Add OWL-ViT model for zero-shot object detection (#17938)
* add owlvit model skeleton * add class and box predictor heads * convert modified flax clip to pytorch * fix box and class predictors * add OwlViTImageTextEmbedder * convert class and box head checkpoints * convert image text embedder checkpoints * add object detection head * fix bugs * update conversion script * update conversion script * fix q,v,k,out weight conversion conversion * add owlvit object detection output * fix bug in image embedder * fix bugs in text embedder * fix positional embeddings * fix bug in inference mode vision pooling * update docs, init tokenizer and processor files * support batch processing * add OwlViTProcessor * remove merge conflicts * readd owlvit imports * fix bug in OwlViTProcessor imports * fix bugs in processor * update docs * fix bugs in processor * update owlvit docs * add OwlViTFeatureExtractor * style changes, add postprocess method to feature extractor * add feature extractor and processor tests * add object detection tests * update conversion script * update config paths * update config paths * fix configuration paths and bugs * fix bugs in OwlViT tests * add import checks to processor * fix docs and minor issues * fix docs and minor issues * fix bugs and issues * fix bugs and issues * fix bugs and issues * fix bugs and issues * update docs and examples * fix bugs and issues * update conversion script, fix positional embeddings * process 2D input ids, update tests * fix style and quality issues * update docs * update docs and imports * update OWL-ViT index.md * fix bug in OwlViT feature ext tests * fix code examples, return_dict by default * return_dict by default * minor fixes, add tests to processor * small fixes * add output_attentions arg to main model * fix bugs * remove output_hidden_states arg from main model * update self.config variables * add option to return last_hidden_states * fix bug in config variables * fix copied from statements * fix small issues and bugs * fix bugs * fix bugs, support greyscale images * run fixup * update repo name * merge OwlViTImageTextEmbedder with obj detection head * fix merge conflict * fix merge conflict * make fixup * fix bugs * fix bugs * add additional processor test
This commit is contained in:
Alara Dirik
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tests/models/owlvit/test_processor_owlvit.py
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241
tests/models/owlvit/test_processor_owlvit.py
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# Copyright 2022 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 OwlViTFeatureExtractor, OwlViTProcessor
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@require_vision
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class OwlViTProcessorTest(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, pad_token="!", **kwargs)
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def get_rust_tokenizer(self, **kwargs):
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return CLIPTokenizerFast.from_pretrained(self.tmpdirname, pad_token="!", **kwargs)
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def get_feature_extractor(self, **kwargs):
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return OwlViTFeatureExtractor.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 = OwlViTProcessor(tokenizer=tokenizer_slow, feature_extractor=feature_extractor)
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processor_slow.save_pretrained(self.tmpdirname)
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processor_slow = OwlViTProcessor.from_pretrained(self.tmpdirname, use_fast=False)
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processor_fast = OwlViTProcessor(tokenizer=tokenizer_fast, feature_extractor=feature_extractor)
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processor_fast.save_pretrained(self.tmpdirname)
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processor_fast = OwlViTProcessor.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, OwlViTFeatureExtractor)
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self.assertIsInstance(processor_fast.feature_extractor, OwlViTFeatureExtractor)
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def test_save_load_pretrained_additional_features(self):
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processor = OwlViTProcessor(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)
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processor = OwlViTProcessor.from_pretrained(
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self.tmpdirname, bos_token="(BOS)", eos_token="(EOS)", do_normalize=False
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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, OwlViTFeatureExtractor)
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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 = OwlViTProcessor(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 = OwlViTProcessor(tokenizer=tokenizer, feature_extractor=feature_extractor)
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input_str = "lower newer"
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encoded_processor = processor(text=input_str, return_tensors="np")
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encoded_tok = tokenizer(input_str, return_tensors="np")
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for key in encoded_tok.keys():
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self.assertListEqual(encoded_tok[key][0].tolist(), encoded_processor[key][0].tolist())
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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 = OwlViTProcessor(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_processor_with_text_list(self):
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model_name = "google/owlvit-base-patch32"
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processor = OwlViTProcessor.from_pretrained(model_name)
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input_text = ["cat", "nasa badge"]
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inputs = processor(text=input_text)
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seq_length = 16
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self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
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self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
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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_processor_with_nested_text_list(self):
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model_name = "google/owlvit-base-patch32"
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processor = OwlViTProcessor.from_pretrained(model_name)
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input_texts = [["cat", "nasa badge"], ["person"]]
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inputs = processor(text=input_texts)
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seq_length = 16
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batch_size = len(input_texts)
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num_max_text_queries = max([len(texts) for texts in input_texts])
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self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
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self.assertEqual(inputs["input_ids"].shape, (batch_size * num_max_text_queries, seq_length))
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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_processor_case(self):
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model_name = "google/owlvit-base-patch32"
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processor = OwlViTProcessor.from_pretrained(model_name)
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input_texts = ["cat", "nasa badge"]
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inputs = processor(text=input_texts)
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seq_length = 16
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input_ids = inputs["input_ids"]
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predicted_ids = [
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[49406, 2368, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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[49406, 6841, 11301, 49407, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0],
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]
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self.assertListEqual(list(inputs.keys()), ["input_ids", "attention_mask"])
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self.assertEqual(inputs["input_ids"].shape, (2, seq_length))
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self.assertListEqual(list(input_ids[0]), predicted_ids[0])
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self.assertListEqual(list(input_ids[1]), predicted_ids[1])
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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 = OwlViTProcessor(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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