def test_accuracy():
accuracy = Accuracy()
accuracy_with_logits = AccuracyWithLogits()
top2 = TopKAccuracy(2)
top3 = TopKAccuracy(3)
fake_accuracy = AverageMetric(accuracy.score)
# First try with a multi class input
x = J.Variable(J.Tensor([[0.9, 0, 0.1, 0], [0.2, 0.3, 0.1, 0.4]]))
# Labels are the indicies
y = J.Variable(J.LongTensor([0, 0]))
assert accuracy(x, y).item() == 50.
assert fake_accuracy(x, y).item() == 50.
# Since applying the softmax again won't change the ordering
assert accuracy_with_logits(x, y) == 50.
assert top2(x, y) == 50.
assert top3(x, y) == 100.
# Now try with binary class input
x_logit = J.Variable(J.Tensor([[100.], [-100.]]))
x = torch.sigmoid(x_logit)
y = J.Variable(J.LongTensor([0, 0]))
assert accuracy(x, y) == 50.
assert fake_accuracy(x, y) == 50.
assert accuracy_with_logits(x_logit, y) == 50.
def test_accumulation():
accuracy = Accuracy()
x = J.Variable(
J.Tensor([[0.9, 0, 0.1, 0], [0.2, 0.3, 0.1, 0.4], [1.0, 0.0, 0.0,
0.0]]))
y = J.Variable(J.LongTensor([1, 2, 3]))
accuracy(x, y)
x = J.Variable(J.Tensor([[0.9, 0, 0.1, 0], [0.2, 0.3, 0.1, 0.4]]))
y = J.Variable(J.LongTensor([0, 0]))
accuracy(x, y)
assert accuracy.accumulate() == 20.
accuracy = accuracy.reset()
accuracy(x, y)
assert accuracy.accumulate() == 50.
def cast_input_to_torch(self, x, volatile=False):
# Remove any missing words
x = [np.array([word for word in sample if word not in self.missing]) for sample in x]
# Get the seq lens and pad it
seq_lens = J.LongTensor([max(len(sample), self.min_len) for sample in x])
x = np.array([L.pad_numpy_to_length(sample, length=seq_lens.max()) for sample in x], dtype=int)
return self.embeddings(Variable(J.from_numpy(x).long(), volatile=volatile)), seq_lens
def cast_input_to_torch(self, x, volatile=False):
# Get the seq lens and pad it
seq_lens = J.LongTensor([[
max(sample.shape[0], self.min_size),
max(sample.shape[1], self.min_size)
] for sample in x])
pad_shape, _ = seq_lens.max(dim=0)
# Simple hack to allow for images to fully resize at the same sizes as the encoder
pad_shape = next_power(pad_shape, len(self.encoder))
x = np.stack([
L.pad_numpy_to_shape(sample, shape=tuple(pad_shape))
for sample in x
])
return Variable(J.from_numpy(x.astype(np.float)).float(),
volatile=volatile), seq_lens
def test_categorical_crossentropy():
x = J.Variable(J.Tensor([[0.5, 0, 0.5, 0]]))
y = J.Variable(J.LongTensor([0]))
np.testing.assert_almost_equal(0.6931471824645996,
categorical_crossentropy(x, y).item())