Just Changes To Graph Convs (5add5bac) · Commits · 钟慕尧 / deepchem

deepchem/feat/mol_graphs.py

+8 −12

Original line number	Diff line number	Diff line
		@@ -86,6 +86,10 @@ class ConvMol(object):
		for deg in range(self.min_deg, self.max_deg + 1)
		]

		self.degree_list = []
		for i, deg in enumerate(range(self.min_deg, self.max_deg + 1)):
		self.degree_list.extend([deg] * deg_size[i])

		# Get the the start indices for items in each block
		self.deg_start = cumulative_sum(deg_size)

		@@ -267,14 +271,9 @@ class ConvMol(object):
		atoms_per_mol = [mol.get_num_atoms() for mol in mols]

		# Get atoms by degree
		atoms_by_deg = [
		mol.get_atoms_with_deg(deg)
		for deg in range(min_deg, max_deg + 1)
		for mol in mols
		]

		# stack the atoms
		all_atoms = np.vstack(atoms_by_deg)
		atoms_by_deg = np.concatenate([x.atom_features for x in mols])
		degree_vector = np.concatenate([x.degree_list for x in mols], axis=0)
		all_atoms = atoms_by_deg[degree_vector.argsort(kind='mergesort')]

		# Sort all atoms by degree.
		# Get the size of each atom list separated by molecule id, then by degree
		@@ -297,8 +296,7 @@ class ConvMol(object):

		# Determines the membership (atom i belongs to membership[i] molecule)
		membership = [
		k
		for deg in range(min_deg, max_deg + 1) for k in range(num_mols)
		k for deg in range(min_deg, max_deg + 1) for k in range(num_mols)
		for i in range(mol_deg_sz[deg][k])
		]

		@@ -371,7 +369,6 @@ class MultiConvMol(object):
		"""

		def __init__(self, nodes, deg_adj_lists, deg_slice, membership, num_mols):

		self.nodes = nodes
		self.deg_adj_lists = deg_adj_lists
		self.deg_slice = deg_slice
		@@ -398,7 +395,6 @@ class WeaveMol(object):
		"""

		def __init__(self, nodes, pairs):

		self.nodes = nodes
		self.pairs = pairs
		self.num_atoms = self.nodes.shape[0]

deepchem/models/tensorgraph/layers.py

+3 −18

Original line number	Diff line number	Diff line
		@@ -2737,27 +2737,12 @@ class GraphGather(Layer):
		# Extract graph topology
		membership = inputs[2]

		# Perform the mol gather

		assert self.batch_size > 1, "graph_gather requires batches larger than 1"

		# Obtain the partitions for each of the molecules
		activated_par = tf.dynamic_partition(atom_features, membership,
		sparse_reps = tf.unsorted_segment_sum(atom_features, membership,
		self.batch_size)
		max_reps = tf.unsorted_segment_max(atom_features, membership,
		self.batch_size)

		# Sum over atoms for each molecule
		sparse_reps = [
		tf.reduce_mean(activated, 0, keepdims=True)
		for activated in activated_par
		]
		max_reps = [
		tf.reduce_max(activated, 0, keepdims=True)
		for activated in activated_par
		]

		# Get the final sparse representations
		sparse_reps = tf.concat(axis=0, values=sparse_reps)
		max_reps = tf.concat(axis=0, values=max_reps)
		mol_features = tf.concat(axis=1, values=[sparse_reps, max_reps])

		if self.activation_fn is not None:

deepchem/models/tensorgraph/models/graph_models.py

+2 −3

Original line number	Diff line number	Diff line
		@@ -355,8 +355,8 @@ class DTNNModel(TensorGraph):
		start = start + num_atoms[im]
		feed_dict[self.atom_number] = np.concatenate(atom_number)
		distance = np.concatenate(distance, 0)
		feed_dict[self.distance] = np.exp(-np.square(distance - self.steps) /
		(2 * self.step_size**2))
		feed_dict[self.distance] = np.exp(
		-np.square(distance - self.steps) / (2 * self.step_size**2))
		feed_dict[self.distance_membership_i] = np.concatenate(
		distance_membership_i)
		feed_dict[self.distance_membership_j] = np.concatenate(
		@@ -598,7 +598,6 @@ class GraphConvModel(TensorGraph):
		self.mode = mode
		self.dense_layer_size = dense_layer_size
		self.graph_conv_layers = graph_conv_layers
		kwargs['use_queue'] = False
		self.number_atom_features = number_atom_features
		self.n_classes = n_classes
		self.uncertainty = uncertainty

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