorch
 
       - if: ${{ matrix.operatingSystem == 'macos-latest' && matrix.pythonVersion == '3.7' }}
         name: Install Anaconda packages on MacOS with python 3.7
         shell: bash -l {0}
         run: |
-          conda install --yes --quiet "pytorch==1.13.1" "torchvision<0.15" captum -c pytorch
+          conda install --yes --quiet "pytorch==1.13.1" "torchvision<0.15" -c pytorch
 
       - if: ${{ matrix.operatingSystem != 'macos-latest' && matrix.pythonVersion != '3.7' }}
         name: Install pytorch on non-MacOS
         shell: bash -l {0}
         run: |
-          conda install --yes --quiet "pytorch<2.1,>1.13.1" "torchvision<0.16" captum cpuonly -c pytorch
+          conda install --yes --quiet "pytorch<2.1,>1.13.1" "torchvision<0.16" cpuonly -c pytorch
 
       - if: ${{ matrix.operatingSystem == 'macos-latest' && matrix.pythonVersion != '3.7' }}
         name: Install Anaconda packages on MacOS, which should not include cpuonly according to official docs
         shell: bash -l {0}
         run: |
-          conda install --yes --quiet "pytorch<2.1,>1.13.1" "torchvision<0.16" captum -c pytorch
+          conda install --yes --quiet "pytorch<2.1,>1.13.1" "torchvision<0.16" -c pytorch
 
       - name: Setup tools
         shell: bash -l {0}
@@ -82,6 +82,7 @@ jobs:
       - name: Install dependencies
         shell: bash -l {0}
         run: |
+          pip install captum
           pip install -r requirements-dev.txt
           pip install .
         working-directory: raiwidgets

.github/workflows/CI-responsibleai-text-vision-pytest.yml

@@ -57,13 +57,13 @@ jobs:
         name: Install pytorch on non-MacOS
         shell: bash -l {0}
         run: |
-          conda install --yes --quiet pytorch==1.13.1 "torchvision<0.15" captum cpuonly -c pytorch
+          conda install --yes --quiet pytorch==1.13.1 "torchvision<0.15" cpuonly -c pytorch
 
       - if: ${{ matrix.operatingSystem == 'macos-latest' }}
         name: Install Anaconda packages on MacOS, which should not include cpuonly according to official docs
         shell: bash -l {0}
         run: |
-          conda install --yes --quiet pytorch==1.13.1 "torchvision<0.15" captum -c pytorch
+          conda install --yes --quiet pytorch==1.13.1 "torchvision<0.15" -c pytorch
 
       - name: Setup tools
         shell: bash -l {0}
@@ -75,6 +75,7 @@ jobs:
       - name: Install dependencies
         shell: bash -l {0}
         run: |
+          pip install captum
           pip install -r requirements-dev.txt
           pip install .
         working-directory: ${{ matrix.packageDirectory }}

responsibleai_vision/responsibleai_vision/rai_vision_insights/rai_vision_insights.py

@@ -650,6 +650,8 @@ def _get_dataset(self):
         for _, image in enumerate(images):
             if isinstance(image, str):
                 image = get_image_from_path(image, self.image_mode)
+            if isinstance(image, list):
+                image = np.array(image)
             s = io.BytesIO()
             # IMshow only accepts floats in range [0, 1]
             try:
@@ -787,7 +789,8 @@ def _format_od_labels(self, y, class_names):
             object_labels_lst = [0] * len(class_names)
             for detection in image:
                 # tracking number of same objects in the image
-                object_labels_lst[int(detection[0] - 1)] += 1
+                object_index = int(detection[0] - 1)
+                object_labels_lst[object_index] += 1
             formatted_labels.append(object_labels_lst)
 
