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test_pytorch_onnx_shape_inference.py
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# Owner(s): ["module: onnx"]
import unittest
import torch
import numpy as np
from torch.onnx.symbolic_helper import (_set_onnx_shape_inference,
_onnx_main_opset,
_set_opset_version)
from test_pytorch_common import skipIfUnsupportedMinOpsetVersion
def expect_tensor(scalar_type, shape=None):
def verify(actual_type):
np.testing.assert_equal(actual_type.scalarType(), scalar_type)
# if shape is not None:
# np.testing.assert_equal(actual_type.sizes(), shape)
if shape is not None:
np.testing.assert_equal(actual_type.varyingSizes(), shape)
return verify
class TestONNXShapeInference(unittest.TestCase):
def __init__(self, *args, **kwargs):
unittest.TestCase.__init__(self, *args, **kwargs)
self.opset_version = _onnx_main_opset
_set_onnx_shape_inference(True)
_set_opset_version(self.opset_version)
def run_test(self, g, n, type_assertion_funcs):
if not isinstance(type_assertion_funcs, list):
type_assertion_funcs = [type_assertion_funcs]
torch._C._jit_pass_onnx_graph_shape_type_inference(g, {}, self.opset_version)
for out, type_assertion_func in zip(n.outputs(), type_assertion_funcs):
type_assertion_func(out.type())
def create_empty_graph(self):
g = torch._C.Graph()
# kick off initialization for ConstantMap.
torch._C._jit_pass_onnx_graph_shape_type_inference(g, {}, self.opset_version)
return g
def insert_tensor_constant(self, g, tensor):
return g.op("Constant", value_t=tensor)
def test_cast(self):
# Test cast with input of unknown scalar type.
g = self.create_empty_graph()
input = g.addInput()
cast_out = g.op("Cast", input, to_i=1)
self.run_test(g, cast_out.node(), expect_tensor("Float"))
def test_constant_of_shape(self):
# Test ConstantOfShape with input of onnx::Shape node.
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(1, 2, 3, 4))
shape = g.op("Shape", constant)
constant_of_shape = g.op("ConstantOfShape", shape, value_t=torch.tensor([2.0]))
self.run_test(g, constant_of_shape.node(), expect_tensor("Float", shape=(1, 2, 3, 4)))
def test_constant_of_shape_static(self):
# Test ConstantOfShape with input of prim::ListConstruct of static tensor
rank = 4
g = self.create_empty_graph()
constants = [self.insert_tensor_constant(g, torch.tensor(i + 1)) for i in range(rank)]
shape = g.op("prim::ListConstruct", *constants)
shape.setType(torch._C.ListType.ofInts())
constant_of_shape = g.op("ConstantOfShape", shape, value_t=torch.tensor([2.0]))
self.run_test(g, constant_of_shape.node(), expect_tensor("Float", shape=(1, 2, 3, 4)))
def test_constant_of_shape_dynamic(self):
# Test ConstantOfShape with input of prim::ListConstruct of dynamic tensor
rank = 4
g = self.create_empty_graph()
inputs = [g.addInput() for i in range(rank)]
shape = g.op("prim::ListConstruct", *inputs)
shape.setType(torch._C.ListType.ofInts())
constant_of_shape = g.op("ConstantOfShape", shape, value_t=torch.tensor([2.0]))
self.run_test(g, constant_of_shape.node(), expect_tensor("Float", shape=(None, None, None, None)))
def test_gather_dynamic_index(self):
g = self.create_empty_graph()
input = g.addInput()
input.setType(input.type().with_dtype(torch.float).with_sizes([None, 3, 16, 16]))
indices = g.addInput()
indices.setType(indices.type().with_dtype(torch.int64).with_sizes([None]))
output = g.op("Gather", input, indices, axis_i=1)
self.run_test(g, output.node(), expect_tensor("Float", shape=([None, None, 16, 16])))
def test_gather_scalar_index(self):
g = self.create_empty_graph()
input = g.addInput()
input.setType(input.type().with_dtype(torch.float).with_sizes([None, 3, 16, 16]))
indices = self.insert_tensor_constant(g, torch.tensor(1))
