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Fix doctest for Blip2ForConditionalGeneration (#26737)
* fix * fix * fix * fix * fix * fix * fix * fix * fix --------- Co-authored-by: ydshieh <ydshieh@users.noreply.github.com>
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@ -1272,14 +1272,10 @@ class Blip2Model(Blip2PreTrainedModel):
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>>> import torch
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>>> from transformers import AutoTokenizer, Blip2Model
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>>> device = "cuda" if torch.cuda.is_available() else "cpu"
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>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
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>>> model.to(device) # doctest: +IGNORE_RESULT
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>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
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>>> tokenizer = AutoTokenizer.from_pretrained("Salesforce/blip2-opt-2.7b")
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>>> inputs = tokenizer(["a photo of a cat", "a photo of a dog"], padding=True, return_tensors="pt").to(device)
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>>> inputs = tokenizer(["a photo of a cat"], padding=True, return_tensors="pt")
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>>> text_features = model.get_text_features(**inputs)
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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@ -1333,16 +1329,12 @@ class Blip2Model(Blip2PreTrainedModel):
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>>> import requests
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>>> from transformers import AutoProcessor, Blip2Model
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>>> device = "cuda" if torch.cuda.is_available() else "cpu"
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>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
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>>> model.to(device) # doctest: +IGNORE_RESULT
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>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
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>>> processor = AutoProcessor.from_pretrained("Salesforce/blip2-opt-2.7b")
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
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>>> inputs = processor(images=image, return_tensors="pt")
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>>> image_outputs = model.get_image_features(**inputs)
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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@ -1381,15 +1373,12 @@ class Blip2Model(Blip2PreTrainedModel):
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>>> import requests
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>>> from transformers import Blip2Processor, Blip2Model
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>>> device = "cuda" if torch.cuda.is_available() else "cpu"
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>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
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>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16)
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>>> model.to(device) # doctest: +IGNORE_RESULT
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>>> model = Blip2Model.from_pretrained("Salesforce/blip2-opt-2.7b")
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
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>>> inputs = processor(images=image, return_tensors="pt")
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>>> qformer_outputs = model.get_qformer_features(**inputs)
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```"""
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output_attentions = output_attentions if output_attentions is not None else self.config.output_attentions
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@ -1654,34 +1643,7 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
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Examples:
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Image captioning (without providing a text prompt):
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import Blip2Processor, Blip2ForConditionalGeneration
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>>> import torch
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>>> device = "cuda" if torch.cuda.is_available() else "cpu"
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>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-opt-2.7b")
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>>> model = Blip2ForConditionalGeneration.from_pretrained(
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... "Salesforce/blip2-opt-2.7b", torch_dtype=torch.float16
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... )
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>>> model.to(device) # doctest: +IGNORE_RESULT
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
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>>> generated_ids = model.generate(**inputs)
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>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
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>>> print(generated_text)
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two cats laying on a couch
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```
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Visual question answering (prompt = question):
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Prepare processor, model and image input
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```python
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>>> from PIL import Image
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@ -1698,7 +1660,22 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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```
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Image captioning (without providing a text prompt):
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```python
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>>> inputs = processor(images=image, return_tensors="pt").to(device, torch.float16)
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>>> generated_ids = model.generate(**inputs)
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>>> generated_text = processor.batch_decode(generated_ids, skip_special_tokens=True)[0].strip()
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>>> print(generated_text)
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two cats laying on a couch
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```
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Visual question answering (prompt = question):
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```python
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>>> prompt = "Question: how many cats are there? Answer:"
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>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.float16)
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@ -1712,20 +1689,10 @@ class Blip2ForConditionalGeneration(Blip2PreTrainedModel):
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This greatly reduces the amount of memory used by the model while maintaining the same performance.
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```python
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>>> from PIL import Image
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>>> import requests
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>>> from transformers import Blip2Processor, Blip2ForConditionalGeneration
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>>> import torch
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>>> processor = Blip2Processor.from_pretrained("Salesforce/blip2-flan-t5-xl")
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>>> model = Blip2ForConditionalGeneration.from_pretrained(
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... "Salesforce/blip2-flan-t5-xl", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.bfloat16
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... "Salesforce/blip2-opt-2.7b", load_in_8bit=True, device_map={"": 0}, torch_dtype=torch.bfloat16
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... ) # doctest: +IGNORE_RESULT
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>>> url = "http://images.cocodataset.org/val2017/000000039769.jpg"
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>>> image = Image.open(requests.get(url, stream=True).raw)
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>>> prompt = "Question: how many cats are there? Answer:"
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>>> inputs = processor(images=image, text=prompt, return_tensors="pt").to(device="cuda", dtype=torch.bfloat16)
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>>> generated_ids = model.generate(**inputs)
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@ -1,4 +1,5 @@
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docs/source/en/generation_strategies.md
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docs/source/en/model_doc/ctrl.md
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docs/source/en/task_summary.md
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src/transformers/models/blip_2/modeling_blip_2.py
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src/transformers/models/ctrl/modeling_ctrl.py
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