Create app.py

app.py

@@ -0,0 +1,235 @@
+import os
+import gradio as gr
+import PIL.Image
+import transformers
+from transformers import PaliGemmaForConditionalGeneration, PaliGemmaProcessor
+import torch
+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 = "gv-hf/paligemma2-10b-mix-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)
+
+@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")
+
+def parse_segmentation(input_image, input_text):
+    out = infer(input_image, input_text, max_new_tokens=200)
+    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
+
+def _get_params(checkpoint):
+    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():
+    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):
+        @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')
+
+_SEGMENT_DETECT_RE = re.compile(
+    r'(.*?)' +
+    r'<loc(\d{4})>' * 4 + r'\s*' +
+    '(?:%s)?' % (r'<seg(\d{3})>' * 16) +
+    r'\s*([^;<>]+)? ?(?:; )?',
+)
+
+_MODEL_PATH = 'vae-oid.npz'
+
+def extract_objs(text, width, height, unique_labels=False):
+    objs = []
+    seen = set()
+    while text:
+        m = _SEGMENT_DETECT_RE.match(text)
+        if not m:
+            break
+            
+        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
+
+with gr.Blocks() as demo:
+    with gr.Tab("Text Generation"):
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="pil", width=512, height=512)
+                text_input = gr.Text(label="Input Text")
+            with gr.Column():
+                text_output = gr.Text(label="Text Output")
+        chat_btn = gr.Button()
+        tokens = gr.Slider(
+            label="Max New Tokens",
+            minimum=10,
+            maximum=200,
+            value=20,
+            step=10,
+        )
+
+        chat_btn.click(
+            fn=infer,
+            inputs=[image, text_input, tokens],
+            outputs=[text_output],
+        )
+
+    with gr.Tab("Segment/Detect"):
+        with gr.Row():
+            with gr.Column():
+                image = gr.Image(type="pil")
+                seg_input = gr.Text(label="Entities to Segment/Detect")
+                seg_btn = gr.Button("Submit")
+            with gr.Column():
+                annotated_image = gr.AnnotatedImage(label="Output")
+        
+        seg_btn.click(
+            fn=parse_segmentation,
+            inputs=[image, seg_input],
+            outputs=[annotated_image],
+        )
+
+if __name__ == "__main__":
+    demo.queue(max_size=10).launch(debug=True)