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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() | |