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When performing complex tasks like image captioning, using a single ML model may not be the best solution.
This notebook shows how to implement a cascade model in Apache Beam using the RunInference API. The RunInference API enables you to run your Beam transforms as part of your pipeline for optimal machine learning inference.
For more information about the RunInference API, review the RunInference notebook or the Beam ML documentation.
Image captioning with cascade models
Image captioning has various applications, such as image indexing for information retrieval, virtual assistant training, and natural language processing.
This example shows how to generate captions on a a large set of images. Apache Beam is the ideal tool to handle this workflow. We use two models for this task:
- BLIP: Generates a set of candidate captions for a given image.
- CLIP: Ranks the generated captions based on accuracy.
The steps to build this pipeline are as follows:
- Read the images.
- Preprocess the images for caption generation for inference with the BLIP model.
- Run inference with BLIP to generate a list of caption candidates.
- Aggregate the generated captions with their source image.
- Preprocess the aggregated image-caption pairs to rank them with CLIP.
- Run inference with CLIP to generate the caption ranking.
- Print the image names and the captions sorted according to their ranking.
The following diagram illustrates the steps in the inference pipelines used in this notebook.
Diagram
from IPython.display import Image
Image(url='https://storage.googleapis.com/apache-beam-samples/image_captioning/beam_ensemble_diagram.png', width=2000)
Dependencies
This section shows how to install the dependencies for this example.
The RunInference library is available in the Apache Beam SDK versions 2.40 and later.
!pip install --upgrade pip --quiet
!pip install transformers==4.30.2 --quiet
!pip install timm==0.4.12 --quiet
!pip install ftfy==6.1.1 --quiet
!pip install spacy==3.4.1 --quiet
!pip install fairscale==0.4.4 --quiet
!pip install apache_beam[gcp]>=2.48.0
# To use the newly installed versions, restart the runtime.
exit()
import requests
import os
import urllib
import json
import io
from io import BytesIO
from typing import Sequence
from typing import Iterator
from typing import Iterable
from typing import Tuple
from typing import Optional
from typing import Dict
from typing import List
from typing import Any
import apache_beam as beam
from apache_beam.ml.inference.base import PredictionResult
from apache_beam.options.pipeline_options import PipelineOptions
from apache_beam.options.pipeline_options import SetupOptions
from apache_beam.ml.inference.base import KeyedModelHandler
from apache_beam.ml.inference.base import PredictionResult
from apache_beam.ml.inference.base import RunInference
from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerTensor
from apache_beam.ml.inference.pytorch_inference import PytorchModelHandlerKeyedTensor
from transformers import CLIPProcessor
from transformers import CLIPTokenizer
from transformers import CLIPModel
from transformers import CLIPConfig
from transformers import CLIPFeatureExtractor
import torch
from torchvision import transforms
from torchvision.transforms.functional import InterpolationMode
import numpy as np
import matplotlib.pyplot as plt
from PIL import Image
Install CLIP dependencies
Download and install the CLIP dependencies.
git lfs installgit clone https://huggingface.co/openai/clip-vit-base-patch32
Git LFS initialized. Cloning into 'clip-vit-base-patch32'... remote: Enumerating objects: 51, done. remote: Counting objects: 100% (6/6), done. remote: Compressing objects: 100% (6/6), done. remote: Total 51 (delta 1), reused 0 (delta 0), pack-reused 45 Unpacking objects: 100% (51/51), done. Filtering content: 100% (3/3), 1.69 GiB | 66.58 MiB/s, done.
# CLIP model and component configs paths
clip_feature_extractor_config_path = '/content/clip-vit-base-patch32/preprocessor_config.json'
clip_tokenizer_vocab_config_path = '/content/clip-vit-base-patch32/vocab.json'
clip_merges_config_path = '/content/clip-vit-base-patch32/merges.txt'
clip_model_config_path = '/content/clip-vit-base-patch32/config.json'
clip_state_dict_path = '/content/clip-vit-base-patch32/pytorch_model.bin'
Install BLIP dependencies
Download and install the BLIP dependencies.
