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@@ -33,3 +33,6 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 *.zip filter=lfs diff=lfs merge=lfs -text
 *.zst filter=lfs diff=lfs merge=lfs -text
 *tfevents* filter=lfs diff=lfs merge=lfs -text
+demo_example/000_reference.png filter=lfs diff=lfs merge=lfs -text
+demo_example/005_source.png filter=lfs diff=lfs merge=lfs -text
+demo_example/007_reference.png filter=lfs diff=lfs merge=lfs -text

app.py

@@ -0,0 +1,366 @@
+import gradio as gr
+import torch
+import torch.nn.functional as F
+from safetensors.numpy import save_file, load_file
+from omegaconf import OmegaConf
+from transformers import AutoConfig
+import cv2
+from PIL import Image
+import numpy as np
+import json
+import os
+#
+from diffusers import StableDiffusionPipeline, StableDiffusionImg2ImgPipeline, StableDiffusionInpaintPipelineLegacy, StableDiffusionInpaintPipeline, DDIMScheduler, AutoencoderKL
+from diffusers import AutoencoderKL, DDPMScheduler, UNet2DConditionModel, DDIMScheduler
+from diffusers import DDIMScheduler, DDPMScheduler, DPMSolverMultistepScheduler
+from diffusers.image_processor import VaeImageProcessor
+#
+from models.pipeline_mimicbrush import MimicBrushPipeline
+from models.ReferenceNet import ReferenceNet
+from models.depth_guider import DepthGuider
+from mimicbrush import MimicBrush_RefNet
+from data_utils import *
+from modelscope.hub.snapshot_download import snapshot_download as ms_snapshot_download
+from huggingface_hub import snapshot_download
+
+
+
+model_dir = ms_snapshot_download('xichen/MimicBrush', cache_dir='./weights', revision='v1.0.1')
+snapshot_download(repo_id="runwayml/stable-diffusion-v1-5", local_dir="./stable-diffusion-v1-5")
+snapshot_download(repo_id="runwayml/stable-diffusion-inpainting", local_dir="./stable-diffusion-inpainting")
+val_configs = OmegaConf.load('./configs/inference.yaml')
+
+# === import Depth Anything ===
+import sys
+sys.path.append("./depthanything")
+from torchvision.transforms import Compose
+from depthanything.fast_import import depth_anything_model 
+from depthanything.depth_anything.util.transform import Resize, NormalizeImage, PrepareForNet
+transform = Compose([
+    Resize(
+        width=518,
+        height=518,
+        resize_target=False,
+        keep_aspect_ratio=True,
+        ensure_multiple_of=14,
+        resize_method='lower_bound',
+        image_interpolation_method=cv2.INTER_CUBIC,
+    ),
+    NormalizeImage(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225]),
+    PrepareForNet(),
+])
+depth_anything_model.load_state_dict(torch.load(val_configs.model_path.depth_model))
+
+
+
+# === load the checkpoint ===
+base_model_path = val_configs.model_path.pretrained_imitativer_path
+vae_model_path = val_configs.model_path.pretrained_vae_name_or_path
+image_encoder_path = val_configs.model_path.image_encoder_path
+ref_model_path = val_configs.model_path.pretrained_reference_path
+mimicbrush_ckpt = val_configs.model_path.mimicbrush_ckpt_path
+device = "cuda"
+
+
+
+def pad_img_to_square(original_image, is_mask=False):
+    width, height = original_image.size
+    
+    if height == width:
+        return original_image
+    
+    if height > width:
+        padding = (height - width) // 2
+        new_size = (height, height)
+    else:
+        padding = (width - height) // 2
+        new_size = (width, width)
+    
+    if is_mask:
+        new_image = Image.new("RGB", new_size, "black")
+    else:
+        new_image = Image.new("RGB", new_size, "white")
+    
+    if height > width:
+        new_image.paste(original_image, (padding, 0))
+    else:
+        new_image.paste(original_image, (0, padding))
+    return new_image
+
+
+def collage_region(low, high, mask):
+    mask = (np.array(mask) > 128).astype(np.uint8)
+    low = np.array(low).astype(np.uint8) 
+    low = (low * 0).astype(np.uint8) 
+    high = np.array(high).astype(np.uint8)
+    mask_3 = mask 
+    collage = low * mask_3 + high * (1-mask_3)
+    collage = Image.fromarray(collage)
+    return collage
+
+
+def resize_image_keep_aspect_ratio(image, target_size = 512):
+    height, width = image.shape[:2]
+    if height > width:
+        new_height = target_size
+        new_width = int(width * (target_size / height))
+    else:
+        new_width = target_size
+        new_height = int(height * (target_size / width))
+    resized_image = cv2.resize(image, (new_width, new_height))
+    return resized_image
+
+
+def crop_padding_and_resize(ori_image, square_image):
+    ori_height, ori_width, _ = ori_image.shape
+    scale = max(ori_height / square_image.shape[0], ori_width / square_image.shape[1])
+    resized_square_image = cv2.resize(square_image, (int(square_image.shape[1] * scale), int(square_image.shape[0] * scale)))
+    padding_size = max(resized_square_image.shape[0] - ori_height, resized_square_image.shape[1] - ori_width)
+    if ori_height < ori_width:
+        top = padding_size // 2
+        bottom = resized_square_image.shape[0] - (padding_size - top)
+        cropped_image = resized_square_image[top:bottom, :,:]
+    else:
+        left = padding_size // 2
+        right = resized_square_image.shape[1] - (padding_size - left)
+        cropped_image = resized_square_image[:, left:right,:]
+    return cropped_image
+
+
+def vis_mask(image, mask):
+    # mask 3 channle 255
+    mask = mask[:,:,0]
+    mask_contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_SIMPLE)
+
+    # Draw outlines, using random colors
+    outline_opacity = 0.5
+    outline_thickness = 5
+    outline_color = np.concatenate([ [255,255,255], [outline_opacity]  ])
+
+    white_mask = np.ones_like(image) * 255
+
+    mask_bin_3 = np.stack([mask,mask,mask],-1) > 128
+    alpha = 0.5 
+    image = ( white_mask * alpha + image * (1-alpha) ) * mask_bin_3 + image * (1-mask_bin_3)
+    cv2.polylines(image, mask_contours, True, outline_color, outline_thickness, cv2.LINE_AA)
+    return image 
+
+
+
+noise_scheduler = DDIMScheduler(
+    num_train_timesteps=1000,
+    beta_start=0.00085,
+    beta_end=0.012,
+    beta_schedule="scaled_linear",
+    clip_sample=False,
+    set_alpha_to_one=False,
+    steps_offset=1,
+)
+
+vae = AutoencoderKL.from_pretrained(vae_model_path).to(dtype=torch.float16)
+unet = UNet2DConditionModel.from_pretrained(base_model_path, subfolder="unet", in_channels=13, low_cpu_mem_usage=False, ignore_mismatched_sizes=True).to(dtype=torch.float16)
+
+pipe = MimicBrushPipeline.from_pretrained(
+    base_model_path,
+    torch_dtype=torch.float16,
+    scheduler=noise_scheduler,
+    vae=vae,
+    unet=unet,
+    feature_extractor=None,
+    safety_checker=None,
+)
+
+depth_guider = DepthGuider()
+referencenet = ReferenceNet.from_pretrained(ref_model_path, subfolder="unet").to(dtype=torch.float16)
+mimicbrush_model = MimicBrush_RefNet(pipe, image_encoder_path, mimicbrush_ckpt,  depth_anything_model, depth_guider, referencenet, device)
+mask_processor = VaeImageProcessor(vae_scale_factor=1, do_normalize=False, do_binarize=True, do_convert_grayscale=True)
+
+
+def infer_single(ref_image, target_image, target_mask, seed = -1, num_inference_steps=50, guidance_scale = 5, enable_shape_control = False):
+    #return ref_image
+    """
+    mask: 0/1 1-channel  np.array
+    image: rgb           np.array
+    """
+    ref_image = ref_image.astype(np.uint8)
+    target_image = target_image.astype(np.uint8)
+    target_mask  = target_mask .astype(np.uint8)
+
+    ref_image = Image.fromarray(ref_image.astype(np.uint8)) 
+    ref_image = pad_img_to_square(ref_image)
+
+    target_image = pad_img_to_square(Image.fromarray(target_image))
+    target_image_low = target_image
+
+
+    target_mask = np.stack([target_mask,target_mask,target_mask],-1).astype(np.uint8) * 255
+    target_mask_np = target_mask.copy()
+    target_mask = Image.fromarray(target_mask) 
+    target_mask = pad_img_to_square(target_mask, True)
+
+    target_image_ori = target_image.copy()
+    target_image = collage_region(target_image_low, target_image, target_mask)
+    
+
+    depth_image = target_image_ori.copy()
+    depth_image = np.array(depth_image)
+    depth_image = transform({'image': depth_image})['image']
+    depth_image = torch.from_numpy(depth_image).unsqueeze(0) / 255
+
+    if not enable_shape_control:
+        depth_image = depth_image * 0
+
+    mask_pt = mask_processor.preprocess(target_mask, height=512, width=512)
+
+    pred, depth_pred = mimicbrush_model.generate(pil_image=ref_image, depth_image = depth_image, num_samples=1, num_inference_steps=num_inference_steps,
+                            seed=seed, image=target_image, mask_image=mask_pt, strength=1.0, guidance_scale=guidance_scale)
+
+
+    depth_pred = F.interpolate(depth_pred, size=(512,512), mode = 'bilinear', align_corners=True)[0][0]
+    depth_pred = (depth_pred - depth_pred.min()) / (depth_pred.max() - depth_pred.min()) * 255.0
+    depth_pred = depth_pred.detach().cpu().numpy().astype(np.uint8)
+    depth_pred = cv2.applyColorMap(depth_pred, cv2.COLORMAP_INFERNO)[:,:,::-1]
+
+    pred = pred[0]
+    pred = np.array(pred).astype(np.uint8)
+    return pred, depth_pred.astype(np.uint8)
+
+
+def inference_single_image(ref_image, 
+                           tar_image, 
+                           tar_mask, 
+                           ddim_steps, 
+                           scale, 
+                           seed,
+                           enable_shape_control,
+                           ):
+    if seed == -1:
+        seed = np.random.randint(10000)
+    pred, depth_pred = infer_single(ref_image, tar_image, tar_mask, seed, num_inference_steps=ddim_steps, guidance_scale = scale, enable_shape_control = enable_shape_control)
+    return pred, depth_pred
+
+
+
+def run_local(base,
+              ref,
+              *args):
+    image = base["image"].convert("RGB")
+    mask = base["mask"].convert("L")
+    image = np.asarray(image)
+    mask = np.asarray(mask)
+    mask = np.where(mask > 128, 1, 0).astype(np.uint8)
+    
+
+    ref_image = ref.convert("RGB")
+    ref_image = np.asarray(ref_image)
+
+    if mask.sum() == 0:
+        raise gr.Error('No mask for the background image.')
+    
+    mask_3 = np.stack([mask,mask,mask],-1).astype(np.uint8) * 255
+
+    mask_alpha = mask_3.copy()
+    for i in range(10):
+        mask_alpha = cv2.GaussianBlur(mask_alpha, (3, 3), 0)
+    
+    synthesis, depth_pred = inference_single_image(ref_image.copy(), image.copy(), mask.copy(), *args)
+
+
+    synthesis = crop_padding_and_resize(image, synthesis)
+    depth_pred = crop_padding_and_resize(image, depth_pred)
+
+
+    mask_3_bin = mask_alpha / 255
+    synthesis = synthesis * mask_3_bin + image * (1-mask_3_bin)
+
+    vis_source = vis_mask(image, mask_3).astype(np.uint8)
+    return [synthesis.astype(np.uint8), depth_pred.astype(np.uint8), vis_source, mask_3]
+
+
+
+with gr.Blocks() as demo:
+    with gr.Column():
+        gr.Markdown("#  MimicBrush: Zero-shot Image Editing with Reference Imitation ")
+        with gr.Row():
+            baseline_gallery = gr.Gallery(label='Output', show_label=True, elem_id="gallery", columns=1, height=768)
+            with gr.Accordion("Advanced Option", open=True):
+                num_samples = 1
+                ddim_steps = gr.Slider(label="Steps", minimum=1, maximum=100, value=50, step=1)
+                scale = gr.Slider(label="Guidance Scale", minimum=-30.0, maximum=30.0, value=5.0, step=0.1)
+                seed = gr.Slider(label="Seed", minimum=-1, maximum=999999999, step=1, value=-1)
+                enable_shape_control = gr.Checkbox(label='Keep the original shape', value=False, interactive = True)
+                
+                gr.Markdown("### Tutorial")
+                gr.Markdown("1. Upload the source image and the reference image")
+                gr.Markdown("2. Mask the to-edit region on the source image  ")
+                gr.Markdown("3. Click generate ")
+                gr.Markdown("#### You shoud click \"keep the original shape\" to conduct texture transfer  ")
+
+    
+        gr.Markdown("# Upload the source image and reference image")
+        gr.Markdown("### Tips: you could adjust the brush size by at the top right")
+
+        with gr.Row():
+            base = gr.Image(label="Background", source="upload", tool="sketch", type="pil", height=512, brush_color='#FFFFFF', mask_opacity=0.5, brush_radius = 100)
+            ref = gr.Image(label="Reference", source="upload", type="pil", height=512 )
+        run_local_button = gr.Button(label="Generate", value="Run")
+        
+
+
+    with gr.Row():
+        gr.Examples(
+        examples=[
+            [
+                './demo_example/005_source.png',
+                './demo_example/005_reference.png', 
+            ],
+            [
+                './demo_example/000_source.png',
+                './demo_example/000_reference.png', 
+            ],
+            [
+                './demo_example/001_source.png',
+                './demo_example/001_reference.png', 
+            ],
+            [
+                './demo_example/002_source.png',
+                './demo_example/002_reference.png', 
+            ],
+            [
+                './demo_example/003_source.png',
+                './demo_example/003_reference.png', 
+            ],     
+            [
+                './demo_example/004_source.png',
+                './demo_example/004_reference.png', 
+            ],
+            [
+                './demo_example/006_source.png',
+                './demo_example/006_reference.png', 
+            ],
+            [
+                './demo_example/007_source.png',
+                './demo_example/007_reference.png', 
+            ],
+        ],
+
+        inputs=[
+                base,
+                ref
+                    ],
+                cache_examples=False,
+                examples_per_page=100)
+
+
+    run_local_button.click(fn=run_local, 
+                           inputs=[base, 
+                                   ref, 
+                                   ddim_steps, 
+                                   scale, 
+                                   seed,
+                                   enable_shape_control
+                                   ], 
+                           outputs=[baseline_gallery]
+                        )
+
+demo.launch(server_name="0.0.0.0")

