mirror of
https://github.com/pytorch/pytorch.git
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Bump protobuf version and refactor tensorboard tests (#154244)
In preparation for https://github.com/pytorch/pytorch/pull/153746, I am bumping protobuf to 5.29.4 and fixing the tensorboard tests first. Pull Request resolved: https://github.com/pytorch/pytorch/pull/154244 Approved by: https://github.com/malfet, https://github.com/cyyever
This commit is contained in:
Justin Chu
committed by
PyTorch MergeBot
PyTorch MergeBot
parent
53ecb8159a
commit
10c51b11ff
@ -166,9 +166,9 @@ pillow==11.0.0
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#Pinned versions: 10.3.0
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#test that import:
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protobuf==3.20.2
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#Description: Google’s data interchange format
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#Pinned versions: 3.20.1
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protobuf==5.29.4
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#Description: Google's data interchange format
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#Pinned versions: 5.29.4
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#test that import: test_tensorboard.py
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psutil
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@ -38,7 +38,7 @@ if [[ "$BUILD_ENVIRONMENT" == *cuda* ]]; then
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fi
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# TODO: Move both of them to Windows AMI
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python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==2.13.0 pytest-subtests==0.13.1
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python -m pip install pytest-rerunfailures==10.3 pytest-cpp==2.3.0 tensorboard==2.13.0 protobuf==5.29.4 pytest-subtests==0.13.1
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# Install Z3 optional dependency for Windows builds.
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python -m pip install z3-solver==4.12.2.0
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@ -25,7 +25,5 @@ pytest-cpp==2.3.0
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z3-solver==4.12.2.0
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tensorboard==2.13.0
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optree==0.13.0
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# NB: test_hparams_* from test_tensorboard is failing with protobuf 5.26.0 in
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# which the stringify metadata is wrong when escaping double quote
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protobuf==3.20.2
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protobuf==5.29.4
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parameterized==0.8.1
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@ -157,7 +157,7 @@ init_command = [
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'types-requests==2.27.25',
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'types-PyYAML==6.0.7',
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'types-tabulate==0.8.8',
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'types-protobuf==3.19.18',
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'types-protobuf==5.29.1.20250403',
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'types-pkg-resources==0.1.3',
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'types-Jinja2==2.11.9',
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'types-colorama==0.4.6',
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@ -1,7 +1,7 @@
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value {
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tag: "dummy"
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audio {
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sample_rate: 44100.0
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sample_rate: 44100
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num_channels: 1
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length_frames: 42
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encoded_audio_string: "RIFFx\000\000\000WAVEfmt \020\000\000\000\001\000\001\000D\254\000\000\210X\001\000\002\000\020\000dataT\000\000\000\000\000\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177\377\177"
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@ -1,23 +1,23 @@
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value {
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tag: "dummy"
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histo {
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max: 1023.0
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num: 1024.0
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sum: 523776.0
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}
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}
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@ -1,23 +1,23 @@
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value {
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tag: "dummy"
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histo {
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max: 1023.0
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}
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@ -1,23 +1,23 @@
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value {
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tag: "dummy"
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histo {
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max: 1023.0
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num: 1024.0
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sum: 523776.0
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sum: 523776
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sum_squares: 357389824
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}
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}
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@ -13,18 +13,18 @@ value {
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size: 3
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}
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}
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float_val: 1.0
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float_val: 1.0
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float_val: 1.0
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float_val: -1.0
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float_val: -1.0
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float_val: 1.0
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float_val: 1.0
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float_val: -1.0
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float_val: -1.0
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float_val: -1.0
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float_val: 1.0
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float_val: -1.0
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float_val: 1
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float_val: 1
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float_val: 1
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float_val: -1
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float_val: -1
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float_val: 1
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float_val: 1
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float_val: -1
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float_val: -1
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float_val: -1
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float_val: 1
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float_val: -1
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}
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metadata {
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plugin_data {
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@ -48,18 +48,18 @@ value {
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size: 3
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}
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}
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float_val: 0.0
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float_val: 2.0
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float_val: 3.0
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float_val: 0.0
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float_val: 3.0
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float_val: 1.0
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float_val: 0.0
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float_val: 1.0
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float_val: 2.0
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float_val: 1.0
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float_val: 3.0
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float_val: 2.0
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float_val: 0
