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# Copyright (c) Facebook, Inc. and its affiliates.
import unittest
import torch
from torch import Tensor
from detectron2.export.torchscript import patch_instances
from detectron2.structures import Boxes, Instances
from detectron2.utils.testing import convert_scripted_instances
class TestInstances(unittest.TestCase):
def test_int_indexing(self):
attr1 = torch.tensor([[0.0, 0.0, 1.0], [0.0, 0.0, 0.5], [0.0, 0.0, 1.0], [0.0, 0.5, 0.5]])
attr2 = torch.tensor([0.1, 0.2, 0.3, 0.4])
instances = Instances((100, 100))
instances.attr1 = attr1
instances.attr2 = attr2
for i in range(-len(instances), len(instances)):
inst = instances[i]
self.assertEqual((inst.attr1 == attr1[i]).all(), True)
self.assertEqual((inst.attr2 == attr2[i]).all(), True)
self.assertRaises(IndexError, lambda: instances[len(instances)])
self.assertRaises(IndexError, lambda: instances[-len(instances) - 1])
def test_script_new_fields(self):
def get_mask(x: Instances) -> torch.Tensor:
return x.mask
class f(torch.nn.Module):
def forward(self, x: Instances):
proposal_boxes = x.proposal_boxes # noqa F841
objectness_logits = x.objectness_logits # noqa F841
return x
class g(torch.nn.Module):
def forward(self, x: Instances):
return get_mask(x)
class g2(torch.nn.Module):
def __init__(self):
super().__init__()
self.g = g()
def forward(self, x: Instances):
proposal_boxes = x.proposal_boxes # noqa F841
return x, self.g(x)
fields = {"proposal_boxes": Boxes, "objectness_logits": Tensor}
with patch_instances(fields):
torch.jit.script(f())
# can't script anymore after exiting the context
with self.assertRaises(Exception):
# will create a ConcreteType for g
torch.jit.script(g2())
new_fields = {"mask": Tensor}
with patch_instances(new_fields):
# will compile g with a different Instances; this should pass
torch.jit.script(g())
with self.assertRaises(Exception):
torch.jit.script(g2())
new_fields = {"mask": Tensor, "proposal_boxes": Boxes}
with patch_instances(new_fields) as NewInstances:
# get_mask will be compiled with a different Instances; this should pass
scripted_g2 = torch.jit.script(g2())
x = NewInstances((3, 4))
x.mask = torch.rand(3)
x.proposal_boxes = Boxes(torch.rand(3, 4))
scripted_g2(x) # it should accept the new Instances object and run successfully
def test_script_access_fields(self):
class f(torch.nn.Module):
def forward(self, x: Instances):
proposal_boxes = x.proposal_boxes
objectness_logits = x.objectness_logits
return proposal_boxes.tensor + objectness_logits
fields = {"proposal_boxes": Boxes, "objectness_logits": Tensor}
with patch_instances(fields):
torch.jit.script(f())
def test_script_len(self):
class f(torch.nn.Module):
def forward(self, x: Instances):
return len(x)
class g(torch.nn.Module):
def forward(self, x: Instances):
return len(x)
image_shape = (15, 15)
fields = {"proposal_boxes": Boxes}
with patch_instances(fields) as new_instance:
script_module = torch.jit.script(f())
x = new_instance(image_shape)
with self.assertRaises(Exception):
script_module(x)
box_tensors = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]])
x.proposal_boxes = Boxes(box_tensors)
length = script_module(x)
self.assertEqual(length, 2)
fields = {"objectness_logits": Tensor}
with patch_instances(fields) as new_instance:
script_module = torch.jit.script(g())
x = new_instance(image_shape)
objectness_logits = torch.tensor([1.0]).reshape(1, 1)
x.objectness_logits = objectness_logits
length = script_module(x)
self.assertEqual(length, 1)
def test_script_has(self):
class f(torch.nn.Module):
def forward(self, x: Instances):
return x.has("proposal_boxes")
image_shape = (15, 15)
fields = {"proposal_boxes": Boxes}
with patch_instances(fields) as new_instance:
script_module = torch.jit.script(f())
x = new_instance(image_shape)
self.assertFalse(script_module(x))
box_tensors = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]])
x.proposal_boxes = Boxes(box_tensors)
self.assertTrue(script_module(x))
def test_script_to(self):
class f(torch.nn.Module):
def forward(self, x: Instances):
return x.to(torch.device("cpu"))
image_shape = (15, 15)
fields = {"proposal_boxes": Boxes, "a": Tensor}
with patch_instances(fields) as new_instance:
script_module = torch.jit.script(f())
x = new_instance(image_shape)
script_module(x)
box_tensors = torch.tensor([[5, 5, 10, 10], [1, 1, 2, 3]])
x.proposal_boxes = Boxes(box_tensors)
x.a = box_tensors
script_module(x)
def test_script_getitem(self):
class f(torch.nn.Module):
def forward(self, x: Instances, idx):
return x[idx]
image_shape = (15, 15)
fields = {"proposal_boxes": Boxes, "a": Tensor}
inst = Instances(image_shape)
inst.proposal_boxes = Boxes(torch.rand(4, 4))
inst.a = torch.rand(4, 10)
idx = torch.tensor([True, False, True, False])
with patch_instances(fields) as new_instance:
script_module = torch.jit.script(f())
out = f()(inst, idx)
out_scripted = script_module(new_instance.from_instances(inst), idx)
self.assertTrue(
torch.equal(out.proposal_boxes.tensor, out_scripted.proposal_boxes.tensor)
)
self.assertTrue(torch.equal(out.a, out_scripted.a))
def test_from_to_instances(self):
orig = Instances((30, 30))
orig.proposal_boxes = Boxes(torch.rand(3, 4))
fields = {"proposal_boxes": Boxes, "a": Tensor}
with patch_instances(fields) as NewInstances:
# convert to NewInstances and back
new1 = NewInstances.from_instances(orig)
new2 = convert_scripted_instances(new1)
self.assertTrue(torch.equal(orig.proposal_boxes.tensor, new1.proposal_boxes.tensor))
self.assertTrue(torch.equal(orig.proposal_boxes.tensor, new2.proposal_boxes.tensor))
def test_script_init_args(self):
def f(x: Tensor):
image_shape = (15, 15)
# __init__ can take arguments
inst = Instances(image_shape, a=x, proposal_boxes=Boxes(x))
inst2 = Instances(image_shape, a=x)
return inst.a, inst2.a
fields = {"proposal_boxes": Boxes, "a": Tensor}
with patch_instances(fields):
script_f = torch.jit.script(f)
x = torch.randn(3, 4)
outputs = script_f(x)
self.assertTrue(torch.equal(outputs[0], x))
self.assertTrue(torch.equal(outputs[1], x))
def test_script_cat(self):
def f(x: Tensor):
image_shape = (15, 15)
# __init__ can take arguments
inst = Instances(image_shape, a=x)
inst2 = Instances(image_shape, a=x)
inst3 = Instances(image_shape, proposal_boxes=Boxes(x))
return inst.cat([inst, inst2]), inst3.cat([inst3, inst3])
fields = {"proposal_boxes": Boxes, "a": Tensor}
with patch_instances(fields):
script_f = torch.jit.script(f)
x = torch.randn(3, 4)
output, output2 = script_f(x)
self.assertTrue(torch.equal(output.a, torch.cat([x, x])))
self.assertFalse(output.has("proposal_boxes"))
self.assertTrue(torch.equal(output2.proposal_boxes.tensor, torch.cat([x, x])))
if __name__ == "__main__":
unittest.main()
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