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format test_monitor.py and test_tensorboard.py (#142003)
Pull Request resolved: https://github.com/pytorch/pytorch/pull/142003 Approved by: https://github.com/StrongerXi, https://github.com/atalman ghstack dependencies: #141409
This commit is contained in:
William Wen
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
22ae34af88
commit
c93dd531d3
@ -29,8 +29,9 @@ skipIfNoTorchVision = unittest.skipIf(not HAS_TORCHVISION, "no torchvision")
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TEST_MATPLOTLIB = True
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try:
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import matplotlib
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if os.environ.get('DISPLAY', '') == '':
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matplotlib.use('Agg')
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if os.environ.get("DISPLAY", "") == "":
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matplotlib.use("Agg")
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import matplotlib.pyplot as plt
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except ImportError:
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TEST_MATPLOTLIB = False
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@ -43,10 +44,10 @@ from torch.testing._internal.common_utils import (
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IS_WINDOWS,
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parametrize,
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run_tests,
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skipIfTorchDynamo,
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TEST_WITH_CROSSREF,
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TestCase,
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xfailIfS390X,
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skipIfTorchDynamo,
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)
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@ -55,8 +56,10 @@ def tensor_N(shape, dtype=float):
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x = (np.arange(numel, dtype=dtype)).reshape(shape)
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return x
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class BaseTestCase(TestCase):
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""" Base class used for all TensorBoard tests """
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"""Base class used for all TensorBoard tests"""
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def setUp(self):
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super().setUp()
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if not TEST_TENSORBOARD:
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@ -92,6 +95,7 @@ if TEST_TENSORBOARD:
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from torch.utils.tensorboard._utils import _prepare_video, convert_to_HWC
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from torch.utils.tensorboard.summary import int_to_half, tensor_proto
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class TestTensorBoardPyTorchNumpy(BaseTestCase):
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def test_pytorch_np(self):
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tensors = [torch.rand(3, 10, 10), torch.rand(1), torch.rand(1, 2, 3, 4, 5)]
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@ -108,7 +112,9 @@ class TestTensorBoardPyTorchNumpy(BaseTestCase):
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# CUDA variable
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if torch.cuda.is_available():
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self.assertIsInstance(make_np(torch.autograd.Variable(tensor).cuda()), np.ndarray)
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self.assertIsInstance(
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make_np(torch.autograd.Variable(tensor).cuda()), np.ndarray
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)
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# python primitive type
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self.assertIsInstance(make_np(0), np.ndarray)
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@ -120,13 +126,13 @@ class TestTensorBoardPyTorchNumpy(BaseTestCase):
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def test_pytorch_write(self):
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with self.createSummaryWriter() as w:
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w.add_scalar('scalar', torch.autograd.Variable(torch.rand(1)), 0)
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w.add_scalar("scalar", torch.autograd.Variable(torch.rand(1)), 0)
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def test_pytorch_histogram(self):
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with self.createSummaryWriter() as w:
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w.add_histogram('float histogram', torch.rand((50,)))
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w.add_histogram('int histogram', torch.randint(0, 100, (50,)))
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w.add_histogram('bfloat16 histogram', torch.rand(50, dtype=torch.bfloat16))
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w.add_histogram("float histogram", torch.rand((50,)))
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w.add_histogram("int histogram", torch.randint(0, 100, (50,)))
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w.add_histogram("bfloat16 histogram", torch.rand(50, dtype=torch.bfloat16))
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def test_pytorch_histogram_raw(self):
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with self.createSummaryWriter() as w:
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@ -135,52 +141,59 @@ class TestTensorBoardPyTorchNumpy(BaseTestCase):
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bins = [0.0, 0.25, 0.5, 0.75, 1.0]
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counts, limits = np.histogram(floats, bins)
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sum_sq = floats.dot(floats).item()
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w.add_histogram_raw('float histogram raw',
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min=floats.min().item(),
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max=floats.max().item(),
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num=num,
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sum=floats.sum().item(),
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sum_squares=sum_sq,
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bucket_limits=limits[1:].tolist(),
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bucket_counts=counts.tolist())
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w.add_histogram_raw(
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"float histogram raw",
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min=floats.min().item(),
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max=floats.max().item(),
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num=num,
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sum=floats.sum().item(),
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sum_squares=sum_sq,
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bucket_limits=limits[1:].tolist(),
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bucket_counts=counts.tolist(),
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)
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ints = make_np(torch.randint(0, 100, (num,)))
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bins = [0, 25, 50, 75, 100]
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counts, limits = np.histogram(ints, bins)
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sum_sq = ints.dot(ints).item()
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w.add_histogram_raw('int histogram raw',
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min=ints.min().item(),
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max=ints.max().item(),
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num=num,
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sum=ints.sum().item(),
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sum_squares=sum_sq,
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bucket_limits=limits[1:].tolist(),
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bucket_counts=counts.tolist())
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w.add_histogram_raw(
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"int histogram raw",
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min=ints.min().item(),
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max=ints.max().item(),
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num=num,
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sum=ints.sum().item(),
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sum_squares=sum_sq,