!git clone https://github.com/salesforce/BLIP
%cd /content/BLIP
Cloning into 'BLIP'... remote: Enumerating objects: 274, done. remote: Counting objects: 100% (156/156), done. remote: Compressing objects: 100% (39/39), done. remote: Total 274 (delta 130), reused 117 (delta 117), pack-reused 118 Receiving objects: 100% (274/274), 7.04 MiB | 13.40 MiB/s, done. Resolving deltas: 100% (150/150), done. /content/BLIP
from BLIP.models.blip import blip_decoder
!gdown 'https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth'
# The blip model is saved as a checkpoint, load it and save it as a state dict since RunInference required
# a state dict for model instantiation
blip_state_dict_path = '/content/BLIP/blip_state_dict.pth'
torch.save(torch.load('/content/BLIP/model*_base_caption.pth')['model'], blip_state_dict_path)
Downloading... From: https://storage.googleapis.com/sfr-vision-language-research/BLIP/models/model*_base_caption.pth To: /content/BLIP/model*_base_caption.pth 100% 896M/896M [00:04<00:00, 198MB/s]
Install I/O helper functions
Download and install the dependencies for the I/O helper functions.
class ReadImagesFromUrl(beam.DoFn):
"""
Read an image from a given URL and return a tuple of the images_url
and image data.
"""
def process(self, element: str) -> Tuple[str, Image.Image]:
response = requests.get(element)
image = Image.open(BytesIO(response.content)).convert('RGB')
return [(element, image)]
class FormatCaptions(beam.DoFn):
"""
Print the image name and its most relevant captions after CLIP ranking.
"""
def __init__(self, number_of_top_captions: int):
self._number_of_top_captions = number_of_top_captions
def process(self, element: Tuple[str, List[str]]):
image_url, caption_list = element
caption_list = caption_list[:self._number_of_top_captions]
img_name = os.path.basename(image_url).rsplit('.')[0]
print(f'Image: {img_name}')
print(f'\tTop {self._number_of_top_captions} captions ranked by CLIP:')
for caption_rank, caption_prob_pair in enumerate(caption_list):
print(f'\t\t{caption_rank+1}: {caption_prob_pair[0]}. (Caption probability: {caption_prob_pair[1]:.2f})')
print('\n')
Intermediate processing functions
Define the preprocessing and postprocessing functions for each of the models.
To prepare the instance for processing bundles of elements by initializing and to cache the processing transform resources, use DoFn.setup().
This step avoids unnecessary re-initializations on every invocation of the processing method.
Define BLIP functions
Define the preprocessing and postprocessing functions for BLIP.
class PreprocessBLIPInput(beam.DoFn):
"""
Process the raw image input to a format suitable for BLIP inference. The processed
images are duplicated to the number of desired captions per image.
Preprocessing transformation taken from:
https://github.com/salesforce/BLIP/blob/d10be550b2974e17ea72e74edc7948c9e5eab884/predict.py
"""
def __init__(self, captions_per_image: int):
self._captions_per_image = captions_per_image
def setup(self):
# Initialize the image transformer.
self._transform = transforms.Compose([
transforms.Resize((384, 384),interpolation=InterpolationMode.BICUBIC),
transforms.ToTensor(),
transforms.Normalize((0.48145466, 0.4578275, 0.40821073), (0.26862954, 0.26130258, 0.27577711))
])
def process(self, element):
image_url, image = element
# The following lines provide a workaround to turn off BatchElements.
preprocessed_img = self._transform(image).unsqueeze(0)
preprocessed_img = preprocessed_img.repeat(self._captions_per_image, 1, 1, 1)
# Parse the processed input to a dictionary to a format suitable for RunInference.
preprocessed_dict = {'inputs': preprocessed_img}
return [(image_url, preprocessed_dict)]
class PostprocessBLIPOutput(beam.DoFn):
"""
Process the PredictionResult to get the generated image captions
"""
def process(self, element : Tuple[str, Iterable[PredictionResult]]):
image_url, prediction = element
return [(image_url, prediction.inference)]
Define CLIP functions
Define the preprocessing and postprocessing functions for CLIP.
class PreprocessCLIPInput(beam.DoFn):
"""
Process the image-caption pair to a format suitable for CLIP inference.
After grouping the raw images with the generated captions, we need to
preprocess them before passing them to the ranking stage (CLIP model).
