SUMIN/HuggingFace_transformer
1
0
Fork 0
Code Issues Pull Requests Actions 7 Packages Projects Releases Wiki Activity

Update doc examples feature extractor -> image processor (#20501)

Browse Source 
* Update doc example feature extractor -> image processor

* Apply suggestions from code review
This commit is contained in:
amyeroberts
2022-11-30 14:50:55 +00:00
committed by GitHub
parent afad0c18d9
commit 17a7b49bda
84 changed files with 497 additions and 458 deletions
Download Patch File Download Diff File Expand all files Collapse all files

14
docs/source/en/model_doc/vision-encoder-decoder.mdx
View File

@@ -68,17 +68,17 @@ To perform inference, one uses the [`generate`] method, which allows to autoregr
>>> import requests
>>> from PIL import Image
>>> from transformers import GPT2TokenizerFast, ViTFeatureExtractor, VisionEncoderDecoderModel
>>> from transformers import GPT2TokenizerFast, ViTImageProcessor, VisionEncoderDecoderModel
>>> # load a fine-tuned image captioning model and corresponding tokenizer and feature extractor
>>> # load a fine-tuned image captioning model and corresponding tokenizer and image processor
>>> model = VisionEncoderDecoderModel.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> tokenizer = GPT2TokenizerFast.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> feature_extractor = ViTFeatureExtractor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> image_processor = ViTImageProcessor.from_pretrained("nlpconnect/vit-gpt2-image-captioning")
>>> # let's perform inference on an image
>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
>>> image = Image.open(requests.get(url, stream=True).raw)
>>> pixel_values = feature_extractor(image, return_tensors="pt").pixel_values
>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values
>>> # autoregressively generate caption (uses greedy decoding by default)
>>> generated_ids = model.generate(pixel_values)
@@ -115,10 +115,10 @@ As you can see, only 2 inputs are required for the model in order to compute a l
images) and `labels` (which are the `input_ids` of the encoded target sequence).
```python
>>> from transformers import ViTFeatureExtractor, BertTokenizer, VisionEncoderDecoderModel
>>> from transformers import ViTImageProcessor, BertTokenizer, VisionEncoderDecoderModel
>>> from datasets import load_dataset
>>> feature_extractor = ViTFeatureExtractor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> image_processor = ViTImageProcessor.from_pretrained("google/vit-base-patch16-224-in21k")
>>> tokenizer = BertTokenizer.from_pretrained("bert-base-uncased")
>>> model = VisionEncoderDecoderModel.from_encoder_decoder_pretrained(
... "google/vit-base-patch16-224-in21k", "bert-base-uncased"
@@ -129,7 +129,7 @@ images) and `labels` (which are the `input_ids` of the encoded target sequence).
>>> dataset = load_dataset("huggingface/cats-image")
>>> image = dataset["test"]["image"][0]
>>> pixel_values = feature_extractor(image, return_tensors="pt").pixel_values
>>> pixel_values = image_processor(image, return_tensors="pt").pixel_values
>>> labels = tokenizer(
... "an image of two cats chilling on a couch",