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bucket_limits=limits[1:].tolist(),
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bucket_counts=counts.tolist(),
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)
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ints = torch.tensor(range(0, 100)).float()
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nbins = 100
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counts = torch.histc(ints, bins=nbins, min=0, max=99)
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limits = torch.tensor(range(nbins))
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sum_sq = ints.dot(ints).item()
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w.add_histogram_raw('int histogram raw',
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min=ints.min().item(),
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max=ints.max().item(),
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num=num,
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sum=ints.sum().item(),
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sum_squares=sum_sq,
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bucket_limits=limits.tolist(),
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bucket_counts=counts.tolist())
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w.add_histogram_raw(
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"int histogram raw",
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min=ints.min().item(),
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max=ints.max().item(),
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num=num,
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sum=ints.sum().item(),
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sum_squares=sum_sq,
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bucket_limits=limits.tolist(),
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bucket_counts=counts.tolist(),
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)
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class TestTensorBoardUtils(BaseTestCase):
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def test_to_HWC(self):
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test_image = np.random.randint(0, 256, size=(3, 32, 32), dtype=np.uint8)
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converted = convert_to_HWC(test_image, 'chw')
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converted = convert_to_HWC(test_image, "chw")
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self.assertEqual(converted.shape, (32, 32, 3))
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test_image = np.random.randint(0, 256, size=(16, 3, 32, 32), dtype=np.uint8)
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converted = convert_to_HWC(test_image, 'nchw')
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converted = convert_to_HWC(test_image, "nchw")
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self.assertEqual(converted.shape, (64, 256, 3))
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test_image = np.random.randint(0, 256, size=(32, 32), dtype=np.uint8)
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converted = convert_to_HWC(test_image, 'hw')
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converted = convert_to_HWC(test_image, "hw")
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self.assertEqual(converted.shape, (32, 32, 3))
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def test_convert_to_HWC_dtype_remains_same(self):
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@ -188,10 +201,13 @@ class TestTensorBoardUtils(BaseTestCase):
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# thus the scale_factor calculated for the image is 1
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test_image = torch.tensor([[[[1, 2, 3], [4, 5, 6]]]], dtype=torch.uint8)
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tensor = make_np(test_image)
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tensor = convert_to_HWC(tensor, 'NCHW')
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tensor = convert_to_HWC(tensor, "NCHW")
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scale_factor = summary._calc_scale_factor(tensor)
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self.assertEqual(scale_factor, 1, msg='Values are already in [0, 255], scale factor should be 1')
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self.assertEqual(
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scale_factor,
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1,
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msg="Values are already in [0, 255], scale factor should be 1",
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)
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def test_prepare_video(self):
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# At each timeframe, the sum over all other
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@ -200,7 +216,7 @@ class TestTensorBoardUtils(BaseTestCase):
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(16, 30, 3, 28, 28),
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(36, 30, 3, 28, 28),
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(19, 29, 3, 23, 19),
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(3, 3, 3, 3, 3)
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(3, 3, 3, 3, 3),
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]
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for s in shapes:
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V_input = np.random.random(s)
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@ -222,6 +238,7 @@ class TestTensorBoardUtils(BaseTestCase):
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y = np.reshape(V_after[f], newshape=(-1))
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np.testing.assert_array_almost_equal(np.sum(x), np.sum(y))
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freqs = [262, 294, 330, 349, 392, 440, 440, 440, 440, 440, 440]
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true_positive_counts = [75, 64, 21, 5, 0]
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@ -231,6 +248,7 @@ false_negative_counts = [0, 11, 54, 70, 75]
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precision = [0.3333333, 0.3786982, 0.5384616, 1.0, 0.0]
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recall = [1.0, 0.8533334, 0.28, 0.0666667, 0.0]
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class TestTensorBoardWriter(BaseTestCase):
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def test_writer(self):
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with self.createSummaryWriter() as writer:
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@ -238,49 +256,66 @@ class TestTensorBoardWriter(BaseTestCase):
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n_iter = 0
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writer.add_hparams(
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{'lr': 0.1, 'bsize': 1},
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{'hparam/accuracy': 10, 'hparam/loss': 10}
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{"lr": 0.1, "bsize": 1}, {"hparam/accuracy": 10, "hparam/loss": 10}
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)
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writer.add_scalar("data/scalar_systemtime", 0.1, n_iter)
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writer.add_scalar("data/scalar_customtime", 0.2, n_iter, walltime=n_iter)
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writer.add_scalar("data/new_style", 0.2, n_iter, new_style=True)
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writer.add_scalars(
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"data/scalar_group",
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{
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"xsinx": n_iter * np.sin(n_iter),
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"xcosx": n_iter * np.cos(n_iter),
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"arctanx": np.arctan(n_iter),
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},
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n_iter,
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)
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writer.add_scalar('data/scalar_systemtime', 0.1, n_iter)
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writer.add_scalar('data/scalar_customtime', 0.2, n_iter, walltime=n_iter)
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writer.add_scalar('data/new_style', 0.2, n_iter, new_style=True)
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writer.add_scalars('data/scalar_group', {
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"xsinx": n_iter * np.sin(n_iter),
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"xcosx": n_iter * np.cos(n_iter),
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"arctanx": np.arctan(n_iter)
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}, n_iter)
