RE-OWOD/tests/structures/test_instances.py

121 lines
4.5 KiB
Python

# Copyright (c) Facebook, Inc. and its affiliates. All Rights Reserved
import unittest
import torch
from detectron2.export.torchscript import patch_instances
from detectron2.structures import Boxes, Instances
from detectron2.utils.env import TORCH_VERSION
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])
@unittest.skipIf(TORCH_VERSION < (1, 7), "Insufficient pytorch version")
def test_script_new_fields(self):
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):
mask = x.mask # noqa F841
return x
class g2(torch.nn.Module):
def forward(self, x: Instances):
proposal_boxes = x.proposal_boxes # noqa F841
return 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):
torch.jit.script(g2())
new_fields = {"mask": "Tensor"}
with patch_instances(new_fields):
torch.jit.script(g())
with self.assertRaises(Exception):
torch.jit.script(g2())
@unittest.skipIf(TORCH_VERSION < (1, 7), "Insufficient pytorch version")
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())
@unittest.skipIf(TORCH_VERSION < (1, 7), "Insufficient pytorch version")
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)
@unittest.skipIf(TORCH_VERSION < (1, 7), "Insufficient pytorch version")
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))
if __name__ == "__main__":
unittest.main()