v1.2.2

README.md

@@ -29,7 +29,7 @@ pip install wilds
 If you have already installed it, please check that you have the latest version:
 ```bash
 python -c "import wilds; print(wilds.__version__)"
-# This should print "1.2.1". If it doesn't, update by running:
+# This should print "1.2.2". If it doesn't, update by running:
 pip install -U wilds
 ```
 
@@ -50,7 +50,10 @@ pip install -e .
 - torch>=1.7.0
 - torch-scatter>=2.0.5
 - torch-geometric>=1.6.1
+- torchvision>=0.8.2
 - tqdm>=4.53.0
+- scikit-learn>=0.20.0
+- scipy>=1.5.4
 
 Running `pip install wilds` or `pip install -e .` will automatically check for and install all of these requirements
 except for the `torch-scatter` and `torch-geometric` packages, which require a [quick manual install](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html#installation-via-binaries).
@@ -63,9 +66,8 @@ These scripts are not part of the installed WILDS package. To use them, you shou
 git clone git@github.com:p-lambda/wilds.git
 ```
 
-To run these scripts, you will need to install these additional dependencies:
+To run these scripts, you will also need to install this additional dependency:
 
-- torchvision>=0.8.2
 - transformers>=3.5.0
 
 All baseline experiments in the paper were run on Python 3.8.5 and CUDA 10.1.

examples/configs/utils.py

@@ -1,3 +1,4 @@
+import copy
 from configs.algorithm import algorithm_defaults
 from configs.model import model_defaults
 from configs.scheduler import scheduler_defaults
@@ -7,41 +8,44 @@
 def populate_defaults(config):
     """Populates hyperparameters with defaults implied by choices
     of other hyperparameters."""
+
+    orig_config = copy.deepcopy(config)
     assert config.dataset is not None, 'dataset must be specified'
     assert config.algorithm is not None, 'algorithm must be specified'
+
     # implied defaults from choice of dataset
     config = populate_config(
-        config, 
+        config,
         dataset_defaults[config.dataset]
     )
 
     # implied defaults from choice of split
     if config.dataset in split_defaults and config.split_scheme in split_defaults[config.dataset]:
         config = populate_config(
-            config, 
+            config,
             split_defaults[config.dataset][config.split_scheme]
         )
-    
+
     # implied defaults from choice of algorithm
     config = populate_config(
-        config, 
+        config,
         algorithm_defaults[config.algorithm]
     )
 
     # implied defaults from choice of loader
     config = populate_config(
-        config, 
+        config,
         loader_defaults
     )
     # implied defaults from choice of model
     if config.model: config = populate_config(
-        config, 
+        config,
         model_defaults[config.model],
     )
-    
+
     # implied defaults from choice of scheduler
     if config.scheduler: config = populate_config(
-        config, 
+        config,
         scheduler_defaults[config.scheduler]
     )
 
@@ -52,12 +56,22 @@ def populate_defaults(config):
 
     # basic checks
     required_fields = [
-        'split_scheme', 'train_loader', 'uniform_over_groups', 'batch_size', 'eval_loader', 'model', 'loss_function', 
+        'split_scheme', 'train_loader', 'uniform_over_groups', 'batch_size', 'eval_loader', 'model', 'loss_function',
         'val_metric', 'val_metric_decreasing', 'n_epochs', 'optimizer', 'lr', 'weight_decay',
-        ] 
+        ]
     for field in required_fields:
         assert getattr(config, field) is not None, f"Must manually specify {field} for this setup."
 
+    # data loader validations
+    # we only raise this error if the train_loader is standard, and
+    # n_groups_per_batch or distinct_groups are
+    # specified by the user (instead of populated as a default)
+    if config.train_loader == 'standard':
+        if orig_config.n_groups_per_batch is not None:
+            raise ValueError("n_groups_per_batch cannot be specified if the data loader is 'standard'. Consider using a 'group' data loader instead.")
+        if orig_config.distinct_groups is not None:
+            raise ValueError("distinct_groups cannot be specified if the data loader is 'standard'. Consider using a 'group' data loader instead.")
