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Update tensorflow_backend.py and linear_solvers.py #17

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Update tensorflow_backend.py and linear_solvers.py #17

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OolongBlender Nov 8, 2022
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Merge branch 'Thinklab-SJTU:main' into main
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Update test_classic_solvers.py
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133 changes: 133 additions & 0 deletions pygmtools/classic_solvers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -210,6 +210,53 @@ def sm(K, n1=None, n2=None, n1max=None, n2max=None, x0=None,
>>> (pygm.hungarian(X) * X_gt).sum() / X_gt.sum()
jt.Var([1.], dtype=float32)


.. dropdown:: Tensorflow Example

::

>>> import tensorflow as tf
>>> import pygmtools as pygm
>>> pygm.BACKEND = 'tensorflow'
>>> _ = tf.random.set_seed(1)

# Generate a batch of isomorphic graphs
>>> batch_size = 10
>>> X_gt = tf.Variable(tf.zeros([batch_size, 4, 4]))
>>> indices = tf.stack([tf.range(4),tf.random.shuffle(tf.range(4))], axis=1)
>>> updates = tf.ones([4])
>>> for i in range(batch_size):
... _ = X_gt[i].assign(tf.tensor_scatter_nd_update(X_gt[i], indices, updates))
>>> A1 = tf.random.uniform([batch_size, 4, 4])
>>> A2 = tf.matmul(tf.matmul(tf.transpose(X_gt, perm=[0, 2, 1]), A1), X_gt)
>>> n1 = n2 = tf.constant([4] * batch_size)

# Build affinity matrix
>>> conn1, edge1, ne1 = pygm.utils.dense_to_sparse(A1)
>>> conn2, edge2, ne2 = pygm.utils.dense_to_sparse(A2)
>>> import functools
>>> gaussian_aff = functools.partial(pygm.utils.gaussian_aff_fn, sigma=1.) # set affinity function
>>> K = pygm.utils.build_aff_mat(None, edge1, conn1, None, edge2, conn2, n1, None, n2, None, edge_aff_fn=gaussian_aff)

# Solve by SM. Note that X is normalized with a squared sum of 1
>>> X = pygm.sm(K, n1, n2)
>>> tf.reduce_sum((X ** 2), axis=[1, 2])
<tf.Tensor: shape=(10,), dtype=float32, numpy=
array([1. , 1.0000001 , 1. , 0.9999999 , 1. ,
1. , 1.0000001 , 0.99999994, 1. , 0.9999998 ],
dtype=float32)>

# Accuracy
>>> tf.reduce_sum((pygm.hungarian(X) * X_gt))/ tf.reduce_sum(X_gt)
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>

This solver supports gradient back-propogation
>>> K = tf.Variable(K)
>>> with tf.GradientTape() as tape:
... y = tf.reduce_sum(pygm.sm(K, n1, n2))
... len(tf.where(tape.gradient(y, K)))
2560

.. note::
If you find this graph matching solver useful for your research, please cite:

Expand Down Expand Up @@ -456,6 +503,52 @@ def rrwm(K, n1=None, n2=None, n1max=None, n2max=None, x0=None,
>>> (pygm.hungarian(X) * X_gt).sum() / X_gt.sum()
jt.Var([1.], dtype=float32)

.. dropdown:: Tensorflow Example

::

>>> import tensorflow as tf
>>> import pygmtools as pygm
>>> pygm.BACKEND = 'tensorflow'
>>> _ = tf.random.set_seed(1)

# Generate a batch of isomorphic graphs
>>> batch_size = 10
>>> X_gt = tf.Variable(tf.zeros([batch_size, 4, 4]))
>>> indices = tf.stack([tf.range(4),tf.random.shuffle(tf.range(4))], axis=1)
>>> updates = tf.ones([4])
>>> for i in range(batch_size):
... _ = X_gt[i].assign(tf.tensor_scatter_nd_update(X_gt[i], indices, updates))
>>> A1 = tf.random.uniform([batch_size, 4, 4])
>>> A2 = tf.matmul(tf.matmul(tf.transpose(X_gt, perm=[0, 2, 1]), A1), X_gt)
>>> n1 = n2 = tf.constant([4] * batch_size)

# Build affinity matrix
>>> conn1, edge1, ne1 = pygm.utils.dense_to_sparse(A1)
>>> conn2, edge2, ne2 = pygm.utils.dense_to_sparse(A2)
>>> import functools
>>> gaussian_aff = functools.partial(pygm.utils.gaussian_aff_fn, sigma=1.) # set affinity function
>>> K = pygm.utils.build_aff_mat(None, edge1, conn1, None, edge2, conn2, n1, None, n2, None, edge_aff_fn=gaussian_aff)

