def test_graph_save_load(tmp_path):
input1 = ak.Input()
input2 = ak.Input()
output1 = ak.DenseBlock()(input1)
output2 = ak.ConvBlock()(input2)
output = ak.Merge()([output1, output2])
output1 = ak.RegressionHead()(output)
output2 = ak.ClassificationHead()(output)
graph = graph_module.Graph(
inputs=[input1, input2],
outputs=[output1, output2],
override_hps=[
hp_module.Choice("dense_block_1/num_layers", [6], default=6)
],
)
path = os.path.join(tmp_path, "graph")
graph.save(path)
graph = graph_module.load_graph(path)
assert len(graph.inputs) == 2
assert len(graph.outputs) == 2
assert isinstance(graph.inputs[0].out_blocks[0], ak.DenseBlock)
assert isinstance(graph.inputs[1].out_blocks[0], ak.ConvBlock)
assert isinstance(graph.override_hps[0], hp_module.Choice)
def test_save_custom_metrics_loss(tmp_path):
def custom_metric(y_pred, y_true):
return 1
def custom_loss(y_pred, y_true):
return y_pred - y_true
head = ak.ClassificationHead(
loss=custom_loss, metrics=["accuracy", custom_metric]
)
input_node = ak.Input()
output_node = head(input_node)
graph = graph_module.Graph(input_node, output_node)
path = os.path.join(tmp_path, "graph")
graph.save(path)
new_graph = graph_module.load_graph(
path,
custom_objects={"custom_metric": custom_metric, "custom_loss": custom_loss},
)
assert new_graph.blocks[0].metrics[1](0, 0) == 1
assert new_graph.blocks[0].loss(3, 2) == 1
def test_graph_save_load(tmp_path):
input1 = ak.Input()
input2 = ak.Input()
output1 = ak.DenseBlock()(input1)
output2 = ak.ConvBlock()(input2)
output = ak.Merge()([output1, output2])
output1 = ak.RegressionHead()(output)
output2 = ak.ClassificationHead()(output)
graph = graph_module.Graph(
inputs=[input1, input2],
outputs=[output1, output2],
)
path = os.path.join(tmp_path, "graph")
graph.save(path)
graph = graph_module.load_graph(path)
assert len(graph.inputs) == 2
assert len(graph.outputs) == 2
assert isinstance(graph.inputs[0].out_blocks[0], ak.DenseBlock)
assert isinstance(graph.inputs[1].out_blocks[0], ak.ConvBlock)
