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README.md

@@ -1,13 +1,124 @@
----
-title: ObjectOrientmodel
-emoji: 👁
-colorFrom: indigo
-colorTo: red
-sdk: gradio
-sdk_version: 5.49.1
-app_file: app.py
-pinned: false
-short_description: Object orientation model
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+<div align="center">
+<h2>Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models</h2>
+
+[**Zehan Wang**](https://scholar.google.com/citations?user=euXK0lkAAAAJ&hl=zh-CN)<sup>1*</sup> · [**Ziang Zhang**](https://scholar.google.com/citations?hl=zh-CN&user=DptGMnYAAAAJ)<sup>1*</sup> · [**Tianyu Pang**](https://scholar.google.com/citations?hl=zh-CN&user=wYDbtFsAAAAJ)<sup>2</sup> · [**Du Chao**](https://scholar.google.com/citations?hl=zh-CN&user=QOp7xW0AAAAJ)<sup>2</sup> · [**Hengshuang Zhao**](https://scholar.google.com/citations?user=4uE10I0AAAAJ&hl&oi=ao)<sup>3</sup> · [**Zhou Zhao**](https://scholar.google.com/citations?user=IIoFY90AAAAJ&hl&oi=ao)<sup>1</sup>
+
+<sup>1</sup>Zhejiang University&emsp;&emsp;&emsp;&emsp;<sup>2</sup>SEA AI Lab&emsp;&emsp;&emsp;&emsp;<sup>3</sup>HKU
+
+*Equal Contribution
+
+
+<a href='https://arxiv.org/abs/2412.18605'><img src='https://img.shields.io/badge/arXiv-Orient Anything-red' alt='Paper PDF'></a>
+<a href='https://orient-anything.github.io'><img src='https://img.shields.io/badge/Project_Page-Orient Anything-green' alt='Project Page'></a>
+<a href='https://huggingface.co/spaces/Viglong/Orient-Anything'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Spaces-blue'></a>
+<a href='https://huggingface.co/papers/2412.18605'><img src='https://img.shields.io/badge/%F0%9F%A4%97%20Hugging%20Face-Paper-yellow'></a>
+</div>
+
+**Orient Anything**, a robust image-based object orientation estimation model. By training on 2M rendered labeled images, it achieves strong zero-shot generalization ability for images in the wild.
+
+![teaser](assets/demo.png)
+
+## News
+* **2025-05-01:** Orient Anything is accepted by ICML 2025!
+* **2024-12-24:** [Paper](https://arxiv.org/abs/2412.18605), [Project Page](https://orient-anything.github.io), [Code](https://github.com/SpatialVision/Orient-Anything), Models, and [Demo](https://huggingface.co/spaces/Viglong/Orient-Anything) are released.
+
+
+
+## Pre-trained models
+
+We provide **three models** of varying scales for robust object orientation estimation in images:
+
+| Model | Params | Checkpoint |
+|:-|-:|:-:|
+| Orient-Anything-Small | 23.3 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/cropsmallEx03/dino_weight.pt) |
+| Orient-Anything-Base | 87.8 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/cropbaseEx032/dino_weight.pt) |
+| Orient-Anything-Large | 305 M | [Download](https://huggingface.co/Viglong/OriNet/blob/main/croplargeEX2/dino_weight.pt) |
+
+## Usage
+
+### 1 Prepraration
+
+```bash
+pip install -r requirements.txt
+```
+
+### 2 Use our models
+#### 2.1 In Gradio app
+Start gradio by executing the following script:
+
+```bash
+python app.py
+```
+then open GUI page(default is https://127.0.0.1:7860) in web browser.
+
+or, you can try it in our [Huggingface-Space](https://huggingface.co/spaces/Viglong/Orient-Anything)
+
+#### 2.2 In Python Scripts
+```python
+from paths import *
+from vision_tower import DINOv2_MLP
+from transformers import AutoImageProcessor
+import torch
+from PIL import Image
+
+import torch.nn.functional as F
+from utils import *
+from inference import *
+
+from huggingface_hub import hf_hub_download
+ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything", filename="croplargeEX2/dino_weight.pt", repo_type="model", cache_dir='./', resume_download=True)
+print(ckpt_path)
+
+save_path = './'
+device = 'cuda' if torch.cuda.is_available() else 'cpu'
+dino = DINOv2_MLP(
+                    dino_mode   = 'large',
+                    in_dim      = 1024,
+                    out_dim     = 360+180+180+2,
+                    evaluate    = True,
+                    mask_dino   = False,
+                    frozen_back = False
+                )
+
+dino.eval()
+print('model create')
+dino.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
+dino = dino.to(device)
+print('weight loaded')
+val_preprocess   = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./')
+
+image_path = '/path/to/image'
+origin_image = Image.open(image_path).convert('RGB')
+angles = get_3angle(origin_image, dino, val_preprocess, device)
+azimuth     = float(angles[0])
+polar       = float(angles[1])
+rotation    = float(angles[2])
+confidence  = float(angles[3])
+
+
+```
+
+
+### Best Practice
+To avoid ambiguity, our model only supports inputs that contain images of a single object. For daily images that usually contain multiple objects, it is a good choice to isolate each object with DINO-grounding and predict the orientation separately.
+```python
+[ToDo]
+```
+### Test-Time Augmentation
+In order to further enhance the robustness of the model，We further propose the test-time ensemble strategy. The input images will be randomly cropped into different variants, and the predicted orientation of different variants will be voted as the final prediction result. We implement this strategy in functions `get_3angle_infer_aug()` and `get_crop_images()`.
+
+## Citation
+
+If you find this project useful, please consider citing:
+
+```bibtex
+@article{orient_anything,
+  title={Orient Anything: Learning Robust Object Orientation Estimation from Rendering 3D Models},
+  author={Wang, Zehan and Zhang, Ziang and Pang, Tianyu and Du, Chao and Zhao, Hengshuang and Zhao, Zhou},
+  journal={arXiv:2412.18605},
+  year={2024}
+}
+```
+
+## Acknowledgement
+Thanks to the open source of the following projects: [Grounded-Segment-Anything](https://github.com/IDEA-Research/Grounded-Segment-Anything), [render-py](https://github.com/tvytlx/render-py)

