def test_save_load_pretrained_additional_features(self):
processor = VisionTextDualEncoderProcessor(
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 = VisionTextDualEncoderProcessor.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,
(BertTokenizer, BertTokenizerFast))
self.assertEqual(processor.feature_extractor.to_json_string(),
feature_extractor_add_kwargs.to_json_string())
self.assertIsInstance(processor.feature_extractor, ViTFeatureExtractor)
def test_tokenizer_decode(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(
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)
def test_inference(self):
model = VisionTextDualEncoderModel.from_pretrained(
"clip-italian/clip-italian", logit_scale_init_value=1)
processor = VisionTextDualEncoderProcessor.from_pretrained(
"clip-italian/clip-italian")
image = Image.open(
"./tests/fixtures/tests_samples/COCO/000000039769.png")
inputs = processor(
text=["una foto di un gatto", "una foto di un cane"],
images=image,
padding=True,
return_tensors="pt")
outputs = model(**inputs)
# verify the logits
self.assertEqual(
outputs.logits_per_image.shape,
(inputs.pixel_values.shape[0], inputs.input_ids.shape[0]))
self.assertEqual(
outputs.logits_per_text.shape,
(inputs.input_ids.shape[0], inputs.pixel_values.shape[0]),
)
expected_logits = torch.tensor([[1.2284727, 0.3104122]])
self.assertTrue(
torch.allclose(outputs.logits_per_image,
expected_logits,
atol=1e-3))
def test_save_load_pretrained_default(self):
tokenizer = self.get_tokenizer()
feature_extractor = self.get_feature_extractor()
processor = VisionTextDualEncoderProcessor(
tokenizer=tokenizer, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = VisionTextDualEncoderProcessor.from_pretrained(
self.tmpdirname)
self.assertEqual(processor.tokenizer.get_vocab(),
tokenizer.get_vocab())
self.assertIsInstance(processor.tokenizer,
(BertTokenizer, BertTokenizerFast))
self.assertEqual(processor.feature_extractor.to_json_string(),
feature_extractor.to_json_string())
self.assertIsInstance(processor.feature_extractor, ViTFeatureExtractor)
def test_tokenizer(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(
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_feature_extractor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(
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)
def test_processor(self):
feature_extractor = self.get_feature_extractor()
tokenizer = self.get_tokenizer()
processor = VisionTextDualEncoderProcessor(
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"])
# test if it raises when no input is passed
with self.assertRaises(ValueError):
processor()