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x = np.zeros((32, 3, 64, 64)) # output from network
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writer.add_images('Image', x, n_iter) # Tensor
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writer.add_image_with_boxes('imagebox',
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np.zeros((3, 64, 64)),
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np.array([[10, 10, 40, 40], [40, 40, 60, 60]]),
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n_iter)
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writer.add_images("Image", x, n_iter) # Tensor
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writer.add_image_with_boxes(
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"imagebox",
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np.zeros((3, 64, 64)),
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np.array([[10, 10, 40, 40], [40, 40, 60, 60]]),
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n_iter,
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)
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x = np.zeros(sample_rate * 2)
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writer.add_audio('myAudio', x, n_iter)
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writer.add_video('myVideo', np.random.rand(16, 48, 1, 28, 28).astype(np.float32), n_iter)
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writer.add_text('Text', 'text logged at step:' + str(n_iter), n_iter)
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writer.add_text('markdown Text', '''a|b\n-|-\nc|d''', n_iter)
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writer.add_histogram('hist', np.random.rand(100, 100), n_iter)
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writer.add_pr_curve('xoxo', np.random.randint(2, size=100), np.random.rand(
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100), n_iter) # needs tensorboard 0.4RC or later
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writer.add_pr_curve_raw('prcurve with raw data', true_positive_counts,
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false_positive_counts,
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true_negative_counts,
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false_negative_counts,
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precision,
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recall, n_iter)
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writer.add_audio("myAudio", x, n_iter)
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writer.add_video(
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"myVideo", np.random.rand(16, 48, 1, 28, 28).astype(np.float32), n_iter
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)
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writer.add_text("Text", "text logged at step:" + str(n_iter), n_iter)
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writer.add_text("markdown Text", """a|b\n-|-\nc|d""", n_iter)
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writer.add_histogram("hist", np.random.rand(100, 100), n_iter)
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writer.add_pr_curve(
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"xoxo", np.random.randint(2, size=100), np.random.rand(100), n_iter
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) # needs tensorboard 0.4RC or later
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writer.add_pr_curve_raw(
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"prcurve with raw data",
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true_positive_counts,
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false_positive_counts,
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true_negative_counts,
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false_negative_counts,
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precision,
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recall,
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n_iter,
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)
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v = np.array([[[1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1]]], dtype=float)
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c = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255]]], dtype=int)
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v = np.array(
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[[[1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1]]], dtype=float
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)
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c = np.array(
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[[[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255]]], dtype=int
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)
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f = np.array([[[0, 2, 3], [0, 3, 1], [0, 1, 2], [1, 3, 2]]], dtype=int)
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writer.add_mesh('my_mesh', vertices=v, colors=c, faces=f)
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writer.add_mesh("my_mesh", vertices=v, colors=c, faces=f)
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class TestTensorBoardSummaryWriter(BaseTestCase):
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def test_summary_writer_ctx(self):
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# after using a SummaryWriter as a ctx it should be closed
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with self.createSummaryWriter() as writer:
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writer.add_scalar('test', 1)
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writer.add_scalar("test", 1)
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self.assertIs(writer.file_writer, None)
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def test_summary_writer_close(self):
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@ -299,182 +334,245 @@ class TestTensorBoardSummaryWriter(BaseTestCase):
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with tempfile.TemporaryDirectory(prefix="test_tensorboard_pathlib") as d:
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p = Path(d)
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with SummaryWriter(p) as writer:
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writer.add_scalar('test', 1)
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writer.add_scalar("test", 1)
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class TestTensorBoardEmbedding(BaseTestCase):
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def test_embedding(self):
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w = self.createSummaryWriter()
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all_features = torch.tensor([[1., 2., 3.], [5., 4., 1.], [3., 7., 7.]])
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all_labels = torch.tensor([33., 44., 55.])
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all_features = torch.tensor([[1.0, 2.0, 3.0], [5.0, 4.0, 1.0], [3.0, 7.0, 7.0]])
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all_labels = torch.tensor([33.0, 44.0, 55.0])
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all_images = torch.zeros(3, 3, 5, 5)
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w.add_embedding(all_features,
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metadata=all_labels,
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label_img=all_images,
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global_step=2)
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w.add_embedding(
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all_features, metadata=all_labels, label_img=all_images, global_step=2
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)
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dataset_label = ['test'] * 2 + ['train'] * 2
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dataset_label = ["test"] * 2 + ["train"] * 2
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all_labels = list(zip(all_labels, dataset_label))
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w.add_embedding(all_features,
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metadata=all_labels,
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label_img=all_images,
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metadata_header=['digit', 'dataset'],
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global_step=2)
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w.add_embedding(
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all_features,
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metadata=all_labels,
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label_img=all_images,
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metadata_header=["digit", "dataset"],
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global_step=2,
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)
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# assert...
