New branch with auto-encoder only

2 files changed, 0 insertions, 94 deletions

diff --git a/test/hpm.py b/test/hpm.py
deleted file mode 100644
index 0aefbb8..0000000
--- a/test/hpm.py
+++ /dev/null
@@ -1,23 +0,0 @@
-import torch
-
-from models.hpm import HorizontalPyramidMatching
-
-T, N, C, H, W = 15, 4, 256, 32, 16
-
-
-def test_default_hpm():
-    hpm = HorizontalPyramidMatching(in_channels=C)
-    x = torch.rand(T, N, C, H, W)
-    x = hpm(x)
-    assert tuple(x.size()) == (1 + 2 + 4, T, N, 128)
-
-
-def test_custom_hpm():
-    hpm = HorizontalPyramidMatching(in_channels=2048,
-                                    out_channels=256,
-                                    scales=(1, 2, 4, 8),
-                                    use_avg_pool=True,
-                                    use_max_pool=False)
-    x = torch.rand(T, N, 2048, H, W)
-    x = hpm(x)
-    assert tuple(x.size()) == (1 + 2 + 4 + 8, T, N, 256)
diff --git a/test/part_net.py b/test/part_net.py
deleted file mode 100644
index 25e92ae..0000000
--- a/test/part_net.py
+++ /dev/null
@@ -1,71 +0,0 @@
-import torch
-
-from models.part_net import FrameLevelPartFeatureExtractor, \
-    TemporalFeatureAggregator, PartNet
-
-T, N, C, H, W = 15, 4, 3, 64, 32
-
-
-def test_default_fpfe():
-    fpfe = FrameLevelPartFeatureExtractor()
-    x = torch.rand(T, N, C, H, W)
-    x = fpfe(x)
-
-    assert tuple(x.size()) == (T * N, 32 * 4, 16, 8)
-
-
-def test_custom_fpfe():
-    feature_channels = 64
-    fpfe = FrameLevelPartFeatureExtractor(
-        in_channels=1,
-        feature_channels=feature_channels,
-        kernel_sizes=((5, 3), (3, 3), (3, 3), (3, 3)),
-        paddings=((2, 1), (1, 1), (1, 1), (1, 1)),
-        halving=(1, 1, 3, 3)
-    )
-    x = torch.rand(T, N, 1, H, W)
-    x = fpfe(x)
-
-    assert tuple(x.size()) == (T * N, feature_channels * 8, 8, 4)
-
-
-def test_default_tfa():
-    in_channels = 32 * 4
-    tfa = TemporalFeatureAggregator(in_channels)
-    x = torch.rand(16, T, N, in_channels)
-    x = tfa(x)
-
-    assert tuple(x.size()) == (16, N, in_channels)
-
-
-def test_custom_tfa():
-    in_channels = 64 * 8
-    num_part = 8
-    tfa = TemporalFeatureAggregator(in_channels=in_channels,
-                                    squeeze_ratio=8, num_part=num_part)
-    x = torch.rand(num_part, T, N, in_channels)
-    x = tfa(x)
-
-    assert tuple(x.size()) == (num_part, N, in_channels)
-
-
-def test_default_part_net():
-    pa = PartNet()
-    x = torch.rand(T, N, C, H, W)
-    x = pa(x)
-
-    assert tuple(x.size()) == (16, N, 32 * 4)
-
-
-def test_custom_part_net():
-    feature_channels = 64
-    pa = PartNet(in_channels=1, feature_channels=feature_channels,
-                 kernel_sizes=((5, 3), (3, 3), (3, 3), (3, 3)),
-                 paddings=((2, 1), (1, 1), (1, 1), (1, 1)),
-                 halving=(1, 1, 3, 3),
-                 squeeze_ratio=8,
-                 num_part=8)
-    x = torch.rand(T, N, 1, H, W)
-    x = pa(x)
-
-    assert tuple(x.size()) == (8, N, pa.tfa_in_channels)