From 4a2b257863a860f523230745ce333afc6040178d Mon Sep 17 00:00:00 2001 From: hadisalman Date: Sun, 12 Jul 2020 16:48:57 +0000 Subject: [PATCH 01/15] Small fix for amp loading backward compatibility --- robustness/train.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/robustness/train.py b/robustness/train.py index c59cc68..66d717c 100644 --- a/robustness/train.py +++ b/robustness/train.py @@ -118,7 +118,7 @@ def lr_func(ep): for i in range(steps_to_take): schedule.step() - if 'amp' in checkpoint and checkpoint['amp'] is not None: + if 'amp' in checkpoint and checkpoint['amp'] not in [None, 'N/A']: amp.load_state_dict(checkpoint['amp']) # TODO: see if there's a smarter way to do this From f9a54a00d9ff7c384406c53fe3a5a47fe69e78b7 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Tue, 28 Jul 2020 07:28:40 -0400 Subject: [PATCH 02/15] Add BREEDS docs --- docs/example_usage/breeds_datasets.rst | 288 +++++++++++++++++++++++++ docs/index.rst | 2 + robustness/datasets.py | 13 +- robustness/loaders.py | 2 + robustness/tools/breeds_helpers.py | 124 ++++++++--- robustness/tools/openimgs_helpers.py | 47 ++-- 6 files changed, 424 insertions(+), 52 deletions(-) create mode 100644 docs/example_usage/breeds_datasets.rst diff --git a/docs/example_usage/breeds_datasets.rst b/docs/example_usage/breeds_datasets.rst new file mode 100644 index 0000000..46b3f7a --- /dev/null +++ b/docs/example_usage/breeds_datasets.rst @@ -0,0 +1,288 @@ +Creating BREEDS subpopulation shift benchmarks +=============================================== + +In this document, we will discuss how to create BREEDS datasets, used in +the `code `_ `release +`_ based on our paper [STM20]_. +These datasets can be used to study model robustness to subpopulation +shift---a specific form of distribution shift wherein the subpopulations of data +(e.g., animal breeds) present in the training set (source domain) are +entirely different from the test set (target domain). +You can create BREEDS datasets from any existing dataset that has +an explicit class hierarchy describing the semantic relationship +between pairs of classes (e.g., ImageNet, OpenImages). +In this walkthrough, we will do so using ImageNet and the corresponding +class hierarchy from [STM20]_ (modification of WordNet to be better-suited +for the object recognition task). + +At a high-level, the procedure to construct BREEDS datasets is as follows: + +1. Use the dataset's class hierarchy to define `superclasses` by grouping + together semantically similar dataset classes (e.g., define the `dog`` superclass + by aggregating all the dog breed classes). +2. Design the classification task to be between different superclasses, so that + we have easy access to the individual subpopulations (i.e., the original + dataset classes). Then, for each superclass, we will partition the subpopulations + into the source (used for model training) and target domain without overlap + (used for evaluation). + + +.. raw:: html + +    Download + a Jupyter notebook containing all the code from this walkthrough!
+
+ +Requirements/Setup +'''''''''''''''''' +To create BREEDS datasets using ImageNet, we need to create a: + +- ``data_dir`` which contains the dataset (in this case, ImageNet) + in PyTorch-readable format. +- ``info_dir`` which contains information about the class hierarchy. + For ImageNet, you can find the relevant files in the + `imagenet_class_hierarchy/modified` subfolder of our + `release `_. + +Part 1: Browsing through the Class Hierarchy +'''''''''''''''''''''''''''''''''''''''''''' +The :class:`~robustness.tools.breeds_helpers.ClassHierarchy` class allows +us to probe the class hierarchy. The class hierarchy has a graph structure, +where a child node is a subclass (an instance) of the parent node and the +original dataset classes are the leaves. We use a top-down approach to obtain +superclasses. Specifically, by defining all nodes as a fixed (user-specified) +distance from the root node as superclasses, and treating all the descendant +leaves (dataset classes) as subpopulations. + +To browse through the ImageNet class hierarchy, let's create an instance +of the ``ClassHierarchy`` class: + +.. code-block:: python + + from robustness.tools.breeds_helpers import ClassHierarchy + hier = ClassHierarchy(info_dir) + + +Here, :samp:`info_dir` should be the path to a folder which contains +information about the class hierarchy +(e.g., ``imagenet_class_hierarchy/modified``). In general, :samp:`info_dir` +must contain the following files: + +- ``dataset_class_info.json``: A list which contains, for every class + in the dataset, a triplet of class number, ID (e.g., WordNet ID) and + name. +- ``class_hierarchy.txt``: Each line should contain + information about an edge in the class hierarchy, represented as + parent node ID followed by child node ID (space separated). +- ``node_names.txt``: Each line should contain a node ID + followed by it's name (tab separated). + + +The :samp:`hier` object contains a ``graph`` attribute that captures the +hierarchy as a ``networkx`` graph. We can explore some high-level properties +of the graph, for instance: + +.. code-block:: python + + import numpy as np + print(f"# Levels in hierarchy: {np.max(list(hier.level_to_nodes.keys()))}") + print(f"# Nodes/level:", + [f"Level {k}: {len(v)}" for k, v in hier.level_to_nodes.items()]) + +We can now use top-down clustering to find superclasses---by selecting all +nodes at a certain depth from the root node (or any other desired ancestor +node): + +.. code-block:: python + + level = 2 # Could be any number smaller than max level + superclasses = hier.get_nodes_at_level(level) + print(f"Superclasses at level {level}:\n") + print(", ".join([f"{hier.HIER_NODE_NAME[s]}" for s in superclasses])) + + +For a specific superclass, we can also inspect all the leaves reachable +from the superclass (which correspond to classes in the dataset): + +.. code-block:: python + + idx = np.random.randint(0, len(superclasses), 1)[0] + superclass = list(superclasses)[idx] + leaves = hier.leaves_reachable(superclass) + print(f"Superclass: {hier.HIER_NODE_NAME[superclass]}\n") + + print(f"Leaves ({len(leaves)}):") + print([f"{hier.LEAF_ID_TO_NAME[l]}" for l in list(leaves)]) + + +We can also visualize subtrees of the graph with the help of +the `networkx` and `pygraphviz` packages. For instance, we can +taks a look at the subtree of the class hierarchy rooted at a +particular superclass: + +.. code-block:: python + + import networkx as nx + from networkx.drawing.nx_agraph import graphviz_layout, to_agraph + import pygraphviz as pgv + from IPython.display import Image + + subtree = nx.ego_graph(hier.graph, superclass, radius=10) + mapping = {n: hier.HIER_NODE_NAME[n] for n in subtree.nodes()} + subtree = to_agraph(nx.relabel_nodes(subtree, mapping)) + subtree.delete_edge(subtree.edges()[0]) + subtree.layout('dot') + subtree.node_attr['color']='blue' + subtree.draw('graph.png', format='png') + Image('graph.png') + +For instance, visualizing tree rooted at the ``fungus`` superclass yields: + +.. image:: ../figures/graph.png + :width: 600 + :alt: Visulization of subtree rooted at a specific superclass. + +Part 2: Creating BREEDS Datasets +''''''''''''''''''''''''''''''''' + +To create a dataset composed of superclasses, we use the +:class:`~robustness.tools.breeds_helpers.BreedsDatasetGenerator`. +Internally, this class instantiates an object of +:class:`~robustness.tools.breeds_helpers.ClassHierarchy` and uses it +to define the superclasses. + +.. code-block:: python + + from robustness.tools.breeds_helpers import BreedsDatasetGenerator + DG = BreedsDatasetGenerator(info_dir) + +Specifically, we will use +py:meth:`~robustness.tools.breeds_helpers.BreedsDatasetGenerator.get_superclasses`. +This function takes in the following arguments (see :meth:`this docstring +` for more details): + +- :samp:`level`: Level in the hierarchy (in terms of distance from the + root node) at which to define superclasses. +- :samp:`Nsubclasses`: Controls the minimum number of subclasses/superclass + in the dataset. If None, it is automatically set to be the size (in terms + of subclasses) of the smallest superclass. +- :samp:`split`: If ``None``, subclasses of a superclass are returned + as is, without partitioning them into the source and target domains. + Else, can be ``rand/good/bad`` depending on whether the subclass split should be + random or less/more adversarially chosen [STM20]_. +- :samp:`ancestor`: If a node ID is specified, superclasses are chosen from + subtree of class hierarchy rooted at this node. Else, if None, :samp:`ancestor` + is set to be the root node. +- :samp:`balanced`: If True, subclasses/superclass is fixed over superclasses. + +For instance, we could create a balanced dataset, with the subclass partition +being less adversarial as follows: + +.. code-block:: python + + ret = DG.get_superclasses(level=2, + Nsubclasses=None, + split="rand", + ancestor=None, + balanced=True) + subclass_ranges, label_map, subclass_tuple, superclasses, _ = ret + +This method returns: + +- :samp:`superclasses` is a list containing the IDs of all the + superclasses. +- :samp:`label_map` is a