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def test_embedding_64(self):
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w = self.createSummaryWriter()
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all_features = torch.tensor([[1., 2., 3.], [5., 4., 1.], [3., 7., 7.]])
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all_labels = torch.tensor([33., 44., 55.])
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all_features = torch.tensor([[1.0, 2.0, 3.0], [5.0, 4.0, 1.0], [3.0, 7.0, 7.0]])
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all_labels = torch.tensor([33.0, 44.0, 55.0])
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all_images = torch.zeros((3, 3, 5, 5), dtype=torch.float64)
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w.add_embedding(all_features,
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metadata=all_labels,
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label_img=all_images,
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global_step=2)
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w.add_embedding(
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all_features, metadata=all_labels, label_img=all_images, global_step=2
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)
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dataset_label = ['test'] * 2 + ['train'] * 2
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dataset_label = ["test"] * 2 + ["train"] * 2
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all_labels = list(zip(all_labels, dataset_label))
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w.add_embedding(all_features,
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metadata=all_labels,
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label_img=all_images,
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metadata_header=['digit', 'dataset'],
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global_step=2)
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w.add_embedding(
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all_features,
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metadata=all_labels,
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label_img=all_images,
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metadata_header=["digit", "dataset"],
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global_step=2,
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)
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class TestTensorBoardSummary(BaseTestCase):
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def test_uint8_image(self):
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'''
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"""
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Tests that uint8 image (pixel values in [0, 255]) is not changed
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'''
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"""
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test_image = np.random.randint(0, 256, size=(3, 32, 32), dtype=np.uint8)
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scale_factor = summary._calc_scale_factor(test_image)
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self.assertEqual(scale_factor, 1, msg='Values are already in [0, 255], scale factor should be 1')
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self.assertEqual(
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scale_factor,
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1,
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msg="Values are already in [0, 255], scale factor should be 1",
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)
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def test_float32_image(self):
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'''
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"""
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Tests that float32 image (pixel values in [0, 1]) are scaled correctly
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to [0, 255]
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'''
|
||||
"""
|
||||
test_image = np.random.rand(3, 32, 32).astype(np.float32)
|
||||
scale_factor = summary._calc_scale_factor(test_image)
|
||||
self.assertEqual(scale_factor, 255, msg='Values are in [0, 1], scale factor should be 255')
|
||||
self.assertEqual(
|
||||
scale_factor, 255, msg="Values are in [0, 1], scale factor should be 255"
|
||||
)
|
||||
|
||||
def test_list_input(self):
|
||||
with self.assertRaises(Exception) as e_info:
|
||||
summary.histogram('dummy', [1, 3, 4, 5, 6], 'tensorflow')
|
||||
summary.histogram("dummy", [1, 3, 4, 5, 6], "tensorflow")
|
||||
|
||||
def test_empty_input(self):
|
||||
with self.assertRaises(Exception) as e_info:
|
||||
summary.histogram('dummy', np.ndarray(0), 'tensorflow')
|
||||
summary.histogram("dummy", np.ndarray(0), "tensorflow")
|
||||
|
||||
def test_image_with_boxes(self):
