[feat] Add FLAVA model (#16654)
* [WIP] Add FLAVA model This PR aims to add [FLAVA](ihttps://arxiv.org/abs/2112.04482) model to the transformers repo. Following checklist delineates the list of things to be done for this PR to be complete: [x] Flava init [x] Flava base models [x] Flava layers [x] Flava Configs [x] Flava encoders [x] Flava pretraining models [ ] Flava classification/retrieval models (To be added in a separate PR) [x] Documentation updates [x] Imports updates [x] Argstring updates [x] Flava pretrained checkpoints [x] Flava tests [x] Flava processors [x] Sanity check [x] Lint
This commit is contained in:
Amanpreet Singh
0
tests/models/flava/__init__.py
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0
tests/models/flava/__init__.py
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347
tests/models/flava/test_feature_extraction_flava.py
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347
tests/models/flava/test_feature_extraction_flava.py
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# coding=utf-8
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# Copyright 2022 Meta Platforms authors and HuggingFace Inc.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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import unittest
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import numpy as np
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from transformers.testing_utils import require_torch, require_vision
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from transformers.utils import is_torch_available, is_vision_available
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from ...test_feature_extraction_common import FeatureExtractionSavingTestMixin, prepare_image_inputs
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if is_torch_available():
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import torch
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if is_vision_available():
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from PIL import Image
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from transformers import FlavaFeatureExtractor
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from transformers.models.flava.feature_extraction_flava import (
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FLAVA_CODEBOOK_MEAN,
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FLAVA_CODEBOOK_STD,
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FLAVA_IMAGE_MEAN,
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FLAVA_IMAGE_STD,
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)
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else:
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FLAVA_IMAGE_MEAN = FLAVA_IMAGE_STD = FLAVA_CODEBOOK_MEAN = FLAVA_CODEBOOK_STD = None
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class FlavaFeatureExtractionTester(unittest.TestCase):
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def __init__(
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self,
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parent,
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batch_size=7,
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num_channels=3,
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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=224,
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do_center_crop=True,
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crop_size=224,
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resample=None,
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do_normalize=True,
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image_mean=FLAVA_IMAGE_MEAN,
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image_std=FLAVA_IMAGE_STD,
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input_size_patches=14,
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total_mask_patches=75,
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mask_group_max_patches=None,
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mask_group_min_patches=16,
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mask_group_min_aspect_ratio=0.3,
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mask_group_max_aspect_ratio=None,
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codebook_do_resize=True,
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codebook_size=112,
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codebook_resample=None,
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codebook_do_center_crop=True,
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codebook_crop_size=112,
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codebook_do_map_pixels=True,
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codebook_do_normalize=True,
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codebook_image_mean=FLAVA_CODEBOOK_MEAN,
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codebook_image_std=FLAVA_CODEBOOK_STD,
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):
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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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self.do_resize = do_resize
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self.min_resolution = min_resolution
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self.max_resolution = max_resolution
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self.size = size
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self.resample = resample if resample is not None else Image.BICUBIC
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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.do_center_crop = do_center_crop
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self.crop_size = crop_size
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self.input_size_patches = input_size_patches
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self.total_mask_patches = total_mask_patches
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self.mask_group_max_patches = mask_group_max_patches
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self.mask_group_min_patches = mask_group_min_patches
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self.mask_group_min_aspect_ratio = mask_group_min_aspect_ratio
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self.mask_group_max_aspect_ratio = mask_group_max_aspect_ratio
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self.codebook_do_resize = codebook_do_resize
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self.codebook_size = codebook_size
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self.codebook_resample = codebook_resample if codebook_resample is not None else Image.LANCZOS
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self.codebook_do_center_crop = codebook_do_center_crop
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self.codebook_crop_size = codebook_crop_size
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self.codebook_do_map_pixels = codebook_do_map_pixels
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self.codebook_do_normalize = codebook_do_normalize
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self.codebook_image_mean = codebook_image_mean
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self.codebook_image_std = codebook_image_std
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def prepare_feat_extract_dict(self):
