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__init__.py
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__init__.py
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"""Image Captioning plugin.
| Copyright 2017-2023, Voxel51, Inc.
| `voxel51.com <https://voxel51.com/>`_
|
"""
from importlib.util import find_spec
import os
from PIL import Image
import fiftyone as fo
import fiftyone.core.utils as fou
import fiftyone.operators as foo
from fiftyone.operators import types
transformers = fou.lazy_import("transformers")
replicate = fou.lazy_import("replicate")
def allows_replicate_models():
"""Returns whether the current environment allows replicate models."""
return (
find_spec("replicate") is not None
and "REPLICATE_API_TOKEN" in os.environ
)
def allows_hf_models():
"""
Returns whether the current environment allows hugging face transformer
models.
"""
return find_spec("transformers") is not None
def get_filepath(sample):
return (
sample.local_path if hasattr(sample, "local_path") else sample.filepath
)
def _execution_mode(ctx, inputs):
delegate = ctx.params.get("delegate", False)
if delegate:
description = "Uncheck this box to execute the operation immediately"
else:
description = "Check this box to delegate execution of this task"
inputs.bool(
"delegate",
default=False,
required=True,
label="Delegate execution?",
description=description,
view=types.CheckboxView(),
)
if delegate:
inputs.view(
"notice",
types.Notice(
label=(
"You've chosen delegated execution. Note that you must "
"have a delegated operation service running in order for "
"this task to be processed. See "
"https://docs.voxel51.com/plugins/index.html#operators "
"for more information"
)
),
)
def run_qwen_vl_chat(sample):
filepath = get_filepath(sample)
return replicate.run(
"lucataco/qwen-vl-chat:50881b153b4d5f72b3db697e2bbad23bb1277ab741c5b52d80cd6ee17ea660e9",
input={
"image": open(filepath, "rb"),
"prompt": "Describe the image in detail.",
},
)
def run_blip2(sample):
filepath = get_filepath(sample)
return replicate.run(
"andreasjansson/blip-2:4b32258c42e9efd4288bb9910bc532a69727f9acd26aa08e175713a0a857a608",
input={
"image": open(filepath, "rb"),
"question": "Describe the image?",
},
)
def run_fuyu8b(sample):
filepath = get_filepath(sample)
return replicate.run(
"lucataco/fuyu-8b:42f23bc876570a46f5a90737086fbc4c3f79dd11753a28eaa39544dd391815e9",
input={"image": open(filepath, "rb"), "prompt": "Describe the image."},
)
def run_llava13b(sample):
filepath = get_filepath(sample)
response = replicate.run(
"yorickvp/llava-13b:2facb4a474a0462c15041b78b1ad70952ea46b5ec6ad29583c0b29dbd4249591",
input={"image": open(filepath, "rb"), "prompt": "Describe the image."},
)
resp_string = ""
for r in response:
resp_string += r
return resp_string
HF_I2T_MODELS = (
"microsoft/git-base",
"Salesforce/blip-image-captioning-base",
"llava-hf/llava-1.5-7b-hf",
"nlpconnect/vit-gpt2-image-captioning",
)
REPLCATE_MODELS = {
"blip2": run_blip2,
"fuyu-8b": run_fuyu8b,
"llava-13b": run_llava13b,
"qwen-vl-chat": run_qwen_vl_chat,
}
def run_hf_model(sample, model_name):
from transformers import pipeline
pipe = pipeline("image-to-text", model=model_name)
image = Image.open(get_filepath(sample))
res = pipe(image, max_new_tokens=100)
if type(res) == list:
res = res[0]
return res["generated_text"]
def generate_sample_caption(sample, model_name):
if model_name in HF_I2T_MODELS:
return run_hf_model(sample, model_name)
else:
return REPLCATE_MODELS[model_name](sample)
class CaptionImages(foo.Operator):
@property
def config(self):
_config = foo.OperatorConfig(
name="caption_images",
label="Caption Images",
dynamic=True,
execute_as_generator=True,
)
_config.icon = "/assets/icon.svg"
return _config
def resolve_input(self, ctx):
inputs = types.Object()
form_view = types.View(
label="Image Captioning",
description="Generate captions for images with state-of-the-art models",
)
rep_flag = allows_replicate_models()
hf_flag = allows_hf_models()
if not rep_flag and not hf_flag:
inputs.message(
"message",
label="No models available. Please set up your environment variables or install `transformers`.",
)
return types.Property(inputs)
available_models = []
if rep_flag:
available_models.extend(REPLCATE_MODELS.keys())
if hf_flag:
available_models.extend(HF_I2T_MODELS)
available_models.sort()
model_choices = types.RadioGroup()
for model in available_models:
model_choices.add_choice(model, label=model)
inputs.enum(
"model_name",
model_choices.values(),
label="Model",
view=types.DropdownView(),
required=True,
)
inputs.str(
"caption_field",
label="Caption field",
description="The name of the field to store the generated captions",
required=True,
)
inputs.view_target(ctx)
_execution_mode(ctx, inputs)
return types.Property(inputs, view=form_view)
async def execute(self, ctx):
sample_collection = ctx.target_view()
model_name = ctx.params["model_name"]
caption_field = ctx.params["caption_field"]
ctx.dataset.add_sample_field(caption_field, ftype=fo.StringField)
num_samples = sample_collection.count()
captions = []
for i, sample in enumerate(
sample_collection.iter_samples(progress=True, autosave=True)
):
captions.append(generate_sample_caption(sample, model_name))
progress_label = f"Loading {i} of {num_samples}"
progress_view = types.ProgressView(label=progress_label)
loading_schema = types.Object()
loading_schema.int("percent_complete", view=progress_view)
show_output_params = {
"outputs": types.Property(loading_schema).to_json(),
"results": {"percent_complete": i / num_samples},
}
yield ctx.trigger("show_output", show_output_params)
sample_collection.set_values(caption_field, captions)
sample_collection.save()
yield ctx.ops.reload_dataset()
def resolve_delegation(self, ctx):
return ctx.params.get("delegate", False)
def register(plugin):
plugin.register(CaptionImages)