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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
)
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