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device agnostic models testing (#27146)

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* device agnostic models testing

* add decorator `require_torch_fp16`

* make style

* apply review suggestion

* Oops, the fp16 decorator was misused
This commit is contained in:
Hz, Ji
2023-11-01 01:12:14 +08:00
committed by GitHub
parent 77930f8a01
commit 50378cbf6c
51 changed files with 369 additions and 154 deletions
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8
tests/models/sam/test_modeling_sam.py
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@@ -22,7 +22,7 @@ import unittest
import requests
from transformers import SamConfig, SamMaskDecoderConfig, SamPromptEncoderConfig, SamVisionConfig, pipeline
from transformers.testing_utils import require_torch, slow, torch_device
from transformers.testing_utils import backend_empty_cache, require_torch, slow, torch_device
from transformers.utils import is_torch_available, is_vision_available
from ...test_configuration_common import ConfigTester
@@ -478,7 +478,7 @@ class SamModelIntegrationTest(unittest.TestCase):
super().tearDown()
# clean-up as much as possible GPU memory occupied by PyTorch
gc.collect()
torch.cuda.empty_cache()
backend_empty_cache(torch_device)
def test_inference_mask_generation_no_point(self):
model = SamModel.from_pretrained("facebook/sam-vit-base")
@@ -772,9 +772,7 @@ class SamModelIntegrationTest(unittest.TestCase):
torch.testing.assert_allclose(iou_scores, EXPECTED_IOU, atol=1e-4, rtol=1e-4)
def test_dummy_pipeline_generation(self):
generator = pipeline(
"mask-generation", model="facebook/sam-vit-base", device=0 if torch.cuda.is_available() else -1
)
generator = pipeline("mask-generation", model="facebook/sam-vit-base", device=torch_device)
raw_image = prepare_image()
_ = generator(raw_image, points_per_batch=64)