Spaces:
Paused
Paused
File size: 4,179 Bytes
30b697a 929aa18 30b697a 929aa18 30b697a |
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 |
import pathlib
import gradio as gr
import spaces
import open_clip
import torch
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model, _, transform = open_clip.create_model_and_transforms(
"coca_ViT-L-14",
pretrained="mscoco_finetuned_laion2B-s13B-b90k"
)
model.to(device)
title="""<h1 align="center">CoCa: Contrastive Captioners</h1>"""
description=(
"""<br> An open source implementation of <strong>CoCa: Contrastive Captioners are Image-Text Foundation Models</strong> <a href=https://arxiv.org/abs/2205.01917>https://arxiv.org/abs/2205.01917.</a>
<br> Built using <a href=https://github.com/mlfoundations/open_clip>open_clip</a> with an effort from <a href=https://laion.ai/>LAION</a>.
<br> For faster inference without waiting in queue, you may duplicate the space and upgrade to GPU in settings.<a href="https://huggingface.co/spaces/laion/CoCa?duplicate=true"> <img style="margin-top: 0em; margin-bottom: 0em" src="https://bit.ly/3gLdBN6" alt="Duplicate Space"></a>"""
)
def output_generate(image):
im = transform(image).unsqueeze(0).to(device)
with torch.no_grad(), torch.cuda.amp.autocast():
generated = model.generate(im, seq_len=20)
return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")
@spaces.GPU
def inference_caption(image, decoding_method="Beam search", rep_penalty=1.2, top_p=0.5, min_seq_len=5, seq_len=20):
im = transform(image).unsqueeze(0).to(device)
generation_type = "beam_search" if decoding_method == "Beam search" else "top_p"
with torch.no_grad(), torch.cuda.amp.autocast():
generated = model.generate(
im,
generation_type=generation_type,
top_p=float(top_p),
min_seq_len=min_seq_len,
seq_len=seq_len,
repetition_penalty=float(rep_penalty)
)
return open_clip.decode(generated[0].detach()).split("<end_of_text>")[0].replace("<start_of_text>", "")
paths = sorted(pathlib.Path("images").glob("*.jpg"))
with gr.Blocks() as iface:
state = gr.State([])
gr.Markdown(title)
gr.Markdown(description)
with gr.Row():
with gr.Column(scale=1):
image_input = gr.Image(type="pil")
# with gr.Row():
sampling = gr.Radio(
choices=["Beam search", "Nucleus sampling"],
value="Beam search",
label="Text Decoding Method",
interactive=True,
)
rep_penalty = gr.Slider(
minimum=1.0,
maximum=5.0,
value=1.0,
step=0.5,
interactive=True,
label="Repeat Penalty (larger value prevents repetition)",
)
top_p = gr.Slider(
minimum=0.0,
maximum=1.0,
value=0.5,
step=0.1,
interactive=True,
label="Top p (used with nucleus sampling)",
)
min_seq_len = gr.Number(
value=5, label="Minimum Sequence Length", precision=0, interactive=True
)
seq_len = gr.Number(
value=20, label="Maximum Sequence Length (has to higher than Minimum)", precision=0, interactive=True
)
with gr.Column(scale=1):
with gr.Column():
caption_output = gr.Textbox(lines=1, label="Caption Output")
caption_button = gr.Button(
value="Caption it!", interactive=True, variant="primary"
)
caption_button.click(
inference_caption,
[
image_input,
sampling,
rep_penalty,
top_p,
min_seq_len,
seq_len
],
[caption_output],
)
examples = gr.Examples(
examples=[path.as_posix() for path in paths],
inputs=[image_input],
)
iface.launch()
|