|
||||
self.assertTrue(compare_image_proto(summary.image_boxes('dummy',
|
||||
tensor_N(shape=(3, 32, 32)),
|
||||
np.array([[10, 10, 40, 40]])),
|
||||
self))
|
||||
self.assertTrue(
|
||||
compare_image_proto(
|
||||
summary.image_boxes(
|
||||
"dummy", tensor_N(shape=(3, 32, 32)), np.array([[10, 10, 40, 40]])
|
||||
),
|
||||
self,
|
||||
)
|
||||
)
|
||||
|
||||
def test_image_with_one_channel(self):
|
||||
self.assertTrue(compare_image_proto(
|
||||
summary.image('dummy',
|
||||
tensor_N(shape=(1, 8, 8)),
|
||||
dataformats='CHW'),
|
||||
self)) # noqa: E131
|
||||
self.assertTrue(
|
||||
compare_image_proto(
|
||||
summary.image("dummy", tensor_N(shape=(1, 8, 8)), dataformats="CHW"),
|
||||
self,
|
||||
)
|
||||
) # noqa: E131
|
||||
|
||||
def test_image_with_one_channel_batched(self):
|
||||
self.assertTrue(compare_image_proto(
|
||||
summary.image('dummy',
|
||||
tensor_N(shape=(2, 1, 8, 8)),
|
||||
dataformats='NCHW'),
|
||||
self)) # noqa: E131
|
||||
self.assertTrue(
|
||||
compare_image_proto(
|
||||
summary.image(
|
||||
"dummy", tensor_N(shape=(2, 1, 8, 8)), dataformats="NCHW"
|
||||
),
|
||||
self,
|
||||
)
|
||||
) # noqa: E131
|
||||
|
||||
def test_image_with_3_channel_batched(self):
|
||||
self.assertTrue(compare_image_proto(
|
||||
summary.image('dummy',
|
||||
tensor_N(shape=(2, 3, 8, 8)),
|
||||
dataformats='NCHW'),
|
||||
self)) # noqa: E131
|
||||
self.assertTrue(
|
||||
compare_image_proto(
|
||||
summary.image(
|
||||
"dummy", tensor_N(shape=(2, 3, 8, 8)), dataformats="NCHW"
|
||||
),
|
||||
self,
|
||||
)
|
||||
) # noqa: E131
|
||||
|
||||
def test_image_without_channel(self):
|
||||
self.assertTrue(compare_image_proto(
|
||||
summary.image('dummy',
|
||||
tensor_N(shape=(8, 8)),
|
||||
dataformats='HW'),
|
||||
self)) # noqa: E131
|
||||
self.assertTrue(
|
||||
compare_image_proto(
|
||||
summary.image("dummy", tensor_N(shape=(8, 8)), dataformats="HW"), self
|
||||
)
|
||||
) # noqa: E131
|
||||
|
||||
def test_video(self):
|
||||
try:
|
||||
import moviepy # noqa: F401
|
||||
except ImportError:
|
||||
return
|
||||
self.assertTrue(compare_proto(summary.video('dummy', tensor_N(shape=(4, 3, 1, 8, 8))), self))
|
||||
summary.video('dummy', np.random.rand(16, 48, 1, 28, 28))
|
||||
summary.video('dummy', np.random.rand(20, 7, 1, 8, 8))
|
||||
self.assertTrue(
|
||||
compare_proto(summary.video("dummy", tensor_N(shape=(4, 3, 1, 8, 8))), self)
|
||||
)
|
||||
summary.video("dummy", np.random.rand(16, 48, 1, 28, 28))
|
||||
summary.video("dummy", np.random.rand(20, 7, 1, 8, 8))
|
||||
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
@xfailIfS390X
|
||||
def test_audio(self):
|
||||
self.assertTrue(compare_proto(summary.audio('dummy', tensor_N(shape=(42,))), self))
|
||||
self.assertTrue(
|
||||
compare_proto(summary.audio("dummy", tensor_N(shape=(42,))), self)
|
||||
)
|
||||
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
def test_text(self):
|
||||
self.assertTrue(compare_proto(summary.text('dummy', 'text 123'), self))
|
||||
self.assertTrue(compare_proto(summary.text("dummy", "text 123"), self))
|
||||
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
def test_histogram_auto(self):
|
||||
self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='auto', max_bins=5), self))
|
||||
self.assertTrue(
|
||||
compare_proto(
|
||||
summary.histogram(
|
||||
"dummy", tensor_N(shape=(1024,)), bins="auto", max_bins=5
|
||||
),
|
||||
self,
|
||||
)
|
||||
)
|
||||
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
def test_histogram_fd(self):
|
||||
self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='fd', max_bins=5), self))
|
||||
self.assertTrue(
|
||||
compare_proto(
|
||||
summary.histogram(
|
||||
"dummy", tensor_N(shape=(1024,)), bins="fd", max_bins=5
|
||||
),
|
||||
self,
|
||||
)
|
||||
)
|
||||
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
def test_histogram_doane(self):
|
||||
self.assertTrue(compare_proto(summary.histogram('dummy', tensor_N(shape=(1024,)), bins='doane', max_bins=5), self))
|
||||
self.assertTrue(
|
||||
compare_proto(
|
||||
summary.histogram(
|
||||
"dummy", tensor_N(shape=(1024,)), bins="doane", max_bins=5
|
||||
),
|
||||
self,
|
||||
)
|
||||
)
|
||||
|
||||
def test_custom_scalars(self):
|
||||
layout = {
|
||||
'Taiwan': {
|
||||
'twse': ['Multiline', ['twse/0050', 'twse/2330']]
|
||||
"Taiwan": {"twse": ["Multiline", ["twse/0050", "twse/2330"]]},
|
||||
"USA": {
|
||||
"dow": ["Margin", ["dow/aaa", "dow/bbb", "dow/ccc"]],
|
||||
"nasdaq": ["Margin", ["nasdaq/aaa", "nasdaq/bbb", "nasdaq/ccc"]],
|
||||
},
|
||||
'USA': {
|
||||
'dow': ['Margin', ['dow/aaa', 'dow/bbb', 'dow/ccc']],
|
||||
'nasdaq': ['Margin', ['nasdaq/aaa', 'nasdaq/bbb', 'nasdaq/ccc']]
|
||||
}
|
||||
}
|
||||
summary.custom_scalars(layout) # only smoke test. Because protobuf in python2/3 serialize dictionary differently.
|
||||
summary.custom_scalars(
|
||||
layout
|
||||
) # only smoke test. Because protobuf in python2/3 serialize dictionary differently.