"""
def __init__(self,
feature_extractor_config_path: str,
tokenizer_vocab_config_path: str,
merges_file_config_path: str):
self._feature_extractor_config_path = feature_extractor_config_path
self._tokenizer_vocab_config_path = tokenizer_vocab_config_path
self._merges_file_config_path = merges_file_config_path
def setup(self):
# Initialize the CLIP feature extractor.
feature_extractor_config = CLIPConfig.from_pretrained(self._feature_extractor_config_path)
feature_extractor = CLIPFeatureExtractor(feature_extractor_config)
# Initialize the CLIP tokenizer.
tokenizer = CLIPTokenizer(self._tokenizer_vocab_config_path,
self._merges_file_config_path)
# Initialize the CLIP processor used to process the image-caption pair.
self._processor = CLIPProcessor(feature_extractor=feature_extractor,
tokenizer=tokenizer)
def process(self, element: Tuple[str, Dict[str, List[Any]]]):
image_url, image_captions_pair = element
# Unpack the image and captions after grouping them with 'CoGroupByKey()'.
image = image_captions_pair['image'][0]
captions = image_captions_pair['captions'][0]
preprocessed_clip_input = self._processor(images = image,
text = captions,
return_tensors="pt",
padding=True)
image_url_caption_pair = (image_url, captions)
return [(image_url_caption_pair, preprocessed_clip_input)]
class RankCLIPOutput(beam.DoFn):
"""
Process the output of CLIP to get the captions sorted by ranking order.
The logits are the output of the CLIP model. Here, we apply a softmax activation
function to the logits to get the probabilistic distribution of the relevance
of each caption to the target image. After that, we sort the captions in descending
order with respect to the probabilities as a caption-probability pair.
"""
def process(self, element : Tuple[Tuple[str, List[str]], Iterable[PredictionResult]]):
(image_url, captions), prediction = element
prediction_results = prediction.inference
prediction_probs = prediction_results.softmax(dim=-1).cpu().detach().numpy()
ranking = np.argsort(-prediction_probs)
sorted_caption_prob_pair = [(captions[idx], prediction_probs[idx]) for idx in ranking]
return [(image_url, sorted_caption_prob_pair)]
Model handlers
A ModelHandler is Beam's method for defining the configuration needed to load and invoke your model. Since both the BLIP and CLIP models use Pytorch and take KeyedTensors as inputs, we will use PytorchModelHandlerKeyedTensor for both.
We will use a KeyedModelHandler for both models to attach a key to the general ModelHandler.
The key is used for the following purposes:
- To keep a reference to the image that the inference is associated with.
- To aggregate transforms of different inputs.
- To run postprocessing steps correctly.
In this example, we use the image_url as the key.
Generate captions with BLIP
Use BLIP to generate a set of candidate captions for a given image.
MAX_CAPTION_LENGTH = 80
MIN_CAPTION_LENGTH = 10
# Increasing Beam search might improve the quality of the captions,
# but also results in more compute time
NUM_BEAMS = 1
def blip_keyed_tensor_inference_fn(
batch: Sequence[Dict[str, torch.Tensor]],
model: torch.nn.Module,
device: str,
inference_args: Optional[Dict[str, Any]] = None,
model_id: Optional[str] = None,
) -> Iterable[PredictionResult]:
# By default, Beam batches inputs for bulk inference and calls model(batch)
# Since we want to call model.generate on a single unbatched input (BLIP/CLIP
# don't handle batched inputs), we define a custom inference function.
captions = model.generate(batch[0]['inputs'],
sample=True,
num_beams=NUM_BEAMS,
max_length=MAX_CAPTION_LENGTH,
min_length=MIN_CAPTION_LENGTH)
return [PredictionResult(batch[0], captions, model_id)]
BLIP_model_handler = PytorchModelHandlerKeyedTensor(
state_dict_path=blip_state_dict_path,
model_class=blip_decoder,
inference_fn=blip_keyed_tensor_inference_fn,
max_batch_size=1)
BLIP_keyed_model_handler = KeyedModelHandler(BLIP_model_handler)
Rank captions with CLIP
Use CLIP to rank the generated captions based on the accuracy with which they represent the image.
def clip_keyed_tensor_inference_fn(
batch: Sequence[Dict[str, torch.Tensor]],
model: torch.nn.Module,
device: str,
inference_args: Optional[Dict[str, Any]] = None,
model_id: Optional[str] = None,
) -> Iterable[PredictionResult]:
# By default, Beam batches inputs for bulk inference and calls model(batch)
# Since we want to call model on a single unbatched input (BLIP/CLIP don't
# handle batched inputs), we define a custom inference function.
output = model(**batch[0], **inference_args)
return [PredictionResult(batch[0], output.logits_per_image[0], model_id)]
CLIP_model_handler = PytorchModelHandlerKeyedTensor(
state_dict_path=clip_state_dict_path,
model_class=CLIPModel,
model_params={'config': CLIPConfig.from_pretrained(clip_model_config_path)},
inference_fn=clip_keyed_tensor_inference_fn,
max_batch_size=1)
CLIP_keyed_model_handler = KeyedModelHandler(CLIP_model_handler)
Specify the images to display
This section demonstrates how to specify the images to display for captioning.