configs/inference.yaml

@@ -0,0 +1,18 @@
+model_path:
+  mimicbrush_ckpt_path: ./weights/mimicbrush/mimicbrush.bin
+  pretrained_reference_path: ./stable-diffusion-v1-5
+  pretrained_imitativer_path: ./stable-diffusion-inpainting
+  pretrained_vae_name_or_path: ./weights/sd-vae-ft-mse
+  image_encoder_path: ./weights/image_encoder
+  depth_model: ./weights/depth_model/depth_anything_vitb14.pth
+data_path:
+  test_set_name: Texture_Within # choose from [ PartComp_Cross, PartComp_Within, Texture_Cross, Texture_Within ] 
+  output_dir: ./test-vis
+  bench_dir_partcomp_cross: /mnt/myworkspace/new_project/MimicBrush_bench/PartComp/CrossInstance
+  bench_dir_partcomp_within: /mnt/myworkspace/new_project/MimicBrush_bench/PartComp/WithinInstance
+  bench_dir_texture_cross: /mnt/myworkspace/new_project/MimicBrush_bench/TextureTrans/CrossInstance
+  bench_dir_texture_within: /mnt/myworkspace/new_project/MimicBrush_bench/TextureTrans/WithinInstance
+
+  
+
+

data_utils.py

@@ -0,0 +1,531 @@
+import numpy as np
+import torch 
+import cv2
+import random
+from PIL import Image
+
+
+def gaussian_blure(img, intens = 5):
+    """
+    高斯模糊
+    :param image_path:
+    :intens 5,10,15,20
+    :return:
+    """
+    img = np.array(img).astype(np.uint8)
+    result = cv2.GaussianBlur(img, (0, 0), intens)
+    result = Image.fromarray(result)
+    return result
+
+def random_mask(mask):
+    h,w = mask.shape[0], mask.shape[1]
+    mask_black = np.zeros_like(mask)
+    box_w = random.uniform(0.4, 0.9) * w
+    box_h = random.uniform(0.4, 0.9) * h
+    box_w = int(box_w)
+    box_h = int(box_h)
+    y1 = random.randint(0, h - box_h)
+    y2 = y1 + box_h
+    x1 = random.randint(0, w - box_w)
+    x2 = x1 + box_w
+    mask_black[y1:y2,x1:x2] = 1
+    mask_black = mask_black.astype(np.uint8)
+    return mask_black
+
+'''
+def random_mask_grid(mask, p=0.50):
+    # 创建一个 h x w 的全零数组，作为初始掩膜
+    h,w = mask.shape[0],mask.shape[1]
+    mask = np.zeros((h, w), dtype=np.uint8)
+    n = random.choice([3,4,5,6,7,8,9,10])
+    
+    # 计算小块的大小
+    block_h = h // n
+    block_w = w // n
+    
+    # 在每个小块中以概率 p 设置为 1
+    for i in range(n):
+        for j in range(n):
+            if np.random.rand() < p:
+                mask[i*block_h:(i+1)*block_h, j*block_w:(j+1)*block_w] = 1
+    return mask
+'''
+
+def get_SIFT(image):
+    orb = cv2.ORB_create(nfeatures=200, edgeThreshold=50)
+    keypoint, descriptor = orb.detectAndCompute(image, None)
+    coordinates = [(int(kp.pt[1]), int(kp.pt[0])) for kp in keypoint]
+    return coordinates
+
+
+'''
+def random_mask_grid(mask, points_list, p=0.0):
+    # 创建一个 h x w 的全零数组，作为初始掩膜
+    h, w = mask.shape[:2]
+    mask = np.zeros((h, w), dtype=np.uint8)
+    n = random.choice([3,4,5,6,7,8,9,10])
+    
+    # 计算小块的大小
+    block_h = h // n
+    block_w = w // n
+    
+    # 统计每个小块内的点个数
+    block_counts = np.zeros((n, n), dtype=np.int32)
+    for point in points_list:
+        y, x = point
+        i = min(y // block_h, n-1)
+        j = min(x // block_w, n-1)
+        block_counts[i, j] += 1
+    
+    # 找出包含点最多的前5个小块
+    top5_blocks = np.argpartition(-block_counts.flatten(), 5)[:5]
+    
+    # 将这些小块对应的像素设为1
+    for idx in top5_blocks:
+        i, j = divmod(idx, n)
+        mask[i*block_h:(i+1)*block_h, j*block_w:(j+1)*block_w] = 1
+    
+    # 在其他小块中按照概率p设置为1
+    for i in range(n):
+        for j in range(n):
+            if (i*n + j) not in top5_blocks and np.random.rand() < p:
+                mask[i*block_h:(i+1)*block_h, j*block_w:(j+1)*block_w] = 1
+    
+    return mask
+'''
+
+def random_mask_grid(mask, points_list, p=0.50, top5_p=0.70, other_p=0.30):
+    # 创建一个 h x w 的全零数组，作为初始掩膜
+    h, w = mask.shape[:2]
+    mask = np.zeros((h, w), dtype=np.uint8)
+    n = random.choice([3,4,5,6,7,8,9,10])
+    
+    # 计算小块的大小
+    block_h = h // n
+    block_w = w // n
+    
+    # 统计每个小块内的点个数
+    block_counts = np.zeros((n, n), dtype=np.int32)
+    for point in points_list:
+        y, x = point
+        i = min(y // block_h, n-1)
+        j = min(x // block_w, n-1)
+        block_counts[i, j] += 1
+    
+    # 找出包含点最多的前5个小块
+    top5_blocks = np.argpartition(-block_counts.flatten(), 5)[:5]
+    
+    # 将这些小块对应的像素设为1
+    for idx in top5_blocks:
+        i, j = divmod(idx, n)
+        if np.random.rand() < top5_p:
+            mask[i*block_h:(i+1)*block_h, j*block_w:(j+1)*block_w] = 1
+    
+    # 在其他小块中按照概率p设置为1
+    for i in range(n):
+        for j in range(n):
+            if (i*n + j) not in top5_blocks and np.random.rand() < other_p:
+                mask[i*block_h:(i+1)*block_h, j*block_w:(j+1)*block_w] = 1
+    
+    return mask
+
+def random_perspective_transform(image, intensity):
+    """
+    对图像进行随机透视变换
+
+    参数:
+    image: 要进行变换的输入图像
+    intensity: 变换的强度,范围从0到1,值越大,变换越明显
+
+    返回值:
+    变换后的图像
+    """
+    height, width = image.shape[:2]
+
+    # 生成随机透视变换的四个目标点
+    x_offset = width * 0.4 * intensity
+    y_offset = height * 0.4 * intensity
+    dst_points = np.float32([[random.uniform(-x_offset, x_offset), random.uniform(-y_offset, y_offset)],
+                             [width - random.uniform(-x_offset, x_offset), random.uniform(-y_offset, y_offset)],
+                             [random.uniform(-x_offset, x_offset), height - random.uniform(-y_offset, y_offset)],
+                             [width - random.uniform(-x_offset, x_offset), height - random.uniform(-y_offset, y_offset)]])
+
+    # 对应的源点是图像的四个角
+    src_points = np.float32([[0, 0], [width, 0], [0, height], [width, height]])
+
+    # 生成透视变换矩阵
+    M = cv2.getPerspectiveTransform(src_points, dst_points)
+
+    # 进行透视变换
+    transformed_image = cv2.warpPerspective(image, M, (width, height))
+    mask = np.ones_like(transformed_image)
+    transformed_mask = cv2.warpPerspective(mask, M, (width, height))> 0.5
+
+    kernel_size = 5
+    kernel = np.ones((kernel_size, kernel_size), np.uint8)
+    transformed_mask = cv2.erode(transformed_mask.astype(np.uint8), kernel, iterations=1).astype(np.uint8)
+
+    white_back = np.ones_like(transformed_image) * 255
+    transformed_image = transformed_image * transformed_mask + white_back * (1-transformed_mask)
+    return transformed_image
+
+
+
+
+def mask_score(mask):
+    '''Scoring the mask according to connectivity.'''
+    mask = mask.astype(np.uint8)
+    if mask.sum() < 10:
+        return 0
+    contours, _ = cv2.findContours(mask, cv2.RETR_EXTERNAL, cv2.CHAIN_APPROX_NONE)
+    cnt_area = [cv2.contourArea(cnt) for cnt in contours]
+    conc_score = np.max(cnt_area) / sum(cnt_area)
+    return conc_score
+
+
+def sobel(img, mask, thresh = 50):
+    '''Calculating the high-frequency map.'''
+    H,W = img.shape[0], img.shape[1]
+    img = cv2.resize(img,(256,256))
+    mask = (cv2.resize(mask,(256,256)) > 0.5).astype(np.uint8)
+    kernel = np.ones((5,5),np.uint8)
+    mask = cv2.erode(mask, kernel, iterations = 2)
+    
+    Ksize = 3
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=Ksize)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=Ksize)
+    sobel_X = cv2.convertScaleAbs(sobelx)
+    sobel_Y = cv2.convertScaleAbs(sobely)
+    scharr = cv2.addWeighted(sobel_X, 0.5, sobel_Y, 0.5, 0)
+    scharr = np.max(scharr,-1) * mask    
+    
+    scharr[scharr < thresh] = 0.0
+    scharr = np.stack([scharr,scharr,scharr],-1)
+    scharr = (scharr.astype(np.float32)/255 * img.astype(np.float32) ).astype(np.uint8)
+    scharr = cv2.resize(scharr,(W,H))
+    return scharr
+
+
+def resize_and_pad(image, box):
+    '''Fitting an image to the box region while keeping the aspect ratio.'''
+    y1,y2,x1,x2 = box
+    H,W = y2-y1, x2-x1
+    h,w =  image.shape[0], image.shape[1]
+    r_box = W / H 
+    r_image = w / h
+    if r_box >= r_image:
+        h_target = H
+        w_target = int(w * H / h) 
+        image = cv2.resize(image, (w_target, h_target))
+
+        w1 = (W - w_target) // 2
+        w2 = W - w_target - w1
+        pad_param = ((0,0),(w1,w2),(0,0))
+        image = np.pad(image, pad_param, 'constant', constant_values=255)
+    else:
+        w_target = W 
+        h_target = int(h * W / w)
+        image = cv2.resize(image, (w_target, h_target))
+
+        h1 = (H-h_target) // 2 
+        h2 = H - h_target - h1
+        pad_param =((h1,h2),(0,0),(0,0))
+        image = np.pad(image, pad_param, 'constant', constant_values=255)
+    return image
+
+
+
+def expand_image_mask(image, mask, ratio=1.4, random = False):
+    # expand image and mask 
+    # pad image with 255 
+    # pad mask with 0
+    h,w = image.shape[0], image.shape[1]
+    H,W = int(h * ratio), int(w * ratio) 
+    if random:
+        h1 = np.random.randint(0, int(H - h))
+        w1 = np.random.randint(0, int(W - w)) 
+    else:
+        h1 = int((H - h) // 2)