|
||||
|
||||
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
def test_mesh(self):
|
||||
v = np.array([[[1, 1, 1], [-1, -1, 1], [1, -1, -1], [-1, 1, -1]]], dtype=float)
|
||||
c = np.array([[[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255]]], dtype=int)
|
||||
c = np.array(
|
||||
[[[255, 0, 0], [0, 255, 0], [0, 0, 255], [255, 0, 255]]], dtype=int
|
||||
)
|
||||
f = np.array([[[0, 2, 3], [0, 3, 1], [0, 1, 2], [1, 3, 2]]], dtype=int)
|
||||
mesh = summary.mesh('my_mesh', vertices=v, colors=c, faces=f, config_dict=None)
|
||||
mesh = summary.mesh("my_mesh", vertices=v, colors=c, faces=f, config_dict=None)
|
||||
self.assertTrue(compare_proto(mesh, self))
|
||||
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
def test_scalar_new_style(self):
|
||||
scalar = summary.scalar('test_scalar', 1.0, new_style=True)
|
||||
scalar = summary.scalar("test_scalar", 1.0, new_style=True)
|
||||
self.assertTrue(compare_proto(scalar, self))
|
||||
with self.assertRaises(AssertionError):
|
||||
summary.scalar('test_scalar2', torch.Tensor([1, 2, 3]), new_style=True)
|
||||
summary.scalar("test_scalar2", torch.Tensor([1, 2, 3]), new_style=True)
|
||||
|
||||
|
||||
def remove_whitespace(string):
|
||||
return string.replace(' ', '').replace('\t', '').replace('\n', '')
|
||||
return string.replace(" ", "").replace("\t", "").replace("\n", "")
|
||||
|
||||
|
||||
def get_expected_file(function_ptr):
|
||||
module_id = function_ptr.__class__.__module__
|
||||
test_file = sys.modules[module_id].__file__
|
||||
# Look for the .py file (since __file__ could be pyc).
|
||||
test_file = ".".join(test_file.split('.')[:-1]) + '.py'
|
||||
test_file = ".".join(test_file.split(".")[:-1]) + ".py"
|
||||
|
||||
# Use realpath to follow symlinks appropriately.
|
||||
test_dir = os.path.dirname(os.path.realpath(test_file))
|
||||
functionName = function_ptr.id().split('.')[-1]
|
||||
return os.path.join(test_dir,
|
||||
"expect",
|
||||
'TestTensorBoard.' + functionName + ".expect")
|
||||
functionName = function_ptr.id().split(".")[-1]
|
||||
return os.path.join(
|
||||
test_dir, "expect", "TestTensorBoard." + functionName + ".expect"
|
||||
)
|
||||
|
||||
|
||||
def read_expected_content(function_ptr):
|
||||
expected_file = get_expected_file(function_ptr)
|
||||
@ -482,10 +580,11 @@ def read_expected_content(function_ptr):
|
||||
with open(expected_file) as f:
|
||||
return f.read()
|
||||
|
||||
|
||||
def compare_image_proto(actual_proto, function_ptr):
|
||||
if expecttest.ACCEPT:
|
||||
expected_file = get_expected_file(function_ptr)
|
||||
with open(expected_file, 'w') as f:
|
||||
with open(expected_file, "w") as f:
|
||||
f.write(text_format.MessageToString(actual_proto))
|
||||
return True
|
||||
expected_str = read_expected_content(function_ptr)
|
||||
@ -497,13 +596,14 @@ def compare_image_proto(actual_proto, function_ptr):
|
||||
expected_img = Image.open(io.BytesIO(expected.image.encoded_image_string))
|
||||
|
||||
return (
|
||||
actual.tag == expected.tag and
|
||||
actual.image.height == expected.image.height and
|
||||
actual.image.width == expected.image.width and
|
||||
actual.image.colorspace == expected.image.colorspace and
|
||||
actual_img == expected_img
|
||||
actual.tag == expected.tag
|
||||
and actual.image.height == expected.image.height
|
||||
and actual.image.width == expected.image.width
|
||||
and actual.image.colorspace == expected.image.colorspace
|
||||
and actual_img == expected_img
|
||||
)
|
||||
|
||||
|
||||
def compare_proto(str_to_compare, function_ptr):
|
||||
if expecttest.ACCEPT:
|
||||
write_proto(str_to_compare, function_ptr)
|
||||
@ -512,11 +612,13 @@ def compare_proto(str_to_compare, function_ptr):
|
||||
str_to_compare = str(str_to_compare)
|
||||
return remove_whitespace(str_to_compare) == remove_whitespace(expected)
|
||||
|
||||
|
||||
def write_proto(str_to_compare, function_ptr):
|
||||
expected_file = get_expected_file(function_ptr)
|
||||
with open(expected_file, 'w') as f:
|
||||
with open(expected_file, "w") as f:
|
||||
f.write(str(str_to_compare))
|
||||
|
||||
|
||||
