887f94bd4f292778622304ba627727a6dacad43f,deepchem/models/tensorgraph/layers.py,GraphConv,create_tensor,#GraphConv#Any#Any#,2414

Before Change

    in_channels = inputs[0].get_shape()[-1].value

    // Generate the nb_affine weights and biases
    self.W_list = [
        initializations.glorot_uniform([in_channels, self.out_channel])
        for k in range(self.num_deg)
    ]
    self.b_list = [
        model_ops.zeros(shape=[
            self.out_channel,
        ]) for k in range(self.num_deg)
    ]

    // Extract atom_features
    atom_features = inputs[0]

After Change

        ]) for k in range(self.num_deg)
    ]

  def create_tensor(self, in_layers=None, set_tensors=True, **kwargs):
    inputs = self._get_input_tensors(in_layers)
    // in_layers = [atom_features, deg_slice, membership, deg_adj_list placeholders...]
    in_channels = inputs[0].get_shape()[-1].value
    if not tfe.in_eager_mode() or not self._built:
      self._create_variables(in_channels)

    // Extract atom_features
    atom_features = inputs[0]

    // Extract graph topology
    deg_slice = inputs[1]
    deg_adj_lists = inputs[3:]

    // Perform the mol conv
    // atom_features = graph_conv(atom_features, deg_adj_lists, deg_slice,
    //                            self.max_deg, self.min_deg, self.W_list,
    //                            self.b_list)

    W = iter(self.W_list)
    b = iter(self.b_list)

    // Sum all neighbors using adjacency matrix
    deg_summed = self.sum_neigh(atom_features, deg_adj_lists)

    // Get collection of modified atom features
    new_rel_atoms_collection = (self.max_degree + 1 - self.min_degree) * [None]

    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)

      // Apply hidden affine to relevant atoms and append
      rel_out = tf.matmul(rel_atoms, next(W)) + next(b)
      self_out = tf.matmul(self_atoms, next(W)) + next(b)
      out = rel_out + self_out

      new_rel_atoms_collection[deg - self.min_degree] = out

    // 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)

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

      new_rel_atoms_collection[deg - self.min_degree] = out

    // Combine all atoms back into the list
    atom_features = tf.concat(axis=0, values=new_rel_atoms_collection)

    if self.activation_fn is not None:
      atom_features = self.activation_fn(atom_features)

    out_tensor = atom_features
    if set_tensors:
      self._record_variable_scope(self.name)
      self.out_tensor = out_tensor
    if tfe.in_eager_mode() and not self._built:
      self._built = True
      self.variables = self.W_list + self.b_list
    return out_tensor

  def sum_neigh(self, atoms, deg_adj_lists):
    Store the summed atoms by degree

Instances