         return formatted_labels
@@ -855,11 +858,12 @@ def _save_ext_data(self, path):
             os.makedirs(mltable_directory, exist_ok=True)
             mltable_data_dict = {}
             if self.test_mltable_path:
-                mltable_dir = self.test_mltable_path.split('/')[-1]
+                test_mltable_path = Path(self.test_mltable_path)
+                mltable_dir = test_mltable_path.name
                 mltable_data_dict[_TEST_MLTABLE_PATH] = mltable_dir
                 test_dir = mltable_directory / mltable_dir
                 shutil.copytree(
-                    Path(self.test_mltable_path), test_dir
+                    test_mltable_path, test_dir
                 )
             if mltable_data_dict:
                 dict_path = mltable_directory / _MLTABLE_METADATA_FILENAME
@@ -1095,12 +1099,14 @@ def _load_ext_data(inst, path):
             mltable_dict = {}
             with open(mltable_dict_path, 'r') as file:
                 mltable_dict = json.load(file)
-
             if mltable_dict.get(_TEST_MLTABLE_PATH, ''):
                 inst.test_mltable_path = str(mltable_directory / mltable_dict[
                     _TEST_MLTABLE_PATH])
                 test_dataset = inst._image_downloader(inst.test_mltable_path)
                 inst.test = test_dataset._images_df
+                if inst.task_type == ModelTask.OBJECT_DETECTION.value:
+                    inst.test = transform_object_detection_labels(
+                        inst.test, target_column, inst._classes)
 
     @staticmethod
     def _load_transformations(inst, path):

responsibleai_vision/tests/common_vision_utils.py

@@ -437,6 +437,16 @@ def _get_model_path(self, path):
         return os.path.join(path, 'image-classification-model')
 
 
+class ObjectDetectionPipelineSerializer(object):
+    def save(self, model, path):
+        pass
+
+    def load(self, path):
+        return retrieve_fridge_object_detection_model(
+            load_fridge_weights=True
+        )
+
+
 class DummyFlowersPipelineSerializer(object):
     def save(self, model, path):
         pass

responsibleai_vision/tests/rai_vision_insights_validator.py

@@ -20,13 +20,15 @@ def validate_rai_vision_insights(
     test_data,
     target_column,
     task_type,
-    ignore_index=False
+    ignore_index=False,
+    ignore_test_data=False
 ):
-    rai_vision_test = rai_vision_insights.test
-    if ignore_index:
-        rai_vision_test = rai_vision_test.reset_index(drop=True)
-        test_data = test_data.reset_index(drop=True)
-    pd.testing.assert_frame_equal(rai_vision_test, test_data)
+    if not ignore_test_data:
+        rai_vision_test = rai_vision_insights.test
+        if ignore_index:
+            rai_vision_test = rai_vision_test.reset_index(drop=True)
+            test_data = test_data.reset_index(drop=True)
+        pd.testing.assert_frame_equal(rai_vision_test, test_data)
     assert rai_vision_insights.target_column == target_column
     assert rai_vision_insights.task_type == task_type
 
@@ -38,7 +40,9 @@ def run_and_validate_serialization(
     class_names,
     label,
     serializer,
-    image_width=None
+    image_width=None,
+    ignore_test_data=False,
+    image_downloader=None
 ):
     """Run and validate serialization.
 
@@ -57,17 +61,28 @@ def run_and_validate_serialization(
     :param image_width: Image width in inches
     :type image_width: int
     """
-    rai_insights = RAIVisionInsights(
-        pred, test, label,
-        task_type=task_type,
-        classes=class_names,
-        serializer=serializer,
-        image_width=image_width)
-
     with TemporaryDirectory() as tmpdir:
         save_1 = Path(tmpdir) / "first_save"
         save_2 = Path(tmpdir) / "second_save"
 
+        test_data_path = None
+        if image_downloader is not None:
+            test_data_path = str(Path(tmpdir) / "fake_downloaded_test_data")
+            dir_path = Path(test_data_path)
+            dir_path.mkdir(exist_ok=True, parents=True)
+            fake_file_path = dir_path / 'fake_file.txt'
+            with open(fake_file_path, 'w') as file:
+                file.write("fake content")
+
+        rai_insights = RAIVisionInsights(
+            pred, test, label,
+            test_data_path=test_data_path,
+            task_type=task_type,
+            classes=class_names,
+            serializer=serializer,
+            image_width=image_width,
+            image_downloader=image_downloader)
+
         # Save it
         rai_insights.save(save_1)
         assert len(os.listdir(save_1 / ManagerNames.EXPLAINER)) == 0
@@ -80,7 +95,11 @@ def run_and_validate_serialization(
         # Validate
         validate_rai_vision_insights(
             rai_2, test,
-            label, task_type)
+            label, task_type,
+            ignore_test_data=ignore_test_data)
+
+        # Test calling get_data works
+        rai_2.get_data()
 