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return {
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"image_mean": self.image_mean,
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"image_std": self.image_std,
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"do_normalize": self.do_normalize,
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"do_resize": self.do_resize,
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"size": self.size,
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"resample": self.resample,
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"do_center_crop": self.do_center_crop,
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"crop_size": self.crop_size,
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"input_size_patches": self.input_size_patches,
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"total_mask_patches": self.total_mask_patches,
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"mask_group_max_patches": self.mask_group_max_patches,
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"mask_group_min_patches": self.mask_group_min_patches,
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"mask_group_min_aspect_ratio": self.mask_group_min_aspect_ratio,
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"mask_group_max_aspect_ratio": self.mask_group_min_aspect_ratio,
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"codebook_do_resize": self.codebook_do_resize,
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"codebook_size": self.codebook_size,
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"codebook_resample": self.codebook_resample,
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"codebook_do_center_crop": self.codebook_do_center_crop,
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"codebook_crop_size": self.codebook_crop_size,
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"codebook_do_map_pixels": self.codebook_do_map_pixels,
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"codebook_do_normalize": self.codebook_do_normalize,
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"codebook_image_mean": self.codebook_image_mean,
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"codebook_image_std": self.codebook_image_std,
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}
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def get_expected_image_size(self):
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return (self.size, self.size) if not isinstance(self.size, tuple) else self.size
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def get_expected_mask_size(self):
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return (
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(self.input_size_patches, self.input_size_patches)
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if not isinstance(self.input_size_patches, tuple)
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else self.input_size_patches
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)
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def get_expected_codebook_image_size(self):
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if not isinstance(self.codebook_size, tuple):
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return (self.codebook_size, self.codebook_size)
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else:
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return self.codebook_size
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@require_torch
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@require_vision
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class FlavaFeatureExtractionTest(FeatureExtractionSavingTestMixin, unittest.TestCase):
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feature_extraction_class = FlavaFeatureExtractor if is_vision_available() else None
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maxDiff = None
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def setUp(self):
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self.feature_extract_tester = FlavaFeatureExtractionTester(self)
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@property
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def feat_extract_dict(self):
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return self.feature_extract_tester.prepare_feat_extract_dict()
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def test_feat_extract_properties(self):
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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self.assertTrue(hasattr(feature_extractor, "image_mean"))
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self.assertTrue(hasattr(feature_extractor, "image_std"))
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self.assertTrue(hasattr(feature_extractor, "do_normalize"))
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self.assertTrue(hasattr(feature_extractor, "do_resize"))
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self.assertTrue(hasattr(feature_extractor, "resample"))
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self.assertTrue(hasattr(feature_extractor, "crop_size"))
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self.assertTrue(hasattr(feature_extractor, "do_center_crop"))
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self.assertTrue(hasattr(feature_extractor, "masking_generator"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_resize"))
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self.assertTrue(hasattr(feature_extractor, "codebook_size"))
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self.assertTrue(hasattr(feature_extractor, "codebook_resample"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_center_crop"))
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self.assertTrue(hasattr(feature_extractor, "codebook_crop_size"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_map_pixels"))
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self.assertTrue(hasattr(feature_extractor, "codebook_do_normalize"))
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self.assertTrue(hasattr(feature_extractor, "codebook_image_mean"))
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self.assertTrue(hasattr(feature_extractor, "codebook_image_std"))
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def test_batch_feature(self):
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pass
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def test_call_pil(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PIL images
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt")
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# Test no bool masked pos
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self.assertFalse("bool_masked_pos" in encoded_images)
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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# Test no bool masked pos
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self.assertFalse("bool_masked_pos" in encoded_images)
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self.assertEqual(
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encoded_images.pixel_values.shape,
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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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expected_height,
