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Remove variables

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This commit is contained in:
Eren Golge
2018-04-25 08:02:56 -07:00
parent 95654de860
commit 7bc27fac82
2 changed files with 80 additions and 88 deletions
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tests/layers_tests.py
+17 -19
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@@ -9,7 +9,7 @@ class PrenetTests(unittest.TestCase):
def test_in_out(self):
layer = Prenet(128, out_features=[256, 128])
dummy_input = T.autograd.Variable(T.rand(4, 128))
dummy_input = T.rand(4, 128)
print(layer)
output = layer(dummy_input)
@@ -21,7 +21,7 @@ class CBHGTests(unittest.TestCase):
def test_in_out(self):
layer = CBHG(128, K=6, projections=[128, 128], num_highways=2)
dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
dummy_input = T.rand(4, 8, 128)
print(layer)
output = layer(dummy_input)
@@ -34,8 +34,8 @@ class DecoderTests(unittest.TestCase):
def test_in_out(self):
layer = Decoder(in_features=256, memory_dim=80, r=2)
dummy_input = T.autograd.Variable(T.rand(4, 8, 256))
dummy_memory = T.autograd.Variable(T.rand(4, 2, 80))
dummy_input = T.rand(4, 8, 256)
dummy_memory = T.rand(4, 2, 80)
output, alignment = layer(dummy_input, dummy_memory)
@@ -48,7 +48,7 @@ class EncoderTests(unittest.TestCase):
def test_in_out(self):
layer = Encoder(128)
dummy_input = T.autograd.Variable(T.rand(4, 8, 128))
dummy_input = T.rand(4, 8, 128)
print(layer)
output = layer(dummy_input)
@@ -62,24 +62,22 @@ class L1LossMaskedTests(unittest.TestCase):
def test_in_out(self):
layer = L1LossMasked()
dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
dummy_target = T.autograd.Variable(T.ones(4, 8, 128).float())
dummy_length = T.autograd.Variable((T.ones(4) * 8).long())
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.ones(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.shape[0] == 0
assert len(output.shape) == 1
assert output.data[0] == 0.0
assert output.item() == 0.0
dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
dummy_length = T.autograd.Variable((T.ones(4) * 8).long())
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.ones(4) * 8).long()
output = layer(dummy_input, dummy_target, dummy_length)
assert output.data[0] == 1.0, "1.0 vs {}".format(output.data[0])
assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])
dummy_input = T.autograd.Variable(T.ones(4, 8, 128).float())
dummy_target = T.autograd.Variable(T.zeros(4, 8, 128).float())
dummy_length = T.autograd.Variable((T.arange(5, 9)).long())
dummy_input = T.ones(4, 8, 128).float()
dummy_target = T.zeros(4, 8, 128).float()
dummy_length = (T.arange(5, 9)).long()
mask = ((_sequence_mask(dummy_length).float() - 1.0)
* 100.0).unsqueeze(2)
output = layer(dummy_input + mask, dummy_target, dummy_length)
assert output.data[0] == 1.0, "1.0 vs {}".format(output.data[0])
assert output.item() == 1.0, "1.0 vs {}".format(output.data[0])