Add Image Processors (#19796)
* Add CLIP image processor * Crop size as dict too * Update warning * Actually use logger this time * Normalize doesn't change dtype of input * Add perceiver image processor * Tidy up * Add DPT image processor * Add Vilt image processor * Tidy up * Add poolformer image processor * Tidy up * Add LayoutLM v2 and v3 imsge processors * Tidy up * Add Flava image processor * Tidy up * Add deit image processor * Tidy up * Add ConvNext image processor * Tidy up * Add levit image processor * Add segformer image processor * Add in post processing * Fix up * Add ImageGPT image processor * Fixup * Add mobilevit image processor * Tidy up * Add postprocessing * Fixup * Add VideoMAE image processor * Tidy up * Add ImageGPT image processor * Fixup * Add ViT image processor * Tidy up * Add beit image processor * Add mobilevit image processor * Tidy up * Add postprocessing * Fixup * Fix up * Fix flava and remove tree module * Fix image classification pipeline failing tests * Update feature extractor in trainer scripts * Update pad_if_smaller to accept tuple and int size * Update for image segmentation pipeline * Update src/transformers/models/perceiver/image_processing_perceiver.py Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com> * Update src/transformers/image_processing_utils.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * Update src/transformers/models/beit/image_processing_beit.py Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com> * PR comments - docstrings; remove accidentally added resize; var names * Update docstrings * Add exception if size is not in the right format * Fix exception check * Fix up * Use shortest_edge in tuple in script Co-authored-by: Alara Dirik <8944735+alaradirik@users.noreply.github.com> Co-authored-by: NielsRogge <48327001+NielsRogge@users.noreply.github.com>
This commit is contained in:
amyeroberts
@@ -44,14 +44,16 @@ class BeitFeatureExtractionTester(unittest.TestCase):
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min_resolution=30,
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max_resolution=400,
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do_resize=True,
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size=20,
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size=None,
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do_center_crop=True,
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crop_size=18,
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crop_size=None,
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do_normalize=True,
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image_mean=[0.5, 0.5, 0.5],
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image_std=[0.5, 0.5, 0.5],
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reduce_labels=False,
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do_reduce_labels=False,
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):
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size = size if size is not None else {"height": 20, "width": 20}
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crop_size = crop_size if crop_size is not None else {"height": 18, "width": 18}
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self.parent = parent
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self.batch_size = batch_size
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self.num_channels = num_channels
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@@ -65,7 +67,7 @@ class BeitFeatureExtractionTester(unittest.TestCase):
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self.do_normalize = do_normalize
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self.image_mean = image_mean
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self.image_std = image_std
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self.reduce_labels = reduce_labels
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self.do_reduce_labels = do_reduce_labels
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def prepare_feat_extract_dict(self):
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return {
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@@ -76,7 +78,7 @@ class BeitFeatureExtractionTester(unittest.TestCase):
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"do_normalize": self.do_normalize,
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"reduce_labels": self.reduce_labels,
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"do_reduce_labels": self.do_reduce_labels,
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}
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@@ -141,8 +143,8 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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@@ -153,8 +155,8 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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@@ -173,8 +175,8 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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@@ -185,8 +187,8 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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@@ -205,8 +207,8 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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@@ -217,8 +219,8 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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@@ -239,16 +241,16 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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@@ -262,16 +264,16 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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self.feature_extract_tester.batch_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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@@ -287,16 +289,16 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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1,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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1,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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@@ -312,16 +314,16 @@ class BeitFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestC
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(
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2,
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self.feature_extract_tester.num_channels,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(
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encoding["labels"].shape,
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(
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2,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size,
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self.feature_extract_tester.crop_size["height"],
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self.feature_extract_tester.crop_size["width"],
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),
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)
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self.assertEqual(encoding["labels"].dtype, torch.long)
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