class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
def test_pytorch_graph(self):
|
||||
dummy_input = (torch.zeros(1, 3),)
|
||||
@ -547,7 +649,8 @@ class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
self.assertEqual(expected_node.input, actual_node.input)
|
||||
self.assertEqual(expected_node.device, actual_node.device)
|
||||
self.assertEqual(
|
||||
sorted(expected_node.attr.keys()), sorted(actual_node.attr.keys()))
|
||||
sorted(expected_node.attr.keys()), sorted(actual_node.attr.keys())
|
||||
)
|
||||
|
||||
def test_nested_nn_squential(self):
|
||||
|
||||
@ -595,7 +698,8 @@ class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
self.assertEqual(expected_node.input, actual_node.input)
|
||||
self.assertEqual(expected_node.device, actual_node.device)
|
||||
self.assertEqual(
|
||||
sorted(expected_node.attr.keys()), sorted(actual_node.attr.keys()))
|
||||
sorted(expected_node.attr.keys()), sorted(actual_node.attr.keys())
|
||||
)
|
||||
|
||||
def test_pytorch_graph_dict_input(self):
|
||||
class Model(torch.nn.Module):
|
||||
@ -614,7 +718,6 @@ class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
def forward(self, x):
|
||||
return {"out": self.l(x)}
|
||||
|
||||
|
||||
dummy_input = torch.zeros(1, 3)
|
||||
|
||||
with self.createSummaryWriter() as w:
|
||||
@ -631,7 +734,6 @@ class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
with self.createSummaryWriter() as w:
|
||||
w.add_graph(ModelDict(), dummy_input, use_strict_trace=False)
|
||||
|
||||
|
||||
def test_mlp_graph(self):
|
||||
dummy_input = (torch.zeros(2, 1, 28, 28),)
|
||||
|
||||
@ -650,8 +752,7 @@ class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
self.fc3 = torch.nn.Linear(1200, 10)
|
||||
|
||||
def forward(self, x, update_batch_stats=True):
|
||||
h = torch.nn.functional.relu(
|
||||
self.fc1(x.view(-1, self.input_len)))
|
||||
h = torch.nn.functional.relu(self.fc1(x.view(-1, self.input_len)))
|
||||
h = self.fc2(h)
|
||||
h = torch.nn.functional.relu(h)
|
||||
h = self.fc3(h)
|
||||
@ -670,42 +771,45 @@ class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
@skipIfNoTorchVision
|
||||
def test_torchvision_smoke(self):
|
||||
model_input_shapes = {
|
||||
'alexnet': (2, 3, 224, 224),
|
||||
'resnet34': (2, 3, 224, 224),
|
||||
'resnet152': (2, 3, 224, 224),
|
||||
'densenet121': (2, 3, 224, 224),
|
||||
'vgg16': (2, 3, 224, 224),
|
||||
'vgg19': (2, 3, 224, 224),
|
||||
'vgg16_bn': (2, 3, 224, 224),
|
||||
'vgg19_bn': (2, 3, 224, 224),
|
||||
'mobilenet_v2': (2, 3, 224, 224),
|
||||
"alexnet": (2, 3, 224, 224),
|
||||
"resnet34": (2, 3, 224, 224),
|
||||
"resnet152": (2, 3, 224, 224),
|
||||
"densenet121": (2, 3, 224, 224),
|
||||
"vgg16": (2, 3, 224, 224),
|
||||
"vgg19": (2, 3, 224, 224),
|
||||
"vgg16_bn": (2, 3, 224, 224),
|
||||
"vgg19_bn": (2, 3, 224, 224),
|
||||
"mobilenet_v2": (2, 3, 224, 224),
|
||||
}
|
||||
for model_name, input_shape in model_input_shapes.items():
|
||||
with self.createSummaryWriter() as w:
|
||||
model = getattr(torchvision.models, model_name)()
|
||||
w.add_graph(model, torch.zeros(input_shape))
|
||||
|
||||
|
||||
class TestTensorBoardFigure(BaseTestCase):
|
||||
@skipIfNoMatplotlib
|
||||
def test_figure(self):
|
||||
writer = self.createSummaryWriter()
|
||||
|
||||
figure, axes = plt.figure(), plt.gca()
|
||||
circle1 = plt.Circle((0.2, 0.5), 0.2, color='r')
|
||||
circle2 = plt.Circle((0.8, 0.5), 0.2, color='g')
|
||||
circle1 = plt.Circle((0.2, 0.5), 0.2, color="r")
|
||||
circle2 = plt.Circle((0.8, 0.5), 0.2, color="g")
|
||||
axes.add_patch(circle1)
|
||||
axes.add_patch(circle2)
|
||||
plt.axis('scaled')
|
||||
plt.axis("scaled")
|
||||
plt.tight_layout()
|
||||
|
||||
writer.add_figure("add_figure/figure", figure, 0, close=False)
|
||||
self.assertTrue(plt.fignum_exists(figure.number))
|
||||
|
||||
writer.add_figure("add_figure/figure", figure, 1)
|
||||
if matplotlib.__version__ != '3.3.0':
|
||||
if matplotlib.__version__ != "3.3.0":
|
||||
self.assertFalse(plt.fignum_exists(figure.number))
|
||||
else:
|
||||
print("Skipping fignum_exists, see https://github.com/matplotlib/matplotlib/issues/18163")