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expected_width,
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),
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)
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def _test_call_framework(self, instance_class, prepare_kwargs):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random tensors
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, **prepare_kwargs)
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for image in image_inputs:
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self.assertIsInstance(image, instance_class)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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encoded_images = feature_extractor(image_inputs, return_image_mask=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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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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expected_height,
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expected_width,
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),
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)
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expected_height, expected_width = self.feature_extract_tester.get_expected_mask_size()
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self.assertEqual(
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encoded_images.bool_masked_pos.shape,
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(
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self.feature_extract_tester.batch_size,
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expected_height,
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expected_width,
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),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_tensors="pt").pixel_values
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.shape,
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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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expected_height,
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expected_width,
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),
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)
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# Test masking
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encoded_images = feature_extractor(image_inputs, return_image_mask=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_image_size()
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self.assertEqual(
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encoded_images.pixel_values.shape,
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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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expected_height,
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expected_width,
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),
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)
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expected_height, expected_width = self.feature_extract_tester.get_expected_mask_size()
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self.assertEqual(
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encoded_images.bool_masked_pos.shape,
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(
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self.feature_extract_tester.batch_size,
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expected_height,
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expected_width,
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),
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)
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def test_call_numpy(self):
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self._test_call_framework(np.ndarray, prepare_kwargs={"numpify": True})
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def test_call_pytorch(self):
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self._test_call_framework(torch.Tensor, prepare_kwargs={"torchify": True})
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def test_masking(self):
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# Initialize feature_extractor
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random.seed(1234)
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False, torchify=True)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_image_mask=True, return_tensors="pt")
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self.assertEqual(encoded_images.bool_masked_pos.sum().item(), 75)
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def test_codebook_pixels(self):
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# Initialize feature_extractor
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feature_extractor = self.feature_extraction_class(**self.feat_extract_dict)
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# create random PIL images
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image_inputs = prepare_image_inputs(self.feature_extract_tester, equal_resolution=False)
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for image in image_inputs:
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self.assertIsInstance(image, Image.Image)
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# Test not batched input
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encoded_images = feature_extractor(image_inputs[0], return_codebook_pixels=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_codebook_image_size()
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self.assertEqual(
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encoded_images.codebook_pixel_values.shape,
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(1, self.feature_extract_tester.num_channels, expected_height, expected_width),
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)
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# Test batched
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encoded_images = feature_extractor(image_inputs, return_codebook_pixels=True, return_tensors="pt")
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expected_height, expected_width = self.feature_extract_tester.get_expected_codebook_image_size()
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self.assertEqual(
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encoded_images.codebook_pixel_values.shape,
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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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expected_height,
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expected_width,
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),
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)
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1224
tests/models/flava/test_modeling_flava.py
Normal file
1224
tests/models/flava/test_modeling_flava.py
Normal file
File diff suppressed because it is too large
Load Diff
234
tests/models/flava/test_processor_flava.py
Normal file
234
tests/models/flava/test_processor_flava.py
Normal file
@@ -0,0 +1,234 @@
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# Copyright 2022 Meta Platforms authors and The HuggingFace Team. All rights reserved.