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float_val: 2
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float_val: 3
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float_val: 0
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float_val: 3
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float_val: 1
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float_val: 0
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float_val: 1
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float_val: 2
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float_val: 1
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float_val: 3
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float_val: 2
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}
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metadata {
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plugin_data {
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@ -83,18 +83,18 @@ value {
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size: 3
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}
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}
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float_val: 255.0
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float_val: 0.0
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float_val: 0.0
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float_val: 0.0
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float_val: 255.0
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float_val: 0.0
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float_val: 0.0
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float_val: 0.0
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float_val: 255.0
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float_val: 255.0
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float_val: 0.0
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float_val: 255.0
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float_val: 255
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float_val: 0
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float_val: 0
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float_val: 0
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float_val: 255
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float_val: 0
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float_val: 0
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float_val: 0
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float_val: 255
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float_val: 255
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float_val: 0
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float_val: 255
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}
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metadata {
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plugin_data {
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@ -2,7 +2,7 @@ value {
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tag: "test_scalar"
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tensor {
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dtype: DT_FLOAT
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float_val: 1.0
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float_val: 1
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}
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metadata {
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plugin_data {
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@ -82,6 +82,34 @@ class BaseTestCase(TestCase):
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if os.path.exists(temp_dir):
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shutil.rmtree(temp_dir)
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def assertProto(self, str_to_compare):
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if expecttest.ACCEPT:
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write_proto(str_to_compare, self)
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return True
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expected = read_expected_content(self)
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str_to_compare = str(str_to_compare)
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self.assertEqual(remove_whitespace(str_to_compare), remove_whitespace(expected))
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def assertImageProto(self, actual_proto):
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if expecttest.ACCEPT:
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expected_file = get_expected_file(self)
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with open(expected_file, "w") as f:
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f.write(text_format.MessageToString(actual_proto))
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return True
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expected_str = read_expected_content(self)
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expected_proto = Summary()
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text_format.Parse(expected_str, expected_proto)
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[actual, expected] = [actual_proto.value[0], expected_proto.value[0]]
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actual_img = Image.open(io.BytesIO(actual.image.encoded_image_string))
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expected_img = Image.open(io.BytesIO(expected.image.encoded_image_string))
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self.assertEqual(actual.tag, expected.tag)
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self.assertEqual(actual.image.height, expected.image.height)
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self.assertEqual(actual.image.width, expected.image.width)
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self.assertEqual(actual.image.colorspace, expected.image.colorspace)
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self.assertEqual(actual_img, expected_img)
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if TEST_TENSORBOARD:
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from google.protobuf import text_format
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@ -417,58 +445,40 @@ class TestTensorBoardSummary(BaseTestCase):
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summary.histogram("dummy", np.ndarray(0), "tensorflow")
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def test_image_with_boxes(self):
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self.assertTrue(
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compare_image_proto(
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summary.image_boxes(
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"dummy", tensor_N(shape=(3, 32, 32)), np.array([[10, 10, 40, 40]])
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),
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self,
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self.assertImageProto(
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summary.image_boxes(
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"dummy", tensor_N(shape=(3, 32, 32)), np.array([[10, 10, 40, 40]])
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)
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)
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def test_image_with_one_channel(self):
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self.assertTrue(
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compare_image_proto(
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summary.image("dummy", tensor_N(shape=(1, 8, 8)), dataformats="CHW"),
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self,
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)
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) # noqa: E131
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self.assertImageProto(
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summary.image("dummy", tensor_N(shape=(1, 8, 8)), dataformats="CHW")
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)
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def test_image_with_one_channel_batched(self):
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self.assertTrue(
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compare_image_proto(
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summary.image(
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"dummy", tensor_N(shape=(2, 1, 8, 8)), dataformats="NCHW"
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),
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self,
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)
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) # noqa: E131
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self.assertImageProto(
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summary.image("dummy", tensor_N(shape=(2, 1, 8, 8)), dataformats="NCHW")
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)
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def test_image_with_3_channel_batched(self):
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self.assertTrue(
