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

[tests] remove TF tests (uses of require_tf) (#38944)

Browse Source 
* remove uses of require_tf

* remove redundant import guards

* this class has no tests

* nits

* del tf rng comment
This commit is contained in:
Joao Gante
2025-06-25 18:29:10 +01:00
committed by GitHub
parent d37f751797
commit 1d45d90e5d
44 changed files with 21 additions and 2504 deletions
Download Patch File Download Diff File Expand all files Collapse all files

59
tests/pipelines/test_pipelines_image_feature_extraction.py
View File

@@ -22,20 +22,16 @@ from transformers import (
TF_MODEL_MAPPING,
TOKENIZER_MAPPING,
ImageFeatureExtractionPipeline,
is_tf_available,
is_torch_available,
is_vision_available,
pipeline,
)
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_tf, require_torch
from transformers.testing_utils import is_pipeline_test, nested_simplify, require_torch
if is_torch_available():
import torch
if is_tf_available():
import tensorflow as tf
if is_vision_available():
from PIL import Image
@@ -73,28 +69,6 @@ class ImageFeatureExtractionPipelineTests(unittest.TestCase):
nested_simplify(outputs[0]),
[-0.056, 0.083, 0.021, 0.038, 0.242, -0.279, -0.033, -0.003, 0.200, -0.192, 0.045, -0.095, -0.077, 0.017, -0.058, -0.063, -0.029, -0.204, 0.014, 0.042, 0.305, -0.205, -0.099, 0.146, -0.287, 0.020, 0.168, -0.052, 0.046, 0.048, -0.156, 0.093]) # fmt: skip
@require_tf
def test_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img)
self.assertEqual(
nested_simplify(outputs[0][0]),
[-1.417, -0.392, -1.264, -1.196, 1.648, 0.885, 0.56, -0.606, -1.175, 0.823, 1.912, 0.081, -0.053, 1.119, -0.062, -1.757, -0.571, 0.075, 0.959, 0.118, 1.201, -0.672, -0.498, 0.364, 0.937, -1.623, 0.228, 0.19, 1.697, -1.115, 0.583, -0.981]) # fmt: skip
@require_tf
def test_small_model_w_pooler_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit-w-pooler", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img, pool=True)
self.assertEqual(
nested_simplify(outputs[0]),
[-0.056, 0.083, 0.021, 0.038, 0.242, -0.279, -0.033, -0.003, 0.200, -0.192, 0.045, -0.095, -0.077, 0.017, -0.058, -0.063, -0.029, -0.204, 0.014, 0.042, 0.305, -0.205, -0.099, 0.146, -0.287, 0.020, 0.168, -0.052, 0.046, 0.048, -0.156, 0.093]) # fmt: skip
@require_torch
def test_image_processing_small_model_pt(self):
feature_extractor = pipeline(
@@ -117,28 +91,6 @@ class ImageFeatureExtractionPipelineTests(unittest.TestCase):
outputs = feature_extractor(img, pool=True)
self.assertEqual(np.squeeze(outputs).shape, (32,))
@require_tf
def test_image_processing_small_model_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
# test with image processor parameters
image_processor_kwargs = {"size": {"height": 300, "width": 300}}
img = prepare_img()
with pytest.raises(ValueError):
# Image doesn't match model input size
feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
image_processor_kwargs = {"image_mean": [0, 0, 0], "image_std": [1, 1, 1]}
img = prepare_img()
outputs = feature_extractor(img, image_processor_kwargs=image_processor_kwargs)
self.assertEqual(np.squeeze(outputs).shape, (226, 32))
# Test pooling option
outputs = feature_extractor(img, pool=True)
self.assertEqual(np.squeeze(outputs).shape, (32,))
@require_torch
def test_return_tensors_pt(self):
feature_extractor = pipeline(
@@ -148,15 +100,6 @@ class ImageFeatureExtractionPipelineTests(unittest.TestCase):
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(torch.is_tensor(outputs))
@require_tf
def test_return_tensors_tf(self):
feature_extractor = pipeline(
task="image-feature-extraction", model="hf-internal-testing/tiny-random-vit", framework="tf"
)
img = prepare_img()
outputs = feature_extractor(img, return_tensors=True)
self.assertTrue(tf.is_tensor(outputs))
def get_test_pipeline(
self,
model,