|
||||
print(
|
||||
"Skipping fignum_exists, see https://github.com/matplotlib/matplotlib/issues/18163"
|
||||
)
|
||||
|
||||
writer.close()
|
||||
|
||||
@ -727,16 +831,24 @@ class TestTensorBoardFigure(BaseTestCase):
|
||||
self.assertTrue(all(plt.fignum_exists(figure.number) is True for figure in figures)) # noqa: F812
|
||||
|
||||
writer.add_figure("add_figure/figure_list", figures, 1)
|
||||
if matplotlib.__version__ != '3.3.0':
|
||||
if matplotlib.__version__ != "3.3.0":
|
||||
self.assertTrue(all(plt.fignum_exists(figure.number) is False for figure in figures)) # noqa: F812
|
||||
else:
|
||||
print("Skipping fignum_exists, see https://github.com/matplotlib/matplotlib/issues/18163")
|
||||
print(
|
||||
"Skipping fignum_exists, see https://github.com/matplotlib/matplotlib/issues/18163"
|
||||
)
|
||||
|
||||
writer.close()
|
||||
|
||||
|
||||
class TestTensorBoardNumpy(BaseTestCase):
|
||||
@unittest.skipIf(IS_WINDOWS, "Skipping on windows, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 ")
|
||||
@unittest.skipIf(
|
||||
IS_WINDOWS,
|
||||
"Skipping on windows, see https://github.com/pytorch/pytorch/pull/109349 ",
|
||||
)
|
||||
@unittest.skipIf(
|
||||
IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
|
||||
)
|
||||
def test_scalar(self):
|
||||
res = make_np(1.1)
|
||||
self.assertIsInstance(res, np.ndarray) and self.assertEqual(res.shape, (1,))
|
||||
@ -751,8 +863,7 @@ class TestTensorBoardNumpy(BaseTestCase):
|
||||
|
||||
def test_pytorch_np_expect_fail(self):
|
||||
with self.assertRaises(NotImplementedError):
|
||||
res = make_np({'pytorch': 1.0})
|
||||
|
||||
res = make_np({"pytorch": 1.0})
|
||||
|
||||
|
||||
class TestTensorProtoSummary(BaseTestCase):
|
||||
@ -766,10 +877,14 @@ class TestTensorProtoSummary(BaseTestCase):
|
||||
@skipIfTorchDynamo("Unsuitable test for Dynamo, behavior changes with version")
|
||||
def test_half_tensor_proto(self, tensor_type, proto_type):
|
||||
float_values = [1.0, 2.0, 3.0]
|
||||
actual_proto = tensor_proto(
|
||||
"dummy",
|
||||
torch.tensor(float_values, dtype=tensor_type),
|
||||
).value[0].tensor
|
||||
actual_proto = (
|
||||
tensor_proto(
|
||||
"dummy",
|
||||
torch.tensor(float_values, dtype=tensor_type),
|
||||
)
|
||||
.value[0]
|
||||
.tensor
|
||||
)
|
||||
self.assertSequenceEqual(
|
||||
[int_to_half(x) for x in actual_proto.half_val],
|
||||
float_values,
|
||||
@ -778,9 +893,7 @@ class TestTensorProtoSummary(BaseTestCase):
|
||||
|
||||
def test_float_tensor_proto(self):
|
||||
float_values = [1.0, 2.0, 3.0]
|
||||
actual_proto = (
|
||||
tensor_proto("dummy", torch.tensor(float_values)).value[0].tensor
|
||||
)
|
||||
actual_proto = tensor_proto("dummy", torch.tensor(float_values)).value[0].tensor
|
||||
self.assertEqual(actual_proto.float_val, float_values)
|
||||
self.assertTrue(actual_proto.dtype == DataType.DT_FLOAT)
|
||||
|
||||
@ -796,24 +909,21 @@ class TestTensorProtoSummary(BaseTestCase):
|
||||
|
||||
def test_scalar_tensor_proto(self):
|
||||
scalar_value = 0.1
|
||||
actual_proto = (
|
||||
tensor_proto("dummy", torch.tensor(scalar_value)).value[0].tensor
|
||||
)
|
||||
actual_proto = tensor_proto("dummy", torch.tensor(scalar_value)).value[0].tensor
|
||||
self.assertAlmostEqual(actual_proto.float_val[0], scalar_value)
|
||||
|
||||
def test_complex_tensor_proto(self):
|
||||
real = torch.tensor([1.0, 2.0])
|
||||
imag = torch.tensor([3.0, 4.0])
|
||||
actual_proto = (
|
||||
tensor_proto("dummy", torch.complex(real, imag)).value[0].tensor
|
||||
)
|
||||
actual_proto = tensor_proto("dummy", torch.complex(real, imag)).value[0].tensor
|
||||
self.assertEqual(actual_proto.scomplex_val, [1.0, 3.0, 2.0, 4.0])
|
||||
|
||||
def test_empty_tensor_proto(self):
|
||||
actual_proto = tensor_proto("dummy", torch.empty(0)).value[0].tensor
|
||||
self.assertEqual(actual_proto.float_val, [])
|
||||
|
||||
|
||||
instantiate_parametrized_tests(TestTensorProtoSummary)
|
||||
|
||||
if __name__ == '__main__':
|
||||
if __name__ == "__main__":
|
||||
run_tests()
|
||||
|
||||
Reference in New Issue
Block a user