Merge pull request #1862 from peastman/conv (cd4e8a5c) · Commits · 钟慕尧 / deepchem

deepchem/feat/mol_graphs.py

+14 −14

Original line number	Diff line number	Diff line
		@@ -22,7 +22,7 @@ def cumulative_sum_minus_last(l, offset=0):
		l: list
		List of integers. Typically small counts.
		"""
		return np.delete(np.insert(np.cumsum(l), 0, 0), -1) + offset
		return np.delete(np.insert(np.cumsum(l, dtype=np.int32), 0, 0), -1) + offset


		def cumulative_sum(l, offset=0):
		@@ -96,7 +96,7 @@ class ConvMol(object):
		]

		# Convert to numpy array
		self.deg_block_indices = np.array(deg_block_indices)
		self.deg_block_indices = np.array(deg_block_indices, dtype=np.int32)

		def get_atoms_with_deg(self, deg):
		"""Retrieves atom_features with the specific degree"""
		@@ -157,7 +157,7 @@ class ConvMol(object):
		to_cat = [self.canon_adj_list[i] for i in indices]
		if len(to_cat) > 0:
		adj_list = np.vstack([self.canon_adj_list[i] for i in indices])
		self.deg_adj_lists[deg - self.min_deg] = adj_list
		self.deg_adj_lists[deg - self.min_deg] = adj_list.astype(np.int32)

		else:
		self.deg_adj_lists[deg - self.min_deg] = np.zeros(
		@@ -333,7 +333,7 @@ class ConvMol(object):
		for deg in range(min_deg, max_deg + 1)
		]

		# Update the old adjcency lists with the new atom indices and then combine
		# Update the old adjacency lists with the new atom indices and then combine
		# all together
		for deg in range(min_deg, max_deg + 1):
		row = 0 # Initialize counter

deepchem/models/layers.py

+19 −23

Original line number	Diff line number	Diff line
		@@ -96,14 +96,13 @@ class GraphConv(tf.keras.layers.Layer):
		# Get collection of modified atom features
		new_rel_atoms_collection = (self.max_degree + 1 - self.min_degree) * [None]

		split_features = tf.split(atom_features, deg_slice[:, 1])
		for deg in range(1, self.max_degree + 1):
		# Obtain relevant atoms for this degree
		rel_atoms = deg_summed[deg - 1]

		# Get self atoms
		begin = tf.stack([deg_slice[deg - self.min_degree, 0], 0])
		size = tf.stack([deg_slice[deg - self.min_degree, 1], -1])
		self_atoms = tf.slice(atom_features, begin, size)
		self_atoms = split_features[deg - self.min_degree]

		# Apply hidden affine to relevant atoms and append
		rel_out = tf.matmul(rel_atoms, next(W)) + next(b)
		@@ -114,16 +113,12 @@ class GraphConv(tf.keras.layers.Layer):

		# Determine the min_deg=0 case
		if self.min_degree == 0:
		deg = 0

		begin = tf.stack([deg_slice[deg - self.min_degree, 0], 0])
		size = tf.stack([deg_slice[deg - self.min_degree, 1], -1])
		self_atoms = tf.slice(atom_features, begin, size)
		self_atoms = split_features[0]

		# Only use the self layer
		out = tf.matmul(self_atoms, next(W)) + next(b)

		new_rel_atoms_collection[deg - self.min_degree] = out
		new_rel_atoms_collection[0] = out

		# Combine all atoms back into the list
		atom_features = tf.concat(axis=0, values=new_rel_atoms_collection)
		@@ -173,12 +168,15 @@ class GraphPool(tf.keras.layers.Layer):

		# Tensorflow correctly processes empty lists when using concat

		split_features = tf.split(atom_features, deg_slice[:, 1])
		for deg in range(1, self.max_degree + 1):
		# Get self atoms
		begin = tf.stack([deg_slice[deg - self.min_degree, 0], 0])
		size = tf.stack([deg_slice[deg - self.min_degree, 1], -1])
		self_atoms = tf.slice(atom_features, begin, size)
		self_atoms = split_features[deg - self.min_degree]

		if deg_adj_lists[deg - 1].shape[0] == 0:
		# There are no neighbors of this degree, so just create an empty tensor directly.
		maxed_atoms = tf.zeros((0, self_atoms.shape[-1]))
		else:
		# Expand dims
		self_atoms = tf.expand_dims(self_atoms, 1)

		@@ -190,9 +188,7 @@ class GraphPool(tf.keras.layers.Layer):
		deg_maxed[deg - self.min_degree] = maxed_atoms

		if self.min_degree == 0:
		begin = tf.stack([deg_slice[0, 0], 0])
		size = tf.stack([deg_slice[0, 1], -1])
		self_atoms = tf.slice(atom_features, begin, size)
		self_atoms = split_features[0]
		deg_maxed[0] = self_atoms

		return tf.concat(axis=0, values=deg_maxed)

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