dictionary mapping a superclass + number (label) to name. +- :samp:`subclass_ranges` is a list, which for + each superclass, contains a list of subclasses included (in both + domains). +- :samp:`subclass_tuple` is a tuple of subclass ranges for + the source and target domains. For instance, + :samp:`subclass_tuple[0]` is a list, which for each superclass, + contains a list of subclasses present in the source domain. + +You can experiment with these parameters to create datasets of different +granularity. For instance, you could specify the :samp:`Nsubclasses` to +restrict the size of every superclass in the dataset, +set the :samp:`ancestor` to be a specific node (e.g., ``n00004258`` +to focus on living things), or set :samp:`balanced` to ``False`` +to get an imbalanced dataset. + +we can take a closer look at the composition of the dataset---what +superclasses/subclasses it contains---using: + +.. code-block:: python + from robustness.tools.breeds_helpers import print_dataset_info + + print_dataset_info(subclass_ranges, + label_map, + subclass_tuple, + superclasses, + hier.LEAF_NUM_TO_NAME) + +Finally, for the source and target domains, we can create datasets +and their corresponding loaders: + +.. code-block:: python + from robustness.datasets import DATASETS + # For the source domain + dataset_source = DATASETS['custom_imagenet'](data_dir, subclass_tuple[0]) + train_loader_source, val_loader_source = dataset_source.make_loaders(num_workers, + batch_size) + # For the target domain + dataset_target = DATASETS['custom_imagenet'](data_dir, subclass_tuple[1]) + train_loader_target, val_loader_target = dataset_source.make_loaders(num_workers, + batch_size) + +You're all set! You can then use this :samp:`custom_dataset` and loaders +just as you would any other existing/custom dataset in the robustness +library. For instance, you can visualize validation set samples from +both domains and their labels using: + +.. code-block:: python + + from robustness.tools.vis_tools import show_image_row + for domain, loader in zip(["Source", "Target"], + [val_loader_source, val_loader_target]): + im, lab = next(iter(loader)) + show_image_row([im], + tlist=[[label_map[int(k)].split(",")[0] for k in lab]], + ylist=[domain], + fontsize=20) + +You can also create superclass tasks where subclasses are not +partitioned across domains: + +.. code-block:: python + + ret = DG.get_superclasses(level=level, + Nsubclasses=Nsubclasses, + split=None, + ancestor=ancestor, + balanced=balanced) + subclass_ranges, label_map, subclass_tuple, superclasses, _ = ret + dataset = DATASETS['custom_imagenet'](data_dir, subclass_ranges) + + print_dataset_info(subclass_ranges, + label_map, + subclass_tuple, + superclasses, + hier.LEAF_NUM_TO_NAME) + +Part 3: Loading in-built BREEDS Datasets +'''''''''''''''''''''''''''''''''''''''' + +Alternatively, we can directly use one of the datasets from our paper +[STM20]_---namely ``Entity13``, ``Entity30``, ``Living17`` +and ``Nonliving26``. Loading any of these datasets is relatively simple: + +.. code-block:: python + + from robustness.tools.breeds_helpers import Living17 + ret = Living17(info_dir, split="rand") + subclass_ranges, label_map, subclass_tuple, superclasses, _ = ret + +You can then use a similar methodology to Part 2 above to probe +dataset information and create datasets and loaders. + diff --git a/docs/index.rst b/docs/index.rst index 379a529..8da2190 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -156,3 +156,5 @@ Contributors .. [EIS+19] Engstrom L., Ilyas A., Santurkar S., Tsipras D., Tran B., Madry A. (2019). Learning Perceptually-Aligned Representations via Adversarial Robustness. arXiv, arXiv:1906.00945 .. [STE+19] Santurkar S., Tsipras D., Tran B., Ilyas A., Engstrom L., Madry A. (2019). Image Synthesis with a Single (Robust) Classifier. arXiv, arXiv:1906.09453 + +.. [STM20] Santurkar S., Tsipras D., Madry A. (2020). : BREEDS: Benchmarks for Subpopulation Shift. diff --git a/robustness/datasets.py b/robustness/datasets.py index bab1b88..2377b92 100644 --- a/robustness/datasets.py +++ b/robustness/datasets.py @@ -447,15 +447,22 @@ class OpenImages(DataSet): dataset for large-scale multi-label and multi-class image classification. Available from https://storage.googleapis.com/openimages/web/index.html. """ - def __init__(self, data_path, **kwargs): + def __init__(self, data_path, custom_grouping=None, **kwargs): """ """ + if custom_grouping is None: + num_classes = 601 + label_mapping = None + else: + num_classes = len(custom_grouping) + label_mapping = get_label_mapping("custom_imagenet", custom_grouping) + ds_kwargs = { - 'num_classes': 601, + 'num_classes': num_classes, 'mean': ch.tensor([0.4859, 0.4131, 0.3083]), 'std': ch.tensor([0.2919, 0.2507, 0.2273]), 'custom_class': openimgs_helpers.OIDatasetFolder, - 'label_mapping': None, + 'label_mapping': label_mapping, 'transform_train': da.TRAIN_TRANSFORMS_IMAGENET, 'transform_test': da.TEST_TRANSFORMS_IMAGENET } diff --git a/robustness/loaders.py b/robustness/loaders.py index d9a39de..fd5bca3 100644 --- a/robustness/loaders.py +++ b/robustness/loaders.py @@ -60,8 +60,10 @@ def make_loaders(workers, batch_size, transforms, data_path, data_aug=True, else: if not only_val: train_set = custom_class(root=data_path, train=True, + label_mapping=label_mapping, download=True, transform=transform_train) test_set = custom_class(root=data_path, train=False, + label_mapping=label_mapping, download=True, transform=transform_test) if not only_val: diff --git a/robustness/tools/breeds_helpers.py b/robustness/tools/breeds_helpers.py index c63d82b..087e3f1 100644 --- a/robustness/tools/breeds_helpers.py +++ b/robustness/tools/breeds_helpers.py @@ -1,6 +1,7 @@ import sys, os import json import numpy as np +import pandas as pd from collections import OrderedDict, Counter import operator import networkx as nx @@ -10,7 +11,7 @@ class ClassHierarchy(): ''' Class representing a general ImageNet-style hierarchy. ''' - def __init__(self, info_dir): + def __init__(self, info_dir, root_wnid='n00001740'): """ Args: info_dir (str) : Path to hierarchy information files. Contains a @@ -18,27 +19,40 @@ def __init__(self, info_dir): "node_names.txt" mapping nodes to names, and "class_info.json". """ - with open(f'{info_dir}/class_info.json', 'r') as f: + # Details about dataset class (sleaves) names, IDS + with open(f'{info_dir}/dataset_class_info.json', 'r') as f: class_info = json.load(f) - - self.IN_WNIDS = [c['wnid'] for c in class_info] - self.WORDNET_TO_NUM = {k['wnid']: k['cid'] for k in class_info} + # Hierarchy represented as edges between parent & child nodes. with open((f'{info_dir}/class_hierarchy.txt')) as f: edges = [l.strip().split() for l in f.readlines()] - - self.graph = self._make_parent_graph(self.IN_WNIDS, edges) + # Information (names, IDs) for intermediate nodes in hierarchy. with open((f'{info_dir}/node_names.txt')) as f: mapping = [l.strip().split('\t') for l in f.readlines()] - self.NODE_NAME = {w[0]: w[1] for w in mapping} - - self.level_dict = self._make_level_dict(self.graph, root='n00001740') + + + # Original dataset classes + self.LEAF_IDS = [c[1] for c in class_info] + self.LEAF_ID_TO_NAME = {c[1]: c[2] for c in class_info} + self.LEAF_ID_TO_NUM = {c[1]: c[0] for c in class_info} + self.LEAF_NUM_TO_NAME = {c[0]: c[2] for c in class_info} + + # Full hierarchy + self.HIER_NODE_NAME = {w[0]: w[1] for w in mapping} + self.graph = self._make_parent_graph(self.LEAF_IDS, edges) + + # Top-down clustering the hierarchy + self.node_to_level = self._make_level_dict(self.graph, root=root_wnid) + self.level_to_nodes = {} + for k, v in self.node_to_level.items(): + if v not in self.level_to_nodes: self.level_to_nodes[v] = [] + self.level_to_nodes[v].append(k) @staticmethod def _make_parent_graph(nodes, edges): """ - Create a graph for the predecessors of the nodes given. + Obtain networkx representation of class hierarchy. Args: nodes [str] : List of node names to traverse upwards. @@ -120,22 +134,22 @@ def leaves_reachable(self, n): todo = [] for w in curr: for p in self.graph.successors(w): - if p in self.IN_WNIDS: + if p in self.LEAF_IDS: leaves.add(p) else: todo.append(p) todo = set(todo) # If the node itself is an ImageNet node - if n in self.IN_WNIDS: leaves = leaves.union([n]) + if n in self.LEAF_IDS: leaves = leaves.union([n]) return leaves def node_name(self, n): """ - Name of a node. + Determine the name of a node. """ - if n in self.NODE_NAME: - return self.NODE_NAME[n] + if n in self.HIER_NODE_NAME: + return self.HIER_NODE_NAME[n] else: return n @@ -149,8 +163,8 @@ def print_node_info(self, nodes): """ for n in nodes: - if n in self.NODE_NAME: - print_str = f"{n}: {self.NODE_NAME[n]}" + if n in self.HIER_NODE_NAME: + print_str = f"{n}: {self.HIER_NODE_NAME[n]}" else: print_str = n @@ -184,7 +198,8 @@ def traverse(self, nodes, direction='down', depth=20): def get_nodes_at_level(self, L, ancestor=None): """ - Find all superclasses at a specified depth. + Find all superclasses at a specified depth within a subtree + of the hierarchy. Args: L (int): Depth in hierarchy (from root node) @@ -199,19 +214,16 @@ def get_nodes_at_level(self, L, ancestor=None): if