def test_architecture(self):
in_channels = 3
X = torch.rand([2, in_channels, 84, 84])
Y = torch.ones([2, 1])
Y[0, 0] = 0
em = embedding_module(in_channels)
rm = relation_module(4)
before_params = [p.clone() for p in rm.parameters()]
optimizer = torch.optim.Adam(rm.parameters())
loss_fn = nn.BCEWithLogitsLoss()
f_X = em(X)
g_f_X = rm(f_X)
loss = loss_fn(g_f_X, Y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
after_params = [p.clone() for p in rm.parameters()]
for b_param, a_param in zip(before_params, after_params):
# Make sure something changed.
# print(b_param)
# print(a_param)
self.assertTrue((b_param != a_param).any())
def test_input_dim_miniImageNet(self):
in_channels = 3
X = torch.ones([1, in_channels, 84, 84])
em = embedding_module(in_channels)
rm = relation_module(4)
f_X = em(X)
g_f_X = rm(f_X)
self.assertTupleEqual(g_f_X.size(), (1, 1))
def test_input_dim_Omniglot(self):
in_channels = 1
X = torch.ones([1, in_channels, 28, 28])
em = embedding_module(in_channels)
rm = relation_module(1)
f_X = em(X)
g_f_X = rm(f_X)
self.assertTupleEqual(g_f_X.size(), (1, 1))
def test_architecture(self):
in_channels = 3
X = torch.ones([1, in_channels, 84, 84])
Y = torch.ones([1, 64, 5, 5])
em = embedding_module(in_channels)
before_params = [p.clone() for p in em.parameters()]
optimizer = torch.optim.Adam(em.parameters())
loss_fn = nn.BCEWithLogitsLoss()
f_X = em(X)
loss = loss_fn(f_X, Y)
optimizer.zero_grad()
loss.backward()
optimizer.step()
after_params = [p.clone() for p in em.parameters()]
for b_param, a_param in zip(before_params, after_params):
# Make sure something changed.
self.assertTrue((b_param != a_param).any())
def test_input_dim_miniImageNet(self):
in_channels = 3
X = torch.ones([1, in_channels, 84, 84])
em = embedding_module(in_channels)
f_X = em(X)
self.assertTupleEqual(f_X.size(), (1, 64, 5, 5))
def test_input_dim_Omniglot(self):
in_channels = 1
X = torch.ones([1, in_channels, 28, 28])
em = embedding_module(in_channels)
f_X = em(X)
self.assertTupleEqual(f_X.size(), (1, 64, 1, 1))