Unit testing on auto-encoder, HPM and Part Net

1 files changed, 99 insertions, 0 deletions

diff --git a/test/auto_encoder.py b/test/auto_encoder.py
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+++ b/test/auto_encoder.py
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+import torch
+
+from models.auto_encoder import Encoder, Decoder, AutoEncoder
+
+N, C, H, W = 128, 3, 64, 32
+
+
+def test_default_encoder():
+    encoder = Encoder()
+    x = torch.rand(N, C, H, W)
+    f_a, f_c, f_p = encoder(x)
+
+    assert tuple(f_a.size()) == (N, 128)
+    assert tuple(f_c.size()) == (N, 128)
+    assert tuple(f_p.size()) == (N, 64)
+
+
+def test_custom_encoder():
+    output_dims = (64, 64, 32)
+    encoder = Encoder(in_channels=1,
+                      feature_channels=32,
+                      output_dims=output_dims)
+    x = torch.rand(N, 1, H, W)
+    f_a, f_c, f_p = encoder(x)
+
+    assert tuple(f_a.size()) == (N, output_dims[0])
+    assert tuple(f_c.size()) == (N, output_dims[1])
+    assert tuple(f_p.size()) == (N, output_dims[2])
+
+
+def test_default_decoder():
+    decoder = Decoder()
+    f_a, f_c, f_p = torch.rand(N, 128), torch.rand(N, 128), torch.rand(N, 64)
+
+    x_trans_conv = decoder(f_a, f_c, f_p)
+    assert tuple(x_trans_conv.size()) == (N, C, H, W)
+    x_no_trans_conv = decoder(f_a, f_c, f_p, no_trans_conv=True)
+    assert tuple(x_no_trans_conv.size()) == (N, 64 * 8, 4, 2)
+
+
+def test_custom_decoder():
+    embedding_dims = (64, 64, 32)
+    feature_channels = 32
+    decoder = Decoder(input_dims=embedding_dims,
+                      feature_channels=feature_channels,
+                      out_channels=1)
+    f_a, f_c, f_p = (torch.rand(N, embedding_dims[0]),
+                     torch.rand(N, embedding_dims[1]),
+                     torch.rand(N, embedding_dims[2]))
+
+    x_trans_conv = decoder(f_a, f_c, f_p)
+    assert tuple(x_trans_conv.size()) == (N, 1, H, W)
+    x_no_trans_conv = decoder(f_a, f_c, f_p, no_trans_conv=True)
+    assert tuple(x_no_trans_conv.size()) == (N, feature_channels * 8, 4, 2)
+
+
+def test_default_auto_encoder():
+    ae = AutoEncoder()
+    x = torch.rand(N, C, H, W)
+    y = torch.randint(74, (N,))
+
+    ae.train()
+    ((x_c, x_p), (f_p_c1, f_p_c2), (xrecon, cano)) = ae(x, x, x, y)
+    assert tuple(x_c.size()) == (N, 64 * 8, 4, 2)
+    assert tuple(x_p.size()) == (N, C, H, W)
+    assert tuple(f_p_c1.size()) == tuple(f_p_c2.size()) == (N, 64)
+    assert tuple(xrecon.size()) == tuple(cano.size()) == ()
+
+    ae.eval()
+    (x_c, x_p) = ae(x, x, x)
+    assert tuple(x_c.size()) == (N, 64 * 8, 4, 2)
+    assert tuple(x_p.size()) == (N, C, H, W)
+
+
+def test_custom_auto_encoder():
+    num_class = 10
+    channels = 1
+    embedding_dims = (64, 64, 32)
+    feature_channels = 32
+    ae = AutoEncoder(num_class=num_class,
+                     channels=channels,
+                     feature_channels=feature_channels,
+                     embedding_dims=embedding_dims)
+    x = torch.rand(N, 1, H, W)
+    y = torch.randint(num_class, (N,))
+
+    ae.train()
+    ((x_c, x_p), (f_p_c1, f_p_c2), (xrecon, cano)) = ae(x, x, x, y)
+    assert tuple(x_c.size()) == (N, feature_channels * 8, 4, 2)
+    assert tuple(x_p.size()) == (N, 1, H, W)
+    assert tuple(f_p_c1.size()) \
+           == tuple(f_p_c2.size()) \
+           == (N, embedding_dims[2])
+    assert tuple(xrecon.size()) == tuple(cano.size()) == ()
+
+    ae.eval()
+    (x_c, x_p) = ae(x, x, x)
+    assert tuple(x_c.size()) == (N, feature_channels * 8, 4, 2)
+    assert tuple(x_p.size()) == (N, 1, H, W)