From 2ac1787e4580521848460215e6b06f4bb1648f06 Mon Sep 17 00:00:00 2001
From: Jordan Gong <jordan.gong@protonmail.com>
Date: Sun, 3 Jan 2021 15:08:19 +0800
Subject: Unit testing on auto-encoder, HPM and Part Net

---
 test/part_net.py | 71 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 71 insertions(+)
 create mode 100644 test/part_net.py

(limited to 'test/part_net.py')

diff --git a/test/part_net.py b/test/part_net.py
new file mode 100644
index 0000000..25e92ae
--- /dev/null
+++ b/test/part_net.py
@@ -0,0 +1,71 @@
+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)
-- 
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