app.py

@@ -0,0 +1,72 @@
+import gradio as gr
+from paths import *
+
+from vision_tower import DINOv2_MLP
+from transformers import AutoImageProcessor
+import torch
+from inference import *
+from utils import *
+
+from huggingface_hub import hf_hub_download
+ckpt_path = hf_hub_download(repo_id="Viglong/Orient-Anything", filename="ronormsigma1/dino_weight.pt", repo_type="model", cache_dir='./', resume_download=True)
+print(ckpt_path)
+
+save_path = './'
+device = 'cpu'
+dino = DINOv2_MLP(
+                    dino_mode   = 'large',
+                    in_dim      = 1024,
+                    out_dim     = 360+180+360+2,
+                    evaluate    = True,
+                    mask_dino   = False,
+                    frozen_back = False
+                )
+
+dino.eval()
+print('model create')
+dino.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
+dino = dino.to(device)
+print('weight loaded')
+val_preprocess   = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./')
+
+def infer_func(img, do_rm_bkg, do_infer_aug):
+    origin_img = Image.fromarray(img)
+    if do_infer_aug:
+        rm_bkg_img = background_preprocess(origin_img, True)
+        angles = get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device)
+    else:
+        rm_bkg_img = background_preprocess(origin_img, do_rm_bkg)
+        angles = get_3angle(rm_bkg_img, dino, val_preprocess, device)
+    
+    phi   = np.radians(angles[0])
+    theta = np.radians(angles[1])
+    gamma = angles[2]
+    confidence = float(angles[3])
+    if confidence > 0.5:
+        render_axis = render_3D_axis(phi, theta, gamma)
+        res_img = overlay_images_with_scaling(render_axis, rm_bkg_img)
+    else:
+        res_img = img
+    
+    # axis_model = "axis.obj"
+    return [res_img, round(float(angles[0]), 2), round(float(angles[1]), 2), round(float(angles[2]), 2), round(float(angles[3]), 2)]
+
+server = gr.Interface(
+    flagging_mode='never',
+    fn=infer_func, 
+    inputs=[
+        gr.Image(height=512, width=512, label="upload your image"),
+        gr.Checkbox(label="Remove Background", value=True),
+        gr.Checkbox(label="Inference time augmentation", value=False)
+    ], 
+    outputs=[
+        gr.Image(height=512, width=512, label="result image"),
+        # gr.Model3D(clear_color=[0.0, 0.0, 0.0, 0.0],  label="3D Model"),
+        gr.Textbox(lines=1, label='Azimuth(0~360°)'),
+        gr.Textbox(lines=1, label='Polar(-90~90°)'),
+        gr.Textbox(lines=1, label='Rotation(-90~90°)'),
+        gr.Textbox(lines=1, label='Confidence(0~1)')
+    ]
+)
+
+server.launch()

assets/axis.obj

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inference.py

@@ -0,0 +1,49 @@
+import torch
+from PIL import Image
+from utils import *
+import torch.nn.functional as F
+import numpy as np
+
+def get_3angle(image, dino, val_preprocess, device):
+    
+    # image = Image.open(image_path).convert('RGB')
+    image_inputs = val_preprocess(images = image)
+    image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device)
+    with torch.no_grad():
+        dino_pred = dino(image_inputs)
+
+    gaus_ax_pred   = torch.argmax(dino_pred[:, 0:360], dim=-1)
+    gaus_pl_pred   = torch.argmax(dino_pred[:, 360:360+180], dim=-1)
+    gaus_ro_pred   = torch.argmax(dino_pred[:, 360+180:360+180+360], dim=-1)
+    confidence     = F.softmax(dino_pred[:, -2:], dim=-1)[0][0]
+    angles = torch.zeros(4)
+    angles[0]  = gaus_ax_pred
+    angles[1]  = gaus_pl_pred - 90
+    angles[2]  = gaus_ro_pred - 180
+    angles[3]  = confidence
+    return angles
+
+def get_3angle_infer_aug(origin_img, rm_bkg_img, dino, val_preprocess, device):
+    
+    # image = Image.open(image_path).convert('RGB')
+    image = get_crop_images(origin_img, num=3) + get_crop_images(rm_bkg_img, num=3)
+    image_inputs = val_preprocess(images = image)
+    image_inputs['pixel_values'] = torch.from_numpy(np.array(image_inputs['pixel_values'])).to(device)
+    with torch.no_grad():
+        dino_pred = dino(image_inputs)
+
+    gaus_ax_pred   = torch.argmax(dino_pred[:, 0:360], dim=-1).to(torch.float32)
+    gaus_pl_pred   = torch.argmax(dino_pred[:, 360:360+180], dim=-1).to(torch.float32)
+    gaus_ro_pred   = torch.argmax(dino_pred[:, 360+180:360+180+360], dim=-1).to(torch.float32)
+    
+    gaus_ax_pred   = remove_outliers_and_average_circular(gaus_ax_pred)
+    gaus_pl_pred   = remove_outliers_and_average(gaus_pl_pred)
+    gaus_ro_pred   = remove_outliers_and_average(gaus_ro_pred)
+    
+    confidence     = torch.mean(F.softmax(dino_pred[:, -2:], dim=-1), dim=0)[0]
+    angles = torch.zeros(4)
+    angles[0]  = gaus_ax_pred
+    angles[1]  = gaus_pl_pred - 90
+    angles[2]  = gaus_ro_pred - 180
+    angles[3]  = confidence
+    return angles

paths.py

@@ -0,0 +1,4 @@
+DINO_SMALL  = "facebook/dinov2-small"
+DINO_BASE   = "facebook/dinov2-base"
+DINO_LARGE  = "facebook/dinov2-large"
+DINO_GIANT  = "facebook/dinov2-giant"

render/__init__.py

@@ -0,0 +1,3 @@
+# flake8: noqa
+from .core import render
+from .model import Model

