def test_compute_losses(sketches, node_feature_mapping, edge_feature_mapping):
sequences = list(map(graph_dataset.sketch_to_sequence, sketches))
dataset = graph_dataset.GraphDataset(sequences,
node_feature_mapping,
edge_feature_mapping,
seed=36)
batch = [dataset[i] for i in range(10)]
batch_input = graph_dataset.collate(batch, node_feature_mapping,
edge_feature_mapping)
feature_dimensions = {
**node_feature_mapping.feature_dimensions,
**edge_feature_mapping.feature_dimensions
}
model = graph_model.make_graph_model(32, feature_dimensions)
model_output = model(batch_input)
losses, _, edge_metrics, node_metrics = graph_model.compute_losses(
model_output, batch_input, feature_dimensions)
assert isinstance(losses, dict)
avg_losses = graph_model.compute_average_losses(
losses, batch_input['graph_counts'])
assert isinstance(avg_losses, dict)
for t in edge_metrics:
assert edge_metrics[t][0].shape == edge_metrics[t][1].shape
for t in edge_metrics:
assert node_metrics[t][0].shape == node_metrics[t][1].shape
def test_dataset(sketches, node_feature_mapping, edge_feature_mapping):
sequences = list(map(data_sequence.sketch_to_sequence, sketches))
dataset = graph_dataset.GraphDataset(sequences,
node_feature_mapping,
edge_feature_mapping,
seed=12)
graph, target = dataset[0]
assert graph_dataset.TargetType.EdgeDistance in graph.sparse_edge_features
assert graph.node_features is not None
assert graph.sparse_node_features is not None
def test_collate_some(sketches, node_feature_mapping, edge_feature_mapping):
sequences = list(map(data_sequence.sketch_to_sequence, sketches))
dataset = graph_dataset.GraphDataset(sequences,
node_feature_mapping,
edge_feature_mapping,
seed=42)
batch = [dataset[i] for i in range(5)]
batch_info = graph_dataset.collate(batch, node_feature_mapping,
edge_feature_mapping)
assert 'edge_numerical' in batch_info
assert batch_info['graph'].node_features is not None
def load_dataset_and_weights_with_mapping(dataset_file,
node_feature_mapping,
edge_feature_mapping,
seed=None):
data = flat_array.load_dictionary_flat(np.load(dataset_file,
mmap_mode='r'))
seqs = data['sequences']
seqs.share_memory_()
ds = dataset.GraphDataset(seqs, node_feature_mapping, edge_feature_mapping,
seed)
return ds, data['sequence_lengths']
def test_compute_model_no_entity_features(sketches, edge_feature_mapping):
sequences = list(map(graph_dataset.sketch_to_sequence, sketches))
dataset = graph_dataset.GraphDataset(sequences,
None,
edge_feature_mapping,
seed=36)
batch = [dataset[i] for i in range(10)]
batch_input = graph_dataset.collate(batch, None, edge_feature_mapping)
model = graph_model.make_graph_model(
32, {**edge_feature_mapping.feature_dimensions},
readout_entity_features=False)
result = model(batch_input)
assert 'node_embedding' in result
def load_dataset_and_weights(dataset_file,
auxiliary_file,
quantization,
seed=None,
entity_features=True,
edge_features=True,
force_entity_categorical_features=False):
data = load_sequences_and_mappings(dataset_file, auxiliary_file,
quantization, entity_features,
edge_features)
if data['entity_feature_mapping'] is None and force_entity_categorical_features:
# Create an entity mapping which only computes the categorical features (i.e. isConstruction and clockwise)
data['entity_feature_mapping'] = dataset.EntityFeatureMapping()
return dataset.GraphDataset(data['sequences'],
data['entity_feature_mapping'],
data['edge_feature_mapping'],
seed=seed), data['weights']
def test_compute_model_subnode(sketches):
sequences = list(map(graph_dataset.sketch_to_sequence, sketches))
dataset = graph_dataset.GraphDataset(sequences, seed=36)
batch = [dataset[i] for i in range(10)]
batch_input = graph_dataset.collate(batch)
model = graph_model.make_graph_model(32,
feature_dimensions={},
readout_entity_features=False,
readout_edge_features=False)
result = model(batch_input)
assert 'node_embedding' in result
losses, _, edge_metrics, node_metrics = graph_model.compute_losses(
result, batch_input, {})
assert isinstance(losses, dict)
avg_losses = graph_model.compute_average_losses(
losses, batch_input['graph_counts'])
assert isinstance(avg_losses, dict)
