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Add the ImageClassificationPipeline (#11598)

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* Add the ImageClassificationPipeline

* Code review

Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>

* Have `load_image` at the module level

Co-authored-by: patrickvonplaten <patrick.v.platen@gmail.com>
This commit is contained in:
Lysandre Debut
2021-05-07 14:08:40 +02:00
committed by GitHub
parent e7bff0aabe
commit 39084ca663
16 changed files with 428 additions and 74 deletions
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17
tests/test_feature_extraction_auto.py
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@@ -16,9 +16,10 @@
import os
import unittest
from transformers import FEATURE_EXTRACTOR_MAPPING, AutoFeatureExtractor, Wav2Vec2FeatureExtractor
from transformers import AutoFeatureExtractor, Wav2Vec2FeatureExtractor
SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR = os.path.join(os.path.dirname(os.path.abspath(__file__)), "fixtures")
SAMPLE_FEATURE_EXTRACTION_CONFIG = os.path.join(
os.path.dirname(os.path.abspath(__file__)), "fixtures/dummy_feature_extractor_config.json"
)
@@ -29,16 +30,10 @@ class AutoFeatureExtractorTest(unittest.TestCase):
config = AutoFeatureExtractor.from_pretrained("facebook/wav2vec2-base-960h")
self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
def test_feature_extractor_from_local_directory(self):
config = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG_DIR)
self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
def test_feature_extractor_from_local_file(self):
config = AutoFeatureExtractor.from_pretrained(SAMPLE_FEATURE_EXTRACTION_CONFIG)
self.assertIsInstance(config, Wav2Vec2FeatureExtractor)
def test_pattern_matching_fallback(self):
"""
In cases where config.json doesn't include a model_type,
perform a few safety checks on the config mapping's order.
"""
# no key string should be included in a later key string (typical failure case)
keys = list(FEATURE_EXTRACTOR_MAPPING.keys())
for i, key in enumerate(keys):
self.assertFalse(any(key in later_key for later_key in keys[i + 1 :]))