# Solve by RRWM. Note that X is normalized with a sum of 1
>>> X = pygm.rrwm(K, n1, n2, beta=100)
>>> tf.reduce_sum(X, axis=[1, 2])
<tf.Tensor: shape=(10,), dtype=float32, numpy=
array([1. , 1. , 1. , 0.99999994, 1. ,
1. , 1. , 0.99999994, 0.99999994, 1.0000001 ],
dtype=float32)>

# Accuracy
>>> tf.reduce_sum((pygm.hungarian(X) * X_gt)) / tf.reduce_sum(X_gt)
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>

# This solver supports gradient back-propogation
>>> K = tf.Variable(K)
>>> with tf.GradientTape(persistent=True) as tape:
... y = tf.reduce_sum(pygm.rrwm(K, n1, n2, beta=100))
... len(tf.where(tape.gradient(y, K)))
768

.. note::
If you find this graph matching solver useful in your research, please cite:

Expand Down Expand Up @@ -687,6 +780,46 @@ def ipfp(K, n1=None, n2=None, n1max=None, n2max=None, x0=None,
>>> (pygm.hungarian(X) * X_gt).sum() / X_gt.sum()
jt.Var([1.], dtype=float32)

.. dropdown:: Tensorflow Example

::

>>> import tensorflow as tf
>>> import pygmtools as pygm
>>> pygm.BACKEND = 'tensorflow'
>>> _ = tf.random.set_seed(1)

# Generate a batch of isomorphic graphs
>>> batch_size = 10
>>> X_gt = tf.Variable(tf.zeros([batch_size, 4, 4]))
>>> indices = tf.stack([tf.range(4),tf.random.shuffle(tf.range(4))], axis=1)
>>> updates = tf.ones([4])
>>> for i in range(batch_size):
... _ = X_gt[i].assign(tf.tensor_scatter_nd_update(X_gt[i], indices, updates))
>>> A1 = tf.random.uniform([batch_size, 4, 4])
>>> A2 = tf.matmul(tf.matmul(tf.transpose(X_gt, perm=[0, 2, 1]), A1), X_gt)
>>> n1 = n2 = tf.constant([4] * batch_size)

# Build affinity matrix
>>> conn1, edge1, ne1 = pygm.utils.dense_to_sparse(A1)
>>> conn2, edge2, ne2 = pygm.utils.dense_to_sparse(A2)
>>> import functools
>>> gaussian_aff = functools.partial(pygm.utils.gaussian_aff_fn, sigma=1.) # set affinity function
>>> K = pygm.utils.build_aff_mat(None, edge1, conn1, None, edge2, conn2, n1, None, n2, None, edge_aff_fn=gaussian_aff)

# Solve by IPFP
>>> X = pygm.ipfp(K, n1, n2)
>>> X[0]
<tf.Tensor: shape=(4, 4), dtype=float32, numpy=
array([[0., 0., 1., 0.],
[0., 1., 0., 0.],
[0., 0., 0., 1.],
[1., 0., 0., 0.]], dtype=float32)>

# Accuracy
>>> tf.reduce_sum((pygm.hungarian(X) * X_gt)) / tf.reduce_sum(X_gt)
<tf.Tensor: shape=(), dtype=float32, numpy=1.0>

.. note::
If you find this graph matching solver useful in your research, please cite:

Expand Down
175 changes: 175 additions & 0 deletions pygmtools/linear_solvers.py
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -495,6 +495,106 @@ def sinkhorn(s, n1=None, n2=None, unmatch1=None, unmatch2=None,
>>> print('row_sum:', x.sum(1), 'col_sum:', x.sum(0))
row_sum: jt.Var([0.8776659 0.52249795 0.45705223 0.8203896 0.70309657], dtype=float32) col_sum: jt.Var([0.7831943 0.30627945 0.73395807 0.61827636 0.938994 ], dtype=float32)

.. dropdown:: Tensorflow Example

::

>>> import tensorflow as tf
>>> import pygmtools as pygm
>>> pygm.BACKEND = 'tensorflow'
>>> np.random.seed(0)