render/canvas.py

@@ -0,0 +1,49 @@
+import typing as t
+
+from PIL import Image, ImageColor, ImageOps, ImageChops, ImageFilter
+import numpy as np
+
+class Canvas:
+    def __init__(self, filename=None, height=500, width=500):
+        self.filename = filename
+        self.height, self.width = height, width
+        self.img = Image.new("RGBA", (self.height, self.width), (0, 0, 0, 0))
+
+    def draw(self, dots, color: t.Union[tuple, str]):
+        if isinstance(color, str):
+            color = ImageColor.getrgb(color)
+        if isinstance(dots, tuple):
+            dots = [dots]
+        for dot in dots:
+            if dot[0]>=self.height or dot[1]>=self.width or dot[0]<0 or dot[1]<0:
+                # print(dot)
+                continue
+            self.img.putpixel(dot, color + (255,))
+
+    def add_white_border(self, border_size=5):
+        # 确保输入图像是 RGBA 模式
+        if self.img.mode != "RGBA":
+            self.img = self.img.convert("RGBA")
+        
+        # 提取 alpha 通道
+        alpha = self.img.getchannel("A")
+        # print(alpha.size)
+        dilated_alpha = alpha.filter(ImageFilter.MaxFilter(size=5))
+        # # print(dilated_alpha.size)
+        white_area = Image.new("RGBA", self.img.size, (255, 255, 255, 255))
+        white_area.putalpha(dilated_alpha)
+        
+        # 合并膨胀后的白色区域与原图像
+        result = Image.alpha_composite(white_area, self.img)
+        # expanded_alpha = ImageOps.expand(alpha, border=border_size, fill=255)
+        # white_border = Image.new("RGBA", image.size, (255, 255, 255, 255))
+        # white_border.putalpha(alpha)
+        return result
+
+    def __enter__(self):
+        return self
+
+    def __exit__(self, type, value, traceback):
+        # self.img = add_white_border(self.img)
+        self.img.save(self.filename)
+        pass

