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[Frontend] TF V2 sparse.todense() test added #7473

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Feb 25, 2021
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[Frontend] TF V2 sparse.todense() test added #7473

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[Frontend] TF V2 sparse.todense() test added
Feb 18, 2021
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Feb 24, 2021
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176 changes: 99 additions & 77 deletions tests/python/frontend/tensorflow/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1915,6 +1915,105 @@ def test_forward_sparse_fill_empty_rows(
)


#######################################################################
# tensorflow.compat.v1.sparse_to_dense
# ---------------
def _test_sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
with tf.Graph().as_default():
indices = tf.placeholder(
shape=sparse_indices.shape, dtype=str(sparse_indices.dtype), name="indices"
)
values = tf.placeholder(
shape=sparse_values.shape, dtype=str(sparse_values.dtype), name="values"
)
oshape = tf.constant(output_shape, shape=output_shape.shape, dtype=str(output_shape.dtype))

if default_value == None:
output = tf.sparse_to_dense(indices, oshape, values)
compare_tf_with_tvm(
[sparse_indices, sparse_values], ["indices:0", "values:0"], output.name
)
else:
dv = tf.placeholder(shape=(), dtype=str(default_value.dtype), name="default_value")
output = tf.sparse_to_dense(indices, oshape, values, dv)
compare_tf_with_tvm(
[sparse_indices, sparse_values, default_value],
["indices:0", "values:0", "default_value:0"],
output.name,
)


def test_forward_sparse_to_dense():
# scalar
_test_sparse_to_dense(
sparse_indices=np.int32(1),
sparse_values=np.int32(3),
default_value=np.int32(0),
output_shape=np.array([5]).astype("int32"),
)

# vector
_test_sparse_to_dense(
sparse_indices=np.array([0, 1, 4]).astype("int32"),
sparse_values=np.array([3, 3, 3]).astype("int32"),
default_value=np.int32(0),
output_shape=np.array([5]).astype("int32"),
)

# vector nXd
_test_sparse_to_dense(
sparse_indices=np.array([[0, 0], [1, 2]]).astype("int32"),
sparse_values=np.array([1, 2]).astype("int32"),
default_value=np.int32(0),
output_shape=np.array([3, 4]).astype("int32"),
)

_test_sparse_to_dense(
sparse_indices=np.array([[0, 0, 0], [1, 2, 3]]).astype("int32"),
sparse_values=np.array([1, 2]).astype("int32"),
default_value=np.int32(4),
output_shape=np.array([2, 3, 4]).astype("int32"),
)

# floats
_test_sparse_to_dense(
sparse_indices=np.array([0, 1, 4]).astype("int32"),
sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
default_value=np.float32(3.5),
output_shape=np.array([5]).astype("int32"),
)

# default value not specified
_test_sparse_to_dense(
sparse_indices=np.array([0, 1, 4]).astype("int32"),
sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
default_value=None,
output_shape=np.array([5]).astype("int32"),
)


#######################################################################
# tensorflow.sparse.to_dense
# ---------------
def _test_sparse_to_dense_v2(indices, values, A_shape, dtype, default_value=None):
with tf.Graph().as_default():
A_sp = tf.sparse.SparseTensor(indices=indices, values=values, dense_shape=A_shape)
B = tf.placeholder(shape=A_shape, dtype=dtype, name="B")

result = tf.sparse.to_dense(A_sp, default_value=default_value) + B
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B_np = np.random.uniform(high=5.0, size=A_shape).astype(dtype)

compare_tf_with_tvm([B_np], [B.name], result.name)


def test_forward_sparse_to_dense_v2():
_test_sparse_to_dense_v2([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], "float32")
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_test_sparse_to_dense_v2([[0, 0], [1, 2]], [4.0, 8.0], [3, 4], "float32", 1.3)
_test_sparse_to_dense_v2([[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [5, 5], "float32")
_test_sparse_to_dense_v2([[0, 0], [1, 3], [4, 3]], [3.0, 6.0, 9.0], [5, 5], "float32", 1.9)


#######################################################################
# StridedSlice
# ------------
Expand Down Expand Up @@ -4227,83 +4326,6 @@ def test_forward_identityn(data_np_list):
_test_identityn(data_np_list)


#######################################################################
# Sparse To Dense
# ---------------
def _test_sparse_to_dense(sparse_indices, sparse_values, default_value, output_shape):
with tf.Graph().as_default():
indices = tf.placeholder(
shape=sparse_indices.shape, dtype=str(sparse_indices.dtype), name="indices"
)
values = tf.placeholder(
shape=sparse_values.shape, dtype=str(sparse_values.dtype), name="values"
)
oshape = tf.constant(output_shape, shape=output_shape.shape, dtype=str(output_shape.dtype))

if default_value == None:
output = tf.sparse_to_dense(indices, oshape, values)
compare_tf_with_tvm(
[sparse_indices, sparse_values], ["indices:0", "values:0"], output.name
)
else:
dv = tf.placeholder(shape=(), dtype=str(default_value.dtype), name="default_value")
output = tf.sparse_to_dense(indices, oshape, values, dv)
compare_tf_with_tvm(
[sparse_indices, sparse_values, default_value],
["indices:0", "values:0", "default_value:0"],
output.name,
)


def test_forward_sparse_to_dense():
# scalar
_test_sparse_to_dense(
sparse_indices=np.int32(1),
sparse_values=np.int32(3),
default_value=np.int32(0),
output_shape=np.array([5]).astype("int32"),
)

# vector
_test_sparse_to_dense(
sparse_indices=np.array([0, 1, 4]).astype("int32"),
sparse_values=np.array([3, 3, 3]).astype("int32"),
default_value=np.int32(0),
output_shape=np.array([5]).astype("int32"),
)

# vector nXd
_test_sparse_to_dense(
sparse_indices=np.array([[0, 0], [1, 2]]).astype("int32"),
sparse_values=np.array([1, 2]).astype("int32"),
default_value=np.int32(0),
output_shape=np.array([3, 4]).astype("int32"),
)

_test_sparse_to_dense(
sparse_indices=np.array([[0, 0, 0], [1, 2, 3]]).astype("int32"),
sparse_values=np.array([1, 2]).astype("int32"),
default_value=np.int32(4),
output_shape=np.array([2, 3, 4]).astype("int32"),
)

# floats
_test_sparse_to_dense(
sparse_indices=np.array([0, 1, 4]).astype("int32"),
sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
default_value=np.float32(3.5),
output_shape=np.array([5]).astype("int32"),
)

# default value not specified
_test_sparse_to_dense(
sparse_indices=np.array([0, 1, 4]).astype("int32"),
sparse_values=np.array([3.1, 3.1, 3.1]).astype("float32"),
default_value=None,
output_shape=np.array([5]).astype("int32"),
)


#######################################################################
# infinity ops
# ------------
Expand Down