ubuntu commited on
Commit
68e5f5a
·
1 Parent(s): e4ae9d7

update genn_astar

Browse files
Files changed (2) hide show
  1. app.py +7 -8
  2. genn_astar.py +0 -2
app.py CHANGED
@@ -19,28 +19,27 @@ def _handle_ged_solve(
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  gexf_1_path: str,
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  gexf_2_path: str
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  ):
 
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  if gexf_1_path is None:
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  raise gr.Error("Please upload file completely!")
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  if gexf_2_path is None:
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  raise gr.Error("Please upload file completely!")
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- start_time = time.time()
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- print("111")
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  dirs = pygm.utils.user_cache_dir("pygmtools")
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- print(dirs)
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- print(os.path.exists(dirs))
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  if not os.path.exists(dirs):
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  os.makedirs(dirs)
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- print("222")
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- shutil.move(src=PRETRAINED_PATH, dst=os.path.join(dirs, PRETRAINED_PATH))
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- print("333")
 
 
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  astar(
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  g1_path=gexf_1_path,
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  g2_path=gexf_2_path,
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  output_path="media",
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  filename="ged_image"
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  )
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- print("444")
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  solved_time = time.time() - start_time
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  message = "Successfully solve the GED problem, using time ({:.3f}s).".format(solved_time)
 
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  gexf_1_path: str,
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  gexf_2_path: str
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  ):
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+ # check the input files
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  if gexf_1_path is None:
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  raise gr.Error("Please upload file completely!")
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  if gexf_2_path is None:
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  raise gr.Error("Please upload file completely!")
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+ # check the pretrained file
 
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  dirs = pygm.utils.user_cache_dir("pygmtools")
 
 
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  if not os.path.exists(dirs):
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  os.makedirs(dirs)
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+ if os.path.exists(PRETRAINED_PATH):
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+ shutil.move(src=PRETRAINED_PATH, dst=os.path.join(dirs, PRETRAINED_PATH))
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+
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+ # begin solve
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+ start_time = time.time()
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  astar(
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  g1_path=gexf_1_path,
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  g2_path=gexf_2_path,
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  output_path="media",
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  filename="ged_image"
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  )
 
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  solved_time = time.time() - start_time
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  message = "Successfully solve the GED problem, using time ({:.3f}s).".format(solved_time)
genn_astar.py CHANGED
@@ -141,9 +141,7 @@ def astar(
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  A2 = torch.tensor(pygm.utils.from_networkx(graph2)).float().to(device)
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  # Caculate the ged
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- print("111111")
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  x_pred = pygm.genn_astar(feat1, feat2, A1, A2, return_network=False)
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- print("222222")
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  # Plot
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  draw(graph1, colors1, labels1, output_filename.format(1), "Graph1")
 
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  A2 = torch.tensor(pygm.utils.from_networkx(graph2)).float().to(device)
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  # Caculate the ged
 
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  x_pred = pygm.genn_astar(feat1, feat2, A1, A2, return_network=False)
 
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  # Plot
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  draw(graph1, colors1, labels1, output_filename.format(1), "Graph1")