render/core.py

@@ -0,0 +1,370 @@
+import typing as t
+from functools import partial
+
+import numpy as np
+from copy import deepcopy
+from .canvas import Canvas
+
+from . import speedup
+
+
+# 2D part
+
+
+class Vec2d:
+    __slots__ = "x", "y", "arr"
+
+    def __init__(self, *args):
+        if len(args) == 1 and isinstance(args[0], Vec3d):
+            self.arr = Vec3d.narr
+        else:
+            assert len(args) == 2
+            self.arr = list(args)
+
+        self.x, self.y = [d if isinstance(d, int) else int(d + 0.5) for d in self.arr]
+
+    def __repr__(self):
+        return f"Vec2d({self.x}, {self.y})"
+
+    def __truediv__(self, other):
+        return (self.y - other.y) / (self.x - other.x)
+
+    def __eq__(self, other):
+        return self.x == other.x and self.y == other.y
+
+
+def draw_line(
+    v1: Vec2d, v2: Vec2d, canvas: Canvas, color: t.Union[tuple, str] = "white"
+):
+    """
+    Draw a line with a specified color
+
+    https://en.wikipedia.org/wiki/Bresenham%27s_line_algorithm
+    """
+    v1, v2 = deepcopy(v1), deepcopy(v2)
+    if v1 == v2:
+        canvas.draw((v1.x, v1.y), color=color)
+        return
+
+    steep = abs(v1.y - v2.y) > abs(v1.x - v2.x)
+    if steep:
+        v1.x, v1.y = v1.y, v1.x
+        v2.x, v2.y = v2.y, v2.x
+    v1, v2 = (v1, v2) if v1.x < v2.x else (v2, v1)
+    slope = abs((v1.y - v2.y) / (v1.x - v2.x))
+    y = v1.y
+    error: float = 0
+    incr = 1 if v1.y < v2.y else -1
+    dots = []
+    for x in range(int(v1.x), int(v2.x + 0.5)):
+        dots.append((int(y), x) if steep else (x, int(y)))
+        error += slope
+        if abs(error) >= 0.5:
+            y += incr
+            error -= 1
+
+    canvas.draw(dots, color=color)
+
+
+def draw_triangle(v1, v2, v3, canvas, color, wireframe=False):
+    """
+    Draw a triangle with 3 ordered vertices
+
+    http://www.sunshine2k.de/coding/java/TriangleRasterization/TriangleRasterization.html
+    """
+    _draw_line = partial(draw_line, canvas=canvas, color=color)
+
+    if wireframe:
+        _draw_line(v1, v2)
+        _draw_line(v2, v3)
+        _draw_line(v1, v3)
+        return
+
+    def sort_vertices_asc_by_y(vertices):
+        return sorted(vertices, key=lambda v: v.y)
+
+    def fill_bottom_flat_triangle(v1, v2, v3):
+        invslope1 = (v2.x - v1.x) / (v2.y - v1.y)
+        invslope2 = (v3.x - v1.x) / (v3.y - v1.y)
+
+        x1 = x2 = v1.x
+        y = v1.y
+
+        while y <= v2.y:
+            _draw_line(Vec2d(x1, y), Vec2d(x2, y))
+            x1 += invslope1
+            x2 += invslope2
+            y += 1
+
+    def fill_top_flat_triangle(v1, v2, v3):
+        invslope1 = (v3.x - v1.x) / (v3.y - v1.y)
+        invslope2 = (v3.x - v2.x) / (v3.y - v2.y)
+
+        x1 = x2 = v3.x
+        y = v3.y
+
+        while y > v2.y:
+            _draw_line(Vec2d(x1, y), Vec2d(x2, y))
+            x1 -= invslope1
+            x2 -= invslope2
+            y -= 1
+
+    v1, v2, v3 = sort_vertices_asc_by_y((v1, v2, v3))
+
+    # 填充
+    if v1.y == v2.y == v3.y:
+        pass
+    elif v2.y == v3.y:
+        fill_bottom_flat_triangle(v1, v2, v3)
+    elif v1.y == v2.y:
+        fill_top_flat_triangle(v1, v2, v3)
+    else:
+        v4 = Vec2d(int(v1.x + (v2.y - v1.y) / (v3.y - v1.y) * (v3.x - v1.x)), v2.y)
+        fill_bottom_flat_triangle(v1, v2, v4)
+        fill_top_flat_triangle(v2, v4, v3)
+
+
+# 3D part
+
+
+class Vec3d:
+    __slots__ = "x", "y", "z", "arr"
+
+    def __init__(self, *args):
+        # for Vec4d cast
+        if len(args) == 1 and isinstance(args[0], Vec4d):
+            vec4 = args[0]
+            arr_value = (vec4.x, vec4.y, vec4.z)
+        else:
+            assert len(args) == 3
+            arr_value = args
+        self.arr = np.array(arr_value, dtype=np.float64)
+        self.x, self.y, self.z = self.arr
+
+    def __repr__(self):
+        return repr(f"Vec3d({','.join([repr(d) for d in self.arr])})")
+
+    def __sub__(self, other):
+        return self.__class__(*[ds - do for ds, do in zip(self.arr, other.arr)])
+
+    def __bool__(self):
+        """ False for zero vector (0, 0, 0)
+        """
+        return any(self.arr)
+
+
+class Mat4d:
+    def __init__(self, narr=None, value=None):
+        self.value = np.matrix(narr) if value is None else value
+
+    def __repr__(self):
+        return repr(self.value)
+
+    def __mul__(self, other):
+        return self.__class__(value=self.value * other.value)
+
+
+class Vec4d(Mat4d):
+    def __init__(self, *narr, value=None):
+        if value is not None:
+            self.value = value
+        elif len(narr) == 1 and isinstance(narr[0], Mat4d):
+            self.value = narr[0].value
+        else:
+            assert len(narr) == 4
+            self.value = np.matrix([[d] for d in narr])
+
+        self.x, self.y, self.z, self.w = (
+            self.value[0, 0],
+            self.value[1, 0],
+            self.value[2, 0],
+            self.value[3, 0],
+        )
+        self.arr = self.value.reshape((1, 4))
+
+
+# Math util
+def normalize(v: Vec3d):
+    return Vec3d(*speedup.normalize(*v.arr))
+
+
+def dot_product(a: Vec3d, b: Vec3d):
+    return speedup.dot_product(*a.arr, *b.arr)
+
+
+def cross_product(a: Vec3d, b: Vec3d):
+    return Vec3d(*speedup.cross_product(*a.arr, *b.arr))
+
+BASE_LIGHT = 0.9
+def get_light_intensity(face) -> float:
+    # lights = [Vec3d(-2, 4, -10), Vec3d(10, 4, -2), Vec3d(8, 8, -8), Vec3d(0, 0, -8)]
+    lights = [Vec3d(-2, 4, -10)]
+    # lights = []
+
+    v1, v2, v3 = face
+    up = normalize(cross_product(v2 - v1, v3 - v1))
+    intensity = BASE_LIGHT
+    for light in lights:
+        intensity += dot_product(up, normalize(light))*0.2
