Spaces:
Runtime error
Runtime error
File size: 4,295 Bytes
455a40f |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 |
# Copyright 2023 The HuggingFace Team. All rights reserved.
#
# 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 shutil
import tempfile
import unittest
import numpy as np
import pytest
from transformers import is_speech_available, is_vision_available
from transformers.testing_utils import require_torch
if is_vision_available():
from transformers import TvltImageProcessor
if is_speech_available():
from transformers import TvltFeatureExtractor
from transformers import TvltProcessor
@require_torch
class TvltProcessorTest(unittest.TestCase):
def setUp(self):
self.checkpoint = "ZinengTang/tvlt-base"
self.tmpdirname = tempfile.mkdtemp()
def get_image_processor(self, **kwargs):
return TvltImageProcessor.from_pretrained(self.checkpoint, **kwargs)
def get_feature_extractor(self, **kwargs):
return TvltFeatureExtractor.from_pretrained(self.checkpoint, **kwargs)
def tearDown(self):
shutil.rmtree(self.tmpdirname)
def test_save_load_pretrained_default(self):
image_processor = self.get_image_processor()
feature_extractor = self.get_feature_extractor()
processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
processor.save_pretrained(self.tmpdirname)
processor = TvltProcessor.from_pretrained(self.tmpdirname)
self.assertIsInstance(processor.feature_extractor, TvltFeatureExtractor)
self.assertIsInstance(processor.image_processor, TvltImageProcessor)
def test_feature_extractor(self):
image_processor = self.get_image_processor()
feature_extractor = self.get_feature_extractor()
processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
audio = np.ones([12000])
audio_dict = feature_extractor(audio, return_tensors="np")
input_processor = processor(audio=audio, return_tensors="np")
for key in audio_dict.keys():
self.assertAlmostEqual(audio_dict[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_image_processor(self):
image_processor = self.get_image_processor()
feature_extractor = self.get_feature_extractor()
processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
images = np.ones([3, 224, 224])
image_dict = image_processor(images, return_tensors="np")
input_processor = processor(images=images, return_tensors="np")
for key in image_dict.keys():
self.assertAlmostEqual(image_dict[key].sum(), input_processor[key].sum(), delta=1e-2)
def test_processor(self):
image_processor = self.get_image_processor()
feature_extractor = self.get_feature_extractor()
processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
audio = np.ones([12000])
images = np.ones([3, 224, 224])
inputs = processor(audio=audio, images=images)
self.assertListEqual(list(inputs.keys()), ["audio_values", "audio_mask", "pixel_values", "pixel_mask"])
# test if it raises when no input is passed
with pytest.raises(ValueError):
processor()
def test_model_input_names(self):
image_processor = self.get_image_processor()
feature_extractor = self.get_feature_extractor()
processor = TvltProcessor(image_processor=image_processor, feature_extractor=feature_extractor)
self.assertListEqual(
processor.model_input_names,
image_processor.model_input_names + feature_extractor.model_input_names,
msg="`processor` and `image_processor`+`feature_extractor` model input names do not match",
)
|