Move test model folders (#17034)
* move test model folders (TODO: fix imports and others) * fix (potentially partially) imports (in model test modules) * fix (potentially partially) imports (in tokenization test modules) * fix (potentially partially) imports (in feature extraction test modules) * fix import utils.test_modeling_tf_core * fix path ../fixtures/ * fix imports about generation.test_generation_flax_utils * fix more imports * fix fixture path * fix get_test_dir * update module_to_test_file * fix get_tests_dir from wrong transformers.utils * update config.yml (CircleCI) * fix style * remove missing imports * update new model script * update check_repo * update SPECIAL_MODULE_TO_TEST_MAP * fix style * add __init__ * update self-scheduled * fix add_new_model scripts * check one way to get location back * python setup.py build install * fix import in test auto * update self-scheduled.yml * update slack notification script * Add comments about artifact names * fix for yolos Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
This commit is contained in:
Yih-Dar
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tests/models/clip/test_processor_clip.py
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189
tests/models/clip/test_processor_clip.py
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# Copyright 2021 The HuggingFace Team. All rights reserved.
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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 json
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import os
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import shutil
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import tempfile
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import unittest
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import numpy as np
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import pytest
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from transformers import CLIPTokenizer, CLIPTokenizerFast
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from transformers.models.clip.tokenization_clip 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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if is_vision_available():
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from PIL import Image
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from transformers import CLIPFeatureExtractor, CLIPProcessor
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@require_vision
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class CLIPProcessorTest(unittest.TestCase):
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def setUp(self):
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self.tmpdirname = tempfile.mkdtemp()
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# fmt: off
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vocab = ["l", "o", "w", "e", "r", "s", "t", "i", "d", "n", "lo", "l</w>", "w</w>", "r</w>", "t</w>", "low</w>", "er</w>", "lowest</w>", "newer</w>", "wider", "<unk>", "<|startoftext|>", "<|endoftext|>"]
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# fmt: on
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vocab_tokens = dict(zip(vocab, range(len(vocab))))
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merges = ["#version: 0.2", "l o", "lo w</w>", "e r</w>", ""]
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self.special_tokens_map = {"unk_token": "<unk>"}
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self.vocab_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["vocab_file"])
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self.merges_file = os.path.join(self.tmpdirname, VOCAB_FILES_NAMES["merges_file"])
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with open(self.vocab_file, "w", encoding="utf-8") as fp:
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fp.write(json.dumps(vocab_tokens) + "\n")
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with open(self.merges_file, "w", encoding="utf-8") as fp:
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fp.write("\n".join(merges))
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feature_extractor_map = {
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"do_resize": True,
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"size": 20,
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"do_center_crop": True,
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"crop_size": 18,
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"do_normalize": True,
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"image_mean": [0.48145466, 0.4578275, 0.40821073],
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"image_std": [0.26862954, 0.26130258, 0.27577711],
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}
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self.feature_extractor_file = os.path.join(self.tmpdirname, FEATURE_EXTRACTOR_NAME)
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with open(self.feature_extractor_file, "w", encoding="utf-8") as fp:
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json.dump(feature_extractor_map, fp)
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def get_tokenizer(self, **kwargs):
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return CLIPTokenizer.from_pretrained(self.tmpdirname, **kwargs)
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def get_rust_tokenizer(self, **kwargs):
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return CLIPTokenizerFast.from_pretrained(self.tmpdirname, **kwargs)
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def get_feature_extractor(self, **kwargs):
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return CLIPFeatureExtractor.from_pretrained(self.tmpdirname, **kwargs)
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def tearDown(self):
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shutil.rmtree(self.tmpdirname)
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def prepare_image_inputs(self):
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"""This function prepares a list of PIL images, or a list of numpy arrays if one specifies numpify=True,
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or a list of PyTorch tensors if one specifies torchify=True.