+    return intensity
+
+
+def look_at(eye: Vec3d, target: Vec3d, up: Vec3d = Vec3d(0, -1, 0)) -> Mat4d:
+    """
+    http://www.songho.ca/opengl/gl_camera.html#lookat
+
+    Args:
+        eye: 摄像机的世界坐标位置
+        target: 观察点的位置
+        up: 就是你想让摄像机立在哪个方向
+            https://stackoverflow.com/questions/10635947/what-exactly-is-the-up-vector-in-opengls-lookat-function
+            这里默认使用了 0, -1, 0， 因为 blender 导出来的模型数据似乎有问题，导致y轴总是反的，于是把摄像机的up也翻一下得了。
+    """
+    f = normalize(eye - target)
+    l = normalize(cross_product(up, f))  # noqa: E741
+    u = cross_product(f, l)
+
+    rotate_matrix = Mat4d(
+        [[l.x, l.y, l.z, 0], [u.x, u.y, u.z, 0], [f.x, f.y, f.z, 0], [0, 0, 0, 1.0]]
+    )
+    translate_matrix = Mat4d(
+        [[1, 0, 0, -eye.x], [0, 1, 0, -eye.y], [0, 0, 1, -eye.z], [0, 0, 0, 1.0]]
+    )
+
+    return Mat4d(value=(rotate_matrix * translate_matrix).value)
+
+
+def perspective_project(r, t, n, f, b=None, l=None):  # noqa: E741
+    """
+    目的：
+        把相机坐标转换成投影在视网膜的范围在(-1, 1)的笛卡尔坐标
+
+    原理：
+        对于x，y坐标，相似三角形可以算出投影点的x，y
+        对于z坐标，是假设了near是-1，far是1，然后带进去算的
+        http://www.songho.ca/opengl/gl_projectionmatrix.html
+        https://www.scratchapixel.com/lessons/3d-basic-rendering/perspective-and-orthographic-projection-matrix/opengl-perspective-projection-matrix
+
+    推导出来的矩阵：
+        [
+            2n/(r-l) 0        (r+l/r-l)   0
+            0        2n/(t-b) (t+b)/(t-b) 0
+            0        0        -(f+n)/f-n  (-2*f*n)/(f-n)
+            0        0        -1          0
+        ]
+
+    实际上由于我们用的视网膜(near pane)是个关于远点对称的矩形，所以矩阵简化为：
+        [
+            n/r      0        0           0
+            0        n/t      0           0
+            0        0        -(f+n)/f-n  (-2*f*n)/(f-n)
+            0        0        -1          0
+        ]
+
+    Args:
+        r: right, t: top, n: near, f: far, b: bottom, l: left
+    """
+    return Mat4d(
+        [
+            [n / r, 0, 0, 0],
+            [0, n / t, 0, 0],
+            [0, 0, -(f + n) / (f - n), (-2 * f * n) / (f - n)],
+            [0, 0, -1, 0],
+        ]
+    )
+
+
+def draw(screen_vertices, world_vertices, model, canvas, wireframe=True):
+    """standard algorithm
+    """
+    for triangle_indices in model.indices:
+        vertex_group = [screen_vertices[idx - 1] for idx in triangle_indices]
+        face = [Vec3d(world_vertices[idx - 1]) for idx in triangle_indices]
+        if wireframe:
+            draw_triangle(*vertex_group, canvas=canvas, color="black", wireframe=True)
+        else:
+            intensity = get_light_intensity(face)
+            if intensity > 0:
+                draw_triangle(
+                    *vertex_group, canvas=canvas, color=(int(intensity * 255),) * 3
+                )
+
+
+def draw_with_z_buffer(screen_vertices, world_vertices, model, canvas):
+    """ z-buffer algorithm
+    """
+    intensities = []
+    triangles = []
+    for i, triangle_indices in enumerate(model.indices):
+        screen_triangle = [screen_vertices[idx - 1] for idx in triangle_indices]
+        uv_triangle = [model.uv_vertices[idx - 1] for idx in model.uv_indices[i]]
+        world_triangle = [Vec3d(world_vertices[idx - 1]) for idx in triangle_indices]
+        intensities.append(abs(get_light_intensity(world_triangle)))
+        # take off the class to let Cython work
+        triangles.append(
+            [np.append(screen_triangle[i].arr, uv_triangle[i]) for i in range(3)]
+        )
+
+    faces = speedup.generate_faces(
+        np.array(triangles, dtype=np.float64), model.texture_width, model.texture_height
+    )
+    for face_dots in faces:
+        for dot in face_dots:
+            intensity = intensities[dot[0]]
+            u, v = dot[3], dot[4]
+            color = model.texture_array[u, v]
+            canvas.draw((dot[1], dot[2]), tuple(int(c * intensity) for c in color[:3]))
+            # TODO: add object rendering mode (no texture)
+            # canvas.draw((dot[1], dot[2]), (int(255 * intensity),) * 3)
+
+
+def render(model, height, width, filename, cam_loc, wireframe=False):
+    """
+    Args:
+        model: the Model object
+        height: cavas height
+        width: cavas width
+        picname: picture file name
+    """
+    model_matrix = Mat4d([[1, 0, 0, 0], [0, 1, 0, 0], [0, 0, 1, 0], [0, 0, 0, 1]])
+    # TODO: camera configration
+    view_matrix = look_at(Vec3d(cam_loc[0], cam_loc[1], cam_loc[2]), Vec3d(0, 0, 0))
+    projection_matrix = perspective_project(0.5, 0.5, 3, 1000)
+
+    world_vertices = []
+
+    def mvp(v):
+        world_vertex = model_matrix * v
+        world_vertices.append(Vec4d(world_vertex))
+        return projection_matrix * view_matrix * world_vertex
+
+    def ndc(v):
+        """
+        各个坐标同时除以 w，得到 NDC 坐标
+        """
+        v = v.value
+        w = v[3, 0]
+        x, y, z = v[0, 0] / w, v[1, 0] / w, v[2, 0] / w
+        return Mat4d([[x], [y], [z], [1 / w]])
+
+    def viewport(v):
+        x = y = 0
+        w, h = width, height
+        n, f = 0.3, 1000
+        return Vec3d(
+            w * 0.5 * v.value[0, 0] + x + w * 0.5,
+            h * 0.5 * v.value[1, 0] + y + h * 0.5,
+            0.5 * (f - n) * v.value[2, 0] + 0.5 * (f + n),
+        )
+
+    # the render pipeline
+    screen_vertices = [viewport(ndc(mvp(v))) for v in model.vertices]
+
+    with Canvas(filename, height, width) as canvas:
+        if wireframe:
+            draw(screen_vertices, world_vertices, model, canvas)
+        else:
+            draw_with_z_buffer(screen_vertices, world_vertices, model, canvas)
+        
+        render_img = canvas.add_white_border().copy()
+    return render_img

