def test_to_sequence_basic(device_id):
dev = cntk_device(device_id)
x = C.input_variable((C.FreeDimension, 2))
x_seq = C.to_sequence(x)
assert len(x_seq.dynamic_axes) == 2
x_data = np.asarray([[[1, 2], [-1000, -1000]], [[3, 4], [5, 6]]], dtype=np.float32)
result = x_seq.eval({x : x_data}, device=dev)
assert np.array_equal(result, x_data)
x = C.input_variable((C.FreeDimension, 2, 3), is_sparse=True)
x_seq_lens = C.input_variable(())
x_seq = C.to_sequence(x, x_seq_lens)
seq1_data = [[[0, 1, 1], [0, 1, 0]], [[1, 0, 0], [1, 0, 1]]]
csr_seq1 = _to_csr(seq1_data)
ndarrayview1 = C.NDArrayView.from_csr(csr_seq1, shape=(2, 2, 3), device=C.cpu())
seq2_data = [[0, 1, 1], [1, 1, 0]]
csr_seq2 = _to_csr([seq2_data, [[0, 0, 0], [0, 0, 0]]])
ndarrayview2 = C.NDArrayView.from_csr(csr_seq2, shape=(2, 2, 3), device=C.cpu())
x_data = C.Value.create(C.input_variable((2, 2, 3), is_sparse=True), [ndarrayview1, ndarrayview2], device=dev).data
x_seq_lens_data = np.asarray([2, 1], dtype=np.float32)
result = x_seq.eval({x : x_data, x_seq_lens : x_seq_lens_data}, device=dev, as_numpy=False)
result_dense = _to_dense(result, True)
assert np.array_equal(result_dense[0], seq1_data)
assert np.array_equal(result_dense[1], [seq2_data])
def test_to_sequence_basic(device_id):
dev = cntk_device(device_id)
x = C.input_variable((C.FreeDimension, 2))
x_seq = C.to_sequence(x)
assert len(x_seq.dynamic_axes) == 2
x_data = np.asarray([[[1, 2], [-1000, -1000]], [[3, 4], [5, 6]]], dtype=np.float32)
result = x_seq.eval({x : x_data}, device=dev)
assert np.array_equal(result, x_data)
x = C.input_variable((C.FreeDimension, 2, 3), is_sparse=True)
x_seq_lens = C.input_variable(())
x_seq = C.to_sequence(x, x_seq_lens)
seq1_data = [[[0, 1, 1], [0, 1, 0]], [[1, 0, 0], [1, 0, 1]]]
csr_seq1 = _to_csr(seq1_data)
ndarrayview1 = C.NDArrayView.from_csr(csr_seq1, shape=(2, 2, 3), device=C.cpu())
seq2_data = [[0, 1, 1], [1, 1, 0]]
csr_seq2 = _to_csr([seq2_data, [[0, 0, 0], [0, 0, 0]]])
ndarrayview2 = C.NDArrayView.from_csr(csr_seq2, shape=(2, 2, 3), device=C.cpu())
x_data = C.Value.create(C.input_variable((2, 2, 3), is_sparse=True), [ndarrayview1, ndarrayview2], device=dev).data
x_seq_lens_data = np.asarray([2, 1], dtype=np.float32)
result = x_seq.eval({x : x_data, x_seq_lens : x_seq_lens_data}, device=dev, as_numpy=False)
result_dense = _to_dense(result, True)
assert np.array_equal(result_dense[0], seq1_data)
assert np.array_equal(result_dense[1], [seq2_data])
def test_2d_non_sequence_sparse_one_hot():
x = C.input_variable((2,))
num_classes = 3
sparse_one_hot = C.one_hot(x, num_classes, sparse_output=True)
indices = np.asarray([[2, 1], [0, 2], [1, 0]])
result = sparse_one_hot.eval({x : indices}, as_numpy=False)
result_dense = _to_dense(result)
assert np.array_equal(result_dense, np.eye(num_classes, dtype=np.float32)[indices])
def test_2d_non_sequence_sparse_one_hot():
x = C.input_variable((2, ))
num_classes = 3
sparse_one_hot = C.one_hot(x, num_classes, sparse_output=True)
indices = np.asarray([[2, 1], [0, 2], [1, 0]])
result = sparse_one_hot.eval({x: indices}, as_numpy=False)
result_dense = _to_dense(result)
assert np.array_equal(result_dense,
np.eye(num_classes, dtype=np.float32)[indices])
def test_2d_sparse_sequences_value(device_id):
dev = cntk_device(device_id)
seq1_data = [[[0, 1, 1], [0, 1, 0]], [[1, 0, 0], [1, 0, 1]]]
csr_seq1 = _to_csr(seq1_data)
ndarrayview1 = C.NDArrayView.from_csr(csr_seq1, shape=(2, 2, 3), device=C.cpu())
seq2_data = [[0, 1, 1], [1, 1, 0]]
csr_seq2 = _to_csr(seq2_data)
ndarrayview2 = C.NDArrayView.from_csr(csr_seq2, shape=(1, 2, 3), device=C.cpu())
x = C.sequence.input_variable((2, 3))
sequence_value = C.Value.create(x, [ndarrayview1, ndarrayview2], device=dev)
assert np.array_equal(_to_dense(sequence_value.data), [seq1_data, [seq2_data, [[0, 0, 0], [0, 0, 0]]]])
def test_ndarrayview_from_csr(device_id):
dev = cntk_device(device_id)
data = [[[0, 1, 1], [0, 1, 0]], [[1, 0, 0], [1, 0, 1]]]
csr_data = _to_csr(data)
ndarrayview = C.NDArrayView.from_csr(csr_data, shape=(2, 2, 3))
assert np.array_equal(_to_dense(ndarrayview), data)
with pytest.raises(ValueError):
ndarrayview = C.NDArrayView.from_csr(csr_data, shape=(3, 2, 3))
with pytest.raises(ValueError):
ndarrayview = C.NDArrayView.from_csr(csr_data, shape=(2, 2, 4))
def test_2d_sequence_sparse_one_hot():
x = C.sequence.input((2, ))
num_classes = 3
sparse_one_hot = C.one_hot(x, num_classes, sparse_output=True)
indices = [np.asarray([[2, 1], [0, 2]]), np.asarray([[1, 0]])]
result = sparse_one_hot.eval({x: indices}, as_numpy=False)
result_dense = _to_dense(result, True)
assert np.array_equal(result_dense[0],
np.eye(num_classes, dtype=np.float32)[indices[0]])
assert np.array_equal(result_dense[1],
np.eye(num_classes, dtype=np.float32)[indices[1]])