+        w1 = int((W -w) // 2)
+    h2 = H - h - h1
+    w2 = W -w - w1
+    pad_param_image = ((h1,h2),(w1,w2),(0,0))
+    pad_param_mask = ((h1,h2),(w1,w2))
+    image = np.pad(image, pad_param_image, 'constant', constant_values=255)
+    mask = np.pad(mask, pad_param_mask, 'constant', constant_values=0)
+    return image, mask
+
+
+def resize_box(yyxx, H,W,h,w):
+    y1,y2,x1,x2 = yyxx
+    y1,y2 = int(y1/H * h), int(y2/H * h)
+    x1,x2 = int(x1/W * w), int(x2/W * w)
+    y1,y2 = min(y1,h), min(y2,h)
+    x1,x2 = min(x1,w), min(x2,w)
+    return (y1,y2,x1,x2)
+
+
+def get_bbox_from_mask(mask):
+    h,w = mask.shape[0],mask.shape[1]
+
+    if mask.sum() < 10:
+        return 0,h,0,w
+    rows = np.any(mask,axis=1)
+    cols = np.any(mask,axis=0)
+    y1,y2 = np.where(rows)[0][[0,-1]]
+    x1,x2 = np.where(cols)[0][[0,-1]]
+    return (y1,y2,x1,x2)
+
+
+def expand_bbox(mask,yyxx,ratio=[1.2,2.0], min_crop=0):
+    y1,y2,x1,x2 = yyxx
+    ratio = np.random.randint( ratio[0] * 10,  ratio[1] * 10 ) / 10
+    H,W = mask.shape[0], mask.shape[1]
+    xc, yc = 0.5 * (x1 + x2), 0.5 * (y1 + y2)
+    h = ratio * (y2-y1+1)
+    w = ratio * (x2-x1+1)
+    h = max(h,min_crop)
+    w = max(w,min_crop)
+
+    x1 = int(xc - w * 0.5)
+    x2 = int(xc + w * 0.5)
+    y1 = int(yc - h * 0.5)
+    y2 = int(yc + h * 0.5)
+
+    x1 = max(0,x1)
+    x2 = min(W,x2)
+    y1 = max(0,y1)
+    y2 = min(H,y2)
+    return (y1,y2,x1,x2)
+
+
+def box2squre(image, box):
+    H,W = image.shape[0], image.shape[1]
+    y1,y2,x1,x2 = box
+    cx = (x1 + x2) // 2
+    cy = (y1 + y2) // 2
+    h,w = y2-y1, x2-x1
+
+    if h >= w:
+        x1 = cx - h//2
+        x2 = cx + h//2
+    else:
+        y1 = cy - w//2
+        y2 = cy + w//2
+    x1 = max(0,x1)
+    x2 = min(W,x2)
+    y1 = max(0,y1)
+    y2 = min(H,y2)
+    return (y1,y2,x1,x2)
+
+
+def pad_to_square(image, pad_value = 255, random = False):
+    H,W = image.shape[0], image.shape[1]
+    if H == W:
+        return image
+
+    padd = abs(H - W)
+    if random:
+        padd_1 = int(np.random.randint(0,padd))
+    else:
+        padd_1 = int(padd / 2)
+    padd_2 = padd - padd_1
+
+    if H > W:
+        pad_param = ((0,0),(padd_1,padd_2),(0,0))
+    else:
+        pad_param = ((padd_1,padd_2),(0,0),(0,0))
+
+    image = np.pad(image, pad_param, 'constant', constant_values=pad_value)
+    return image
+
+
+
+def box_in_box(small_box, big_box):
+    y1,y2,x1,x2 = small_box
+    y1_b, _, x1_b, _ = big_box
+    y1,y2,x1,x2 = y1 - y1_b ,y2 - y1_b, x1 - x1_b ,x2 - x1_b
+    return (y1,y2,x1,x2 )
+
+
+
+def shuffle_image(image, N):
+    height, width = image.shape[:2]
+    
+    block_height = height // N
+    block_width = width // N
+    blocks = []
+    
+    for i in range(N):
+        for j in range(N):
+            block = image[i*block_height:(i+1)*block_height, j*block_width:(j+1)*block_width]
+            blocks.append(block)
+    
+    np.random.shuffle(blocks)
+    shuffled_image = np.zeros((height, width, 3), dtype=np.uint8)
+
+    for i in range(N):
+        for j in range(N):
+            shuffled_image[i*block_height:(i+1)*block_height, j*block_width:(j+1)*block_width] = blocks[i*N+j]
+    return shuffled_image
+
+
+def get_mosaic_mask(image, fg_mask, N=16, ratio = 0.5):
+    ids = [i for i in range(N * N)]
+    masked_number = int(N * N * ratio)
+    masked_id = np.random.choice(ids, masked_number, replace=False)
+    
+
+    
+    height, width = image.shape[:2]
+    mask = np.ones((height, width))
+    
+    block_height = height // N
+    block_width = width // N
+    
+    b_id = 0
+    for i in range(N):
+        for j in range(N):
+            if b_id in masked_id:
+                mask[i*block_height:(i+1)*block_height, j*block_width:(j+1)*block_width] = mask[i*block_height:(i+1)*block_height, j*block_width:(j+1)*block_width] * 0
+            b_id += 1
+    mask = mask * fg_mask
+    mask3 = np.stack([mask,mask,mask],-1).copy().astype(np.uint8)
+    noise = q_x(image)
+    noise_mask = image * mask3 + noise * (1-mask3)
+    return noise_mask
+
+def extract_canney_noise(image, mask, dilate=True):
+    h,w = image.shape[0],image.shape[1]
+    mask = cv2.resize(mask.astype(np.uint8),(w,h)) > 0.5
+    kernel = np.ones((8, 8), dtype=np.uint8)
+    mask =  cv2.erode(mask.astype(np.uint8), kernel, 10)
+
+    canny = cv2.Canny(image, 50,100) * mask
+    kernel = np.ones((8, 8), dtype=np.uint8)
+    mask = (cv2.dilate(canny, kernel, 5) > 128).astype(np.uint8)
+    mask = np.stack([mask,mask,mask],-1)
+
+    pure_noise = q_x(image, t=1) * 0 + 255
+    canny_noise = mask * image + (1-mask) * pure_noise
+    return canny_noise
+
+
+def get_random_structure(size):
+    choice = np.random.randint(1, 5)
+
+    if choice == 1:
+        return cv2.getStructuringElement(cv2.MORPH_RECT, (size, size))
+    elif choice == 2:
+        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size))
+    elif choice == 3:
+        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size, size//2))
+    elif choice == 4:
+        return cv2.getStructuringElement(cv2.MORPH_ELLIPSE, (size//2, size))
+
+def random_dilate(seg, min=3, max=10):
+    size = np.random.randint(min, max)
+    kernel = get_random_structure(size)
+    seg = cv2.dilate(seg,kernel,iterations = 1)
+    return seg
+
+def random_erode(seg, min=3, max=10):
+    size = np.random.randint(min, max)
+    kernel = get_random_structure(size)
+    seg = cv2.erode(seg,kernel,iterations = 1)
+    return seg
+
+def compute_iou(seg, gt):
+    intersection = seg*gt
+    union = seg+gt
+    return (np.count_nonzero(intersection) + 1e-6) / (np.count_nonzero(union) + 1e-6)
+
+
+def select_max_region(mask):
+    nums, labels, stats, centroids = cv2.connectedComponentsWithStats(mask, connectivity=8)
+    background = 0
+    for row in range(stats.shape[0]):
+        if stats[row, :][0] == 0 and stats[row, :][1] == 0:
+            background = row
+    stats_no_bg = np.delete(stats, background, axis=0)
+    max_idx = stats_no_bg[:, 4].argmax()
+    max_region = np.where(labels==max_idx+1, 1, 0)
+
+    return max_region.astype(np.uint8)
+
+
+
+def perturb_mask(gt, min_iou = 0.3,  max_iou = 0.99):
+    iou_target = np.random.uniform(min_iou, max_iou)
+    h, w = gt.shape
+    gt = gt.astype(np.uint8)
+    seg = gt.copy()
+    
+    # Rare case
+    if h <= 2 or w <= 2:
+        print('GT too small, returning original')
+        return seg
+
+    # Do a bunch of random operations
+    for _ in range(250):
+        for _ in range(4):
+            lx, ly = np.random.randint(w), np.random.randint(h)
+            lw, lh = np.random.randint(lx+1,w+1), np.random.randint(ly+1,h+1)
+
+            # Randomly set one pixel to 1/0. With the following dilate/erode, we can create holes/external regions
+            if np.random.rand() < 0.1:
+                cx = int((lx + lw) / 2)
+                cy = int((ly + lh) / 2)
+                seg[cy, cx] = np.random.randint(2) * 255
+
+            # Dilate/erode
+            if np.random.rand() < 0.5:
+                seg[ly:lh, lx:lw] = random_dilate(seg[ly:lh, lx:lw])
+            else:
+                seg[ly:lh, lx:lw] = random_erode(seg[ly:lh, lx:lw])
+            
+            seg = np.logical_or(seg, gt).astype(np.uint8)
+            #seg = select_max_region(seg) 
+
+        if compute_iou(seg, gt) < iou_target:
+            break
+    seg = select_max_region(seg.astype(np.uint8)) 
+    return seg.astype(np.uint8)
+
+
+def q_x(x_0,t=65):
+    '''Adding noise for and given image.'''
+    x_0 = torch.from_numpy(x_0).float() / 127.5 - 1
+    num_steps = 100
+    
+    betas = torch.linspace(-6,6,num_steps)
+    betas = torch.sigmoid(betas)*(0.5e-2 - 1e-5)+1e-5
+
+    alphas = 1-betas
+    alphas_prod = torch.cumprod(alphas,0)
+    
+    alphas_prod_p = torch.cat([torch.tensor([1]).float(),alphas_prod[:-1]],0)
+    alphas_bar_sqrt = torch.sqrt(alphas_prod)
+    one_minus_alphas_bar_log = torch.log(1 - alphas_prod)
+    one_minus_alphas_bar_sqrt = torch.sqrt(1 - alphas_prod)
+    
+    noise = torch.randn_like(x_0)
+    alphas_t = alphas_bar_sqrt[t]
+    alphas_1_m_t = one_minus_alphas_bar_sqrt[t]
+    return (alphas_t * x_0 + alphas_1_m_t * noise).numpy()  * 127.5 + 127.5 
+
+
+def extract_target_boundary(img, target_mask):
+    Ksize = 3
+    sobelx = cv2.Sobel(img, cv2.CV_64F, 1, 0, ksize=Ksize)
+    sobely = cv2.Sobel(img, cv2.CV_64F, 0, 1, ksize=Ksize)
+
+    # sobel-x
+    sobel_X = cv2.convertScaleAbs(sobelx)
+    # sobel-y
+    sobel_Y = cv2.convertScaleAbs(sobely)
+    # sobel-xy
+    scharr = cv2.addWeighted(sobel_X, 0.5, sobel_Y, 0.5, 0)
+    scharr = np.max(scharr,-1).astype(np.float32)/255
+    scharr = scharr *  target_mask.astype(np.float32)
+    return scharr