output = g.op("Gather", input, indices, axis_i=1)
self.run_test(g, output.node(), expect_tensor("Float", shape=([None, 16, 16])))
def test_reshape(self):
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(2, 16, 5, 5))
constant_2 = self.insert_tensor_constant(g, torch.tensor([2, 0, -1]))
shape = g.op("Reshape", constant, constant_2)
self.run_test(g, shape.node(), expect_tensor("Float", shape=(2, 16, 25)))
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(2, 16, 5, 4))
constant_2 = self.insert_tensor_constant(g, torch.tensor([-1, 0, 4]))
shape = g.op("Reshape", constant, constant_2)
self.run_test(g, shape.node(), expect_tensor("Float", shape=(10, 16, 4)))
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(2, 16, 5, 4))
constant_2 = self.insert_tensor_constant(g, torch.tensor([-1, 0, 0]))
shape = g.op("Reshape", constant, constant_2)
self.run_test(g, shape.node(), expect_tensor("Float", shape=(8, 16, 5)))
def test_reshape_symbolic(self):
g = self.create_empty_graph()
input = g.addInput()
input.setType(input.type().with_sizes([None, None, 2, 8]))
constant = self.insert_tensor_constant(g, torch.tensor([0, 0, -1]))
output = g.op("Reshape", input, constant)
self.run_test(g, output.node(), expect_tensor(None, shape=(None, None, 16)))
@skipIfUnsupportedMinOpsetVersion(14)
def test_reshape_allowzero(self):
g = self.create_empty_graph()
input = g.addInput()
input.setType(input.type().with_sizes([3, 4, 0]))
constant = self.insert_tensor_constant(g, torch.tensor([0, 4, 3]))
output = g.op("Reshape", input, constant, allowzero_i=1)
self.run_test(g, output.node(), expect_tensor(None, shape=(0, 4, 3)))
def test_slice(self):
g = self.create_empty_graph()
input = g.addInput()
input.setType(input.type().with_sizes([None, None]))
start_input = g.addInput()
start_input.setType(start_input.type().with_sizes([None]))
end = self.insert_tensor_constant(g, torch.tensor([3]))
axis = self.insert_tensor_constant(g, torch.tensor([0]))
step = self.insert_tensor_constant(g, torch.tensor([1]))
slice = g.op("Slice", input, start_input, end, axis, step)
self.run_test(g, slice.node(), expect_tensor(None, shape=(None, None)))
def test_broadcast_matmul(self):
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(5, 1, 2))
constant_2 = self.insert_tensor_constant(g, torch.ones(3, 1, 2, 1))
shape = g.op("MatMul", constant, constant_2)
self.run_test(g, shape.node(), expect_tensor("Float", shape=(3, 5, 1, 1)))
# test when first input is of rank 1
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(2))
constant_2 = self.insert_tensor_constant(g, torch.ones(3, 1, 2, 1))
shape = g.op("MatMul", constant, constant_2)
self.run_test(g, shape.node(), expect_tensor("Float", shape=(3, 1, 1)))
# test when second input is of rank 1
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(5, 1, 2))
constant_2 = self.insert_tensor_constant(g, torch.ones(2))
shape = g.op("MatMul", constant, constant_2)
self.run_test(g, shape.node(), expect_tensor("Float", shape=(5, 1)))
# test when both inputs are of rank 1
g = self.create_empty_graph()
constant = self.insert_tensor_constant(g, torch.ones(2))
constant_2 = self.insert_tensor_constant(g, torch.ones(2))
shape = g.op("MatMul", constant, constant_2)
self.run_test(g, shape.node(), expect_tensor("Float", shape=()))
def test_expand(self):
g = self.create_empty_graph()
input = g.addInput()
constant = self.insert_tensor_constant(g, torch.ones(2, 4))
input.setType(constant.type().with_sizes([None, None]))
shape = g.op("Shape", input)
expand = g.op("Expand", constant, shape)
self.run_test(g, expand.node(), expect_tensor("Float", shape=(None, None)))
if __name__ == '__main__':
unittest.main()