Merge branch 'main' of https://github.com/sisl/InteractionImitation into main

scratch/johannes/normalization.py

@@ -0,0 +1,38 @@
+import torch
+import torchvision
+
+from torchvision import transforms
+from torch.utils.data import DataLoader
+
+train_set = torchvision.datasets.FashionMNIST(
+    root='./data'
+    ,train=True
+    ,download=True
+    ,transform=transforms.Compose([
+        transforms.ToTensor()
+    ])
+)
+
+loader = DataLoader(train_set, batch_size=len(train_set), num_workers=1)
+# load whole dataset
+input_data, out_data = next(iter(loader))
+out_data = out_data.float()
+# compute mean and std only over batch dimension
+m_in, s_in = input_data.mean(dim=0), input_data.std(dim=0)
+m_out, s_out = out_data.mean(dim=0), out_data.std(dim=0)
+
+input_tf = transforms.Normalize(m_in, s_in)
+out_tf = transforms.Normalize(m_out, s_out)
+
+transformed_input = input_tf(input_data)
+transformed_output = torch.sigmoid(out_tf(out_data))
+
+# scale sigmoid output [0, 1] to acceleration interval [a_min, a_max]
+a_min, a_max = (-4, 2)
+# compute m and s such that normalization with m and s results in desired scaling
+s = 1 / (a_max - a_min)
+m = - s * a_min
+scaling = transforms.Normalize(m, s)
+
+# DOES NOT WORK SINCE TORCHVISION NORMALIZE WORKS ONLY ON IMAGES
+scaled_output = scaling(transformed_output)

src/nets/util.py

@@ -2,13 +2,11 @@ import torch
 from torch.nn import functional
 
 def parse_functional(functional_config):
-    if functional_config is None:
-        return None
-    elif isinstance(functional_config, str):
+    if isinstance(functional_config, str):
         if functional_config == 'relu':
             return functional.relu
         elif functional_config == 'sigmoid':
-            return functional.sigmoid
+            return torch.sigmoid
         elif functional_config == 'softmax':
             return functional.softmax
-    
+    return None

tests/nets/test_deepsets.py

@@ -24,7 +24,11 @@ def test_constructor():
     phi_config["output_dim"] = 2
     phi_config["final_activation"] = "sigmoid"
     phi = ds.Phi.from_config(phi_config)
-    assert phi.final_activation == torch.nn.functional.sigmoid
+    assert phi.final_activation == torch.sigmoid
+
+    phi_config["final_activation"] = "relu"
+    phi = ds.Phi.from_config(phi_config)
+    assert phi.final_activation == torch.nn.functional.relu
 
 def test_phi():
     input_dim = 5