def test_merge():
"""
Functional test of merge().
"""
# Subgraph 1
sg1 = empty_graph("Graph 1")
n1 = node('Add', inputs=['x1', 'x2'], outputs=['sum'])
sg1 = add_node(sg1, n1)
sg1 = add_input(sg1, 'x1', "FLOAT", [1])
sg1 = add_input(sg1, 'x2', "FLOAT", [1])
sg1 = add_output(sg1, 'sum', "FLOAT", [1])
# Subgraph 2
sg2 = empty_graph("Graph 2")
sg2 = add_constant(sg2, "const", np.array([7]), "FLOAT")
n2 = node("Equal", inputs=['sum', 'const'], outputs=['equal'])
sg2 = add_node(sg2, n2)
sg2 = add_input(sg2, 'sum', "FLOAT", [1])
sg2 = add_output(sg2, 'equal', "BOOL", [1])
g = merge(sg1, sg2, outputs=["sum"], inputs=["sum"])
data = {
"x1": np.array([2]).astype(np.float32),
"x2": np.array([5]).astype(np.float32)
}
result = run(g, inputs=data, outputs=["equal"])
assert result[
0], "Sum of 2 and 5 should be equal to constant 7. Merged failed."
img_arr = np.array(np.round(result[0]), dtype=np.uint8)
out = Image.fromarray(img_arr, mode="RGB")
out.save("images/1-Resized.JPG") # Yes, this works.
# Store the resize onnx:
so.graph_to_file(sg1, "onnx/resize-image-450x600-300x400.onnx")
# So, now we have a working (sub)graph that resizes an image (which obviously we can just load next time)
# Now, we open up the original image processing graph
sg2 = so.graph_from_file("onnx/check-container.onnx")
# The outputs of sg1 and the inputs of sg2 need to match; lets examine them
so.list_outputs(sg1)
so.list_inputs(sg2)
# Merge the two graphs, the outputs will be merged with the inputs in order of appearance:
g = so.merge(sg1, sg2, outputs=["small_image"], inputs=["in"])
so.check(g)
so.display(g)
# And now it works with the large image:
result = so.run(g, inputs=large_input, outputs=['result'])
# Print the result
if result[0]:
print("The container in the large image is empty.")
else:
print("The container in the large image is filled.")
# Store the merged graph
g = so.graph_to_file(g, "onnx/check-container-resize.onnx")
pass
for cn in n.outputs:
if cn.name[-1:] != 'b':
cn.name = f'{cn.name}b'
else:
pass
graph2_inputs = [i.name for i in graph2.inputs]
print(f'graph2 inputs: {graph2_inputs}')
onnx.save(gs.export_onnx(graph2), "graph2.onnx")
"""
graph1 outputs: [
'317a',
'852a',
'870a',
'897a',
'836a'
]
graph2 inputs: [
'0b',
'input.1b',
'input.13b',
'input.25b',
'input.37b'
]
"""
sg1 = so.graph_from_file('graph1.onnx')
sg2 = so.graph_from_file('graph2.onnx')
sg3 = so.merge(sg1, sg2, outputs=graph1_outputs, inputs=graph2_inputs)
so.graph_to_file(sg3, MODEL3)
outputs=['out_3_1'],
name="node_3_1")
g3 = so.add_input(g3, 'in_3_1', "FLOAT", [1])
g3 = so.add_input(g3, 'in_3_2', "FLOAT", [1])
g3 = so.add_output(g3, 'out_3_1', "FLOAT", [1])
g3 = so.add_node(g3, n3)
# so.display(g3)
# data = {
# "in_3_1": np.array([2]).astype(np.float32),
# "in_3_2": np.array([5]).astype(np.float32)}
# print(so.run(g3, inputs=data, outputs=["out_3_1"]))
# # MERGE:
# # Merge takes two complete graphs and links the output of the parent to the inputs of the child.
# # Merge assumes the result is complete.
g_merge = so.merge(sg1=g1, sg2=g2, io_match=[("out_1_1", "in_2_1")])
# so.display(g_merge)
# data = {"in_1_1": np.array([2]).astype(np.float32)}
# print(so.run(g_merge, inputs=data, outputs=["out_2_1"]))
# # JOIN:
# # Join takes two parents and links their outputs to one child
# # Join assumes the result is complete.
g_join = so.join(pg1=g1,
pg2=g2,
cg=g3,
pg1_match=[("out_1_1", "in_3_1")],
pg2_match=[("out_2_1", "in_3_2")])
# so.display(g_join)
# data = {
# "in_1_1": np.array([2]).astype(np.float32),