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Fix quality due to ruff release

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This commit is contained in:
Sylvain
2023-03-22 20:45:08 -04:00
parent 73fdc8c5b4
commit ef28df0572
28 changed files with 40 additions and 58 deletions
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4
tests/models/mask2former/test_image_processing_mask2former.py
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@@ -298,9 +298,7 @@ class Mask2FormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Te
high = num_labels
if is_instance_map:
labels_expanded = list(range(num_labels)) * 2
instance_id_to_semantic_id = {
instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
}
instance_id_to_semantic_id = dict(enumerate(labels_expanded))
annotations = [
np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
]

4
tests/models/maskformer/test_image_processing_maskformer.py
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@@ -298,9 +298,7 @@ class MaskFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Tes
high = num_labels
if is_instance_map:
labels_expanded = list(range(num_labels)) * 2
instance_id_to_semantic_id = {
instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
}
instance_id_to_semantic_id = dict(enumerate(labels_expanded))
annotations = [
np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
]

4
tests/models/oneformer/test_image_processing_oneformer.py
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@@ -329,9 +329,7 @@ class OneFormerImageProcessingTest(ImageProcessingSavingTestMixin, unittest.Test
high = num_labels
if is_instance_map:
labels_expanded = list(range(num_labels)) * 2
instance_id_to_semantic_id = {
instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
}
instance_id_to_semantic_id = dict(enumerate(labels_expanded))
annotations = [
np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
]

4
tests/models/oneformer/test_processor_oneformer.py
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@@ -401,9 +401,7 @@ class OneFormerProcessingTest(unittest.TestCase):
high = num_labels
if is_instance_map:
labels_expanded = list(range(num_labels)) * 2
instance_id_to_semantic_id = {
instance_id: label_id for instance_id, label_id in enumerate(labels_expanded)
}
instance_id_to_semantic_id = dict(enumerate(labels_expanded))
annotations = [
np.random.randint(0, high * 2, (img.size[1], img.size[0])).astype(np.uint8) for img in image_inputs
]

7
tests/pipelines/test_pipelines_automatic_speech_recognition.py
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@@ -56,11 +56,8 @@ if is_torch_available():
@is_pipeline_test
class AutomaticSpeechRecognitionPipelineTests(unittest.TestCase):
model_mapping = {
k: v
for k, v in (list(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items()) if MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING else [])
+ (MODEL_FOR_CTC_MAPPING.items() if MODEL_FOR_CTC_MAPPING else [])
}
model_mapping = dict((list(MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING.items()) if MODEL_FOR_SPEECH_SEQ_2_SEQ_MAPPING else [])
+ (MODEL_FOR_CTC_MAPPING.items() if MODEL_FOR_CTC_MAPPING else []))
def get_test_pipeline(self, model, tokenizer, processor):
if tokenizer is None:

7
tests/pipelines/test_pipelines_image_segmentation.py
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@@ -80,14 +80,11 @@ def mask_to_test_readable_only_shape(mask: Image) -> Dict:
@require_timm
@require_torch
class ImageSegmentationPipelineTests(unittest.TestCase):
model_mapping = {
k: v
for k, v in (
model_mapping = dict((
list(MODEL_FOR_IMAGE_SEGMENTATION_MAPPING.items()) if MODEL_FOR_IMAGE_SEGMENTATION_MAPPING else []
)
+ (MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING.items() if MODEL_FOR_SEMANTIC_SEGMENTATION_MAPPING else [])
+ (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else [])
}
+ (MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING.items() if MODEL_FOR_INSTANCE_SEGMENTATION_MAPPING else []))
def get_test_pipeline(self, model, tokenizer, processor):
image_segmenter = ImageSegmentationPipeline(model=model, image_processor=processor)