app.py

import gradio as gr
import PIL.Image
import transformers
from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
import torch
import os
import string
import functools
import re
import flax.linen as nn
import jax
import jax.numpy as jnp
import numpy as np
import spaces


model_id = "google/paligemma-3b-ft-coco35l-448"
COLORS = ['#4285f4', '#db4437', '#f4b400', '#0f9d58', '#e48ef1']
device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
model = PaliGemmaForConditionalGeneration.from_pretrained(model_id).eval().to(device)
processor = PaliGemmaProcessor.from_pretrained(model_id)

###### Transformers Inference
@spaces.GPU
def infer(
    image: PIL.Image.Image,
    text: str,
    max_new_tokens: int
) -> str:
    inputs = processor(text=text, images=image, return_tensors="pt").to(device)
    with torch.inference_mode():
      generated_ids = model.generate(
          **inputs,
          max_new_tokens=max_new_tokens,
          do_sample=False
      )
    result = processor.batch_decode(generated_ids, skip_special_tokens=True)
    return result[0][len(text):].lstrip("\n")

##### Parse segmentation output tokens into masks
##### Also returns bounding boxes with their labels

def parse_segmentation(input_image, input_text):
  out = infer(input_image, input_text, max_new_tokens=100)
  objs = extract_objs(out.lstrip("\n"), input_image.size[0], input_image.size[1], unique_labels=True)
  labels = set(obj.get('name') for obj in objs if obj.get('name'))
  color_map = {l: COLORS[i % len(COLORS)] for i, l in enumerate(labels)}
  highlighted_text = [(obj['content'], obj.get('name')) for obj in objs]
  annotated_img = (
    input_image,
    [
        (
            obj['mask'] if obj.get('mask') is not None else obj['xyxy'],
            obj['name'] or '',
        )
        for obj in objs
        if 'mask' in obj or 'xyxy' in obj
    ],
)
  has_annotations = bool(annotated_img[1])
  return annotated_img



######## Demo

INTRO_TEXT = """## PaliGemma demo\n\n
| [Github](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md) 
| [Blogpost](https://huggingface.co/blog/paligemma) 
|\n\n
PaliGemma is an open vision-language model by Google, inspired by [PaLI-3](https://arxiv.org/abs/2310.09199) and 
built with open components such as the [SigLIP](https://arxiv.org/abs/2303.15343) 
vision model and the [Gemma](https://arxiv.org/abs/2403.08295) language model. PaliGemma is designed as a versatile 
model for transfer to a wide range of vision-language tasks such as image and short video caption, visual question 
answering, text reading, object detection and object segmentation.
\n\n
This space includes models fine-tuned on a mix of downstream tasks, **inferred via 🤗 transformers**.
See the [Blogpost](https://huggingface.co/blog/paligemma) and 
[README](https://github.com/google-research/big_vision/blob/main/big_vision/configs/proj/paligemma/README.md) 
for detailed information how to use and fine-tune PaliGemma models.
\n\n
**This is an experimental research model.** Make sure to add appropriate guardrails when using the model for applications.
"""


with gr.Blocks(css="style.css") as demo:
  gr.Markdown(INTRO_TEXT)
  with gr.Tab("Text Generation"):
    with gr.Column():
        image = gr.Image(type="pil")
        text_input = gr.Text(label="Input Text")

        text_output = gr.Text(label="Text Output")
        chat_btn = gr.Button()
        tokens = gr.Slider(
            label="Max New Tokens",
            info="Set to larger for longer generation.",
            minimum=10,
            maximum=100,
            value=20,
            step=10,
        )

    chat_inputs = [
        image,
        text_input,
        tokens
        ]
    chat_outputs = [
        text_output
    ]
    chat_btn.click(
        fn=infer,
        inputs=chat_inputs,
        outputs=chat_outputs,
    )
    
    examples = [["./bee.jpg", "What is on the flower?"],
               ["./examples/bowie.jpg", "Who is this?"],
               ["./examples/emu.jpg", "What animal is this?"],
               ["./howto.jpg", "What does this image show?"],
               ["./examples/password.jpg", "What is the password?"],
               ["./examples/ulges.jpg", "Who is the author of this book?"]]
    gr.Markdown("Example images are licensed CC0 by [akolesnikoff@](https://github.com/akolesnikoff), [mbosnjak@](https://github.com/mbosnjak), [maximneumann@](https://github.com/maximneumann) and [merve](https://huggingface.co/merve).")

    gr.Examples(
        examples=examples,
        inputs=chat_inputs,
    )
  with gr.Tab("Segment/Detect"):
    image = gr.Image(type="pil")
    seg_input = gr.Text(label="Entities to Segment/Detect")
    seg_btn = gr.Button("Submit")
    annotated_image = gr.AnnotatedImage(label="Output")
    
    examples = [["./cats.png", "segment cats"],
                ["./bee.jpg", "detect bee"],
               ["./examples/barsik.jpg", "segment cat"],
               ["./bird.jpg", "segment bird ; bird ; plant"]]
    gr.Markdown("Example images are licensed CC0 by [akolesnikoff@](https://github.com/akolesnikoff), [mbosnjak@](https://github.com/mbosnjak), [maximneumann@](https://github.com/maximneumann) and [merve](https://huggingface.co/merve).")
    gr.Examples(
        examples=examples,
        inputs=[image, seg_input],
    )

    seg_inputs = [
        image,
        seg_input
        ]
    seg_outputs = [
        annotated_image
    ]
    seg_btn.click(
        fn=parse_segmentation,
        inputs=seg_inputs,
        outputs=seg_outputs,
    )