depthanything/depth_anything/blocks.py

@@ -0,0 +1,153 @@
+import torch.nn as nn
+
+
+def _make_scratch(in_shape, out_shape, groups=1, expand=False):
+    scratch = nn.Module()
+
+    out_shape1 = out_shape
+    out_shape2 = out_shape
+    out_shape3 = out_shape
+    if len(in_shape) >= 4:
+        out_shape4 = out_shape
+
+    if expand:
+        out_shape1 = out_shape
+        out_shape2 = out_shape*2
+        out_shape3 = out_shape*4
+        if len(in_shape) >= 4:
+            out_shape4 = out_shape*8
+
+    scratch.layer1_rn = nn.Conv2d(
+        in_shape[0], out_shape1, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer2_rn = nn.Conv2d(
+        in_shape[1], out_shape2, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    scratch.layer3_rn = nn.Conv2d(
+        in_shape[2], out_shape3, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+    )
+    if len(in_shape) >= 4:
+        scratch.layer4_rn = nn.Conv2d(
+            in_shape[3], out_shape4, kernel_size=3, stride=1, padding=1, bias=False, groups=groups
+        )
+
+    return scratch
+
+
+class ResidualConvUnit(nn.Module):
+    """Residual convolution module.
+    """
+
+    def __init__(self, features, activation, bn):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super().__init__()
+
+        self.bn = bn
+
+        self.groups=1
+
+        self.conv1 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+        
+        self.conv2 = nn.Conv2d(
+            features, features, kernel_size=3, stride=1, padding=1, bias=True, groups=self.groups
+        )
+
+        if self.bn==True:
+            self.bn1 = nn.BatchNorm2d(features)
+            self.bn2 = nn.BatchNorm2d(features)
+
+        self.activation = activation
+
+        self.skip_add = nn.quantized.FloatFunctional()
+
+    def forward(self, x):
+        """Forward pass.
+
+        Args:
+            x (tensor): input
+
+        Returns:
+            tensor: output
+        """
+        
+        out = self.activation(x)
+        out = self.conv1(out)
+        if self.bn==True:
+            out = self.bn1(out)
+       
+        out = self.activation(out)
+        out = self.conv2(out)
+        if self.bn==True:
+            out = self.bn2(out)
+
+        if self.groups > 1:
+            out = self.conv_merge(out)
+
+        return self.skip_add.add(out, x)
+
+
+class FeatureFusionBlock(nn.Module):
+    """Feature fusion block.
+    """
+
+    def __init__(self, features, activation, deconv=False, bn=False, expand=False, align_corners=True, size=None):
+        """Init.
+
+        Args:
+            features (int): number of features
+        """
+        super(FeatureFusionBlock, self).__init__()
+
+        self.deconv = deconv
+        self.align_corners = align_corners
+
+        self.groups=1
+
+        self.expand = expand
+        out_features = features
+        if self.expand==True:
+            out_features = features//2
+        
+        self.out_conv = nn.Conv2d(features, out_features, kernel_size=1, stride=1, padding=0, bias=True, groups=1)
+
+        self.resConfUnit1 = ResidualConvUnit(features, activation, bn)
+        self.resConfUnit2 = ResidualConvUnit(features, activation, bn)
+        
+        self.skip_add = nn.quantized.FloatFunctional()
+
+        self.size=size
+
+    def forward(self, *xs, size=None):
+        """Forward pass.
+
+        Returns:
+            tensor: output
+        """
+        output = xs[0]
+
+        if len(xs) == 2:
+            res = self.resConfUnit1(xs[1])
+            output = self.skip_add.add(output, res)
+
+        output = self.resConfUnit2(output)
+
+        if (size is None) and (self.size is None):
+            modifier = {"scale_factor": 2}
+        elif size is None:
+            modifier = {"size": self.size}
+        else:
+            modifier = {"size": size}
+
+        output = nn.functional.interpolate(
+            output, **modifier, mode="bilinear", align_corners=self.align_corners
+        )
+
+        output = self.out_conv(output)
+
+        return output

