Implement Batch All Triplet Loss

1 files changed, 65 insertions, 0 deletions

diff --git a/test/rgb_part_net.py b/test/rgb_part_net.py
new file mode 100644
index 0000000..1d754a0
--- /dev/null
+++ b/test/rgb_part_net.py
@@ -0,0 +1,65 @@
+import torch
+
+from models import RGBPartNet
+
+P, K = 2, 4
+N, T, C, H, W = P * K, 10, 3, 64, 32
+
+
+def rand_x1_x2_y(n, t, c, h, w):
+    x1 = torch.rand(n, t, c, h, w)
+    x2 = torch.rand(n, t, c, h, w)
+    y = []
+    for p in range(P):
+        y += [p] * K
+    y = torch.as_tensor(y)
+    return x1, x2, y
+
+
+def test_default_rgb_part_net():
+    rgb_pa = RGBPartNet()
+    x1, x2, y = rand_x1_x2_y(N, T, C, H, W)
+
+    rgb_pa.train()
+    loss, metrics = rgb_pa(x1, x2, y)
+    _, _, _, _ = metrics
+    assert tuple(loss.size()) == ()
+    assert isinstance(_, float)
+
+    rgb_pa.eval()
+    x = rgb_pa(x1, x2)
+    assert tuple(x.size()) == (23, N, 256)
+
+
+def test_custom_rgb_part_net():
+    hpm_scales = (1, 2, 4, 8)
+    tfa_num_parts = 8
+    embedding_dims = 1024
+    rgb_pa = RGBPartNet(num_class=10,
+                        ae_in_channels=1,
+                        ae_feature_channels=32,
+                        f_a_c_p_dims=(64, 64, 32),
+                        hpm_scales=hpm_scales,
+                        hpm_use_avg_pool=True,
+                        hpm_use_max_pool=False,
+                        fpfe_feature_channels=64,
+                        fpfe_kernel_sizes=((5, 3), (3, 3), (3, 3), (3, 3)),
+                        fpfe_paddings=((2, 1), (1, 1), (1, 1), (1, 1)),
+                        fpfe_halving=(1, 1, 3, 3),
+                        tfa_squeeze_ratio=8,
+                        tfa_num_parts=tfa_num_parts,
+                        embedding_dims=1024,
+                        triplet_margin=0.4)
+    x1, x2, y = rand_x1_x2_y(N, T, 1, H, W)
+
+    rgb_pa.train()
+    loss, metrics = rgb_pa(x1, x2, y)
+    _, _, _, _ = metrics
+    assert tuple(loss.size()) == ()
+    assert isinstance(_, float)
+
+    rgb_pa.eval()
+    x = rgb_pa(x1, x2)
+    assert tuple(x.size()) == (
+        sum(hpm_scales) + tfa_num_parts, N, embedding_dims
+    )