Spaces:
Runtime error
Runtime error
File size: 5,775 Bytes
d992c15 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 |
import os
import datasets
import fuego
import gradio as gr
from datasets import load_dataset
from huggingface_hub import HfFolder, create_repo, delete_repo, login
from PIL import Image
datasets.disable_caching()
login(token=os.getenv("HUGGING_FACE_HUB_TOKEN", HfFolder.get_token()), add_to_git_credential=True)
labeled_samples_repo_id = create_repo("actlearn_labeled_samples", exist_ok=True, repo_type="dataset").repo_id
unlabled_samples_repo_id = create_repo("actlearn_unlabeled_samples", exist_ok=True, repo_type="dataset").repo_id
to_label_samples_repo_id = create_repo("actlearn_to_label_samples", exist_ok=True, repo_type="dataset").repo_id
test_dataset_repo_id = create_repo("actlearn_test_mnist", exist_ok=True, repo_type="dataset").repo_id
model_repo_id = create_repo("actlearn_mnist_model", exist_ok=True).repo_id
idx = 0
try:
data_to_label = load_dataset(to_label_samples_repo_id)
imgs = data_to_label["train"]["image"]
except:
imgs = None
data_to_label = None
def get_image():
global idx
if imgs is None:
return None
new_img = imgs[idx]
idx += 1
return new_img
labeled_data = []
information = """# Active Learning Demo
This demo showcases Active Learning, which is great when labeling is expensive. In this demo, you will label images by choosing a digit (0-9).
How does this work?
* There is a large pool of unlabeled images
* A model is trained with the few labeled images
* We can then use the model to pick the images with the lowest confidence or with the lowest probability of corresponding to an image. These are the images for which the model is confused, so by improving them, the quality of the model can improve much more than queries for which the model was already doing well!
* In this UI, you will be provided a couple of images to label
* Once all the provided images are labeled, the model is retrained, and a new set of images is chosen!
"""
training_info = """## Model Retraining
There are new labeled images. The model is retraining. Follow progress in the "fuego" space that was spun up for you in your profile.
"""
with gr.Blocks() as demo:
gr.Markdown(information)
img_to_label = gr.Image(shape=[28, 28], value=get_image(), visible=True if imgs is not None else False)
label_dropdown = gr.Dropdown(
choices=[0, 1, 2, 3, 4, 5, 6, 7, 8, 9], interactive=True, value=0, visible=True if imgs is not None else False
)
save_btn = gr.Button("Save label", visible=True if imgs is not None else False)
output_box = gr.Markdown(value=training_info, visible=False)
reload_btn = gr.Button("Reload", visible=False if imgs is not None else True)
def save_data(img, label):
global labeled_data
global idx
labeled_data.append([img, label])
if imgs is not None and len(imgs) == idx:
# Remove dataset of queries to label
# datasets library does not allow pushing an empty dataset, so as a
# workaround we just delete the repo
delete_repo(repo_id=to_label_samples_repo_id, repo_type="dataset")
create_repo(repo_id=to_label_samples_repo_id, repo_type="dataset")
# Push to training dataset
labeled_dataset = load_dataset(labeled_samples_repo_id)["train"]
feature = datasets.Image(decode=False)
for img, label in labeled_data:
# Hack due to https://github.com/huggingface/datasets/issues/4796
labeled_dataset = labeled_dataset.add_item(
{"image": feature.encode_example(Image.fromarray(img)), "label": label}
)
labeled_dataset.push_to_hub(labeled_samples_repo_id)
# Clean up data
labeled_data = []
idx = 0
fuego.run("training/run.py", "training/requirements.txt", space_id="actlearn-fuego-runner")
# Update UI
return {
img_to_label: gr.update(visible=False),
label_dropdown: gr.update(visible=False),
save_btn: gr.update(visible=False),
output_box: gr.update(visible=True, value=training_info),
reload_btn: gr.update(visible=True),
}
else:
return {img_to_label: gr.update(value=get_image())}
def reload_data():
global data_to_label
global imgs
try:
# See if there is new data to be labeled
data_to_label = load_dataset(to_label_samples_repo_id)
imgs = data_to_label["train"]["image"]
except Exception:
imgs = None
data_to_label = None
return {
img_to_label: gr.update(visible=False, value=None),
label_dropdown: gr.update(visible=False),
save_btn: gr.update(visible=False),
output_box: gr.update(visible=True, value="No more images to label"),
reload_btn: gr.update(visible=True),
}
if len(imgs) == 0:
return
else:
global idx
idx = 0
return {
img_to_label: gr.update(visible=True, value=get_image()),
label_dropdown: gr.update(visible=True),
save_btn: gr.update(visible=True),
output_box: gr.update(visible=False),
reload_btn: gr.update(visible=False),
}
save_btn.click(
save_data,
inputs=[img_to_label, label_dropdown],
outputs=[img_to_label, label_dropdown, save_btn, output_box, reload_btn],
)
reload_btn.click(reload_data, outputs=[img_to_label, label_dropdown, save_btn, output_box, reload_btn])
if __name__ == "__main__":
demo.launch(debug=True)
|