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import gradio as gr
import torch
import io
from PIL import Image
from transformers import (
AutoImageProcessor,
AutoTokenizer,
AutoModelForCausalLM,
)
import numpy as np
model_root = "qihoo360/fg-clip-base"
model = AutoModelForCausalLM.from_pretrained(model_root,trust_remote_code=True)
device = model.device
tokenizer = AutoTokenizer.from_pretrained(model_root)
image_processor = AutoImageProcessor.from_pretrained(model_root)
import math
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
def postprocess_result(probs, labels):
pro_output = {labels[i]: probs[i] for i in range(len(labels))}
return pro_output
def Retrieval(image, candidate_labels):
"""
Takes an image and a comma-separated string of candidate labels,
and returns the classification scores.
"""
image_size=224
image = image.convert("RGB")
image = image.resize((image_size,image_size))
image_input = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].to(device)
walk_short_pos = True
caption_input = torch.tensor(tokenizer(candidate_labels, max_length=77, padding="max_length", truncation=True).input_ids, dtype=torch.long, device=device)
with torch.no_grad():
image_feature = model.get_image_features(image_input)
text_feature = model.get_text_features(caption_input,walk_short_pos=walk_short_pos)
image_feature = image_feature / image_feature.norm(p=2, dim=-1, keepdim=True)
text_feature = text_feature / text_feature.norm(p=2, dim=-1, keepdim=True)
logits_per_image = image_feature @ text_feature.T
logits_per_image = model.logit_scale.exp() * logits_per_image
probs = logits_per_image.softmax(dim=1)
results = probs[0].tolist()
return results
def Get_Densefeature(image, candidate_labels):
"""
Takes an image and a comma-separated string of candidate labels,
and returns the classification scores.
"""
candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",") if label !=""]
# print(candidate_labels)
image_size=224
image = image.convert("RGB")
image = image.resize((image_size,image_size))
image_input = image_processor.preprocess(image, return_tensors='pt')['pixel_values'].to(device)
with torch.no_grad():
dense_image_feature = model.get_image_dense_features(image_input)
captions = [candidate_labels[0]]
caption_input = torch.tensor(tokenizer(captions, max_length=77, padding="max_length", truncation=True).input_ids, dtype=torch.long, device=device)
text_feature = model.get_text_features(caption_input,walk_short_pos=True)
text_feature = text_feature / text_feature.norm(p=2, dim=-1, keepdim=True)
dense_image_feature = dense_image_feature / dense_image_feature.norm(p=2, dim=-1, keepdim=True)
similarity = dense_image_feature.squeeze() @ text_feature.squeeze().T
similarity = similarity.cpu().numpy()
patch_size = int(math.sqrt(similarity.shape[0]))
original_shape = (patch_size, patch_size)
show_image = similarity.reshape(original_shape)
# normalized = (show_image - show_image.min()) / (show_image.max() - show_image.min())
# def viridis_colormap(x):
# r = np.clip(1.1746 * x - 0.1776, 0, 1)
# g = np.clip(2.0 * x - 0.7, 0, 1)
# b = np.clip(-2.0 * x + 1.7, 0, 1)
# return np.stack([r, g, b], axis=-1)
# color_mapped = viridis_colormap(normalized)
# color_mapped_uint8 = (color_mapped * 255).astype(np.uint8)
# pil_img = Image.fromarray(color_mapped_uint8)
# pil_img = pil_img.resize((512,512))
fig = plt.figure(figsize=(6, 6))
plt.imshow(show_image)
plt.title('similarity Visualization')
plt.axis('off')
buf = io.BytesIO()
plt.savefig(buf, format='png')
buf.seek(0)
plt.close(fig)
pil_img = Image.open(buf)
# buf.close()
return pil_img
def infer(image, candidate_labels):
candidate_labels = [label.lstrip(" ") for label in candidate_labels.split(",") if label !=""]
fg_probs = Retrieval(image, candidate_labels)
return postprocess_result(fg_probs,candidate_labels)
with gr.Blocks() as demo:
gr.Markdown("# FG-CLIP Retrieval")
gr.Markdown(
"This app uses the FG-CLIP model (qihoo360/fg-clip-base) for retrieval on CPU :"
)
gr.Markdown(
"(Run Densefeature) only support only one class!"
)
with gr.Row():
with gr.Column():
image_input = gr.Image(type="pil")
text_input = gr.Textbox(label="Input a list of labels (comma seperated)")
run_button = gr.Button("Run Retrieval", visible=True)
dfs_button = gr.Button("Run Densefeature", visible=True)
with gr.Column():
fg_output = gr.Label(label="FG-CLIP Output", num_top_classes=11)
dfs_output = gr.Image(label="Similarity Visualization", type="pil")
examples = [
# ["./baklava.jpg", "dessert on a plate, a serving of baklava, a plate and spoon"],
# ["./dog.jpg", "A light brown wood stool, A bucket with a body made of dark brown plastic, A black velvet back cover for a cellular telephone, A green ball with a perforated pattern, A light blue plastic helmet made of plastic, A grey slipper made of wool, A newspaper with white and black perforated printed on a paper texture, A blue dog with a white colored head, A yellow sponge with a dark green rough surface, A book with white, dark orange and brown pages made of paper, A black ceramic scarf with a body made of fabric."],
["./Landscape.jpg", "red grass, yellow grass, green grass"],
["./cat.jpg", "two sleeping cats, two cats playing, three cats laying down"],
["./cat_dfclor.jpg", "white cat,"],
]
gr.Examples(
examples=examples,
inputs=[image_input, text_input],
# outputs=fg_output,
# fn=infer,
)
run_button.click(fn=infer, inputs=[image_input, text_input], outputs=fg_output)
dfs_button.click(fn=Get_Densefeature, inputs=[image_input, text_input], outputs=dfs_output)
demo.launch() |