[FIX] pass flake8

examples/bdd_oia/bridge.py

@@ -3,6 +3,7 @@ from typing import List, Any
 from ablkit.data import ListData
 from ablkit.bridge import SimpleBridge
 
+
 class BDDBridge(SimpleBridge):
     def idx_to_pseudo_label(self, data_examples: ListData) -> List[List[Any]]:
         pred_idx = data_examples.pred_idx  # [ ndarray(1,nc),... ]
@@ -20,4 +21,4 @@ class BDDBridge(SimpleBridge):
             sub_list = np.array([self.reasoner.label_to_idx[_lab] for _lab in sub_list])
             abduced_idx.append(sub_list)
         data_examples.abduced_idx = abduced_idx
-        return data_examples.abduced_idx
+        return data_examples.abduced_idx

examples/bdd_oia/dataset/data_util.py

@@ -14,5 +14,6 @@ def get_dataset(fname, get_pseudo_label=True):
     Y = [tuple(y) for y in Y]
     return X, pseudo_label, Y
 
-if __name__ == '__main__':
-    dataset = get_dataset("val.npz")
+
+if __name__ == "__main__":
+    dataset = get_dataset("val.npz")

examples/bdd_oia/main.py

@@ -8,7 +8,7 @@ from ablkit.data.evaluation import SymbolAccuracy
 from ablkit.reasoning import Reasoner
 from ablkit.utils import ABLLogger, print_log
 
-from models.nn import *
+from models.nn import ConceptNet
 from models.bdd_nn import BDDNN
 from models.bdd_model import BDDABLModel
 from reasoning.bddkb import BDDKB
@@ -19,12 +19,13 @@ from metric import BDDReasoningMetric
 
 def multi_label_confidence_dist(data_example, candidates, candidates_idxs, reasoning_results):
     pred_prob = data_example.pred_prob.T  # nc x 1
-    pred_prob = np.concatenate([1-pred_prob, pred_prob], axis=1)  # nc x 2
+    pred_prob = np.concatenate([1 - pred_prob, pred_prob], axis=1)  # nc x 2
     cols = np.arange(len(candidates_idxs[0]))[None, :]
     corr_prob = pred_prob[cols, candidates_idxs]
-    costs = - np.sum(np.log(corr_prob + 1e-6), axis=1)
+    costs = -np.sum(np.log(corr_prob + 1e-6), axis=1)
     return costs
 
+
 def get_args():
     parser = argparse.ArgumentParser(description="BDD-OIA example")
     parser.add_argument(
@@ -62,6 +63,7 @@ def get_args():
     args = parser.parse_args()
     return args
 
+
 def main():
     args = get_args()
 
@@ -116,7 +118,7 @@ def main():
         kb,
         dist_func=multi_label_confidence_dist,
         max_revision=args.max_revision,
-        require_more_revision=args.require_more_revision
+        require_more_revision=args.require_more_revision,
     )
 
     # -- Building Evaluation Metrics --------------------

examples/bdd_oia/metric.py

@@ -3,6 +3,7 @@ from typing import Optional
 from ablkit.reasoning import KBBase
 from ablkit.data import BaseMetric, ListData
 
+
 class BDDReasoningMetric(BaseMetric):
     def __init__(self, kb: KBBase, prefix: Optional[str] = None) -> None:
         super().__init__(prefix)
@@ -13,7 +14,9 @@ class BDDReasoningMetric(BaseMetric):
         y_list = data_examples.Y
         x_list = data_examples.X
         for pred_pseudo_label, y, x in zip(pred_pseudo_label_list, y_list, x_list):
-            pred_y = self.kb.logic_forward(pred_pseudo_label, *(x,) if self.kb._num_args == 2 else ())
+            pred_y = self.kb.logic_forward(
+                pred_pseudo_label, *(x,) if self.kb._num_args == 2 else ()
+            )
             for py, yy in zip(pred_y, y):
                 self.results.append(int(py == yy))
 
@@ -21,4 +24,4 @@ class BDDReasoningMetric(BaseMetric):
         results = self.results
         metrics = dict()
         metrics["reasoning_accuracy"] = sum(results) / len(results)
-        return metrics
+        return metrics

examples/bdd_oia/models/bdd_model.py

@@ -5,6 +5,7 @@ from ablkit.data import ListData
 from ablkit.learning import ABLModel
 from ablkit.utils import reform_list
 
+
 class BDDABLModel(ABLModel):
     def predict(self, data_examples: ListData) -> Dict:
         model = self.base_model
@@ -21,4 +22,4 @@ class BDDABLModel(ABLModel):
         data_examples.pred_idx = label
         data_examples.pred_prob = prob
 