### Postprocessing Utils for Segmentation Tokens
### Segmentation tokens are passed to another VAE which decodes them to a mask

_MODEL_PATH = 'vae-oid.npz'

_SEGMENT_DETECT_RE = re.compile(
    r'(.*?)' +
    r'<loc(\d{4})>' * 4 + r'\s*' +
    '(?:%s)?' % (r'<seg(\d{3})>' * 16) +
    r'\s*([^;<>]+)? ?(?:; )?',
)


def _get_params(checkpoint):
  """Converts PyTorch checkpoint to Flax params."""

  def transp(kernel):
    return np.transpose(kernel, (2, 3, 1, 0))

  def conv(name):
    return {
        'bias': checkpoint[name + '.bias'],
        'kernel': transp(checkpoint[name + '.weight']),
    }

  def resblock(name):
    return {
        'Conv_0': conv(name + '.0'),
        'Conv_1': conv(name + '.2'),
        'Conv_2': conv(name + '.4'),
    }

  return {
      '_embeddings': checkpoint['_vq_vae._embedding'],
      'Conv_0': conv('decoder.0'),
      'ResBlock_0': resblock('decoder.2.net'),
      'ResBlock_1': resblock('decoder.3.net'),
      'ConvTranspose_0': conv('decoder.4'),
      'ConvTranspose_1': conv('decoder.6'),
      'ConvTranspose_2': conv('decoder.8'),
      'ConvTranspose_3': conv('decoder.10'),
      'Conv_1': conv('decoder.12'),
  }


def _quantized_values_from_codebook_indices(codebook_indices, embeddings):
  batch_size, num_tokens = codebook_indices.shape
  assert num_tokens == 16, codebook_indices.shape
  unused_num_embeddings, embedding_dim = embeddings.shape

  encodings = jnp.take(embeddings, codebook_indices.reshape((-1)), axis=0)
  encodings = encodings.reshape((batch_size, 4, 4, embedding_dim))
  return encodings


@functools.cache
def _get_reconstruct_masks():
  """Reconstructs masks from codebook indices.
  Returns:
    A function that expects indices shaped `[B, 16]` of dtype int32, each
    ranging from 0 to 127 (inclusive), and that returns a decoded masks sized
    `[B, 64, 64, 1]`, of dtype float32, in range [-1, 1].
  """

  class ResBlock(nn.Module):
    features: int

    @nn.compact
    def __call__(self, x):
      original_x = x
      x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
      x = nn.relu(x)
      x = nn.Conv(features=self.features, kernel_size=(3, 3), padding=1)(x)
      x = nn.relu(x)
      x = nn.Conv(features=self.features, kernel_size=(1, 1), padding=0)(x)
      return x + original_x

  class Decoder(nn.Module):
    """Upscales quantized vectors to mask."""

    @nn.compact
    def __call__(self, x):
      num_res_blocks = 2
      dim = 128
      num_upsample_layers = 4

      x = nn.Conv(features=dim, kernel_size=(1, 1), padding=0)(x)
      x = nn.relu(x)

      for _ in range(num_res_blocks):
        x = ResBlock(features=dim)(x)

      for _ in range(num_upsample_layers):
        x = nn.ConvTranspose(
            features=dim,
            kernel_size=(4, 4),
            strides=(2, 2),
            padding=2,
            transpose_kernel=True,
        )(x)
        x = nn.relu(x)
        dim //= 2

      x = nn.Conv(features=1, kernel_size=(1, 1), padding=0)(x)

      return x

  def reconstruct_masks(codebook_indices):
    quantized = _quantized_values_from_codebook_indices(
        codebook_indices, params['_embeddings']
    )
    return Decoder().apply({'params': params}, quantized)

  with open(_MODEL_PATH, 'rb') as f:
    params = _get_params(dict(np.load(f)))

  return jax.jit(reconstruct_masks, backend='cpu')
def extract_objs(text, width, height, unique_labels=False):
  """Returns objs for a string with "<loc>" and "<seg>" tokens."""
  objs = []
  seen = set()
  while text:
    m = _SEGMENT_DETECT_RE.match(text)
    if not m:
      break
    print("m", m)
    gs = list(m.groups())
    before = gs.pop(0)
    name = gs.pop()
    y1, x1, y2, x2 = [int(x) / 1024 for x in gs[:4]]
    
    y1, x1, y2, x2 = map(round, (y1*height, x1*width, y2*height, x2*width))
    seg_indices = gs[4:20]
    if seg_indices[0] is None:
      mask = None
    else:
      seg_indices = np.array([int(x) for x in seg_indices], dtype=np.int32)
      m64, = _get_reconstruct_masks()(seg_indices[None])[..., 0]
      m64 = np.clip(np.array(m64) * 0.5 + 0.5, 0, 1)
      m64 = PIL.Image.fromarray((m64 * 255).astype('uint8'))
      mask = np.zeros([height, width])
      if y2 > y1 and x2 > x1:
        mask[y1:y2, x1:x2] = np.array(m64.resize([x2 - x1, y2 - y1])) / 255.0

    content = m.group()
    if before:
      objs.append(dict(content=before))
      content = content[len(before):]
    while unique_labels and name in seen:
      name = (name or '') + "'"
    seen.add(name)
    objs.append(dict(
        content=content, xyxy=(x1, y1, x2, y2), mask=mask, name=name))
    text = text[len(before) + len(content):]

  if text:
    objs.append(dict(content=text))

  return objs

#########

if __name__ == "__main__":
    demo.queue(max_size=10).launch(debug=True)