render/model.py

@@ -0,0 +1,31 @@
+import numpy
+from PIL import Image
+from .core import Vec4d
+
+
+class Model:
+    def __init__(self, filename, texture_filename):
+        """
+        https://en.wikipedia.org/wiki/Wavefront_.obj_file#Vertex_normal_indices
+        """
+        self.vertices = []
+        self.uv_vertices = []
+        self.uv_indices = []
+        self.indices = []
+
+        texture = Image.open(texture_filename)
+        self.texture_array = numpy.array(texture)
+        self.texture_width, self.texture_height = texture.size
+
+        with open(filename) as f:
+            for line in f:
+                if line.startswith("v "):
+                    x, y, z = [float(d) for d in line.strip("v").strip().split(" ")]
+                    self.vertices.append(Vec4d(x, y, z, 1))
+                elif line.startswith("vt "):
+                    u, v = [float(d) for d in line.strip("vt").strip().split(" ")]
+                    self.uv_vertices.append([u, v])
+                elif line.startswith("f "):
+                    facet = [d.split("/") for d in line.strip("f").strip().split(" ")]
+                    self.indices.append([int(d[0]) for d in facet])
+                    self.uv_indices.append([int(d[1]) for d in facet])

render/speedup.py

@@ -0,0 +1,101 @@
+# import cython
+import numpy as np
+from math import sqrt
+
+
+def normalize(x, y, z):
+    unit = sqrt(x * x + y * y + z * z)
+    if unit == 0:
+        return 0, 0, 0
+    return x / unit, y / unit, z / unit
+
+
+def get_min_max(a, b, c):
+    min = a
+    max = a
+    if min > b:
+        min = b
+    if min > c:
+        min = c
+    if max < b:
+        max = b
+    if max < c:
+        max = c
+    return int(min), int(max)
+
+def dot_product(a0, a1, a2, b0, b1, b2):
+    r = a0 * b0 + a1 * b1 + a2 * b2
+    return r
+
+
+def cross_product(a0, a1, a2, b0, b1, b2):
+    x = a1 * b2 - a2 * b1
+    y = a2 * b0 - a0 * b2
+    z = a0 * b1 - a1 * b0
+    return x,y,z
+
+
+# @cython.boundscheck(False)
+def generate_faces(triangles, width, height):
+    """ draw the triangle faces with z buffer
+
+    Args:
+        triangles: groups of vertices
+
+    FYI:
+        * zbuffer, https://github.com/ssloy/tinyrenderer/wiki/Lesson-3:-Hidden-faces-removal-(z-buffer)
+        * uv mapping and perspective correction
+    """
+    i, j, k, length     = 0, 0, 0, 0
+    bcy, bcz, x, y, z   = 0.,0.,0.,0.,0.
+    a, b, c             = [0.,0.,0.],[0.,0.,0.],[0.,0.,0.]
+    m, bc               = [0.,0.,0.],[0.,0.,0.]
+    uva, uvb, uvc       = [0.,0.],[0.,0.],[0.,0.]
+    minx, maxx, miny, maxy = 0,0,0,0
+    length = triangles.shape[0]
+    zbuffer = {}
+    faces = []
+
+    for i in range(length):
+        a = triangles[i, 0, 0], triangles[i, 0, 1], triangles[i, 0, 2]
+        b = triangles[i, 1, 0], triangles[i, 1, 1], triangles[i, 1, 2]
+        c = triangles[i, 2, 0], triangles[i, 2, 1], triangles[i, 2, 2]
+        uva = triangles[i, 0, 3], triangles[i, 0, 4]
+        uvb = triangles[i, 1, 3], triangles[i, 1, 4]
+        uvc = triangles[i, 2, 3], triangles[i, 2, 4]
+        minx, maxx = get_min_max(a[0], b[0], c[0])
+        miny, maxy = get_min_max(a[1], b[1], c[1])
+        pixels = []
+        for j in range(minx, maxx + 2):
+            for k in range(miny - 1, maxy + 2):
+                # 必须显式转换成 double 参与底下的运算，不然结果是错的
+                x = j
+                y = k
+
+                m[0], m[1], m[2] = cross_product(c[0] - a[0], b[0] - a[0], a[0] - x, c[1] - a[1], b[1] - a[1], a[1] - y)
+                if abs(m[2]) > 0:
+                    bcy = m[1] / m[2]
+                    bcz = m[0] / m[2]
+                    bc = (1 - bcy - bcz, bcy, bcz)
+                else:
+                    continue
+
+                # here, -0.00001 because of the precision lose
+                if bc[0] < -0.00001 or bc[1] < -0.00001 or bc[2] < -0.00001:
+                    continue
+
+                z = 1 / (bc[0] / a[2] + bc[1] / b[2] + bc[2] / c[2])
+
+                # Blender 导出来的 uv 数据，跟之前的顶点数据有一样的问题，Y轴是个反的，
+                # 所以这里的纹理图片要旋转一下才能 work
+                v = (uva[0] * bc[0] / a[2] + uvb[0] * bc[1] / b[2] + uvc[0] * bc[2] / c[2]) * z * width
+                u = height - (uva[1] * bc[0] / a[2] + uvb[1] * bc[1] / b[2] + uvc[1] * bc[2] / c[2]) * z * height
+
+                # https://en.wikipedia.org/wiki/Pairing_function
+                idx = ((x + y) * (x + y + 1) + y) / 2
+                if zbuffer.get(idx) is None or zbuffer[idx] < z:
+                    zbuffer[idx] = z
+                    pixels.append((i, j, k, int(u) - 1, int(v) - 1))
+
+        faces.append(pixels)
+    return faces