ancestor is not None: valid = set(self.traverse([ancestor], direction="down")) - nodes= set() - for k, v in self.level_dict.items() : - if v == L and (ancestor is None or k in valid): - nodes.add(k) - + nodes = set([v for v in self.level_to_nodes[L] + if ancestor is None or v in valid]) return nodes class BreedsDatasetGenerator(): ''' Class for generating datasets from ImageNet superclasses. ''' - def __init__(self, info_dir): - self.hierarchy = ClassHierarchy(info_dir) + def __init__(self, info_dir, root_wnid='n00001740'): + self.hierarchy = ClassHierarchy(info_dir, root_wnid=root_wnid) def split_superclass(self, superclass_wnid, Nsubclasses, balanced, split_type, rng=np.random.RandomState(2)): @@ -307,8 +319,8 @@ def split_superclass(self, superclass_wnid, Nsubclasses, balanced, split[1].extend(spare[:Nh - l[1]]) assert len(split[0]) == len(split[1]) and not set(split[0]).intersection(split[1]) - class_ranges = ([self.hierarchy.WORDNET_TO_NUM[s] for s in split[0]], - [self.hierarchy.WORDNET_TO_NUM[s] for s in split[1]]) + class_ranges = ([self.hierarchy.LEAF_ID_TO_NUM[s] for s in split[0]], + [self.hierarchy.LEAF_ID_TO_NUM[s] for s in split[1]]) return class_ranges @@ -323,8 +335,7 @@ def get_superclasses(self, level, Nsubclasses=None, Args: level (int): Depth in hierarchy (from root node) - Nsubclasses (int): Number of subclasses per superclass - (not used when balanced is True) + Nsubclasses (int): Minimum number of subclasses per superclass balanced (bool): Whether or not the dataset should be balanced over superclasses split ("good"/"bad"/"rand"/None): If None, superclasses are @@ -344,8 +355,9 @@ def get_superclasses(self, level, Nsubclasses=None, a list of subclasses for a given superclass in the dataset superclasses (list): WordNet IDs of superclasses - all_subclasses (list): List of all possible subclasses included in - superclass + all_subclasses (list): For each superclass, a list of all + possible descendants (including + those omitted from `subclass_ranges`) """ @@ -394,7 +406,7 @@ def get_superclasses(self, level, Nsubclasses=None, Ns = [len(d) for d in all_subclasses] wnids = [list(rng.choice(d, n, replace=False)) for n, d in zip(Ns, all_subclasses)] - subclass_ranges = [[self.hierarchy.WORDNET_TO_NUM[w] for w in c] for c in wnids] + subclass_ranges = [[self.hierarchy.LEAF_ID_TO_NUM[w] for w in c] for c in wnids] else: for sci, sc in enumerate(sorted(superclasses)): class_tup = self.split_superclass(sc, Nsubclasses=Nsubclasses, @@ -406,9 +418,53 @@ def get_superclasses(self, level, Nsubclasses=None, return subclass_ranges, label_map, subclass_tuple, superclasses, all_subclasses +def print_dataset_info(subclass_ranges, + label_map, + subclass_tuple, + superclasses, + label_map_sub): + """ + Obtain a dataframe with information about the + superclasses/subclasses included in the dataset. -# Some standard datasets from the BREEDS paper. + Args: + subclass_ranges (list): Each entry is a list of subclasses + for a given superclass in the dataset + label_map (dict): Map from class number to superclass name + subclass_tuple (tuple): Tuple of lists capturing the split; + each list entry is + a list of subclasses for a given + superclass in the dataset + superclasses (list): WordNet IDs of superclasses + label_map_sub (dict): Map from subclass number to subclass name + (equivalent to label map for original dataset) + Returns: + dataDf (pandas DataFrame): Columns contain relevant information + about the datast + + """ + def print_names(class_idx): + return [f'{label_map_sub[r].split(",")[0]} ({r})' + for r in class_idx] + + data = {'superclass': [], + 'subclasses': []} + if len(subclass_tuple[0]): + data.update({'source domain': [], + 'target domain': []}) + + for i, (k, v) in enumerate(label_map.items()): + data['superclass'].append(f'{v}') + data['subclasses'].append(print_names(subclass_ranges[i])) + if len(subclass_tuple[0]): + data['source domain'].append(print_names(subclass_tuple[0][i])) + data['target domain'].append(print_names(subclass_tuple[1][i])) + dataDf = pd.DataFrame(data) + return dataDf + + +# Some standard datasets from the BREEDS paper. def Entity13(info_dir, split=None): """ ENTITY-13 Dataset diff --git a/robustness/tools/openimgs_helpers.py b/robustness/tools/openimgs_helpers.py index 0e52eea..bfc74d9 100644 --- a/robustness/tools/openimgs_helpers.py +++ b/robustness/tools/openimgs_helpers.py @@ -74,24 +74,34 @@ def get_image_annotations_mode(class_names, data_dir, mode="train"): return img_to_label -def make_dataset(dir, mode, sample_info, class_to_idx, extensions): +def make_dataset(dir, mode, sample_info, + class_to_idx, class_to_idx_comp, extensions): images = [] + allowed_labels = set(class_to_idx.keys()) + Nclasses = len(set(class_to_idx.values())) for k, v in sample_info.items(): img_path = os.path.join(dir, "images", mode, k + ".jpg") - pos_labels = [l for l in v.keys() if v[l]['conf'][0] == '1'] - neg_labels = [l for l in v.keys() if v[l]['conf'][0] == '0'] - label = [0] * len(class_to_idx) - for p in pos_labels: - label[class_to_idx[p]] = 1 - for n in neg_labels: - label[class_to_idx[n]] = -1 - - item = (img_path, label) - images.append(item) + pos_labels = set([l for l in v.keys() if v[l]['conf'][0] == '1']) + neg_labels = set([l for l in v.keys() if v[l]['conf'][0] == '0']) + + pos_labels = pos_labels.intersection(allowed_labels) + neg_labels = neg_labels.intersection(allowed_labels) + if Nclasses == 601 or len(pos_labels) != 0: + label = [0] * Nclasses + all_labels = [0] * 601 + for p in pos_labels: + label[class_to_idx[p]] = 1 + all_labels[class_to_idx_comp[p]] = 1 + for n in neg_labels: + if label[class_to_idx[n]] == 0: + label[class_to_idx[n]] = -1 + all_labels[class_to_idx_comp[n]] = -1 + item = (img_path, label, all_labels) + images.append(item) return images @@ -118,6 +128,9 @@ def __init__(self, root, train=True, extensions=IMG_EXTENSIONS, target_transform=target_transform_oi, label_mapping=None, download=False): classes, class_to_idx, code_to_class = self._find_classes(root) + class_to_idx_comp = {k: v for k, v in class_to_idx.items()} + if label_mapping is not None: + classes, class_to_idx = label_mapping(classes, class_to_idx) mode = "train" if train else "test" sample_info = get_image_annotations_mode(code_to_class, @@ -125,7 +138,9 @@ def __init__(self, root, train=True, extensions=IMG_EXTENSIONS, data_dir=root) - samples = make_dataset(root, mode, sample_info, class_to_idx, extensions) + samples = make_dataset(root, mode, sample_info, + class_to_idx, class_to_idx_comp, + extensions) if len(samples) == 0: raise(RuntimeError("Found 0 files in subfolders of: " + root + "\n" "Supported extensions are: " + ",".join(extensions))) @@ -138,6 +153,7 @@ def __init__(self, root, train=True, extensions=IMG_EXTENSIONS, self.class_to_idx = class_to_idx self.samples = samples self.targets = [s[1] for s in samples] + self.all_targets = [s[2] for s in samples] self.transform = transform self.target_transform = target_transform @@ -158,14 +174,15 @@ def __getitem__(self, index): Returns: tuple: (sample, target) where target is class_index of the target class. """ - path, target = self.samples[index] + path, target, comp_target = self.samples[index] sample = self.loader(path) if self.transform is not None: sample = self.transform(sample) if self.target_transform is not None: target = self.target_transform(target) - - return sample, target + if self.target_transform is not None: + comp_target = self.target_transform(comp_target) + return sample, target, comp_target def __len__(self): return len(self.samples) From 84d69bef836c6829a0b79a905fabf69e3fc11bbf Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Tue, 28 Jul 2020 11:57:35 -0400 Subject: [PATCH 03/15] Edit notebook link --- docs/example_usage/breeds_datasets.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/example_usage/breeds_datasets.rst b/docs/example_usage/breeds_datasets.rst index 46b3f7a..598031b 100644 --- a/docs/example_usage/breeds_datasets.rst +++ b/docs/example_usage/breeds_datasets.rst @@ -30,7 +30,7 @@ At a high-level, the procedure to construct BREEDS datasets is as follows: .. raw:: html    Download + href="https://github.com/MadryLab/BREEDS-Benchmarks/blob/master/Constructing%20BREEDS%20datasets.ipynb">Download a Jupyter notebook containing all the code from this walkthrough!