depthanything/depth_anything/dpt.py

@@ -0,0 +1,189 @@
+import argparse
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from huggingface_hub import PyTorchModelHubMixin, hf_hub_download
+import os
+from depth_anything.blocks import FeatureFusionBlock, _make_scratch
+
+
+def _make_fusion_block(features, use_bn, size = None):
+    return FeatureFusionBlock(
+        features,
+        nn.ReLU(False),
+        deconv=False,
+        bn=use_bn,
+        expand=False,
+        align_corners=True,
+        size=size,
+    )
+
+
+class DPTHead(nn.Module):
+    def __init__(self, nclass, in_channels, features=256, use_bn=False, out_channels=[256, 512, 1024, 1024], use_clstoken=False):
+        super(DPTHead, self).__init__()
+        
+        self.nclass = nclass
+        self.use_clstoken = use_clstoken
+        
+        self.projects = nn.ModuleList([
+            nn.Conv2d(
+                in_channels=in_channels,
+                out_channels=out_channel,
+                kernel_size=1,
+                stride=1,
+                padding=0,
+            ) for out_channel in out_channels
+        ])
+        
+        self.resize_layers = nn.ModuleList([
+            nn.ConvTranspose2d(
+                in_channels=out_channels[0],
+                out_channels=out_channels[0],
+                kernel_size=4,
+                stride=4,
+                padding=0),
+            nn.ConvTranspose2d(
+                in_channels=out_channels[1],
+                out_channels=out_channels[1],
+                kernel_size=2,
+                stride=2,
+                padding=0),
+            nn.Identity(),
+            nn.Conv2d(
+                in_channels=out_channels[3],
+                out_channels=out_channels[3],
+                kernel_size=3,
+                stride=2,
+                padding=1)
+        ])
+        
+        if use_clstoken:
+            self.readout_projects = nn.ModuleList()
+            for _ in range(len(self.projects)):
+                self.readout_projects.append(
+                    nn.Sequential(
+                        nn.Linear(2 * in_channels, in_channels),
+                        nn.GELU()))
+        
+        self.scratch = _make_scratch(
+            out_channels,
+            features,
+            groups=1,
+            expand=False,
+        )
+
+        self.scratch.stem_transpose = None
+        
+        self.scratch.refinenet1 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet2 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet3 = _make_fusion_block(features, use_bn)
+        self.scratch.refinenet4 = _make_fusion_block(features, use_bn)
+
+        head_features_1 = features
+        head_features_2 = 32
+        
+        if nclass > 1:
+            self.scratch.output_conv = nn.Sequential(
+                nn.Conv2d(head_features_1, head_features_1, kernel_size=3, stride=1, padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(head_features_1, nclass, kernel_size=1, stride=1, padding=0),
+            )
+        else:
+            self.scratch.output_conv1 = nn.Conv2d(head_features_1, head_features_1 // 2, kernel_size=3, stride=1, padding=1)
+            
+            self.scratch.output_conv2 = nn.Sequential(
+                nn.Conv2d(head_features_1 // 2, head_features_2, kernel_size=3, stride=1, padding=1),
+                nn.ReLU(True),
+                nn.Conv2d(head_features_2, 1, kernel_size=1, stride=1, padding=0),
+                nn.ReLU(True),
+                nn.Identity(),
+            )
+            
+    def forward(self, out_features, patch_h, patch_w):
+        out = []
+        for i, x in enumerate(out_features):
+            if self.use_clstoken:
+                x, cls_token = x[0], x[1]
+                readout = cls_token.unsqueeze(1).expand_as(x)
+                x = self.readout_projects[i](torch.cat((x, readout), -1))
+            else:
+                x = x[0]
+            
+            x = x.permute(0, 2, 1).reshape((x.shape[0], x.shape[-1], patch_h, patch_w))
+            
+            x = self.projects[i](x)
+            x = self.resize_layers[i](x)
+            
+            out.append(x)
+        
+        layer_1, layer_2, layer_3, layer_4 = out
+        
+        layer_1_rn = self.scratch.layer1_rn(layer_1)
+        layer_2_rn = self.scratch.layer2_rn(layer_2)
+        layer_3_rn = self.scratch.layer3_rn(layer_3)
+        layer_4_rn = self.scratch.layer4_rn(layer_4)
+        
+        path_4 = self.scratch.refinenet4(layer_4_rn, size=layer_3_rn.shape[2:])
+        path_3 = self.scratch.refinenet3(path_4, layer_3_rn, size=layer_2_rn.shape[2:])
+        path_2 = self.scratch.refinenet2(path_3, layer_2_rn, size=layer_1_rn.shape[2:])
+        path_1 = self.scratch.refinenet1(path_2, layer_1_rn)
+        
+        out = self.scratch.output_conv1(path_1)
+        out = F.interpolate(out, (int(patch_h * 14), int(patch_w * 14)), mode="bilinear", align_corners=True)
+        out = self.scratch.output_conv2(out)
+        
+        return out
+        
+        
+class DPT_DINOv2(nn.Module):
+    def __init__(self, encoder='vitl', features=256, out_channels=[256, 512, 1024, 1024], use_bn=False, use_clstoken=False, localhub=True):
+        super(DPT_DINOv2, self).__init__()
+        
+        assert encoder in ['vits', 'vitb', 'vitl']
+        
+        # in case the Internet connection is not stable, please load the DINOv2 locally
+        if localhub:
+            #self.pretrained = torch.hub.load('torchhub/facebookresearch_dinov2_main', 'dinov2_{:}14'.format(encoder), source='local', pretrained=False)
+            root = os.path.dirname(os.path.dirname(os.path.realpath(__file__))) 
+            self.pretrained = torch.hub.load(f'{root}/torchhub/facebookresearch_dinov2_main', 'dinov2_{:}14'.format(encoder), source='local', pretrained=False)
+        else:
+            self.pretrained = torch.hub.load('facebookresearch/dinov2', 'dinov2_{:}14'.format(encoder))
+        
+        dim = self.pretrained.blocks[0].attn.qkv.in_features
+        
+        self.depth_head = DPTHead(1, dim, features, use_bn, out_channels=out_channels, use_clstoken=use_clstoken)
+        
+    def forward(self, x):
+        h, w = x.shape[-2:]
+        
+        features = self.pretrained.get_intermediate_layers(x, 4, return_class_token=True)
+        
+        patch_h, patch_w = h // 14, w // 14
+
+        depth = self.depth_head(features, patch_h, patch_w)
+        depth = F.interpolate(depth, size=(h, w), mode="bilinear", align_corners=True)
+        depth = F.relu(depth)
+
+        return depth.squeeze(1)
+
+
+class DepthAnything(DPT_DINOv2, PyTorchModelHubMixin):
+    def __init__(self, config):
+        super().__init__(**config)
+
+
+if __name__ == '__main__':
+    parser = argparse.ArgumentParser()
+    parser.add_argument(
+        "--encoder",
+        default="vits",
+        type=str,
+        choices=["vits", "vitb", "vitl"],
+    )
+    args = parser.parse_args()
+    
+    model = DepthAnything.from_pretrained("LiheYoung/depth_anything_{:}14".format(args.encoder))
+    
+    print(model)
+    

depthanything/depth_anything/util/transform.py

@@ -0,0 +1,248 @@
+import random
+from PIL import Image, ImageOps, ImageFilter
+import torch
+from torchvision import transforms
+import torch.nn.functional as F
+
+import numpy as np
+import cv2
+import math
+
+
+def apply_min_size(sample, size, image_interpolation_method=cv2.INTER_AREA):
+    """Rezise the sample to ensure the given size. Keeps aspect ratio.
+
+    Args:
+        sample (dict): sample
+        size (tuple): image size
+
+    Returns:
+        tuple: new size
+    """
+    shape = list(sample["disparity"].shape)
+
+    if shape[0] >= size[0] and shape[1] >= size[1]:
+        return sample
+
+    scale = [0, 0]
+    scale[0] = size[0] / shape[0]
+    scale[1] = size[1] / shape[1]
+
+    scale = max(scale)
+
+    shape[0] = math.ceil(scale * shape[0])
+    shape[1] = math.ceil(scale * shape[1])
+
+    # resize
+    sample["image"] = cv2.resize(
+        sample["image"], tuple(shape[::-1]), interpolation=image_interpolation_method
+    )
+
+    sample["disparity"] = cv2.resize(
+        sample["disparity"], tuple(shape[::-1]), interpolation=cv2.INTER_NEAREST
+    )
+    sample["mask"] = cv2.resize(
+        sample["mask"].astype(np.float32),
+        tuple(shape[::-1]),
+        interpolation=cv2.INTER_NEAREST,
+    )
+    sample["mask"] = sample["mask"].astype(bool)
+
+    return tuple(shape)
+
+
+class Resize(object):
+    """Resize sample to given size (width, height).
+    """
+
+    def __init__(
+        self,
+        width,
+        height,
+        resize_target=True,
+        keep_aspect_ratio=False,
+        ensure_multiple_of=1,
+        resize_method="lower_bound",
+        image_interpolation_method=cv2.INTER_AREA,
+    ):
+        """Init.
+
+        Args:
+            width (int): desired output width
+            height (int): desired output height
+            resize_target (bool, optional):
+                True: Resize the full sample (image, mask, target).
+                False: Resize image only.
+                Defaults to True.
+            keep_aspect_ratio (bool, optional):
+                True: Keep the aspect ratio of the input sample.
+                Output sample might not have the given width and height, and
+                resize behaviour depends on the parameter 'resize_method'.
+                Defaults to False.
+            ensure_multiple_of (int, optional):
+                Output width and height is constrained to be multiple of this parameter.
+                Defaults to 1.
+            resize_method (str, optional):
+                "lower_bound": Output will be at least as large as the given size.
+                "upper_bound": Output will be at max as large as the given size. (Output size might be smaller than given size.)
+                "minimal": Scale as least as possible.  (Output size might be smaller than given size.)
+                Defaults to "lower_bound".
+        """
+        self.__width = width
+        self.__height = height
+
+        self.__resize_target = resize_target
+        self.__keep_aspect_ratio = keep_aspect_ratio
+        self.__multiple_of = ensure_multiple_of
+        self.__resize_method = resize_method
+        self.__image_interpolation_method = image_interpolation_method
+
+    def constrain_to_multiple_of(self, x, min_val=0, max_val=None):
+        y = (np.round(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if max_val is not None and y > max_val:
+            y = (np.floor(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        if y < min_val:
+            y = (np.ceil(x / self.__multiple_of) * self.__multiple_of).astype(int)
+
+        return y
+
+    def get_size(self, width, height):
+        # determine new height and width
+        scale_height = self.__height / height
+        scale_width = self.__width / width
+
+        if self.__keep_aspect_ratio:
+            if self.__resize_method == "lower_bound":
+                # scale such that output size is lower bound
+                if scale_width > scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "upper_bound":
+                # scale such that output size is upper bound
+                if scale_width < scale_height:
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            elif self.__resize_method == "minimal":
+                # scale as least as possbile
+                if abs(1 - scale_width) < abs(1 - scale_height):
+                    # fit width
+                    scale_height = scale_width
+                else:
+                    # fit height
+                    scale_width = scale_height
+            else:
+                raise ValueError(
+                    f"resize_method {self.__resize_method} not implemented"
+                )
+
+        if self.__resize_method == "lower_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, min_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, min_val=self.__width
+            )
+        elif self.__resize_method == "upper_bound":
+            new_height = self.constrain_to_multiple_of(
+                scale_height * height, max_val=self.__height
+            )
+            new_width = self.constrain_to_multiple_of(
+                scale_width * width, max_val=self.__width
+            )
+        elif self.__resize_method == "minimal":
+            new_height = self.constrain_to_multiple_of(scale_height * height)
+            new_width = self.constrain_to_multiple_of(scale_width * width)
+        else:
+            raise ValueError(f"resize_method {self.__resize_method} not implemented")
+
+        return (new_width, new_height)
+
+    def __call__(self, sample):
+        width, height = self.get_size(
+            sample["image"].shape[1], sample["image"].shape[0]
+        )
+
+        # resize sample
+        sample["image"] = cv2.resize(
+            sample["image"],
+            (width, height),
+            interpolation=self.__image_interpolation_method,
+        )
+
+        if self.__resize_target:
+            if "disparity" in sample:
+                sample["disparity"] = cv2.resize(
+                    sample["disparity"],
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+
+            if "depth" in sample:
+                sample["depth"] = cv2.resize(
+                    sample["depth"], (width, height), interpolation=cv2.INTER_NEAREST
+                )
+
+            if "semseg_mask" in sample:
+                # sample["semseg_mask"] = cv2.resize(
+                #     sample["semseg_mask"], (width, height), interpolation=cv2.INTER_NEAREST
+                # )
+                sample["semseg_mask"] = F.interpolate(torch.from_numpy(sample["semseg_mask"]).float()[None, None, ...], (height, width), mode='nearest').numpy()[0, 0]
+                
+            if "mask" in sample:
+                sample["mask"] = cv2.resize(
+                    sample["mask"].astype(np.float32),
+                    (width, height),
+                    interpolation=cv2.INTER_NEAREST,
+                )
+                # sample["mask"] = sample["mask"].astype(bool)
+
+        # print(sample['image'].shape, sample['depth'].shape)
+        return sample
+
+
+class NormalizeImage(object):
+    """Normlize image by given mean and std.
+    """
+
+    def __init__(self, mean, std):
+        self.__mean = mean
+        self.__std = std
+
+    def __call__(self, sample):
+        sample["image"] = (sample["image"] - self.__mean) / self.__std
+
+        return sample
+
+
+class PrepareForNet(object):
+    """Prepare sample for usage as network input.
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        image = np.transpose(sample["image"], (2, 0, 1))
+        sample["image"] = np.ascontiguousarray(image).astype(np.float32)
+
+        if "mask" in sample:
+            sample["mask"] = sample["mask"].astype(np.float32)
+            sample["mask"] = np.ascontiguousarray(sample["mask"])
+        
+        if "depth" in sample:
+            depth = sample["depth"].astype(np.float32)
+            sample["depth"] = np.ascontiguousarray(depth)
+            
+        if "semseg_mask" in sample:
+            sample["semseg_mask"] = sample["semseg_mask"].astype(np.float32)
+            sample["semseg_mask"] = np.ascontiguousarray(sample["semseg_mask"])
+
+        return sample

