Delete app.py.v1

app.py.v1

@@ -1,100 +0,0 @@
-import spaces
-from pip._internal import main
-
-main(['install', 'timm==1.0.8'])
-import timm
-
-print("installed", timm.__version__)
-import gradio as gr
-from inference import sam_preprocess, beit3_preprocess
-from model.evf_sam import EvfSamModel
-from transformers import AutoTokenizer
-import torch
-import numpy as np
-import sys
-import os
-
-version = "YxZhang/evf-sam"
-model_type = "ori"
-
-tokenizer = AutoTokenizer.from_pretrained(
-    version,
-    padding_side="right",
-    use_fast=False,
-)
-
-kwargs = {
-    "torch_dtype": torch.half,
-}
-model = EvfSamModel.from_pretrained(version, low_cpu_mem_usage=True,
-                                    **kwargs).eval()
-model.to('cuda')
-
-
-@spaces.GPU
-@torch.no_grad()
-def pred(image_np, prompt):
-    original_size_list = [image_np.shape[:2]]
-
-    image_beit = beit3_preprocess(image_np, 224).to(dtype=model.dtype,
-                                                    device=model.device)
-
-    image_sam, resize_shape = sam_preprocess(image_np, model_type=model_type)
-    image_sam = image_sam.to(dtype=model.dtype, device=model.device)
-
-    input_ids = tokenizer(
-        prompt, return_tensors="pt")["input_ids"].to(device=model.device)
-
-    # infer
-    pred_mask = model.inference(
-        image_sam.unsqueeze(0),
-        image_beit.unsqueeze(0),
-        input_ids,
-        resize_list=[resize_shape],
-        original_size_list=original_size_list,
-    )
-    pred_mask = pred_mask.detach().cpu().numpy()[0]
-    pred_mask = pred_mask > 0
-
-    visualization = image_np.copy()
-    visualization[pred_mask] = (image_np * 0.5 +
-                                pred_mask[:, :, None].astype(np.uint8) *
-                                np.array([50, 120, 220]) * 0.5)[pred_mask]
-
-    return visualization / 255.0, pred_mask.astype(np.float16)
-
-
-desc = """
-<div><h3>EVF-SAM: Early Vision-Language Fusion for Text-Prompted Segment Anything Model</h3>
-<p>EVF-SAM extends SAM's capabilities with text-prompted segmentation, achieving high accuracy in Referring Expression Segmentation.</p></div>
-<div style='display:flex; gap: 0.25rem; align-items: center'><a href="https://arxiv.org/abs/2406.20076"><img src="https://img.shields.io/badge/arXiv-Paper-red"></a><a href="https://github.com/hustvl/EVF-SAM"><img src="https://img.shields.io/badge/GitHub-Code-blue"></a></div>
-"""
-
-# desc_title_str = '<div align ="center"><img src="assets/logo.jpg" width="20%"><h3> Early Vision-Language Fusion for Text-Prompted Segment Anything Model</h3></div>'
-# desc_link_str = '[![arxiv paper](https://img.shields.io/badge/arXiv-Paper-red)](https://arxiv.org/abs/2406.20076)'
-
-demo = gr.Interface(
-    fn=pred,
-    inputs=[
-        gr.components.Image(type="numpy", label="Image", image_mode="RGB"),
-        gr.components.Textbox(
-            label="Prompt",
-            info=
-            "Use a phrase or sentence to describe the object you want to segment. Currently we only support English"
-        )
-    ],
-    outputs=[
-        gr.components.Image(type="numpy", label="visulization"),
-        gr.components.Image(type="numpy", label="mask")
-    ],
-    examples=[["assets/zebra.jpg", "zebra top left"],
-              ["assets/bus.jpg", "bus going to south common"],
-              [
-                  "assets/carrots.jpg",
-                  "3carrots in center with ice and greenn leaves"
-              ]],
-    title="📷 EVF-SAM: Referring Expression Segmentation",
-    description=desc,
-    allow_flagging="never")
-# demo.launch()
-demo.launch()