init

.gitignore

@@ -0,0 +1,5 @@
+__pycache__/**
+.locks/*
+.gradio/*
+models--facebook--dinov2-large/*
+models--Viglong--OriNet/*

app.py

@@ -0,0 +1,139 @@
+import gradio as gr
+from paths import *
+import numpy as np
+from vision_tower import DINOv2_MLP
+from transformers import AutoImageProcessor
+import torch
+import os
+import matplotlib.pyplot as plt
+import io
+from PIL import Image
+
+from huggingface_hub import hf_hub_download
+ckpt_path = hf_hub_download(repo_id="Viglong/OriNet", filename="celarge/dino_weight.pt", repo_type="model", cache_dir='./')
+print(ckpt_path)
+
+save_path = './'
+device = 'cpu'
+dino = DINOv2_MLP(
+                    dino_mode   = 'large',
+                    in_dim      = 1024,
+                    out_dim     = 360+180+60+2,
+                    evaluate    = True,
+                    mask_dino   = False,
+                    frozen_back = False
+                ).to(device)
+
+dino.eval()
+print('model create')
+dino.load_state_dict(torch.load(ckpt_path, map_location='cpu'))
+print('weight loaded')
+val_preprocess   = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./')
+
+
+def get_3angle(image):
+    
+    # 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+60], dim=-1)
+    angles = torch.zeros(3)
+    angles[0]  = gaus_ax_pred
+    angles[1]  = gaus_pl_pred - 90
+    angles[2]  = gaus_ro_pred - 30
+    
+    return angles
+
+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 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
+
+# def generate_mutimodal(title, context, img):
+#     return f"Title:{title}\nContext:{context}\n...{img}"
+
+def generate_mutimodal(img):
+    angles = get_3angle(img)
+    
+    fig, ax = plt.subplots(figsize=(8, 8))
+
+    h, w, c = img.shape
+    if h>w:
+        extent = [-5*w/h, 5*w/h, -5, 5]
+    else:
+        extent = [-5, 5, -5*h/w, 5*h/w]
+    ax.imshow(img, extent=extent, zorder=0, aspect ='auto')  # extent 设置图片的显示范围
+
+    origin = np.array([0, 0])
+
+    # # 设置旋转角度
+    phi   = np.radians(angles[0])
+    theta = np.radians(angles[1])
+    gamma = np.radians(-1*angles[2])
+
+    # 旋转后的向量
+    rot_x, rot_y, rot_z = get_proj2D_XYZ(phi, theta, gamma)
+
+    draw_axis(ax, origin, rot_y, 'g')
+    draw_axis(ax, origin, rot_z, 'b')
+    draw_axis(ax, origin, rot_x, 'r')
+
+    # 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)
+    
+    res_img = figure_to_img(fig)
+    # axis_model = "axis.obj"
+    return [res_img, float(angles[0]), float(angles[1]), float(angles[2])]
+
+server = gr.Interface(
+    flagging_mode='never',
+    fn=generate_mutimodal, 
+    inputs=[
+        gr.Image(height=512, width=512, label="upload your image")
+    ], 
+    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°)')
+    ]
+)
+
+server.launch()

axis.obj

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+# www.blender.org
+mtllib axis.mtl
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demo.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import argparse
+import os
+import glob
+
+from transformers import AutoProcessor, AutoTokenizer, AutoImageProcessor
+import pandas as pd
+from paths import *
+import numpy as np
+from vision_tower import DINOv2_MLP
+from PIL import Image
+save_path = './'
+device = 'cpu'
+dino = DINOv2_MLP(
+                    dino_mode   = 'large',
+                    in_dim      = 1024,
+                    out_dim     = 360+180+60+2,
+                    evaluate    = True,
+                    mask_dino   = False,
+                    frozen_back = False
+                ).to(device)
+
+dino.eval()
+
+dino.load_state_dict(torch.load(os.path.join(save_path, 'dino_weight.pt'), map_location='cpu'))
+val_preprocess   = AutoImageProcessor.from_pretrained(DINO_LARGE, cache_dir='./')
+
+
+def get_3angle(image_path):
+    
+    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+60], dim=-1)
+    angles = torch.zeros(3)
+    angles[0]  = gaus_ax_pred
+    angles[1]  = gaus_pl_pred - 90
+    angles[2]  = gaus_ro_pred - 30
+    
+    return angles
+
+with torch.no_grad():
+    obj_angles = []
+    img_paths = glob.glob(os.path.join('/home/aiops/wangzh/wangjialei/data_preprocess/meta/sa_10099.jpg'))
+    img_paths.sort()
+    for image_path in img_paths:
+        # image_path = f'/home/aiops/wangzh/zza/Objaverse_render_extract/coco/demo_image/3D/{i}.png'
+        image_name = image_path.split('/')[-1]
+        print(image_name)
+        angles = get_3angle(image_path)
+        obj_angles.append(angles)
+        # print(f'cat/{i}.png', angles)
+    obj_angles = torch.stack(obj_angles, dim=0)
+    print('wild', obj_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"

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)
+