depthanything/fast_import.py

@@ -0,0 +1,13 @@
+from depth_anything.dpt import DepthAnything
+import numpy as np
+import cv2
+import torch
+from torchvision.transforms import Compose
+
+model_configs = {
+    'vitl': {'encoder': 'vitl', 'features': 256, 'out_channels': [256, 512, 1024, 1024]},
+    'vitb': {'encoder': 'vitb', 'features': 128, 'out_channels': [96, 192, 384, 768]},
+    'vits': {'encoder': 'vits', 'features': 64, 'out_channels': [48, 96, 192, 384]}
+}
+encoder = 'vitb' # or 'vitb', 'vits'
+depth_anything_model = DepthAnything(model_configs[encoder]).cuda().eval()

depthanything/torchhub/README.md

@@ -0,0 +1,3 @@
+# Local PyTorch Hub
+
+This directory is for loading the DINOv2 encoder locally in case of no Internet connection.

depthanything/torchhub/facebookresearch_dinov2_main/CODE_OF_CONDUCT.md

@@ -0,0 +1,80 @@
+# Code of Conduct
+
+## Our Pledge
+
+In the interest of fostering an open and welcoming environment, we as
+contributors and maintainers pledge to make participation in our project and
+our community a harassment-free experience for everyone, regardless of age, body
+size, disability, ethnicity, sex characteristics, gender identity and expression,
+level of experience, education, socio-economic status, nationality, personal
+appearance, race, religion, or sexual identity and orientation.
+
+## Our Standards
+
+Examples of behavior that contributes to creating a positive environment
+include:
+
+* Using welcoming and inclusive language
+* Being respectful of differing viewpoints and experiences
+* Gracefully accepting constructive criticism
+* Focusing on what is best for the community
+* Showing empathy towards other community members
+
+Examples of unacceptable behavior by participants include:
+
+* The use of sexualized language or imagery and unwelcome sexual attention or
+advances
+* Trolling, insulting/derogatory comments, and personal or political attacks
+* Public or private harassment
+* Publishing others' private information, such as a physical or electronic
+address, without explicit permission
+* Other conduct which could reasonably be considered inappropriate in a
+professional setting
+
+## Our Responsibilities
+
+Project maintainers are responsible for clarifying the standards of acceptable
+behavior and are expected to take appropriate and fair corrective action in
+response to any instances of unacceptable behavior.
+
+Project maintainers have the right and responsibility to remove, edit, or
+reject comments, commits, code, wiki edits, issues, and other contributions
+that are not aligned to this Code of Conduct, or to ban temporarily or
+permanently any contributor for other behaviors that they deem inappropriate,
+threatening, offensive, or harmful.
+
+## Scope
+
+This Code of Conduct applies within all project spaces, and it also applies when
+an individual is representing the project or its community in public spaces.
+Examples of representing a project or community include using an official
+project e-mail address, posting via an official social media account, or acting
+as an appointed representative at an online or offline event. Representation of
+a project may be further defined and clarified by project maintainers.
+
+This Code of Conduct also applies outside the project spaces when there is a
+reasonable belief that an individual's behavior may have a negative impact on
+the project or its community.
+
+## Enforcement
+
+Instances of abusive, harassing, or otherwise unacceptable behavior may be
+reported by contacting the project team at <[email protected]>. All
+complaints will be reviewed and investigated and will result in a response that
+is deemed necessary and appropriate to the circumstances. The project team is
+obligated to maintain confidentiality with regard to the reporter of an incident.
+Further details of specific enforcement policies may be posted separately.
+
+Project maintainers who do not follow or enforce the Code of Conduct in good
+faith may face temporary or permanent repercussions as determined by other
+members of the project's leadership.
+
+## Attribution
+
+This Code of Conduct is adapted from the [Contributor Covenant][homepage], version 1.4,
+available at https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
+
+[homepage]: https://www.contributor-covenant.org
+
+For answers to common questions about this code of conduct, see
+https://www.contributor-covenant.org/faq

depthanything/torchhub/facebookresearch_dinov2_main/CONTRIBUTING.md

@@ -0,0 +1,31 @@
+# Contributing to DINOv2
+We want to make contributing to this project as easy and transparent as
+possible.
+
+## Pull Requests
+We actively welcome your pull requests.
+
+1. Fork the repo and create your branch from `main`.
+2. If you've added code that should be tested, add tests.
+3. If you've changed APIs, update the documentation.
+4. Ensure the test suite passes.
+5. Make sure your code lints.
+6. If you haven't already, complete the Contributor License Agreement ("CLA").
+
+## Contributor License Agreement ("CLA")
+In order to accept your pull request, we need you to submit a CLA. You only need
+to do this once to work on any of Meta's open source projects.
+
+Complete your CLA here: <https://code.facebook.com/cla>
+
+## Issues
+We use GitHub issues to track public bugs. Please ensure your description is
+clear and has sufficient instructions to be able to reproduce the issue.
+
+Meta has a [bounty program](https://www.facebook.com/whitehat/) for the safe
+disclosure of security bugs. In those cases, please go through the process
+outlined on that page and do not file a public issue.
+
+## License
+By contributing to DINOv2, you agree that your contributions will be licensed
+under the LICENSE file in the root directory of this source tree.

depthanything/torchhub/facebookresearch_dinov2_main/LICENSE

@@ -0,0 +1,400 @@
+
+Attribution-NonCommercial 4.0 International
+
+=======================================================================
+
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+does not provide legal services or legal advice. Distribution of
+Creative Commons public licenses does not create a lawyer-client or
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+information available on an "as-is" basis. Creative Commons gives no
+warranties regarding its licenses, any material licensed under their
+terms and conditions, or any related information. Creative Commons
+disclaims all liability for damages resulting from their use to the
+fullest extent possible.
+
+Using Creative Commons Public Licenses
+
+Creative Commons public licenses provide a standard set of terms and
+conditions that creators and other rights holders may use to share
+original works of authorship and other material subject to copyright
+and certain other rights specified in the public license below. The
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+
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depthanything/torchhub/facebookresearch_dinov2_main/MODEL_CARD.md

