Merge commit 'refs/pull/18/head' of github.com:matatonic/openedai-vision

backend/minimonkey.py

@@ -0,0 +1,174 @@
+
+import numpy as np
+import torch
+import torchvision.transforms as T
+from PIL import Image
+from torchvision.transforms.functional import InterpolationMode
+from transformers import AutoModel, AutoTokenizer
+
+IMAGENET_MEAN = (0.485, 0.456, 0.406)
+IMAGENET_STD = (0.229, 0.224, 0.225)
+
+def build_transform(input_size):
+    MEAN, STD = IMAGENET_MEAN, IMAGENET_STD
+    transform = T.Compose([
+        T.Lambda(lambda img: img.convert('RGB') if img.mode != 'RGB' else img),
+        T.Resize((input_size, input_size), interpolation=InterpolationMode.BICUBIC),
+        T.ToTensor(),
+        T.Normalize(mean=MEAN, std=STD)
+    ])
+    return transform
+
+def find_closest_aspect_ratio(aspect_ratio, target_ratios, width, height, image_size):
+    best_ratio_diff = float('inf')
+    best_ratio = (1, 1)
+    area = width * height
+    for ratio in target_ratios:
+        target_aspect_ratio = ratio[0] / ratio[1]
+        ratio_diff = abs(aspect_ratio - target_aspect_ratio)
+        if ratio_diff < best_ratio_diff:
+            best_ratio_diff = ratio_diff
+            best_ratio = ratio
+        elif ratio_diff == best_ratio_diff:
+            if area > 0.5 * image_size * image_size * ratio[0] * ratio[1]:
+                best_ratio = ratio
+    return best_ratio
+
+def dynamic_preprocess(image, min_num=1, max_num=12, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, target_ratios, orig_width, orig_height, image_size)
+
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images, target_aspect_ratio
+
+
+def dynamic_preprocess2(image, min_num=1, max_num=12, prior_aspect_ratio=None, image_size=448, use_thumbnail=False):
+    orig_width, orig_height = image.size
+    aspect_ratio = orig_width / orig_height
+
+    # calculate the existing image aspect ratio
+    target_ratios = set(
+        (i, j) for n in range(min_num, max_num + 1) for i in range(1, n + 1) for j in range(1, n + 1) if
+        i * j <= max_num and i * j >= min_num)
+    target_ratios = sorted(target_ratios, key=lambda x: x[0] * x[1])
+    new_target_ratios = []
+    for i in target_ratios:
+        if prior_aspect_ratio[0]%i[0] or prior_aspect_ratio[1]%i[1]:
+            new_target_ratios.append(i)
+        else:
+            continue
+    # find the closest aspect ratio to the target
+    target_aspect_ratio = find_closest_aspect_ratio(
+        aspect_ratio, new_target_ratios, orig_width, orig_height, image_size)
+    # calculate the target width and height
+    target_width = image_size * target_aspect_ratio[0]
+    target_height = image_size * target_aspect_ratio[1]
+    blocks = target_aspect_ratio[0] * target_aspect_ratio[1]
+
+    # resize the image
+    resized_img = image.resize((target_width, target_height))
+    processed_images = []
+    for i in range(blocks):
+        box = (
+            (i % (target_width // image_size)) * image_size,
+            (i // (target_width // image_size)) * image_size,
+            ((i % (target_width // image_size)) + 1) * image_size,
+            ((i // (target_width // image_size)) + 1) * image_size
+        )
+        # split the image
+        split_img = resized_img.crop(box)
+        processed_images.append(split_img)
+    assert len(processed_images) == blocks
+    if use_thumbnail and len(processed_images) != 1:
+        thumbnail_img = image.resize((image_size, image_size))
+        processed_images.append(thumbnail_img)
+    return processed_images
+
+def load_image(image, input_size=448, min_num=1, max_num=12):
+    image = image.convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images, target_aspect_ratio = dynamic_preprocess(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values, target_aspect_ratio
+
+def load_image2(image, input_size=448, min_num=1, max_num=12, target_aspect_ratio=None):
+    image = image.convert('RGB')
+    transform = build_transform(input_size=input_size)
+    images = dynamic_preprocess2(image, image_size=input_size, use_thumbnail=True, min_num=min_num, max_num=max_num, prior_aspect_ratio=target_aspect_ratio)
+    pixel_values = [transform(image) for image in images]
+    pixel_values = torch.stack(pixel_values)
+    return pixel_values
+
+
+from vision_qna import *
+
+# mx262/MiniMonkey
+import transformers
+transformers.logging.set_verbosity_error()
+
+class VisionQnA(VisionQnABase):
+    model_name: str = "minimonkey"
+    format: str = '' # phi15-ish
+    #vision_layers: List[str] = ["vision", "vision_tower", "resampler", "visual", "in_proj","out_proj","c_fc","c_proj"]
+
+    def __init__(self, model_id: str, device: str, device_map: str = 'auto', extra_params = {}, format = None):
+        super().__init__(model_id, device, device_map, extra_params, format)
+
+        self.tokenizer = AutoTokenizer.from_pretrained(model_id, trust_remote_code=self.params.get('trust_remote_code', False))
+        self.model = AutoModel.from_pretrained(**self.params).eval()
+
+        self.loaded_banner()
+
+    async def stream_chat_with_images(self, request: ImageChatRequest) -> AsyncGenerator[str, None]:
+        query, history, images, system_message = await prompt_history_images_system_from_messages(
+            request.messages, img_tok='', url_handler=url_to_image)
+
+        # set the max number of tiles in `max_num`
+        pixel_values, target_aspect_ratio = load_image(images[0], min_num=4, max_num=12)
+        pixel_values = pixel_values.to(torch.bfloat16).to(self.model.device)
+        pixel_values2 = load_image2(images[0], min_num=3, max_num=7, target_aspect_ratio=target_aspect_ratio)
+        pixel_values2 = pixel_values2.to(torch.bfloat16).to(self.model.device)
+        pixel_values = torch.cat([pixel_values2[:-1], pixel_values[:-1], pixel_values2[-1:]], 0)
+
+        generation_config = dict(do_sample=False, max_new_tokens=512)
+
+        answer, history = self.model.chat(self.tokenizer, pixel_values, target_aspect_ratio, query, generation_config, history=None, return_history=True)
+
+        if isinstance(answer, str):
+            answer = [answer]
+
+        for new_text in answer:
+            if isinstance(new_text, str):
+                yield new_text