SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

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

Browse Source 
* 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
Download Patch File Download Diff File Expand all files Collapse all files

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):