[MNT] solve flake8 error in examples

examples/dataset_image_workflow/utils.py

@@ -9,19 +9,15 @@
 @torch.no_grad()
 def evaluate(model, evaluate_set: Dataset, device=None, distribution=True):
  device = choose_device(0) if device is None else device
-
  if isinstance(model, nn.Module):
  model.eval()
- mapping = lambda m, x: m(x)
- else:
- mapping = lambda m, x: m.predict(x)
 
  criterion = nn.CrossEntropyLoss(reduction="sum")
  total, correct, loss = 0, 0, torch.as_tensor(0.0, dtype=torch.float32, device=device)
  dataloader = DataLoader(evaluate_set, batch_size=1024, shuffle=True)
  for i, (X, y) in enumerate(dataloader):
  X, y = X.to(device), y.to(device)
- out = mapping(model, X)
+ out = model(X) if isinstance(model, nn.Module) else model.predict(X)
  if not torch.is_tensor(out):
  out = torch.from_numpy(out).to(device)
 

examples/dataset_image_workflow/workflow.py

@@ -49,7 +49,7 @@ def _plot_labeled_peformance_curves(self, all_user_curves_data):
 
  plt.xlabel("Amout of Labeled User Data", fontsize=14)
  plt.ylabel("1 - Accuracy", fontsize=14)
- plt.title(f"Results on Image Experimental Scenario", fontsize=16)
+ plt.title("Results on Image Experimental Scenario", fontsize=16)
  plt.legend(fontsize=14)
  plt.tight_layout()
  plt.savefig(os.path.join(self.fig_path, "image_labeled_curves.svg"), bbox_inches="tight", dpi=700)
@@ -61,7 +61,7 @@ def _prepare_market(self, rebuild=False):
  self.user_semantic = client.get_semantic_specification(self.image_benchmark.learnware_ids[0])
  self.user_semantic["Name"]["Values"] = ""
 
- if len(self.image_market) == 0 or rebuild == True:
+ if len(self.image_market) == 0 or rebuild is True:
  for learnware_id in self.image_benchmark.learnware_ids:
  with tempfile.TemporaryDirectory(prefix="image_benchmark_") as tempdir:
  zip_path = os.path.join(tempdir, f"{learnware_id}.zip")
@@ -71,7 +71,7 @@ def _prepare_market(self, rebuild=False):
  client.download_learnware(learnware_id, zip_path)
  self.image_market.add_learnware(zip_path, semantic_spec)
  break
- except:
+ except Exception:
  time.sleep(1)
  continue
 

examples/dataset_text_workflow/workflow.py

@@ -64,7 +64,7 @@ def _plot_labeled_peformance_curves(self, all_user_curves_data):
 
  plt.xlabel("Amout of Labeled User Data", fontsize=14)
  plt.ylabel("1 - Accuracy", fontsize=14)
- plt.title(f"Results on Text Experimental Scenario", fontsize=16)
+ plt.title("Results on Text Experimental Scenario", fontsize=16)
  plt.legend(fontsize=14)
  plt.tight_layout()
  plt.savefig(os.path.join(self.fig_path, "text_labeled_curves.svg"), bbox_inches="tight", dpi=700)
@@ -76,7 +76,7 @@ def _prepare_market(self, rebuild=False):
  self.user_semantic = client.get_semantic_specification(self.text_benchmark.learnware_ids[0])
  self.user_semantic["Name"]["Values"] = ""
 
- if len(self.text_market) == 0 or rebuild == True:
+ if len(self.text_market) == 0 or rebuild is True:
  for learnware_id in self.text_benchmark.learnware_ids:
  with tempfile.TemporaryDirectory(prefix="text_benchmark_") as tempdir:
  zip_path = os.path.join(tempdir, f"{learnware_id}.zip")
@@ -86,7 +86,7 @@ def _prepare_market(self, rebuild=False):
  client.download_learnware(learnware_id, zip_path)
  self.text_market.add_learnware(zip_path, semantic_spec)
  break
- except:
+ except Exception:
  time.sleep(1)
  continue