+
     return config
 
 def populate_config(config, template: dict, force_compatibility=False):
@@ -78,7 +92,7 @@ def populate_config(config, template: dict, force_compatibility=False):
                 d_config[key] = val
             elif d_config[key] != val and force_compatibility:
                 raise ValueError(f"Argument {key} must be set to {val}")
-                
+
         else: # config[key] expected to be a kwarg dict
             for kwargs_key, kwargs_val in val.items():
                 if kwargs_key not in d_config[key] or d_config[key][kwargs_key] is None:

examples/models/detection/fasterrcnn.py

@@ -27,7 +27,6 @@
 from torchvision.models.utils import load_state_dict_from_url
 from torchvision.ops import misc as misc_nn_ops
 from torchvision.ops import MultiScaleRoIAlign
-from torchvision.models.detection import _utils as det_utils
 from torchvision.models.detection.anchor_utils import AnchorGenerator
 from torchvision.models.detection.generalized_rcnn import GeneralizedRCNN
 from torchvision.models.detection.faster_rcnn import TwoMLPHead
@@ -127,11 +126,11 @@ def compute_loss(self, objectness, pred_bbox_deltas, labels, regression_targets)
             sampled_neg_inds = torch.where(torch.cat(sampled_neg_inds, dim=0))[0]
             sampled_inds = torch.cat([sampled_pos_inds, sampled_neg_inds], dim=0)
 
-            box_loss.append(det_utils.smooth_l1_loss(
+            box_loss.append(F.smooth_l1_loss(
                 pred_bbox_deltas_[sampled_pos_inds],
                 regression_targets_[sampled_pos_inds],
                 beta=1 / 9,
-                size_average=False,
+                reduction='sum',
             ) / (sampled_inds.numel()))
 
             objectness_loss.append(F.binary_cross_entropy_with_logits(
@@ -226,11 +225,11 @@ def fastrcnn_loss(class_logits, box_regression, labels, regression_targets):
 
         box_regression_ = box_regression_.reshape(N, -1, 4)
 
-        box_loss_ = det_utils.smooth_l1_loss(
+        box_loss_ = F.smooth_l1_loss(
             box_regression_[sampled_pos_inds_subset, labels_pos],
             regression_targets_[sampled_pos_inds_subset],
             beta=1 / 9,
-            size_average=False,
+            reduction='sum',
         )
         box_loss.append(box_loss_ / labels_.numel())
 

examples/run_expt.py

@@ -155,8 +155,9 @@ def main():
         split_scheme=config.split_scheme,
         **config.dataset_kwargs)
 
-    # To implement data augmentation (i.e., have different transforms
-    # at training time vs. test time), modify these two lines:
+    # To modify data augmentation, modify the following code block.
+    # If you want to use transforms that modify both `x` and `y`,
+    # set `do_transform_y` to True when initializing the `WILDSSubset` below.
     train_transform = initialize_transform(
         transform_name=config.transform,
         config=config,

examples/transforms.py

@@ -7,8 +7,9 @@
 
 def initialize_transform(transform_name, config, dataset, is_training):
     """
-    Transforms should take in a single (x, y)
-    and return (transformed_x, transformed_y).
+    By default, transforms should take in `x` and return `transformed_x`.
+    For transforms that take in `(x, y)` and return `(transformed_x, transformed_y)`,
+    set `do_transform_y` to True when initializing the WILDSSubset.    
     """
     if transform_name is None:
         return None
@@ -25,11 +26,6 @@ def initialize_transform(transform_name, config, dataset, is_training):
     else:
         raise ValueError(f"{transform_name} not recognized")
 
-def transform_input_only(input_transform):
-    def transform(x, y):
-        return input_transform(x), y
-    return transform
-
 def initialize_bert_transform(config):
     assert 'bert' in config.model
     assert config.max_token_length is not None
@@ -55,7 +51,7 @@ def transform(text):
                 dim=2)
         x = torch.squeeze(x, dim=0) # First shape dim is always 1
         return x
-    return transform_input_only(transform)
+    return transform
 
 def getBertTokenizer(model):
     if model == 'bert-base-uncased':
@@ -79,7 +75,7 @@ def initialize_image_base_transform(config, dataset):
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
         ]
     transform = transforms.Compose(transform_steps)
-    return transform_input_only(transform)
+    return transform
 
 def initialize_image_resize_and_center_crop_transform(config, dataset):
     """
@@ -98,7 +94,7 @@ def initialize_image_resize_and_center_crop_transform(config, dataset):
         transforms.ToTensor(),
         transforms.Normalize([0.485, 0.456, 0.406], [0.229, 0.224, 0.225])
     ])
-    return transform_input_only(transform)
+    return transform
 
 def initialize_poverty_transform(is_training):
     if is_training:
@@ -115,7 +111,7 @@ def transform_rgb(img):
             img[:3] = rgb_transform(img[:3][[2,1,0]])[[2,1,0]]
             return img
         transform = transforms.Lambda(lambda x: transform_rgb(x))
-        return transform_input_only(transform)
+        return transform
     else:
         return None
 
@@ -148,4 +144,4 @@ def random_rotation(x: torch.Tensor) -> torch.Tensor:
             t_standardize,
         ]
     transform = transforms.Compose(transforms_ls)
-    return transform_input_only(transform)
+    return transform

setup.py

@@ -22,14 +22,16 @@
     long_description_content_type="text/markdown",
     install_requires = [
         'numpy>=1.19.1',
+        'ogb>=1.2.6',
+        'outdated>=0.2.0',
         'pandas>=1.1.0',
-        'scikit-learn>=0.20.0',
         'pillow>=7.2.0',
+        'pytz>=2020.4',
         'torch>=1.7.0',
-        'ogb>=1.2.6',
+        'torchvision>=0.8.2',
         'tqdm>=4.53.0',
-        'outdated>=0.2.0',
-        'pytz>=2020.4',
+        'scikit-learn>=0.20.0',
+        'scipy>=1.5.4'
     ],
     license='MIT',
     packages=setuptools.find_packages(exclude=['dataset_preprocessing', 'examples', 'examples.models', 'examples.models.bert']),

wilds/common/metrics/all_metrics.py

@@ -1,10 +1,10 @@
+import numpy as np
 import torch
 import torch.nn as nn
+import torch.nn.functional as F
 from torchvision.ops.boxes import box_iou
 from torchvision.models.detection._utils import Matcher
 from torchvision.ops import nms, box_convert
-import numpy as np
-import torch.nn.functional as F
 from wilds.common.metrics.metric import Metric, ElementwiseMetric, MultiTaskMetric
 from wilds.common.metrics.loss import ElementwiseLoss
 from wilds.common.utils import avg_over_groups, minimum, maximum, get_counts
@@ -243,12 +243,17 @@ def _accuracy(self, src_boxes,pred_boxes ,  iou_threshold):
         total_pred = len(pred_boxes)
         if total_gt > 0 and total_pred > 0:
             # Define the matcher and distance matrix based on iou
-            matcher = Matcher(iou_threshold,iou_threshold,allow_low_quality_matches=False)
-            match_quality_matrix = box_iou(src_boxes,pred_boxes)
+            matcher = Matcher(
+                iou_threshold,
+                iou_threshold,
+                allow_low_quality_matches=False)
+            match_quality_matrix = box_iou(
+                src_boxes,
+                pred_boxes)
             results = matcher(match_quality_matrix)
             true_positive = torch.count_nonzero(results.unique() != -1)
             matched_elements = results[results > -1]
-            #in Matcher, a pred element can be matched only twice
+            # in Matcher, a pred element can be matched only twice
             false_positive = (
                 torch.count_nonzero(results == -1) +
                 (len(matched_elements) - len(matched_elements.unique()))

wilds/datasets/wilds_dataset.py

@@ -433,11 +433,16 @@ def standard_group_eval(metric, grouper, y_pred, y_true, metadata, aggregate=Tru
 
 
 class WILDSSubset(WILDSDataset):
-    def __init__(self, dataset, indices, transform):
+    def __init__(self, dataset, indices, transform, do_transform_y=False):
         """
-        This acts like torch.utils.data.Subset, but on WILDSDatasets.
-        We pass in transform explicitly because it can potentially vary at
-        training vs. test time, if we're using data augmentation.
+        This acts like `torch.utils.data.Subset`, but on `WILDSDatasets`.
+        We pass in `transform` (which is used for data augmentation) explicitly
+        because it can potentially vary on the training vs. test subsets.
+
+        `do_transform_y` (bool): When this is false (the default),
+                                 `self.transform ` acts only on  `x`.
+                                 Set this to true if `self.transform` should
+                                 operate on `(x,y)` instead of just `x`.
         """
         self.dataset = dataset
         self.indices = indices
@@ -449,11 +454,15 @@ def __init__(self, dataset, indices, transform):
             if hasattr(dataset, attr_name):
                 setattr(self, attr_name, getattr(dataset, attr_name))
         self.transform = transform
+        self.do_transform_y = do_transform_y
 
     def __getitem__(self, idx):
         x, y, metadata = self.dataset[self.indices[idx]]
         if self.transform is not None:
-            x, y = self.transform(x, y)
+            if self.do_transform_y:
+                x, y = self.transform(x, y)
+            else:
+                x = self.transform(x)
         return x, y, metadata
 
     def __len__(self):

wilds/version.py

@@ -4,7 +4,7 @@
 import logging
 from threading import Thread
 
-__version__ = '1.2.1'
+__version__ = '1.2.2'
 
 try:
     os.environ['OUTDATED_IGNORE'] = '1'