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compare_image_proto(
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summary.image(
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"dummy", tensor_N(shape=(2, 3, 8, 8)), dataformats="NCHW"
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),
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self,
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)
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) # noqa: E131
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self.assertImageProto(
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summary.image("dummy", tensor_N(shape=(2, 3, 8, 8)), dataformats="NCHW")
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)
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def test_image_without_channel(self):
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self.assertTrue(
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compare_image_proto(
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summary.image("dummy", tensor_N(shape=(8, 8)), dataformats="HW"), self
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)
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) # noqa: E131
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self.assertImageProto(
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summary.image("dummy", tensor_N(shape=(8, 8)), dataformats="HW")
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)
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def test_video(self):
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try:
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import moviepy # noqa: F401
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except ImportError:
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return
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self.assertTrue(
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compare_proto(summary.video("dummy", tensor_N(shape=(4, 3, 1, 8, 8))), self)
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)
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self.assertProto(summary.video("dummy", tensor_N(shape=(4, 3, 1, 8, 8))))
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summary.video("dummy", np.random.rand(16, 48, 1, 28, 28))
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summary.video("dummy", np.random.rand(20, 7, 1, 8, 8))
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@ -477,52 +487,37 @@ class TestTensorBoardSummary(BaseTestCase):
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)
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@xfailIfS390X
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def test_audio(self):
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self.assertTrue(
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compare_proto(summary.audio("dummy", tensor_N(shape=(42,))), self)
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)
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self.assertProto(summary.audio("dummy", tensor_N(shape=(42,))))
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@unittest.skipIf(
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IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
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)
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def test_text(self):
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self.assertTrue(compare_proto(summary.text("dummy", "text 123"), self))
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self.assertProto(summary.text("dummy", "text 123"))
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@unittest.skipIf(
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IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
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)
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def test_histogram_auto(self):
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self.assertTrue(
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compare_proto(
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summary.histogram(
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"dummy", tensor_N(shape=(1024,)), bins="auto", max_bins=5
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),
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self,
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)
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self.assertProto(
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summary.histogram("dummy", tensor_N(shape=(1024,)), bins="auto", max_bins=5)
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)
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@unittest.skipIf(
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IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
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)
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def test_histogram_fd(self):
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self.assertTrue(
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compare_proto(
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summary.histogram(
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"dummy", tensor_N(shape=(1024,)), bins="fd", max_bins=5
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),
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self,
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)
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self.assertProto(
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summary.histogram("dummy", tensor_N(shape=(1024,)), bins="fd", max_bins=5)
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)
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@unittest.skipIf(
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IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
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)
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def test_histogram_doane(self):
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self.assertTrue(
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compare_proto(
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summary.histogram(
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"dummy", tensor_N(shape=(1024,)), bins="doane", max_bins=5
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),
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self,
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self.assertProto(
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summary.histogram(
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"dummy", tensor_N(shape=(1024,)), bins="doane", max_bins=5
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)
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)
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@ -548,14 +543,14 @@ class TestTensorBoardSummary(BaseTestCase):
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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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mesh = summary.mesh("my_mesh", vertices=v, colors=c, faces=f, config_dict=None)
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self.assertTrue(compare_proto(mesh, self))
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self.assertProto(mesh)
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@unittest.skipIf(
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IS_MACOS, "Skipping on mac, see https://github.com/pytorch/pytorch/pull/109349 "
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)
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def test_scalar_new_style(self):
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scalar = summary.scalar("test_scalar", 1.0, new_style=True)
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self.assertTrue(compare_proto(scalar, self))
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self.assertProto(scalar)
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with self.assertRaises(AssertionError):
|
||||
summary.scalar("test_scalar2", torch.Tensor([1, 2, 3]), new_style=True)
|
||||
|
||||
@ -585,38 +580,6 @@ def read_expected_content(function_ptr):
|
||||
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:
|
||||
f.write(text_format.MessageToString(actual_proto))
|
||||
return True
|
||||
expected_str = read_expected_content(function_ptr)
|
||||
expected_proto = Summary()
|
||||
text_format.Parse(expected_str, expected_proto)
|
||||
|
||||
[actual, expected] = [actual_proto.value[0], expected_proto.value[0]]
|
||||
actual_img = Image.open(io.BytesIO(actual.image.encoded_image_string))
|
||||
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
|
||||
)
|
||||
|
||||
|
||||
def compare_proto(str_to_compare, function_ptr):
|
||||
if expecttest.ACCEPT:
|
||||
write_proto(str_to_compare, function_ptr)
|
||||
return True
|
||||
expected = read_expected_content(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:
|
||||
@ -657,7 +620,6 @@ class TestTensorBoardPytorchGraph(BaseTestCase):
|
||||
)
|
||||
|
||||
def test_nested_nn_squential(self):
|
||||
|
||||
dummy_input = torch.randn(2, 3)
|
||||
|
||||
class InnerNNSquential(torch.nn.Module):
|
||||
|
||||
Reference in New Issue
Block a user