images_url = ['https://storage.googleapis.com/apache-beam-samples/image_captioning/Paris-sunset.jpeg',
'https://storage.googleapis.com/apache-beam-samples/image_captioning/Wedges.jpeg',
'https://storage.googleapis.com/apache-beam-samples/image_captioning/Hamsters.jpeg']
Visualize the images to use for captioning.
license_txt_url = 'https://storage.googleapis.com/apache-beam-samples/image_captioning/LICENSE.txt'
license_dict = json.loads(urllib.request.urlopen(license_txt_url).read().decode("utf-8"))
for image_url in images_url:
response = requests.get(image_url)
image = Image.open(BytesIO(response.content)).convert('RGB')
image_author = license_dict[image_url]
fig = plt.figure()
title = f"{os.path.basename(image_url).rsplit('.')[0]} \n Author: {image_author}"
fig.suptitle(title, fontsize=12)
plt.axis('off')
plt.imshow(image)
Initialize the pipeline run parameters
Specify the number of captions generated per image and the number of captions to display with each image.
# Number of captions generated per image.
NUM_CAPTIONS_PER_IMAGE = 10
# Number of top captions to display.
NUM_TOP_CAPTIONS_TO_DISPLAY = 3
Run the pipeline
This example uses raw images from the read_images pipeline as inputs for both models. Each model needs to preprocess the raw images differently, because they require a different embedding representation for image captioning and for image-captions pair ranking.
To aggregate the raw images with the generated caption by their key (the image URL), this example uses CoGroupByKey. This process produces a tuple of image-captions pairs that is then passed to the CLIP transform and used for ranking.
with beam.Pipeline() as pipeline:
read_images = (
pipeline
| "ReadUrl" >> beam.Create(images_url)
| "ReadImages" >> beam.ParDo(ReadImagesFromUrl()))
blip_caption_generation = (
read_images
| "PreprocessBlipInput" >> beam.ParDo(PreprocessBLIPInput(NUM_CAPTIONS_PER_IMAGE))
| "GenerateCaptions" >> RunInference(BLIP_keyed_model_handler)
| "PostprocessCaptions" >> beam.ParDo(PostprocessBLIPOutput()))
clip_captions_ranking = (
({'image' : read_images, 'captions': blip_caption_generation})
| "CreateImageCaptionPair" >> beam.CoGroupByKey()
| "PreprocessClipInput" >> beam.ParDo(
PreprocessCLIPInput(
clip_feature_extractor_config_path,
clip_tokenizer_vocab_config_path,
clip_merges_config_path))
| "GetRankingLogits" >> RunInference(CLIP_keyed_model_handler)
| "RankClipOutput" >> beam.ParDo(RankCLIPOutput())
)
clip_captions_ranking | "FormatCaptions" >> beam.ParDo(FormatCaptions(NUM_TOP_CAPTIONS_TO_DISPLAY))
Image: Paris-sunset
Top 3 captions ranked by CLIP:
1: the setting sun is reflected in an orange setting sky over paris. (Caption probability: 0.28)
2: the sun rising above the eiffel tower over paris. (Caption probability: 0.23)
3: the sun setting over the eiffel tower and rooftops. (Caption probability: 0.15)
Image: Wedges
Top 3 captions ranked by CLIP:
1: sweet potato fries with ketchup served in bowl. (Caption probability: 0.73)
2: this is a plate of sweet potato fries with ketchup. (Caption probability: 0.16)
3: sweet potato fries and a dipping sauce are on the tray. (Caption probability: 0.06)
Image: Hamsters
Top 3 captions ranked by CLIP:
1: person holding two small animals in their hands. (Caption probability: 0.62)
2: a person's hand holding a small hamster in front of them. (Caption probability: 0.20)
3: a person holding a small animal in their hands. (Caption probability: 0.09)
Conclusion
After running the pipeline, you can see the captions generated by the BLIP model and ranked by the CLIP model with all of our pre/postprocessing logic applied. As you can see, running multi-model inference is easy with the power of Beam.
Resources
- RunInference API: an official guide to the RunInference API.
- RunInference Demo: an ensemble model demo in Colab.
- The advantages of having a DAG and what it unlocks for you: a guide on the advantages of using a Beam DAG for ML workflow orchestration and inference.