# 2-dimensional (non-batched) input
>>> s_2d = tf.constant(np.random.rand(5, 5))
>>> s_2d
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ],
[0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152],
[0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606],
[0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215],
[0.97861834, 0.79915856, 0.46147936, 0.78052918, 0.11827443]])>
>>> x = pygm.sinkhorn(s_2d)
>>> x
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0.18880224, 0.24990915, 0.19202217, 0.16034278, 0.20892366],
[0.18945066, 0.17240445, 0.23345011, 0.22194762, 0.18274716],
[0.23713583, 0.204348 , 0.18271243, 0.23114583, 0.1446579 ],
[0.11731039, 0.1229692 , 0.23823909, 0.19961588, 0.32186549],
[0.26730088, 0.2503692 , 0.15357619, 0.18694789, 0.1418058 ]])>
>>> print('row_sum:', tf.reduce_sum(x,axis=1), 'col_sum:', tf.reduce_sum(x, axis=0))
row_sum: tf.Tensor([1. 1.00000001 0.99999998 1.00000005 0.99999997], shape=(5,), dtype=float64) col_sum: tf.Tensor([1. 1. 1. 1. 1.], shape=(5,), dtype=float64)

# 3-dimensional (batched) input
>>> s_3d = tf.constant(np.random.rand(3, 5, 5))
>>> x = pygm.sinkhorn(s_3d)
>>> print('row_sum:', tf.reduce_sum(x, axis=2))
row_sum: tf.Tensor(
[[1. 1. 1. 1. 1. ]
[0.99999998 1.00000002 0.99999999 1.00000003 0.99999999]
[1. 1. 1. 1. 1. ]], shape=(3, 5), dtype=float64)
>>> print('col_sum:', tf.reduce_sum(x, axis=1))
col_sum: tf.Tensor(
[[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]
[1. 1. 1. 1. 1.]], shape=(3, 5), dtype=float64)

# If the 3-d tensor are with different number of nodes
>>> n1 = tf.constant([3, 4, 5])
>>> n2 = tf.constant([3, 4, 5])
>>> x = pygm.sinkhorn(s_3d, n1, n2)
>>> x[0] # non-zero size: 3x3
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0.36665934, 0.21498158, 0.41835906, 0. , 0. ],
[0.27603621, 0.44270207, 0.28126175, 0. , 0. ],
[0.35730445, 0.34231636, 0.3003792 , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ],
[0. , 0. , 0. , 0. , 0. ]])>
>>> x[1] # non-zero size: 4x4
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0.28847831, 0.20583051, 0.34242091, 0.16327021, 0. ],
[0.22656752, 0.30153021, 0.19407969, 0.27782262, 0. ],
[0.25346378, 0.19649853, 0.32565049, 0.22438715, 0. ],
[0.23149039, 0.29614075, 0.13784891, 0.33452002, 0. ],
[0. , 0. , 0. , 0. , 0. ]])>
>>> x[2] # non-zero size: 5x5
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0.20147352, 0.19541986, 0.24942798, 0.17346397, 0.18021467],
[0.21050732, 0.17620948, 0.18645469, 0.20384684, 0.22298167],
[0.18319623, 0.18024007, 0.17619871, 0.1664133 , 0.29395169],
[0.20754376, 0.2236443 , 0.19658101, 0.20570847, 0.16652246],
[0.19727917, 0.22448629, 0.19133762, 0.25056742, 0.13632951]])>

# non-squared input
>>> s_non_square = tf.constant(np.random.rand(4, 5))
>>> x = pygm.sinkhorn(s_non_square, dummy_row=True) # set dummy_row=True for non-squared cases
>>> print('row_sum:', tf.reduce_sum(x,axis=1), 'col_sum:', tf.reduce_sum(x,axis=0))
row_sum: tf.Tensor([1. 1. 1. 1.], shape=(4,), dtype=float64) col_sum: tf.Tensor([0.78239609 0.80485526 0.80165627 0.80004254 0.81104984], shape=(5,), dtype=float64)

# allow matching to void nodes by setting unmatch1 and unmatch2
>>> s_2d = tf.constant(np.random.randn(5, 5))
>>> s_2d
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[ 0.01050002, 1.78587049, 0.12691209, 0.40198936, 1.8831507 ],
[-1.34775906, -1.270485 , 0.96939671, -1.17312341, 1.94362119],
[-0.41361898, -0.74745481, 1.92294203, 1.48051479, 1.86755896],
[ 0.90604466, -0.86122569, 1.91006495, -0.26800337, 0.8024564 ],
[ 0.94725197, -0.15501009, 0.61407937, 0.92220667, 0.37642553]])>
>>> unmatch1 = tf.constant(np.random.randn(5))
>>> unmatch1
<tf.Tensor: shape=(5,), dtype=float64, numpy=array([-1.09940079, 0.29823817, 1.3263859 , -0.69456786, -0.14963454])>
>>> unmatch2 = tf.constant(np.random.randn(5))
>>> unmatch2
<tf.Tensor: shape=(5,), dtype=float64, numpy=array([-0.43515355, 1.84926373, 0.67229476, 0.40746184, -0.76991607])>
>>> x = pygm.sinkhorn(s_2d, unmatch1=unmatch1, unmatch2=unmatch2, max_iter=40)
>>> x
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0.12434101, 0.23913991, 0.05663597, 0.13943479, 0.31811425],
[0.03084473, 0.01085787, 0.12689067, 0.02784578, 0.3260589 ],
[0.03192548, 0.00745004, 0.13391025, 0.16087345, 0.12289304],
[0.29820536, 0.01659601, 0.32997174, 0.06988242, 0.10573396],
[0.29787774, 0.0322356 , 0.08654936, 0.22023996, 0.06619393]])>
>>> print('row_sum:', tf.reduce_sum(x, axis=1), 'col_sum:', tf.reduce_sum(x, axis=0))
row_sum: tf.Tensor([0.87766593 0.52249794 0.45705226 0.82038949 0.70309659], shape=(5,), dtype=float64) col_sum: tf.Tensor([0.78319431 0.30627943 0.733958 0.61827641 0.93899407], shape=(5,), dtype=float64)