         # Save again
         rai_2.save(save_2)

responsibleai_vision/tests/test_rai_vision_insights.py

@@ -146,7 +146,7 @@ def predict_proba(self, X):
         class_names = np.array(['can', 'carton',
                                 'milk_bottle', 'water_bottle'])
         # test case where there are different numbers of objects in labels
-        data = data.iloc[[1, 50, 120]]
+        data = data.iloc[[1, 50, 120]].reset_index(drop=True)
         run_rai_insights(wrapped_model, data, ImageColumns.LABEL,
                          task_type, class_names,
                          test_error_analysis=True,

responsibleai_vision/tests/test_rai_vision_insights_save_and_load_scenarios.py

@@ -4,20 +4,36 @@
 import shutil
 from pathlib import Path
 from tempfile import TemporaryDirectory
+from unittest.mock import patch
 
+import numpy as np
 import PIL
 import pytest
 from common_vision_utils import (DummyFlowersPipelineSerializer,
                                  ImageClassificationPipelineSerializer,
+                                 ObjectDetectionPipelineSerializer,
                                  create_dummy_model,
                                  create_image_classification_pipeline,
-                                 load_flowers_dataset, load_imagenet_dataset,
-                                 load_imagenet_labels)
+                                 load_flowers_dataset,
+                                 load_fridge_object_detection_dataset,
+                                 load_imagenet_dataset, load_imagenet_labels,
+                                 retrieve_fridge_object_detection_model)
 from rai_vision_insights_validator import run_and_validate_serialization
 
 from responsibleai_vision import ModelTask, RAIVisionInsights
 from responsibleai_vision.common.constants import ImageColumns
 
+FRIDGE_CLASS_NAMES = np.array(['can', 'carton',
+                               'milk_bottle', 'water_bottle'])
+
+
+class FakeImageDownloader:
+    def __init__(self, test_mltable_path):
+        self._images_df = self.get_data()
+
+    def get_data(self):
+        return None
+
 
 class TestRAIVisionInsightsSaveAndLoadScenarios(object):
 
@@ -78,3 +94,37 @@ def test_loading_rai_insights_without_model_file(self):
             with pytest.raises(OSError, match=match_msg):
                 without_model_rai_insights = RAIVisionInsights.load(save_path)
                 assert without_model_rai_insights.model is None
+
+    @pytest.mark.parametrize('automl_format', [True, False])
+    def test_rai_insights_object_detection(self, automl_format):
+        data = load_fridge_object_detection_dataset(automl_format)
+        model = retrieve_fridge_object_detection_model(
+            load_fridge_weights=True
+        )
+        task_type = ModelTask.OBJECT_DETECTION
+        test = data[:3]
+        label = ImageColumns.LABEL
+        serializer = ObjectDetectionPipelineSerializer()
+
+        run_and_validate_serialization(
+            model, test, task_type, FRIDGE_CLASS_NAMES, label,
+            serializer, ignore_test_data=True)
+
+    def test_rai_insights_image_downloader_object_detection(self):
+        data = load_fridge_object_detection_dataset(True)
+        model = retrieve_fridge_object_detection_model(
+            load_fridge_weights=True
+        )
+        task_type = ModelTask.OBJECT_DETECTION
+        test = data[:3]
+        label = ImageColumns.LABEL
+        serializer = ObjectDetectionPipelineSerializer()
+
+        get_data = ('test_rai_vision_insights_save_and_load_scenarios'
+                    '.FakeImageDownloader.get_data')
+        with patch(get_data) as mock_images_df:
+            mock_images_df.return_value = test.copy()
+            run_and_validate_serialization(
+                model, test, task_type, FRIDGE_CLASS_NAMES, label,
+                serializer, ignore_test_data=True,
+                image_downloader=FakeImageDownloader)