From e5b27d11b61d7924dd534015c138bfeb3d9815c3 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Wed, 29 Jul 2020 10:04:05 -0400 Subject: [PATCH 04/15] Update --- docs/example_usage/breeds_datasets.rst | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/docs/example_usage/breeds_datasets.rst b/docs/example_usage/breeds_datasets.rst index 598031b..01e0de5 100644 --- a/docs/example_usage/breeds_datasets.rst +++ b/docs/example_usage/breeds_datasets.rst @@ -209,7 +209,7 @@ set the :samp:`ancestor` to be a specific node (e.g., ``n00004258`` to focus on living things), or set :samp:`balanced` to ``False`` to get an imbalanced dataset. -we can take a closer look at the composition of the dataset---what +We can take a closer look at the composition of the dataset---what superclasses/subclasses it contains---using: .. code-block:: python From d78990b4e26a19fc80ebe28dcd5d244700eaf6f8 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Wed, 29 Jul 2020 10:07:51 -0400 Subject: [PATCH 05/15] Add missing file --- docs/example_usage/breeds_datasets.rst | 4 ++-- docs/figures/graph.png | Bin 0 -> 55478 bytes 2 files changed, 2 insertions(+), 2 deletions(-) create mode 100644 docs/figures/graph.png diff --git a/docs/example_usage/breeds_datasets.rst b/docs/example_usage/breeds_datasets.rst index 01e0de5..b7189e2 100644 --- a/docs/example_usage/breeds_datasets.rst +++ b/docs/example_usage/breeds_datasets.rst @@ -213,6 +213,7 @@ We can take a closer look at the composition of the dataset---what superclasses/subclasses it contains---using: .. code-block:: python + from robustness.tools.breeds_helpers import print_dataset_info print_dataset_info(subclass_ranges, @@ -225,12 +226,11 @@ Finally, for the source and target domains, we can create datasets and their corresponding loaders: .. code-block:: python + from robustness.datasets import DATASETS - # For the source domain dataset_source = DATASETS['custom_imagenet'](data_dir, subclass_tuple[0]) train_loader_source, 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literal 0 HcmV?d00001 From b3fd75f886d6c7182bff9faf6517d80a2714b589 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Wed, 29 Jul 2020 17:59:50 -0400 Subject: [PATCH 06/15] Update helpers (wip) --- docs/example_usage/breeds_datasets.rst | 132 +++++++++++-------------- robustness/tools/breeds_helpers.py | 39 ++++---- 2 files changed, 73 insertions(+), 98 deletions(-) diff --git a/docs/example_usage/breeds_datasets.rst b/docs/example_usage/breeds_datasets.rst index b7189e2..e84b8b7 100644 --- a/docs/example_usage/breeds_datasets.rst +++ b/docs/example_usage/breeds_datasets.rst @@ -1,31 +1,27 @@ Creating BREEDS subpopulation shift benchmarks =============================================== -In this document, we will discuss how to create BREEDS datasets, used in -the `code `_ `release -`_ based on our paper [STM20]_. -These datasets can be used to study model robustness to subpopulation -shift---a specific form of distribution shift wherein the subpopulations of data -(e.g., animal breeds) present in the training set (source domain) are -entirely different from the test set (target domain). -You can create BREEDS datasets from any existing dataset that has -an explicit class hierarchy describing the semantic relationship -between pairs of classes (e.g., ImageNet, OpenImages). -In this walkthrough, we will do so using ImageNet and the corresponding -class hierarchy from [STM20]_ (modification of WordNet to be better-suited -for the object recognition task). - -At a high-level, the procedure to construct BREEDS datasets is as follows: - -1. Use the dataset's class hierarchy to define `superclasses` by grouping - together semantically similar dataset classes (e.g., define the `dog`` superclass - by aggregating all the dog breed classes). -2. Design the classification task to be between different superclasses, so that - we have easy access to the individual subpopulations (i.e., the original - dataset classes). Then, for each superclass, we will partition the subpopulations - into the source (used for model training) and target domain without overlap - (used for evaluation). - +In this document, we will discuss how to create BREEDS datasets [STM20]_. +Given any existing dataset that comes with a class hierarchy (e.g. ImageNet, +OpenImages), BREEDS allows you to make a derivative classification task that +can be used to measure robustness to subpopulation shift. To do this, we: + +1. Group together semantically-simlar classes ("breeds") in the dataset + into superclasses. +2. Define a classification task in terms of these superclasses---with + the twist that the "breeds" used in the training set from each superclasses + are disjoint from the "breeds" used in the test set. + +As a primitive example, one could take ImageNet (which contains many cat and +dog breed classes), and use the BREEDS methodology to come up with a derivative +"cats vs dogs" task, where the training set would contain one set of breeds +(e.g. Egyptian cat, Tabby Cat vs Labrador, Golden Retriever) and the test set +would contain another set (e.g. Persian cat, alley cat vs Mastiff, Poodle). + +BREEDS allows you to create subpopulation shift benchmarks of varying difficulty +automatically, without having to manually group or split up classes, and can be +applied to any dataset which has a class hierarchy. In this walkthrough, we will +use ImageNet and the corresponding class hierarchy from [STM20]_. .. raw:: html @@ -38,61 +34,43 @@ Requirements/Setup '''''''''''''''''' To create BREEDS datasets using ImageNet, we need to create a: -- ``data_dir`` which contains the dataset (in this case, ImageNet) +- ``data_dir`` which contains the dataset in PyTorch-readable format. -- ``info_dir`` which contains information about the class hierarchy. - For ImageNet, you can find the relevant files in the - `imagenet_class_hierarchy/modified` subfolder of our - `release `_. +- ``info_dir`` which contains the following information (files) about + the class hierarchy: + + - ``dataset_class_info.json``: A list whose entries are triplets of + class number, class ID and class name, for each dataset class. + - ``class_hierarchy.txt``: Every line denotes an edge---parent ID followed by + child ID (space separated)---in the class hierarchy. + - ``node_names.txt``: Each line contains the ID of a node followed by + it's name (tab separated). + For ImageNet, you can download the relevant files from `here `_ and move them + to ``info_dir``. Part 1: Browsing through the Class Hierarchy '''''''''''''''''''''''''''''''''''''''''''' -The :class:`~robustness.tools.breeds_helpers.ClassHierarchy` class allows -us to probe the class hierarchy. The class hierarchy has a graph structure, -where a child node is a subclass (an instance) of the parent node and the -original dataset classes are the leaves. We use a top-down approach to obtain -superclasses. Specifically, by defining all nodes as a fixed (user-specified) -distance from the root node as superclasses, and treating all the descendant -leaves (dataset classes) as subpopulations. -To browse through the ImageNet class hierarchy, let's create an instance -of the ``ClassHierarchy`` class: +We can use :class:`~robustness.tools.breeds_helpers.ClassHierarchy` to +examine a dataset's (here, ImageNet) class hierarchy. .. code-block:: python from robustness.tools.breeds_helpers import ClassHierarchy + import numpy as np hier = ClassHierarchy(info_dir) - - -Here, :samp:`info_dir` should be the path to a folder which contains -information about the class hierarchy -(e.g., ``imagenet_class_hierarchy/modified``). In general, :samp:`info_dir` -must contain the following files: - -- ``dataset_class_info.json``: A list which contains, for every class - in the dataset, a triplet of class number, ID (e.g., WordNet ID) and - name. -- ``class_hierarchy.txt``: Each line should contain - information about an edge in the class hierarchy, represented as - parent node ID followed by child node ID (space separated). -- ``node_names.txt``: Each line should contain a node ID - followed by it's name (tab separated). - - -The :samp:`hier` object contains a ``graph`` attribute that captures the -hierarchy as a ``networkx`` graph. We can explore some high-level properties -of the graph, for instance: - -.. code-block:: python - - import numpy as np - print(f"# Levels in hierarchy: {np.max(list(hier.level_to_nodes.keys()))}") - print(f"# Nodes/level:", + print(f"# Levels in hierarchy: {np.max(list(hier.level_to_nodes.keys()))}") + print(f"# Nodes/level:", [f"Level {k}: {len(v)}" for k, v in hier.level_to_nodes.items()]) -We can now use top-down clustering to find superclasses---by selecting all -nodes at a certain depth from the root node (or any other desired ancestor -node): +The :samp:`hier` object has a ``graph`` attribute, which represents the class +hierarchy as a ``networkx`` graph. In this graph, the children of a node +correspond to its subclasses (e.g., Labrador would be a child of the dog +class in our primitive example). Note that all the original dataset classes +will be the leaves of this graph. + +We can then use this graph to define superclasses---all nodes at a user-specified +depth from the root node. For example: .. code-block:: python @@ -101,19 +79,19 @@ node): print(f"Superclasses at level {level}:\n") print(", ".join([f"{hier.HIER_NODE_NAME[s]}" for s in superclasses])) - -For a specific superclass, we can also inspect all the leaves reachable -from the superclass (which correspond to classes in the dataset): +Each superclass is made up of multiple "breeds", which simply correspond to +the leaves (original dataset classes) that are its descendants in the class +hierarchy: .. code-block:: python idx = np.random.randint(0, len(superclasses), 1)[0] superclass = list(superclasses)[idx] - leaves = hier.leaves_reachable(superclass) + subclasses = hier.leaves_reachable(superclass) print(f"Superclass: {hier.HIER_NODE_NAME[superclass]}\n") - print(f"Leaves ({len(leaves)}):") - print([f"{hier.LEAF_ID_TO_NAME[l]}" for