Fix graphgym activation function dictionary, now not including inst…

…ances anymore (pyg-team#4978)

* Individual activation function instances

* Removed unnecessary import

* Updated changelog

* Revert "Removed unnecessary import"

This reverts commit f48bc2c.

* fix test

* fix test

CHANGELOG.md

@@ -54,6 +54,7 @@ The format is based on [Keep a Changelog](http://keepachangelog.com/en/1.0.0/).
 - Added support for graph-level outputs in `to_hetero` ([#4582](https://github.com/pyg-team/pytorch_geometric/pull/4582))
 - Added `CHANGELOG.md` ([#4581](https://github.com/pyg-team/pytorch_geometric/pull/4581))
 ### Changed
+- Changed `act_dict` (part of `graphgym`) to create individual instances instead of reusing the same ones everywhere ([4978](https://github.com/pyg-team/pytorch_geometric/pull/4978))
 - Fixed issue where one-hot tensors were passed to `F.one_hot` ([4970](https://github.com/pyg-team/pytorch_geometric/pull/4970))
 - Fixed `bool` arugments in `argparse` in `benchmark/` ([#4967](https://github.com/pyg-team/pytorch_geometric/pull/4967))
 - Fixed `BasicGNN` for `num_layers=1`, which now respects a desired number of `out_channels` ([#4943](https://github.com/pyg-team/pytorch_geometric/pull/4943))

test/graphgym/test_graphgym.py

@@ -95,7 +95,7 @@ def test_run_single_graphgym(auto_resume, skip_train_eval, use_trivial_metric):
     assert isinstance(scheduler[0], torch.optim.lr_scheduler.CosineAnnealingLR)
 
     cfg.params = params_count(model)
-    assert cfg.params == 23880
+    assert cfg.params == 23883
 
     train(model, datamodule, logger=True,
           trainer_config={"enable_progress_bar": False})
@@ -136,7 +136,7 @@ def test_graphgym_module(tmpdir):
     assert isinstance(scheduler[0], torch.optim.lr_scheduler.CosineAnnealingLR)
 
     cfg.params = params_count(model)
-    assert cfg.params == 23880
+    assert cfg.params == 23883
 
     keys = {"loss", "true", "pred_score", "step_end_time"}
     # test training step

test/graphgym/test_register.py

@@ -16,7 +16,7 @@ def test_register():
         'relu', 'selu', 'prelu', 'elu', 'lrelu_01', 'lrelu_025', 'lrelu_05',
         'identity'
     ]
-    assert str(register.act_dict['relu']) == 'ReLU()'
+    assert str(register.act_dict['relu']()) == 'ReLU()'
 
     register.register_act('lrelu_03', torch.nn.LeakyReLU(0.3))
     assert len(register.act_dict) == 9

torch_geometric/graphgym/models/act.py

@@ -3,11 +3,40 @@
 from torch_geometric.graphgym.config import cfg
 from torch_geometric.graphgym.register import register_act
 
+
+def relu():
+    return nn.ReLU(inplace=cfg.mem.inplace)
+
+
+def selu():
+    return nn.SELU(inplace=cfg.mem.inplace)
+
+
+def prelu():
+    return nn.PReLU()
+
+
+def elu():
+    return nn.ELU(inplace=cfg.mem.inplace)
+
+
+def lrelu_01():
+    return nn.LeakyReLU(0.1, inplace=cfg.mem.inplace)
+
+
+def lrelu_025():
+    return nn.LeakyReLU(0.25, inplace=cfg.mem.inplace)
+
+
+def lrelu_05():
+    return nn.LeakyReLU(0.5, inplace=cfg.mem.inplace)
+
+
 if cfg is not None:
-    register_act('relu', nn.ReLU(inplace=cfg.mem.inplace))
-    register_act('selu', nn.SELU(inplace=cfg.mem.inplace))
-    register_act('prelu', nn.PReLU())
-    register_act('elu', nn.ELU(inplace=cfg.mem.inplace))
-    register_act('lrelu_01', nn.LeakyReLU(0.1, inplace=cfg.mem.inplace))
-    register_act('lrelu_025', nn.LeakyReLU(0.25, inplace=cfg.mem.inplace))
-    register_act('lrelu_05', nn.LeakyReLU(0.5, inplace=cfg.mem.inplace))
+    register_act('relu', relu)
+    register_act('selu', selu)
+    register_act('prelu', prelu)
+    register_act('elu', elu)
+    register_act('lrelu_01', lrelu_01)
+    register_act('lrelu_025', lrelu_025)
+    register_act('lrelu_05', lrelu_05)

torch_geometric/graphgym/models/layer.py

@@ -96,7 +96,7 @@ def __init__(self, name, layer_config: LayerConfig, **kwargs):
                 nn.Dropout(p=layer_config.dropout,
                            inplace=layer_config.mem_inplace))
         if layer_config.has_act:
-            layer_wrapper.append(register.act_dict[layer_config.act])
+            layer_wrapper.append(register.act_dict[layer_config.act]())
         self.post_layer = nn.Sequential(*layer_wrapper)
 
     def forward(self, batch):