.. note::

Expand Down Expand Up @@ -914,6 +1014,81 @@ def hungarian(s, n1=None, n2=None, unmatch1=None, unmatch2=None,
[0. 0. 0. 0. 0.]
[0. 0. 0. 0. 0.]], dtype=float32)

.. dropdown:: Tensorflow Example

::

>>> import tensorflow as tf
>>> import pygmtools as pygm
>>> pygm.BACKEND = 'tensorflow'
>>> np.random.seed(0)

# 2-dimensional (non-batched) input
>>> s_2d = tf.constant(np.random.rand(5, 5))
>>> s_2d
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0.5488135 , 0.71518937, 0.60276338, 0.54488318, 0.4236548 ],
[0.64589411, 0.43758721, 0.891773 , 0.96366276, 0.38344152],
[0.79172504, 0.52889492, 0.56804456, 0.92559664, 0.07103606],
[0.0871293 , 0.0202184 , 0.83261985, 0.77815675, 0.87001215],
[0.97861834, 0.79915856, 0.46147936, 0.78052918, 0.11827443]])>
>>> x = pygm.hungarian(s_2d)
>>> x
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0., 1., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 1.],
[1., 0., 0., 0., 0.]])>

# 3-dimensional (batched) input
>>> s_3d = tf.constant(np.random.rand(3, 5, 5))
>>> n1 = n2 = tf.constant([3, 4, 5])
>>> x = pygm.hungarian(s_3d, n1, n2)
>>> x
<tf.Tensor: shape=(3, 5, 5), dtype=float64, numpy=
array([[[0., 0., 1., 0., 0.],
[0., 1., 0., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]],
<BLANKLINE>
[[1., 0., 0., 0., 0.],
[0., 1., 0., 0., 0.],
[0., 0., 1., 0., 0.],
[0., 0., 0., 1., 0.],
[0., 0., 0., 0., 0.]],
<BLANKLINE>
[[0., 0., 1., 0., 0.],
[1., 0., 0., 0., 0.],
[0., 0., 0., 0., 1.],
[0., 1., 0., 0., 0.],
[0., 0., 0., 1., 0.]]])>

# allow matching to void nodes by setting unmatch1 and unmatch2
>>> s_2d = tf.constant(np.random.randn(5, 5))
>>> s_2d
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[-1.16514984, 0.90082649, 0.46566244, -1.53624369, 1.48825219],
[ 1.89588918, 1.17877957, -0.17992484, -1.07075262, 1.05445173],
[-0.40317695, 1.22244507, 0.20827498, 0.97663904, 0.3563664 ],
[ 0.70657317, 0.01050002, 1.78587049, 0.12691209, 0.40198936],
[ 1.8831507 , -1.34775906, -1.270485 , 0.96939671, -1.17312341]])>
>>> unmatch1 = tf.constant(np.random.randn(5))
>>> unmatch1
<tf.Tensor: shape=(5,), dtype=float64, numpy=array([ 1.94362119, -0.41361898, -0.74745481, 1.92294203, 1.48051479])>
>>> unmatch2 = tf.constant(np.random.randn(5))
>>> unmatch2
<tf.Tensor: shape=(5,), dtype=float64, numpy=array([ 1.86755896, 0.90604466, -0.86122569, 1.91006495, -0.26800337])>
>>> x = pygm.hungarian(s_2d, unmatch1=unmatch1, unmatch2=unmatch2)
>>> x
<tf.Tensor: shape=(5, 5), dtype=float64, numpy=
array([[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 1.],
[0., 0., 1., 0., 0.],
[0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0.]])>

.. note::

If you find this graph matching solver useful for your research, please cite:
Expand Down
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