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Don't use LayoutLMv2 and LayoutLMv3 in some pipeline tests (#22774)

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* fix

---------

Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
2023-04-17 17:45:20 +02:00
committed by GitHub
parent ea7b0a539a
commit 5269718cb7
6 changed files with 39 additions and 26 deletions
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22
tests/models/layoutlmv2/test_modeling_layoutlmv2.py
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@@ -273,33 +273,11 @@ class LayoutLMv2ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
{
"document-question-answering": LayoutLMv2ForQuestionAnswering,
"feature-extraction": LayoutLMv2Model,
"question-answering": LayoutLMv2ForQuestionAnswering,
"text-classification": LayoutLMv2ForSequenceClassification,
"token-classification": LayoutLMv2ForTokenClassification,
"zero-shot": LayoutLMv2ForSequenceClassification,
}
if is_torch_available()
else {}
)
# TODO: Fix the failed tests
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
if pipeline_test_casse_name in [
"QAPipelineTests",
"TextClassificationPipelineTests",
"TokenClassificationPipelineTests",
"ZeroShotClassificationPipelineTests",
]:
# `LayoutLMv2Config` was never used in pipeline tests (`test_pt_LayoutLMv2Config_XXX`) due to lack of tiny
# config. With new tiny model creation, it is available, but we need to fix the failed tests.
return True
return super().is_pipeline_test_to_skip(
pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
)
def setUp(self):
self.model_tester = LayoutLMv2ModelTester(self)
self.config_tester = ConfigTester(self, config_class=LayoutLMv2Config, hidden_size=37)

8
tests/models/layoutlmv3/test_modeling_layoutlmv3.py
View File

@@ -289,10 +289,6 @@ class LayoutLMv3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
{
"document-question-answering": LayoutLMv3ForQuestionAnswering,
"feature-extraction": LayoutLMv3Model,
"question-answering": LayoutLMv3ForQuestionAnswering,
"text-classification": LayoutLMv3ForSequenceClassification,
"token-classification": LayoutLMv3ForTokenClassification,
"zero-shot": LayoutLMv3ForSequenceClassification,
}
if is_torch_available()
else {}
@@ -302,6 +298,10 @@ class LayoutLMv3ModelTest(ModelTesterMixin, PipelineTesterMixin, unittest.TestCa
def is_pipeline_test_to_skip(
self, pipeline_test_casse_name, config_class, model_architecture, tokenizer_name, processor_name
):
# `DocumentQuestionAnsweringPipeline` is expected to work with this model, but it combines the text and visual
# embedding along the sequence dimension (dim 1), which causes an error during post-processing as `p_mask` has
# the sequence dimension of the text embedding only.
# (see the line `embedding_output = torch.cat([embedding_output, visual_embeddings], dim=1)`)
return True
def setUp(self):