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