requirements.txt

@@ -0,0 +1,9 @@
+torch==2.2.1
+transformers==4.38
+matplotlib
+pillow==10.2.0
+huggingface-hub==0.26.5
+gradio==5.9.0
+numpy==1.26.4
+onnxruntime
+rembg

utils.py

@@ -0,0 +1,304 @@
+import rembg
+import random
+import torch
+import numpy as np
+from PIL import Image, ImageOps
+import PIL
+from typing import Any
+import matplotlib.pyplot as plt
+import io
+
+def resize_foreground(
+    image: Image,
+    ratio: float,
+) -> Image:
+    image = np.array(image)
+    assert image.shape[-1] == 4
+    alpha = np.where(image[..., 3] > 0)
+    y1, y2, x1, x2 = (
+        alpha[0].min(),
+        alpha[0].max(),
+        alpha[1].min(),
+        alpha[1].max(),
+    )
+    # crop the foreground
+    fg = image[y1:y2, x1:x2]
+    # pad to square
+    size = max(fg.shape[0], fg.shape[1])
+    ph0, pw0 = (size - fg.shape[0]) // 2, (size - fg.shape[1]) // 2
+    ph1, pw1 = size - fg.shape[0] - ph0, size - fg.shape[1] - pw0
+    new_image = np.pad(
+        fg,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+
+    # compute padding according to the ratio
+    new_size = int(new_image.shape[0] / ratio)
+    # pad to size, double side
+    ph0, pw0 = (new_size - size) // 2, (new_size - size) // 2
+    ph1, pw1 = new_size - size - ph0, new_size - size - pw0
+    new_image = np.pad(
+        new_image,
+        ((ph0, ph1), (pw0, pw1), (0, 0)),
+        mode="constant",
+        constant_values=((0, 0), (0, 0), (0, 0)),
+    )
+    new_image = Image.fromarray(new_image)
+    return new_image
+
+def remove_background(image: Image,
+    rembg_session: Any = None,
+    force: bool = False,
+    **rembg_kwargs,
+) -> Image:
+    do_remove = True
+    if image.mode == "RGBA" and image.getextrema()[3][0] < 255:
+        do_remove = False
+    do_remove = do_remove or force
+    if do_remove:
+        image = rembg.remove(image, session=rembg_session, **rembg_kwargs)
+    return image
+
+def random_crop(image, crop_scale=(0.8, 0.95)):
+    """
+    随机裁切图片
+        image (numpy.ndarray):  (H, W, C)。
+        crop_scale (tuple): (min_scale, max_scale)。
+    """
+    assert isinstance(image, Image.Image), "iput must be PIL.Image.Image"
+    assert len(crop_scale) == 2 and 0 < crop_scale[0] <= crop_scale[1] <= 1
+
+    width, height = image.size
+
+    # 计算裁切的高度和宽度
+    crop_width = random.randint(int(width * crop_scale[0]), int(width * crop_scale[1]))
+    crop_height = random.randint(int(height * crop_scale[0]), int(height * crop_scale[1]))
+
+    # 随机选择裁切的起始点
+    left = random.randint(0, width - crop_width)
+    top = random.randint(0, height - crop_height)
+
+    # 裁切图片
+    cropped_image = image.crop((left, top, left + crop_width, top + crop_height))
+
+    return cropped_image
+
+def get_crop_images(img, num=3):
+    cropped_images = []
+    for i in range(num):
+        cropped_images.append(random_crop(img))
+    return cropped_images
+
+def background_preprocess(input_image, do_remove_background):
+
+    rembg_session = rembg.new_session() if do_remove_background else None
+
+    if do_remove_background:
+        input_image = remove_background(input_image, rembg_session)
+        input_image = resize_foreground(input_image, 0.85)
+
+    return input_image
+
+def remove_outliers_and_average(tensor, threshold=1.5):
+    assert tensor.dim() == 1, "dimension of input Tensor must equal to 1"
+
+    q1 = torch.quantile(tensor, 0.25)
+    q3 = torch.quantile(tensor, 0.75)
+    iqr = q3 - q1
+
+    lower_bound = q1 - threshold * iqr
+    upper_bound = q3 + threshold * iqr
+
+    non_outliers = tensor[(tensor >= lower_bound) & (tensor <= upper_bound)]
+
+    if len(non_outliers) == 0:
+        return tensor.mean().item()
+
+    return non_outliers.mean().item()
+
+
+def remove_outliers_and_average_circular(tensor, threshold=1.5):
+    assert tensor.dim() == 1, "dimension of input Tensor must equal to 1"
+
+    # 将角度转换为二维平面上的点
+    radians = tensor * torch.pi / 180.0
+    x_coords = torch.cos(radians)
+    y_coords = torch.sin(radians)
+
+    # 计算平均向量
+    mean_x = torch.mean(x_coords)
+    mean_y = torch.mean(y_coords)
+
+    differences = torch.sqrt((x_coords - mean_x) * (x_coords - mean_x) + (y_coords - mean_y) * (y_coords - mean_y))
+
+    # 计算四分位数和 IQR
+    q1 = torch.quantile(differences, 0.25)
+    q3 = torch.quantile(differences, 0.75)
+    iqr = q3 - q1
+
+    # 计算上下限
+    lower_bound = q1 - threshold * iqr
+    upper_bound = q3 + threshold * iqr
+
+    # 筛选非离群点
+    non_outliers = tensor[(differences >= lower_bound) & (differences <= upper_bound)]
+
+    if len(non_outliers) == 0:
+        mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi
+        mean_angle = (mean_angle + 360) % 360
+        return mean_angle  # 如果没有非离群点，返回 None
+
+    # 对非离群点再次计算平均向量
+    radians = non_outliers * torch.pi / 180.0
+    x_coords = torch.cos(radians)
+    y_coords = torch.sin(radians)
+
+    mean_x = torch.mean(x_coords)
+    mean_y = torch.mean(y_coords)
+
+    mean_angle = torch.atan2(mean_y, mean_x) * 180.0 / torch.pi
+    mean_angle = (mean_angle + 360) % 360
+
+    return mean_angle
+
+def scale(x):
+    # print(x)
+    # if abs(x[0])<0.1 and abs(x[1])<0.1:
+        
+    #     return x*5
+    # else:
+    #     return x
+    return x*3
+
+def get_proj2D_XYZ(phi, theta, gamma):
+    x = np.array([-1*np.sin(phi)*np.cos(gamma) - np.cos(phi)*np.sin(theta)*np.sin(gamma), np.sin(phi)*np.sin(gamma) - np.cos(phi)*np.sin(theta)*np.cos(gamma)])
+    y = np.array([-1*np.cos(phi)*np.cos(gamma) + np.sin(phi)*np.sin(theta)*np.sin(gamma), np.cos(phi)*np.sin(gamma) + np.sin(phi)*np.sin(theta)*np.cos(gamma)])
+    z = np.array([np.cos(theta)*np.sin(gamma), np.cos(theta)*np.cos(gamma)])
+    x = scale(x)
+    y = scale(y)
+    z = scale(z)
+    return x, y, z
+
+# 绘制3D坐标轴
+def draw_axis(ax, origin, vector, color, label=None):
+    ax.quiver(origin[0], origin[1], vector[0], vector[1], angles='xy', scale_units='xy', scale=1, color=color)
+    if label!=None:
+        ax.text(origin[0] + vector[0] * 1.1, origin[1] + vector[1] * 1.1, label, color=color, fontsize=12)
+
+def matplotlib_2D_arrow(angles, rm_bkg_img):
+    fig, ax = plt.subplots(figsize=(8, 8))
+
+    # 设置旋转角度
+    phi   = np.radians(angles[0])
+    theta = np.radians(angles[1])
+    gamma = np.radians(-1*angles[2])
+
+    w, h = rm_bkg_img.size
+    if h>w:
+        extent = [-5*w/h, 5*w/h, -5, 5]
+    else:
+        extent = [-5, 5, -5*h/w, 5*h/w]
+    ax.imshow(rm_bkg_img, extent=extent, zorder=0, aspect ='auto')  # extent 设置图片的显示范围
+
+    origin = np.array([0, 0])
+
+    # 旋转后的向量
+    rot_x, rot_y, rot_z = get_proj2D_XYZ(phi, theta, gamma)
+
+    # draw arrow
+    arrow_attr = [{'point':rot_x, 'color':'r', 'label':'front'}, 
+                  {'point':rot_y, 'color':'g', 'label':'right'}, 
+                  {'point':rot_z, 'color':'b', 'label':'top'}]
+    
+    if phi> 45 and phi<=225:
+        order = [0,1,2]
+    elif phi > 225 and phi < 315:
+        order = [2,0,1]
+    else:
+        order = [2,1,0]
+    
+    for i in range(3):
+        draw_axis(ax, origin, arrow_attr[order[i]]['point'], arrow_attr[order[i]]['color'], arrow_attr[order[i]]['label'])
+        # draw_axis(ax, origin, rot_y, 'g', label='right')
+        # draw_axis(ax, origin, rot_z, 'b', label='top')
+        # draw_axis(ax, origin, rot_x, 'r', label='front')
+
+    # 关闭坐标轴和网格
+    ax.set_axis_off()
+    ax.grid(False)
+
+    # 设置坐标范围
+    ax.set_xlim(-5, 5)
+    ax.set_ylim(-5, 5)
+
+def figure_to_img(fig):
+    with io.BytesIO() as buf:
+        fig.savefig(buf, format='JPG', bbox_inches='tight')
+        buf.seek(0)
+        image = Image.open(buf).copy()
+    return image
+
+from render import render, Model
+import math
+axis_model = Model("./assets/axis.obj", texture_filename="./assets/axis.png")
+def render_3D_axis(phi, theta, gamma):
+    radius = 240
+    # camera_location = [radius * math.cos(phi), radius * math.sin(phi), radius * math.tan(theta)]
+    # print(camera_location)
+    camera_location = [-1*radius * math.cos(phi), -1*radius * math.tan(theta), radius * math.sin(phi)]
+    img = render(
+        # Model("res/jinx.obj", texture_filename="res/jinx.tga"),
+        axis_model,
+        height=512,
+        width=512,
+        filename="tmp_render.png",
+        cam_loc = camera_location
+    )
+    img = img.rotate(gamma)
+    return img
+
+def overlay_images_with_scaling(center_image: Image.Image, background_image, target_size=(512, 512)):
+    """
+    调整前景图像大小为 512x512，将背景图像缩放以适配，并中心对齐叠加
+    :param center_image: 前景图像
+    :param background_image: 背景图像
+    :param target_size: 前景图像的目标大小，默认 (512, 512)
+    :return: 叠加后的图像
+    """
+    # 确保输入图像为 RGBA 模式
+    if center_image.mode != "RGBA":
+        center_image = center_image.convert("RGBA")
+    if background_image.mode != "RGBA":
+        background_image = background_image.convert("RGBA")
+    
+    # 调整前景图像大小
+    center_image = center_image.resize(target_size)
+    
+    # 缩放背景图像，确保其适合前景图像的尺寸
+    bg_width, bg_height = background_image.size
+    
+    # 按宽度或高度等比例缩放背景
+    scale = target_size[0] / max(bg_width, bg_height)
+    new_width = int(bg_width * scale)
+    new_height = int(bg_height * scale)
+    resized_background = background_image.resize((new_width, new_height))
+    # 计算需要的填充量
+    pad_width = target_size[0] - new_width
+    pad_height = target_size[0] - new_height
+
+    # 计算上下左右的 padding
+    left = pad_width // 2
+    right = pad_width - left
+    top = pad_height // 2
+    bottom = pad_height - top
+
+    # 添加 padding
+    resized_background = ImageOps.expand(resized_background, border=(left, top, right, bottom), fill=(255,255,255,255))
+    
+    # 将前景图像叠加到背景图像上
+    result = resized_background.copy()
+    result.paste(center_image, (0, 0), mask=center_image)
+    
+    return result