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"""
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image_inputs = [np.random.randint(255, size=(3, 30, 400), dtype=np.uint8)]
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image_inputs = [Image.fromarray(np.moveaxis(x, 0, -1)) for x in image_inputs]
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return image_inputs
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def test_save_load_pretrained_default(self):
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tokenizer_slow = self.get_tokenizer()
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tokenizer_fast = self.get_rust_tokenizer()
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feature_extractor = self.get_feature_extractor()
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processor_slow = CLIPProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor)
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processor_slow.save_pretrained(self.tmpdirname)
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processor_slow = CLIPProcessor.from_pretrained(self.tmpdirname, use_fast=False)
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processor_fast = CLIPProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor)
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processor_fast.save_pretrained(self.tmpdirname)
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processor_fast = CLIPProcessor.from_pretrained(self.tmpdirname)
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self.assertEqual(processor_slow.tokenizer.get_vocab(), tokenizer_slow.get_vocab())
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self.assertEqual(processor_fast.tokenizer.get_vocab(), tokenizer_fast.get_vocab())
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self.assertEqual(tokenizer_slow.get_vocab(), tokenizer_fast.get_vocab())
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self.assertIsInstance(processor_slow.tokenizer, CLIPTokenizer)
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self.assertIsInstance(processor_fast.tokenizer, CLIPTokenizerFast)
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self.assertEqual(processor_slow.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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self.assertEqual(processor_fast.feature_extractor.to_json_string(), feature_extractor.to_json_string())
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self.assertIsInstance(processor_slow.feature_extractor, CLIPFeatureExtractor)
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self.assertIsInstance(processor_fast.feature_extractor, CLIPFeatureExtractor)
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def test_save_load_pretrained_additional_features(self):
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processor = CLIPProcessor(tokenizer=self.get_tokenizer(), feature_extractor=self.get_feature_extractor())
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processor.save_pretrained(self.tmpdirname)
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tokenizer_add_kwargs = self.get_tokenizer(bos_token="(BOS)", eos_token="(EOS)")
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feature_extractor_add_kwargs = self.get_feature_extractor(do_normalize=False, padding_value=1.0)
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processor = CLIPProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False, padding_value=1.0
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)
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self.assertEqual(processor.tokenizer.get_vocab(), tokenizer_add_kwargs.get_vocab())
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self.assertIsInstance(processor.tokenizer, CLIPTokenizerFast)
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self.assertEqual(processor.feature_extractor.to_json_string(), feature_extractor_add_kwargs.to_json_string())
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self.assertIsInstance(processor.feature_extractor, CLIPFeatureExtractor)
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def test_feature_extractor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = CLIPProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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image_input = self.prepare_image_inputs()
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input_feat_extract = feature_extractor(image_input, return_tensors="np")
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input_processor = processor(images=image_input, return_tensors="np")
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for key in input_feat_extract.keys():
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self.assertAlmostEqual(input_feat_extract[key].sum(), input_processor[key].sum(), delta=1e-2)
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def test_tokenizer(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = CLIPProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "lower newer"
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encoded_processor = processor(text=input_str)
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encoded_tok = tokenizer(input_str)
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key], encoded_processor[key])
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def test_processor(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = CLIPProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "lower newer"
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image_input = self.prepare_image_inputs()
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inputs = processor(text=input_str, images=image_input)
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self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask", "pixel_values"])
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# test if it raises when no input is passed
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with pytest.raises(ValueError):
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processor()
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def test_tokenizer_decode(self):
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feature_extractor = self.get_feature_extractor()
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tokenizer = self.get_tokenizer()
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processor = CLIPProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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predicted_ids = [[1, 4, 5, 8, 1, 0, 8], [3, 4, 3, 1, 1, 8, 9]]
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decoded_processor = processor.batch_decode(predicted_ids)
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decoded_tok = tokenizer.batch_decode(predicted_ids)
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self.assertListEqual(decoded_tok, decoded_processor)
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