Implement DeepChem's GraphPoolLayer with PyTorch's custom layer

Introduction

Following yesterday's GraphConvLayer, I implemented DeepChem's GraphPoolLayer in a custom layer in Pytorch.

environment

Source

I ported DeepChem's GraphPoolLayer to PyTorch and tried to feed the output result of the previous GraphConvLayer to the created GraphPoolLayer.

import torch
from torch.utils import data
from deepchem.feat.graph_features import ConvMolFeaturizer
from deepchem.feat.mol_graphs import ConvMol
import torch.nn as nn
import numpy as np


class GraphConv(nn.Module):

    def __init__(self,
               in_channel,
               out_channel,
               min_deg=0,
               max_deg=10,
               activation=lambda x: x
               ):

        super().__init__()
        self.in_channel = in_channel
        self.out_channel = out_channel
        self.min_degree = min_deg
        self.max_degree = max_deg

        num_deg = 2 * self.max_degree + (1 - self.min_degree)

        self.W_list = [
            nn.Parameter(torch.Tensor(
                np.random.normal(size=(in_channel, out_channel))).double())
            for k in range(num_deg)]

        self.b_list = [
            nn.Parameter(torch.Tensor(np.zeros(out_channel)).double()) for k in range(num_deg)]

    def forward(self, atom_features, deg_slice, deg_adj_lists):

        #print("deg_adj_list")
        #print(deg_adj_lists)

        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 = deg_slice[deg - self.min_degree, 0]
            size = deg_slice[deg - self.min_degree, 1]

            self_atoms = torch.narrow(atom_features, 0, int(begin), int(size))

            # Apply hidden affine to relevant atoms and append
            rel_out = torch.matmul(rel_atoms, next(W)) + next(b)
            self_out = torch.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 = deg_slice[deg - self.min_degree, 0]
            size = deg_slice[deg - self.min_degree, 1]
            self_atoms = torch.narrow(atom_features, 0, int(begin), int(size))

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

            new_rel_atoms_collection[deg - self.min_degree] = out

        # Combine all atoms back into the list
        #print(new_rel_atoms_collection)
        atom_features = torch.cat(new_rel_atoms_collection, 0)

        return atom_features


    def sum_neigh(self, atoms, deg_adj_lists):
        """Store the summed atoms by degree"""
        deg_summed = self.max_degree * [None]

        for deg in range(1, self.max_degree + 1):
            index = torch.tensor(deg_adj_lists[deg - 1], dtype=torch.int64)
            gathered_atoms = atoms[index]

            # Sum along neighbors as well as self, and store
            summed_atoms = torch.sum(gathered_atoms, 1)
            deg_summed[deg - 1] = summed_atoms

        return deg_summed


class GraphPool(nn.Module):

    def __init__(self, min_degree=0, max_degree=10):
        super().__init__()
        self.min_degree = min_degree
        self.max_degree = max_degree


    def forward(self, atom_features, deg_slice, deg_adj_lists):

        # Perform the mol gather
        deg_maxed = (self.max_degree + 1 - self.min_degree) * [None]

        # Tensorflow correctly processes empty lists when using concat
        for deg in range(1, self.max_degree + 1):
            # Get self atoms
            begin = deg_slice[deg - self.min_degree, 0]
            size = deg_slice[deg - self.min_degree, 1]
            self_atoms = torch.narrow(atom_features, 0, int(begin), int(size))

            # Expand dims
            self_atoms = torch.unsqueeze(self_atoms, 1)

            # always deg-1 for deg_adj_lists
            index = torch.tensor(deg_adj_lists[deg - 1], dtype=torch.int64)

            gathered_atoms = atom_features[index]
            gathered_atoms = torch.cat([self_atoms, gathered_atoms], 1)

            if gathered_atoms.shape[0] > 0:
                maxed_atoms = torch.max(gathered_atoms, 1)[0]
            else:
                maxed_atoms = torch.Tensor([])

            deg_maxed[deg - self.min_degree] = maxed_atoms

        if self.min_degree == 0:
            begin = deg_slice[0, 0]
            size = deg_slice[0, 1]
            self_atoms = torch.narrow(atom_features, 0, int(begin), int(size))
            deg_maxed[0] = self_atoms

        return torch.cat(deg_maxed, 0)


class GCNDataset(data.Dataset):

    def __init__(self, smiles_list, label_list):
        self.smiles_list = smiles_list
        self.label_list = label_list

    def __len__(self):
        return len(self.smiles_list)

    def __getitem__(self, index):
        return self.smiles_list[index], self.label_list[index]


def gcn_collate_fn(batch):
    from rdkit import Chem
    cmf = ConvMolFeaturizer()

    mols = []
    labels = []