|
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#
|
||||
# Licensed under the Apache License, Version 2.0 (the "License");
|
||||
# you may not use this file except in compliance with the License.
|
||||
# You may obtain a copy of the License at
|
||||
#
|
||||
# http://www.apache.org/licenses/LICENSE-2.0
|
||||
#
|
||||
# Unless required by applicable law or agreed to in writing, software
|
||||
# distributed under the License is distributed on an "AS IS" BASIS,
|
||||
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
|
||||
# See the License for the specific language governing permissions and
|
||||
# limitations under the License.
|
||||
|
||||
import json
|
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import os
|
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import random
|
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import shutil
|
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import tempfile
|
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import unittest
|
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|
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import numpy as np
|
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import pytest
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|
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from transformers import BertTokenizer, BertTokenizerFast
|
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from transformers.models.bert.tokenization_bert import VOCAB_FILES_NAMES
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from transformers.testing_utils import require_vision
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from transformers.utils import FEATURE_EXTRACTOR_NAME, is_vision_available
|
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|
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|
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if is_vision_available():
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from PIL import Image
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|
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from transformers import FlavaFeatureExtractor, FlavaProcessor
|
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from transformers.models.flava.feature_extraction_flava import (
|
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FLAVA_CODEBOOK_MEAN,
|
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FLAVA_CODEBOOK_STD,
|
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FLAVA_IMAGE_MEAN,
|
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FLAVA_IMAGE_STD,
|
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)
|
||||
|
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|
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@require_vision
|
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class FlavaProcessorTest(unittest.TestCase):
|
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def setUp(self):
|
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self.tmpdirname = tempfile.mkdtemp()
|
||||
|
||||
# fmt: off
|
||||
vocab_tokens = ["[UNK]", "[CLS]", "[SEP]", "[PAD]", "[MASK]", "want", "##want", "##ed", "wa", "un", "runn", "##ing", ",", "low", "lowest"]
|
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# fmt: on
|
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
|
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|
||||
with open(self.vocab_file, "w", encoding="utf-8") as fp:
|
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fp.write("".join([x + "\n" for x in vocab_tokens]))
|
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|
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feature_extractor_map = {
|
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"image_mean": FLAVA_IMAGE_MEAN,
|
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"image_std": FLAVA_IMAGE_STD,
|
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"do_normalize": True,
|
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"do_resize": True,
|
||||
"size": 224,
|
||||
"do_center_crop": True,
|
||||
"crop_size": 224,
|
||||
"input_size_patches": 14,
|
||||
"total_mask_patches": 75,
|
||||
"mask_group_max_patches": None,
|
||||
"mask_group_min_patches": 16,
|
||||
"mask_group_min_aspect_ratio": 0.3,
|
||||
"mask_group_max_aspect_ratio": None,
|
||||
"codebook_do_resize": True,
|
||||
"codebook_size": 112,
|
||||
"codebook_resample": None,
|
||||
"codebook_do_center_crop": True,
|
||||
"codebook_crop_size": 112,
|
||||
"codebook_do_map_pixels": True,
|
||||
"codebook_do_normalize": True,
|
||||
"codebook_image_mean": FLAVA_CODEBOOK_MEAN,
|
||||
"codebook_image_std": FLAVA_CODEBOOK_STD,
|
||||
}
|
||||
|
||||
self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
|
||||
with open(self.feature_extractor_file, "w", encoding="utf-8") as fp:
|
||||
json.dump(feature_extractor_map, fp)
|
||||
|
||||
def get_tokenizer(self, **kwargs):
|
||||
return BertTokenizer.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_rust_tokenizer(self, **kwargs):
|
||||
return BertTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def get_feature_extractor(self, **kwargs):
|
||||
return FlavaFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
|
||||
|
||||
def tearDown(self):
|
||||
shutil.rmtree(self.tmpdirname)
|
||||
|
||||
def prepare_image_inputs(self):
|
||||
"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
|
||||
or a list of PyTorch tensors if one specifies torchify=True.
