train_test.py

# Copyright 2020 The Flax Authors.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# Lint as: python3
"""Tests for flax.examples.graph.train."""

from absl.testing import absltest
import train
from train import GNN

from flax import nn
from flax import optim
import jax
from jax import random
from jax import test_util as jtu
import jax.numpy as jnp

# Parse absl flags test_srcdir and test_tmpdir.
jax.config.parse_flags_with_absl()


class TrainTest(jtu.JaxTestCase):

  def test_permutation_invariance(self):

    num_nodes = 4
    num_features = 2
    rng = random.PRNGKey(0)

    # Generate random graph.
    adjacency = random.randint(rng, (num_nodes, num_nodes), 0, 2)
    node_feats = random.normal(rng, (num_nodes, num_features))
    sources, targets = jnp.where(adjacency)

    # Get permuted graph.
    perm = random.permutation(rng, jnp.arange(num_nodes))
    node_feats_perm = node_feats[perm]
    adjacency_perm = adjacency[perm]
    for j in range(len(adjacency)):
      adjacency_perm = jax.ops.index_update(
          adjacency_perm, j, adjacency_perm[j][perm])
    sources_perm, targets_perm = jnp.where(adjacency_perm)

    # Create GNN.
    _, initial_params = GNN.init(
      rng, node_x=node_feats, edge_x=None, sources=sources, targets=targets)
    model = nn.Model(GNN, initial_params)

    # Feedforward both original and permuted graph.
    logits = model(node_feats, None, sources, targets)
    logits_perm = model(node_feats_perm, None, sources_perm, targets_perm)

    self.assertAllClose(logits[perm], logits_perm, check_dtypes=False)

  def test_single_train_step(self):
    prng = random.PRNGKey(0)
    #       0 (0)
    #      / \
    # (0) 1 - 2 (1)
    edge_list = [(0, 0), (1, 2), (2, 0)]
    node_labels = [0, 0, 1]
    node_feats, node_labels, sources, targets = train.create_graph_data(edge_list=edge_list,
                                                                        node_labels=node_labels)
    _, initial_params = GNN.init(prng, node_x=node_feats, edge_x=None,
                                 sources=sources, targets=targets)
    model = nn.Model(GNN, initial_params)
    optimizer = optim.Adam(learning_rate=0.01).create(model)
    _, loss = train.train_step(optimizer=optimizer, node_feats=node_feats,
                               sources=sources, targets=targets)
    
    self.assertGreater(loss, 0.0)


if __name__ == '__main__':
  absltest.main()