    for sample, label in batch:
        mols.append(Chem.MolFromSmiles(sample))
        labels.append(torch.tensor(label))

    conv_mols = cmf.featurize(mols)
    multiConvMol = ConvMol.agglomerate_mols(conv_mols)

    atom_feature = torch.tensor(multiConvMol.get_atom_features(), dtype=torch.float64)
    deg_slice = torch.tensor(multiConvMol.deg_slice, dtype=torch.float64)
    membership = torch.tensor(multiConvMol.membership, dtype=torch.float64)
    deg_adj_lists = []

    for i in range(1, len(multiConvMol.get_deg_adjacency_lists())):
        deg_adj_lists.append(multiConvMol.get_deg_adjacency_lists()[i])

    return atom_feature, deg_slice, membership, deg_adj_lists,  labels


def main():
    dataset = GCNDataset(["CCC", "CCCC", "CCCCC"], [1, 0, 1])
    dataloader = data.DataLoader(dataset, batch_size=3, shuffle=False, collate_fn =gcn_collate_fn)

    gc = GraphConv(75, 20)
    gp = GraphPool()
    for atom_feature, deg_slice, membership, deg_adj_lists, labels in dataloader:
        print("atom_feature")
        print(atom_feature)
        print("deg_slice")
        print(deg_slice)
        print("membership")
        print(membership)
        print("result")
        gc_out = gc(atom_feature, deg_slice, deg_adj_lists)
        gp_out = gp(gc_out, deg_slice, deg_adj_lists)
        print(gp_out)

if __name__ == "__main__":
    main()

result

Yes, don't. For the time being, the resulting shape is the number of atoms x 20 dimensions, which seems to be because it maintains the dimensions output by GraphConvLayer. I like this white box feeling as usual (the comments are exactly the same as the last time, so I've omitted it). However, the calculation is slightly different from TensorFlow, and it takes time to check it a little.

tensor([[ 1.8113e+00,  1.1862e+00,  1.3068e+00,  1.8266e+00,  6.0706e-03,
          7.2303e+00, -8.7022e-01,  1.1336e+00, -5.1411e+00, -3.3319e-02,
          1.8048e+00,  4.7143e+00,  3.8385e+00,  1.7524e+00,  5.2120e+00,
          2.8675e+00,  4.8746e+00, -2.5079e+00,  8.1260e+00,  7.8020e+00],
        [ 1.8113e+00,  1.1862e+00,  1.3068e+00,  1.8266e+00,  6.0706e-03,
          7.2303e+00, -8.7022e-01,  1.1336e+00, -5.1411e+00, -3.3319e-02,
          1.8048e+00,  4.7143e+00,  3.8385e+00,  1.7524e+00,  5.2120e+00,
          2.8675e+00,  4.8746e+00, -2.5079e+00,  8.1260e+00,  7.8020e+00],
        [ 3.0749e+00,  2.2618e+00,  8.2658e-02,  3.1331e+00,  6.0706e-03,
          4.5357e+00, -8.7022e-01,  1.1336e+00, -5.9143e+00, -3.3319e-02,
          1.8048e+00,  4.7143e+00,  5.9190e+00,  1.7524e+00,  5.2120e+00,
          1.5569e+00,  3.0329e+00, -2.5079e+00,  4.3327e+00,  4.7906e+00],
        [ 3.0749e+00,  2.2618e+00,  8.2658e-02,  3.1331e+00,  6.0706e-03,
          4.5357e+00, -8.7022e-01,  1.1336e+00, -5.9143e+00, -3.3319e-02,
          1.8048e+00,  4.7143e+00,  5.9190e+00,  1.7524e+00,  5.2120e+00,
          1.5569e+00,  3.0329e+00, -2.5079e+00,  4.3327e+00,  4.7906e+00],
        [ 3.0749e+00,  2.2618e+00,  8.2658e-02,  3.1331e+00,  6.0706e-03,
          4.5357e+00, -8.7022e-01,  1.1336e+00, -5.9143e+00, -3.3319e-02,
          1.8048e+00,  4.7143e+00,  5.9190e+00,  1.7524e+00,  5.2120e+00,
          1.5569e+00,  3.0329e+00, -2.5079e+00,  4.3327e+00,  4.7906e+00],
        [ 3.0749e+00,  2.2618e+00,  8.2658e-02,  3.1331e+00,  6.0706e-03,
          4.5357e+00, -8.7022e-01,  1.1336e+00, -5.9143e+00, -3.3319e-02,
          1.8048e+00,  4.7143e+00,  5.9190e+00,  1.7524e+00,  5.2120e+00,
          1.5569e+00,  3.0329e+00, -2.5079e+00,  4.3327e+00,  4.7906e+00]],
       dtype=torch.float64, grad_fn=<MaxBackward0>)

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