Format code

benchmarks/pso.py

@@ -1,11 +1,13 @@
 """Benchmark the performance of PSO algorithm in EvoX."""
 
 import time
+
 import torch
-from torch.profiler import profile, ProfilerActivity
-from evox.core import vmap, Problem, use_state, jit
-from evox.workflows import StdWorkflow
+from torch.profiler import ProfilerActivity, profile
+
 from evox.algorithms import PSO
+from evox.core import Problem, jit, use_state, vmap
+from evox.workflows import StdWorkflow
 
 
 def run_pso():

benchmarks/utils.py

@@ -1,7 +1,8 @@
 """Benchmark the performance of utils functions in EvoX"""
 
 import torch
-from torch.profiler import profile, ProfilerActivity
+from torch.profiler import ProfilerActivity, profile
+
 from evox.core import jit
 from evox.utils import switch
 

docs/fix_notebook_translation.py

@@ -1,10 +1,11 @@
 """
-This script is used to wrap the translated strings in jupyter notebooks in the docs.po file
+This script is used to wrap the translated strings in jupyter notebooks in the docs.po
 """
 
-import polib
-import json
 import copy
+import json
+
+import polib
 
 po = polib.pofile("docs/source/locale/zh/LC_MESSAGES/docs.po")
 

unit_test/algorithms/test_pso.py

@@ -1,8 +1,10 @@
 from unittest import TestCase
+
 import torch
-from evox.core import vmap, Problem, use_state, jit
-from evox.workflows import StdWorkflow
+
 from evox.algorithms import PSO
+from evox.core import Problem, jit, use_state, vmap
+from evox.workflows import StdWorkflow
 
 
 class TestPSO(TestCase):

unit_test/core/test_jit_util.py

@@ -1,7 +1,9 @@
 import unittest
 from functools import partial
+
 import torch
-from evox.core import vmap, jit
+
+from evox.core import jit, vmap
 
 
 @partial(vmap, example_ndim=2)

unit_test/core/test_module.py

@@ -1,14 +1,14 @@
 import unittest
+from typing import Dict, List
+
 import torch
 import torch.nn as nn
-from typing import Dict, List
 
-from evox.core import jit_class, ModuleBase, trace_impl, use_state
+from evox.core import ModuleBase, jit_class, trace_impl, use_state
 
 
 @jit_class
 class DummyModule(ModuleBase):
-
     def __init__(self, threshold=0.5):
         super().__init__()
         self.threshold = threshold
@@ -36,7 +36,6 @@ def g(self, p: torch.Tensor) -> torch.Tensor:
 
 
 class TestModule(unittest.TestCase):
-
     def setUp(self):
         self.test_instance = DummyModule()
 

unit_test/problems/test_hpo_wrapper.py

@@ -1,9 +1,11 @@
 import unittest
+
 import torch
 from torch import nn
-from evox.core import jit_class, Problem, Algorithm, trace_impl, Parameter
+
+from evox.core import Algorithm, Parameter, Problem, jit_class, trace_impl
+from evox.problems.hpo_wrapper import HPOFitnessMonitor, HPOProblemWrapper
 from evox.workflows import StdWorkflow
-from evox.problems.hpo_wrapper import HPOProblemWrapper, HPOFitnessMonitor
 
 
 @jit_class

unit_test/utils/test_control_flow.py

@@ -1,4 +1,5 @@
 import unittest
+
 import torch
 
 from evox.core import ModuleBase, jit, jit_class, trace_impl, use_state, vmap

unit_test/utils/test_jit_fix.py

@@ -1,5 +1,7 @@
 import unittest
+
 import torch
+
 from evox.core import jit
 from evox.utils import switch
 

unit_test/workflows/test_std_workflow.py

@@ -1,21 +1,22 @@
 import unittest
+
 import torch
 import torch.nn as nn
+
 from evox.core import (
-    vmap,
-    trace_impl,
-    use_state,
-    jit,
-    jit_class,
     Algorithm,
     Problem,
+    jit,
+    jit_class,
+    trace_impl,
+    use_state,
+    vmap,
 )
-from evox.workflows import StdWorkflow, EvalMonitor
+from evox.workflows import EvalMonitor, StdWorkflow
 
 
 @jit_class
 class BasicProblem(Problem):
-
     def __init__(self):
         super().__init__()
         self._eval_fn = vmap(BasicProblem._single_eval, trace=False)
@@ -34,7 +35,6 @@ def trace_evaluate(self, pop: torch.Tensor):
 
 @jit_class
 class BasicAlgorithm(Algorithm):
-
     def __init__(self, pop_size: int, lb: torch.Tensor, ub: torch.Tensor):
         super().__init__()
         assert (
@@ -73,7 +73,6 @@ def trace_step(self):
 
 
 class TestStdWorkflow(unittest.TestCase):
-
     def setUp(self):
         torch.set_default_device("cuda" if torch.cuda.is_available() else "cpu")
         self.algo = BasicAlgorithm(10, -10 * torch.ones(2), 10 * torch.ones(2))