def test_cross_simple(mnist_graph):
G = create_graph(mnist_graph, opts={"load_tensors":True})
G.add_dimensions()
groups, neurons = cl.discover_groups(G)
assert groups and neurons, "Nothing discovered"
cl.process_groups(groups)
cl.update_parameters(neurons)
def test_cross_mini(two_conv_graph):
G = two_conv_graph
output1 = execute(G, [np.full([10, 10, 2], 1)])
groups, neurons = cl.discover_groups(G)
assert groups and neurons, "Nothing discovered"
cl.process_groups(groups)
cl.update_parameters(neurons)
output2 = execute(G, [np.full([10, 10, 2], 1)])
assert np.max(np.abs(output1[3][0] - output2[3][0])) < 0.00001
def test_cross_fused(mnist_graph):
G = create_graph(mnist_graph, opts={"load_tensors":True})
G.add_dimensions()
matcher = MatchAllGapConv()
matcher.match(G)
G.add_dimensions()
groups, neurons = cl.discover_groups(G)
assert groups and neurons, "Nothing discovered"
cl.process_groups(groups)
cl.update_parameters(neurons)
def test_cross_large(vww_graph, vww_images):
G = create_graph(vww_graph, opts={"load_tensors": True})
G.add_dimensions()
input_tensor = import_data(vww_images[4], offset=0, divisor=255)
output1 = execute(G, [input_tensor])
groups, neurons = cl.discover_groups(G, do_relun=True)
group_inputs = [
G.in_edges(grp[0][0]['name'])[0].from_node.step_idx for grp in groups
]
group_outputs = [grp[-1][-1]['node'].step_idx for grp in groups]
assert groups and neurons, "Nothing discovered"
cl.process_groups(groups, threshold=0.0001)
cl.update_parameters(neurons)
output2 = execute(G, [input_tensor])
assert max(
[np.max(np.abs(output1[i][0] - output2[i][0]))
for i in group_inputs]) < 0.0001
assert max(
[np.max(np.abs(output1[i][0] - output2[i][0]))
for i in group_outputs]) < 0.0001
assert np.max(np.abs(output1[-1][0] - output2[-1][0])) < 0.0001