@@ -0,0 +1,201 @@
+# Model Card for DINOv2-S/B/L/g
+
+These are Vision Transformer models trained following the method described in the paper:
+"DINOv2: Learning Robust Visual Features without Supervision"
+
+We provide 4 models: 1 ViT-g trained from scratch, and 3 ViT-S/B/L models distilled from the ViT-g.
+
+## Model Details
+The model takes an image as input and returns a class token and patch tokens.
+
+The embedding dimension is: 
+- 384 for ViT-S.
+- 768 for ViT-B.
+- 1024 for ViT-L.
+- 1536 for ViT-g.
+
+The models follow a Transformer architecture, with a patch size of 14.
+
+For a 224x224 image, this results in 1 class token + 256 patch tokens.
+
+The models can accept larger images provided the image shapes are multiples of the patch size (14). 
+If this condition is not verified, the model will crop to the closest smaller multiple of the patch size.
+
+### Model Description
+
+- **Developed by:** Meta AI
+- **Model type:** Vision Transformer
+- **License:** CC-BY-NC
+
+- **Repository:** https://github.com/facebookresearch/dinov2
+- **Paper:** https://arxiv.org/abs/2304.07193
+- **Demo:** https://dinov2.metademolab.com/
+
+## Uses
+
+The models are vision backbones providing multi-purpose features for downstream tasks.
+
+### Direct Use
+
+The models can be used without fine-tuning, with downstream classifiers as simple as linear layers, to obtain competitive results:
+- on depth estimation, semantic segmentation, using linear layers.
+- on image classification, using k-NN classifiers on the class token.
+- on image classification, with logistic regression classifiers applied on the class token.
+- on image classification, with a linear layer applied on the class token and the average of the patch tokens.
+- on image retrieval using nearest neighbors.
+
+### Downstream Use
+
+It is technically possible to perform fine-tuning on the models, for small gains (we measured +2% on ImageNet-1k classification). 
+We recommend keeping this as a very last step and only when necessary, as the features already provide good performance out-of-the-box.
+
+## Bias, Risks, and Limitations
+
+Despite improvements thanks to the training method not using annotations, we still observe significant biases in our models toward rich households from Western countries.
+
+### Recommendations
+
+We expect fine-tuning will increase the biases in the features produced by the model as they will be tuned to the fine-tuning labels.
+
+## How to Get Started with the Model
+
+Use the code below to get started with the model.
+
+```python
+import torch
+dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
+dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14')
+dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14')
+dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14')
+```
+
+## Training Details
+
+### Training Data
+
+- **Training data:** LVD-142M (see paper)
+- **Training regime:** fp16 using PyTorch-FSDP mixed-precision.
+
+### Training Procedure 
+
+- **Training objective:**
+  - DINO self-distillation loss with multi-crop
+  - iBOT masked-image modeling loss
+  - KoLeo regularization on [CLS] tokens
+- **Architectures:**
+  - ViT-S (21M params): Patch size 14, embedding dimension 384, 6 heads, MLP FFN
+  - ViT-B (86M params): Patch size 14, embedding dimension 768, 12 heads, MLP FFN
+  - ViT-L (0.3B params): Patch size 14, embedding dimension 1024, 16 heads, MLP FFN
+  - ViT-g (1.1B params): Patch size 14, embedding dimension 1536, 24 heads, SwiGLU FFN
+- **Distillation:**
+  - Distillation follows the standard DINOv2 pretraining procedure, except the teacher is a pretrained ViT-g, frozen.
+
+## Evaluation
+
+We refer users to the associated paper for the evaluation protocols.
+
+<table>
+  <tr>
+    <th>model</th>
+    <th colspan="3">ImageNet-1k</th>
+    <th>NYU-Depth v2</th>
+    <th>SUN-RGBD</th>
+    <th>ADE20k</th>
+    <th>iNaturalist 2018</th>
+    <th>Oxford-H</th>
+  </tr>
+  <tr>
+    <th rowspan="2">task</th>
+    <th>classif. (acc)</th>
+    <th>classif. (acc)</th>
+    <th>classif. V2 (acc)</th>
+    <th>depth (RMSE)</th>
+    <th>depth (RMSE)</th>
+    <th>segm. (mAP)</th>
+    <th>classif. (acc)</th>
+    <th>retrieval (mAP)</th>
+  </tr>
+  <tr>
+    <!-- <th>^</th> -->
+    <th>k-NN</th>
+    <th>linear</th>
+    <th>linear</th>
+    <th>linear<br />4 layers</th>
+    <th>NYU-D transfer</th>
+    <th>multiscale</th>
+    <th>linear</th>
+    <th>nearest neighbor</th>
+  </tr>
+  <tr>
+    <td>ViT-S/14</td>
+    <td align="right">79.0%</td>
+    <td align="right">81.1%</td>
+    <td align="right">70.8%</td> 
+    <td align="right">0.417</td> 
+    <td align="right">0.431</td> 
+    <td align="right">47.2</td> 
+    <td align="right">69.5%</td> 
+    <td align="right">43.2</td> 
+  </tr>
+  <tr>
+    <td>ViT-B/14</td>
+    <td align="right">82.1%</td>
+    <td align="right">84.5%</td>
+    <td align="right">74.9%</td>
+    <td align="right">0.362</td> 
+    <td align="right">0.400</td> 
+    <td align="right">51.3</td> 
+    <td align="right">76.3%</td> 
+    <td align="right">49.5</td> 
+  </tr>
+  <tr>
+    <td>ViT-L/14</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.3%</td>
+    <td align="right">77.6%</td>
+    <td align="right">0.333</td> 
+    <td align="right">0.396</td> 
+    <td align="right">53.1</td> 
+    <td align="right">79.8%</td> 
+    <td align="right">54.0</td> 
+  </tr>
+  <tr>
+    <td>ViT-g/14</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.5%</td>
+    <td align="right">78.4%</td>
+    <td align="right">0.298</td> 
+    <td align="right">0.362</td> 
+    <td align="right">53.0</td> 
+    <td align="right">81.6%</td> 
+    <td align="right">52.3</td> 
+  </tr>
+</table>
+
+## Environmental Impact
+
+- **Hardware Type:** Nvidia A100
+- **Hours used:** 22,000 for ViT-g, 4,500 for ViT-S distillation, 5,300 for ViT-B distillation, 8,000 for ViT-L distillation
+- **Cloud Provider:** Private infra
+- **Compute Region:** USA
+- **Carbon Emitted:** 7t CO2eq
+
+#### Hardware
+
+Nvidia A100 GPUs
+
+#### Software
+
+PyTorch 2.0,
+xFormers 0.0.18
+
+**BibTeX**
+
+```
+@misc{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
+  journal={arXiv:2304.07193},
+  year={2023}
+}
+```

depthanything/torchhub/facebookresearch_dinov2_main/README.md

@@ -0,0 +1,277 @@
+# DINOv2: Learning Robust Visual Features without Supervision
+
+**[Meta AI Research, FAIR](https://ai.facebook.com/research/)**
+
+Maxime Oquab,
+Timothée Darcet,
+Théo Moutakanni,
+Huy V. Vo,
+Marc Szafraniec,
+Vasil Khalidov,
+Patrick Labatut,
+Armand Joulin,
+Piotr Bojanowski
+
+[[`Paper`](https://arxiv.org/abs/2304.07193)] [[`Blog`](https://ai.facebook.com/blog/dino-v2-computer-vision-self-supervised-learning/)] [[`Demo`](https://dinov2.metademolab.com)] [[`BibTeX`](#citing-dinov2)]
+
+PyTorch implementation and pretrained models for DINOv2. For details, see the paper: **[DINOv2: Learning Robust Visual Features without Supervision](https://arxiv.org/abs/2304.07193)**.
+
+DINOv2 models produce high-performance visual features that can be directly employed with classifiers as simple as linear layers on a variety of computer vision tasks; these visual features are robust and perform well across domains without any requirement for fine-tuning. The models were pretrained on a dataset of 142 M images without using any labels or annotations.
+
+https://github.com/facebookresearch/dinov2/assets/60359573/f168823e-7922-415a-b429-578badf5c356
+
+<div align="center">
+  Visualization of the three first principal components of the patch features of all frames, mapped to RGB values.
+</div>
+
+## Pretrained models
+
+<table style="margin: auto">
+  <tr>
+    <th>model</th>
+    <th># of<br />params</th>
+    <th>ImageNet<br />k-NN</th>
+    <th>ImageNet<br />linear</th>
+    <th>download</th>
+  </tr>
+  <tr>
+    <td>ViT-S/14 distilled</td>
+    <td align="right">21 M</td>
+    <td align="right">79.0%</td>
+    <td align="right">81.1%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_pretrain.pth">backbone only</a></td>
+  </tr>
+  <tr>
+    <td>ViT-B/14 distilled</td>
+    <td align="right">86 M</td>
+    <td align="right">82.1%</td>
+    <td align="right">84.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_pretrain.pth">backbone only</a></td>
+  </tr>
+  <tr>
+    <td>ViT-L/14 distilled</td>
+    <td align="right">300 M</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.3%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_pretrain.pth">backbone only</a></td>
+  </tr>
+  <tr>
+    <td>ViT-g/14</td>
+    <td align="right">1,100 M</td>
+    <td align="right">83.5%</td>
+    <td align="right">86.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth">backbone only</a></td>
+  </tr>
+</table>
+
+### Pretrained models via PyTorch Hub
+
+Please follow the instructions [here](https://pytorch.org/get-started/locally/) to install PyTorch (the only required dependency for loading the model). Installing PyTorch with CUDA support is strongly recommended.
+
+A corresponding [model card](MODEL_CARD.md) is included in the repository.
+
+```python
+import torch
+
+dinov2_vits14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vits14')
+dinov2_vitb14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitb14')
+dinov2_vitl14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitl14')
+dinov2_vitg14 = torch.hub.load('facebookresearch/dinov2', 'dinov2_vitg14')
+```
+
+## Installation
+
+The training and evaluation code requires PyTorch 2.0 and [xFormers](https://github.com/facebookresearch/xformers) 0.0.18 as well as a number of other 3rd party packages. Note that the code has only been tested with the specified versions and also expects a Linux environment. To setup all the required dependencies for training and evaluation, please follow the instructions below:
+
+*[conda](https://docs.conda.io/projects/conda/en/latest/user-guide/getting-started.html)* **(Recommended)** - Clone the repository and then create and activate a `dinov2` conda environment using the provided environment definition:
+
+```shell
+conda env create -f conda.yaml
+conda activate dinov2
+```
+
+*[pip](https://pip.pypa.io/en/stable/getting-started/)* - Clone the repository and then use the provided `requirements.txt` to install the dependencies:
+
+```shell
+pip install -r requirements.txt
+```
+
+## Data preparation
+
+### ImageNet-1k
+
+The root directory of the dataset should hold the following contents:
+
+- `<ROOT>/test/ILSVRC2012_test_00000001.JPEG`
+- `<ROOT>/test/[..]`
+- `<ROOT>/test/ILSVRC2012_test_00100000.JPEG`
+- `<ROOT>/train/n01440764/n01440764_10026.JPEG`
+- `<ROOT>/train/[...]`
+- `<ROOT>/train/n15075141/n15075141_9993.JPEG`
+- `<ROOT>/val/n01440764/ILSVRC2012_val_00000293.JPEG`
+- `<ROOT>/val/[...]`
+- `<ROOT>/val/n15075141/ILSVRC2012_val_00049174.JPEG`
+- `<ROOT>/labels.txt`
+
+The provided dataset implementation expects a few additional metadata files to be present under the extra directory:
+
+- `<EXTRA>/class-ids-TRAIN.npy`
+- `<EXTRA>/class-ids-VAL.npy`
+- `<EXTRA>/class-names-TRAIN.npy`
+- `<EXTRA>/class-names-VAL.npy`
+- `<EXTRA>/entries-TEST.npy`
+- `<EXTRA>/entries-TRAIN.npy`
+- `<EXTRA>/entries-VAL.npy`
+
+These metadata files can be generated (once) with the following lines of Python code:
+
+```python
+from dinov2.data.datasets import ImageNet
+
+for split in ImageNet.Split:
+    dataset = ImageNet(split=split, root="<ROOT>", extra="<EXTRA>")
+    dataset.dump_extra()
+```
+
+Note that the root and extra directories do not have to be distinct directories.
+
+### ImageNet-22k
+
+Please adapt the [dataset class](dinov2/data/datasets/image_net_22k.py) to match your local setup.
+
+<br />
+
+:warning: To execute the commands provided in the next sections for training and evaluation, the `dinov2` package should be included in the Python module search path, i.e. simply prefix the command to run with `PYTHONPATH=.`.
+
+## Training
+
+### Fast setup: training DINOv2 ViT-L/16 on ImageNet-1k
+
+Run DINOv2 training on 4 A100-80GB nodes (32 GPUs) in a SLURM cluster environment with submitit:
+
+```shell
+python dinov2/run/train/train.py \
+    --nodes 4 \
+    --config-file dinov2/configs/train/vitl16_short.yaml \
+    --output-dir <PATH/TO/OUTPUT/DIR> \
+    train.dataset_path=ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+Training time is approximately 1 day and the resulting checkpoint should reach 81.6% on k-NN eval and 82.9% on linear eval.
+
+The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation.
+
+### Long setup: training DINOv2 ViT-L/14 on ImageNet-22k
+
+Run DINOv2 training on 12 A100-80GB nodes (96 GPUs) in a SLURM cluster environment with submitit:
+
+```shell
+python dinov2/run/train/train.py \
+    --nodes 12 \
+    --config-file dinov2/configs/train/vitl14.yaml \
+    --output-dir <PATH/TO/OUTPUT/DIR> \
+    train.dataset_path=ImageNet22k:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+Training time is approximately 3.3 days and the resulting checkpoint should reach 82.0% on k-NN eval and 84.5% on linear eval.
+
+The training code saves the weights of the teacher in the `eval` folder every 12500 iterations for evaluation.
+
+
+## Evaluation
+
+The training code regularly saves the teacher weights. In order to evaluate the model, run the following evaluation on a single node:
+
+### k-NN classification on ImageNet-1k
+
+```shell
+python dinov2/run/eval/knn.py \
+    --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
+    --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
+    --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/knn \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+### Logistic regression classification on ImageNet-1k
+
+```shell
+python dinov2/run/eval/log_regression.py \
+    --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
+    --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
+    --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/logreg \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+### Linear classification with data augmentation on ImageNet-1k
+
+```shell
+python dinov2/run/eval/linear.py \
+    --config-file <PATH/TO/OUTPUT/DIR>/config.yaml \
+    --pretrained-weights <PATH/TO/OUTPUT/DIR>/eval/training_24999/teacher_checkpoint.pth \
+    --output-dir <PATH/TO/OUTPUT/DIR>/eval/training_24999/linear \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+We release the weights from evaluating the different models:
+
+<table style="margin: auto">
+  <tr>
+    <th>model</th>
+    <th>ImageNet<br />top-1</th>
+    <th>linear evaluation</th>
+  </tr>
+  <tr>
+    <td>ViT-S/14 distilled</td>
+    <td align="right">81.1%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vits14/dinov2_vits14_linear_head.pth">linear head weights</a></td>
+  </tr>
+  <tr>
+    <td>ViT-B/14 distilled</td>
+    <td align="right">84.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitb14/dinov2_vitb14_linear_head.pth">linear head weights</a></td>
+  </tr>
+  <tr>
+    <td>ViT-L/14 distilled</td>
+    <td align="right">86.3%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitl14/dinov2_vitl14_linear_head.pth">linear head weights</a></td>
+  </tr>
+  <tr>
+    <td>ViT-g/14</td>
+    <td align="right">86.5%</td>
+    <td><a href="https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_linear_head.pth">linear head weights</a></td>
+  </tr>
+</table>
+
+The performance of the provided pretrained model weights can be evaluated as follows on ImageNet-1k:
+
+```shell
+python dinov2/run/eval/linear.py \
+    --config-file dinov2/configs/eval/vitg14_pretrain.yaml \
+    --pretrained-weights https://dl.fbaipublicfiles.com/dinov2/dinov2_vitg14/dinov2_vitg14_pretrain.pth \
+    --train-dataset ImageNet:split=TRAIN:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET> \
+    --val-dataset ImageNet:split=VAL:root=<PATH/TO/DATASET>:extra=<PATH/TO/DATASET>
+```
+
+## License
+
+DINOv2 code and model weights are released under the CC-BY-NC 4.0 license. See [LICENSE](LICENSE) for additional details.
+
+## Contributing
+
+See [contributing](CONTRIBUTING.md) and the [code of conduct](CODE_OF_CONDUCT.md).
+
+## Citing DINOv2
+
+If you find this repository useful, please consider giving a star :star: and citation :t-rex::
+
+```
+@misc{oquab2023dinov2,
+  title={DINOv2: Learning Robust Visual Features without Supervision},
+  author={Oquab, Maxime and Darcet, Timothée and Moutakanni, Theo and Vo, Huy V. and Szafraniec, Marc and Khalidov, Vasil and Fernandez, Pierre and Haziza, Daniel and Massa, Francisco and El-Nouby, Alaaeldin and Howes, Russell and Huang, Po-Yao and Xu, Hu and Sharma, Vasu and Li, Shang-Wen and Galuba, Wojciech and Rabbat, Mike and Assran, Mido and Ballas, Nicolas and Synnaeve, Gabriel and Misra, Ishan and Jegou, Herve and Mairal, Julien and Labatut, Patrick and Joulin, Armand and Bojanowski, Piotr},
+  journal={arXiv:2304.07193},
+  year={2023}
+}
+```