l in list(leaves)]) + print(f"Subclasses ({len(subclasses)}):") + print([f"{hier.LEAF_ID_TO_NAME[l]}" for l in list(subclasses)]) We can also visualize subtrees of the graph with the help of @@ -186,7 +164,7 @@ being less adversarial as follows: split="rand", ancestor=None, balanced=True) - subclass_ranges, label_map, subclass_tuple, superclasses, _ = ret + subclass_ranges, label_map, subclass_tuple, superclasses = ret This method returns: @@ -279,8 +257,8 @@ and ``Nonliving26``. Loading any of these datasets is relatively simple: .. code-block:: python - from robustness.tools.breeds_helpers import Living17 - ret = Living17(info_dir, split="rand") + from robustness.tools.breeds_helpers import make_living17 + ret = make_living17(info_dir, split="rand") subclass_ranges, label_map, subclass_tuple, superclasses, _ = ret You can then use a similar methodology to Part 2 above to probe diff --git a/robustness/tools/breeds_helpers.py b/robustness/tools/breeds_helpers.py index 087e3f1..e00d77d 100644 --- a/robustness/tools/breeds_helpers.py +++ b/robustness/tools/breeds_helpers.py @@ -18,6 +18,15 @@ def __init__(self, info_dir, root_wnid='n00001740'): "class_hierarchy.txt" file with one edge per line, a "node_names.txt" mapping nodes to names, and "class_info.json". """ + + required_files = ['dataset_class_info.json', + 'class_hierarchy.txt', + 'node_names.txt'] + + for f in required_files: + if not os.path.exists(f'{info_dir}/{f}'): + raise Exception(f"Missing files: `info_dir` does not " + "contain required file {f}") # Details about dataset class (sleaves) names, IDS with open(f'{info_dir}/dataset_class_info.json', 'r') as f: @@ -355,10 +364,6 @@ def get_superclasses(self, level, Nsubclasses=None, a list of subclasses for a given superclass in the dataset superclasses (list): WordNet IDs of superclasses - all_subclasses (list): For each superclass, a list of all - possible descendants (including - those omitted from `subclass_ranges`) - """ rng = np.random.RandomState(random_seed) @@ -416,7 +421,7 @@ def get_superclasses(self, level, Nsubclasses=None, subclass_tuple[1].append(class_tup[1]) subclass_ranges.append(class_tup[0] + class_tup[1]) - return subclass_ranges, label_map, subclass_tuple, superclasses, all_subclasses + return superclasses, subclass_ranges, subclass_tuple, label_map def print_dataset_info(subclass_ranges, label_map, @@ -465,9 +470,9 @@ def print_names(class_idx): # Some standard datasets from the BREEDS paper. -def Entity13(info_dir, split=None): +def make_entity13(info_dir, split=None): """ - ENTITY-13 Dataset + Obtain superclass/subclass information for the ENTITY-13 dataset Args: info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass @@ -480,8 +485,6 @@ def Entity13(info_dir, split=None): a list of subclasses for a given superclass in the dataset superclasses (list): WordNet IDs of superclasses - all_subclasses (list): List of all possible subclasses included in - superclass """ @@ -494,9 +497,9 @@ def Entity13(info_dir, split=None): random_seed=2, verbose=False) -def Entity30(info_dir, split=None): +def make_entity30(info_dir, split=None): """ - ENTITY-30 Dataset + Obtain superclass/subclass information for the ENTITY-30 dataset Args: info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass @@ -509,8 +512,6 @@ def Entity30(info_dir, split=None): a list of subclasses for a given superclass in the dataset superclasses (list): WordNet IDs of superclasses - all_subclasses (list): List of all possible subclasses included in - superclass """ DG = BreedsDatasetGenerator(info_dir) @@ -522,9 +523,9 @@ def Entity30(info_dir, split=None): random_seed=2, verbose=False) -def Living17(info_dir, split=None): +def make_living17(info_dir, split=None): """ - LIVING-17 Dataset + Obtain superclass/subclass information for the LIVING-17 dataset Args: info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass @@ -537,8 +538,6 @@ def Living17(info_dir, split=None): a list of subclasses for a given superclass in the dataset superclasses (list): WordNet IDs of superclasses - all_subclasses (list): List of all possible subclasses included in - superclass """ DG = BreedsDatasetGenerator(info_dir) @@ -550,9 +549,9 @@ def Living17(info_dir, split=None): random_seed=2, verbose=False) -def Nonliving26(info_dir, split=None): +def make_nonliving26(info_dir, split=None): """ - NONLIVING-26 Dataset + Obtain superclass/subclass information for the NONLIVING-26 dataset. Args: info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass @@ -565,8 +564,6 @@ def Nonliving26(info_dir, split=None): a list of subclasses for a given superclass in the dataset superclasses (list): WordNet IDs of superclasses - all_subclasses (list): List of all possible subclasses included in - superclass """ DG = BreedsDatasetGenerator(info_dir) From 0dad37c16cace254308154abb74ea5f0a5af5685 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Wed, 29 Jul 2020 18:58:38 -0400 Subject: [PATCH 07/15] Address comments --- docs/example_usage/breeds_datasets.rst | 95 +++++++++------- robustness/tools/breeds_helpers.py | 152 ++++++++++++++----------- 2 files changed, 140 insertions(+), 107 deletions(-) diff --git a/docs/example_usage/breeds_datasets.rst b/docs/example_usage/breeds_datasets.rst index e84b8b7..48a51a2 100644 --- a/docs/example_usage/breeds_datasets.rst +++ b/docs/example_usage/breeds_datasets.rst @@ -45,14 +45,25 @@ To create BREEDS datasets using ImageNet, we need to create a: child ID (space separated)---in the class hierarchy. - ``node_names.txt``: Each line contains the ID of a node followed by it's name (tab separated). - For ImageNet, you can download the relevant files from `here `_ and move them - to ``info_dir``. + +For ImageNet, you can manually download the relevant files for the class hierarchy +from `here `_ and move them to ``info_dir``. Alternatively, you can do it automatically +using by specifying an empty ``info_dir`` to +:meth:`~robustness.tools.breeds_helpers.BreedsDatasetGenerator.get_superclasses`: + +.. code-block:: python + + from robustness.tools.breeds_helpers import setup_breeds + setup_breeds(info_dir) + Part 1: Browsing through the Class Hierarchy '''''''''''''''''''''''''''''''''''''''''''' We can use :class:`~robustness.tools.breeds_helpers.ClassHierarchy` to -examine a dataset's (here, ImageNet) class hierarchy. +examine a dataset's (here, ImageNet) class hierarchy. Here, ``info_dir`` +should contain the requisite files for the class hierarchy (from the Setup +step): .. code-block:: python @@ -128,7 +139,7 @@ To create a dataset composed of superclasses, we use the :class:`~robustness.tools.breeds_helpers.BreedsDatasetGenerator`. Internally, this class instantiates an object of :class:`~robustness.tools.breeds_helpers.ClassHierarchy` and uses it -to define the superclasses. +to define the superclasses. .. code-block:: python @@ -136,7 +147,7 @@ to define the superclasses. DG = BreedsDatasetGenerator(info_dir) Specifically, we will use -py:meth:`~robustness.tools.breeds_helpers.BreedsDatasetGenerator.get_superclasses`. +:meth:`~robustness.tools.breeds_helpers.BreedsDatasetGenerator.get_superclasses`. This function takes in the following arguments (see :meth:`this docstring ` for more details): @@ -159,26 +170,25 @@ being less adversarial as follows: .. code-block:: python - ret = DG.get_superclasses(level=2, - Nsubclasses=None, - split="rand", - ancestor=None, - balanced=True) - subclass_ranges, label_map, subclass_tuple, superclasses = ret + ret = DG.get_superclasses(level=2, + Nsubclasses=None, + split="rand", + ancestor=None, + balanced=True) + superclasses, subclass_split, label_map = ret This method returns: - :samp:`superclasses` is a list containing the IDs of all the superclasses. -- :samp:`label_map` is a dictionary mapping a superclass - number (label) to name. -- :samp:`subclass_ranges` is a list, which for - each superclass, contains a list of subclasses included (in both - domains). - :samp:`subclass_tuple` is a tuple of subclass ranges for the source and target domains. For instance, :samp:`subclass_tuple[0]` is a list, which for each superclass, contains a list of subclasses present in the source domain. + If ``split=None``, subclass_tuple[1] is empty and can be + ignored. +- :samp:`label_map` is a dictionary mapping a superclass + number (label) to name. You can experiment with these parameters to create datasets of different granularity. For instance, you could specify the :samp:`Nsubclasses` to @@ -194,24 +204,27 @@ superclasses/subclasses it contains---using: from robustness.tools.breeds_helpers import print_dataset_info - print_dataset_info(subclass_ranges, - label_map, - subclass_tuple, - superclasses, - hier.LEAF_NUM_TO_NAME) + print_dataset_info(superclasses, + subclass_split, + label_map, + hier.LEAF_NUM_TO_NAME) Finally, for the source and target domains, we can create datasets and their corresponding loaders: .. code-block:: python - from robustness.datasets import DATASETS - dataset_source = DATASETS['custom_imagenet'](data_dir, subclass_tuple[0]) - train_loader_source, val_loader_source = dataset_source.make_loaders(num_workers, - batch_size) - dataset_target = DATASETS['custom_imagenet'](data_dir, subclass_tuple[1]) - train_loader_target, val_loader_target = dataset_source.make_loaders(num_workers, - batch_size) + from robustness import datasets + + train_subclasses, test_subclasses = subclass_split + + dataset_source = datasets.CustomImageNet(data_dir, train_subclasses) + loaders_source = dataset_source.make_loaders(num_workers, batch_size) + train_loader_source, val_loader_source = loaders_source + + dataset_target = datasets.CustomImageNet(data_dir, test_subclasses) + loaders_target = dataset_source.make_loaders(num_workers, batch_size) + train_loader_target, val_loader_target = loaders_target You're all set! You can then use this :samp:`custom_dataset` and loaders just as you would any other existing/custom dataset in the robustness @@ -234,18 +247,19 @@ partitioned across domains: .. code-block:: python - ret = DG.get_superclasses(level=level, - Nsubclasses=Nsubclasses, + ret = DG.get_superclasses(level=2, + Nsubclasses=2, split=None, - ancestor=ancestor, - balanced=balanced) - subclass_ranges, label_map, subclass_tuple, superclasses, _ = ret - dataset = DATASETS['custom_imagenet'](data_dir, subclass_ranges) + ancestor=None, + balanced=True) + superclasses, subclass_split, label_map = ret + all_subclasses = subclass_split[0] - print_dataset_info(subclass_ranges, + dataset = datasets.CustomImageNet(data_dir, all_subclasses) + + print_dataset_info(superclasses, + subclass_split, label_map, - subclass_tuple, - superclasses, hier.LEAF_NUM_TO_NAME) Part 3: Loading in-built BREEDS Datasets @@ -259,7 +273,12 @@ and ``Nonliving26``. Loading any of these datasets is relatively simple: from robustness.tools.breeds_helpers import make_living17 ret = make_living17(info_dir, split="rand") - subclass_ranges, label_map, subclass_tuple, superclasses, _ = ret + superclasses, subclass_split, label_map = ret + + print_dataset_info(superclasses, + subclass_split, + label_map, + hier.LEAF_NUM_TO_NAME) You can then use a similar methodology to Part 2 above to probe dataset information and create datasets and loaders. diff --git a/robustness/tools/breeds_helpers.py b/robustness/tools/breeds_helpers.py index e00d77d..47fb043 100644 --- a/robustness/tools/breeds_helpers.py +++ b/robustness/tools/breeds_helpers.py @@ -2,11 +2,24 @@ import json import numpy as np import pandas as pd +import urllib from collections import OrderedDict, Counter import operator import networkx as nx from ..datasets import DATASETS +REQUIRED_FILES = ['dataset_class_info.json', + 'class_hierarchy.txt', + 'node_names.txt'] +IMAGENET_FILE_PATH = "https://github.com/MadryLab/BREEDS-Benchmarks/tree/master/imagenet_class_hierarchy/modified" + +def setup_breeds(info_dir, url=IMAGENET_FILE_PATH): + print(f"Downloading files from {url} to {info_dir}") + if not os.path.exists(info_dir): + os.makedirs(info_dir) + for f in REQUIRED_FILES: + urllib.request.urlretrieve(url + f"/{f}", info_dir + f"/{f}") + class ClassHierarchy(): ''' Class representing a general ImageNet-style hierarchy. @@ -19,11 +32,7 @@ def __init__(self, info_dir, root_wnid='n00001740'): "node_names.txt" mapping nodes to names, and "class_info.json". """ - required_files = ['dataset_class_info.json', - 'class_hierarchy.txt', - 'node_names.txt'] - - for f in required_files: + for f in REQUIRED_FILES: if not os.path.exists(f'{info_dir}/{f}'): raise Exception(f"Missing files: `info_dir` does not " "contain required file {f}") @@ -355,15 +364,15 @@ def get_superclasses(self, level, Nsubclasses=None, rng (RandomState): Random number generator Returns: - subclass_ranges (list): Each entry is a list of subclasses - for a given superclass in the dataset - label_map (dict): Map from class number to superclass name - subclass_tuple (tuple): Tuple of lists capturing the split. - If split is None, lists are empty. - Otherwise, each list entry is - a list of subclasses for a given - superclass in the dataset superclasses (list): WordNet IDs of superclasses + subclass_splits (tuple): Tuple entries correspond to the source + and target domains respectively. A + tuple entry is a list, where each + element is a list of subclasses to + be included in a given superclass in + that domain. If split is None, + the second tuple element is empty. + label_map (dict): Map from (super)class number to superclass name """ rng = np.random.RandomState(random_seed) @@ -400,8 +409,6 @@ def get_superclasses(self, level, Nsubclasses=None, label_map = {} for ri, r in enumerate(superclasses): label_map[ri] = self.hierarchy.node_name(r) - - subclass_ranges, subclass_tuple = [], ([], []) if split is None: @@ -412,36 +419,37 @@ def get_superclasses(self, level, Nsubclasses=None, wnids = [list(rng.choice(d, n, replace=False)) for n, d in zip(Ns, all_subclasses)] subclass_ranges = [[self.hierarchy.LEAF_ID_TO_NUM[w] for w in c] for c in wnids] + subclass_splits = (subclass_ranges, []) else: + subclass_splits = ([], []) for sci, sc in enumerate(sorted(superclasses)): class_tup = self.split_superclass(sc, Nsubclasses=Nsubclasses, balanced=balanced, split_type=split, rng=rng) - subclass_tuple[0].append(class_tup[0]) - subclass_tuple[1].append(class_tup[1]) - subclass_ranges.append(class_tup[0] + class_tup[1]) + subclass_splits[0].append(class_tup[0]) + subclass_splits[1].append(class_tup[1]) - return superclasses, subclass_ranges, subclass_tuple, label_map + return superclasses, subclass_splits, label_map -def print_dataset_info(subclass_ranges, +def print_dataset_info(superclasses, + subclass_splits, label_map, - subclass_tuple, - superclasses, label_map_sub): """ Obtain a dataframe with information about the superclasses/subclasses included in the dataset. Args: - subclass_ranges (list): Each entry is a list of subclasses - for a given superclass in the dataset - label_map (dict): Map from class number to superclass name - subclass_tuple (tuple): Tuple of lists capturing the split; - each list entry is - a list of subclasses for a given - superclass in the dataset - superclasses (list): WordNet IDs of superclasses - label_map_sub (dict): Map from subclass number to subclass name + superclasses (list): WordNet IDs of superclasses + subclass_splits (tuple): Tuple entries correspond to the source + and target domains respectively. A + tuple entry is a list, where each + element is a list of subclasses to + be included in a given superclass in + that domain. If split is None, + the second tuple element is empty. + label_map (dict): Map from (super)class number to superclass name + label_map_sub (dict): Map from subclass number to subclass name (equivalent to label map for original dataset) Returns: dataDf (pandas DataFrame): Columns contain relevant information @@ -451,19 +459,21 @@ def print_dataset_info(subclass_ranges, def print_names(class_idx): return [f'{label_map_sub[r].split(",")[0]} ({r})' for r in class_idx] - - data = {'superclass': [], - 'subclasses': []} - if len(subclass_tuple[0]): - data.update({'source domain': [], - 'target domain': []}) + data = {'superclass': []} + contains_split = len(subclass_splits[1]) + if contains_split: + data.update({'subclasses (source)': [], + 'subclasses (target)': []}) + else: + data.update({'subclasses': []}) for i, (k, v) in enumerate(label_map.items()): data['superclass'].append(f'{v}') - data['subclasses'].append(print_names(subclass_ranges[i])) - if len(subclass_tuple[0]): - data['source domain'].append(print_names(subclass_tuple[0][i])) - data['target domain'].append(print_names(subclass_tuple[1][i])) + if contains_split: + data['subclasses (source)'].append(print_names(subclass_splits[0][i])) + data['subclasses (target)'].append(print_names(subclass_splits[1][i])) + else: + data['subclasses'].append(print_names(subclass_splits[0][i])) dataDf = pd.DataFrame(data) return dataDf @@ -477,14 +487,15 @@ def make_entity13(info_dir, split=None): info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass Returns: - subclass_ranges (list): Each entry is a list of subclasses - for a given superclass in the dataset - label_map (dict): Map from class number to superclass name - subclass_tuple (tuple): Tuple of lists capturing the split; - each list entry is - a list of subclasses for a given - superclass in the dataset superclasses (list): WordNet IDs of superclasses + subclass_splits (tuple): Tuple entries correspond to the source + and target domains respectively. A + tuple entry is a list, where each + element is a list of subclasses to + be included in a given superclass in + that domain. If split is None, + the second tuple element is empty. + label_map (dict): Map from (super)class number to superclass name """ @@ -504,14 +515,15 @@ def make_entity30(info_dir, split=None): info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass Returns: - subclass_ranges (list): Each entry is a list of subclasses - for a given superclass in the dataset - label_map (dict): Map from class number to superclass name - subclass_tuple (tuple): Tuple of lists capturing the split; - each list entry is - a list of subclasses for a given - superclass in the dataset superclasses (list): WordNet IDs of superclasses + subclass_splits (tuple): Tuple entries correspond to the source + and target domains