|
||||
"""
|
||||
|
||||
image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
|
||||
|
||||
image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
|
||||
|
||||
return image_inputs
|
||||
|
||||
def test_save_load_pretrained_default(self):
|
||||
tokenizer_slow = self.get_tokenizer()
|
||||
tokenizer_fast = self.get_rust_tokenizer()
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
|
||||
processor_slow = FlavaProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor)
|
||||
processor_slow.save_pretrained(self.tmpdirname)
|
||||
processor_slow = FlavaProcessor.from_pretrained(self.tmpdirname, use_fast=False)
|
||||
|
||||
processor_fast = FlavaProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor)
|
||||
processor_fast.save_pretrained(self.tmpdirname)
|
||||
processor_fast = FlavaProcessor.from_pretrained(self.tmpdirname)
|
||||
|
||||
self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
|
||||
self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
|
||||
self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
|
||||
self.assertIsInstance(processor_slow.tokenizer, BertTokenizer)
|
||||
self.assertIsInstance(processor_fast.tokenizer, BertTokenizerFast)
|
||||
|
||||
self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string())
|
||||
self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string())
|
||||
self.assertIsInstance(processor_slow.feature_extractor, FlavaFeatureExtractor)
|
||||
self.assertIsInstance(processor_fast.feature_extractor, FlavaFeatureExtractor)
|
||||
|
||||
def test_save_load_pretrained_additional_features(self):
|
||||
processor = FlavaProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
|
||||
processor.save_pretrained(self.tmpdirname)
|
||||
|
||||
tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
|
||||
feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
|
||||
|
||||
processor = FlavaProcessor.from_pretrained(
|
||||
self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
|
||||
)
|
||||
|
||||
self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
|
||||
self.assertIsInstance(processor.tokenizer, BertTokenizerFast)
|
||||
|
||||
self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
|
||||
self.assertIsInstance(processor.feature_extractor, FlavaFeatureExtractor)
|
||||
|
||||
def test_feature_extractor(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
image_input = self.prepare_image_inputs()
|
||||
|
||||
input_feat_extract = feature_extractor(image_input, return_tensors="np")
|
||||
input_processor = processor(images=image_input, return_tensors="np")
|
||||
|
||||
for key in input_feat_extract.keys():
|
||||
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
|
||||
|
||||
# With rest of the args
|
||||
random.seed(1234)
|
||||
input_feat_extract = feature_extractor(
|
||||
image_input, return_image_mask=True, return_codebook_pixels=True, return_tensors="np"
|
||||
)
|
||||
random.seed(1234)
|
||||
input_processor = processor(
|
||||
images=image_input, return_image_mask=True, return_codebook_pixels=True, return_tensors="np"
|
||||
)
|
||||
|
||||
for key in input_feat_extract.keys():
|
||||
self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
|
||||
|
||||
def test_tokenizer(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
input_str = "lower newer"
|
||||
|
||||
encoded_processor = processor(text=input_str)
|
||||
|
||||
encoded_tok = tokenizer(input_str)
|
||||
|
||||
for key in encoded_tok.keys():
|
||||
self.assertListEqual(encoded_tok[key], encoded_processor[key])
|
||||
|
||||
def test_processor(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
input_str = "lower newer"
|
||||
image_input = self.prepare_image_inputs()
|
||||
|
||||
inputs = processor(text=input_str, images=image_input)
|
||||
|
||||
self.assertListEqual(list(inputs.keys()), ["input_ids", "token_type_ids", "attention_mask", "pixel_values"])
|
||||
|
||||
# add extra args
|
||||
inputs = processor(text=input_str, images=image_input, return_codebook_pixels=True, return_image_mask=True)
|
||||
|
||||
self.assertListEqual(
|
||||
list(inputs.keys()),
|
||||
[
|
||||
"input_ids",
|
||||
"token_type_ids",
|
||||
"attention_mask",
|
||||
"pixel_values",
|
||||
"codebook_pixel_values",
|
||||
"bool_masked_pos",
|
||||
],
|
||||
)
|
||||
|
||||
# test if it raises when no input is passed
|
||||
with pytest.raises(ValueError):
|
||||
processor()
|
||||
|
||||
def test_tokenizer_decode(self):
|
||||
feature_extractor = self.get_feature_extractor()
|
||||
tokenizer = self.get_tokenizer()
|
||||
|
||||
processor = FlavaProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
|
||||
|
||||
predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
|
||||
|
||||
decoded_processor = processor.batch_decode(predicted_ids)
|
||||
decoded_tok = tokenizer.batch_decode(predicted_ids)
|
||||
|
||||
self.assertListEqual(decoded_tok, decoded_processor)
|
||||
Reference in New Issue
Block a user