depthanything/torchhub/facebookresearch_dinov2_main/conda.yaml

@@ -0,0 +1,22 @@
+name: dinov2
+channels:
+  - defaults
+  - pytorch
+  - nvidia
+  - xformers
+  - conda-forge
+dependencies:
+  - python=3.9
+  - pytorch::pytorch=2.0.0
+  - pytorch::pytorch-cuda=11.7.0
+  - pytorch::torchvision=0.15.0
+  - omegaconf
+  - torchmetrics=0.10.3
+  - fvcore
+  - iopath
+  - xformers::xformers=0.0.18
+  - pip
+  - pip:
+    - git+https://github.com/facebookincubator/submitit
+    - --extra-index-url https://pypi.nvidia.com
+    - cuml-cu11

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/__init__.py

@@ -0,0 +1,7 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+__version__ = "0.0.1"

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/__init__.py

@@ -0,0 +1,23 @@
+# Copyright (c) Meta Platforms, Inc. and affiliates.
+# All rights reserved.
+#
+# This source code is licensed under the license found in the
+# LICENSE file in the root directory of this source tree.
+
+import pathlib
+
+from omegaconf import OmegaConf
+
+
+def load_config(config_name: str):
+    config_filename = config_name + ".yaml"
+    return OmegaConf.load(pathlib.Path(__file__).parent.resolve() / config_filename)
+
+
+dinov2_default_config = load_config("ssl_default_config")
+
+
+def load_and_merge_config(config_name: str):
+    default_config = OmegaConf.create(dinov2_default_config)
+    loaded_config = load_config(config_name)
+    return OmegaConf.merge(default_config, loaded_config)

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitb14_pretrain.yaml

@@ -0,0 +1,6 @@
+student:
+  arch: vit_base
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitg14_pretrain.yaml

@@ -0,0 +1,7 @@
+student:
+  arch: vit_giant2
+  patch_size: 14
+  ffn_layer: swiglufused
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vitl14_pretrain.yaml

@@ -0,0 +1,6 @@
+student:
+  arch: vit_large
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/eval/vits14_pretrain.yaml

@@ -0,0 +1,6 @@
+student:
+  arch: vit_small
+  patch_size: 14
+crops:
+  global_crops_size: 518  # this is to set up the position embeddings properly
+  local_crops_size: 98

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/ssl_default_config.yaml

@@ -0,0 +1,115 @@
+MODEL:
+  WEIGHTS: ''
+compute_precision:
+  grad_scaler: true
+  teacher:
+    backbone:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    dino_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    ibot_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+  student:
+    backbone:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp16
+        buffer_dtype: fp32
+    dino_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp32
+        buffer_dtype: fp32
+    ibot_head:
+      sharding_strategy: SHARD_GRAD_OP
+      mixed_precision:
+        param_dtype: fp16
+        reduce_dtype: fp32
+        buffer_dtype: fp32
+dino:
+  loss_weight: 1.0
+  head_n_prototypes: 65536
+  head_bottleneck_dim: 256
+  head_nlayers: 3
+  head_hidden_dim: 2048
+  koleo_loss_weight: 0.1
+ibot:
+  loss_weight: 1.0
+  mask_sample_probability: 0.5
+  mask_ratio_min_max:
+  - 0.1
+  - 0.5
+  separate_head: false
+  head_n_prototypes: 65536
+  head_bottleneck_dim: 256
+  head_nlayers: 3
+  head_hidden_dim: 2048
+train:
+  batch_size_per_gpu: 64
+  dataset_path: ImageNet:split=TRAIN
+  output_dir: .
+  saveckp_freq: 20
+  seed: 0
+  num_workers: 10
+  OFFICIAL_EPOCH_LENGTH: 1250
+  cache_dataset: true
+  centering: "centering" # or "sinkhorn_knopp"
+student:
+  arch: vit_large
+  patch_size: 16
+  drop_path_rate: 0.3
+  layerscale: 1.0e-05
+  drop_path_uniform: true
+  pretrained_weights: ''
+  ffn_layer: "mlp"
+  block_chunks: 0
+  qkv_bias: true
+  proj_bias: true
+  ffn_bias: true
+teacher:
+  momentum_teacher: 0.992
+  final_momentum_teacher: 1
+  warmup_teacher_temp: 0.04
+  teacher_temp: 0.07
+  warmup_teacher_temp_epochs: 30
+optim:
+  epochs: 100
+  weight_decay: 0.04
+  weight_decay_end: 0.4
+  base_lr: 0.004  # learning rate for a batch size of 1024
+  lr: 0.  # will be set after applying scaling rule
+  warmup_epochs: 10
+  min_lr: 1.0e-06
+  clip_grad: 3.0
+  freeze_last_layer_epochs: 1
+  scaling_rule: sqrt_wrt_1024
+  patch_embed_lr_mult: 0.2
+  layerwise_decay: 0.9
+  adamw_beta1: 0.9
+  adamw_beta2: 0.999
+crops:
+  global_crops_scale:
+  - 0.32
+  - 1.0
+  local_crops_number: 8
+  local_crops_scale:
+  - 0.05
+  - 0.32
+  global_crops_size: 224
+  local_crops_size: 96
+evaluation:
+  eval_period_iterations: 12500

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitg14.yaml

@@ -0,0 +1,26 @@
+dino:
+  head_n_prototypes: 131072
+  head_bottleneck_dim: 384
+ibot:
+  separate_head: true
+  head_n_prototypes: 131072
+train:
+  batch_size_per_gpu: 12
+  dataset_path: ImageNet22k
+  centering: sinkhorn_knopp
+student:
+  arch: vit_giant2
+  patch_size: 14
+  drop_path_rate: 0.4
+  ffn_layer: swiglufused
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.994
+optim:
+  epochs: 500
+  weight_decay_end: 0.2
+  base_lr: 2.0e-04  # learning rate for a batch size of 1024
+  warmup_epochs: 80
+  layerwise_decay: 1.0
+crops:
+  local_crops_size: 98

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl14.yaml

@@ -0,0 +1,26 @@
+dino:
+  head_n_prototypes: 131072
+  head_bottleneck_dim: 384
+ibot:
+  separate_head: true
+  head_n_prototypes: 131072
+train:
+  batch_size_per_gpu: 32
+  dataset_path: ImageNet22k
+  centering: sinkhorn_knopp
+student:
+  arch: vit_large
+  patch_size: 14
+  drop_path_rate: 0.4
+  ffn_layer: swiglufused
+  block_chunks: 4
+teacher:
+  momentum_teacher: 0.994
+optim:
+  epochs: 500
+  weight_decay_end: 0.2
+  base_lr: 2.0e-04  # learning rate for a batch size of 1024
+  warmup_epochs: 80
+  layerwise_decay: 1.0
+crops:
+  local_crops_size: 98

depthanything/torchhub/facebookresearch_dinov2_main/dinov2/configs/train/vitl16_short.yaml

@@ -0,0 +1,6 @@
+# this corresponds to the default config
+train:
+  dataset_path: ImageNet:split=TRAIN
+  batch_size_per_gpu: 64
+student:
+  block_chunks: 4