respectively. A + tuple entry is a list, where each + element is a list of subclasses to + be included in a given superclass in + that domain. If split is None, + the second tuple element is empty. + label_map (dict): Map from (super)class number to superclass name """ DG = BreedsDatasetGenerator(info_dir) @@ -530,14 +542,15 @@ def make_living17(info_dir, split=None): info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass Returns: - subclass_ranges (list): Each entry is a list of subclasses - for a given superclass in the dataset - label_map (dict): Map from class number to superclass name - subclass_tuple (tuple): Tuple of lists capturing the split; - each list entry is - a list of subclasses for a given - superclass in the dataset superclasses (list): WordNet IDs of superclasses + subclass_splits (tuple): Tuple entries correspond to the source + and target domains respectively. A + tuple entry is a list, where each + element is a list of subclasses to + be included in a given superclass in + that domain. If split is None, + the second tuple element is empty. + label_map (dict): Map from (super)class number to superclass name """ DG = BreedsDatasetGenerator(info_dir) @@ -556,14 +569,15 @@ def make_nonliving26(info_dir, split=None): info_dir (str) : Path to ImageNet information files split ("good"/"bad"/"rand"/None): Nature of subclass Returns: - subclass_ranges (list): Each entry is a list of subclasses - for a given superclass in the dataset - label_map (dict): Map from class number to superclass name - subclass_tuple (tuple): Tuple of lists capturing the split; - each list entry is - a list of subclasses for a given - superclass in the dataset superclasses (list): WordNet IDs of superclasses + subclass_splits (tuple): Tuple entries correspond to the source + and target domains respectively. A + tuple entry is a list, where each + element is a list of subclasses to + be included in a given superclass in + that domain. If split is None, + the second tuple element is empty. + label_map (dict): Map from (super)class number to superclass name """ DG = BreedsDatasetGenerator(info_dir) From eaeadc0cb2a6a1e87be7f09c824e1571d88ac7e3 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Thu, 30 Jul 2020 08:15:07 -0400 Subject: [PATCH 08/15] Add pipeline illustration --- docs/example_usage/Figures/breeds_pipeline.png | Bin 0 -> 658254 bytes .../Figures/breeds_superclasses.png} | Bin docs/example_usage/breeds_datasets.rst | 11 +++++++++-- 3 files changed, 9 insertions(+), 2 deletions(-) create mode 100644 docs/example_usage/Figures/breeds_pipeline.png rename docs/{figures/graph.png => example_usage/Figures/breeds_superclasses.png} (100%) diff --git a/docs/example_usage/Figures/breeds_pipeline.png b/docs/example_usage/Figures/breeds_pipeline.png new file mode 100644 index 0000000000000000000000000000000000000000..5c366b5ece43a589b638e2bf29235aa3a1ceab89 GIT binary patch literal 658254 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08:18:09 -0400 Subject: [PATCH 09/15] Address comments --- docs/index.rst | 2 ++ 1 file changed, 2 insertions(+) diff --git a/docs/index.rst b/docs/index.rst index 8da2190..4bd3c95 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -19,6 +19,8 @@ upcoming code releases. A few projects using the library include: [EIS+19]_ - `Code `_ for "Image Synthesis with a Single (Robust) Classifier" [STE+19]_ +- `Code `_ for + "BREEDS: Benchmarks for Subpopulation Shift" [STM20]_ We demonstrate how to use the library in a set of walkthroughs and our API reference. Functionality provided by the library includes: From 3d85937fcded98e33a1e1b4b57332e90babee947 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Tue, 4 Aug 2020 18:57:43 -0400 Subject: [PATCH 10/15] Remove label mapping arg --- robustness/loaders.py | 2 -- 1 file changed, 2 deletions(-) diff --git a/robustness/loaders.py b/robustness/loaders.py index fd5bca3..d9a39de 100644 --- a/robustness/loaders.py +++ b/robustness/loaders.py @@ -60,10 +60,8 @@ def make_loaders(workers, batch_size, transforms, data_path, data_aug=True, else: if not only_val: train_set = custom_class(root=data_path, train=True, - label_mapping=label_mapping, download=True, transform=transform_train) test_set = custom_class(root=data_path, train=False, - label_mapping=label_mapping, download=True, transform=transform_test) if not only_val: From fe7be2af37c26d58aef629439f205e2c39169e34 Mon Sep 17 00:00:00 2001 From: dtsip Date: Tue, 4 Aug 2020 18:59:01 -0400 Subject: [PATCH 11/15] minor --- robustness/tools/breeds_helpers.py | 42 +++++++++++++++--------------- 1 file changed, 21 insertions(+), 21 deletions(-) diff --git a/robustness/tools/breeds_helpers.py b/robustness/tools/breeds_helpers.py index 47fb043..e4c3525 100644 --- a/robustness/tools/breeds_helpers.py +++ b/robustness/tools/breeds_helpers.py @@ -11,14 +11,15 @@ REQUIRED_FILES = ['dataset_class_info.json', 'class_hierarchy.txt', 'node_names.txt'] -IMAGENET_FILE_PATH = "https://github.com/MadryLab/BREEDS-Benchmarks/tree/master/imagenet_class_hierarchy/modified" +BREEDS_URL = "https://github.com/MadryLab/BREEDS-Benchmarks/tree/master/imagenet_class_hierarchy/modified" -def setup_breeds(info_dir, url=IMAGENET_FILE_PATH): +def setup_breeds(info_dir, url=BREEDS_URL): print(f"Downloading files from {url} to {info_dir}") if not os.path.exists(info_dir): os.makedirs(info_dir) for f in REQUIRED_FILES: - urllib.request.urlretrieve(url + f"/{f}", info_dir + f"/{f}") + urllib.request.urlretrieve(f"{url}/{f}", + os.path.join(info_dir, f)) class ClassHierarchy(): ''' @@ -33,20 +34,20 @@ def __init__(self, info_dir, root_wnid='n00001740'): """ for f in REQUIRED_FILES: - if not os.path.exists(f'{info_dir}/{f}'): + if not os.path.exists(os.path.join(info_dir, f)): raise Exception(f"Missing files: `info_dir` does not " "contain required file {f}") - # Details about dataset class (sleaves) names, IDS - with open(f'{info_dir}/dataset_class_info.json', 'r') as f: + # Details about dataset class names (leaves), IDS + with open(os.path.join(info_dir, "dataset_class_info.json")) as f: class_info = json.load(f) # Hierarchy represented as edges between parent & child nodes. - with open((f'{info_dir}/class_hierarchy.txt')) as f: + with open(os.path.join(info_dir, "class_hierarchy.txt")) as f: edges = [l.strip().split() for l in f.readlines()] # Information (names, IDs) for intermediate nodes in hierarchy. - with open((f'{info_dir}/node_names.txt')) as f: + with open(os.path.join(info_dir, "node_names.txt")) as f: mapping = [l.strip().split('\t') for l in f.readlines()] @@ -60,7 +61,7 @@ def __init__(self, info_dir, root_wnid='n00001740'): self.HIER_NODE_NAME = {w[0]: w[1] for w in mapping} self.graph = self._make_parent_graph(self.LEAF_IDS, edges) - # Top-down clustering the hierarchy + # Arrange nodes in levels (top-down) self.node_to_level = self._make_level_dict(self.graph, root=root_wnid) self.level_to_nodes = {} for k, v in self.node_to_level.items(): @@ -151,11 +152,11 @@ def leaves_reachable(self, n): curr = todo todo = [] for w in curr: - for p in self.graph.successors(w): - if p in self.LEAF_IDS: - leaves.add(p) + for c in self.graph.successors(w): + if c in self.LEAF_IDS: + leaves.add(c) else: - todo.append(p) + todo.append(c) todo = set(todo) # If the node itself is an ImageNet node @@ -177,7 +178,7 @@ def print_node_info(self, nodes): about a given set of nodes. Args: - nodes (list) : List of WordNet IDs of relevant nodes + nodes (list) : List of WordNet IDs for relevant nodes """ for n in nodes: @@ -189,13 +190,13 @@ def print_node_info(self, nodes): print_str += f" ({len(self.leaves_reachable(n))})" print(print_str) - def traverse(self, nodes, direction='down', depth=20): + def traverse(self, nodes, direction='down', depth=100): """ - Find all nodes accessible from a set of nodes. + Find all nodes accessible from a set of given nodes. Args: - nodes (list) : List of WordNet IDs of relevant nodes - direction ("up"/"down"): Allowed traversal directions + nodes (list) : List of WordNet IDs for relevant nodes + direction ("up"/"down"): Traversal direction depth (int): Maximum depth to traverse (from nodes) Returns: @@ -274,8 +275,7 @@ def split_superclass(self, superclass_wnid, Nsubclasses, balanced, # Map each descendant to its ImageNet subclasses desc_map = {} for d in desc: - dcurr = sorted(list(self.hierarchy.leaves_reachable(d))) - desc_map[d] = dcurr + desc_map[d] = sorted(list(self.hierarchy.leaves_reachable(d))) # Map sorted by nodes that have the maximum number of children desc_sorted = sorted(desc_map.items(), key=lambda x: -len(x[1])) @@ -587,4 +587,4 @@ def make_nonliving26(info_dir, split=None): split=split, balanced=True, random_seed=2, - verbose=False) \ No newline at end of file + verbose=False) From 05f9e684426ce89d5f0cc43453e5541b8f0580ee Mon Sep 17 00:00:00 2001 From: dtsip Date: Tue, 4 Aug 2020 22:20:29 -0400 Subject: [PATCH 12/15] minor --- robustness/tools/breeds_helpers.py | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/robustness/tools/breeds_helpers.py b/robustness/tools/breeds_helpers.py index e4c3525..f5c688e 100644 --- a/robustness/tools/breeds_helpers.py +++ b/robustness/tools/breeds_helpers.py @@ -282,12 +282,13 @@ def split_superclass(self, superclass_wnid, Nsubclasses, balanced, # If not balanced, we will pick as many subclasses as possible # from this superclass (ignoring Nsubclasses) + assert Nsubclasses >= 2 if not balanced: S = sum([len(d) for d in desc_map.values()]) assert S >= Nsubclasses Nsubclasses = S if Nsubclasses % 2 != 0: - Nsubclasses = max(Nsubclasses - 1, 2) + Nsubclasses -= 1 # Split superclasses into two disjoint sets assert Nsubclasses % 2 == 0 @@ -309,7 +310,7 @@ def split_superclass(self, superclass_wnid, Nsubclasses, balanced, split, spare = ([], []), [] for k, v in desc_sorted: - l = [len(s) + 0 for s in split] + l = [len(s) for s in split] if split_type == "bad": if l[0] <= l[1] and l[0] < Nh: if len(v) > Nh - l[0]: spare.extend(v[Nh-l[0]:]) From b05da342a4c3427b2fa42a9e809ba8d72fb2ca16 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Tue, 4 Aug 2020 22:34:38 -0400 Subject: [PATCH 13/15] Add links from docs main page --- docs/api/robustness.tools.breeds_helpers.rst | 7 +++++++ docs/api/robustness.tools.rst | 1 + 2 files changed, 8 insertions(+) create mode 100644 docs/api/robustness.tools.breeds_helpers.rst diff --git a/docs/api/robustness.tools.breeds_helpers.rst b/docs/api/robustness.tools.breeds_helpers.rst new file mode 100644 index 0000000..bf852bf --- /dev/null +++ b/docs/api/robustness.tools.breeds_helpers.rst @@ -0,0 +1,7 @@ +robustness.tools.breeds\_helpers module +================================== + +.. automodule:: robustness.tools.breeds_helpers + :members: + :undoc-members: + :show-inheritance: diff --git a/docs/api/robustness.tools.rst b/docs/api/robustness.tools.rst index be09c10..ca02677 100644 --- a/docs/api/robustness.tools.rst +++ b/docs/api/robustness.tools.rst @@ -11,6 +11,7 @@ Submodules robustness.tools.helpers robustness.tools.label_maps robustness.tools.vis_tools + robustness.tools.breeds_helpers Module contents --------------- From 92dc6a244c57be38d51f5e59ebd3532cc1a64791 Mon Sep 17 00:00:00 2001 From: Shibani Santurkar Date: Tue, 4 Aug 2020 22:36:53 -0400 Subject: [PATCH 14/15] Add links from main docs to breeds helpers --- docs/index.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/docs/index.rst b/docs/index.rst index 4bd3c95..579206e 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -136,6 +136,7 @@ Walkthroughs example_usage/training_lib_part_1 example_usage/training_lib_part_2 example_usage/custom_imagenet + example_usage/breeds_datasets example_usage/changelog API Reference From 2a057c846eccf1b4b8f2a3c51c4cd7d758a5b367 Mon Sep 17 00:00:00 2001 From: dtsip Date: Tue, 4 Aug 2020 22:53:27 -0400 Subject: [PATCH 15/15] breeds doc minor --- docs/example_usage/breeds_datasets.rst | 51 +++++++++++++++----------- 1 file changed, 30 insertions(+), 21 deletions(-) diff --git a/docs/example_usage/breeds_datasets.rst b/docs/example_usage/breeds_datasets.rst index 9a1461c..2b4876f 100644 --- a/docs/example_usage/breeds_datasets.rst +++ b/docs/example_usage/breeds_datasets.rst @@ -3,8 +3,9 @@ Creating BREEDS subpopulation shift benchmarks In this document, we will discuss how to create BREEDS datasets [STM20]_. Given any existing dataset that comes with a class hierarchy (e.g. ImageNet, -OpenImages), BREEDS allows you to make a derivative classification task that -can be used to measure robustness to subpopulation shift. To do this, we: +OpenImages), the BREEDS methodology allows you to make a derivative +classification task that can be used to measure robustness to subpopulation +shift. To do this, we: 1. Group together semantically-simlar classes ("breeds") in the dataset into superclasses. @@ -12,22 +13,24 @@ can be used to measure robustness to subpopulation shift. To do this, we: the twist that the "breeds" used in the training set from each superclasses are disjoint from the "breeds" used in the test set. -As a primitive example, one could take ImageNet (which contains many cat and -dog breed classes), and use the BREEDS methodology to come up with a derivative -"cats vs dogs" task, where the training set would contain one set of breeds -(e.g. Egyptian cat, Tabby Cat vs Labrador, Golden Retriever) and the test set -would contain another set (e.g. Persian cat, alley cat vs Mastiff, Poodle). Here, -is a (simplified) pictorial illustration of the BREEDS approach: +As a primitive example, one could take ImageNet (which contains many classes +corresponding to cat and dog breeds), and use the BREEDS methodology to come up +with a derivative "cats vs. dogs" task, where the training set would contain one +set of breeds (e.g., Egyptian cat and Tabby Cat vs. Labrador and Golden +Retriever) and the test set would contain another set (e.g. Persian cat and +alley cat vs Mastiff and Poodle). Here is a pictorial illustration of the BREEDS +approach: .. image:: Figures/breeds_pipeline.png :width: 600 :align: center :alt: Illustration of the BREEDS dataset creation pipeline. -BREEDS allows you to create subpopulation shift benchmarks of varying difficulty -automatically, without having to manually group or split up classes, and can be -applied to any dataset which has a class hierarchy. In this walkthrough, we will -use ImageNet and the corresponding class hierarchy from [STM20]_. +This methodology allows you to create subpopulation shift benchmarks of varying +difficulty automatically, without having to manually group or split up classes, +and can be applied to any dataset which has a class hierarchy. In this +walkthrough, we will use ImageNet and the corresponding class hierarchy from +[STM20]_. .. raw:: html @@ -40,7 +43,7 @@ Requirements/Setup '''''''''''''''''' To create BREEDS datasets using ImageNet, we need to create a: -- ``data_dir`` which contains the dataset +- ``data_dir`` which contains the ImageNet dataset in PyTorch-readable format. - ``info_dir`` which contains the following information (files) about the class hierarchy: @@ -52,14 +55,17 @@ To create BREEDS datasets using ImageNet, we need to create a: - ``node_names.txt``: Each line contains the ID of a node followed by it's name (tab separated). -For ImageNet, you can manually download the relevant files for the class hierarchy -from `here `_ and move them to ``info_dir``. Alternatively, you can do it automatically -using by specifying an empty ``info_dir`` to +For convenience, we provide the relevant files for the (modified) class +hierarchy `here +`_. +You can manually download them and move them to ``info_dir`` or do it +automatically by specifying an empty ``info_dir`` to :meth:`~robustness.tools.breeds_helpers.BreedsDatasetGenerator.get_superclasses`: .. code-block:: python from robustness.tools.breeds_helpers import setup_breeds + setup_breeds(info_dir) @@ -75,6 +81,7 @@ step): from robustness.tools.breeds_helpers import ClassHierarchy import numpy as np + hier = ClassHierarchy(info_dir) print(f"# Levels in hierarchy: {np.max(list(hier.level_to_nodes.keys()))}") print(f"# Nodes/level:", @@ -151,6 +158,7 @@ to define the superclasses. .. code-block:: python from robustness.tools.breeds_helpers import BreedsDatasetGenerator + DG = BreedsDatasetGenerator(info_dir) Specifically, we will use @@ -166,7 +174,7 @@ This function takes in the following arguments (see :meth:`this docstring - :samp:`split`: If ``None``, subclasses of a superclass are returned as is, without partitioning them into the source and target domains. Else, can be ``rand/good/bad`` depending on whether the subclass split should be - random or less/more adversarially chosen [STM20]_. + random or less/more adversarially chosen (see paper for details). - :samp:`ancestor`: If a node ID is specified, superclasses are chosen from subtree of class hierarchy rooted at this node. Else, if None, :samp:`ancestor` is set to be the root node. @@ -188,11 +196,11 @@ This method returns: - :samp:`superclasses` is a list containing the IDs of all the superclasses. -- :samp:`subclass_tuple` is a tuple of subclass ranges for +- :samp:`subclass_split` is a tuple of subclass ranges for the source and target domains. For instance, - :samp:`subclass_tuple[0]` is a list, which for each superclass, + :samp:`subclass_split[0]` is a list, which for each superclass, contains a list of subclasses present in the source domain. - If ``split=None``, subclass_tuple[1] is empty and can be + If ``split=None``, subclass_split[1] is empty and can be ignored. - :samp:`label_map` is a dictionary mapping a superclass number (label) to name. @@ -233,7 +241,7 @@ and their corresponding loaders: loaders_target = dataset_source.make_loaders(num_workers, batch_size) train_loader_target, val_loader_target = loaders_target -You're all set! You can then use this :samp:`custom_dataset` and loaders +You're all set! You can then use this dataset and loaders just as you would any other existing/custom dataset in the robustness library. For instance, you can visualize validation set samples from both domains and their labels using: @@ -241,6 +249,7 @@ both domains and their labels using: .. code-block:: python from robustness.tools.vis_tools import show_image_row + for domain, loader in zip(["Source", "Target"], [val_loader_source, val_loader_target]): im, lab = next(iter(loader))