Add unit tests for readout.py

osculari/models/readout.py

@@ -22,6 +22,8 @@
     "load_paradigm_2afc",
     "load_paradigm_ooo",
     "ProbeNet",
+    "OddOneOutNet",
+    "Classifier2AFC",
     "ActivationLoader",
     "FeatureExtractor"
 ]

tests/models/readout_test.py

@@ -0,0 +1,85 @@
+"""
+Unit tests for readout.py
+"""
+
+import pytest
+import torch
+from torch.testing import assert_close
+
+from osculari.models import OddOneOutNet, load_paradigm_ooo, load_paradigm_2afc
+
+
+def test_odd_one_out_net_few_inputs():
+    with pytest.raises(RuntimeError):
+        _ = OddOneOutNet(input_nodes=2, merge_paradigm='cat',
+                         architecture='taskonomy_autoencoding', weights=None, layers='block0',
+                         img_size=224)
+
+
+def test_odd_one_out_net_init_cat():
+    # Test the initialization of OddOneOutNet
+    input_nodes = 4
+    net = OddOneOutNet(input_nodes=input_nodes, merge_paradigm='cat',
+                       architecture='taskonomy_autoencoding', weights=None, layers='block0',
+                       img_size=224)
+    assert net.input_nodes == input_nodes
+    assert net.fc.out_features == input_nodes
+
+
+def test_odd_one_out_net_init_diff():
+    # Test the initialization of OddOneOutNet
+    input_nodes = 4
+    net = OddOneOutNet(input_nodes=input_nodes, merge_paradigm='diff',
+                       architecture='taskonomy_autoencoding', weights=None, layers='block0',
+                       img_size=224)
+    assert net.input_nodes == input_nodes
+    assert net.fc.out_features == 1
+
+
+@pytest.mark.parametrize("merge_paradigm,expected", [("cat", 4), ("diff", 4)])
+def test_odd_one_out_net_forward_cat(merge_paradigm, expected):
+    # Test the forward pass of OddOneOutNet with merge_paradigm='cat'
+    input_nodes = 4
+    img_size = 224
+    net = OddOneOutNet(input_nodes=input_nodes, merge_paradigm=merge_paradigm,
+                       architecture='taskonomy_autoencoding', weights=None, layers='block0',
+                       img_size=img_size)
+
+    x1 = torch.randn(2, 3, img_size, img_size)
+    x2 = torch.randn(2, 3, img_size, img_size)
+    x3 = torch.randn(2, 3, img_size, img_size)
+    x4 = torch.randn(2, 3, img_size, img_size)
+
+    output = net(x1, x2, x3, x4)
+    assert output.shape == (2, input_nodes)
+
+
+def test_odd_one_out_net_serialization():
+    # Test the serialization of OddOneOutNet
+    input_nodes = 4
+    net = OddOneOutNet(input_nodes=input_nodes, merge_paradigm='cat',
+                       architecture='taskonomy_autoencoding', weights='taskonomy_autoencoding',
+                       layers='block0', img_size=224)
+
+    net_params = net.serialisation_params()
+    new_net = load_paradigm_ooo(net_params)
+
+    # Ensure that the parameters are correctly loaded
+    assert net.input_nodes == new_net.input_nodes
+    assert net.merge_paradigm == new_net.merge_paradigm
+    assert_close(net.state_dict(), new_net.state_dict())
+
+
+def test_odd_one_out_net_loss_function():
+    # Test the loss function of OddOneOutNet
+    input_nodes = 4
+    net = OddOneOutNet(input_nodes=input_nodes, merge_paradigm='cat',
+                       architecture='taskonomy_autoencoding', weights=None, layers='block0',
+                       img_size=224)
+
+    # Assuming a batch size of 2
+    output = torch.randn(2, input_nodes)
+    target = torch.randint(0, input_nodes, (2,), dtype=torch.long)
+
+    loss = net.loss_function(output, target)
+    assert loss.item() >= 0