vision_tower.py

@@ -0,0 +1,161 @@
+import torch
+from torch import nn
+import torch.nn.init as init
+import torch.nn.functional as F
+
+from paths import *
+
+from typing import Dict, List, Optional, Set, Tuple, Union
+from transformers import AutoImageProcessor, AutoModel, Dinov2Model
+from transformers.models.dinov2.modeling_dinov2 import Dinov2Embeddings
+from transformers.models.dinov2.configuration_dinov2 import Dinov2Config
+import numpy as np
+from contextlib import nullcontext
+
+def get_activation(activation):
+    if activation.lower() == 'gelu':
+        return nn.GELU()
+    elif activation.lower() == 'rrelu':
+        return nn.RReLU(inplace=True)
+    elif activation.lower() == 'selu':
+        return nn.SELU(inplace=True)
+    elif activation.lower() == 'silu':
+        return nn.SiLU(inplace=True)
+    elif activation.lower() == 'hardswish':
+        return nn.Hardswish(inplace=True)
+    elif activation.lower() == 'leakyrelu':
+        return nn.LeakyReLU(inplace=True)
+    elif activation.lower() == 'sigmoid':
+        return nn.Sigmoid()
+    elif activation.lower() == 'tanh':
+        return nn.Tanh()
+    else:
+        return nn.ReLU(inplace=True)
+
+
+
+class MLP_dim(nn.Module):
+    def __init__(
+        self, in_dim=512, out_dim=1024, bias=True, activation='relu'):
+        super().__init__()
+        self.act = get_activation(activation)
+        self.net1 = nn.Sequential(
+            nn.Linear(in_dim, int(out_dim), bias=bias),
+            nn.BatchNorm1d(int(out_dim)),
+            self.act
+        )
+        self.net2 = nn.Sequential(
+            nn.Linear(int(out_dim), out_dim, bias=bias),
+            nn.BatchNorm1d(out_dim)
+        )
+
+    def forward(self, x):
+        return self.net2(self.net1(x))
+
+class FLIP_Dinov2Embeddings(Dinov2Embeddings):
+    """
+    Construct the CLS token, mask token, position and patch embeddings.
+    """
+
+    def __init__(self, config: Dinov2Config) -> None:
+        super().__init__(config)
+
+    def forward(self, pixel_values: torch.Tensor, bool_masked_pos: Optional[torch.Tensor] = None) -> torch.Tensor:
+        batch_size, _, height, width = pixel_values.shape
+        target_dtype = self.patch_embeddings.projection.weight.dtype
+        embeddings = self.patch_embeddings(pixel_values.to(dtype=target_dtype))
+
+        # add the [CLS] token to the embedded patch tokens
+        cls_tokens = self.cls_token.expand(batch_size, -1, -1)
+        embeddings = torch.cat((cls_tokens, embeddings), dim=1)
+
+        # add positional encoding to each token
+        embeddings = embeddings + self.interpolate_pos_encoding(embeddings, height, width)
+
+        if bool_masked_pos is not None:
+            # embeddings = torch.where(
+            #     bool_masked_pos.unsqueeze(-1), self.mask_token.to(embeddings.dtype).unsqueeze(0), embeddings
+            # )
+            B,S,D = embeddings.shape
+            batch_indices = torch.arange(B).unsqueeze(1)
+            embeddings = embeddings[batch_indices, bool_masked_pos]
+
+        embeddings = self.dropout(embeddings)
+
+        return embeddings
+
+class FLIP_DINOv2(Dinov2Model):
+    def __init__(self, config):
+        super().__init__(config)
+        
+        self.embeddings = FLIP_Dinov2Embeddings(config)
+        
+class DINOv2_MLP(nn.Module):
+    def __init__(self, 
+                 dino_mode,
+                 in_dim,
+                 out_dim,
+                 evaluate,
+                 mask_dino,
+                 frozen_back
+                ) -> None:
+        super().__init__()
+        # self.dinov2 = AutoModel.from_pretrained(DINO_BASE)
+        if dino_mode == 'base':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_BASE, cache_dir='./')
+        elif dino_mode == 'large':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_LARGE, cache_dir='./')
+        elif dino_mode == 'small':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_SMALL, cache_dir='./')
+        elif dino_mode == 'giant':
+            self.dinov2 = FLIP_DINOv2.from_pretrained(DINO_GIANT, cache_dir='./')
+        
+        self.down_sampler = MLP_dim(in_dim=in_dim, out_dim=out_dim)
+        self.random_mask  = False
+        if not evaluate:
+            self.init_weights(self.down_sampler)
+            self.random_mask = mask_dino
+        if frozen_back:
+            self.forward_mode = torch.no_grad()
+        else:
+            self.forward_mode = nullcontext()
+        
+    def forward(self, img_inputs):
+        device = self.get_device()
+        # print(img_inputs['pixel_values'].shape)
+        
+        with self.forward_mode:
+            if self.random_mask:
+                B = len(img_inputs['pixel_values'])
+                S = 256
+                indices = []
+                for i in range(B):
+                    tmp = torch.randperm(S)[:S//2]
+                    tmp = tmp.sort().values + 1
+                    indices.append(tmp)
+                indices = torch.stack(indices, dim=0)
+                indices = torch.cat([torch.zeros(B, 1, dtype=torch.long, device='cpu'), indices], dim=1)
+                # print(indices.shape)
+                img_inputs['bool_masked_pos'] = indices.to(device)
+                
+            dino_outputs = self.dinov2(**img_inputs)
+            dino_seq = dino_outputs.last_hidden_state
+            # B,S,_ = dino_seq.shape
+            # dino_seq = dino_seq.view(B*S,-1)
+            dino_seq = dino_seq[:,0,:]
+        
+        down_sample_out = self.down_sampler(dino_seq)
+        # down_sample_out = down_sample_out.view(B,S,-1)
+        # down_sample_out = down_sample_out[:,0,:]
+        
+        return down_sample_out
+    
+    def get_device(self):
+        return next(self.parameters()).device
+    
+    def init_weights(self, m):
+        if isinstance(m, nn.Linear):
+            init.xavier_uniform_(m.weight)
+            if m.bias is not None:
+                init.constant_(m.bias, 0)
+