-        return {"label": label, "prob": prob}
+        return {"label": label, "prob": prob}

examples/bdd_oia/models/bdd_nn.py

@@ -1,10 +1,9 @@
 import logging
-import os
-from typing import Any, Callable, List, Optional, Tuple, Union
+from typing import Any, Callable, List, Optional
 
 import numpy
 import torch
-from torch.utils.data import DataLoader, Dataset
+from torch.utils.data import DataLoader
 
 from ablkit.learning import BasicNN, PredictionDataset, ClassificationDataset
 from ablkit.utils.logger import print_log
@@ -15,11 +14,11 @@ class MultiLabelClassificationDataset(ClassificationDataset):
         if (not isinstance(X, list)) or (not isinstance(Y, list)):
             raise ValueError("X and Y should be of type list.")
         self.X = X
-        self.Y = torch.FloatTensor(numpy.stack(Y, axis=0)) # float32 for BCELoss
+        self.Y = torch.FloatTensor(numpy.stack(Y, axis=0))  # float32 for BCELoss
         self.transform = transform
 
-class BDDNN(BasicNN):
 
+class BDDNN(BasicNN):
     def predict(
         self,
         data_loader: Optional[DataLoader] = None,

examples/bdd_oia/models/nn.py

@@ -1,5 +1,6 @@
 from torch import nn
 
+
 class SimpleNet(nn.Module):
     def __init__(self, num_features=2048, num_concepts=21):
         super(SimpleNet, self).__init__()
@@ -8,6 +9,7 @@ class SimpleNet(nn.Module):
     def forward(self, x):
         return self.fc(x)
 
+
 class ConceptNet(nn.Module):
     def __init__(self, num_features=2048, num_concepts=21):
         super(ConceptNet, self).__init__()
@@ -15,7 +17,7 @@ class ConceptNet(nn.Module):
         self.fc = nn.Sequential(
             nn.Linear(num_features, intermidate_dim),
             nn.SiLU(),
-            nn.Linear(intermidate_dim, num_concepts)
+            nn.Linear(intermidate_dim, num_concepts),
         )
 
     def forward(self, x):

examples/bdd_oia/reasoning/bddkb.py

@@ -1,6 +1,7 @@
 # -*- coding: utf-8 -*-
 from ablkit.reasoning import KBBase
 
+
 class BDDKB(KBBase):
     def __init__(self, pseudo_label_list=None):
         if pseudo_label_list is None:
@@ -20,9 +21,29 @@ class BDDKB(KBBase):
         (1, 0, 1, 1) 196
         """
         assert len(attrs) == 21
-        green_light, follow, road_clear, red_light, traffic_sign, car, person, rider, other_obstacle, \
-        left_lane, left_green_light, left_follow, no_left_lane, left_obstacle, left_solid_line, \
-        right_lane, right_green_light, right_follow, no_right_lane, right_obstacle, right_solid_line = attrs
+        (
+            green_light,
+            follow,
+            road_clear,
+            red_light,
+            traffic_sign,
+            car,
+            person,
+            rider,
+            other_obstacle,
+            left_lane,
+            left_green_light,
+            left_follow,
+            no_left_lane,
+            left_obstacle,
+            left_solid_line,
+            right_lane,
+            right_green_light,
+            right_follow,
+            no_right_lane,
+            right_obstacle,
+            right_solid_line,
+        ) = attrs
 
         illegal_return = (0, 0, 0, 0)
         if red_light == green_light == 1:
@@ -30,8 +51,8 @@ class BDDKB(KBBase):
         obstacle = car or person or rider or other_obstacle
         if road_clear == obstacle:
             return illegal_return
-        move_forward = (green_light or follow or road_clear)
-        stop = (red_light or traffic_sign or obstacle)
+        move_forward = green_light or follow or road_clear
+        stop = red_light or traffic_sign or obstacle
         if stop:
             move_forward = 0
 
@@ -43,4 +64,4 @@ class BDDKB(KBBase):
         cannot_turn_right = no_right_lane or right_obstacle or right_solid_line
         turn_right = can_turn_right and int(not cannot_turn_right)
 
-        return move_forward, stop, turn_left, turn_right
+        return move_forward, stop, turn_left, turn_right

examples/bdd_oia/requirements.txt

@@ -1,2 +1 @@
-torch
 ablkit

examples/hwf/main.py

@@ -81,7 +81,7 @@ def main():
         "--label-smoothing",
         type=float,
         default=0.2,
-        help="label smoothing in cross entropy loss (default : 0.2)"
+        help="label smoothing in cross entropy loss (default : 0.2)",
     )
     parser.add_argument(
         "--lr", type=float, default=1e-3, help="base model learning rate (default : 0.001)"
@@ -138,7 +138,12 @@ def main():
 
     # Build BasicNN
     base_model = BasicNN(
-        cls, loss_fn, optimizer, device=device, batch_size=args.batch_size, num_epochs=args.epochs,
+        cls,
+        loss_fn,
+        optimizer,
+        device=device,
+        batch_size=args.batch_size,
+        num_epochs=args.epochs,
     )
 
     # Build ABLModel