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A10G
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import gradio as gr
from transformers import AutoModel, AutoTokenizer, AutoImageProcessor
import torch
import torchvision.transforms as T
from PIL import Image
from torchvision.transforms.functional import InterpolationMode
# Define the path to your model
import os
from huggingface_hub import login
hf_token = os.environ.get('hf_token', None)
path = "h2oai/h2o-mississippi-2b"
# image preprocesing
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=6, 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=6, image_size=448, use_thumbnail=False, prior_aspect_ratio=None):
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 = []
if prior_aspect_ratio is not None:
for i in target_ratios:
if prior_aspect_ratio[0]%i[0] != 0 and prior_aspect_ratio[1]%i[1] != 0:
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_image1(image_file, input_size=448, min_num=1, max_num=12):
if isinstance(image_file, str):
image = Image.open(image_file).convert('RGB')
else:
image = image_file
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_file, input_size=448, min_num=1, max_num=12, target_aspect_ratio=None):
if isinstance(image_file, str):
image = Image.open(image_file).convert('RGB')
else:
image = image_file
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
def load_image_msac(file_name):
pixel_values, target_aspect_ratio = load_image1(file_name, min_num=1, max_num=6)
pixel_values = pixel_values.to(torch.bfloat16).cuda()
pixel_values2 = load_image2(file_name, min_num=3, max_num=6, target_aspect_ratio=target_aspect_ratio)
pixel_values2 = pixel_values2.to(torch.bfloat16).cuda()
pixel_values = torch.cat([pixel_values2[:-1], pixel_values[:-1], pixel_values2[-1:]], 0)
return pixel_values
# Load the model and tokenizer
model = AutoModel.from_pretrained(
path,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
trust_remote_code=True,
use_auth_token=hf_token
).eval().cuda()
tokenizer = AutoTokenizer.from_pretrained(
path,
trust_remote_code=True,
use_fast=False,
use_auth_token=hf_token
)
tokenizer.pad_token = tokenizer.unk_token
tokenizer.eos_token = "<|end|>"
model.generation_config.pad_token_id = tokenizer.pad_token_id
def inference(image, prompt):
# Check if both image and prompt are provided
if image is None or prompt.strip() == "":
return "Please provide both an image and a prompt."
# Process the image and get pixel_values
pixel_values = load_image_msac(image)
# Set generation config
generation_config = dict(
num_beams=1,
max_new_tokens=2048,
do_sample=False,
)
# Generate the response
response = model.chat(
tokenizer,
pixel_values,
prompt,
generation_config
)
return response
# Build the Gradio interface
with gr.Blocks() as demo:
gr.Markdown("H2O-Mississippi")
with gr.Row():
image_input = gr.Image(type="pil", label="Upload an Image")
prompt_input = gr.Textbox(label="Enter your prompt here")
response_output = gr.Textbox(label="Model Response")
with gr.Row():
submit_button = gr.Button("Submit")
clear_button = gr.Button("Clear")
# When the submit button is clicked, call the inference function
submit_button.click(
fn=inference,
inputs=[image_input, prompt_input],
outputs=response_output
)
# Define the clear button action
def clear_all():
return None, "", ""
clear_button.click(
fn=clear_all,
inputs=None,
outputs=[image_input, prompt_input, response_output]
)
demo.launch()
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